system

Abstract

The system according to this embodiment aims to centrally manage users' health data and provide appropriate health advice. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a corporate provision unit. The collection unit collects user health data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides health advice to the user based on the analysis results obtained by the analysis unit. The corporate provision unit provides the data collected by the collection unit to companies.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the user's health data has not been sufficiently managed centrally and appropriate health advice has not been provided, leaving room for improvement.

[0005] The system according to the embodiment aims to centrally manage the user's health data and provide appropriate health advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a corporate data provision unit. The data collection unit collects user health data. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides health advice to the user based on the analysis results obtained by the analysis unit. The corporate data provision unit provides the data collected by the data collection unit to the corporate data provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage users' health data and provide appropriate health advice. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health concierge system according to an embodiment of the present invention is a system that centrally manages a user's health data and provides necessary health advice. This health concierge system collects health data such as the user's diet, basal body temperature, metabolism, weight, blood pressure, exercise (pedometer), and blood oxygen, and automatically manages chronic illnesses, medication, lifestyle habits, and exercise habits based on this data. Furthermore, it analyzes the collected health data and provides appropriate health advice to the user. In addition, the collected health data can be provided to companies as big data and used for purposes such as recruiting monitors. For example, when collecting a user's health data, meal data is obtained from a meal recording app and basal body temperature data is obtained from a basal body temperature measurement app. In this way, the user's health data can be collected in a centralized manner. Next, based on the collected data, chronic illnesses, medication, lifestyle habits, and exercise habits are automatically managed. For example, if a user has a chronic illness, medication information and lifestyle habits are managed according to that illness. In addition, regarding exercise habits, an appropriate exercise plan can be proposed based on the user's exercise data. Furthermore, the collected health data is analyzed and appropriate health advice is provided to the user. For example, the user's meal data is analyzed and areas for improvement in nutritional balance are suggested. Furthermore, the system can analyze basal body temperature data and provide advice on health management. In this way, it can provide personalized advice tailored to the user's health condition. In addition, the collected health data can be provided to companies as big data and used for purposes such as recruiting monitors. For example, health-related companies can use users' health data to collect data necessary for developing new products. Also, when recruiting monitors, appropriate monitors can be selected based on the user's health data. Thus, the health concierge system of the present invention not only centrally manages users' health data and provides appropriate health advice, but can also contribute to the development of various health-related services and products by providing it to companies as big data. As a result, the health concierge system can efficiently collect, analyze, provide advice on, and provide users' health data to companies.

[0029] The health concierge system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a corporate data provision unit. The data collection unit collects the user's health data. The user's health data includes, but is not limited to, examples of diet, basal body temperature, metabolism, weight, blood pressure, exercise (pedometer), and blood oxygen. For example, the data collection unit can acquire diet data from a diet recording app and basal body temperature data from a basal body temperature measurement app. The data collection unit can also acquire exercise data from a pedometer app and blood oxygen data from a dedicated measurement app. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit automatically manages chronic illnesses, medication, lifestyle habits, and exercise habits based on the collected data. For example, if the user has a chronic illness, the analysis unit manages medication information and lifestyle habits according to that illness. The analysis unit can also propose an appropriate exercise plan based on the user's exercise data. The data provision unit provides health advice to the user based on the analysis results obtained by the analysis unit. The provisioning department, for example, analyzes the user's dietary data and suggests improvements to their nutritional balance. The provisioning department can also analyze basal body temperature data and provide advice on health management. For example, the provisioning department suggests improvements to the user's dietary data. Furthermore, the provisioning department can provide advice on health management based on basal body temperature data. The corporate provisioning department provides the data collected by the collection department to companies. For example, the corporate provisioning department can provide the collected health data to companies as big data and utilize it for purposes such as recruiting monitors. For example, health-related companies can utilize the user's health data to collect data necessary for developing new products. The corporate provisioning department can also select appropriate monitors based on the user's health data when recruiting monitors. As a result, the health concierge system according to this embodiment can efficiently collect, analyze, provide advice on, and provide user health data to companies.

[0030] The data collection unit can collect data on meals, basal body temperature, metabolism, weight, blood pressure, exercise (pedometer), and blood oxygen levels. For example, the data collection unit can acquire meal data from a meal logging app. For example, a user uses an app to record their meals, and the data collection unit acquires that data. The data collection unit can also acquire basal body temperature data from a basal body temperature measurement app. For example, a user uses an app to measure their basal body temperature, and the data collection unit acquires that data. Furthermore, the data collection unit can acquire exercise data from a pedometer app. For example, a user uses an app to record their steps, and the data collection unit acquires that data. The data collection unit can also acquire blood oxygen data from a dedicated measurement app. For example, a user uses an app to measure their blood oxygen levels, and the data collection unit acquires that data. By collecting diverse health data, more detailed health management becomes possible. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a meal logging app into a generating AI, and have the generating AI perform analysis of the meal data.

[0031] The analysis unit can automatically manage chronic illnesses, medication, lifestyle, and exercise habits based on the collected data. For example, the analysis unit can manage chronic illnesses based on the collected data. For instance, if a user has a chronic illness, it can manage medication information and lifestyle habits according to that illness. The analysis unit can also manage medication information based on the collected data. For example, it can manage the types of medications the user is taking and their medication schedule. Furthermore, the analysis unit can also manage lifestyle habits based on the collected data. For example, it can manage the user's sleep patterns and eating habits. The analysis unit can also manage exercise habits based on the collected data. For example, it can suggest an appropriate exercise plan based on the user's exercise data. This enables automatic health management based on the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the management of chronic illnesses and medication information.

[0032] The service provider can analyze the user's dietary data and suggest improvements to their nutritional balance. For example, the service provider can analyze the user's dietary data and suggest improvements to their nutritional balance. For example, it can evaluate the intake and balance of nutrients based on the user's dietary data and suggest improvements. The service provider can also suggest dietary improvement plans based on the user's dietary data. For example, it can suggest a balanced meal plan based on the user's dietary data. Furthermore, the service provider can provide advice on increasing the intake of specific nutrients based on the user's dietary data. For example, it can provide advice on increasing the intake of vitamins and minerals based on the user's dietary data. In this way, health management is improved by analyzing the user's dietary data and suggesting improvements to their nutritional balance. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's dietary data into a generating AI and have the generating AI generate suggestions for improving nutritional balance.

