System

Abstract

To provide a system that enables real-time monitoring of the health conditions of elderly individuals in rural areas or the like and allows for rapid response when necessary.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, and a transmission unit. The reception unit inputs the health condition of a user. The analysis unit analyzes the information input through the reception unit. The transmission unit transmits data based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to grasp the health status of elderly people in rural areas in real time and take emergency measures.

[0005] The system according to the embodiment aims to grasp the health status of elderly people and others in rural areas in real time and to take necessary measures promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a sending unit. The reception unit inputs the health condition of a user. The analysis unit analyzes the information input by the reception unit. The sending unit sends data based on the analysis result obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can grasp the health status of elderly people and others in rural areas in real time and can take necessary measures quickly. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The health management system according to the embodiment of the present invention is a system that can be used in cases where the health condition and daily life of elderly people in rural areas cannot be visualized and emergency response is not possible. This health management system allows users to input their health status and their own condition into the generation AI, and when a response is required, it can automatically send data summarizing the symptoms to the nearest hospital. For example, a user inputs their health condition and condition into the generation AI. For example, a specific symptom such as "I've had chest pain recently" is input. This information is sent to the generation AI. The generation AI then analyzes the input information and evaluates the user's health condition. The generation AI determines what disease or condition the input symptoms are related to based on past data and medical knowledge. For example, it determines that chest pain may be related to a heart problem. If the generation AI determines that a response is required, it automatically sends data summarizing the symptoms to the nearest hospital. This data includes the user's specific symptoms and the evaluation results of the health condition. For example, it includes content such as "The user has reported that he has had chest pain recently, and a heart problem is suspected." This system allows elderly people in rural areas and others to easily understand their health condition and quickly contact a medical institution if necessary. This will allow emergency responses to be made without delay and appropriate medical care to be received. As a result, the health management system will enable elderly people in rural areas to easily understand their own health conditions and quickly contact medical institutions if necessary.

[0029] The health management system according to the embodiment includes a reception unit, an analysis unit, and a sending unit. The reception unit inputs the health condition of the user. The health condition of the user includes, for example, body temperature, blood pressure, details of symptoms, and the like, but is not limited to such examples. The reception unit allows the user to input specific symptoms such as "I've had chest pain recently." The analysis unit uses a generation AI to analyze the information input by the reception unit. The analysis is performed, for example, based on the type of data and an analysis algorithm, but is not limited to such examples. For example, the generation AI determines what disease or condition the input symptoms are related to based on past data and medical knowledge. The sending unit sends the data based on the analysis results obtained by the analysis unit. The sending is performed, for example, based on a sending destination and a sending means, but is not limited to such examples. For example, the sending unit identifies the nearest hospital and sends data including the evaluation results of the user's specific symptoms and health condition. This makes it possible for the health management system according to the embodiment to efficiently input, analyze, and send the user's health condition.

[0030] The reception unit inputs the user's health condition. The user's health condition includes, for example, body temperature, blood pressure, details of symptoms, etc., but is not limited to such examples. Specifically, the reception unit provides an interface for the user to input health information through a dedicated application or website using a device such as a smartphone, tablet, or personal computer. The user can manually input the body temperature and blood pressure, or can automatically obtain data from a device connected via Bluetooth (registered trademark) or Wi-Fi. For example, a health management device such as a smart watch or a blood pressure monitor can measure the user's body temperature and blood pressure and transmit the data to the reception unit. The user can also input specific symptoms and changes in physical condition in detail. For example, specific symptoms such as "I've had chest pain recently," "I've had a persistent headache," and "I have no appetite" can be input in text format. Furthermore, the reception unit can organize the information input by the user and ask additional questions as necessary. For example, questions can be automatically generated to collect detailed information such as how often the chest pain occurs, how strong the pain is, and whether there are any other related symptoms. This allows the reception unit to collect detailed and accurate information on the user's health condition and prepare to provide it to the analysis unit.

[0031] The analysis unit uses the generation AI to analyze the information input by the reception unit. The analysis is performed, for example, based on the type of data and the analysis algorithm, but is not limited to such examples. Specifically, the generation AI uses natural language processing (NLP) technology to analyze the text data input by the user and extracts symptom patterns and related keywords. For example, if the input is "chest pain," the generation AI refers to past databases and medical literature to identify diseases and conditions that may be related to the chest pain. In addition, for numerical data such as body temperature and blood pressure, statistical analysis and machine learning algorithms are used to detect outliers and trends. For example, if the user's body temperature is continuously high, additional analysis can be performed to identify the cause of the fever. Furthermore, the generation AI determines what disease or condition the input symptom is related to based on past data and medical knowledge. For example, it can evaluate which of the following is more likely to cause chest pain, such as heart disease, stomach problems, or muscle pain, and list them in order of risk. The analysis results are used as basic data for comprehensively evaluating the user's health condition and proposing necessary measures and next steps. This allows the analysis unit to quickly and accurately analyze the user's health condition and support appropriate responses.

[0032] The sending unit sends data based on the analysis results obtained by the analysis unit. The sending is performed, for example, based on the destination and the sending means, but is not limited to such examples. Specifically, the sending unit identifies an appropriate medical institution or specialist for the user based on the analysis results, and provides the information to the user. For example, if the user's symptoms are determined to be a high risk of heart disease, the sending unit can provide information on the nearest cardiologist or hospital and support the reservation procedure. In addition, the sending unit can send data including the evaluation results of the user's specific symptoms and health condition to the medical institution or specialist. This allows the medical institution to grasp the necessary information in advance before the user visits the hospital, and prepare for a prompt and appropriate diagnosis and treatment. Furthermore, the sending unit can notify the user of advice based on the analysis results and the next step. For example, if the symptoms are mild, advice on how to deal with them at home and how to improve lifestyle habits can be provided, and if the symptoms are severe, the sending unit can instruct the user to visit a medical institution immediately. As a sending means, a method that is most convenient for the user can be selected, such as email, SMS, or in-app notification. This allows the sending unit to provide appropriate information and support based on the analysis results, and to support the user's health management.

