system

Abstract

The system according to this embodiment aims to allow users to easily seek health consultations and, if necessary, to collaborate with hospitals. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a conversation unit, and a collaboration unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The conversation unit converses with the user based on the data analyzed by the analysis unit. The collaboration unit collaborates with the hospital based on the information obtained by the conversation unit.

Description

Technical Field

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

Background Art

[0002] A method disclosed in Patent Document 1 is a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that communication with doctors is scarce and there is no place where a simple health consultation can be made.

[0005] The system according to the embodiment aims to enable a user to easily conduct a health consultation and cooperate with a hospital as needed.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a conversation unit, and a cooperation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The conversation unit converses with the user based on the data analyzed by the analysis unit. The cooperation unit cooperates with a hospital based on the information obtained by the conversation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to easily seek health consultations and, if necessary, collaborate with hospitals. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system that utilizes generative AI to provide communication similar to that of a local doctor. This health management system links information with hospitals and pharmacies and stores medical records, visit information, and prescription drug information. Next, it collects data on the user's usual health condition from a wearable device. Based on this data, the generative AI converses with the user and provides advice on their health condition. For example, if a user says, "I haven't been feeling well lately," the generative AI analyzes the stored data and provides appropriate advice. Furthermore, the generative AI links with a hospital as needed and encourages the user to see a doctor. This mechanism allows users to easily consult about their health, just like a local doctor in the old days. First, it links information with hospitals and pharmacies and stores medical records, visit information, and prescription drug information. At this time, data provided by each medical institution is centrally managed, allowing for a comprehensive understanding of the user's health condition. For example, it stores information such as which hospitals the user visits and which medications they are prescribed. Next, it collects data on the user's usual health condition from a wearable device. This allows for the acquisition of detailed data such as the user's heart rate, exercise level, and sleep status. For example, the system collects information such as how much exercise a user does on a daily basis and the quality of their sleep. Based on this data, the generating AI converses with the user and provides advice on their health. For example, if a user says, "I haven't been feeling well lately," the generating AI analyzes the accumulated data and provides appropriate advice. For instance, if the cause is lack of exercise, the generating AI will advise, "You should exercise more." If the cause is lack of sleep, it will advise, "You should get more sleep." Furthermore, the generating AI will coordinate with hospitals as needed and encourage the user to seek medical attention. For example, if a user says, "I've been experiencing chest pain lately," the generating AI will analyze the accumulated data and, if it determines that there may be a serious health problem, it will coordinate with hospitals and encourage the user to seek medical attention. In this way, users can receive medical attention at the appropriate time. This system allows users to easily consult about their health, much like a local doctor in the old days.For example, when users experience minor changes in their physical condition or feel anxious, they can consult the AI ​​generator to receive appropriate advice. Furthermore, by collaborating with medical institutions as needed, they can receive prompt and appropriate medical care. This allows for more efficient and effective health management for users. As a result, the health management system can comprehensively manage the user's health status and provide appropriate advice.

[0029] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a conversation unit, and a communication unit. The data collection unit collects data. For example, the data collection unit collects medical records, visit information, and prescription drug information from hospitals and pharmacies. The data collection unit can also collect data on the user's usual health status from wearable devices. For example, the data collection unit obtains detailed data such as the user's heart rate, exercise level, and sleep status. Furthermore, the data collection unit can also collect data such as the user's diet and stress level. For example, the data collection unit collects information such as how much exercise the user does on a daily basis and the quality of their sleep. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit evaluates the user's health status based on the collected data. For example, the analysis unit analyzes data such as the user's heart rate, exercise level, and sleep status to evaluate the health status. Furthermore, the analysis unit can also analyze data such as the user's diet and stress level. For example, the analysis unit analyzes the user's diet and evaluates the nutritional balance. The conversation unit converses with the user based on the data analyzed by the analysis unit. The conversation unit, for example, when a user consults the system saying, "I haven't been feeling well lately," provides appropriate advice based on data analyzed by the analysis unit. For example, if the conversation unit determines that lack of exercise is the cause, it will advise, "You should exercise more." If the conversation unit determines that lack of sleep is the cause, it will advise, "You should try to get more sleep." Furthermore, the conversation unit can also advise, based on the user's diet, "You should try to eat a balanced diet." The collaboration unit collaborates with hospitals based on the information obtained by the conversation unit. For example, if a user consults the system saying, "I've been having chest pain lately," and the collaboration unit determines, based on data analyzed by the analysis unit, that there may be a serious health problem, it will collaborate with a hospital and encourage the user to seek medical attention. For example, the collaboration unit will introduce the user to an appropriate medical institution according to their health condition. The collaboration unit can also collaborate with appropriate medical institutions based on the user's medical visit information and prescription information. As a result, the health management system according to this embodiment can comprehensively manage the user's health condition and provide appropriate advice.

[0030] The data collection unit collects data. For example, it collects medical records, visit information, and prescription drug information from hospitals and pharmacies. Specifically, it links with hospital electronic medical record systems to obtain patient medical records, test results, and information on prescribed medications. From pharmacies, it can collect prescription drug receipt history and medication usage status. The data collection unit can also collect data on the user's daily health status from wearable devices. Wearable devices monitor the user's heart rate, exercise level, sleep status, etc., in real time and send this data to a cloud server. For example, smartwatches and fitness trackers fall into this category, and these devices record the user's daily activity level, sleep quality, and heart rate fluctuations in detail. Furthermore, the data collection unit can also collect data such as the user's diet and stress level. For example, by recording the user's diet through a smartphone app, it is possible to understand calorie intake and nutritional balance. Stress levels are also evaluated based on sensors on wearable devices and the user's self-report. In this way, the data collection unit can centrally collect diverse data on the user's health status and support comprehensive health management. Furthermore, the data collection unit securely manages this data and implements appropriate security measures to protect privacy. For example, it implements data encryption and access control to protect users' personal information. This enables the data collection unit to achieve reliable data collection and improve the reliability and security of the entire system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit evaluates the user's health status based on the collected data. Specifically, it uses AI to analyze the data and comprehensively evaluate the user's health status. For example, it analyzes data such as heart rate, exercise level, and sleep status to evaluate the user's cardiac health, exercise habits, and sleep quality. Based on past data and statistical information, the AI ​​can detect abnormal patterns and trends, enabling early detection of health risks. The analysis unit can also analyze data such as the user's diet and stress level. For example, it analyzes the diet to evaluate nutritional balance. The AI ​​calculates the calories and nutrient intake from the meal and evaluates whether the user's diet is healthy. Furthermore, it analyzes the stress level to evaluate the user's mental health status. In this way, the analysis unit can evaluate the user's health status from multiple angles and identify individual health risks. In addition, the analysis unit generates personalized advice tailored to the user's health status. For example, if lack of exercise is the cause, it provides specific advice on increasing exercise. Also, if nutritional balance is poor, it provides specific suggestions on how to maintain a balanced diet. This allows the analysis unit to analyze the user's health status in detail and provide appropriate advice tailored to their individual needs.