[0033] The service provider can analyze basal body temperature data and provide advice on health management. For example, the service provider can analyze basal body temperature data and provide advice on health management. For example, it can monitor changes in the user's health based on their basal body temperature data and provide appropriate advice. The service provider can also suggest areas for improvement in health based on basal body temperature data. For example, it can suggest areas for improvement in health based on the user's basal body temperature data. Furthermore, the service provider can propose specific health management plans based on basal body temperature data. For example, it can propose specific health management plans based on the user's basal body temperature data. In this way, by analyzing basal body temperature data and providing advice on health management, the user's health management improves. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input basal body temperature data into a generating AI and have the generating AI execute health management advice.

[0034] The Corporate Provision Department can provide collected health data to companies and use it for recruiting monitors. For example, the Corporate Provision Department can provide collected health data to companies and use it for recruiting monitors. For example, the Corporate Provision Department can provide users' health data to health-related companies to collect data necessary for developing new products. The Corporate Provision Department can also select appropriate monitors based on users' health data when recruiting monitors. For example, the Corporate Provision Department can select monitors that meet specific conditions based on users' health data. In this way, by providing collected health data to companies and using it for recruiting monitors, it becomes possible to provide data that meets the needs of companies. Some or all of the above processing in the Corporate Provision Department may be performed using AI, for example, or not using AI. For example, the Corporate Provision Department can input collected health data into a generating AI and have the generating AI select the data to provide to companies.

[0035] The data collection unit can analyze the user's past health data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data that the user has frequently collected in the past. For example, it can prioritize collecting dietary data that the user has frequently recorded in the past. The data collection unit can also suggest the optimal collection time based on the user's past collection history. For example, it can suggest the optimal collection time based on the time of day when the user has collected the most data in the past. The data collection unit can also adjust the collection frequency based on the user's past collection history. For example, it can increase the collection frequency of data that the user has frequently collected in the past to collect more detailed data. This allows the optimal collection method to be selected by analyzing the user's past health data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0036] The data collection unit can filter health data based on the user's current lifestyle or areas of interest. For example, if the user is on a diet, the data collection unit will prioritize collecting dietary data. For example, if the user is using a diet app, it will prioritize collecting that data. The data collection unit can also collect detailed exercise data if the user is interested in exercise. For example, if the user is using a fitness app, it will prioritize collecting that data. The data collection unit can also prioritize collecting data related to a chronic illness if the user has one. For example, if the user is using a diabetes management app, it will prioritize collecting that data. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit will prioritize the collection of blood oxygen data. For example, it will collect blood oxygen data while the user is engaged in activities at high altitude. The data collection unit can also collect detailed exercise data if the user is at a sports facility. For example, it will collect exercise data while the user is exercising at a gym. The data collection unit can also prioritize the collection of lifestyle data if the user is at home. For example, it will collect dietary and sleep data while the user is living at home. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts about dieting on social media, the data collection unit will prioritize collecting dietary data. For example, if a user frequently posts about dieting, the data will prioritize collecting that information. The data collection unit can also collect detailed exercise data if a user posts about exercise. For example, if a user frequently posts about exercise, the data will be collected in detail. The data collection unit can also collect balanced overall health data if a user posts about health. For example, if a user frequently posts about health, the data collection unit will collect balanced dietary data, exercise data, vital sign data, etc. This allows for the collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit performs a detailed analysis on important health data. For instance, it performs a detailed analysis on the user's vital sign data and data related to pre-existing conditions. The analysis unit can also perform a simplified analysis on general health data. For example, it performs a simplified analysis on the user's diet data and exercise data. The analysis unit can also perform a detailed analysis on data of high interest to the user. For example, it performs a detailed analysis on health data of particular interest to the user. This allows for more detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a nutritional balance analysis algorithm to dietary data. For example, it can apply an algorithm that evaluates nutritional balance based on the user's dietary data. The analysis unit can also apply an exercise effect analysis algorithm to exercise data. For example, it can apply an algorithm that evaluates the effect of exercise based on the user's exercise data. The analysis unit can also apply a health management analysis algorithm to basal body temperature data. For example, it can apply an algorithm that manages health based on the user's basal body temperature data. By applying different analysis algorithms depending on the category of health data, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of health data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit may prioritize the analysis of the most recent health data. For instance, it may prioritize the analysis of the user's most recent vital sign data and dietary data. The analysis unit can also analyze overall trends, including historical data. For example, it may analyze overall health trends based on the user's past health data. Furthermore, the analysis unit can focus on analyzing data collected during a specific period. For example, it may analyze the user's health status during a specific period based on the data collected during that period. This allows for prioritizing the analysis based on when the health data was collected, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the health data collection period into a generating AI and have the generating AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of health data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For instance, it can prioritize the analysis of the user's vital sign data or data related to pre-existing conditions. The analysis unit can also postpone the analysis of less relevant data. For example, it can postpone the analysis of the user's general health data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, it can evaluate the relevance of the user's health data and dynamically adjust the order of analysis. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The service provider can adjust the level of detail of health advice based on the user's health condition when providing it. For example, if the user's health condition is good, the service provider can provide concise advice. For example, if the user's health condition is good, it can provide a concise summary report. The service provider can also provide detailed advice if the user's health condition is deteriorating. For example, if the user's health condition is deteriorating, it can provide a detailed text report. Furthermore, the service provider can dynamically adjust the level of detail of the advice according to the user's health condition. For example, it can evaluate the user's health condition and dynamically adjust the level of detail of the advice. This allows for the provision of more appropriate advice by adjusting the level of detail based on the user's health condition. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's health condition data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0044] The service provider can apply different advice algorithms depending on the user's lifestyle when providing health advice. For example, the service provider can suggest improvements to nutritional balance based on eating habits. For example, it can suggest improvements to nutritional balance based on the user's eating habit data. The service provider can also suggest exercise plans based on exercise habits. For example, it can suggest an appropriate exercise plan based on the user's exercise habit data. The service provider can also provide comprehensive health advice based on the user's overall lifestyle. For example, it can provide comprehensive health advice based on the user's lifestyle data. By applying different advice algorithms depending on the user's lifestyle, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's lifestyle data into a generating AI and have the generating AI execute the application of an appropriate advice algorithm.