[0033] The analysis unit can perform analysis based on past data or medical knowledge. The analysis unit performs analysis based on past data such as past medical records and health check results. The analysis unit can also perform analysis based on medical knowledge such as medical papers and medical guidelines. For example, the analysis unit refers to past medical records to evaluate whether the user's current symptoms are related to past symptoms. The analysis unit also refers to medical guidelines to determine whether the user's symptoms are related to a specific disease. As a result, the analysis unit performs analysis based on past data and medical knowledge, improving the accuracy of the analysis.

[0034] The sending unit can identify nearby hospitals and send the data. The sending unit can identify nearby hospitals based on, for example, geographical distance or specialty. For example, the sending unit can identify the hospital closest to the user's current location and send the data to that hospital. The sending unit can also identify a hospital with a specialty that can deal with the user's symptoms and send the data to that hospital. For example, if the sending unit determines that the user's chest pain is related to a heart problem, it can identify a hospital that specializes in heart problems and send the data to that hospital. This allows the sending unit to identify the nearest hospital and send the data, enabling a quick response.

[0035] The reception unit can input the specific symptoms of the user. For example, the reception unit can input the specific symptoms of the user, such as "I've had chest pain recently." The specific symptoms include, for example, the degree of pain and the presence or absence of fever, but are not limited to such examples. For example, when the user inputs "I have a headache," the reception unit can input the degree and frequency of the pain in detail. In addition, when the user inputs "I have a cough," the reception unit can also input the frequency and duration of the cough in detail. In this way, the reception unit can grasp the detailed health condition by inputting the specific symptoms of the user.

[0036] The reception unit can analyze the user's past health condition input history and select an appropriate input method. For example, the reception unit automatically displays symptoms that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. The reception unit can also predict and suggest symptoms to be input during a specific time period from the user's past input history. For example, if the user inputs "I have a headache in the morning" in the past, the reception unit displays similar symptoms as candidates the next time the user inputs. The reception unit also preferentially suggests voice input the next time the user inputs if the user used voice input frequently in the past. In this way, the reception unit can provide an optimal input method by analyzing the past input history.

[0037] The reception unit can filter the health condition based on the user's current living situation and areas of interest when the health condition is input. For example, when the user inputs the recent living situation, the reception unit proposes related health condition input items based on the information. The reception unit can also customize the input items based on the user's areas of interest (for example, a specific disease or health management method). Furthermore, the reception unit can filter the input items based on the user's living situation (for example, stress level or exercise habit). For example, when the user inputs "I've been stressed recently," the reception unit preferentially proposes input items for health conditions related to stress. When the user inputs "I don't get enough exercise," the reception unit proposes input items for health conditions related to exercise. As a result, the reception unit can filter the input items based on the living situation and areas of interest, allowing for more relevant input.

[0038] The reception unit can preferentially input information with high relevance based on the geographical location information of the user when inputting the health condition. For example, when the user lives in a specific area, the reception unit prioritizes input items related to diseases prevalent in that area. In addition, when the user is traveling, the reception unit can also prioritize input items related to health risks at the travel destination. Furthermore, when the user visits a specific medical institution, the reception unit can also preferentially input information required by the medical institution. For example, when the user inputs "I live in an area where influenza is prevalent recently," the reception unit preferentially suggests input items related to diseases prevalent in that area. In addition, when the user inputs "I felt sick at the travel destination," the reception unit preferentially suggests input items related to health risks at the travel destination. In this way, the reception unit can preferentially input information with high relevance by considering the geographical location information.

[0039] The reception unit can analyze the user's social media activities and input related information when inputting the health condition. For example, the reception unit suggests input items based on health-related posts shared by the user on social media. The reception unit can also estimate the user's recent living situation and stress level from the user's social media activities and customize the input items. The reception unit can also suggest related information as input items from health-related accounts that the user follows on social media. For example, if the user posts on social media that "I get tired easily recently," the reception unit suggests input items for related health conditions based on that information. Also, if the user posts on social media that "I'm stressed," the reception unit suggests input items related to stress based on that information. In this way, the reception unit can input related information by analyzing social media activities.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the health condition during the analysis. For example, the analysis unit performs a detailed analysis of information related to an important health condition. The analysis unit can also perform a simplified analysis of information related to a minor health condition. Furthermore, the analysis unit can adjust the display method of the analysis result according to the importance of the health condition. For example, the analysis unit provides a detailed analysis result when the user's symptoms are serious. The analysis unit provides a simplified analysis result when the user's symptoms are minor. In this way, the analysis unit can analyze important information in detail by adjusting the level of detail of the analysis based on the importance of the health condition.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the health condition during analysis. For example, the analysis unit applies a cardiac analysis algorithm to a heart-related health condition. The analysis unit can also apply a respiratory analysis algorithm to a respiratory-related health condition. The analysis unit can also apply a digestive analysis algorithm to a digestive-related health condition. For example, the analysis unit uses a cardiac analysis algorithm when analyzing a symptom related to the user's heart. The analysis unit uses a respiratory analysis algorithm when analyzing a symptom related to the user's respiratory system. This allows the analysis unit to perform more accurate analysis by applying an analysis algorithm according to the category.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the health condition. For example, the analysis unit prioritizes analysis of recently submitted health condition information. The analysis unit can also prioritize analysis of highly urgent health condition information. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. For example, the analysis unit prioritizes analysis of recently submitted health condition information by the user. Furthermore, if the user's health condition is urgent, the analysis unit prioritizes analysis of that information. In this way, the analysis unit can prioritize analysis of highly urgent information by determining the priority of analysis based on the time of submission.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the health condition during analysis. For example, the analysis unit prioritizes analysis of highly relevant health condition information. The analysis unit can also postpone less relevant health condition information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the health condition. For example, the analysis unit prioritizes analysis of information highly relevant to the user's symptoms. The analysis unit can also postpone information less relevant to the user's symptoms. In this way, the analysis unit can prioritize analysis of important information by adjusting the order of analysis based on relevance.