[0032] The conversation unit converses with the user based on data analyzed by the analysis unit. For example, if a user says, "I haven't been feeling well lately," the conversation unit will provide appropriate advice based on the data analyzed by the analysis unit. Specifically, it uses natural language processing technology to understand the user's statements and generate appropriate responses. For example, if a user says, "I haven't been feeling well lately," the conversation unit will identify the cause of the poor health based on the data provided by the analysis unit and provide specific advice. If the cause is lack of exercise, it will advise, "Let's exercise more." If the cause is lack of sleep, it will advise, "Let's try to get more sleep." Furthermore, the conversation unit can also advise, based on the user's diet, "Let's try to eat a balanced diet." Through dialogue with the user, the conversation unit can also collect detailed information about the user's health condition and feed it back to the analysis unit. This allows the conversation unit to continuously monitor the user's health condition through dialogue and provide appropriate advice. Furthermore, the conversation unit can continuously improve the content of its advice based on user feedback. For example, if a user reports the results of following the advice, the conversation unit provides that information to the analysis unit to evaluate the effectiveness of the advice. This allows the conversational unit to provide appropriate advice tailored to individual needs through dialogue with the user, thereby supporting the user's health management.

[0033] The Collaboration Department collaborates with hospitals based on information obtained by the Conversation Department. For example, if a user reports experiencing chest pain, the Collaboration Department, based on data analyzed by the Analysis Department, may determine that there is a potential serious health problem and will collaborate with a hospital to encourage the user to seek medical attention. Specifically, the Collaboration Department will refer the user to an appropriate medical institution based on their health condition. For example, if the chest pain may be related to the heart, it will refer the user to a cardiology specialist and instruct them to seek immediate medical attention. The Collaboration Department can also collaborate with appropriate medical institutions based on the user's medical history and prescription information. For example, if the user is already receiving treatment at a specific hospital, the Collaboration Department can collaborate with that hospital to arrange an appointment. Furthermore, the Collaboration Department can share data on the user's health condition with medical institutions to aid in diagnosis and treatment. This allows the Collaboration Department to comprehensively manage the user's health condition and provide prompt and appropriate medical services through collaboration with appropriate medical institutions. In addition, the Collaboration Department can continuously monitor data on the user's health condition and strengthen collaboration with medical institutions as needed. For example, if the user's health condition rapidly deteriorates, the Collaboration Department can immediately collaborate with medical institutions to provide emergency response. This allows the collaborative department to continuously monitor the user's health status and provide prompt and appropriate medical services.

[0034] The data collection unit can collect data from wearable devices. For example, the data collection unit collects data from wearable devices such as smartwatches and fitness trackers. For example, the data collection unit can acquire data such as heart rate, exercise level, and sleep status from a smartwatch. The data collection unit can also collect data such as exercise data and calorie consumption from a fitness tracker. Furthermore, the data collection unit can also collect data such as stress level and body temperature from wearable devices. For example, the data collection unit can acquire data from a wearable device that measures stress levels to understand the user's stress level. This allows for a detailed understanding of the user's health condition through data collection from wearable devices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from wearable devices into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can analyze the collected data and evaluate the user's health status. For example, the analysis unit can analyze the user's heart rate, exercise level, sleep status, etc., based on the collected data to evaluate their health status. For example, the analysis unit can analyze heart rate data to evaluate the user's cardiac health status. The analysis unit can also analyze exercise data to evaluate the user's exercise habits. Furthermore, the analysis unit can analyze sleep data to evaluate the quality of the user's sleep. For example, the analysis unit can evaluate the depth and duration of the user's sleep based on the sleep data. This allows for an accurate evaluation of the user's health status through data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0036] The conversation unit can converse with the user and provide advice regarding their health. For example, if the user says, "I haven't been feeling well lately," the conversation unit will provide appropriate advice based on data analyzed by the analysis unit. For example, if the conversation unit determines that lack of exercise is the cause, it will advise, "You should exercise more." If the cause is lack of sleep, it will advise, "You should try to get more sleep." Furthermore, based on the user's diet, the conversation unit can also advise, "You should try to eat a balanced diet." In this way, appropriate health advice can be provided through conversation with the user. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input the content of the conversation with the user into a generating AI and have the generating AI generate the advice.

[0037] The collaboration unit can, as needed, collaborate with hospitals and encourage users to seek medical attention. For example, if a user reports experiencing chest pain, the collaboration unit, based on data analyzed by the analysis unit, may determine that there is a possibility of a serious health problem and will collaborate with hospitals to encourage the user to seek medical attention. For example, the collaboration unit may recommend an appropriate medical institution based on the user's health condition. The collaboration unit can also collaborate with appropriate medical institutions based on the user's medical history and prescription information. This allows users to receive appropriate medical care through collaboration with hospitals. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input data on the user's health condition into a generating AI and have the generating AI select an appropriate medical institution.

