System

Abstract

To provide a system according to an embodiment that prepares a specific action plan by utilizing information on an individual's health and provides medical advice and health counseling in an emergency.SOLUTION: A system comprises a collection unit, an analysis unit, a proposal unit, and an advice unit. The collection unit collects information on an individual's health. The analysis unit analyzes the information collected by the collection unit. The proposal unit prepares a specific action plan on the basis of an analysis result obtained by the analysis unit. The advice unit conducts medical advice and health counseling in an emergency on the basis of the action plan prepared by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Previous technologies have had the disadvantage that they have not been able to effectively utilize personal health information to create specific action plans or provide medical advice in emergencies.

[0005] The system according to the embodiment aims to utilize personal health information to create a specific action plan and provide medical advice and health consultation in an emergency. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an advice unit. The collection unit collects personal health information. The analysis unit analyzes the information collected by the collection unit. The proposal unit makes a specific action plan based on the analysis results obtained by the analysis unit. The advice unit provides medical advice and health consultation in an emergency based on the action plan made by the proposal unit. Effect of the Invention

[0007] The system according to the embodiment can utilize personal health information to develop specific action plans and provide medical advice and health consultations in emergency situations. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The health consultation system according to the embodiment of the present invention utilizes personal health information collected through the spread of personal identification cards and wearable devices, and is a system that can provide personalized health consultations, such as planning specific actions for lifestyle improvement and body shaping on a daily basis, providing medical advice in emergencies, and providing health consultations when traveling abroad. This health consultation system collects personal health information, analyzes it with AI, and evaluates each individual's health condition and lifestyle. For example, it analyzes heart rate and step count data to detect signs of lack of exercise and overwork. Next, based on the analysis results, the AI ​​creates specific action plans for lifestyle improvement and body shaping on a daily basis. For example, if lack of exercise is detected, the AI ​​suggests specific actions such as "walk for 30 minutes every day." It also suggests improvements to nutritional balance based on dietary records. In addition, medical advice is provided in emergencies. For example, if the heart rate is abnormally high, the AI ​​advises "consult a doctor immediately." In addition, when traveling abroad, information on local medical institutions and advice on health management at the travel destination are also provided. In this way, personalized health consultations are possible, improving the quality of life. This allows the health consultation system to automatically collect and analyze an individual's health information and provide appropriate action plans and medical advice.

[0029] The health consultation system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an advice unit. The collection unit collects personal health information. The personal health information includes, for example, heart rate, blood pressure, body temperature, number of steps, sleep data, and the like, but is not limited to such examples. The collection unit collects health information using, for example, a wearable device or a smartphone app. The collection unit can also collect health information using medical equipment. For example, the collection unit collects heart rate and number of steps using a smart watch. A meal record can also be collected using a smartphone app. Blood pressure and body temperature can also be collected using medical equipment. The analysis unit analyzes the information collected by the collection unit. The analysis is performed, for example, using statistical analysis of data or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit analyzes data on heart rate and number of steps to detect signs of lack of exercise and overwork. A machine learning algorithm can also be used to evaluate the health condition. The proposal unit makes a specific action plan based on the analysis result obtained by the analysis unit. The action plan includes, for example, an exercise plan, a meal plan, a sleep improvement plan, and the like, but is not limited to such examples. For example, if a lack of exercise is detected, the suggestion unit suggests a specific action such as "walk for 30 minutes every day." Based on the dietary records, the suggestion unit can also suggest improvements to nutritional balance. The advice unit provides medical advice and health consultation in an emergency based on the action plan established by the suggestion unit. For example, if the heart rate is abnormally high, the advice unit can provide advice such as "consult a doctor immediately." When traveling abroad, information on local medical institutions and advice on health management at the travel destination can also be provided. As a result, the health consultation system according to the embodiment can collect and analyze personal health information and provide appropriate action plans and medical advice. Some or all of the above-mentioned processes in the collection unit, analysis unit, proposal unit, and advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs data acquired from a wearable device to the generation AI, the analysis unit analyzes the data using the generation AI, the proposal unit creates an action plan using the generation AI, and the advice unit provides medical advice using the generation AI.

[0030] The collection unit collects health information of an individual. The health information of an individual includes, but is not limited to, for example, heart rate, blood pressure, body temperature, number of steps, and sleep data. The collection unit collects health information using, for example, a wearable device or a smartphone app. Specifically, wearable devices include smart watches and fitness trackers. These devices monitor heart rate, number of steps, calories burned, sleep patterns, and the like in real time and collect data. The smartphone app allows the user to manually input food records, water intake, weight fluctuations, and the like. The collection unit can also collect health information using medical devices. Examples include home blood pressure monitors, thermometers, and pulse oximeters. These medical devices transmit data to a smartphone or cloud via Bluetooth (registered trademark) or Wi-Fi, and the collection unit centrally manages the data. Furthermore, the collection unit can also capture the results of regular health checkups and test data at medical institutions. This allows the collection unit to collect a wide range of health information using a variety of devices and means and comprehensively grasp the health condition of an individual. The collected data is stored in a secure cloud environment and made accessible to the analysis and proposal units. The frequency and accuracy of data collection can be adjusted according to the user's needs and health condition, allowing for flexible response to specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the information collected by the collection unit. The analysis is performed, for example, using statistical analysis of data or machine learning algorithms, but is not limited to such examples. Specifically, the heart rate and step count data are analyzed to detect signs of lack of exercise and overwork. For example, the heart rate fluctuation pattern can be analyzed to evaluate the stress level and the health condition of the heart. By analyzing the step count data, the amount of daily activity and exercise habits can be understood, and the risk of lack of exercise can be evaluated. The health condition can also be evaluated using a machine learning algorithm. For example, a model that predicts a specific health risk is constructed based on past data, and a risk assessment is performed in real time. Furthermore, the analysis unit can detect patterns that are different from the norm or abnormal data using an anomaly detection algorithm, and issue an early warning. For example, an alert is issued when the heart rate rises suddenly or blood pressure is abnormally high. The analysis unit can also analyze long-term health trends and predict future health risks using past data and statistical information. As a result, the analysis unit can quickly and accurately analyze the collected data and grasp the individual's health condition in real time. Furthermore, the analysis unit uses a data visualization tool to provide the analysis results to the user in an easy-to-understand form. This allows users to intuitively understand their own health condition and take appropriate action.

[0032] The suggestion unit makes a specific action plan based on the analysis results obtained by the analysis unit. The action plan includes, but is not limited to, an exercise plan, a meal plan, a sleep improvement plan, and the like. Specifically, if a lack of exercise is detected, a specific action such as "walk for 30 minutes every day" is suggested. Furthermore, an exercise plan customized individually according to the user's lifestyle and health condition can be provided. For example, appropriate exercise intensity and frequency can be suggested based on past exercise data and fitness level. A proposal for improving nutritional balance can be made based on a meal record. For example, if a specific nutrient is lacking, foods that contain a lot of that nutrient are suggested. Specific advice is also provided on the timing and amount of meals. The sleep data can be analyzed and suggestions made to improve sleep quality. For example, advice is provided on how to relax before going to bed and on maintaining an appropriate sleeping environment. The suggestion unit can present these action plans to the user in an easy-to-understand manner and provide reminders and motivation tools to encourage execution. For example, the daily progress is checked through a smartphone app and feedback is provided for goal achievement. As a result, the suggestion unit can provide a specific action plan for the user to maintain and improve healthy lifestyle habits and support the execution.