[0044] At the time of sending, the sending unit can select the optimal sending method by analyzing the user's past health condition. For example, the sending unit selects the optimal sending method based on the sending method used by the user in the past. The sending unit can also select the most appropriate sending method from the user's past health condition. Furthermore, the sending unit can analyze the user's past health condition and select the most efficient sending method. For example, if the user requested sending by email in the past, the sending unit selects a method to send by email next time. Also, if the user's past health condition was serious, the sending unit selects a rapid sending method. In this way, the sending unit can provide the optimal sending method by analyzing the past health condition.

[0045] The sending unit can customize the means of sending based on the user's current living situation at the time of sending. For example, when the user is busy, the sending unit selects a quick and simple sending method. Also, when the user is relaxed, the sending unit can select a sending method including detailed information. Furthermore, the sending unit can customize the optimal sending means based on the user's living situation. For example, when the user inputs "I'm busy at work," the sending unit selects a quick and simple sending method. Also, when the user inputs "I have time on a holiday," the sending unit selects a sending method including detailed information. This allows the sending unit to customize the sending means based on the living situation, enabling more appropriate sending.

[0046] The sending unit can select an appropriate sending method based on the geographical location information of the user at the time of sending. For example, when the user lives in a specific area, the sending unit selects the optimal sending method for a medical institution in that area. Also, when the user is traveling, the sending unit can also select the optimal sending method for a medical institution at the travel destination. Furthermore, the sending unit can select the optimal sending method based on the geographical location information of the user. For example, when the user inputs "living in a specific area," the sending unit selects the optimal sending method for a medical institution in that area. Also, when the user inputs "traveling," the sending unit selects the optimal sending method for a medical institution at the travel destination. In this way, the sending unit can provide the optimal sending method by taking into account the geographical location information.

[0047] The sending unit can analyze the user's social media activity at the time of sending and suggest a sending means. The sending unit can suggest an optimal sending means based on, for example, health-related posts shared by the user on social media. The sending unit can also estimate the user's recent living situation and stress level from the user's social media activity and customize the sending means. The sending unit can also suggest related information as a sending means from health-related accounts that the user follows on social media. For example, if the user posts on social media that "I get tired easily these days," the sending unit suggests an optimal sending means based on that information. Also, if the user posts on social media that "I'm stressed," the sending unit customizes the sending means based on that information. In this way, the sending unit can suggest an optimal sending means by analyzing social media activity.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] When inputting the user's health condition, the reception unit can automatically acquire the user's lifestyle habits and daily activity data to complement the input contents. For example, the reception unit acquires data from the user's smart watch or fitness tracker and automatically records the amount of daily exercise and sleep patterns. The reception unit can also acquire data from the user's food record app and reflect the meal contents and calorie intake in the input contents. Furthermore, the reception unit can acquire data from a device that measures the user's stress level and input the health condition taking into account the influence of stress. This allows the reception unit to input the health condition more accurately and comprehensively by utilizing the user's lifestyle habits and daily activity data.

[0050] The analysis unit can take environmental data into consideration when analyzing the user's health condition. For example, the analysis unit acquires air quality data of the user's living environment and performs analysis related to respiratory symptoms. The analysis unit can also acquire weather data of the user's area and evaluate the impact of temperature and humidity fluctuations on the health condition. Furthermore, the analysis unit can acquire noise levels and light environment data of the user's living environment and analyze the impact of these environmental factors on stress and sleep. This allows the analysis unit to analyze the health condition with higher accuracy by taking environmental data into consideration.

[0051] The sending unit can encrypt the data when sending the user's health condition data. For example, the sending unit encrypts the data before transmitting the user's health condition data to prevent unauthorized access by a third party. The sending unit can also provide a key for decrypting the data when the destination medical institution receives the data. Furthermore, the sending unit can record the data transmission history and monitor the safety of the transmission. In this way, the sending unit can protect the user's privacy and enable safe data transmission by encrypting the data.

[0052] The reception unit can use a voice assistant when inputting the user's health condition. For example, when the user reports the health condition by voice, the reception unit uses the voice assistant to ask questions and collect detailed information. The reception unit can also use the voice assistant to analyze the contents of the user's speech and automatically select appropriate input items. Furthermore, the reception unit can also use the voice assistant to convert the contents of the user's speech into text and record it as input contents. In this way, the reception unit can make it easier for the user to input the health condition by using the voice assistant.

[0053] The analysis unit can take the genetic information into account when analyzing the user's health condition. For example, the analysis unit obtains the user's genetic test results and evaluates genetic risk factors. The analysis unit can also analyze the risk of genetically related diseases by taking the user's family history into account. Furthermore, the analysis unit can propose an individualized health management plan based on the user's genetic information. This allows the analysis unit to take the genetic information into account and provide more accurate analysis of the health condition and individualized health management.