[0038] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze the user's past heart rate data and increase the frequency of heart rate data collection if abnormalities are found. The data collection unit can also analyze the user's past exercise data and strengthen the collection of exercise data if a lack of exercise is observed. Furthermore, the data collection unit can analyze the user's past sleep data and collect sleep data in more detail if the quality of sleep is poor. For example, the data collection unit can select the optimal data collection method based on the user's past health data. This allows for the selection of the optimal data collection method by analyzing past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current lifestyle and activity level during data collection. For example, if the user is exercising, the data collection unit can prioritize collecting exercise data and temporarily suspend the collection of other data. The data collection unit can also prioritize collecting heart rate and sleep data if the user is resting. Furthermore, if the user is working, the data collection unit can collect data related to stress levels and concentration. For example, the data collection unit filters data collection based on the user's current lifestyle and activity level, enabling data collection tailored to the user's lifestyle and activity level. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's lifestyle and activity level into a generating AI and have the generating AI perform the data collection filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is near a hospital, the data collection unit can prioritize the collection of medical-related data. It can also prioritize the collection of exercise data if the user is at a sports facility. Furthermore, if the user is at home, the data collection unit can prioritize the collection of relaxation and sleep data. For example, the data collection unit prioritizes the collection of highly relevant data based on the user's geographical location information. This allows for the priority collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant health data during data collection. For example, if the user posts on social media indicating they are stressed, the data collection unit can collect stress-related data. The data collection unit can also collect exercise data if the user posts about exercise. Furthermore, if the user posts about sleep, the data collection unit can collect sleep data. For example, the data collection unit collects relevant health data based on the user's social media activity. This allows for the collection of relevant health data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant health data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit will analyze important health data (heart rate, blood pressure, etc.) in detail. The analysis unit can also analyze secondary data (exercise level, diet, etc.) in a simplified manner. Furthermore, the analysis unit can quickly perform a detailed analysis of urgent data (abnormal heart rate, etc.). For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the collected data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a time-series analysis algorithm to heart rate data. It can also apply a pattern recognition algorithm to exercise data. Furthermore, it can apply a clustering algorithm to sleep data. For example, the analysis unit applies different analysis algorithms depending on the data category. This enables highly accurate analysis by applying analysis algorithms appropriate to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit prioritizes the analysis of recently collected data. The analysis unit can also perform a detailed analysis of current data while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data with high urgency (such as abnormal heart rate). For example, the analysis unit determines the priority of analysis based on the data collection timing. This enables efficient analysis by determining priorities based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0045] The analysis unit can adjust the order of analysis based on the relationships between the data during the analysis. For example, the analysis unit may consider the relationship between heart rate and exercise data during the analysis. It can also consider the relationship between sleep data and stress data during the analysis. Furthermore, it can consider the relationship between diet data and blood glucose data during the analysis. For example, the analysis unit adjusts the order of analysis based on the relationships between the data. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0046] The conversation unit can adjust the level of detail of advice based on the user's health condition during a conversation. For example, if the user's health condition is good, the conversation unit will provide concise advice. If the user's health condition is unstable, the conversation unit can also provide detailed advice. Furthermore, if the user's health condition is deteriorating, the conversation unit can provide urgent advice. For example, the conversation unit adjusts the level of detail of advice based on the user's health condition. This allows for the provision of appropriate advice by adjusting the level of detail according to the user's health condition. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input user health condition data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0047] The conversation unit can provide appropriate advice during a conversation based on the user's past consultation history. For example, the conversation unit can provide advice by referring to the content of the user's past consultations. The conversation unit can also find patterns in the user's past consultation history and provide appropriate advice. Furthermore, the conversation unit can analyze the user's past consultation history and provide the most effective advice. For example, the conversation unit provides appropriate advice based on the user's past consultation history. This allows the conversation unit to provide the user with the most optimal advice by providing advice based on past consultation history. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input the user's past consultation history data into a generating AI and have the generating AI perform the task of providing appropriate advice.

[0048] The conversational unit can prioritize advice based on the user's lifestyle during a conversation. For example, if the user's lifestyle is unhealthy, the conversational unit will prioritize advice on improving that lifestyle. If the user's lifestyle is healthy, the conversational unit can also provide advice on maintaining it. Furthermore, if there are problems with the user's lifestyle, the conversational unit can prioritize providing specific improvement measures. For example, the conversational unit determines the priority of advice based on the user's lifestyle. This allows for the provision of appropriate advice by prioritizing advice based on the user's lifestyle. Some or all of the above processing in the conversational unit may be performed using AI, for example, or without AI. For example, the conversational unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of determining the priority of advice.

[0049] The conversation unit can adjust the content of its advice based on the user's areas of interest during a conversation. For example, if the user is interested in exercise, the conversation unit will provide advice on exercise. It can also provide advice on diet if the user is interested in diet. Furthermore, if the user is interested in sleep, it can provide advice on sleep. For example, the conversation unit adjusts the content of its advice based on the user's areas of interest. This allows for the provision of more appropriate advice by adjusting the content of the advice based on the user's areas of interest. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input user area of ​​interest data into a generating AI and have the generating AI adjust the content of the advice.

[0050] The collaboration unit can adjust the level of detail of the collaboration based on the user's health status during the collaboration process. For example, if the user's health status is good, the collaboration unit can provide a simple collaboration method. If the user's health status is unstable, the collaboration unit can also provide a detailed collaboration method. Furthermore, if the user's health status is deteriorating, the collaboration unit can provide an emergency collaboration method. For example, the collaboration unit adjusts the level of detail of the collaboration based on the user's health status. This allows for appropriate medical collaboration by adjusting the level of detail of the collaboration according to the user's health status. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without using AI. For example, the collaboration unit can input user health status data into a generating AI and have the generating AI perform the adjustment of the level of detail of the collaboration.

[0051] The collaboration unit can select an appropriate medical institution based on the user's past medical history during the collaboration process. For example, the collaboration unit may prioritize selecting medical institutions the user has visited in the past. The collaboration unit can also select medical institutions with specialists based on the user's past medical history. Furthermore, the collaboration unit can analyze the user's past medical history and select the most appropriate medical institution. For example, the collaboration unit may select an appropriate medical institution based on the user's past medical history. This enables appropriate medical collaboration by selecting medical institutions based on past medical history. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's past medical history data into a generating AI and have the generating AI perform the selection of an appropriate medical institution.

[0052] The collaboration unit can select the most suitable medical institution by considering the user's geographical location information during the collaboration process. For example, the collaboration unit can prioritize selecting medical institutions close to the user's current location. It can also select medical institutions along the user's commuting route. Furthermore, it can select medical institutions near places the user frequently visits. For example, the collaboration unit can select the most suitable medical institution based on the user's geographical location information. This enables rapid and appropriate medical collaboration through the selection of medical institutions based on geographical location information. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of the most suitable medical institution.

[0053] The collaboration unit can analyze a user's social media activity and collaborate with relevant medical institutions during the collaboration process. For example, the collaboration unit can collaborate with medical institutions recommended by the user on social media. It can also collaborate with medical institutions that the user follows on social media. Furthermore, the collaboration unit can collaborate with medical institutions that have received high ratings from the user on social media. For example, the collaboration unit collaborates with relevant medical institutions based on the user's social media activity. This enables medical collaboration tailored to the user by collaborating with medical institutions based on social media activity. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's social media activity data into a generating AI and have the generating AI execute the collaboration with relevant medical institutions.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] A health management system can analyze a user's past health data and predict future health risks. For example, it can analyze past heart rate data to predict the risk of heart disease. It can also analyze past exercise data to predict health risks due to lack of exercise. Furthermore, it can analyze past sleep data to predict health risks due to sleep deprivation. This allows for the prediction of future health risks and the implementation of preventive measures based on the user's past health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into a generating AI and have the generating AI perform health risk predictions.

[0056] The health management system can adjust the content of health advice based on the user's geographical location. For example, if the user is at high altitude, it can provide advice on exercise at high altitude. If the user is in an urban area, it can provide advice on stress management in urban areas. Furthermore, if the user is at the beach, it can provide advice on relaxation methods at the beach. This allows for the provision of appropriate health advice based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI adjust the content of the advice.

[0057] A health management system can analyze a user's past health data and provide optimal health advice. For example, it can analyze past heart rate data and provide advice to maintain heart health. It can also analyze past exercise data and provide advice to improve exercise habits. Furthermore, it can analyze past sleep data and provide advice to improve sleep quality. This allows the system to provide optimal health advice based on the user's past health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into a generating AI and have the generating AI perform the task of providing optimal health advice.