[0033] The advice unit provides medical advice and health consultations in an emergency based on the action plan drawn up by the proposal unit. Specifically, if the heart rate is abnormally high, the advice is given such as "consult a doctor immediately." Furthermore, if the user reports a specific symptom, the advice unit can recommend an appropriate way to deal with the symptom and to visit a medical institution. For example, if the user has chest pain or shortness of breath, the advice unit can advise the user to visit a medical institution immediately as there may be a heart problem. When traveling abroad, the advice unit can provide information on local medical institutions and advice on health management at the travel destination. For example, the advice unit can provide health management points according to the local climate and food culture, and contact information for medical institutions that can be used in an emergency. The advice unit monitors the user's health condition and progress of the action plan, and provides appropriate advice as necessary. For example, if the user experiences poor health while executing the exercise plan, the advice unit can identify the cause and advise on an appropriate way to deal with the problem. Furthermore, the advice unit can quickly respond to questions and consultations from the user and provide reliable information. For example, the advice unit can provide information on specific diseases and symptoms to support the user in making appropriate decisions. In this way, the advice unit can provide support for the user to maintain a healthy lifestyle and take appropriate measures in emergencies.

[0034] The collection unit can collect health information such as heart rate, number of steps, sleep data, and meal records from a personal identification card or a wearable device. The collection unit collects, for example, health information such as heart rate, number of steps, sleep data, and meal records from a personal identification card or a wearable device. Examples of personal identification cards include, but are not limited to, health insurance cards, driver's licenses, and employee ID cards. Examples of wearable devices include, but are not limited to, smart watches and fitness trackers. For example, the collection unit collects heart rate and number of steps using a smart watch. Sleep data can also be collected using a fitness tracker. Meal records can also be collected using a smartphone app. In this way, detailed health data can be obtained by collecting health information from a personal identification card or a wearable device. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from a wearable device to a generation AI and collect data using the generation AI.

[0035] The analysis unit can analyze the collected data and detect signs of lack of exercise or overwork. The analysis unit, for example, analyzes the collected data and detects signs of lack of exercise or overwork. Signs of lack of exercise or overwork include, but are not limited to, cases where the number of steps is below a certain level and where the heart rate is above a certain level. The analysis unit detects signs of lack of exercise, for example, when the number of steps is below a certain level. Signs of overwork can also be detected when the heart rate is above a certain level. The health condition can also be evaluated using a machine learning algorithm. As a result, signs of lack of exercise or overwork can be detected early by analyzing the collected data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and analyze the data using the generation AI.

[0036] The suggestion unit can make specific action plans for lifestyle improvement and body shaping on a daily basis based on the analysis results. For example, the suggestion unit makes specific action plans for lifestyle improvement and body shaping on a daily basis based on the analysis results. Lifestyle improvement and body shaping include, but are not limited to, for example, dietary improvement, exercise recommendations, and stress management. For example, the suggestion unit can suggest specific actions such as "walking for 30 minutes every day" when lack of exercise is detected. Based on the diet record, it is also possible to make suggestions for improving nutritional balance. It is also possible to suggest relaxation methods for stress management. This makes it possible to make specific action plans based on the analysis results, thereby improving lifestyles according to individuals. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the analysis results to a generation AI and use the generation AI to make an action plan.

[0037] The advice unit can provide emergency medical advice such as "consult a doctor" when the heart rate is abnormally high. The advice unit provides emergency medical advice such as "consult a doctor" when the heart rate is abnormally high. An abnormally high heart rate includes, but is not limited to, a case where the heart rate exceeds 100 bpm. For example, the advice unit provides advice such as "consult a doctor immediately" when the heart rate exceeds 100 bpm. This allows for a rapid response by providing emergency medical advice. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input heart rate data to a generation AI and provide medical advice using the generation AI.

[0038] The advice unit can provide information on local medical institutions and advice on health management at the travel destination when traveling abroad. The advice unit, for example, provides information on local medical institutions and advice on health management at the travel destination when traveling abroad. Information on local medical institutions includes, for example, the location of the hospital, contact information, medical department, etc., but is not limited to such examples. The advice unit provides, for example, the location and contact information of the hospital at the travel destination. Advice on health management at the travel destination includes, for example, health management methods suited to the local climate and advice on local meals, but is not limited to such examples. As a result, by providing information on local medical institutions and advice on health management when traveling abroad, health management during the travel becomes easier. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input information on the travel destination into the generation AI and provide advice on health management using the generation AI.

[0039] The collection unit can analyze the user's past health data and select the optimal collection method. The collection unit, for example, analyzes the user's past health data and selects the optimal collection method. The optimal collection method includes, for example, data accuracy, collection frequency, and user burden, but is not limited to such examples. For example, if the user's heart rate fluctuated greatly in the past, the collection unit can increase the frequency of collecting the heart rate. If the user's step count was low in the past, the collection unit can also increase the frequency of collecting the step count. If the user's sleep data was irregular in the past, the collection unit can also increase the frequency of collecting the sleep data. In this way, the optimal collection method can be selected by analyzing the past health data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past health data into a generation AI and select the optimal collection method using the generation AI.

[0040] The collection unit can perform filtering based on the user's current living situation and activity level when collecting health information. For example, the collection unit performs filtering based on the user's current living situation and activity level when collecting health information. Filtering includes, for example, the type of data to be collected and the timing of collection, but is not limited to such examples. For example, when the user is exercising, the collection unit preferentially collects data related to exercise. When the user is resting, the collection unit can also preferentially collect heart rate and sleep data. When the user is eating, the collection unit can also preferentially collect meal records. In this way, by filtering based on the current living situation and activity level, more relevant health information can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's living situation data to the generation AI and perform filtering using the generation AI.

[0041] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting health information. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting health information. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, when the user is at a gym, the collection unit preferentially collects data related to exercise. When the user is at home, data related to daily life can also be preferentially collected. When the user is traveling, health information at the travel destination can also be preferentially collected. In this way, by considering the geographical location information, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and preferentially collect highly relevant data using the generation AI.

[0042] The collection unit can analyze the social media activity of the user and collect related data when collecting health information. For example, the collection unit analyzes the social media activity of the user and collects related data when collecting health information. Social media activities include, for example, analysis of post content, analysis of followers, and the like, but are not limited to such examples. For example, when the user posts about exercise on social media, the collection unit preferentially collects exercise data. When the user posts about meals, the collection unit can also preferentially collect meal records. When the user posts about stress, the collection unit can also preferentially collect stress level data. In this way, related health information can be collected by analyzing social media activities. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media data of the user to the generation AI and collect related data using the generation AI.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the health data during the analysis. The importance of the health data includes, for example, the type of data, the frequency of collection, and the health condition of the user, but is not limited to such examples. For example, the analysis unit performs a detailed analysis when the heart rate data is important. When the step count data is important, a detailed analysis can also be performed. When the sleep data is important, a detailed analysis can also be performed. As a result, important data can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the health data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the health data to a generation AI and adjust the level of detail of the analysis using the generation AI.