[0054] The reception unit can incorporate game elements when inputting the user's health condition. For example, the reception unit can provide a mechanism that allows the user to earn points every time the user inputs his / her health condition. The reception unit can also provide health advice or benefits when the user earns a certain number of points. Furthermore, the reception unit can provide a ranking function that allows the user to compete with other users, and encourage the input of the health condition. In this way, the reception unit can incorporate game elements to enable the user to enjoy inputting his / her health condition.

[0055] The analysis unit can utilize real-time data when analyzing the user's health condition. For example, the analysis unit uses real-time heart rate data obtained from the user's smartwatch for analysis. The analysis unit can also use real-time location information obtained from the user's smartphone for analysis and take environmental factors into account. Furthermore, the analysis unit can use real-time activity data of the user for analysis and evaluate the amount of exercise and stress level. This allows the analysis unit to analyze the health condition more accurately and in a timely manner by utilizing real-time data.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The reception unit inputs the user's health condition. The user's health condition includes, for example, body temperature, blood pressure, and details of symptoms. The user can input specific symptoms such as "I've been having chest pains recently." Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis is based on the type of data and the analysis algorithm. The generation AI determines what disease or condition the entered symptoms are related to based on past data and medical knowledge. Step 3: The sending unit sends the data based on the analysis results obtained by the analysis unit. The data is sent based on the destination and the means of sending. For example, the sending unit identifies the nearest hospital and sends data including the user's specific symptoms and the evaluation results of the health condition.

[0058] (Example 2) The health management system according to the embodiment of the present invention is a system that can be used in cases where the health condition and daily life of elderly people in rural areas cannot be visualized and emergency response is not possible. This health management system allows users to input their health status and their own condition into the generation AI, and when a response is required, it can automatically send data summarizing the symptoms to the nearest hospital. For example, a user inputs their health condition and condition into the generation AI. For example, a specific symptom such as "I've had chest pain recently" is input. This information is sent to the generation AI. The generation AI then analyzes the input information and evaluates the user's health condition. The generation AI determines what disease or condition the input symptoms are related to based on past data and medical knowledge. For example, it determines that chest pain may be related to a heart problem. If the generation AI determines that a response is required, it automatically sends data summarizing the symptoms to the nearest hospital. This data includes the user's specific symptoms and the evaluation results of the health condition. For example, it includes content such as "The user has reported that he has had chest pain recently, and a heart problem is suspected." This system allows elderly people in rural areas and others to easily understand their health condition and quickly contact a medical institution if necessary. This will allow emergency responses to be made without delay and appropriate medical care to be received. As a result, the health management system will enable elderly people in rural areas to easily understand their own health conditions and quickly contact medical institutions if necessary.

[0059] The health management system according to the embodiment includes a reception unit, an analysis unit, and a sending unit. The reception unit inputs the health condition of the user. The health condition of the user includes, for example, body temperature, blood pressure, details of symptoms, and the like, but is not limited to such examples. The reception unit allows the user to input specific symptoms such as "I've had chest pain recently." The analysis unit uses a generation AI to analyze the information input by the reception unit. The analysis is performed, for example, based on the type of data and an analysis algorithm, but is not limited to such examples. For example, the generation AI determines what disease or condition the input symptoms are related to based on past data and medical knowledge. The sending unit sends the data based on the analysis results obtained by the analysis unit. The sending is performed, for example, based on a sending destination and a sending means, but is not limited to such examples. For example, the sending unit identifies the nearest hospital and sends data including the evaluation results of the user's specific symptoms and health condition. This makes it possible for the health management system according to the embodiment to efficiently input, analyze, and send the user's health condition.

[0060] The reception unit inputs the user's health condition. The user's health condition includes, for example, body temperature, blood pressure, details of symptoms, etc., but is not limited to such examples. Specifically, the reception unit provides an interface in which the user inputs health information through a dedicated application or website using a device such as a smartphone, tablet, or personal computer. The user can manually input the body temperature and blood pressure, or can automatically obtain data from a device connected via Bluetooth or Wi-Fi. For example, a health management device such as a smart watch or blood pressure monitor can measure the user's body temperature and blood pressure and transmit the data to the reception unit. The user can also input specific symptoms and changes in physical condition in detail. For example, specific symptoms such as "I've had chest pain recently," "I've had a persistent headache," and "I have no appetite" can be input in text format. Furthermore, the reception unit can organize the information input by the user and ask additional questions as necessary. For example, questions can be automatically generated to collect detailed information such as how often the chest pain occurs, how intense the pain is, and whether there are any other related symptoms. This allows the reception unit to collect detailed and accurate information on the user's health condition and prepare to provide it to the analysis unit.

[0061] The analysis unit uses the generation AI to analyze the information input by the reception unit. The analysis is performed, for example, based on the type of data and the analysis algorithm, but is not limited to such examples. Specifically, the generation AI uses natural language processing (NLP) technology to analyze the text data input by the user and extracts symptom patterns and related keywords. For example, if the input is "chest pain," the generation AI refers to past databases and medical literature to identify diseases and conditions that may be related to the chest pain. In addition, for numerical data such as body temperature and blood pressure, statistical analysis and machine learning algorithms are used to detect outliers and trends. For example, if the user's body temperature is continuously high, additional analysis can be performed to identify the cause of the fever. Furthermore, the generation AI determines what disease or condition the input symptom is related to based on past data and medical knowledge. For example, it can evaluate which of the following is more likely to cause chest pain, such as heart disease, stomach problems, or muscle pain, and list them in order of risk. The analysis results are used as basic data for comprehensively evaluating the user's health condition and proposing necessary measures and next steps. This allows the analysis unit to quickly and accurately analyze the user's health condition and support appropriate responses.