[0045] The service provider can prioritize health advice based on when the user's health data was collected. For example, it can provide advice based on the most recent health data, such as the user's most recent vital signs data or dietary data. It can also provide advice based on overall trends, including past data, such as the user's past health data. It can also provide advice based on data collected during a specific period, such as the user's health status during that period, based on data collected by the user during that period. This allows the service provider to provide advice based on the most recent data by prioritizing advice based on when the user's health data was collected. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the health data collection period into a generating AI and have the generating AI determine the priority of the advice.

[0046] The service provider can adjust the order of advice based on the relevance of the user's health data when providing health advice. For example, the service provider can provide advice based on highly relevant data, such as the user's vital sign data or data related to pre-existing conditions. The service provider can also postpone less relevant data, such as the user's general health data. Furthermore, the service provider can dynamically adjust the order of advice based on the relevance of the data, such as evaluating the relevance of the user's health data and dynamically adjusting the order of advice. This allows for the provision of more relevant advice by adjusting the order of advice based on the relevance of the user's health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0047] The Corporate Data Provisioning Department can adjust the level of detail of the data provided based on the company's needs at the time of provision. For example, if a company requires detailed data, the Corporate Data Provisioning Department will provide detailed data. For example, it will provide detailed health data necessary for a company to develop a new product. The Corporate Data Provisioning Department can also provide concise data if a company requires concise data. For example, it will provide concise health data necessary for a company to conduct a marketing survey. Furthermore, the Corporate Data Provisioning Department can dynamically adjust the level of detail of the data according to the company's needs. For example, it will adjust the level of detail of the data provided based on the company's request. This allows for the provision of more appropriate data by adjusting the level of detail of the data provided based on the company's needs. Some or all of the above processing in the Corporate Data Provisioning Department may be performed using AI, for example, or not using AI. For example, the Corporate Data Provisioning Department can input company needs data into a generating AI and have the generating AI perform the adjustment of the level of detail of the data provided.

[0048] The Corporate Data Provisioning Department can apply different data provision algorithms depending on the industry of the company when providing data to companies. For example, the Corporate Data Provisioning Department can provide detailed health data to health-related companies, for example, to health-related companies, to provide detailed health data necessary for the development of new products. The Corporate Data Provisioning Department can also prioritize providing dietary data to food-related companies, for example, to provide dietary data necessary for the development of new products. The Corporate Data Provisioning Department can also prioritize providing exercise data to exercise-related companies, for example, to provide exercise-related companies, to provide exercise data necessary for the development of new products. In this way, more appropriate data can be provided by applying different data provision algorithms depending on the industry of the company. Some or all of the above processing in the Corporate Data Provisioning Department may be performed using AI, for example, or not using AI. For example, the Corporate Data Provisioning Department can input company industry data into a generating AI and have the generating AI execute the application of an appropriate data provision algorithm.

[0049] The Corporate Data Provisioning Department can prioritize providing highly relevant data by considering the geographical location of the companies when providing data to them. For example, if a company is located at high altitude, the Corporate Data Provisioning Department can prioritize providing blood oxygen data. For example, a company located at high altitude can provide blood oxygen data necessary for new product development. The Corporate Data Provisioning Department can also provide detailed exercise data if a company is located at a sports facility. For example, a company located at a sports facility can provide exercise data necessary for new product development. Furthermore, if a company is located in an urban area, the Corporate Data Provisioning Department can prioritize providing lifestyle data. For example, a company located in an urban area can provide lifestyle data necessary for marketing research. In this way, by considering the geographical location of the companies, highly relevant data can be prioritized. Some or all of the above processing in the Corporate Data Provisioning Department may be performed using AI, for example, or without AI. For example, the Corporate Data Provisioning Department can input the geographical location data of companies into a generating AI and have the generating AI perform the provision of highly relevant data.

[0050] The Corporate Data Provision Department can analyze a company's past data usage history and select the optimal data provision method. For example, the Corporate Data Provision Department can select the optimal data provision method based on data the company has used in the past. For example, it can select the optimal data provision method based on health data the company has used in the past. The Corporate Data Provision Department can also adjust the frequency of data provision based on the company's past data usage history. For example, it can increase the frequency of provision of data that the company has used frequently in the past. The Corporate Data Provision Department can also analyze a company's past data usage history and select the most efficient data provision method. For example, it can select the optimal data provision method based on the data provision method the company has used most efficiently in the past. In this way, the optimal data provision method can be selected by analyzing a company's past data usage history. Some or all of the above processes in the Corporate Data Provision Department may be performed using AI, for example, or without AI. For example, the Corporate Data Provision Department can input the company's past data usage history data into a generating AI and have the generating AI select the optimal data provision method.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The data collection unit can adjust data collection based on the user's current weather conditions when collecting user health data. For example, if the user is in a hot and humid environment, the data collection unit can prioritize collecting water intake data to account for the risk of dehydration. It can also collect detailed body temperature data if the user is in a cold environment. Furthermore, if the user is in pollen season, the data collection unit can prioritize collecting data related to allergy symptoms. This allows for the collection of more appropriate health data by considering weather conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather information data into a generating AI and have the generating AI adjust the timing of data collection.

[0053] The analysis unit can analyze a user's health data while taking the user's genetic information into consideration. For example, the analysis unit can assess the risk of specific diseases based on the user's genetic information. It can also propose an optimal nutritional intake plan based on the user's genetic information. Furthermore, the analysis unit can evaluate individual differences in exercise effectiveness and propose an optimal exercise plan based on the user's genetic information. This allows for more personalized health management by considering genetic information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input genetic information data into a generating AI and have the generating AI adjust the level of detail in the analysis.

[0054] The service provider can provide health advice tailored to the user's living environment based on the user's health data. For example, if the user lives in an urban area, the service provider can provide advice on stress management and air quality. If the user lives in a rural area, the service provider can also provide advice on health risks related to agricultural work. Furthermore, if the user lives in a senior living facility, the service provider can provide advice on specific health risks. This enables more appropriate health management by providing health advice tailored to the living environment. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input living environment data into a generating AI and have the generating AI adjust the level of detail of the advice.