[0058] A health management system can prioritize health advice based on the user's lifestyle. For example, if the user's lifestyle is unhealthy, it will prioritize advice on improving that lifestyle. If the user's lifestyle is healthy, it can also provide advice on maintaining it. Furthermore, if there are problems with the user's lifestyle, it can prioritize providing specific improvement measures. This allows for the provision of appropriate advice by prioritizing advice based on the user's lifestyle. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input the user's lifestyle data into a generating AI and have the generating AI determine the priority of advice.

[0059] The health management system can analyze a user's social media activity and collect relevant health data. For example, if a user posts on social media indicating they are stressed, stress-related data can be collected. Similarly, if a user posts about exercise, exercise data can be collected. Furthermore, if a user posts about sleep, sleep data can be collected. This allows for the collection of relevant health data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant health data.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects data. For example, the data collection unit collects medical records, visit information, and prescription drug information from hospitals and pharmacies. The data collection unit can also collect data on the user's normal health status from wearable devices. For example, the data collection unit obtains detailed data such as the user's heart rate, exercise level, and sleep status. Furthermore, the data collection unit can also collect data such as the user's diet and stress level. For example, the data collection unit collects information such as how much exercise the user does on a daily basis and the quality of their sleep. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit evaluates the user's health status based on the collected data. For example, the analysis unit analyzes data such as the user's heart rate, exercise level, and sleep status to evaluate their health status. The analysis unit can also analyze data such as the user's diet and stress level. For example, the analysis unit analyzes the user's diet to evaluate nutritional balance. Step 3: The conversation unit engages in conversation with the user based on the data analyzed by the analysis unit. For example, if the user says, "I haven't been feeling well lately," the conversation unit will provide appropriate advice based on the data analyzed by the analysis unit. For example, if the conversation unit determines that lack of exercise is the cause, it will advise, "You should exercise more." If the conversation unit determines that lack of sleep is the cause, it will advise, "You should try to get more sleep." Furthermore, based on the user's diet, the conversation unit can also advise, "You should try to eat a balanced diet." Step 4: The liaison unit collaborates with hospitals based on information obtained by the conversation unit. For example, if a user reports "I've been experiencing chest pain recently," the liaison unit, based on data analyzed by the analysis unit, may determine that there is a possibility of a serious health problem and will collaborate with a hospital to encourage the user to seek medical attention. For example, the liaison unit will refer the user to an appropriate medical institution based on their health condition. The liaison unit can also collaborate with appropriate medical institutions based on the user's medical history and prescription information.

[0062] (Example of form 2) The health management system according to an embodiment of the present invention is a system that utilizes generative AI to provide communication similar to that of a local doctor. This health management system links information with hospitals and pharmacies and stores medical records, visit information, and prescription drug information. Next, it collects data on the user's usual health condition from a wearable device. Based on this data, the generative AI converses with the user and provides advice on their health condition. For example, if a user says, "I haven't been feeling well lately," the generative AI analyzes the stored data and provides appropriate advice. Furthermore, the generative AI links with a hospital as needed and encourages the user to see a doctor. This mechanism allows users to easily consult about their health, just like a local doctor in the old days. First, it links information with hospitals and pharmacies and stores medical records, visit information, and prescription drug information. At this time, data provided by each medical institution is centrally managed, allowing for a comprehensive understanding of the user's health condition. For example, it stores information such as which hospitals the user visits and which medications they are prescribed. Next, it collects data on the user's usual health condition from a wearable device. This allows for the acquisition of detailed data such as the user's heart rate, exercise level, and sleep status. For example, the system collects information such as how much exercise a user does on a daily basis and the quality of their sleep. Based on this data, the generating AI converses with the user and provides advice on their health. For example, if a user says, "I haven't been feeling well lately," the generating AI analyzes the accumulated data and provides appropriate advice. For instance, if the cause is lack of exercise, the generating AI will advise, "You should exercise more." If the cause is lack of sleep, it will advise, "You should get more sleep." Furthermore, the generating AI will coordinate with hospitals as needed and encourage the user to seek medical attention. For example, if a user says, "I've been experiencing chest pain lately," the generating AI will analyze the accumulated data and, if it determines that there may be a serious health problem, it will coordinate with hospitals and encourage the user to seek medical attention. In this way, users can receive medical attention at the appropriate time. This system allows users to easily consult about their health, much like a local doctor in the old days.For example, when users experience minor changes in their physical condition or feel anxious, they can consult the AI ​​generator to receive appropriate advice. Furthermore, by collaborating with medical institutions as needed, they can receive prompt and appropriate medical care. This allows for more efficient and effective health management for users. As a result, the health management system can comprehensively manage the user's health status and provide appropriate advice.

[0063] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a conversation unit, and a communication unit. The data collection unit collects data. For example, the data collection unit collects medical records, visit information, and prescription drug information from hospitals and pharmacies. The data collection unit can also collect data on the user's usual health status from wearable devices. For example, the data collection unit obtains detailed data such as the user's heart rate, exercise level, and sleep status. Furthermore, the data collection unit can also collect data such as the user's diet and stress level. For example, the data collection unit collects information such as how much exercise the user does on a daily basis and the quality of their sleep. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit evaluates the user's health status based on the collected data. For example, the analysis unit analyzes data such as the user's heart rate, exercise level, and sleep status to evaluate the health status. Furthermore, the analysis unit can also analyze data such as the user's diet and stress level. For example, the analysis unit analyzes the user's diet and evaluates the nutritional balance. The conversation unit converses with the user based on the data analyzed by the analysis unit. The conversation unit, for example, when a user consults the system saying, "I haven't been feeling well lately," provides appropriate advice based on data analyzed by the analysis unit. For example, if the conversation unit determines that lack of exercise is the cause, it will advise, "You should exercise more." If the conversation unit determines that lack of sleep is the cause, it will advise, "You should try to get more sleep." Furthermore, the conversation unit can also advise, based on the user's diet, "You should try to eat a balanced diet." The collaboration unit collaborates with hospitals based on the information obtained by the conversation unit. For example, if a user consults the system saying, "I've been having chest pain lately," and the collaboration unit determines, based on data analyzed by the analysis unit, that there may be a serious health problem, it will collaborate with a hospital and encourage the user to seek medical attention. For example, the collaboration unit will introduce the user to an appropriate medical institution according to their health condition. The collaboration unit can also collaborate with appropriate medical institutions based on the user's medical visit information and prescription information. As a result, the health management system according to this embodiment can comprehensively manage the user's health condition and provide appropriate advice.