[0062] The sending unit sends data based on the analysis results obtained by the analysis unit. The sending is performed, for example, based on the destination and the sending means, but is not limited to such examples. Specifically, the sending unit identifies an appropriate medical institution or specialist for the user based on the analysis results, and provides the information to the user. For example, if the user's symptoms are determined to be a high risk of heart disease, the sending unit can provide information on the nearest cardiologist or hospital and support the reservation procedure. In addition, the sending unit can send data including the evaluation results of the user's specific symptoms and health condition to the medical institution or specialist. This allows the medical institution to grasp the necessary information in advance before the user visits the hospital, and prepare for a prompt and appropriate diagnosis and treatment. Furthermore, the sending unit can notify the user of advice based on the analysis results and the next step. For example, if the symptoms are mild, advice on how to deal with them at home and how to improve lifestyle habits can be provided, and if the symptoms are severe, the sending unit can instruct the user to visit a medical institution immediately. As a sending means, a method that is most convenient for the user can be selected, such as email, SMS, or in-app notification. This allows the sending unit to provide appropriate information and support based on the analysis results, and to support the user's health management.

[0063] The analysis unit can perform analysis based on past data or medical knowledge. The analysis unit performs analysis based on past data such as past medical records and health check results. The analysis unit can also perform analysis based on medical knowledge such as medical papers and medical guidelines. For example, the analysis unit refers to past medical records to evaluate whether the user's current symptoms are related to past symptoms. The analysis unit also refers to medical guidelines to determine whether the user's symptoms are related to a specific disease. As a result, the analysis unit performs analysis based on past data and medical knowledge, improving the accuracy of the analysis.

[0064] The sending unit can identify nearby hospitals and send the data. The sending unit can identify nearby hospitals based on, for example, geographical distance or specialty. For example, the sending unit can identify the hospital closest to the user's current location and send the data to that hospital. The sending unit can also identify a hospital with a specialty that can deal with the user's symptoms and send the data to that hospital. For example, if the sending unit determines that the user's chest pain is related to a heart problem, it can identify a hospital that specializes in heart problems and send the data to that hospital. This allows the sending unit to identify the nearest hospital and send the data, enabling a quick response.

[0065] The reception unit can input the specific symptoms of the user. For example, the reception unit can input the specific symptoms of the user, such as "I've had chest pain recently." The specific symptoms include, for example, the degree of pain and the presence or absence of fever, but are not limited to such examples. For example, when the user inputs "I have a headache," the reception unit can input the degree and frequency of the pain in detail. In addition, when the user inputs "I have a cough," the reception unit can also input the frequency and duration of the cough in detail. In this way, the reception unit can grasp the detailed health condition by inputting the specific symptoms of the user.

[0066] The reception unit can estimate the user's emotion and adjust the input method of the health condition based on the estimated user's emotion. The reception unit estimates the user's emotion using, for example, technologies such as facial expression recognition and voice analysis. For example, when the user feels anxious, the reception unit provides a simple and intuitive interface and minimizes the input procedure. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to input the health condition quickly. This allows the reception unit to adjust the input method according to the user's emotion, thereby enabling more appropriate input. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0067] The reception unit can analyze the user's past health condition input history and select an appropriate input method. For example, the reception unit automatically displays symptoms that the user frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. The reception unit can also predict and suggest symptoms to be input during a specific time period from the user's past input history. For example, if the user inputs "I have a headache in the morning" in the past, the reception unit displays similar symptoms as candidates the next time the user inputs. The reception unit also preferentially suggests voice input the next time the user inputs if the user used voice input frequently in the past. In this way, the reception unit can provide an optimal input method by analyzing the past input history.

[0068] The reception unit can filter the health condition based on the user's current living situation and areas of interest when the health condition is input. For example, when the user inputs the recent living situation, the reception unit proposes related health condition input items based on the information. The reception unit can also customize the input items based on the user's areas of interest (for example, a specific disease or health management method). Furthermore, the reception unit can filter the input items based on the user's living situation (for example, stress level or exercise habit). For example, when the user inputs "I've been stressed recently," the reception unit preferentially proposes input items for health conditions related to stress. When the user inputs "I don't get enough exercise," the reception unit proposes input items for health conditions related to exercise. As a result, the reception unit can filter the input items based on the living situation and areas of interest, allowing for more relevant input.

[0069] The reception unit can estimate the user's emotion and determine the priority of the health condition to be input based on the estimated user's emotion. The reception unit estimates the user's emotion using, for example, a technology such as facial expression recognition or voice analysis. For example, when the user feels anxious, the reception unit prioritizes the input of important health conditions. In addition, when the user is relaxed, the reception unit can also prompt the user to input detailed health conditions. Furthermore, when the user is in a hurry, the reception unit can also prioritize the input of the most important health conditions. In this way, the reception unit can prioritize input of important information by determining the priority of input according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0070] The reception unit can preferentially input information with high relevance based on the geographical location information of the user when inputting the health condition. For example, when the user lives in a specific area, the reception unit prioritizes input items related to diseases prevalent in that area. In addition, when the user is traveling, the reception unit can also prioritize input items related to health risks at the travel destination. Furthermore, when the user visits a specific medical institution, the reception unit can also preferentially input information required by the medical institution. For example, when the user inputs "I live in an area where influenza is prevalent recently," the reception unit preferentially suggests input items related to diseases prevalent in that area. In addition, when the user inputs "I felt sick at the travel destination," the reception unit preferentially suggests input items related to health risks at the travel destination. In this way, the reception unit can preferentially input information with high relevance by considering the geographical location information.