[0055] The Corporate Data Provisioning Department can adjust the level of detail of collected health data when providing it to companies, depending on the company's research and development stage. For example, if a company is conducting early-stage research, the Corporate Data Provisioning Department can provide a broad dataset. If a company is in the final stages of product development, it can provide specific data in detail. Furthermore, if a company is conducting market research, the Corporate Data Provisioning Department can provide concise data. By adjusting the level of detail of the data according to the company's research and development stage, it becomes possible to provide more appropriate data. Some or all of the above processing in the Corporate Data Provisioning Department may be performed using AI, for example, or not. For example, the Corporate Data Provisioning Department can input research and development stage data into a generating AI and have the generating AI perform the adjustment of the level of detail of the data.

[0056] The data collection unit can adjust the frequency of data collection based on the user's activity level when collecting user health data. For example, the data collection unit can increase the frequency of data collection when the user is at a high activity level, and decrease it when the user is at a low activity level. Furthermore, it can collect data at a balanced frequency when the user is at a moderate activity level. By adjusting the frequency of data collection based on the activity level, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input activity level data into a generating AI and have the generating AI adjust the frequency of data collection.

[0057] The analysis unit can adjust the level of detail of the analysis based on the user's dietary patterns when analyzing the user's health data. For example, if the user has a balanced diet, the analysis unit can perform a simplified analysis. If the user has an unbalanced diet, it can perform a detailed analysis. Furthermore, if the user is on a specific diet, it can perform an analysis tailored to that diet. By adjusting the level of detail of the analysis based on the dietary patterns, a more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input dietary pattern data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects the user's health data. This data includes information on diet, basal body temperature, metabolism, weight, blood pressure, exercise (pedometer), and blood oxygen levels. For example, the data collection unit can obtain dietary data from a meal tracking app and basal body temperature data from a basal body temperature measurement app. It can also obtain exercise data from a pedometer app and blood oxygen data from a dedicated measurement app. Step 2: The analysis unit analyzes the data collected by the collection unit. Based on the collected data, the analysis unit automatically manages information such as chronic illnesses, medication, lifestyle, and exercise habits. For example, if a user has a chronic illness, it manages medication information and lifestyle habits according to that illness. It can also suggest an appropriate exercise plan based on the user's exercise data. Step 3: The service provider provides health advice to the user based on the analysis results obtained by the analysis unit. The service provider analyzes the user's dietary data and suggests areas for improvement in nutritional balance. It can also analyze basal body temperature data and provide advice on managing one's health. Step 4: The Corporate Provisioning Department provides the data collected by the Collection Department to companies. The Corporate Provisioning Department provides the collected health data to companies as big data, which can be used for purposes such as recruiting monitors. For example, health-related companies can use users' health data to collect data necessary for developing new products. Also, when recruiting monitors, appropriate monitors can be selected based on the users' health data.

[0060] (Example of form 2) The health concierge system according to an embodiment of the present invention is a system that centrally manages a user's health data and provides necessary health advice. This health concierge system collects health data such as the user's diet, basal body temperature, metabolism, weight, blood pressure, exercise (pedometer), and blood oxygen, and automatically manages chronic illnesses, medication, lifestyle habits, and exercise habits based on this data. Furthermore, it analyzes the collected health data and provides appropriate health advice to the user. In addition, the collected health data can be provided to companies as big data and used for purposes such as recruiting monitors. For example, when collecting a user's health data, meal data is obtained from a meal recording app and basal body temperature data is obtained from a basal body temperature measurement app. In this way, the user's health data can be collected in a centralized manner. Next, based on the collected data, chronic illnesses, medication, lifestyle habits, and exercise habits are automatically managed. For example, if a user has a chronic illness, medication information and lifestyle habits are managed according to that illness. In addition, regarding exercise habits, an appropriate exercise plan can be proposed based on the user's exercise data. Furthermore, the collected health data is analyzed and appropriate health advice is provided to the user. For example, the user's meal data is analyzed and areas for improvement in nutritional balance are suggested. Furthermore, the system can analyze basal body temperature data and provide advice on health management. In this way, it can provide personalized advice tailored to the user's health condition. In addition, the collected health data can be provided to companies as big data and used for purposes such as recruiting monitors. For example, health-related companies can use users' health data to collect data necessary for developing new products. Also, when recruiting monitors, appropriate monitors can be selected based on the user's health data. Thus, the health concierge system of the present invention not only centrally manages users' health data and provides appropriate health advice, but can also contribute to the development of various health-related services and products by providing it to companies as big data. As a result, the health concierge system can efficiently collect, analyze, provide advice on, and provide users' health data to companies.

[0061] The health concierge system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a corporate data provision unit. The data collection unit collects the user's health data. The user's health data includes, but is not limited to, examples of diet, basal body temperature, metabolism, weight, blood pressure, exercise (pedometer), and blood oxygen. For example, the data collection unit can acquire diet data from a diet recording app and basal body temperature data from a basal body temperature measurement app. The data collection unit can also acquire exercise data from a pedometer app and blood oxygen data from a dedicated measurement app. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit automatically manages chronic illnesses, medication, lifestyle habits, and exercise habits based on the collected data. For example, if the user has a chronic illness, the analysis unit manages medication information and lifestyle habits according to that illness. The analysis unit can also propose an appropriate exercise plan based on the user's exercise data. The data provision unit provides health advice to the user based on the analysis results obtained by the analysis unit. The provisioning department, for example, analyzes the user's dietary data and suggests improvements to their nutritional balance. The provisioning department can also analyze basal body temperature data and provide advice on health management. For example, the provisioning department suggests improvements to the user's dietary data. Furthermore, the provisioning department can provide advice on health management based on basal body temperature data. The corporate provisioning department provides the data collected by the collection department to companies. For example, the corporate provisioning department can provide the collected health data to companies as big data and utilize it for purposes such as recruiting monitors. For example, health-related companies can utilize the user's health data to collect data necessary for developing new products. The corporate provisioning department can also select appropriate monitors based on the user's health data when recruiting monitors. As a result, the health concierge system according to this embodiment can efficiently collect, analyze, provide advice on, and provide user health data to companies.

[0062] The data collection unit can collect data on meals, basal body temperature, metabolism, weight, blood pressure, exercise (pedometer), and blood oxygen levels. For example, the data collection unit can acquire meal data from a meal logging app. For example, a user uses an app to record their meals, and the data collection unit acquires that data. The data collection unit can also acquire basal body temperature data from a basal body temperature measurement app. For example, a user uses an app to measure their basal body temperature, and the data collection unit acquires that data. Furthermore, the data collection unit can acquire exercise data from a pedometer app. For example, a user uses an app to record their steps, and the data collection unit acquires that data. The data collection unit can also acquire blood oxygen data from a dedicated measurement app. For example, a user uses an app to measure their blood oxygen levels, and the data collection unit acquires that data. By collecting diverse health data, more detailed health management becomes possible. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a meal logging app into a generating AI, and have the generating AI perform analysis of the meal data.