[0044] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of health data during analysis. The analysis algorithms include, for example, regression analysis, clustering, deep learning, and the like, but are not limited to such examples. The analysis unit, for example, applies a heart rate variability analysis algorithm to heart rate data. A walking pattern analysis algorithm can also be applied to step count data. A sleep stage analysis algorithm can also be applied to sleep data. This allows for more accurate analysis by applying different analysis algorithms depending on the category of health data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input health data to a generation AI and apply different analysis algorithms using the generation AI.

[0045] The analysis unit can determine the priority of the analysis based on the collection time of the health data at the time of analysis. The analysis unit, for example, determines the priority of the analysis based on the collection time of the health data at the time of analysis. The collection time of the health data includes, for example, the latest data, past data, seasonal data, etc., but is not limited to such examples. The analysis unit, for example, preferentially analyzes recently collected data. It is also possible to analyze current data while referring to past data. It is also possible to focus on analyzing data collected in a specific period. In this way, by determining the priority of the analysis based on the collection time of the health data, it is possible to preferentially analyze the latest data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the health data to a generation AI and determine the priority of the analysis using the generation AI.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the health data during analysis. The relevance of the health data includes, for example, correlation and co-occurrence of data, but is not limited to such examples. The analysis unit, for example, associates and analyzes heart rate data and step count data. Sleep data and diet records can also be associated and analyzed. Exercise data and stress level data can also be associated and analyzed. By adjusting the order of analysis based on the relevance of the health data, it is possible to preferentially analyze highly related data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input health data to a generation AI and adjust the order of analysis using the generation AI.

[0047] The suggestion unit can adjust the level of detail of the proposal based on the importance of the action plan at the time of proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the action plan at the time of proposal. The importance of the action plan includes, for example, health risk, urgency, user's goal, etc., but is not limited to such examples. For example, the suggestion unit provides a detailed proposal for an important action plan. A simple proposal can be provided for a general action plan. A quick proposal can be provided for an action plan with high urgency. Thereby, by adjusting the level of detail of the proposal based on the importance of the action plan, an important action plan can be proposed in detail. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the action plan data to the generation AI and adjust the level of detail of the proposal using the generation AI.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the action plan at the time of suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the action plan at the time of suggestion. The suggestion algorithm includes, for example, a rule-based algorithm, a machine learning algorithm, an optimization algorithm, and the like, but is not limited to such examples. For example, the suggestion unit applies an exercise algorithm to an exercise plan. A nutrition algorithm can also be applied to a meal plan. A sleep algorithm can also be applied to a sleep plan. Thereby, by applying different suggestion algorithms depending on the category of the action plan, more appropriate suggestions can be made. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the action plan data to a generation AI and apply different suggestion algorithms using the generation AI.

[0049] The suggestion unit can determine the priority of the proposal based on the submission time of the action plan at the time of proposal. The suggestion unit, for example, determines the priority of the proposal based on the submission time of the action plan at the time of proposal. The submission time of the action plan includes, for example, a submission deadline, urgency, a user's schedule, etc., but is not limited to such examples. The suggestion unit, for example, preferentially proposes an action plan with a high urgency. It is also possible to preferentially propose an action plan with a close submission deadline. It is also possible to postpone the proposal of a long-term action plan. In this way, by determining the priority of the proposal based on the submission time of the action plan, it is possible to preferentially propose an action plan with a high urgency. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the action plan data to the generation AI and determine the priority of the proposal using the generation AI.

[0050] The suggestion unit can adjust the order of the proposal based on the relevance of the action plans at the time of proposal. For example, the suggestion unit adjusts the order of the proposal based on the relevance of the action plans at the time of proposal. The relevance of the action plans includes, for example, interdependence between the plans, common goals, and the like, but is not limited to such examples. For example, the suggestion unit suggests an exercise plan and a meal plan in association with each other. It is also possible to suggest a sleep plan and a stress management plan in association with each other. It is also possible to suggest a health management plan and a lifestyle improvement plan in association with each other. Thereby, by adjusting the order of the proposal based on the relevance of the action plans, it is possible to preferentially suggest an action plan with high relevance. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the action plan data to the generation AI and adjust the order of the proposal using the generation AI.

[0051] The advice unit can select the optimal advice method by analyzing the user's past health data when giving advice. For example, the advice unit can select the optimal advice method by analyzing the user's past health data when giving advice. The optimal advice method includes, for example, past advice history, the user's preferences, and health conditions, but is not limited to such examples. For example, if the user had a high heart rate in the past, the advice unit can advise how to lower the heart rate. If the user has lacked exercise in the past, the advice unit can also provide advice to encourage exercise. If the user has lacked sleep in the past, the advice unit can also advise how to improve sleep. In this way, the optimal advice method can be selected by analyzing the past health data. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input past health data into a generation AI and select the optimal advice method using the generation AI.

[0052] The advice unit can customize the means of advice based on the user's current living situation when giving advice. The advice unit, for example, customizes the means of advice based on the user's current living situation when giving advice. The means of advice include, for example, text messages, voice guidance, video calls, and the like, but are not limited to such examples. For example, when the user is at work, the advice unit provides advice that can be implemented in a short time. When the user is resting, the advice unit can also provide advice on how to relax. When the user is traveling, the advice unit can also provide advice on health management at the travel destination. In this way, by customizing the means of advice based on the current living situation, more appropriate advice can be provided. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the user's living situation data into the generation AI and customize the means of advice using the generation AI.

[0053] The advice unit can select the optimal advice method in consideration of the geographical location information of the user when giving advice. For example, the advice unit selects the optimal advice method in consideration of the geographical location information of the user when giving advice. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, when the user is at a gym, the advice unit provides advice on exercise. When the user is at home, the advice unit can also provide advice on daily life. When the user is traveling, the advice unit can also provide advice on health management at the travel destination. In this way, the optimal advice method can be selected by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the geographical location information of the user to the generation AI and select the optimal advice method using the generation AI.

[0054] The advice unit can analyze the social media activity of the user at the time of giving advice and propose a means of advice. For example, the advice unit can analyze the social media activity of the user at the time of giving advice and propose a means of advice. Social media activity includes, for example, analysis of the content of posts, analysis of followers, and the like, but is not limited to such examples. For example, when the user posts about exercise on social media, the advice unit provides advice about exercise. When the user posts about diet, the advice unit can also provide advice about diet. When the user posts about stress, the advice unit can also provide advice about stress reduction. In this way, by analyzing social media activity, it is possible to provide relevant advice. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the social media data of the user into a generation AI and use the generation AI to propose a means of advice.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The health consultation system can further collect data on the user's living environment and make suggestions for improving the living environment based on the analysis results. For example, by collecting the temperature, humidity, and noise level of the user's living environment and analyzing this data, it can make suggestions for maintaining a comfortable living environment. Specifically, if the temperature is too high, it recommends using an air conditioner, and if the humidity is too low, it suggests using a humidifier. In addition, if the noise level is high, it can also suggest using earplugs or moving to a quieter place. This can comprehensively improve the user's living environment and contribute to maintaining health.

[0057] The collection unit can analyze the user's social media activity and use it to collect health information. For example, if the user posts about exercise on social media, the frequency and type of exercise can be understood by analyzing the content of the post. In addition, by analyzing posts about food, the user's eating habits can be understood and the information can be used to suggest improvements to nutritional balance. Furthermore, by analyzing posts about stress, the user's stress level can be estimated and suggestions for stress management can be made. In this way, more detailed health information can be collected by utilizing the user's social media activity, and health management appropriate for each individual can be performed.