[0071] The reception unit can analyze the user's social media activities and input related information when inputting the health condition. For example, the reception unit suggests input items based on health-related posts shared by the user on social media. The reception unit can also estimate the user's recent living situation and stress level from the user's social media activities and customize the input items. The reception unit can also suggest related information as input items from health-related accounts that the user follows on social media. For example, if the user posts on social media that "I get tired easily recently," the reception unit suggests input items for related health conditions based on that information. Also, if the user posts on social media that "I'm stressed," the reception unit suggests input items related to stress based on that information. In this way, the reception unit can input related information by analyzing social media activities.

[0072] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, technologies such as facial expression recognition and voice analysis. For example, the analysis unit provides a simple and easy-to-understand analysis result when the user feels anxious. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide an analysis result that focuses on the main points when the user is in a hurry. This allows the analysis unit to provide a more appropriate analysis result by adjusting the expression method of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the health condition during the analysis. For example, the analysis unit performs a detailed analysis of information related to an important health condition. The analysis unit can also perform a simplified analysis of information related to a minor health condition. Furthermore, the analysis unit can adjust the display method of the analysis result according to the importance of the health condition. For example, the analysis unit provides a detailed analysis result when the user's symptoms are serious. The analysis unit provides a simplified analysis result when the user's symptoms are minor. In this way, the analysis unit can analyze important information in detail by adjusting the level of detail of the analysis based on the importance of the health condition.

[0074] The analysis unit can apply different analysis algorithms depending on the category of the health condition during analysis. For example, the analysis unit applies a cardiac analysis algorithm to a heart-related health condition. The analysis unit can also apply a respiratory analysis algorithm to a respiratory-related health condition. The analysis unit can also apply a digestive analysis algorithm to a digestive-related health condition. For example, the analysis unit uses a cardiac analysis algorithm when analyzing a symptom related to the user's heart. The analysis unit uses a respiratory analysis algorithm when analyzing a symptom related to the user's respiratory system. This allows the analysis unit to perform more accurate analysis by applying an analysis algorithm according to the category.

[0075] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, techniques such as facial expression recognition and voice analysis. For example, the analysis unit provides a short and to the point analysis result when the user is feeling anxious. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide a concise analysis result when the user is in a hurry. This allows the analysis unit to provide a more appropriate analysis result by adjusting the length of the analysis according to the user's emotion. The emotion estimation is realized using, for example, an emotion engine or a generation AI using an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0076] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the health condition. For example, the analysis unit prioritizes analysis of recently submitted health condition information. The analysis unit can also prioritize analysis of highly urgent health condition information. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. For example, the analysis unit prioritizes analysis of recently submitted health condition information by the user. Furthermore, if the user's health condition is urgent, the analysis unit prioritizes analysis of that information. In this way, the analysis unit can prioritize analysis of highly urgent information by determining the priority of analysis based on the time of submission.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the health condition during analysis. For example, the analysis unit prioritizes analysis of highly relevant health condition information. The analysis unit can also postpone less relevant health condition information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the health condition. For example, the analysis unit prioritizes analysis of information highly relevant to the user's symptoms. The analysis unit can also postpone information less relevant to the user's symptoms. In this way, the analysis unit can prioritize analysis of important information by adjusting the order of analysis based on relevance.

[0078] The sending unit can estimate the user's emotion and adjust the sending method based on the estimated user's emotion. The sending unit estimates the user's emotion using, for example, techniques such as facial expression recognition and voice analysis. For example, when the user feels anxious, the sending unit selects a method for sending quickly and reliably. In addition, when the user is relaxed, the sending unit can also select a sending method including detailed information. Furthermore, when the user is in a hurry, the sending unit can also select the quickest sending method. This allows the sending unit to adjust the sending method according to the user's emotion, thereby enabling more appropriate sending. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] At the time of sending, the sending unit can select the optimal sending method by analyzing the user's past health condition. For example, the sending unit selects the optimal sending method based on the sending method used by the user in the past. The sending unit can also select the most appropriate sending method from the user's past health condition. Furthermore, the sending unit can analyze the user's past health condition and select the most efficient sending method. For example, if the user requested sending by email in the past, the sending unit selects a method to send by email next time. Also, if the user's past health condition was serious, the sending unit selects a rapid sending method. In this way, the sending unit can provide the optimal sending method by analyzing the past health condition.

[0080] The sending unit can customize the means of sending based on the user's current living situation at the time of sending. For example, when the user is busy, the sending unit selects a quick and simple sending method. Also, when the user is relaxed, the sending unit can select a sending method including detailed information. Furthermore, the sending unit can customize the optimal sending means based on the user's living situation. For example, when the user inputs "I'm busy at work," the sending unit selects a quick and simple sending method. Also, when the user inputs "I have time on a holiday," the sending unit selects a sending method including detailed information. This allows the sending unit to customize the sending means based on the living situation, enabling more appropriate sending.