[0064] The data collection unit collects data. For example, it collects medical records, visit information, and prescription drug information from hospitals and pharmacies. Specifically, it links with hospital electronic medical record systems to obtain patient medical records, test results, and information on prescribed medications. From pharmacies, it can collect prescription drug receipt history and medication usage status. The data collection unit can also collect data on the user's daily health status from wearable devices. Wearable devices monitor the user's heart rate, exercise level, sleep status, etc., in real time and send this data to a cloud server. For example, smartwatches and fitness trackers fall into this category, and these devices record the user's daily activity level, sleep quality, and heart rate fluctuations in detail. Furthermore, the data collection unit can also collect data such as the user's diet and stress level. For example, by recording the user's diet through a smartphone app, it is possible to understand calorie intake and nutritional balance. Stress levels are also evaluated based on sensors on wearable devices and the user's self-report. In this way, the data collection unit can centrally collect diverse data on the user's health status and support comprehensive health management. Furthermore, the data collection unit securely manages this data and implements appropriate security measures to protect privacy. For example, it implements data encryption and access control to protect users' personal information. This enables the data collection unit to achieve reliable data collection and improve the reliability and security of the entire system.

[0065] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit evaluates the user's health status based on the collected data. Specifically, it uses AI to analyze the data and comprehensively evaluate the user's health status. For example, it analyzes data such as heart rate, exercise level, and sleep status to evaluate the user's cardiac health, exercise habits, and sleep quality. Based on past data and statistical information, the AI ​​can detect abnormal patterns and trends, enabling early detection of health risks. The analysis unit can also analyze data such as the user's diet and stress level. For example, it analyzes the diet to evaluate nutritional balance. The AI ​​calculates the calories and nutrient intake from the meal and evaluates whether the user's diet is healthy. Furthermore, it analyzes the stress level to evaluate the user's mental health status. In this way, the analysis unit can evaluate the user's health status from multiple angles and identify individual health risks. In addition, the analysis unit generates personalized advice tailored to the user's health status. For example, if lack of exercise is the cause, it provides specific advice on increasing exercise. Also, if nutritional balance is poor, it provides specific suggestions on how to maintain a balanced diet. This allows the analysis unit to analyze the user's health status in detail and provide appropriate advice tailored to their individual needs.

[0066] The conversation unit converses with the user based on data analyzed by the analysis unit. For example, if a user says, "I haven't been feeling well lately," the conversation unit will provide appropriate advice based on the data analyzed by the analysis unit. Specifically, it uses natural language processing technology to understand the user's statements and generate appropriate responses. For example, if a user says, "I haven't been feeling well lately," the conversation unit will identify the cause of the poor health based on the data provided by the analysis unit and provide specific advice. If the cause is lack of exercise, it will advise, "Let's exercise more." If the cause is lack of sleep, it will advise, "Let's try to get more sleep." Furthermore, the conversation unit can also advise, based on the user's diet, "Let's try to eat a balanced diet." Through dialogue with the user, the conversation unit can also collect detailed information about the user's health condition and feed it back to the analysis unit. This allows the conversation unit to continuously monitor the user's health condition through dialogue and provide appropriate advice. Furthermore, the conversation unit can continuously improve the content of its advice based on user feedback. For example, if a user reports the results of following the advice, the conversation unit provides that information to the analysis unit to evaluate the effectiveness of the advice. This allows the conversational unit to provide appropriate advice tailored to individual needs through dialogue with the user, thereby supporting the user's health management.

[0067] The Collaboration Department collaborates with hospitals based on information obtained by the Conversation Department. For example, if a user reports experiencing chest pain, the Collaboration Department, based on data analyzed by the Analysis Department, may determine that there is a potential serious health problem and will collaborate with a hospital to encourage the user to seek medical attention. Specifically, the Collaboration Department will refer the user to an appropriate medical institution based on their health condition. For example, if the chest pain may be related to the heart, it will refer the user to a cardiology specialist and instruct them to seek immediate medical attention. The Collaboration Department can also collaborate with appropriate medical institutions based on the user's medical history and prescription information. For example, if the user is already receiving treatment at a specific hospital, the Collaboration Department can collaborate with that hospital to arrange an appointment. Furthermore, the Collaboration Department can share data on the user's health condition with medical institutions to aid in diagnosis and treatment. This allows the Collaboration Department to comprehensively manage the user's health condition and provide prompt and appropriate medical services through collaboration with appropriate medical institutions. In addition, the Collaboration Department can continuously monitor data on the user's health condition and strengthen collaboration with medical institutions as needed. For example, if the user's health condition rapidly deteriorates, the Collaboration Department can immediately collaborate with medical institutions to provide emergency response. This allows the collaborative department to continuously monitor the user's health status and provide prompt and appropriate medical services.

[0068] The data collection unit can collect data from wearable devices. For example, the data collection unit collects data from wearable devices such as smartwatches and fitness trackers. For example, the data collection unit can acquire data such as heart rate, exercise level, and sleep status from a smartwatch. The data collection unit can also collect data such as exercise data and calorie consumption from a fitness tracker. Furthermore, the data collection unit can also collect data such as stress level and body temperature from wearable devices. For example, the data collection unit can acquire data from a wearable device that measures stress levels to understand the user's stress level. This allows for a detailed understanding of the user's health condition through data collection from wearable devices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from wearable devices into a generating AI and have the generating AI perform data analysis.

[0069] The analysis unit can analyze the collected data and evaluate the user's health status. For example, the analysis unit can analyze the user's heart rate, exercise level, sleep status, etc., based on the collected data to evaluate their health status. For example, the analysis unit can analyze heart rate data to evaluate the user's cardiac health status. The analysis unit can also analyze exercise data to evaluate the user's exercise habits. Furthermore, the analysis unit can analyze sleep data to evaluate the quality of the user's sleep. For example, the analysis unit can evaluate the depth and duration of the user's sleep based on the sleep data. This allows for an accurate evaluation of the user's health status through data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0070] The conversation unit can converse with the user and provide advice regarding their health. For example, if the user says, "I haven't been feeling well lately," the conversation unit will provide appropriate advice based on data analyzed by the analysis unit. For example, if the conversation unit determines that lack of exercise is the cause, it will advise, "You should exercise more." If the cause is lack of sleep, it will advise, "You should try to get more sleep." Furthermore, based on the user's diet, the conversation unit can also advise, "You should try to eat a balanced diet." In this way, appropriate health advice can be provided through conversation with the user. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input the content of the conversation with the user into a generating AI and have the generating AI generate the advice.

