system

Abstract

The system according to this embodiment aims to identify the user's risk of lifestyle-related diseases in advance and propose specific improvement measures. [Solution] The system according to the embodiment comprises a recording unit, an analysis unit, a specification unit, a proposal unit, and a pickup unit. The recording unit records the user's lifestyle habits. The analysis unit analyzes the data recorded by the recording unit. The specification unit identifies the risk of lifestyle-related diseases based on the data analyzed by the analysis unit. The proposal unit proposes specific improvement measures based on the risks identified by the specification unit. The pickup unit picks up potential hospitals based on the improvement measures proposed by the proposal unit.

Description

Technical Field

[0004] ,

[0006] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the risk of a user's lifestyle disease has not been sufficiently identified in advance, and specific improvement measures have not been sufficiently proposed, leaving room for improvement.

[0005] The system according to the embodiment aims to identify in advance the risk of a user's lifestyle disease and propose specific improvement measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recording unit, an analysis unit, a selection unit, a proposal unit, and a pickup unit. The recording unit records the user's lifestyle habits. The analysis unit analyzes the data recorded by the recording unit. The selection unit identifies the risk of lifestyle-related diseases based on the data analyzed by the analysis unit. The proposal unit proposes specific improvement measures based on the risks identified by the selection unit. The pickup unit selects potential hospitals based on the improvement measures proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify a user's risk of lifestyle-related diseases in advance and propose specific improvement measures. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) A healthcare system according to an embodiment of the present invention is a system that automatically records and analyzes a user's lifestyle habits and identifies the risk of lifestyle-related diseases in advance. This healthcare system records the user's lifestyle habits, and a generating AI analyzes the recorded data to identify the risk of lifestyle-related diseases. Furthermore, it proposes specific improvement measures based on the identified risks and selects potential hospitals in case the user becomes ill. This allows the user to improve their lifestyle habits and maintain their health before becoming ill. In addition, even if the user becomes ill, they can receive prompt and appropriate medical care, leading to a reduction in medical expenses. For example, if lack of exercise is identified as a risk factor, the generating AI sets daily exercise goals for the user and provides advice on how to achieve them. Also, if the user's sleep patterns are irregular, the generating AI provides advice on how to promote regular sleep. In this way, the healthcare system can maintain the user's health and reduce medical expenses.

[0029] The healthcare system according to this embodiment comprises a recording unit, an analysis unit, a identification unit, a suggestion unit, and a pickup unit. The recording unit records the user's lifestyle habits. The recording unit records the user's steps, heart rate, sleep patterns, etc., using IoT devices such as smartwatches and fitness trackers. The recording unit records the user's steps using a smartwatch, for example. The recording unit can also record the user's heart rate using a fitness tracker. The recording unit can also record the user's sleep patterns using IoT devices. The analysis unit analyzes the data recorded by the recording unit. The analysis unit analyzes the recorded data using a generative AI, for example. The generative AI analyzes the data using a text generation AI (e.g., LLM), for example. The analysis unit can also analyze the data using a multimodal generative AI. The analysis unit can also analyze data patterns using a generative AI. The identification unit identifies the risk of lifestyle-related diseases based on the data analyzed by the analysis unit. The identification unit identifies the risk using a generative AI, for example. The generation AI identifies risks, for example, using a text generation AI (e.g., LLM). The identification unit can also identify risks using a multimodal generation AI. Furthermore, the identification unit can identify risk patterns using the generation AI. The proposal unit proposes specific improvement measures based on the risks identified by the identification unit. The proposal unit proposes improvement measures, for example, using a generation AI. The generation AI proposes improvement measures, for example, using a text generation AI (e.g., LLM). Furthermore, the proposal unit can propose improvement measures using a multimodal generation AI. Furthermore, the proposal unit can propose improvement measures patterns using the generation AI. The pick-up unit selects hospital candidates based on the improvement measures proposed by the proposal unit. The pick-up unit selects hospital candidates, for example, using a generation AI. The generation AI selects hospital candidates, for example, using a text generation AI (e.g., LLM). Furthermore, the pick-up unit can select hospital candidates using a multimodal generation AI. Furthermore, the pick-up unit can also select hospital candidate patterns using the generation AI.As a result, the healthcare system according to this embodiment can provide comprehensive health support by automatically recording and analyzing the user's lifestyle, identifying the risk of lifestyle-related diseases in advance, proposing specific improvement measures, and selecting potential hospitals.

[0030] The recording unit records the user's lifestyle habits. For example, it uses IoT devices such as smartwatches and fitness trackers to record the user's steps, heart rate, and sleep patterns. Specifically, smartwatches are worn on the user's wrist and use built-in accelerometers and gyroscopes to count steps. They also measure heart rate in real time using heart rate sensors and transmit the data to the cloud. Fitness trackers record the user's exercise volume and calorie consumption, monitoring their daily activity level. Furthermore, these devices detect nighttime movement and heart rate fluctuations to record the user's sleep patterns, analyzing the duration of deep and light sleep. This allows the recording unit to collect detailed data on the user's lifestyle habits and gain a comprehensive understanding of their health. The recorded data can be viewed by the user through a dedicated application and can also be shared with medical professionals. This allows users to understand their health status in real time and take concrete actions to improve their lifestyle habits as needed.

[0031] The analysis unit analyzes the data recorded by the recording unit. The analysis unit analyzes the recorded data using, for example, a generative AI. The generative AI analyzes the data using, for example, a text generation AI (e.g., LLM). Specifically, the generative AI receives data such as the user's step count, heart rate, and sleep patterns as input, and detects trends and anomalies in the user's health status from this data. For example, it analyzes the user's exercise habits from step count data and evaluates stress levels and cardiac health status from heart rate data. It also analyzes sleep pattern data to identify sleep quality and the possibility of sleep disorders. Furthermore, the analysis unit can also use multimodal generative AI to comprehensively analyze different types of data. This allows for a more comprehensive evaluation of the user's health status and the detection of patterns and anomalies that might be overlooked with individual data alone. The analysis results are provided to the user in a visually easy-to-understand format, allowing for an intuitive understanding of changes in health status and risk factors. In this way, the analysis unit plays an important role in analyzing the user's health status in detail and detecting problems early.

[0032] The identification unit identifies the risk of lifestyle-related diseases based on data analyzed by the analysis unit. The identification unit identifies risks using, for example, generative AI. The generative AI identifies risks using, for example, text generation AI (e.g., LLM). Specifically, the generative AI builds a model to identify risk factors for lifestyle-related diseases based on data provided by the analysis unit. This model is based on historical data and medical knowledge, and evaluates the risk in comparison with the user's data. For example, abnormal fluctuations in heart rate, lack of exercise, and poor sleep quality are identified as risk factors for lifestyle-related diseases. The identification unit can also use multimodal generative AI to comprehensively analyze multiple data sources and identify risk patterns. This allows the identification unit to comprehensively evaluate the user's health status and detect lifestyle-related disease risks early. The identified risks are notified to the user in a specific form, and information is provided to take necessary measures. In this way, the identification unit plays an important role in identifying the user's health risks early and supporting a preventive approach.

[0033] The proposal department proposes specific improvement measures based on the risks identified by the identification department. The proposal department proposes improvement measures using, for example, a generative AI. The generative AI proposes improvement measures using, for example, a text generation AI (e.g., LLM). Specifically, the generative AI proposes the optimal improvement measures for the user based on the risk information provided by the identification department. For example, if lack of exercise is identified, the generative AI proposes an exercise plan that suits the user's lifestyle and preferences. Also, if an abnormality in heart rate is detected, it proposes methods for stress management and relaxation. Furthermore, if the quality of sleep is poor, it can propose improvements to the sleep environment and a review of sleep habits. The proposal department can also use a multimodal generative AI to provide individual improvement measures tailored to the user's health condition and lifestyle. In this way, the proposal department provides users with specific and actionable improvement measures and supports the improvement of their health. The proposed improvement measures are notified to the user through their smartphone or a dedicated application, and the user can implement them in their daily life. In this way, the proposal department plays an important role in supporting the improvement of the user's health and contributing to the prevention of lifestyle-related diseases.