[0058] The collection unit can collect health information taking into account the geographical location information of the user. For example, when the user is at the gym, data related to exercise can be collected preferentially. When the user is at home, data related to daily life can also be collected. When the user is traveling, health information at the travel destination can also be collected. In this way, more relevant health information can be collected by taking into account the geographical location information.

[0059] The analysis unit can determine the priority of analysis based on the time when the health data was collected. For example, it can prioritize analysis of recently collected data. It can also analyze current data while referring to past data. It can also focus on analyzing data collected during a specific period. In this way, by determining the priority of analysis based on the time when the health data was collected, it is possible to prioritize analysis of the latest data.

[0060] The suggestion unit can apply different suggestion algorithms depending on the category of the action plan. For example, an exercise algorithm can be applied to an exercise plan, and a nutrition algorithm can be applied to a meal plan. A sleep algorithm can also be applied to a sleep plan. This allows for more appropriate suggestions by applying different suggestion algorithms depending on the category of the action plan.

[0061] The advice unit can select the optimal advice method by analyzing the user's past health data. For example, if the user has had a high heart rate in the past, advice can be given on how to lower the heart rate. If the user has lacked exercise in the past, advice can be given to encourage exercise. If the user has lacked sleep in the past, advice can be given on how to improve sleep. In this way, the optimal advice method can be selected by analyzing past health data.

[0062] The advice unit can select the optimal advice method by taking into account the geographical location information of the user. For example, when the user is at a gym, advice on exercise can be provided. When the user is at home, advice on daily life can be provided. When the user is traveling, advice on health management at the travel destination can be provided. In this way, the optimal advice method can be selected by taking into account the geographical location information.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The collection unit collects personal health information. The personal health information includes heart rate, blood pressure, body temperature, number of steps, sleep data, etc. The collection unit collects this information using a wearable device, a smartphone app, and medical equipment. For example, the collection unit collects heart rate and number of steps using a smart watch, collects food records using a smartphone app, and collects blood pressure and body temperature using medical equipment. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using statistical analysis of the data and machine learning algorithms. For example, heart rate and step count data can be analyzed to detect signs of lack of exercise or overwork. Machine learning algorithms can also be used to evaluate health status. Step 3: The suggestion unit creates a specific action plan based on the analysis results obtained by the analysis unit. The action plan includes an exercise plan, a meal plan, a sleep improvement plan, etc. For example, if a lack of exercise is detected, a specific action such as "walk for 30 minutes every day" is suggested. It can also make suggestions for improving nutritional balance based on food records. Step 4: The Advisory Department provides emergency medical advice and health consultations based on the action plan drawn up by the Proposal Department. For example, if the heart rate is abnormally high, the Advisory Department will advise the user to "consult a doctor immediately." When traveling abroad, the Advisory Department can also provide information on local medical institutions and advice on health management at the travel destination.

[0065] (Example 2) The health consultation system according to the embodiment of the present invention utilizes personal health information collected through the spread of personal identification cards and wearable devices, and is a system that can provide personalized health consultations, such as planning specific actions for lifestyle improvement and body shaping on a daily basis, providing medical advice in emergencies, and providing health consultations when traveling abroad. This health consultation system collects personal health information, analyzes it with AI, and evaluates each individual's health condition and lifestyle. For example, it analyzes heart rate and step count data to detect signs of lack of exercise and overwork. Next, based on the analysis results, the AI ​​creates specific action plans for lifestyle improvement and body shaping on a daily basis. For example, if lack of exercise is detected, the AI ​​suggests specific actions such as "walk for 30 minutes every day." It also suggests improvements to nutritional balance based on dietary records. In addition, medical advice is provided in emergencies. For example, if the heart rate is abnormally high, the AI ​​advises "consult a doctor immediately." In addition, when traveling abroad, information on local medical institutions and advice on health management at the travel destination are also provided. In this way, personalized health consultations are possible, improving the quality of life. This allows the health consultation system to automatically collect and analyze an individual's health information and provide appropriate action plans and medical advice.

[0066] The health consultation system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an advice unit. The collection unit collects personal health information. The personal health information includes, for example, heart rate, blood pressure, body temperature, number of steps, sleep data, and the like, but is not limited to such examples. The collection unit collects health information using, for example, a wearable device or a smartphone app. The collection unit can also collect health information using medical equipment. For example, the collection unit collects heart rate and number of steps using a smart watch. A meal record can also be collected using a smartphone app. Blood pressure and body temperature can also be collected using medical equipment. The analysis unit analyzes the information collected by the collection unit. The analysis is performed, for example, using statistical analysis of data or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit analyzes data on heart rate and number of steps to detect signs of lack of exercise and overwork. A machine learning algorithm can also be used to evaluate the health condition. The proposal unit makes a specific action plan based on the analysis result obtained by the analysis unit. The action plan includes, for example, an exercise plan, a meal plan, a sleep improvement plan, and the like, but is not limited to such examples. For example, if a lack of exercise is detected, the suggestion unit suggests a specific action such as "walk for 30 minutes every day." Based on the dietary records, the suggestion unit can also suggest improvements to nutritional balance. The advice unit provides medical advice and health consultation in an emergency based on the action plan established by the suggestion unit. For example, if the heart rate is abnormally high, the advice unit can provide advice such as "consult a doctor immediately." When traveling abroad, information on local medical institutions and advice on health management at the travel destination can also be provided. As a result, the health consultation system according to the embodiment can collect and analyze personal health information and provide appropriate action plans and medical advice. Some or all of the above-mentioned processes in the collection unit, analysis unit, proposal unit, and advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs data acquired from a wearable device to the generation AI, the analysis unit analyzes the data using the generation AI, the proposal unit creates an action plan using the generation AI, and the advice unit provides medical advice using the generation AI.

[0067] The collection unit collects health information of an individual. The health information of an individual includes, but is not limited to, for example, heart rate, blood pressure, body temperature, number of steps, and sleep data. The collection unit collects health information using, for example, a wearable device or a smartphone app. Specifically, wearable devices include smart watches and fitness trackers. These devices monitor heart rate, number of steps, calories burned, sleep patterns, and the like in real time and collect data. The smartphone app allows the user to manually input food records, water intake, weight fluctuations, and the like. The collection unit can also collect health information using medical devices. Examples include home blood pressure monitors, thermometers, and pulse oximeters. These medical devices transmit data to a smartphone or cloud via Bluetooth or Wi-Fi, and the collection unit centrally manages the data. In addition, the collection unit can also capture the results of regular health checkups and test data at medical institutions. This allows the collection unit to collect a wide range of health information using a variety of devices and means and comprehensively grasp the health condition of an individual. The collected data is stored in a secure cloud environment and can be accessed by the analysis unit and the proposal unit. The frequency and accuracy of data collection can be adjusted according to the user's needs and health condition, allowing for flexible response to specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0068] The analysis unit analyzes the information collected by the collection unit. The analysis is performed, for example, using statistical analysis of data or machine learning algorithms, but is not limited to such examples. Specifically, the heart rate and step count data are analyzed to detect signs of lack of exercise and overwork. For example, the heart rate fluctuation pattern can be analyzed to evaluate the stress level and the health condition of the heart. By analyzing the step count data, the amount of daily activity and exercise habits can be understood, and the risk of lack of exercise can be evaluated. The health condition can also be evaluated using a machine learning algorithm. For example, a model that predicts a specific health risk is constructed based on past data, and a risk assessment is performed in real time. Furthermore, the analysis unit can detect patterns that are different from the norm or abnormal data using an anomaly detection algorithm, and issue an early warning. For example, an alert is issued when the heart rate rises suddenly or blood pressure is abnormally high. The analysis unit can also analyze long-term health trends and predict future health risks using past data and statistical information. As a result, the analysis unit can quickly and accurately analyze the collected data and grasp the individual's health condition in real time. Furthermore, the analysis unit uses a data visualization tool to provide the analysis results to the user in an easy-to-understand form. This allows users to intuitively understand their own health condition and take appropriate action.