[0063] The analysis unit can automatically manage chronic illnesses, medication, lifestyle, and exercise habits based on the collected data. For example, the analysis unit can manage chronic illnesses based on the collected data. For instance, if a user has a chronic illness, it can manage medication information and lifestyle habits according to that illness. The analysis unit can also manage medication information based on the collected data. For example, it can manage the types of medications the user is taking and their medication schedule. Furthermore, the analysis unit can also manage lifestyle habits based on the collected data. For example, it can manage the user's sleep patterns and eating habits. The analysis unit can also manage exercise habits based on the collected data. For example, it can suggest an appropriate exercise plan based on the user's exercise data. This enables automatic health management based on the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the management of chronic illnesses and medication information.

[0064] The service provider can analyze the user's dietary data and suggest improvements to their nutritional balance. For example, the service provider can analyze the user's dietary data and suggest improvements to their nutritional balance. For example, it can evaluate the intake and balance of nutrients based on the user's dietary data and suggest improvements. The service provider can also suggest dietary improvement plans based on the user's dietary data. For example, it can suggest a balanced meal plan based on the user's dietary data. Furthermore, the service provider can provide advice on increasing the intake of specific nutrients based on the user's dietary data. For example, it can provide advice on increasing the intake of vitamins and minerals based on the user's dietary data. In this way, health management is improved by analyzing the user's dietary data and suggesting improvements to their nutritional balance. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's dietary data into a generating AI and have the generating AI generate suggestions for improving nutritional balance.

[0065] The service provider can analyze basal body temperature data and provide advice on health management. For example, the service provider can analyze basal body temperature data and provide advice on health management. For example, it can monitor changes in the user's health based on their basal body temperature data and provide appropriate advice. The service provider can also suggest areas for improvement in health based on basal body temperature data. For example, it can suggest areas for improvement in health based on the user's basal body temperature data. Furthermore, the service provider can propose specific health management plans based on basal body temperature data. For example, it can propose specific health management plans based on the user's basal body temperature data. In this way, by analyzing basal body temperature data and providing advice on health management, the user's health management improves. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input basal body temperature data into a generating AI and have the generating AI execute health management advice.

[0066] The Corporate Provision Department can provide collected health data to companies and use it for recruiting monitors. For example, the Corporate Provision Department can provide collected health data to companies and use it for recruiting monitors. For example, the Corporate Provision Department can provide users' health data to health-related companies to collect data necessary for developing new products. The Corporate Provision Department can also select appropriate monitors based on users' health data when recruiting monitors. For example, the Corporate Provision Department can select monitors that meet specific conditions based on users' health data. In this way, by providing collected health data to companies and using it for recruiting monitors, it becomes possible to provide data that meets the needs of companies. Some or all of the above processing in the Corporate Provision Department may be performed using AI, for example, or not using AI. For example, the Corporate Provision Department can input collected health data into a generating AI and have the generating AI select the data to provide to companies.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can adjust the timing to collect health data during relaxed periods. For example, it can collect health data during the time when the user is relaxed after work. The data collection unit can also increase the frequency of health data collection and collect more detailed data when the user is relaxed. For example, it can collect health data frequently during the time when the user is relaxed on a holiday. The data collection unit can also shorten the collection timing and collect only the minimum necessary data when the user is in a hurry. For example, it can collect the necessary data quickly during the busy morning hours. By adjusting the timing of health data collection according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0068] The data collection unit can analyze the user's past health data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data that the user has frequently collected in the past. For example, it can prioritize collecting dietary data that the user has frequently recorded in the past. The data collection unit can also suggest the optimal collection time based on the user's past collection history. For example, it can suggest the optimal collection time based on the time of day when the user has collected the most data in the past. The data collection unit can also adjust the collection frequency based on the user's past collection history. For example, it can increase the collection frequency of data that the user has frequently collected in the past to collect more detailed data. This allows the optimal collection method to be selected by analyzing the user's past health data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0069] The data collection unit can filter health data based on the user's current lifestyle or areas of interest. For example, if the user is on a diet, the data collection unit will prioritize collecting dietary data. For example, if the user is using a diet app, it will prioritize collecting that data. The data collection unit can also collect detailed exercise data if the user is interested in exercise. For example, if the user is using a fitness app, it will prioritize collecting that data. The data collection unit can also prioritize collecting data related to a chronic illness if the user has one. For example, if the user is using a diabetes management app, it will prioritize collecting that data. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0070] The data collection unit can estimate the user's emotions and prioritize the health data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the user is using a stress management app, it will prioritize collecting that data. The data collection unit can also collect overall health data in a balanced manner if the user is relaxed. For example, it will collect diet data, exercise data, vital sign data, etc., in a balanced manner during times when the user is relaxed. The data collection unit can also prioritize collecting only the most important data if the user is in a hurry. For example, it will collect only the minimum necessary data during times when the user is busy. This allows for the priority collection of more important data by prioritizing health data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the health data to be collected.