[0034] The selection unit selects hospital candidates based on the improvement measures proposed by the proposal unit. The selection unit uses, for example, a generative AI to select hospital candidates. The generative AI uses, for example, a text generation AI (e.g., LLM) to select hospital candidates. Specifically, the generative AI selects the most suitable hospital or medical institution according to the user's place of residence and identified risks. For example, if the user has heart problems, it will select a cardiology clinic or hospital. If a sleep disorder is identified, it can suggest a sleep clinic. The selection unit can also use a multimodal generative AI to select the most suitable medical institution according to the user's health condition and lifestyle. In this way, the selection unit can quickly provide the user with appropriate medical institutions and support them in receiving the necessary medical services. The selected hospital candidates are notified via the user's smartphone or a dedicated application, and the user can easily make reservations or inquiries. In this way, the selection unit plays an important role in comprehensively supporting the user's health management and assisting in the provision of prompt and appropriate medical services.

[0035] The recording unit can record the user's steps, heart rate, sleep patterns, etc., using IoT devices such as smartwatches and fitness trackers. For example, the recording unit can record the user's steps using a smartwatch. The recording unit can also record the user's heart rate using a fitness tracker. The recording unit can also record the user's sleep patterns using an IoT device. This allows for detailed recording of the user's lifestyle habits using IoT devices. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input data acquired from a smartwatch into a generating AI and have the generating AI perform data analysis.

[0036] The analysis unit can identify the user's risk of lifestyle-related diseases based on the data recorded by the recording unit. The analysis unit can, for example, analyze the recorded data using a generation AI to identify the risk. The generation AI can, for example, analyze the data using a text generation AI (e.g., LLM) to identify the risk. The analysis unit can also analyze the data using a multimodal generation AI to identify the risk. Furthermore, the analysis unit can analyze data patterns using a generation AI to identify the risk. This allows for an accurate assessment of the user's health status by identifying the risk based on the recorded data. 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 data acquired from the recording unit into a generation AI and have the generation AI perform risk identification.

[0037] The suggestion unit can set daily exercise goals for the user based on identified risks and provide advice for achieving them. The suggestion unit can, for example, use a generative AI to set exercise goals and provide advice. The generative AI can, for example, use a text generation AI (e.g., LLM) to set exercise goals and provide advice. The suggestion unit can also set exercise goals and provide advice using a multimodal generative AI. Furthermore, the suggestion unit can use a generative AI to set exercise goal patterns and provide advice. This allows for the provision of specific exercise goals and advice to the user, thereby promoting improvements in lifestyle habits. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input risk data obtained from the identification unit into the generative AI and have the generative AI perform the setting of exercise goals and the provision of advice.

[0038] The suggestion unit can provide users with advice to promote regular sleep based on identified risks. The suggestion unit can, for example, use a generative AI to suggest sleep improvement measures. The generative AI can, for example, use a text generation AI (e.g., LLM) to suggest sleep improvement measures. The suggestion unit can also suggest sleep improvement measures using a multimodal generative AI. Furthermore, the suggestion unit can suggest patterns of sleep improvement measures using a generative AI. This can promote improvements in lifestyle habits by providing users with specific sleep improvement measures. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input risk data obtained from the identification unit into the generative AI and have the generative AI execute the suggestion of sleep improvement measures.

[0039] The selection unit can suggest the most suitable hospital based on the user's place of residence, hospital ratings, and specialist information. The selection unit can, for example, use a generative AI to select hospital candidates. The generative AI can, for example, use a text generation AI (e.g., LLM) to select hospital candidates. The selection unit can also use a multimodal generative AI to select hospital candidates. Furthermore, the selection unit can use a generative AI to select patterns of hospital candidates. This allows the user to receive appropriate medical care quickly by suggesting the most suitable hospital. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input improvement data obtained from the suggestion unit into the generative AI and have the generative AI perform the selection of hospital candidates.

[0040] The recording unit can optimize the recording frequency by referring to the user's past health data during recording. For example, if the recording unit finds from the user's past health data that the user's health tends to deteriorate during certain time periods, it can increase the recording frequency during those times. For example, if the recording unit finds from the user's past data that the user's exercise level tends to increase on weekends, it can also increase the recording frequency on weekends. For example, if the recording unit finds from the user's past data that the user's health fluctuates during certain seasons, it can also adjust the recording frequency for those seasons. This optimizes the recording frequency by referring to the user's past health data, enabling efficient data collection. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input past health data into a generating AI and have the generating AI perform the optimization of the recording frequency.

[0041] The recording unit can correct data based on the user's living environment (e.g., temperature and humidity) during recording. For example, the recording unit can correct exercise data in a hot and humid environment to calculate accurate calorie consumption. The recording unit can also correct heart rate data in a low-temperature environment to accurately assess stress levels. The recording unit can also correct sleep data considering the difference between indoor and outdoor environments. By correcting the data while considering the user's living environment, more accurate data can be obtained. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input living environment data into a generating AI and have the generating AI perform the data correction.

[0042] The recording unit can automatically record the user's meal content and integrate it with lifestyle data during recording. For example, the recording unit can analyze the meal content and automatically record calories and nutrients simply by having the user take a picture of the meal. The recording unit can also analyze the meal content and integrate it with lifestyle data simply by having the user input the meal menu. For example, the recording unit can estimate the meal content and integrate it with lifestyle data simply by having the user record the time of the meal. This enables comprehensive health management by automatically recording the user's meal content and integrating it with lifestyle data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input meal content data into a generating AI and have the generating AI perform data analysis and integration.

[0043] The recording unit can analyze the user's exercise patterns in real time during recording and issue alerts if it detects an anomaly. For example, the recording unit will issue an alert in real time if the user's heart rate increases rapidly. The recording unit can also issue an alert in real time if the user's exercise pattern is different from normal. For example, the recording unit can also issue an alert in real time if it detects abnormal movements during exercise. This enables a rapid response by analyzing the user's exercise patterns in real time and issuing alerts when an anomaly is detected. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input exercise pattern data into a generating AI and have the generating AI perform real-time analysis and issue alerts.

[0044] The analysis unit can improve the accuracy of the analysis by referring to past analysis results during the analysis process. For example, the analysis unit can extract specific patterns from past analysis results and reflect them in the current analysis. For example, the analysis unit can also adjust the analysis algorithm based on past analysis results to improve accuracy. For example, the analysis unit can refer to past analysis results and set criteria for detecting outliers. In this way, the accuracy of the analysis can be improved by referring to past analysis results. 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 past analysis results into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0045] The analysis unit can perform risk assessments based on the user's genetic information during analysis. For example, the analysis unit can assess specific genetic risks based on the user's genetic information. The analysis unit can also analyze the user's genetic information and identify the risk of lifestyle-related diseases. The analysis unit can also perform personalized risk assessments by taking the user's genetic information into consideration. This enables personalized risk assessments by considering the user's genetic information. 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 user's genetic information into a generating AI and have the generating AI perform the risk assessment.

[0046] The analysis unit can analyze a user's lifestyle data by comparing it with data from other users. For example, the analysis unit can compare a user's lifestyle data with other users of the same age group to perform a risk assessment. The analysis unit can also compare a user's lifestyle data with other users in the same region to perform a risk assessment. The analysis unit can also compare a user's lifestyle data with other users in the same occupation to perform a risk assessment. This allows for a more accurate risk assessment by comparing the data with that of other users. 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 data from other users into a generating AI and have the generating AI perform a comparative analysis.

[0047] The analysis unit can evaluate the user's stress level during analysis and associate it with the risk of lifestyle-related diseases. For example, the analysis unit can evaluate the user's stress level and associate it with the risk of lifestyle-related diseases. The analysis unit can also analyze the user's stress level and reflect it in the risk assessment. For example, the analysis unit can identify the risk of lifestyle-related diseases by considering the user's stress level. This makes it possible to perform a more accurate risk assessment by evaluating the user's stress level and associating it with the risk of lifestyle-related diseases. 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 stress level data into a generating AI and have the generating AI perform the risk assessment.