[0081] The sending unit can estimate the user's emotion and determine the priority of sending based on the estimated user's emotion. The sending unit estimates the user's emotion using, for example, a technology such as facial expression recognition or voice analysis. For example, when the user feels anxious, the sending unit preferentially sends the most important information. In addition, when the user is relaxed, the sending unit can also perform sending including detailed information. Furthermore, when the user is in a hurry, the sending unit can also prioritize information to be sent quickly. In this way, the sending unit can prioritize important information by determining the priority of sending according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0082] The sending unit can select an appropriate sending method based on the geographical location information of the user at the time of sending. For example, when the user lives in a specific area, the sending unit selects the optimal sending method for a medical institution in that area. Also, when the user is traveling, the sending unit can also select the optimal sending method for a medical institution at the travel destination. Furthermore, the sending unit can select the optimal sending method based on the geographical location information of the user. For example, when the user inputs "living in a specific area," the sending unit selects the optimal sending method for a medical institution in that area. Also, when the user inputs "traveling," the sending unit selects the optimal sending method for a medical institution at the travel destination. In this way, the sending unit can provide the optimal sending method by taking into account the geographical location information.

[0083] The sending unit can analyze the user's social media activity at the time of sending and suggest a sending means. The sending unit can suggest an optimal sending means based on, for example, health-related posts shared by the user on social media. The sending unit can also estimate the user's recent living situation and stress level from the user's social media activity and customize the sending means. The sending unit can also suggest related information as a sending means from health-related accounts that the user follows on social media. For example, if the user posts on social media that "I get tired easily these days," the sending unit suggests an optimal sending means based on that information. Also, if the user posts on social media that "I'm stressed," the sending unit customizes the sending means based on that information. In this way, the sending unit can suggest an optimal sending means by analyzing social media activity.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] When inputting the user's health condition, the reception unit can automatically acquire the user's lifestyle habits and daily activity data to complement the input contents. For example, the reception unit acquires data from the user's smart watch or fitness tracker and automatically records the amount of daily exercise and sleep patterns. The reception unit can also acquire data from the user's food record app and reflect the meal contents and calorie intake in the input contents. Furthermore, the reception unit can acquire data from a device that measures the user's stress level and input the health condition taking into account the influence of stress. This allows the reception unit to input the health condition more accurately and comprehensively by utilizing the user's lifestyle habits and daily activity data.

[0086] The analysis unit can take environmental data into consideration when analyzing the user's health condition. For example, the analysis unit acquires air quality data of the user's living environment and performs analysis related to respiratory symptoms. The analysis unit can also acquire weather data of the user's area and evaluate the impact of temperature and humidity fluctuations on the health condition. Furthermore, the analysis unit can acquire noise levels and light environment data of the user's living environment and analyze the impact of these environmental factors on stress and sleep. This allows the analysis unit to analyze the health condition with higher accuracy by taking environmental data into consideration.

[0087] The sending unit can encrypt the data when sending the user's health condition data. For example, the sending unit encrypts the data before transmitting the user's health condition data to prevent unauthorized access by a third party. The sending unit can also provide a key for decrypting the data when the destination medical institution receives the data. Furthermore, the sending unit can record the data transmission history and monitor the safety of the transmission. In this way, the sending unit can protect the user's privacy and enable safe data transmission by encrypting the data.

[0088] The reception unit can use a voice assistant when inputting the user's health condition. For example, when the user reports the health condition by voice, the reception unit uses the voice assistant to ask questions and collect detailed information. The reception unit can also use the voice assistant to analyze the contents of the user's speech and automatically select appropriate input items. Furthermore, the reception unit can also use the voice assistant to convert the contents of the user's speech into text and record it as input contents. In this way, the reception unit can make it easier for the user to input the health condition by using the voice assistant.

[0089] The reception unit can estimate the user's emotion and provide feedback on the input content based on the estimated user's emotion. For example, when the user feels anxious, the reception unit provides feedback that gives the user a sense of security in response to the input content. In addition, when the user feels relaxed, the reception unit can provide detailed feedback to deepen understanding of the health condition. Furthermore, when the user is in a hurry, the reception unit can provide concise and quick feedback. In this way, the reception unit can provide feedback according to the user's emotion, enabling more appropriate input of the health condition.

[0090] The analysis unit can take the genetic information into account when analyzing the user's health condition. For example, the analysis unit obtains the user's genetic test results and evaluates genetic risk factors. The analysis unit can also analyze the risk of genetically related diseases by taking the user's family history into account. Furthermore, the analysis unit can propose an individualized health management plan based on the user's genetic information. This allows the analysis unit to take the genetic information into account and provide more accurate analysis of the health condition and individualized health management.

[0091] The reception unit can incorporate game elements when inputting the user's health condition. For example, the reception unit can provide a mechanism that allows the user to earn points every time the user inputs his / her health condition. The reception unit can also provide health advice or benefits when the user earns a certain number of points. Furthermore, the reception unit can provide a ranking function that allows the user to compete with other users, and encourage the input of the health condition. In this way, the reception unit can incorporate game elements to enable the user to enjoy inputting his / her health condition.

[0092] The reception unit can estimate the user's emotions and determine the priority of input contents based on the estimated user's emotions. For example, when the user feels anxious, the reception unit prioritizes input of important health conditions. In addition, when the user feels relaxed, the reception unit can also prompt the user to input detailed health conditions. Furthermore, when the user is in a hurry, the reception unit can also prioritize input of the most important health conditions. In this way, the reception unit can prioritize input of important information by determining the priority of inputs according to the user's emotions.

[0093] The analysis unit can utilize real-time data when analyzing the user's health condition. For example, the analysis unit uses real-time heart rate data obtained from the user's smartwatch for analysis. The analysis unit can also use real-time location information obtained from the user's smartphone for analysis and take environmental factors into account. Furthermore, the analysis unit can use real-time activity data of the user for analysis and evaluate the amount of exercise and stress level. This allows the analysis unit to analyze the health condition more accurately and in a timely manner by utilizing real-time data.