[0071] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit will prioritize the collection of blood oxygen data. For example, it will collect blood oxygen data while the user is engaged in activities at high altitude. The data collection unit can also collect detailed exercise data if the user is at a sports facility. For example, it will collect exercise data while the user is exercising at a gym. The data collection unit can also prioritize the collection of lifestyle data if the user is at home. For example, it will collect dietary and sleep data while the user is living at home. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0072] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts about dieting on social media, the data collection unit will prioritize collecting dietary data. For example, if a user frequently posts about dieting, the data will prioritize collecting that information. The data collection unit can also collect detailed exercise data if a user posts about exercise. For example, if a user frequently posts about exercise, the data will be collected in detail. The data collection unit can also collect balanced overall health data if a user posts about health. For example, if a user frequently posts about health, the data collection unit will collect balanced dietary data, exercise data, vital sign data, etc. This allows for the collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is stressed, it can provide visual analysis results using graphs and charts. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is relaxed, it can provide a detailed text report. The analysis unit can also provide concise analysis results if the user is in a hurry. For example, if the user is in a hurry, it can provide a brief summary report. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit performs a detailed analysis on important health data. For instance, it performs a detailed analysis on the user's vital sign data and data related to pre-existing conditions. The analysis unit can also perform a simplified analysis on general health data. For example, it performs a simplified analysis on the user's diet data and exercise data. The analysis unit can also perform a detailed analysis on data of high interest to the user. For example, it performs a detailed analysis on health data of particular interest to the user. This allows for more detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a nutritional balance analysis algorithm to dietary data. For example, it can apply an algorithm that evaluates nutritional balance based on the user's dietary data. The analysis unit can also apply an exercise effect analysis algorithm to exercise data. For example, it can apply an algorithm that evaluates the effect of exercise based on the user's exercise data. The analysis unit can also apply a health management analysis algorithm to basal body temperature data. For example, it can apply an algorithm that manages health based on the user's basal body temperature data. By applying different analysis algorithms depending on the category of health data, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of health data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis result. For example, if the user is stressed, it can provide a brief summary report. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, it can provide a detailed text report. The analysis unit can also provide a concise analysis result if the user is in a hurry. For example, if the user is in a hurry, it can provide a concise analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0077] The analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit may prioritize the analysis of the most recent health data. For instance, it may prioritize the analysis of the user's most recent vital sign data and dietary data. The analysis unit can also analyze overall trends, including historical data. For example, it may analyze overall health trends based on the user's past health data. Furthermore, the analysis unit can focus on analyzing data collected during a specific period. For example, it may analyze the user's health status during a specific period based on the data collected during that period. This allows for prioritizing the analysis based on when the health data was collected, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the health data collection period into a generating AI and have the generating AI determine the analysis priority.

[0078] The analysis unit can adjust the order of analysis based on the relevance of health data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For instance, it can prioritize the analysis of the user's vital sign data or data related to pre-existing conditions. The analysis unit can also postpone the analysis of less relevant data. For example, it can postpone the analysis of the user's general health data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, it can evaluate the relevance of the user's health data and dynamically adjust the order of analysis. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0079] The service provider can estimate the user's emotions and adjust the way health advice is presented based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple and easy-to-understand advice. For example, if the user is stressed, it can provide visual advice using graphs and charts. The service provider can also provide detailed advice if the user is relaxed. For example, if the user is relaxed, it can provide a detailed text report. The service provider can also provide concise advice if the user is in a hurry. For example, if the user is in a hurry, it can provide a concise summary report. By adjusting the way health advice is presented based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​adjust how health advice is expressed.

[0080] The service provider can adjust the level of detail of health advice based on the user's health condition when providing it. For example, if the user's health condition is good, the service provider can provide concise advice. For example, if the user's health condition is good, it can provide a concise summary report. The service provider can also provide detailed advice if the user's health condition is deteriorating. For example, if the user's health condition is deteriorating, it can provide a detailed text report. Furthermore, the service provider can dynamically adjust the level of detail of the advice according to the user's health condition. For example, it can evaluate the user's health condition and dynamically adjust the level of detail of the advice. This allows for the provision of more appropriate advice by adjusting the level of detail based on the user's health condition. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's health condition data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0081] The service provider can apply different advice algorithms depending on the user's lifestyle when providing health advice. For example, the service provider can suggest improvements to nutritional balance based on eating habits. For example, it can suggest improvements to nutritional balance based on the user's eating habit data. The service provider can also suggest exercise plans based on exercise habits. For example, it can suggest an appropriate exercise plan based on the user's exercise habit data. The service provider can also provide comprehensive health advice based on the user's overall lifestyle. For example, it can provide comprehensive health advice based on the user's lifestyle data. By applying different advice algorithms depending on the user's lifestyle, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's lifestyle data into a generating AI and have the generating AI execute the application of an appropriate advice algorithm.

[0082] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the service provider can provide short, concise advice. For example, if the user is stressed, it can provide a brief summary report. The service provider can also provide detailed advice if the user is relaxed. For example, if the user is relaxed, it can provide a detailed text report. The service provider can also provide concise advice if the user is in a hurry. For example, if the user is in a hurry, it can provide concise advice. By adjusting the length of the advice based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of the advice.

[0083] The service provider can prioritize health advice based on when the user's health data was collected. For example, it can provide advice based on the most recent health data, such as the user's most recent vital signs data or dietary data. It can also provide advice based on overall trends, including past data, such as the user's past health data. It can also provide advice based on data collected during a specific period, such as the user's health status during that period, based on data collected by the user during that period. This allows the service provider to provide advice based on the most recent data by prioritizing advice based on when the user's health data was collected. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the health data collection period into a generating AI and have the generating AI determine the priority of the advice.

[0084] The service provider can adjust the order of advice based on the relevance of the user's health data when providing health advice. For example, the service provider can provide advice based on highly relevant data, such as the user's vital sign data or data related to pre-existing conditions. The service provider can also postpone less relevant data, such as the user's general health data. Furthermore, the service provider can dynamically adjust the order of advice based on the relevance of the data, such as evaluating the relevance of the user's health data and dynamically adjusting the order of advice. This allows for the provision of more relevant advice by adjusting the order of advice based on the relevance of the user's health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the relevance of health data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0085] The enterprise data provider can estimate the user's emotions and adjust the timing of data provision to the enterprise based on the estimated emotions. For example, if the user is stressed, the enterprise data provider can provide data during relaxed times. For example, it can provide data during times when the user is relaxed after work. The enterprise data provider can also increase the frequency of data provision when the user is relaxed. For example, it can increase the frequency of data provision during times when the user is relaxed on holidays. The enterprise data provider can also shorten the timing of data provision when the user is in a hurry. For example, it can provide data in a short time when the user is busy. By adjusting the timing of data provision to the enterprise based on the user's emotions, data can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the enterprise data provider may be performed using AI, for example, or not using AI. For example, the corporate service provider can input user emotion data into a generating AI and have the AI ​​adjust the timing of data delivery.