[0048] The identification unit can perform a risk assessment by referring to the user's past medical history at the time of identification. For example, the identification unit assesses a specific risk based on the user's past medical history. The identification unit can also refer to the user's past medical history and reflect it in the risk assessment. The identification unit can also identify risks by considering the user's past medical history. This improves the accuracy of the risk assessment by referring to the user's past medical history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input the user's past medical history data into a generating AI and have the generating AI perform the risk assessment.

[0049] The identification unit can perform a risk assessment considering the user's family history at the time of identification. For example, the identification unit can assess genetic risk based on the user's family history. The identification unit can also refer to the user's family history and reflect it in the risk assessment. The identification unit can also perform risk identification considering the user's family history. This allows for the assessment of genetic risk by considering the user's family history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's family history data into a generating AI and have the generating AI perform the risk assessment.

[0050] The identification unit can perform a risk assessment by comparing the user's lifestyle data with regional health data at the time of identification. For example, the identification unit can perform a risk assessment by comparing the user's lifestyle data with the regional average. The identification unit can also, for example, compare the user's lifestyle data with regional health data and reflect this in the risk assessment. The identification unit can also, for example, identify risks by comparing the user's lifestyle data with regional health data. This improves the accuracy of the user's risk assessment by comparing it with regional health data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input regional health data into a generating AI and have the generating AI perform the risk assessment.

[0051] The identification unit can perform risk assessments at the time of identification, taking into account the user's occupation and lifestyle. For example, the identification unit can assess specific risks based on the user's occupation. The identification unit can also refer to the user's lifestyle and reflect it in the risk assessment. The identification unit can also identify risks by considering the user's occupation and lifestyle. This allows for more individualized risk assessments by considering the user's occupation and lifestyle. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's occupation and lifestyle data into a generating AI and have the generating AI perform the risk assessment.

[0052] The suggestion unit can make optimal suggestions by referring to the user's past behavioral history when making suggestions. For example, the suggestion unit can suggest an optimal exercise plan based on the user's past exercise history. For example, the suggestion unit can also suggest an optimal meal plan based on the user's past eating history. For example, the suggestion unit can also suggest optimal sleep improvement measures based on the user's past sleep history. This allows for more effective suggestions by referring to the user's past behavioral history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past behavioral history data into a generating AI and have the generating AI execute the optimal suggestion.

[0053] The suggestion unit can customize the suggested content when making suggestions, taking into account the user's living environment. For example, the suggestion unit can customize an exercise plan considering the user's living environment (e.g., whether they live in an urban or suburban area). The suggestion unit can also customize a meal plan considering the user's climate conditions. The suggestion unit can also customize sleep improvement measures considering the user's daily rhythm. This allows for more personalized suggestions by taking the user's living environment into account. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input living environment data into a generating AI and have the generating AI perform the customization of the suggested content.

[0054] The suggestion unit can propose nutritional balances while considering the user's diet. For example, the suggestion unit can analyze the user's diet and propose ways to improve the nutritional balance. For example, the suggestion unit can recommend the intake of specific nutrients based on the user's diet. For example, the suggestion unit can customize meal plans while considering the user's diet. This makes it possible to propose more appropriate nutritional balances by considering the user's diet. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input meal data into a generating AI and have the generating AI execute nutritional balance proposals.

[0055] The suggestion unit can propose an exercise program considering the user's exercise patterns when making a suggestion. For example, the suggestion unit can analyze the user's exercise patterns and propose an optimal exercise program. For example, the suggestion unit can also propose a program with adjusted exercise intensity based on the user's exercise history. For example, the suggestion unit can customize the exercise plan considering the user's exercise patterns. This makes it possible to propose a more effective exercise program by considering the user's exercise patterns. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input exercise pattern data into a generating AI and have the generating AI execute the exercise program proposal.

[0056] The selection unit can select the most suitable hospital by referring to the user's past medical history during the selection process. For example, the selection unit can select a hospital with a specific specialist based on the user's past medical history. The selection unit can also select the most suitable hospital by referring to the user's past medical history. The selection unit can also select hospital candidates by considering the user's past medical history. This allows for the selection of a more appropriate hospital by referring to the user's past medical history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input past medical history data into a generating AI and have the generating AI perform the selection of hospital candidates.

[0057] The selection unit can select hospitals while considering the user's insurance information. For example, the selection unit can select hospitals within the scope of insurance coverage based on the user's insurance information. The selection unit can also select the most suitable hospital by referring to the user's insurance information. The selection unit can also select hospital candidates while considering the user's insurance information. In this way, by considering the user's insurance information, it is possible to select hospitals within the scope of insurance coverage. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input insurance information data into a generating AI and have the generating AI perform the selection of hospital candidates.

[0058] The pickup unit can select a hospital that is easily accessible, taking into account the user's mode of transportation. For example, if the user is using a car, the pickup unit can select a hospital with ample parking. If the user is using public transportation, the pickup unit can also select a hospital close to a train station or bus stop. If the user is traveling on foot, the pickup unit can also select a hospital within walking distance. In this way, the system can select a hospital that is easily accessible by considering the user's mode of transportation. Some or all of the above processing in the pickup unit may be performed using AI, for example, or without AI. For example, the pickup unit can input transportation data into a generating AI and have the generating AI select hospital candidates.

[0059] The selection unit can select hospitals with specialists based on the user's medical needs during the selection process. For example, if the user has a specific illness, the selection unit can select a hospital with a specialist in that illness. For example, if the user needs a specific treatment, the selection unit can also select a hospital that provides that treatment. For example, if the user needs a specific test, the selection unit can also select a hospital that provides that test. This allows users to receive more appropriate medical care by selecting hospitals with specialists based on their medical needs. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input medical needs data into a generating AI and have the generating AI perform the selection of hospital candidates.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] A healthcare system can include a recording unit that collects user lifestyle data, as well as a frequency adjustment unit that optimizes recording frequency by referencing the user's past health data. For example, if the user's past health data shows a tendency for their health to deteriorate during certain time periods, the recording unit can be instructed to increase the recording frequency during those times. Similarly, if the user's past data shows a tendency for increased exercise on weekends, the recording frequency on weekends can also be increased. Furthermore, if the user's past data shows fluctuations in their health during certain seasons, the recording frequency can be adjusted accordingly. This allows for optimized recording frequency and efficient data collection by referencing the user's past health data.

[0062] A healthcare system can include, in addition to a recording unit that collects user lifestyle data, an environmental correction unit that corrects the data based on the user's living environment (e.g., temperature and humidity). For example, it can correct exercise data in hot and humid environments and instruct the recording unit to calculate accurate calorie expenditure. It can also correct heart rate data in low-temperature environments and accurately assess stress levels. Furthermore, it can correct sleep data considering the difference between indoor and outdoor environments. By correcting the data while considering the user's living environment, more accurate data can be obtained.

[0063] A healthcare system can include a recording unit that collects user lifestyle data, as well as a meal recording unit that automatically records the user's meals and integrates them with lifestyle data. For example, if the user simply takes a picture of their meal, the AI ​​can analyze the meal content and instruct the recording unit to automatically record calories and nutrients. Alternatively, if the user simply enters the menu of their meal, the AI ​​can analyze the meal content and integrate it with lifestyle data. Furthermore, if the user simply records the time of their meal, the AI ​​can estimate the meal content and integrate it with lifestyle data. This enables comprehensive health management by automatically recording the user's meals and integrating them with lifestyle data.

[0064] A healthcare system can include a recording unit that collects user lifestyle data, as well as an anomaly detection unit that analyzes the user's exercise patterns in real time and issues alerts if abnormalities are detected. For example, if the user's heart rate suddenly increases, the recording unit can be instructed to issue an alert in real time. It can also issue an alert in real time if the user's exercise pattern is different from normal. Furthermore, it can issue an alert in real time if it detects abnormal movements during exercise. This allows for a rapid response by analyzing the user's exercise patterns in real time and issuing alerts when anomalies are detected.