[0071] The collaboration unit can, as needed, collaborate with hospitals and encourage users to seek medical attention. For example, if a user reports experiencing chest pain, the collaboration unit, based on data analyzed by the analysis unit, may determine that there is a possibility of a serious health problem and will collaborate with hospitals to encourage the user to seek medical attention. For example, the collaboration unit may recommend an appropriate medical institution based on the user's health condition. The collaboration unit can also collaborate with appropriate medical institutions based on the user's medical history and prescription information. This allows users to receive appropriate medical care through collaboration with hospitals. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input data on the user's health condition into a generating AI and have the generating AI select an appropriate medical institution.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to obtain more detailed health data. Furthermore, if the user is in a hurry, the data collection unit can temporarily stop data collection and resume it later. For example, the data collection unit can monitor the user's emotions in real time and adjust the timing of data collection according to changes in emotions. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0073] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze the user's past heart rate data and increase the frequency of heart rate data collection if abnormalities are found. The data collection unit can also analyze the user's past exercise data and strengthen the collection of exercise data if a lack of exercise is observed. Furthermore, the data collection unit can analyze the user's past sleep data and collect sleep data in more detail if the quality of sleep is poor. For example, the data collection unit can select the optimal data collection method based on the user's past health data. This allows for the selection of the optimal data collection method by analyzing past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0074] The data collection unit can filter data based on the user's current lifestyle and activity level during data collection. For example, if the user is exercising, the data collection unit can prioritize collecting exercise data and temporarily suspend the collection of other data. The data collection unit can also prioritize collecting heart rate and sleep data if the user is resting. Furthermore, if the user is working, the data collection unit can collect data related to stress levels and concentration. For example, the data collection unit filters data collection based on the user's current lifestyle and activity level, enabling data collection tailored to the user's lifestyle and activity level. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the user's lifestyle and activity level into a generating AI and have the generating AI perform the data collection filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. It can also prioritize collecting heart rate and sleep data if the user is relaxed. Furthermore, if the user is excited, it can prioritize collecting exercise data. For example, the data collection unit can monitor the user's emotions in real time and determine the priority of data to collect according to changes in emotions. This allows for the priority collection of important data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data priority.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is near a hospital, the data collection unit can prioritize the collection of medical-related data. It can also prioritize the collection of exercise data if the user is at a sports facility. Furthermore, if the user is at home, the data collection unit can prioritize the collection of relaxation and sleep data. For example, the data collection unit prioritizes the collection of highly relevant data based on the user's geographical location information. This allows for the priority collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0077] The data collection unit can analyze the user's social media activity and collect relevant health data during data collection. For example, if the user posts on social media indicating they are stressed, the data collection unit can collect stress-related data. The data collection unit can also collect exercise data if the user posts about exercise. Furthermore, if the user posts about sleep, the data collection unit can collect sleep data. For example, the data collection unit collects relevant health data based on the user's social media activity. This allows for the collection of relevant health data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant health data.

[0078] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit will analyze stress-related data in detail. If the user is relaxed, the analysis unit can also analyze overall health data in a balanced manner. Furthermore, if the user is in a hurry, the analysis unit can quickly analyze only the important data. For example, the analysis unit can monitor the user's emotions in real time and adjust the data analysis method according to changes in emotions. This allows for more appropriate analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit will analyze important health data (heart rate, blood pressure, etc.) in detail. The analysis unit can also analyze secondary data (exercise level, diet, etc.) in a simplified manner. Furthermore, the analysis unit can quickly perform a detailed analysis of urgent data (abnormal heart rate, etc.). For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the collected data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a time-series analysis algorithm to heart rate data. It can also apply a pattern recognition algorithm to exercise data. Furthermore, it can apply a clustering algorithm to sleep data. For example, the analysis unit applies different analysis algorithms depending on the data category. This enables highly accurate analysis by applying analysis algorithms appropriate to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit can monitor the user's emotions in real time and adjust the display method of the analysis results according to changes in emotions. This allows for the provision of highly visible analysis results by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0082] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit prioritizes the analysis of recently collected data. The analysis unit can also perform a detailed analysis of current data while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data with high urgency (such as abnormal heart rate). For example, the analysis unit determines the priority of analysis based on the data collection timing. This enables efficient analysis by determining priorities based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0083] The analysis unit can adjust the order of analysis based on the relationships between the data during the analysis. For example, the analysis unit may consider the relationship between heart rate and exercise data during the analysis. It can also consider the relationship between sleep data and stress data during the analysis. Furthermore, it can consider the relationship between diet data and blood glucose data during the analysis. For example, the analysis unit adjusts the order of analysis based on the relationships between the data. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0084] The conversational unit can estimate the user's emotions and adjust the way the conversation is expressed based on those emotions. For example, if the user is stressed, the conversational unit will proceed in a calm tone. Conversely, if the user is relaxed, the conversational unit can proceed in a friendly tone. Furthermore, if the user is in a hurry, the conversational unit can engage in a quick and concise conversation. For example, the conversational unit can monitor the user's emotions in real time and adjust the way the conversation is expressed in response to changes in those emotions. This allows for more appropriate communication by adjusting the way the conversation is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversational unit may be performed using AI, or not using AI. For example, the conversational unit can input user emotion data into the generative AI and have the generative AI adjust the way the conversation is expressed.

[0085] The conversation unit can adjust the level of detail of advice based on the user's health condition during a conversation. For example, if the user's health condition is good, the conversation unit will provide concise advice. If the user's health condition is unstable, the conversation unit can also provide detailed advice. Furthermore, if the user's health condition is deteriorating, the conversation unit can provide urgent advice. For example, the conversation unit adjusts the level of detail of advice based on the user's health condition. This allows for the provision of appropriate advice by adjusting the level of detail according to the user's health condition. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input user health condition data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0086] The conversation unit can provide appropriate advice during a conversation based on the user's past consultation history. For example, the conversation unit can provide advice by referring to the content of the user's past consultations. The conversation unit can also find patterns in the user's past consultation history and provide appropriate advice. Furthermore, the conversation unit can analyze the user's past consultation history and provide the most effective advice. For example, the conversation unit provides appropriate advice based on the user's past consultation history. This allows the conversation unit to provide the user with the most optimal advice by providing advice based on past consultation history. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input the user's past consultation history data into a generating AI and have the generating AI perform the task of providing appropriate advice.

[0087] The conversational unit can estimate the user's emotions and adjust the length of the conversation based on the estimated emotions. For example, if the user is stressed, the conversational unit will engage in short, to-the-point conversations. Conversely, if the user is relaxed, the conversational unit can engage in longer conversations with more detailed explanations. Furthermore, if the user is in a hurry, the conversational unit can engage in quick and concise conversations. For example, the conversational unit can monitor the user's emotions in real time and adjust the length of the conversation in response to changes in emotions. This allows for more appropriate communication by adjusting the length of the conversation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversational unit may be performed using AI, or not using AI. For example, the conversational unit can input user emotion data into a generative AI and have the generative AI adjust the length of the conversation.