[0069] The suggestion unit makes a specific action plan based on the analysis results obtained by the analysis unit. The action plan includes, but is not limited to, an exercise plan, a meal plan, a sleep improvement plan, and the like. Specifically, if a lack of exercise is detected, a specific action such as "walk for 30 minutes every day" is suggested. Furthermore, an exercise plan customized individually according to the user's lifestyle and health condition can be provided. For example, appropriate exercise intensity and frequency can be suggested based on past exercise data and fitness level. A proposal for improving nutritional balance can be made based on a meal record. For example, if a specific nutrient is lacking, foods that contain a lot of that nutrient are suggested. Specific advice is also provided on the timing and amount of meals. The sleep data can be analyzed and suggestions made to improve sleep quality. For example, advice is provided on how to relax before going to bed and on maintaining an appropriate sleeping environment. The suggestion unit can present these action plans to the user in an easy-to-understand manner and provide reminders and motivation tools to encourage execution. For example, the daily progress is checked through a smartphone app and feedback is provided for goal achievement. As a result, the suggestion unit can provide a specific action plan for the user to maintain and improve healthy lifestyle habits and support the execution.

[0070] The advice unit provides medical advice and health consultations in an emergency based on the action plan drawn up by the proposal unit. Specifically, if the heart rate is abnormally high, the advice is given such as "consult a doctor immediately." Furthermore, if the user reports a specific symptom, the advice unit can recommend an appropriate way to deal with the symptom and to visit a medical institution. For example, if the user has chest pain or shortness of breath, the advice unit can advise the user to visit a medical institution immediately as there may be a heart problem. When traveling abroad, the advice unit can provide information on local medical institutions and advice on health management at the travel destination. For example, the advice unit can provide health management points according to the local climate and food culture, and contact information for medical institutions that can be used in an emergency. The advice unit monitors the user's health condition and progress of the action plan, and provides appropriate advice as necessary. For example, if the user experiences poor health while executing the exercise plan, the advice unit can identify the cause and advise on an appropriate way to deal with the problem. Furthermore, the advice unit can quickly respond to questions and consultations from the user and provide reliable information. For example, the advice unit can provide information on specific diseases and symptoms to support the user in making appropriate decisions. In this way, the advice unit can provide support for the user to maintain a healthy lifestyle and take appropriate measures in emergencies.

[0071] The collection unit can collect health information such as heart rate, number of steps, sleep data, and meal records from a personal identification card or a wearable device. The collection unit collects, for example, health information such as heart rate, number of steps, sleep data, and meal records from a personal identification card or a wearable device. Examples of personal identification cards include, but are not limited to, health insurance cards, driver's licenses, and employee ID cards. Examples of wearable devices include, but are not limited to, smart watches and fitness trackers. For example, the collection unit collects heart rate and number of steps using a smart watch. Sleep data can also be collected using a fitness tracker. Meal records can also be collected using a smartphone app. In this way, detailed health data can be obtained by collecting health information from a personal identification card or a wearable device. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from a wearable device to a generation AI and collect data using the generation AI.

[0072] The analysis unit can analyze the collected data and detect signs of lack of exercise or overwork. The analysis unit, for example, analyzes the collected data and detects signs of lack of exercise or overwork. Signs of lack of exercise or overwork include, but are not limited to, cases where the number of steps is below a certain level and where the heart rate is above a certain level. The analysis unit detects signs of lack of exercise, for example, when the number of steps is below a certain level. Signs of overwork can also be detected when the heart rate is above a certain level. The health condition can also be evaluated using a machine learning algorithm. As a result, signs of lack of exercise or overwork can be detected early by analyzing the collected data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and analyze the data using the generation AI.

[0073] The suggestion unit can make specific action plans for lifestyle improvement and body shaping on a daily basis based on the analysis results. For example, the suggestion unit makes specific action plans for lifestyle improvement and body shaping on a daily basis based on the analysis results. Lifestyle improvement and body shaping include, but are not limited to, for example, dietary improvement, exercise recommendations, and stress management. For example, the suggestion unit can suggest specific actions such as "walking for 30 minutes every day" when lack of exercise is detected. Based on the diet record, it is also possible to make suggestions for improving nutritional balance. It is also possible to suggest relaxation methods for stress management. This makes it possible to make specific action plans based on the analysis results, thereby improving lifestyles according to individuals. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the analysis results to a generation AI and use the generation AI to make an action plan.

[0074] The advice unit can provide emergency medical advice such as "consult a doctor" when the heart rate is abnormally high. The advice unit provides emergency medical advice such as "consult a doctor" when the heart rate is abnormally high. An abnormally high heart rate includes, but is not limited to, a case where the heart rate exceeds 100 bpm. For example, the advice unit provides advice such as "consult a doctor immediately" when the heart rate exceeds 100 bpm. This allows for a rapid response by providing emergency medical advice. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input heart rate data to a generation AI and provide medical advice using the generation AI.

[0075] The advice unit can provide information on local medical institutions and advice on health management at the travel destination when traveling abroad. The advice unit, for example, provides information on local medical institutions and advice on health management at the travel destination when traveling abroad. Information on local medical institutions includes, for example, the location of the hospital, contact information, medical department, etc., but is not limited to such examples. The advice unit provides, for example, the location and contact information of the hospital at the travel destination. Advice on health management at the travel destination includes, for example, health management methods suited to the local climate and advice on local meals, but is not limited to such examples. As a result, by providing information on local medical institutions and advice on health management when traveling abroad, health management during the travel becomes easier. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input information on the travel destination into the generation AI and provide advice on health management using the generation AI.

[0076] The collection unit can estimate the user's emotion and adjust the collection timing of the health information based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the collection timing of the health information based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. For example, when the user is feeling stressed, the collection unit collects health information during a time period when the user is relaxed. When the user is relaxed, the collection timing can also be shortened and the minimum amount of information required can be collected when the user is in a hurry. In this way, by adjusting the collection timing based on the user's emotion, the health information can be collected at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and adjust the collection timing using the generation AI.

[0077] The collection unit can analyze the user's past health data and select the optimal collection method. The collection unit, for example, analyzes the user's past health data and selects the optimal collection method. The optimal collection method includes, for example, data accuracy, collection frequency, and user burden, but is not limited to such examples. For example, if the user's heart rate fluctuated greatly in the past, the collection unit can increase the frequency of collecting the heart rate. If the user's step count was low in the past, the collection unit can also increase the frequency of collecting the step count. If the user's sleep data was irregular in the past, the collection unit can also increase the frequency of collecting the sleep data. In this way, the optimal collection method can be selected by analyzing the past health data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past health data into a generation AI and select the optimal collection method using the generation AI.