[0094] The sending unit can estimate the user's emotion and adjust the sending method based on the estimated user's emotion. For example, when the user feels anxious, the sending unit selects a method for sending quickly and reliably. Also, when the user feels relaxed, the sending unit can select a sending method including detailed information. Furthermore, when the user is in a hurry, the sending unit can select the quickest sending method. In this way, the sending unit can adjust the sending method according to the user's emotion, thereby enabling more appropriate sending.

[0095] The process flow of the second embodiment will be briefly described below.

[0096] Step 1: The reception unit inputs the user's health condition. The user's health condition includes, for example, body temperature, blood pressure, and details of symptoms. The user can input specific symptoms such as "I've been having chest pains recently." Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis is based on the type of data and the analysis algorithm. The generation AI determines what disease or condition the entered symptoms are related to based on past data and medical knowledge. Step 3: The sending unit sends the data based on the analysis results obtained by the analysis unit. The data is sent based on the destination and the means of sending. For example, the sending unit identifies the nearest hospital and sends data including the user's specific symptoms and the evaluation results of the health condition.

[0097] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0099] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0100] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and sending unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user can input the health condition. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The sending unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sends data to the nearest hospital based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] 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.

[0105] 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 the voice according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0107] 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 performed in a secure state.

[0108] Fig. 4 shows an example of 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0114] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0115] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and sending unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user can input the health condition. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The sending unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sends data to the nearest hospital based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] 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.

[0121] 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 the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] 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 performed in a secure state.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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 identification processing unit 290 using these models.

[0128] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 voice indicating a user input for 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.

[0130] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and sending unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the user can input the health condition. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The sending unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sends data to the nearest hospital based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] 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.

[0137] 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 the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0139] 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 performed in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0145] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] 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 a voice indicating a user input for 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.

[0147] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0148] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0149] Each of the multiple elements including the reception unit, analysis unit, and sending unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user can input the health condition. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The sending unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sends data to the nearest hospital based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0150] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0151] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0153] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0155] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0157] 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.

[0158] 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 Universal Serial Bus (USB) 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.

[0159] In addition, 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 upon request from the data processing device 12.

[0160] 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.

[0161] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0162] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0163] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0164] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0166] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0168] (Appendix 1) A reception unit for inputting a user's health condition; an analysis unit that analyzes the information input by the reception unit; A sending unit that sends data based on the analysis result obtained by the analysis unit. A system characterized by: (Appendix 2) The analysis unit is Analyze based on past data or medical knowledge 2. The system of claim 1, (Appendix 3) The sending unit is Identify nearby hospitals and send data 2. The system of claim 1, (Appendix 4) The reception unit is Enter the user's specific symptoms 2. The system of claim 1, (Appendix 5) The reception unit is Estimating a user's emotions and adjusting the input method of health status based on the estimated user's emotions 2. The system of claim 1, (Appendix 6) The reception unit is Analyze the user's past health condition input history and select the appropriate input method 2. The system of claim 1, (Appendix 7) The reception unit is When entering health information, filtering is performed based on the user's current lifestyle and interests. 2. The system of claim 1, (Appendix 8) The reception unit is Estimate the user's emotions and determine the priority of the health conditions to be input based on the estimated user emotions. 2. The system of claim 1, (Appendix 9) The reception unit is When entering health information, the most relevant information is prioritized based on the user's geographic location 2. The system of claim 1, (Appendix 10) The reception unit is Analyzes user's social media activity and populates relevant information when entering health information 2. The system of claim 1, (Appendix 11) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, adjust the level of analysis detail based on the importance of health conditions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, different analysis algorithms are applied depending on the health category. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, prioritize analysis based on when health status was submitted 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, adjust the order of analysis based on the relevance of health conditions 2. The system of claim 1, (Appendix 17) The sending unit is Infer user emotions and adjust delivery methods based on the estimated user emotions 2. The system of claim 1, (Appendix 18) The sending unit is At the time of delivery, the system analyzes the user's past health condition to select the optimal delivery method. 2. The system of claim 1, (Appendix 19) The sending unit is At the time of delivery, customize the delivery method based on the user's current life situation 2. The system of claim 1, (Appendix 20) The sending unit is Estimate user emotions and prioritize delivery based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The sending unit is At the time of delivery, the appropriate delivery method is selected based on the user's geographic location information. 2. The system of claim 1, (Appendix 22) The sending unit is At the time of delivery, the system analyzes the user's social media activity to suggest delivery methods. 2. The system of claim 1, [Explanation of symbols]

[0169] 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 reception unit for inputting a user's health condition; an analysis unit that analyzes the information input by the reception unit; A sending unit that sends data based on the analysis result obtained by the analysis unit. A system characterized by:

2. The analysis unit is Analyze based on past data or medical knowledge 2. The system of claim 1 .

3. The sending unit is Identify nearby hospitals and send data 2. The system of claim 1 .

4. The reception unit is Enter the user's specific symptoms 2. The system of claim 1 .

5. The reception unit is Estimating a user's emotions and adjusting the input method of health status based on the estimated user's emotions 2. The system of claim 1 .

6. The reception unit is Analyze the user's past health condition input history and select the appropriate input method 2. The system of claim 1 .

7. The reception unit is When entering health information, filtering is performed based on the user's current lifestyle and interests.

2. The system of claim 1 .

8. The reception unit is Estimate the user's emotions and determine the priority of the health conditions to be input based on the estimated user emotions.

2. The system of claim 1 .

Images