[0086] The Corporate Data Provisioning Department can adjust the level of detail of the data provided based on the company's needs at the time of provision. For example, if a company requires detailed data, the Corporate Data Provisioning Department will provide detailed data. For example, it will provide detailed health data necessary for a company to develop a new product. The Corporate Data Provisioning Department can also provide concise data if a company requires concise data. For example, it will provide concise health data necessary for a company to conduct a marketing survey. Furthermore, the Corporate Data Provisioning Department can dynamically adjust the level of detail of the data according to the company's needs. For example, it will adjust the level of detail of the data provided based on the company's request. This allows for the provision of more appropriate data by adjusting the level of detail of the data provided based on the company's needs. Some or all of the above processing in the Corporate Data Provisioning Department may be performed using AI, for example, or not using AI. For example, the Corporate Data Provisioning Department can input company needs data into a generating AI and have the generating AI perform the adjustment of the level of detail of the data provided.

[0087] The Corporate Data Provisioning Department can apply different data provision algorithms depending on the industry of the company when providing data to companies. For example, the Corporate Data Provisioning Department can provide detailed health data to health-related companies, for example, to health-related companies, to provide detailed health data necessary for the development of new products. The Corporate Data Provisioning Department can also prioritize providing dietary data to food-related companies, for example, to provide dietary data necessary for the development of new products. The Corporate Data Provisioning Department can also prioritize providing exercise data to exercise-related companies, for example, to provide exercise-related companies, to provide exercise data necessary for the development of new products. In this way, more appropriate data can be provided by applying different data provision algorithms depending on the industry of the company. Some or all of the above processing in the Corporate Data Provisioning Department may be performed using AI, for example, or not using AI. For example, the Corporate Data Provisioning Department can input company industry data into a generating AI and have the generating AI execute the application of an appropriate data provision algorithm.

[0088] The enterprise data provider can estimate the user's emotions and prioritize the data provided based on those emotions. For example, if the user is stressed, the enterprise data provider will prioritize providing stress-related data. For example, if the user is using a stress management app, it will prioritize providing that data. The enterprise data provider can also provide a balanced set of overall health data if the user is relaxed. For example, it will provide a balanced set of diet data, exercise data, and vital signs data during times when the user is relaxed. The enterprise data provider can also prioritize providing only essential data if the user is in a hurry. For example, it will provide only the minimum necessary data during times when the user is busy. This allows for the prioritization of more important data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the enterprise data provider may be performed using AI or not. For example, the corporate service provider can input user emotion data into a generating AI and have the AI ​​determine the priority of the data to be provided.

[0089] The Corporate Data Provisioning Department can prioritize providing highly relevant data by considering the geographical location of the companies when providing data to them. For example, if a company is located at high altitude, the Corporate Data Provisioning Department can prioritize providing blood oxygen data. For example, a company located at high altitude can provide blood oxygen data necessary for new product development. The Corporate Data Provisioning Department can also provide detailed exercise data if a company is located at a sports facility. For example, a company located at a sports facility can provide exercise data necessary for new product development. Furthermore, if a company is located in an urban area, the Corporate Data Provisioning Department can prioritize providing lifestyle data. For example, a company located in an urban area can provide lifestyle data necessary for marketing research. In this way, by considering the geographical location of the companies, highly relevant data can be prioritized. Some or all of the above processing in the Corporate Data Provisioning Department may be performed using AI, for example, or without AI. For example, the Corporate Data Provisioning Department can input the geographical location data of companies into a generating AI and have the generating AI perform the provision of highly relevant data.

[0090] The Corporate Data Provision Department can analyze a company's past data usage history and select the optimal data provision method. For example, the Corporate Data Provision Department can select the optimal data provision method based on data the company has used in the past. For example, it can select the optimal data provision method based on health data the company has used in the past. The Corporate Data Provision Department can also adjust the frequency of data provision based on the company's past data usage history. For example, it can increase the frequency of provision of data that the company has used frequently in the past. The Corporate Data Provision Department can also analyze a company's past data usage history and select the most efficient data provision method. For example, it can select the optimal data provision method based on the data provision method the company has used most efficiently in the past. In this way, the optimal data provision method can be selected by analyzing a company's past data usage history. Some or all of the above processes in the Corporate Data Provision Department may be performed using AI, for example, or without AI. For example, the Corporate Data Provision Department can input the company's past data usage history data into a generating AI and have the generating AI select the optimal data provision method.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The data collection unit can adjust data collection based on the user's current weather conditions when collecting user health data. For example, if the user is in a hot and humid environment, the data collection unit can prioritize collecting water intake data to account for the risk of dehydration. It can also collect detailed body temperature data if the user is in a cold environment. Furthermore, if the user is in pollen season, the data collection unit can prioritize collecting data related to allergy symptoms. This allows for the collection of more appropriate health data by considering weather conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input weather information data into a generating AI and have the generating AI adjust the timing of data collection.

[0093] The analysis unit can analyze a user's health data while taking the user's genetic information into consideration. For example, the analysis unit can assess the risk of specific diseases based on the user's genetic information. It can also propose an optimal nutritional intake plan based on the user's genetic information. Furthermore, the analysis unit can evaluate individual differences in exercise effectiveness and propose an optimal exercise plan based on the user's genetic information. This allows for more personalized health management by considering genetic information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input genetic information data into a generating AI and have the generating AI adjust the level of detail in the analysis.

[0094] The service provider can provide health advice tailored to the user's living environment based on the user's health data. For example, if the user lives in an urban area, the service provider can provide advice on stress management and air quality. If the user lives in a rural area, the service provider can also provide advice on health risks related to agricultural work. Furthermore, if the user lives in a senior living facility, the service provider can provide advice on specific health risks. This enables more appropriate health management by providing health advice tailored to the living environment. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input living environment data into a generating AI and have the generating AI adjust the level of detail of the advice.

[0095] The Corporate Data Provisioning Department can adjust the level of detail of collected health data when providing it to companies, depending on the company's research and development stage. For example, if a company is conducting early-stage research, the Corporate Data Provisioning Department can provide a broad dataset. If a company is in the final stages of product development, it can provide specific data in detail. Furthermore, if a company is conducting market research, the Corporate Data Provisioning Department can provide concise data. By adjusting the level of detail of the data according to the company's research and development stage, it becomes possible to provide more appropriate data. Some or all of the above processing in the Corporate Data Provisioning Department may be performed using AI, for example, or not. For example, the Corporate Data Provisioning Department can input research and development stage data into a generating AI and have the generating AI perform the adjustment of the level of detail of the data.

[0096] The data collection unit can estimate the user's emotions and adjust the types of health data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting stress-related data. If the user is relaxed, it can also collect overall health data in a balanced manner. Furthermore, if the user is in a hurry, it can prioritize collecting only the most important data. This allows for the collection of more important data by adjusting the types of health data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the types of health data to be collected.