[0065] A healthcare system can include a historical data reference unit in addition to an analysis unit that analyzes users' lifestyle data, to improve the accuracy of the analysis by referring to past analysis results. For example, it can extract specific patterns from past analysis results and instruct the analysis unit to reflect them in the current analysis. It can also adjust the analysis algorithm based on past analysis results to improve accuracy. Furthermore, it can refer to past analysis results to set criteria for detecting outliers. In this way, the accuracy of the analysis can be improved by referring to past analysis results.

[0066] A healthcare system can include a genetic information analysis unit that performs risk assessments based on the user's genetic information, in addition to an analysis unit that analyzes the user's lifestyle data. For example, the analysis unit can be instructed to assess specific genetic risks based on the user's genetic information. It can also analyze the user's genetic information to identify the risk of lifestyle-related diseases. Furthermore, it can perform personalized risk assessments that take the user's genetic information into consideration. This makes personalized risk assessments possible by considering the user's genetic information.

[0067] A healthcare system can include an analysis unit that analyzes a user's lifestyle data, as well as a comparative analysis unit that compares and analyzes that user's lifestyle data with that of other users. For example, the analysis unit can be instructed to compare a user's lifestyle data with that of other users of the same age group and perform a risk assessment. It can also compare a user's lifestyle data with that of other users in the same region and perform a risk assessment. Furthermore, it can compare a user's lifestyle data with that of other users in the same occupation and perform a risk assessment. This allows for a more accurate risk assessment by comparing data with that of other users.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The recording unit records the user's lifestyle habits. The recording unit records the user's steps, heart rate, sleep patterns, etc., using IoT devices such as smartwatches and fitness trackers. Step 2: The analysis unit analyzes the data recorded by the recording unit. The analysis unit analyzes the recorded data using, for example, a generation AI. The generation AI analyzes the data patterns using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The identification unit identifies the risk of lifestyle-related diseases based on the data analyzed by the analysis unit. The identification unit identifies the risk using, for example, a generative AI. The generative AI identifies the risk pattern using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The proposal team proposes specific improvement measures based on the risks identified by the identification team. The proposal team proposes improvement measures using, for example, a generative AI. The generative AI proposes improvement measures patterns using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 5: The Picking Unit selects hospital candidates based on the improvement measures proposed by the Proposal Unit. The Picking Unit selects hospital candidates using, for example, a Generative AI. The Generative AI selects patterns of hospital candidates using, for example, a Text Generation AI (e.g., LLM) or a Multimodal Generative AI.

[0070] (Example of form 2) A healthcare system according to an embodiment of the present invention is a system that automatically records and analyzes a user's lifestyle habits and identifies the risk of lifestyle-related diseases in advance. This healthcare system records the user's lifestyle habits, and a generating AI analyzes the recorded data to identify the risk of lifestyle-related diseases. Furthermore, it proposes specific improvement measures based on the identified risks and selects potential hospitals in case the user becomes ill. This allows the user to improve their lifestyle habits and maintain their health before becoming ill. In addition, even if the user becomes ill, they can receive prompt and appropriate medical care, leading to a reduction in medical expenses. For example, if lack of exercise is identified as a risk factor, the generating AI sets daily exercise goals for the user and provides advice on how to achieve them. Also, if the user's sleep patterns are irregular, the generating AI provides advice on how to promote regular sleep. In this way, the healthcare system can maintain the user's health and reduce medical expenses.

[0071] The healthcare system according to this embodiment comprises a recording unit, an analysis unit, a identification unit, a suggestion unit, and a pickup unit. The recording unit records the user's lifestyle habits. The recording unit records the user's steps, heart rate, sleep patterns, etc., using IoT devices such as smartwatches and fitness trackers. The recording unit records the user's steps using a smartwatch, for example. The recording unit can also record the user's heart rate using a fitness tracker. The recording unit can also record the user's sleep patterns using IoT devices. The analysis unit analyzes the data recorded by the recording unit. The analysis unit analyzes the recorded data using a generative AI, for example. The generative AI analyzes the data using a text generation AI (e.g., LLM), for example. The analysis unit can also analyze the data using a multimodal generative AI. The analysis unit can also analyze data patterns using a generative AI. The identification unit identifies the risk of lifestyle-related diseases based on the data analyzed by the analysis unit. The identification unit identifies the risk using a generative AI, for example. The generation AI identifies risks, for example, using a text generation AI (e.g., LLM). The identification unit can also identify risks using a multimodal generation AI. Furthermore, the identification unit can identify risk patterns using the generation AI. The proposal unit proposes specific improvement measures based on the risks identified by the identification unit. The proposal unit proposes improvement measures, for example, using a generation AI. The generation AI proposes improvement measures, for example, using a text generation AI (e.g., LLM). Furthermore, the proposal unit can propose improvement measures using a multimodal generation AI. Furthermore, the proposal unit can propose improvement measures patterns using the generation AI. The pick-up unit selects hospital candidates based on the improvement measures proposed by the proposal unit. The pick-up unit selects hospital candidates, for example, using a generation AI. The generation AI selects hospital candidates, for example, using a text generation AI (e.g., LLM). Furthermore, the pick-up unit can select hospital candidates using a multimodal generation AI. Furthermore, the pick-up unit can also select hospital candidate patterns using the generation AI.As a result, the healthcare system according to this embodiment can provide comprehensive health support by automatically recording and analyzing the user's lifestyle, identifying the risk of lifestyle-related diseases in advance, proposing specific improvement measures, and selecting potential hospitals.

[0072] The recording unit records the user's lifestyle habits. For example, it uses IoT devices such as smartwatches and fitness trackers to record the user's steps, heart rate, and sleep patterns. Specifically, smartwatches are worn on the user's wrist and use built-in accelerometers and gyroscopes to count steps. They also measure heart rate in real time using heart rate sensors and transmit the data to the cloud. Fitness trackers record the user's exercise volume and calorie consumption, monitoring their daily activity level. Furthermore, these devices detect nighttime movement and heart rate fluctuations to record the user's sleep patterns, analyzing the duration of deep and light sleep. This allows the recording unit to collect detailed data on the user's lifestyle habits and gain a comprehensive understanding of their health. The recorded data can be viewed by the user through a dedicated application and can also be shared with medical professionals. This allows users to understand their health status in real time and take concrete actions to improve their lifestyle habits as needed.

[0073] The analysis unit analyzes the data recorded by the recording unit. The analysis unit analyzes the recorded data using, for example, a generative AI. The generative AI analyzes the data using, for example, a text generation AI (e.g., LLM). Specifically, the generative AI receives data such as the user's step count, heart rate, and sleep patterns as input, and detects trends and anomalies in the user's health status from this data. For example, it analyzes the user's exercise habits from step count data and evaluates stress levels and cardiac health status from heart rate data. It also analyzes sleep pattern data to identify sleep quality and the possibility of sleep disorders. Furthermore, the analysis unit can also use multimodal generative AI to comprehensively analyze different types of data. This allows for a more comprehensive evaluation of the user's health status and the detection of patterns and anomalies that might be overlooked with individual data alone. The analysis results are provided to the user in a visually easy-to-understand format, allowing for an intuitive understanding of changes in health status and risk factors. In this way, the analysis unit plays an important role in analyzing the user's health status in detail and detecting problems early.

[0074] The identification unit identifies the risk of lifestyle-related diseases based on data analyzed by the analysis unit. The identification unit identifies risks using, for example, generative AI. The generative AI identifies risks using, for example, text generation AI (e.g., LLM). Specifically, the generative AI builds a model to identify risk factors for lifestyle-related diseases based on data provided by the analysis unit. This model is based on historical data and medical knowledge, and evaluates the risk in comparison with the user's data. For example, abnormal fluctuations in heart rate, lack of exercise, and poor sleep quality are identified as risk factors for lifestyle-related diseases. The identification unit can also use multimodal generative AI to comprehensively analyze multiple data sources and identify risk patterns. This allows the identification unit to comprehensively evaluate the user's health status and detect lifestyle-related disease risks early. The identified risks are notified to the user in a specific form, and information is provided to take necessary measures. In this way, the identification unit plays an important role in identifying the user's health risks early and supporting a preventive approach.