[0088] The conversational unit can prioritize advice based on the user's lifestyle during a conversation. For example, if the user's lifestyle is unhealthy, the conversational unit will prioritize advice on improving that lifestyle. If the user's lifestyle is healthy, the conversational unit can also provide advice on maintaining it. Furthermore, if there are problems with the user's lifestyle, the conversational unit can prioritize providing specific improvement measures. For example, the conversational unit determines the priority of advice based on the user's lifestyle. This allows for the provision of appropriate advice by prioritizing advice based on the user's lifestyle. Some or all of the above processing in the conversational unit may be performed using AI, for example, or without AI. For example, the conversational unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of determining the priority of advice.

[0089] The conversation unit can adjust the content of its advice based on the user's areas of interest during a conversation. For example, if the user is interested in exercise, the conversation unit will provide advice on exercise. It can also provide advice on diet if the user is interested in diet. Furthermore, if the user is interested in sleep, it can provide advice on sleep. For example, the conversation unit adjusts the content of its advice based on the user's areas of interest. This allows for the provision of more appropriate advice by adjusting the content of the advice based on the user's areas of interest. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input user area of ​​interest data into a generating AI and have the generating AI adjust the content of the advice.

[0090] The collaboration unit can estimate the user's emotions and adjust the method of collaboration with the hospital based on the estimated emotions. For example, if the user is stressed, the collaboration unit can provide a quick and concise method of collaboration. If the user is relaxed, the collaboration unit can also provide a detailed method of collaboration. Furthermore, if the user is in a hurry, the collaboration unit can provide a method of collaboration that can be completed in the shortest possible time. For example, the collaboration unit can monitor the user's emotions in real time and adjust the method of collaboration with the hospital in response to changes in emotions. This allows for more appropriate medical collaboration by adjusting the collaboration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or not using AI. For example, the collaboration unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the collaboration method.

[0091] The collaboration unit can adjust the level of detail of the collaboration based on the user's health status during the collaboration process. For example, if the user's health status is good, the collaboration unit can provide a simple collaboration method. If the user's health status is unstable, the collaboration unit can also provide a detailed collaboration method. Furthermore, if the user's health status is deteriorating, the collaboration unit can provide an emergency collaboration method. For example, the collaboration unit adjusts the level of detail of the collaboration based on the user's health status. This allows for appropriate medical collaboration by adjusting the level of detail of the collaboration according to the user's health status. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without using AI. For example, the collaboration unit can input user health status data into a generating AI and have the generating AI perform the adjustment of the level of detail of the collaboration.

[0092] The collaboration unit can select an appropriate medical institution based on the user's past medical history during the collaboration process. For example, the collaboration unit may prioritize selecting medical institutions the user has visited in the past. The collaboration unit can also select medical institutions with specialists based on the user's past medical history. Furthermore, the collaboration unit can analyze the user's past medical history and select the most appropriate medical institution. For example, the collaboration unit may select an appropriate medical institution based on the user's past medical history. This enables appropriate medical collaboration by selecting medical institutions based on past medical history. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's past medical history data into a generating AI and have the generating AI perform the selection of an appropriate medical institution.

[0093] The collaboration unit can estimate the user's emotions and determine the priority of collaboration based on the estimated emotions. For example, if the user is feeling stressed, the collaboration unit can initiate collaboration quickly. Furthermore, if the user is relaxed, the collaboration unit can initiate collaboration in the shortest possible time if the user is in a hurry. For example, the collaboration unit can monitor the user's emotions in real time and determine the priority of collaboration according to changes in emotions. This enables rapid and appropriate medical collaboration by determining the priority of collaboration according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI, or not using AI. For example, the collaboration unit can input user emotion data into the generative AI and have the generative AI determine the priority of collaboration.

[0094] The collaboration unit can select the most suitable medical institution by considering the user's geographical location information during the collaboration process. For example, the collaboration unit can prioritize selecting medical institutions close to the user's current location. It can also select medical institutions along the user's commuting route. Furthermore, it can select medical institutions near places the user frequently visits. For example, the collaboration unit can select the most suitable medical institution based on the user's geographical location information. This enables rapid and appropriate medical collaboration through the selection of medical institutions based on geographical location information. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of the most suitable medical institution.

[0095] The collaboration unit can analyze a user's social media activity and collaborate with relevant medical institutions during the collaboration process. For example, the collaboration unit can collaborate with medical institutions recommended by the user on social media. It can also collaborate with medical institutions that the user follows on social media. Furthermore, the collaboration unit can collaborate with medical institutions that have received high ratings from the user on social media. For example, the collaboration unit collaborates with relevant medical institutions based on the user's social media activity. This enables medical collaboration tailored to the user by collaborating with medical institutions based on social media activity. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the user's social media activity data into a generating AI and have the generating AI execute the collaboration with relevant medical institutions.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] A health management system can estimate a user's emotions and adjust the content of health advice based on those emotions. For example, if a user is stressed, it can prioritize providing advice on relaxation methods and stress reduction. If the user is relaxed, it can also provide advice on maintaining good health. Furthermore, if the user is excited, it can provide advice on exercise and activity. This allows for the provision of appropriate health advice tailored to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversation unit may be performed using AI, or not using AI. For example, the conversation unit can input user emotion data into the generative AI and have the generative AI adjust the content of the advice.

[0098] A health management system can analyze a user's past health data and predict future health risks. For example, it can analyze past heart rate data to predict the risk of heart disease. It can also analyze past exercise data to predict health risks due to lack of exercise. Furthermore, it can analyze past sleep data to predict health risks due to sleep deprivation. This allows for the prediction of future health risks and the implementation of preventive measures based on the user's past health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into a generating AI and have the generating AI perform health risk predictions.

[0099] A health management system can estimate a user's emotions and adjust how health data is displayed based on those emotions. For example, if a user is stressed, it can provide a simple and easy-to-read display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. This allows for the provision of highly visible health data by adjusting the display method according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0100] The health management system can adjust the content of health advice based on the user's geographical location. For example, if the user is at high altitude, it can provide advice on exercise at high altitude. If the user is in an urban area, it can provide advice on stress management in urban areas. Furthermore, if the user is at the beach, it can provide advice on relaxation methods at the beach. This allows for the provision of appropriate health advice based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI adjust the content of the advice.

[0101] The health management system can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of data collection can be increased to obtain more detailed health data. Furthermore, if the user is in a hurry, data collection can be temporarily stopped and resumed later. This reduces the user's burden by adjusting the frequency of data collection according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not using AI. For example, the collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the frequency of data collection.

[0102] A health management system can analyze a user's past health data and provide optimal health advice. For example, it can analyze past heart rate data and provide advice to maintain heart health. It can also analyze past exercise data and provide advice to improve exercise habits. Furthermore, it can analyze past sleep data and provide advice to improve sleep quality. This allows the system to provide optimal health advice based on the user's past health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into a generating AI and have the generating AI perform the task of providing optimal health advice.