[0097] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can perform the analysis during a relaxed time. If the user is relaxed, the frequency of the analysis can be increased, and a more detailed analysis can be performed. Furthermore, if the user is in a hurry, the analysis timing can be shortened, and only the minimum necessary analysis can be performed. By adjusting the timing of the analysis based on the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the timing of the analysis.

[0098] The service provider can estimate the user's emotions and adjust the tone of advice based on the estimated emotions. For example, if the user is stressed, the service provider can provide advice in a gentle tone. If the user is relaxed, it can provide advice in a positive tone. Furthermore, if the user is in a hurry, it can provide advice in a concise and direct tone. By adjusting the tone of advice based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the tone of advice.

[0099] The enterprise data provider can estimate the user's emotions and adjust the frequency of data provision to the enterprise based on the estimated emotions. For example, if the enterprise data provider is stressed, it can reduce the frequency of data provision. Conversely, if the user is relaxed, it can increase the frequency of data provision. Furthermore, if the user is in a hurry, it can shorten the frequency of data provision and provide only the minimum necessary data. By adjusting the frequency of data provision to the enterprise based on the user's emotions, data can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the enterprise data provider may be performed using AI or not. For example, the enterprise data provider can input user emotion data into a generative AI and have the generative AI adjust the frequency of data provision.

[0100] The data collection unit can adjust the frequency of data collection based on the user's activity level when collecting user health data. For example, the data collection unit can increase the frequency of data collection when the user is at a high activity level, and decrease it when the user is at a low activity level. Furthermore, it can collect data at a balanced frequency when the user is at a moderate activity level. By adjusting the frequency of data collection based on the activity level, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input activity level data into a generating AI and have the generating AI adjust the frequency of data collection.

[0101] The analysis unit can adjust the level of detail of the analysis based on the user's dietary patterns when analyzing the user's health data. For example, if the user has a balanced diet, the analysis unit can perform a simplified analysis. If the user has an unbalanced diet, it can perform a detailed analysis. Furthermore, if the user is on a specific diet, it can perform an analysis tailored to that diet. By adjusting the level of detail of the analysis based on the dietary patterns, a more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input dietary pattern data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The data collection unit collects the user's health data. This data includes information on diet, basal body temperature, metabolism, weight, blood pressure, exercise (pedometer), and blood oxygen levels. For example, the data collection unit can obtain dietary data from a meal tracking app and basal body temperature data from a basal body temperature measurement app. It can also obtain exercise data from a pedometer app and blood oxygen data from a dedicated measurement app. Step 2: The analysis unit analyzes the data collected by the collection unit. Based on the collected data, the analysis unit automatically manages information such as chronic illnesses, medication, lifestyle, and exercise habits. For example, if a user has a chronic illness, it manages medication information and lifestyle habits according to that illness. It can also suggest an appropriate exercise plan based on the user's exercise data. Step 3: The service provider provides health advice to the user based on the analysis results obtained by the analysis unit. The service provider analyzes the user's dietary data and suggests areas for improvement in nutritional balance. It can also analyze basal body temperature data and provide advice on managing one's health. Step 4: The Corporate Provisioning Department provides the data collected by the Collection Department to companies. The Corporate Provisioning Department provides the collected health data to companies as big data, which can be used for purposes such as recruiting monitors. For example, health-related companies can use users' health data to collect data necessary for developing new products. Also, when recruiting monitors, appropriate monitors can be selected based on the users' health data.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0106] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and corporate provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user health data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides health advice to the user based on the analysis results. The corporate provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides the collected data to companies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0114] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0115] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0116] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0117] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0118] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and corporate provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's health data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides health advice to the user based on the analysis results. The corporate provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides the collected data to a company. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0128] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and corporate provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user health data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides health advice to the user based on the analysis results. The corporate provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides the collected data to companies. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0144] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0148] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and corporate provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user health data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides health advice to the user based on the analysis results. The corporate provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides the collected data to companies. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0157] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0159] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0160] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0161] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0165] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0167] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0168] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0169] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0170] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0172] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0173] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0174] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0175] (Note 1) A collection unit that collects user health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provisioning unit that provides health advice to the user based on the analysis results obtained by the aforementioned analysis unit, The system includes a corporate data provision unit that provides data collected by the aforementioned data collection unit to a company. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data on diet, basal body temperature, metabolism, weight, blood pressure, exercise (pedometer), and blood oxygen levels. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, it automatically manages pre-existing conditions, medications, lifestyle habits, and exercise habits. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We analyze users' meal data and suggest areas for improvement in their nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We analyze basal body temperature data and provide advice on managing your health. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned corporate provision department, We will provide the collected health data to companies and use it to recruit monitors. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way health advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing health advice, adjust the level of detail of the advice based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing health advice, different advice algorithms are applied depending on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing health advice, the priority of the advice is determined based on when the user's health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing health advice, the order of advice is adjusted based on the relevance of the user's health data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned corporate provision department, We estimate user sentiment and adjust the timing of data provision to companies based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned corporate provision department, When providing data to a company, we adjust the level of detail provided based on the company's needs. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned corporate provision department, When providing data to companies, different data provision algorithms are applied depending on the company's industry. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned corporate provision department, The system estimates the user's emotions and prioritizes the data provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned corporate provision department, When providing data to companies, we prioritize providing highly relevant data by taking into account the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned corporate provision department, We analyze a company's past data usage history and select the optimal data delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provisioning unit that provides health advice to the user based on the analysis results obtained by the aforementioned analysis unit, The system includes a corporate data provision unit that provides data collected by the aforementioned data collection unit to a company. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data on diet, basal body temperature, metabolism, weight, blood pressure, exercise (pedometer), and blood oxygen levels. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, it automatically manages pre-existing conditions, medications, lifestyle habits, and exercise habits. The system according to feature 1.

4. The aforementioned supply unit is, We analyze users' meal data and suggest areas for improvement in their nutritional balance. The system according to feature 1.

5. The aforementioned supply unit is, We analyze basal body temperature data and provide advice on managing your health. The system according to feature 1.

6. The aforementioned corporate provision department, We will provide the collected health data to companies and use it to recruit monitors. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system according to feature 1.

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

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