[0075] The proposal department proposes specific improvement measures based on the risks identified by the identification department. The proposal department proposes improvement measures using, for example, a generative AI. The generative AI proposes improvement measures using, for example, a text generation AI (e.g., LLM). Specifically, the generative AI proposes the optimal improvement measures for the user based on the risk information provided by the identification department. For example, if lack of exercise is identified, the generative AI proposes an exercise plan that suits the user's lifestyle and preferences. Also, if an abnormality in heart rate is detected, it proposes methods for stress management and relaxation. Furthermore, if the quality of sleep is poor, it can propose improvements to the sleep environment and a review of sleep habits. The proposal department can also use a multimodal generative AI to provide individual improvement measures tailored to the user's health condition and lifestyle. In this way, the proposal department provides users with specific and actionable improvement measures and supports the improvement of their health. The proposed improvement measures are notified to the user through their smartphone or a dedicated application, and the user can implement them in their daily life. In this way, the proposal department plays an important role in supporting the improvement of the user's health and contributing to the prevention of lifestyle-related diseases.

[0076] The selection unit selects hospital candidates based on the improvement measures proposed by the proposal unit. The selection unit uses, for example, a generative AI to select hospital candidates. The generative AI uses, for example, a text generation AI (e.g., LLM) to select hospital candidates. Specifically, the generative AI selects the most suitable hospital or medical institution according to the user's place of residence and identified risks. For example, if the user has heart problems, it will select a cardiology clinic or hospital. If a sleep disorder is identified, it can suggest a sleep clinic. The selection unit can also use a multimodal generative AI to select the most suitable medical institution according to the user's health condition and lifestyle. In this way, the selection unit can quickly provide the user with appropriate medical institutions and support them in receiving the necessary medical services. The selected hospital candidates are notified via the user's smartphone or a dedicated application, and the user can easily make reservations or inquiries. In this way, the selection unit plays an important role in comprehensively supporting the user's health management and assisting in the provision of prompt and appropriate medical services.

[0077] The recording unit can record the user's steps, heart rate, sleep patterns, etc., using IoT devices such as smartwatches and fitness trackers. For example, the recording unit can record the user's steps using a smartwatch. The recording unit can also record the user's heart rate using a fitness tracker. The recording unit can also record the user's sleep patterns using an IoT device. This allows for detailed recording of the user's lifestyle habits using IoT devices. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input data acquired from a smartwatch into a generating AI and have the generating AI perform data analysis.

[0078] The analysis unit can identify the user's risk of lifestyle-related diseases based on the data recorded by the recording unit. The analysis unit can, for example, analyze the recorded data using a generation AI to identify the risk. The generation AI can, for example, analyze the data using a text generation AI (e.g., LLM) to identify the risk. The analysis unit can also analyze the data using a multimodal generation AI to identify the risk. Furthermore, the analysis unit can analyze data patterns using a generation AI to identify the risk. This allows for an accurate assessment of the user's health status by identifying the risk based on the recorded data. 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 data acquired from the recording unit into a generation AI and have the generation AI perform risk identification.

[0079] The suggestion unit can set daily exercise goals for the user based on identified risks and provide advice for achieving them. The suggestion unit can, for example, use a generative AI to set exercise goals and provide advice. The generative AI can, for example, use a text generation AI (e.g., LLM) to set exercise goals and provide advice. The suggestion unit can also set exercise goals and provide advice using a multimodal generative AI. Furthermore, the suggestion unit can use a generative AI to set exercise goal patterns and provide advice. This allows for the provision of specific exercise goals and advice to the user, thereby promoting improvements in lifestyle habits. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input risk data obtained from the identification unit into the generative AI and have the generative AI perform the setting of exercise goals and the provision of advice.

[0080] The suggestion unit can provide users with advice to promote regular sleep based on identified risks. The suggestion unit can, for example, use a generative AI to suggest sleep improvement measures. The generative AI can, for example, use a text generation AI (e.g., LLM) to suggest sleep improvement measures. The suggestion unit can also suggest sleep improvement measures using a multimodal generative AI. Furthermore, the suggestion unit can suggest patterns of sleep improvement measures using a generative AI. This can promote improvements in lifestyle habits by providing users with specific sleep improvement measures. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input risk data obtained from the identification unit into the generative AI and have the generative AI execute the suggestion of sleep improvement measures.

[0081] The selection unit can suggest the most suitable hospital based on the user's place of residence, hospital ratings, and specialist information. The selection unit can, for example, use a generative AI to select hospital candidates. The generative AI can, for example, use a text generation AI (e.g., LLM) to select hospital candidates. The selection unit can also use a multimodal generative AI to select hospital candidates. Furthermore, the selection unit can use a generative AI to select patterns of hospital candidates. This allows the user to receive appropriate medical care quickly by suggesting the most suitable hospital. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input improvement data obtained from the suggestion unit into the generative AI and have the generative AI perform the selection of hospital candidates.

[0082] The recording unit can estimate the user's emotions and adjust the type of data recorded based on the estimated emotions. For example, the recording unit estimates the user's emotions using an emotion estimation algorithm. For example, if the user is stressed, the recording unit prioritizes recording heart rate and stress level. For example, if the user is relaxed, the recording unit can also record sleep patterns and heart rate during relaxation in detail. For example, if the user is exercising, the recording unit can prioritize recording exercise intensity and calories burned. This allows for the collection of more appropriate data by adjusting the type of data recorded according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input user emotion data into a generative AI and have the generative AI adjust the type of data to be recorded.

[0083] The recording unit can optimize the recording frequency by referring to the user's past health data during recording. For example, if the recording unit finds from the user's past health data that the user's health tends to deteriorate during certain time periods, it can increase the recording frequency during those times. For example, if the recording unit finds from the user's past data that the user's exercise level tends to increase on weekends, it can also increase the recording frequency on weekends. For example, if the recording unit finds from the user's past data that the user's health fluctuates during certain seasons, it can also adjust the recording frequency for those seasons. This optimizes the recording frequency by referring to the user's past health data, enabling efficient data collection. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input past health data into a generating AI and have the generating AI perform the optimization of the recording frequency.

[0084] The recording unit can correct data based on the user's living environment (e.g., temperature and humidity) during recording. For example, the recording unit can correct exercise data in a hot and humid environment to calculate accurate calorie consumption. The recording unit can also correct heart rate data in a low-temperature environment to accurately assess stress levels. The recording unit can also correct sleep data considering the difference between indoor and outdoor environments. By correcting the data while considering the user's living environment, more accurate data can be obtained. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input living environment data into a generating AI and have the generating AI perform the data correction.

[0085] The recording unit can estimate the user's emotions and determine the priority of data to record based on the estimated user emotions. For example, the recording unit estimates the user's emotions using an emotion estimation algorithm. For example, if the user is stressed, the recording unit prioritizes recording stress-related data. For example, if the user is relaxed, the recording unit may also prioritize recording physiological data related to relaxation. For example, if the user is exercising, the recording unit may also prioritize recording exercise-related data. This allows important data to be recorded preferentially by determining the priority of data to record according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's emotion data into a generative AI and have the generative AI determine the priority of data to record.

[0086] The recording unit can automatically record the user's meal content and integrate it with lifestyle data during recording. For example, the recording unit can analyze the meal content and automatically record calories and nutrients simply by having the user take a picture of the meal. The recording unit can also analyze the meal content and integrate it with lifestyle data simply by having the user input the meal menu. For example, the recording unit can estimate the meal content and integrate it with lifestyle data simply by having the user record the time of the meal. This enables comprehensive health management by automatically recording the user's meal content and integrating it with lifestyle data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input meal content data into a generating AI and have the generating AI perform data analysis and integration.