[0078] The collection unit can perform filtering based on the user's current living situation and activity level when collecting health information. For example, the collection unit performs filtering based on the user's current living situation and activity level when collecting health information. Filtering includes, for example, the type of data to be collected and the timing of collection, but is not limited to such examples. For example, when the user is exercising, the collection unit preferentially collects data related to exercise. When the user is resting, the collection unit can also preferentially collect heart rate and sleep data. When the user is eating, the collection unit can also preferentially collect meal records. In this way, by filtering based on the current living situation and activity level, more relevant health information can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's living situation data to the generation AI and perform filtering using the generation AI.

[0079] The collection unit can estimate the user's emotion and determine the priority of the health information to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of the health information to be collected based on the estimated user's emotion. The priority of the health information includes, for example, the importance and urgency of the data, but is not limited to such examples. For example, when the user is stressed, the collection unit preferentially collects data on the heart rate and stress level. When the user is relaxed, normal health information can also be collected. When the user is in a hurry, the minimum necessary health information can also be collected. In this way, important information can be preferentially collected by determining the priority of the health information based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and determine the priority of the health information using the generation AI.

[0080] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting health information. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting health information. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, when the user is at a gym, the collection unit preferentially collects data related to exercise. When the user is at home, data related to daily life can also be preferentially collected. When the user is traveling, health information at the travel destination can also be preferentially collected. In this way, by considering the geographical location information, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and preferentially collect highly relevant data using the generation AI.

[0081] The collection unit can analyze the social media activity of the user and collect related data when collecting health information. For example, the collection unit analyzes the social media activity of the user and collects related data when collecting health information. Social media activities include, for example, analysis of post content, analysis of followers, and the like, but are not limited to such examples. For example, when the user posts about exercise on social media, the collection unit preferentially collects exercise data. When the user posts about meals, the collection unit can also preferentially collect meal records. When the user posts about stress, the collection unit can also preferentially collect stress level data. In this way, related health information can be collected by analyzing social media activities. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media data of the user to the generation AI and collect related data using the generation AI.

[0082] The analysis unit can estimate the user's emotion and adjust the method of expression of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the method of expression of the analysis based on the estimated user's emotion. The method of expression of the analysis includes, for example, graph display, text display, alert display, etc., but is not limited to such examples. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. When the user is relaxed, a detailed analysis result can also be provided. When the user is in a hurry, an analysis result with the main points can also be provided. In this way, by adjusting the method of expression of the analysis based on the user's emotion, a more appropriate analysis result can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and adjust the method of expression of the analysis using the generation AI.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the health data during the analysis. The importance of the health data includes, for example, the type of data, the frequency of collection, and the health condition of the user, but is not limited to such examples. For example, the analysis unit performs a detailed analysis when the heart rate data is important. When the step count data is important, a detailed analysis can also be performed. When the sleep data is important, a detailed analysis can also be performed. As a result, important data can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the health data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the health data to a generation AI and adjust the level of detail of the analysis using the generation AI.

[0084] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of health data during analysis. The analysis algorithms include, for example, regression analysis, clustering, deep learning, and the like, but are not limited to such examples. The analysis unit, for example, applies a heart rate variability analysis algorithm to heart rate data. A walking pattern analysis algorithm can also be applied to step count data. A sleep stage analysis algorithm can also be applied to sleep data. This allows for more accurate analysis by applying different analysis algorithms depending on the category of health data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input health data to a generation AI and apply different analysis algorithms using the generation AI.

[0085] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. The length of the analysis includes, for example, the analysis time, the amount of data, the user's request, and the like, but is not limited to such examples. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. When the user is relaxed, a detailed analysis result can also be provided. When the user is excited, a visually stimulating analysis result can also be provided. In this way, by adjusting the length of the analysis based on the user's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and adjust the length of the analysis using the generation AI.

[0086] The analysis unit can determine the priority of the analysis based on the collection time of the health data at the time of analysis. The analysis unit, for example, determines the priority of the analysis based on the collection time of the health data at the time of analysis. The collection time of the health data includes, for example, the latest data, past data, seasonal data, etc., but is not limited to such examples. The analysis unit, for example, preferentially analyzes recently collected data. It is also possible to analyze current data while referring to past data. It is also possible to focus on analyzing data collected in a specific period. In this way, by determining the priority of the analysis based on the collection time of the health data, it is possible to preferentially analyze the latest data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the health data to a generation AI and determine the priority of the analysis using the generation AI.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the health data during analysis. The relevance of the health data includes, for example, correlation and co-occurrence of data, but is not limited to such examples. The analysis unit, for example, associates and analyzes heart rate data and step count data. Sleep data and diet records can also be associated and analyzed. Exercise data and stress level data can also be associated and analyzed. By adjusting the order of analysis based on the relevance of the health data, it is possible to preferentially analyze highly related data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input health data to a generation AI and adjust the order of analysis using the generation AI.

[0088] The suggestion unit can estimate the user's emotion and adjust the way of expressing the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way of expressing the proposal based on the estimated user's emotion. The way of expressing the proposal includes, for example, text display, voice guidance, visual display, etc., but is not limited to such examples. For example, when the user is nervous, the suggestion unit provides a simple and highly visible proposal. When the user is relaxed, a detailed proposal can also be provided. When the user is in a hurry, a proposal that covers the main points can also be provided. In this way, by adjusting the way of expressing the proposal based on the user's emotion, a more appropriate proposal can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and adjust the way of expressing the proposal using the generation AI.

[0089] The suggestion unit can adjust the level of detail of the proposal based on the importance of the action plan at the time of proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the action plan at the time of proposal. The importance of the action plan includes, for example, health risk, urgency, user's goal, etc., but is not limited to such examples. For example, the suggestion unit provides a detailed proposal for an important action plan. A simple proposal can be provided for a general action plan. A quick proposal can be provided for an action plan with high urgency. Thereby, by adjusting the level of detail of the proposal based on the importance of the action plan, an important action plan can be proposed in detail. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the action plan data to the generation AI and adjust the level of detail of the proposal using the generation AI.

[0090] The suggestion unit can apply different suggestion algorithms depending on the category of the action plan at the time of suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the action plan at the time of suggestion. The suggestion algorithm includes, for example, a rule-based algorithm, a machine learning algorithm, an optimization algorithm, and the like, but is not limited to such examples. For example, the suggestion unit applies an exercise algorithm to an exercise plan. A nutrition algorithm can also be applied to a meal plan. A sleep algorithm can also be applied to a sleep plan. Thereby, by applying different suggestion algorithms depending on the category of the action plan, more appropriate suggestions can be made. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the action plan data to a generation AI and apply different suggestion algorithms using the generation AI.

[0091] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the proposal based on the estimated user's emotion. The length of the proposal includes, for example, the level of detail of the proposal content, the user's request, and the like, but is not limited to such examples. For example, the suggestion unit provides a short and to the point proposal when the user is in a hurry. When the user is relaxed, a detailed proposal can also be provided. When the user is excited, a visually stimulating proposal can also be provided. In this way, by adjusting the length of the proposal based on the user's emotion, a more appropriate proposal can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and adjust the length of the proposal using the generation AI.