[0103] A health management system can estimate a user's emotions and adjust the method of analyzing health data based on those emotions. For example, if a user is stressed, stress-related data can be analyzed in detail. If a user is relaxed, overall health data can be analyzed in a balanced manner. Furthermore, if a user is in a hurry, only important data can be quickly analyzed. This allows for more appropriate analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the data analysis method.

[0104] A health management system can prioritize health advice based on the user's lifestyle. For example, if the user's lifestyle is unhealthy, it will prioritize advice on improving that lifestyle. If the user's lifestyle is healthy, it can also provide advice on maintaining it. Furthermore, if there are problems with the user's lifestyle, it can prioritize providing specific improvement measures. This allows for the provision of appropriate advice by prioritizing advice based on the user's lifestyle. Some or all of the above processing in the conversation unit may be performed using AI, for example, or without AI. For example, the conversation unit can input the user's lifestyle data into a generating AI and have the generating AI determine the priority of advice.

[0105] A health management system can estimate a user's emotions and adjust the way health advice is presented based on those emotions. For example, if a user is stressed, advice can be provided in a calm tone. If the user is relaxed, advice can be provided in a friendly tone. Furthermore, if the user is in a hurry, quick and concise advice can be provided. This allows for more appropriate communication by adjusting the way advice is presented according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversational unit may be performed using AI or not. For example, the conversational unit can input user emotion data into the generative AI and have the generative AI adjust the way advice is presented.

[0106] The health management system can analyze a user's social media activity and collect relevant health data. For example, if a user posts on social media indicating they are stressed, stress-related data can be collected. Similarly, if a user posts about exercise, exercise data can be collected. Furthermore, if a user posts about sleep, sleep data can be collected. This allows for the collection of relevant health data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant health data.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects data. For example, the data collection unit collects medical records, visit information, and prescription drug information from hospitals and pharmacies. The data collection unit can also collect data on the user's normal health status from wearable devices. For example, the data collection unit obtains detailed data such as the user's heart rate, exercise level, and sleep status. Furthermore, the data collection unit can also collect data such as the user's diet and stress level. For example, the data collection unit collects information such as how much exercise the user does on a daily basis and the quality of their sleep. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit evaluates the user's health status based on the collected data. For example, the analysis unit analyzes data such as the user's heart rate, exercise level, and sleep status to evaluate their health status. The analysis unit can also analyze data such as the user's diet and stress level. For example, the analysis unit analyzes the user's diet to evaluate nutritional balance. Step 3: The conversation unit engages in conversation with the user based on the data analyzed by the analysis unit. For example, if the user says, "I haven't been feeling well lately," the conversation unit will provide appropriate advice based on the data analyzed by the analysis unit. For example, if the conversation unit determines that lack of exercise is the cause, it will advise, "You should exercise more." If the conversation unit determines that lack of sleep is the cause, it will advise, "You should try to get more sleep." Furthermore, based on the user's diet, the conversation unit can also advise, "You should try to eat a balanced diet." Step 4: The liaison unit collaborates with hospitals based on information obtained by the conversation unit. For example, if a user reports "I've been experiencing chest pain recently," the liaison unit, based on data analyzed by the analysis unit, may determine that there is a possibility of a serious health problem and will collaborate with a hospital to encourage the user to seek medical attention. For example, the liaison unit will refer the user to an appropriate medical institution based on their health condition. The liaison unit can also collaborate with appropriate medical institutions based on the user's medical history and prescription information.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, conversation unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects information from hospitals and pharmacies using the camera 42 and communication I / F 44 of the smart device 14, and also acquires data from wearable devices. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to evaluate the user's health status. The conversation unit is implemented in the specific processing unit 46A of the smart device 14, and converses with the user based on the analysis results and provides appropriate advice. The collaboration unit is implemented in the specific processing unit 290 of the data processing unit 12, and collaborates with hospitals as needed to encourage medical examinations. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, conversation unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects information from hospitals and pharmacies using the camera 42 and communication I / F 44 of the smart glasses 214, and also acquires data from wearable devices. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to evaluate the user's health status. The conversation unit is implemented in the specific processing unit 46A of the smart glasses 214, for example, and converses with the user based on the analysis results and provides appropriate advice. The collaboration unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and collaborates with hospitals to prompt medical examinations as needed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, conversation unit, and collaboration unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects information from hospitals and pharmacies using the camera 42 and communication I / F 44 of the headset terminal 314, and also acquires data from wearable devices. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to evaluate the user's health status. The conversation unit is implemented, for example, by the control unit 46A of the headset terminal 314, which converses with the user based on the analysis results and provides appropriate advice. The collaboration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which collaborates with hospitals as needed to encourage medical examinations. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, conversation unit, and collaboration unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects information from hospitals and pharmacies using the camera 42 and communication I / F 44 of the robot 414, and also acquires data from wearable devices. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to evaluate the user's health status. The conversation unit is implemented, for example, by the control unit 46A of the robot 414, and converses with the user based on the analysis results and provides appropriate advice. The collaboration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and collaborates with hospitals as needed to facilitate medical examinations. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A conversation unit that converses with the user based on the data analyzed by the aforementioned analysis unit, The system includes a communication unit that communicates with hospitals based on information obtained by the aforementioned communication unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data from wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to assess the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned conversation section is, Conversing with users and providing advice on their health status. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, We will coordinate with hospitals as needed and encourage patients to seek medical attention. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant health data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned conversation section is, It estimates the user's emotions and adjusts the way the conversation is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned conversation section is, During conversations, the level of detail in advice is adjusted based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned conversation section is, During conversations, provide appropriate advice based on the user's past consultation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned conversation section is, It estimates the user's emotions and adjusts the length of the conversation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned conversation section is, During conversations, the system prioritizes advice based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned conversation section is, During conversations, the advice is tailored based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, The system estimates the user's emotions and adjusts the method of collaboration with hospitals based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, When connecting, the level of detail of the connection is adjusted based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, During integration, the system selects an appropriate medical institution based on the user's past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of collaborations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, During integration, the system selects the most suitable medical institution by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, During the integration process, the system analyzes the user's social media activity and connects with relevant medical institutions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A conversation unit that converses with the user based on the data analyzed by the aforementioned analysis unit, The system includes a communication unit that communicates with hospitals based on information obtained by the aforementioned communication unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data from wearable devices. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to assess the user's health status. The system according to feature 1.

4. The aforementioned conversation section is, Conversing with users and providing advice on their health status. The system according to feature 1.

5. The aforementioned linkage unit is, We will coordinate with hospitals as needed and encourage patients to seek medical attention. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and activity level. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

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