[0087] The recording unit can analyze the user's exercise patterns in real time during recording and issue alerts if it detects an anomaly. For example, the recording unit will issue an alert in real time if the user's heart rate increases rapidly. The recording unit can also issue an alert in real time if the user's exercise pattern is different from normal. For example, the recording unit can also issue an alert in real time if it detects abnormal movements during exercise. This enables a rapid response by analyzing the user's exercise patterns in real time and issuing alerts when an anomaly is detected. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input exercise pattern data into a generating AI and have the generating AI perform real-time analysis and issue alerts.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, the analysis unit estimates the user's emotions using an emotion estimation algorithm. For example, if the user is stressed, the analysis unit uses an analysis algorithm that emphasizes stress-related data. For example, if the user is relaxed, the analysis unit may also use an analysis algorithm that emphasizes physiological data during relaxation. For example, if the user is exercising, the analysis unit may also use an analysis algorithm that emphasizes exercise-related data. By adjusting the analysis algorithm according to the user's emotions, more accurate analysis results can be obtained. 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, a text generation AI (e.g., LLM) or a multimodal generation AI. 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 user's emotion data into the generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0089] The analysis unit can improve the accuracy of the analysis by referring to past analysis results during the analysis process. For example, the analysis unit can extract specific patterns from past analysis results and reflect them in the current analysis. For example, the analysis unit can also adjust the analysis algorithm based on past analysis results to improve accuracy. For example, the analysis unit can refer to past analysis results and set criteria for detecting outliers. In this way, the accuracy of the analysis can be improved by referring to past analysis results. 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 past analysis results into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0090] The analysis unit can perform risk assessments based on the user's genetic information during analysis. For example, the analysis unit can assess specific genetic risks based on the user's genetic information. The analysis unit can also analyze the user's genetic information and identify the risk of lifestyle-related diseases. The analysis unit can also perform personalized risk assessments by taking the user's genetic information into consideration. This enables personalized risk assessments by considering the user's genetic information. 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 user's genetic information into a generating AI and have the generating AI perform the risk assessment.

[0091] 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, the analysis unit estimates the user's emotions using an emotion estimation algorithm. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This allows for the provision of more appropriate information by adjusting the display method of the analysis results 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, a text generation AI (e.g., LLM) or a multimodal generation AI. 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 user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0092] The analysis unit can analyze a user's lifestyle data by comparing it with data from other users. For example, the analysis unit can compare a user's lifestyle data with other users of the same age group to perform a risk assessment. The analysis unit can also compare a user's lifestyle data with other users in the same region to perform a risk assessment. The analysis unit can also compare a user's lifestyle data with other users in the same occupation to perform a risk assessment. This allows for a more accurate risk assessment by comparing the data with that of other users. 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 data from other users into a generating AI and have the generating AI perform a comparative analysis.

[0093] The analysis unit can evaluate the user's stress level during analysis and associate it with the risk of lifestyle-related diseases. For example, the analysis unit can evaluate the user's stress level and associate it with the risk of lifestyle-related diseases. The analysis unit can also analyze the user's stress level and reflect it in the risk assessment. For example, the analysis unit can identify the risk of lifestyle-related diseases by considering the user's stress level. This makes it possible to perform a more accurate risk assessment by evaluating the user's stress level and associating it with the risk of lifestyle-related diseases. 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 stress level data into a generating AI and have the generating AI perform the risk assessment.

[0094] The identification unit can estimate the user's emotions and adjust risk identification criteria based on the estimated user emotions. For example, the identification unit estimates the user's emotions using an emotion estimation algorithm. For example, if the user is stressed, the identification unit strengthens stress-related risk identification criteria. For example, if the user is relaxed, the identification unit can also adjust risk identification criteria based on physiological data during relaxation. For example, if the user is exercising, the identification unit can also adjust exercise-related risk identification criteria. This allows for more accurate risk identification by adjusting risk identification criteria 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 identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of risk identification criteria.

[0095] The identification unit can perform a risk assessment by referring to the user's past medical history at the time of identification. For example, the identification unit assesses a specific risk based on the user's past medical history. The identification unit can also refer to the user's past medical history and reflect it in the risk assessment. The identification unit can also identify risks by considering the user's past medical history. This improves the accuracy of the risk assessment by referring to the user's past medical history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input the user's past medical history data into a generating AI and have the generating AI perform the risk assessment.

[0096] The identification unit can perform a risk assessment considering the user's family history at the time of identification. For example, the identification unit can assess genetic risk based on the user's family history. The identification unit can also refer to the user's family history and reflect it in the risk assessment. The identification unit can also perform risk identification considering the user's family history. This allows for the assessment of genetic risk by considering the user's family history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's family history data into a generating AI and have the generating AI perform the risk assessment.

[0097] The identification unit can estimate the user's emotions and determine the priority of risk identification based on the estimated user emotions. For example, the identification unit estimates the user's emotions using an emotion estimation algorithm. For example, if the user is stressed, the identification unit will prioritize identifying stress-related risks. For example, if the user is relaxed, the identification unit can also determine the priority of risk identification based on physiological data during relaxation. For example, if the user is exercising, the identification unit can also prioritize identifying exercise-related risks. In this way, by determining the priority of risk identification according to the user's emotions, important risks can be identified preferentially. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or 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 processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input user emotion data into the generative AI and have the generative AI perform the determination of the priority of risk identification.

[0098] The identification unit can perform a risk assessment by comparing the user's lifestyle data with regional health data at the time of identification. For example, the identification unit can perform a risk assessment by comparing the user's lifestyle data with the regional average. The identification unit can also, for example, compare the user's lifestyle data with regional health data and reflect this in the risk assessment. The identification unit can also, for example, identify risks by comparing the user's lifestyle data with regional health data. This improves the accuracy of the user's risk assessment by comparing it with regional health data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input regional health data into a generating AI and have the generating AI perform the risk assessment.

[0099] The identification unit can perform risk assessments at the time of identification, taking into account the user's occupation and lifestyle. For example, the identification unit can assess specific risks based on the user's occupation. The identification unit can also refer to the user's lifestyle and reflect it in the risk assessment. The identification unit can also identify risks by considering the user's occupation and lifestyle. This allows for more individualized risk assessments by considering the user's occupation and lifestyle. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's occupation and lifestyle data into a generating AI and have the generating AI perform the risk assessment.

[0100] The suggestion unit can estimate the user's emotions and adjust its suggestions based on those emotions. For example, the suggestion unit can estimate the user's emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the suggestion unit can offer suggestions to reduce stress. For example, if the user is relaxed, the suggestion unit can offer suggestions to maintain that relaxation. For example, if the user is exercising, the suggestion unit can offer suggestions to maximize the effects of the exercise. By adjusting the suggestions according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the suggestions.

[0101] The suggestion unit can make optimal suggestions by referring to the user's past behavioral history when making suggestions. For example, the suggestion unit can suggest an optimal exercise plan based on the user's past exercise history. For example, the suggestion unit can also suggest an optimal meal plan based on the user's past eating history. For example, the suggestion unit can also suggest optimal sleep improvement measures based on the user's past sleep history. This allows for more effective suggestions by referring to the user's past behavioral history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past behavioral history data into a generating AI and have the generating AI execute the optimal suggestion.

[0102] The suggestion unit can customize the suggested content when making suggestions, taking into account the user's living environment. For example, the suggestion unit can customize an exercise plan considering the user's living environment (e.g., whether they live in an urban or suburban area). The suggestion unit can also customize a meal plan considering the user's climate conditions. The suggestion unit can also customize sleep improvement measures considering the user's daily rhythm. This allows for more personalized suggestions by taking the user's living environment into account. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input living environment data into a generating AI and have the generating AI perform the customization of the suggested content.

[0103] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, the suggestion unit estimates the user's emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the suggestion unit will prioritize suggestions for stress reduction. For example, if the user is relaxed, the suggestion unit may also prioritize suggestions for maintaining relaxation. For example, if the user is exercising, the suggestion unit may also prioritize suggestions for maximizing the effects of exercise. This allows important suggestions to be prioritized by determining the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0104] The suggestion unit can propose nutritional balances while considering the user's diet. For example, the suggestion unit can analyze the user's diet and propose ways to improve the nutritional balance. For example, the suggestion unit can recommend the intake of specific nutrients based on the user's diet. For example, the suggestion unit can customize meal plans while considering the user's diet. This makes it possible to propose more appropriate nutritional balances by considering the user's diet. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input meal data into a generating AI and have the generating AI execute nutritional balance proposals.