[0092] The suggestion unit can determine the priority of the proposal based on the submission time of the action plan at the time of proposal. The suggestion unit, for example, determines the priority of the proposal based on the submission time of the action plan at the time of proposal. The submission time of the action plan includes, for example, a submission deadline, urgency, a user's schedule, etc., but is not limited to such examples. The suggestion unit, for example, preferentially proposes an action plan with a high urgency. It is also possible to preferentially propose an action plan with a close submission deadline. It is also possible to postpone the proposal of a long-term action plan. In this way, by determining the priority of the proposal based on the submission time of the action plan, it is possible to preferentially propose an action plan with a high urgency. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the action plan data to the generation AI and determine the priority of the proposal using the generation AI.

[0093] The suggestion unit can adjust the order of the proposal based on the relevance of the action plans at the time of proposal. For example, the suggestion unit adjusts the order of the proposal based on the relevance of the action plans at the time of proposal. The relevance of the action plans includes, for example, interdependence between the plans, common goals, and the like, but is not limited to such examples. For example, the suggestion unit suggests an exercise plan and a meal plan in association with each other. It is also possible to suggest a sleep plan and a stress management plan in association with each other. It is also possible to suggest a health management plan and a lifestyle improvement plan in association with each other. Thereby, by adjusting the order of the proposal based on the relevance of the action plans, it is possible to preferentially suggest an action plan with high relevance. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the action plan data to the generation AI and adjust the order of the proposal using the generation AI.

[0094] The advice unit can estimate the user's emotion and adjust the advice method based on the estimated user's emotion. The advice unit can estimate the user's emotion, for example, and adjust the advice method based on the estimated user's emotion. The advice method includes, for example, a text message, a voice guide, a video call, and the like, but is not limited to such examples. For example, when the user is nervous, the advice unit can provide advice in a calm voice. When the user is relaxed, the advice unit can also provide advice in a bright voice. When the user is in a hurry, the advice unit can also provide quick and concise advice. This allows more appropriate advice to be provided by adjusting the advice method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotion data into the generation AI and adjust the advice method using the generation AI.

[0095] The advice unit can select the optimal advice method by analyzing the user's past health data when giving advice. For example, the advice unit can select the optimal advice method by analyzing the user's past health data when giving advice. The optimal advice method includes, for example, past advice history, the user's preferences, and health conditions, but is not limited to such examples. For example, if the user had a high heart rate in the past, the advice unit can advise how to lower the heart rate. If the user has lacked exercise in the past, the advice unit can also provide advice to encourage exercise. If the user has lacked sleep in the past, the advice unit can also advise how to improve sleep. In this way, the optimal advice method can be selected by analyzing the past health data. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input past health data into a generation AI and select the optimal advice method using the generation AI.

[0096] The advice unit can customize the means of advice based on the user's current living situation when giving advice. The advice unit, for example, customizes the means of advice based on the user's current living situation when giving advice. The means of advice include, for example, text messages, voice guidance, video calls, and the like, but are not limited to such examples. For example, when the user is at work, the advice unit provides advice that can be implemented in a short time. When the user is resting, the advice unit can also provide advice on how to relax. When the user is traveling, the advice unit can also provide advice on health management at the travel destination. In this way, by customizing the means of advice based on the current living situation, more appropriate advice can be provided. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the user's living situation data into the generation AI and customize the means of advice using the generation AI.

[0097] The advice unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. The advice unit can estimate the user's emotion, for example, and determine the priority of advice based on the estimated user's emotion. The priority of advice includes, for example, the importance of advice, urgency, and user's request, but is not limited to such examples. For example, when the user is stressed, the advice unit can give priority to advice on stress reduction. When the user is relaxed, advice on normal health management can also be provided. When the user is in a hurry, advice with high urgency can also be given priority. In this way, important advice can be provided preferentially by determining the priority of advice based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotion data to the generation AI and determine the priority of advice using the generation AI.

[0098] The advice unit can select the optimal advice method in consideration of the geographical location information of the user when giving advice. For example, the advice unit selects the optimal advice method in consideration of the geographical location information of the user when giving advice. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, when the user is at a gym, the advice unit provides advice on exercise. When the user is at home, the advice unit can also provide advice on daily life. When the user is traveling, the advice unit can also provide advice on health management at the travel destination. In this way, the optimal advice method can be selected by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the geographical location information of the user to the generation AI and select the optimal advice method using the generation AI.

[0099] The advice unit can analyze the social media activity of the user at the time of giving advice and propose a means of advice. For example, the advice unit can analyze the social media activity of the user at the time of giving advice and propose a means of advice. Social media activity includes, for example, analysis of the content of posts, analysis of followers, and the like, but is not limited to such examples. For example, when the user posts about exercise on social media, the advice unit provides advice about exercise. When the user posts about diet, the advice unit can also provide advice about diet. When the user posts about stress, the advice unit can also provide advice about stress reduction. In this way, by analyzing social media activity, it is possible to provide relevant advice. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the social media data of the user into a generation AI and use the generation AI to propose a means of advice.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The health consultation system can further collect data on the user's living environment and make suggestions for improving the living environment based on the analysis results. For example, by collecting the temperature, humidity, and noise level of the user's living environment and analyzing this data, it can make suggestions for maintaining a comfortable living environment. Specifically, if the temperature is too high, it recommends using an air conditioner, and if the humidity is too low, it suggests using a humidifier. In addition, if the noise level is high, it can also suggest using earplugs or moving to a quieter place. This can comprehensively improve the user's living environment and contribute to maintaining health.

[0102] The collection unit can analyze the user's social media activity and use it to collect health information. For example, if the user posts about exercise on social media, the frequency and type of exercise can be understood by analyzing the content of the post. In addition, by analyzing posts about food, the user's eating habits can be understood and the information can be used to suggest improvements to nutritional balance. Furthermore, by analyzing posts about stress, the user's stress level can be estimated and suggestions for stress management can be made. In this way, more detailed health information can be collected by utilizing the user's social media activity, and health management appropriate for each individual can be performed.

[0103] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated emotions. For example, if the user is feeling stressed, the analysis results can be provided in a simple, highly visible format. If the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. This makes it possible to provide more appropriate information by adjusting the way in which the analysis results are presented according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or a generation AI.

[0104] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated emotions. For example, if the user is feeling stressed, suggestions for relaxation methods and stress reduction can be given priority. If the user is relaxed, suggestions for improving exercise and diet can also be made. If the user is in a hurry, suggestions that can be implemented in a short time can also be made. This allows for more appropriate health management by adjusting the content of the suggestions based on the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or a generation AI.

[0105] The advice unit can estimate the user's emotions and adjust the method of advice based on the estimated emotions. For example, if the user is nervous, advice can be given in a calm voice. If the user is relaxed, advice can be given in a bright voice. If the user is in a hurry, quick and concise advice can be given. In this way, more appropriate advice can be provided by adjusting the method of advice based on the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or a generation AI.

[0106] The collection unit can collect health information taking into account the geographical location information of the user. For example, when the user is at the gym, data related to exercise can be collected preferentially. When the user is at home, data related to daily life can also be collected. When the user is traveling, health information at the travel destination can also be collected. In this way, more relevant health information can be collected by taking into account the geographical location information.