[0105] The suggestion unit can propose an exercise program considering the user's exercise patterns when making a suggestion. For example, the suggestion unit can analyze the user's exercise patterns and propose an optimal exercise program. For example, the suggestion unit can also propose a program with adjusted exercise intensity based on the user's exercise history. For example, the suggestion unit can customize the exercise plan considering the user's exercise patterns. This makes it possible to propose a more effective exercise program by considering the user's exercise patterns. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input exercise pattern data into a generating AI and have the generating AI execute the exercise program proposal.

[0106] The selection unit can estimate the user's emotions and adjust the hospital selection criteria based on the estimated emotions. For example, the selection unit estimates the user's emotions using an emotion estimation algorithm. For example, if the user is feeling stressed, the selection unit will prioritize selecting hospitals specializing in stress reduction. For example, if the user is relaxed, the selection unit can also select hospitals that help maintain relaxation. For example, if the user is exercising, the selection unit can also select hospitals with exercise-related specialists. This allows for the selection of a more appropriate hospital by adjusting the hospital selection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 selection unit may be performed using AI, or not using AI. For example, the selection unit can input user emotion data into a generative AI and have the generative AI adjust the hospital selection criteria.

[0107] The selection unit can select the most suitable hospital by referring to the user's past medical history during the selection process. For example, the selection unit can select a hospital with a specific specialist based on the user's past medical history. The selection unit can also select the most suitable hospital by referring to the user's past medical history. The selection unit can also select hospital candidates by considering the user's past medical history. This allows for the selection of a more appropriate hospital by referring to the user's past medical history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input past medical history data into a generating AI and have the generating AI perform the selection of hospital candidates.

[0108] The selection unit can select hospitals while considering the user's insurance information. For example, the selection unit can select hospitals within the scope of insurance coverage based on the user's insurance information. The selection unit can also select the most suitable hospital by referring to the user's insurance information. The selection unit can also select hospital candidates while considering the user's insurance information. In this way, by considering the user's insurance information, it is possible to select hospitals within the scope of insurance coverage. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input insurance information data into a generating AI and have the generating AI perform the selection of hospital candidates.

[0109] The pickup unit can estimate the user's emotions and adjust the display method of hospital candidates based on the estimated user emotions. The pickup unit estimates the user's emotions, for example, using an emotion estimation algorithm. For example, if the user is stressed, the pickup unit provides a simple and highly visible display method. For example, if the user is relaxed, the pickup unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the pickup unit can also provide a display method that gets straight to the point. This makes it possible to provide more appropriate information by adjusting the display method of hospital candidates 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the pickup unit may be performed using AI, for example, or without AI. For example, the pickup unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the display method of hospital candidates.

[0110] The pickup unit can select a hospital that is easily accessible, taking into account the user's mode of transportation. For example, if the user is using a car, the pickup unit can select a hospital with ample parking. If the user is using public transportation, the pickup unit can also select a hospital close to a train station or bus stop. If the user is traveling on foot, the pickup unit can also select a hospital within walking distance. In this way, the system can select a hospital that is easily accessible by considering the user's mode of transportation. Some or all of the above processing in the pickup unit may be performed using AI, for example, or without AI. For example, the pickup unit can input transportation data into a generating AI and have the generating AI select hospital candidates.

[0111] The selection unit can select hospitals with specialists based on the user's medical needs during the selection process. For example, if the user has a specific illness, the selection unit can select a hospital with a specialist in that illness. For example, if the user needs a specific treatment, the selection unit can also select a hospital that provides that treatment. For example, if the user needs a specific test, the selection unit can also select a hospital that provides that test. This allows users to receive more appropriate medical care by selecting hospitals with specialists based on their medical needs. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input medical needs data into a generating AI and have the generating AI perform the selection of hospital candidates.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] A healthcare system can include, in addition to a recording unit that collects user lifestyle data, an emotion adjustment unit that estimates the user's emotions and adjusts the data collection method based on those emotions. For example, if the user is stressed, the emotion adjustment unit can instruct the recording unit to prioritize recording heart rate and stress levels. If the user is relaxed, the emotion adjustment unit can also instruct the recording unit to record sleep patterns and heart rate during relaxation in detail. Furthermore, if the user is exercising, the emotion adjustment unit can instruct the recording unit to focus on recording exercise intensity and calories burned. This allows for the collection of more appropriate data by adjusting the type of data recorded according to the user's emotions.

[0114] A healthcare system can include a recording unit that collects user lifestyle data, as well as a frequency adjustment unit that optimizes recording frequency by referencing the user's past health data. For example, if the user's past health data shows a tendency for their health to deteriorate during certain time periods, the recording unit can be instructed to increase the recording frequency during those times. Similarly, if the user's past data shows a tendency for increased exercise on weekends, the recording frequency on weekends can also be increased. Furthermore, if the user's past data shows fluctuations in their health during certain seasons, the recording frequency can be adjusted accordingly. This allows for optimized recording frequency and efficient data collection by referencing the user's past health data.

[0115] A healthcare system can include, in addition to a recording unit that collects user lifestyle data, an environmental correction unit that corrects the data based on the user's living environment (e.g., temperature and humidity). For example, it can correct exercise data in hot and humid environments and instruct the recording unit to calculate accurate calorie expenditure. It can also correct heart rate data in low-temperature environments and accurately assess stress levels. Furthermore, it can correct sleep data considering the difference between indoor and outdoor environments. By correcting the data while considering the user's living environment, more accurate data can be obtained.

[0116] A healthcare system can include a recording unit that collects user lifestyle data, as well as a meal recording unit that automatically records the user's meals and integrates them with lifestyle data. For example, if the user simply takes a picture of their meal, the AI ​​can analyze the meal content and instruct the recording unit to automatically record calories and nutrients. Alternatively, if the user simply enters the menu of their meal, the AI ​​can analyze the meal content and integrate it with lifestyle data. Furthermore, if the user simply records the time of their meal, the AI ​​can estimate the meal content and integrate it with lifestyle data. This enables comprehensive health management by automatically recording the user's meals and integrating them with lifestyle data.

[0117] A healthcare system can include a recording unit that collects user lifestyle data, as well as an anomaly detection unit that analyzes the user's exercise patterns in real time and issues alerts if abnormalities are detected. For example, if the user's heart rate suddenly increases, the recording unit can be instructed to issue an alert in real time. It can also issue an alert in real time if the user's exercise pattern is different from normal. Furthermore, it can issue an alert in real time if it detects abnormal movements during exercise. This allows for a rapid response by analyzing the user's exercise patterns in real time and issuing alerts when anomalies are detected.

[0118] A healthcare system can include an emotion analysis unit that estimates the user's emotions and adjusts the analysis algorithm based on those emotions, in addition to an analysis unit that analyzes the user's lifestyle data. For example, if the user is stressed, the emotion analysis unit instructs the analysis unit to use an analysis algorithm that emphasizes stress-related data. If the user is relaxed, an analysis algorithm that emphasizes physiological data during relaxation can be used. Furthermore, if the user is exercising, an analysis algorithm that emphasizes exercise-related data can be used. By adjusting the analysis algorithm according to the user's emotions, more accurate analysis results can be obtained.

[0119] A healthcare system can include a historical data reference unit in addition to an analysis unit that analyzes users' lifestyle data, to improve the accuracy of the analysis by referring to past analysis results. For example, it can extract specific patterns from past analysis results and instruct the analysis unit to reflect them in the current analysis. It can also adjust the analysis algorithm based on past analysis results to improve accuracy. Furthermore, it can refer to past analysis results to set criteria for detecting outliers. In this way, the accuracy of the analysis can be improved by referring to past analysis results.

[0120] A healthcare system can include a genetic information analysis unit that performs risk assessments based on the user's genetic information, in addition to an analysis unit that analyzes the user's lifestyle data. For example, the analysis unit can be instructed to assess specific genetic risks based on the user's genetic information. It can also analyze the user's genetic information to identify the risk of lifestyle-related diseases. Furthermore, it can perform personalized risk assessments that take the user's genetic information into consideration. This makes personalized risk assessments possible by considering the user's genetic information.

[0121] A healthcare system can include an analysis unit that analyzes the user's lifestyle data, as well as an emotion display unit that estimates the user's emotions and adjusts the display method of the analysis results based on those emotions. For example, if the user is stressed, the emotion display unit instructs the analysis unit to provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide more appropriate information.

[0122] A healthcare system can include an analysis unit that analyzes a user's lifestyle data, as well as a comparative analysis unit that compares and analyzes that user's lifestyle data with that of other users. For example, the analysis unit can be instructed to compare a user's lifestyle data with that of other users of the same age group and perform a risk assessment. It can also compare a user's lifestyle data with that of other users in the same region and perform a risk assessment. Furthermore, it can compare a user's lifestyle data with that of other users in the same occupation and perform a risk assessment. This allows for a more accurate risk assessment by comparing data with that of other users.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The recording unit records the user's lifestyle habits. The recording unit records the user's steps, heart rate, sleep patterns, etc., using IoT devices such as smartwatches and fitness trackers. Step 2: The analysis unit analyzes the data recorded by the recording unit. The analysis unit analyzes the recorded data using, for example, a generation AI. The generation AI analyzes the data patterns using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The identification unit identifies the risk of lifestyle-related diseases based on the data analyzed by the analysis unit. The identification unit identifies the risk using, for example, a generative AI. The generative AI identifies the risk pattern using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The proposal team proposes specific improvement measures based on the risks identified by the identification team. The proposal team proposes improvement measures using, for example, a generative AI. The generative AI proposes improvement measures patterns using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 5: The Picking Unit selects hospital candidates based on the improvement measures proposed by the Proposal Unit. The Picking Unit selects hospital candidates using, for example, a Generative AI. The Generative AI selects patterns of hospital candidates using, for example, a Text Generation AI (e.g., LLM) or a Multimodal Generative AI.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the recording unit, analysis unit, identification unit, proposal unit, and pickup unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recording unit records the user's lifestyle habits using the computer 36 and camera 42 of the smart device 14. The analysis unit analyzes the recorded data using the identification unit 290 of the data processing unit 12. The identification unit identifies the risk of lifestyle-related diseases using the identification unit 290 of the data processing unit 12. The proposal unit proposes specific improvement measures using the identification unit 290 of the data processing unit 12. The pickup unit picks up hospital candidates using the identification unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[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 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.

[0133] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0144] Each of the multiple elements described above, including the recording unit, analysis unit, identification unit, proposal unit, and pickup unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit records the user's lifestyle habits using the computer 36 and camera 42 of the smart glasses 214. The analysis unit analyzes the recorded data, for example, by the identification processing unit 290 of the data processing unit 12. The identification unit identifies the risk of lifestyle-related diseases, for example, by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes specific improvement measures, for example, by the identification processing unit 290 of the data processing unit 12. The pickup unit picks up hospital candidates, for example, by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[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 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.

[0149] 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.

[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 (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).

[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] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the recording unit, analysis unit, identification unit, proposal unit, and pickup unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit records the user's lifestyle habits using the computer 36 and camera 42 of the headset terminal 314. The analysis unit analyzes the recorded data using the identification unit 290 of the data processing unit 12. The identification unit identifies the risk of lifestyle-related diseases using the identification unit 290 of the data processing unit 12. The proposal unit proposes specific improvement measures using the identification unit 290 of the data processing unit 12. The pickup unit picks up hospital candidates using the identification unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the recording unit, analysis unit, identification unit, proposal unit, and pickup unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the recording unit records the user's lifestyle habits using the robot 414's computer 36 and camera 42. The analysis unit analyzes the recorded data using, for example, the identification unit 290 of the data processing unit 12. The identification unit identifies the risk of lifestyle-related diseases using, for example, the identification unit 290 of the data processing unit 12. The proposal unit proposes specific improvement measures using, for example, the identification unit 290 of the data processing unit 12. The pickup unit picks up hospital candidates using, for example, the identification unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A recording unit that records the user's lifestyle habits, An analysis unit that analyzes the data recorded by the recording unit, An identification unit identifies the risk of lifestyle-related diseases based on the data analyzed by the aforementioned analysis unit, A proposal unit that proposes specific improvement measures based on the risks identified by the aforementioned identification unit, The system includes a pickup unit that selects hospital candidates based on the improvement measures proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned recording unit is IoT devices such as smartwatches and fitness trackers are used to record the user's steps, heart rate, sleep patterns, and other data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the data recorded by the recording unit, the user's risk of lifestyle-related diseases is identified. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on identified risks, the system sets daily exercise goals for users and provides advice on how to achieve them. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on identified risks, provide users with advice to promote regular sleep patterns. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned pickup unit is Based on the user's location, hospital ratings, and specialist information, we suggest the most suitable hospital. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recording unit is It estimates the user's emotions and adjusts the type of data recorded based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recording unit is During recording, the frequency of recording is optimized by referencing the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recording unit is During recording, the data is corrected based on the user's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recording unit is It estimates the user's emotions and determines the priority of data to record based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recording unit is During recording, the system automatically records the user's meal content and integrates it with lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recording unit is During recording, the system analyzes the user's movement patterns in real time and issues an alert if an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, past analysis results are referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, risk assessment is performed based on the user's genetic information. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned analysis unit, During analysis, the user's lifestyle data is compared with data from other users. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the user's stress level is assessed and associated with the risk of lifestyle-related diseases. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, We estimate user sentiment and adjust risk identification criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, At specific times, risk assessment is performed by referring to the user's past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, At specific times, a risk assessment is performed considering the user's family history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, It estimates user sentiment and determines risk identification priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, At specific times, risk assessment is performed by comparing the user's lifestyle data with local health data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The specified part is, At specific times, a risk assessment is conducted, taking into account the user's occupation and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making suggestions, we refer to the user's past behavior history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, customize the proposal content to take into account the user's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making suggestions, we will propose nutritionally balanced options that take into account the user's diet. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, we suggest an exercise program that takes into account the user's movement patterns. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned pickup unit is The system estimates user sentiment and adjusts the hospital candidate selection criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned pickup unit is During the selection process, the system references the user's past medical history to choose the most suitable hospital. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned pickup unit is When selecting a hospital, the system takes the user's insurance information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned pickup unit is The system estimates the user's emotions and adjusts how hospital candidates are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned pickup unit is When picking up patients, we select hospitals that are easily accessible, taking into account the user's mode of transportation. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned pickup unit is During the selection process, hospitals with specialists are chosen based on the user's medical needs. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 recording unit that records the user's lifestyle habits, An analysis unit that analyzes the data recorded by the recording unit, An identification unit identifies the risk of lifestyle-related diseases based on the data analyzed by the aforementioned analysis unit, A proposal unit that proposes specific improvement measures based on the risks identified by the aforementioned identification unit, The system includes a pickup unit that selects hospital candidates based on the improvement measures proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The recording unit is, IoT devices such as smartwatches and fitness trackers are used to record the user's steps, heart rate, sleep patterns, and other data. The system according to feature 1.

3. The aforementioned analysis unit, Based on the data recorded by the aforementioned recording unit, the user's risk of lifestyle-related diseases is identified. The system according to feature 1.

4. The aforementioned proposal section is, Based on identified risks, the system sets daily exercise goals for users and provides advice on how to achieve them. The system according to feature 1.

5. The aforementioned proposal section is, Based on identified risks, provide users with advice to promote regular sleep patterns. The system according to feature 1.

6. The aforementioned pickup unit is Based on the user's location, hospital ratings, and specialist information, we suggest the most suitable hospital. The system according to feature 1.

7. The recording unit is, It estimates the user's emotions and adjusts the type of data recorded based on the estimated user emotions. The system according to feature 1.

8. The recording unit is, During recording, the frequency of recording is optimized by referencing the user's past health data. The system according to feature 1.

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