[0107] The analysis unit can determine the priority of analysis based on the time when the health data was collected. For example, it can prioritize analysis of recently collected data. It can also analyze current data while referring to past data. It can also focus on analyzing data collected during a specific period. In this way, by determining the priority of analysis based on the time when the health data was collected, it is possible to prioritize analysis of the latest data.

[0108] The suggestion unit can apply different suggestion algorithms depending on the category of the action plan. For example, an exercise algorithm can be applied to an exercise plan, and a nutrition algorithm can be applied to a meal plan. A sleep algorithm can also be applied to a sleep plan. This allows for more appropriate suggestions by applying different suggestion algorithms depending on the category of the action plan.

[0109] The advice unit can select the optimal advice method by analyzing the user's past health data. For example, if the user has had a high heart rate in the past, advice can be given on how to lower the heart rate. If the user has lacked exercise in the past, advice can be given to encourage exercise. If the user has lacked sleep in the past, advice can be given on how to improve sleep. In this way, the optimal advice method can be selected by analyzing past health data.

[0110] The advice unit can select the optimal advice method by taking into account the geographical location information of the user. For example, when the user is at a gym, advice on exercise can be provided. When the user is at home, advice on daily life can be provided. When the user is traveling, advice on health management at the travel destination can be provided. In this way, the optimal advice method can be selected by taking into account the geographical location information.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The collection unit collects personal health information. The personal health information includes heart rate, blood pressure, body temperature, number of steps, sleep data, etc. The collection unit collects this information using a wearable device, a smartphone app, and medical equipment. For example, the collection unit collects heart rate and number of steps using a smart watch, collects food records using a smartphone app, and collects blood pressure and body temperature using medical equipment. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using statistical analysis of the data and machine learning algorithms. For example, heart rate and step count data can be analyzed to detect signs of lack of exercise or overwork. Machine learning algorithms can also be used to evaluate health status. Step 3: The suggestion unit creates a specific action plan based on the analysis results obtained by the analysis unit. The action plan includes an exercise plan, a meal plan, a sleep improvement plan, etc. For example, if a lack of exercise is detected, a specific action such as "walk for 30 minutes every day" is suggested. It can also make suggestions for improving nutritional balance based on food records. Step 4: The Advisory Department provides emergency medical advice and health consultations based on the action plan drawn up by the Proposal Department. For example, if the heart rate is abnormally high, the Advisory Department will advise the user to "consult a doctor immediately." When traveling abroad, the Advisory Department can also provide information on local medical institutions and advice on health management at the travel destination.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0115] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and advice unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects personal health information using the computer 36 and the camera 42 of the smart device 14. The analysis unit analyzes the collected data, for example, by the specific processing unit 290 of the data processing device 12. The suggestion unit makes an action plan based on the analysis result, for example, by the specific processing unit 290 of the data processing device 12. The advice unit provides medical advice in an emergency, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0124] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and advice unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects personal health information using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data, for example, by the specific processing unit 290 of the data processing device 12. The suggestion unit makes an action plan based on the analysis result, for example, by the specific processing unit 290 of the data processing device 12. The advice unit provides medical advice in an emergency, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

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

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and advice unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects personal health information using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit analyzes the collected data, for example, by the specific processing unit 290 of the data processing device 12. The suggestion unit makes an action plan based on the analysis result, for example, by the specific processing unit 290 of the data processing device 12. The advice unit provides medical advice in an emergency, for example, by the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and advice unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects personal health information using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected data, for example, by the specific processing unit 290 of the data processing device 12. The suggestion unit makes an action plan based on the analysis result, for example, by the specific processing unit 290 of the data processing device 12. The advice unit provides medical advice in an emergency, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A collection department that collects personal health information; an analysis unit that analyzes the information collected by the collection unit; a proposal unit that creates a specific action plan based on the analysis result obtained by the analysis unit; and an advice unit that provides medical advice and health consultation in an emergency based on the action plan established by the suggestion unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect health information from personal identification cards and wearable devices, such as heart rate, steps, sleep data, and food records 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the collected data to detect signs of lack of exercise or overwork 2. The system of claim 1, (Appendix 4) The suggestion unit, Based on the analysis results, create a specific action plan for daily lifestyle improvements and body shaping 2. The system of claim 1, (Appendix 5) The advice unit: Providing emergency medical advice, such as "consult a doctor" if your heart rate is abnormally high 2. The system of claim 1, (Appendix 6) The advice unit: Providing information on local medical facilities and advice on health management when traveling abroad 2. The system of claim 1, (Appendix 7) The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting health information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze the user's past health data and select the optimal collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting health information, it will be filtered based on the user's current lifestyle and activity level. 2. The system of claim 1, (Appendix 10) The collecting unit includes: The user's emotions are estimated, and the priority of health information to be collected is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting health information, the system takes into account the user's geographic location to prioritize collection of relevant data. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting health information, analyze users' social media activity and collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the health data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of health data. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the health data was collected. 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of health data 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, When proposing, adjust the level of detail in the proposal based on the importance of the action plan 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of the action plan. 2. The system of claim 1, (Appendix 22) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 23) The suggestion unit, At the time of proposal, the proposals will be prioritized based on the time of submission of the action plan. 2. The system of claim 1, (Appendix 24) The suggestion unit, When making proposals, adjust the order of proposals based on their relevance to the action plan 2. The system of claim 1, (Appendix 25) The advice unit: To estimate a user's emotion and adjust a method of providing advice based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The advice unit: When providing advice, the system analyzes the user's past health data to select the most appropriate advice method. 2. The system of claim 1, (Appendix 27) The advice unit: At the time of advice, customize the advice delivery method based on the user's current life situation 2. The system of claim 1, (Appendix 28) The advice unit: Estimating a user's emotions and prioritizing advice based on the estimated user's emotions 2. The system of claim 1, (Appendix 29) The advice unit: When providing advice, the system selects the most appropriate advice method by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The advice unit: When providing advice, the system analyzes the user's social media activity to suggest advice measures. 2. The system of claim 1, [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects personal health information; an analysis unit that analyzes the information collected by the collection unit; a proposal unit that creates a specific action plan based on the analysis result obtained by the analysis unit; and an advice unit that provides medical advice and health consultation in an emergency based on the action plan established by the suggestion unit. A system characterized by:

2. The collecting unit includes: Collect health information from personal identification cards and wearable devices, such as heart rate, steps, sleep data, and food records 2. The system of claim 1 .

3. The analysis unit is Analyze the collected data to detect signs of lack of exercise or overwork 2. The system of claim 1 .

4. The suggestion unit, Based on the analysis results, create a specific action plan for daily lifestyle improvements and body shaping 2. The system of claim 1 .

5. The advice unit: Providing emergency medical advice if your heart rate is abnormally high 2. The system of claim 1 .

6. The advice unit: Providing information on local medical facilities and advice on health management when traveling abroad 2. The system of claim 1 .

7. The collecting unit includes: To estimate a user's emotion and adjust the timing of collection of health information based on the estimated user's emotion.

2. The system of claim 1 .

8. The collecting unit includes: Analyze the user's past health data and select the optimal collection method 2. The system of claim 1 .

Images