system

Abstract

The system according to this embodiment aims to provide appropriate health advice based on each user's medical history and lifestyle patterns. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides appropriate advice to the user based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that appropriate health advice based on the medical history and life patterns of individual users has not been sufficiently provided.

[0005] The system according to the embodiment aims to provide appropriate health advice based on the medical history and life patterns of individual users.

Means for Solving the Problems

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

[0007] The system according to this embodiment can provide appropriate health advice based on each user's medical history and lifestyle patterns. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor २8, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​Home Doctor System, according to an embodiment of the present invention, is a system that contributes to daily health management by providing physical and psychological health information from the latest medical papers, industry information, public health data, and individual medical histories. Furthermore, it supports a healthier lifestyle by analyzing not only individual medical histories but also communication data to identify physical and psychological health risks from excessive device use and unbalanced lifestyle patterns, and by providing optimal advice. For example, the AI ​​Home Doctor System collects the latest medical papers, industry information, and public health data and provides health information based on this information. For example, by providing information on new treatments and preventive measures, users can acquire the latest medical knowledge. Next, it analyzes individual medical histories to understand the user's health status. For example, it evaluates the current health status based on past medical records and test results and provides necessary health management advice. Furthermore, it analyzes communication data to identify health risks from excessive device use and unbalanced lifestyle patterns. For example, it analyzes smartphone usage time and sleep patterns to evaluate the impact of excessive device use on health. This allows the system to provide appropriate advice to the user and support a healthy lifestyle. In this way, the AI ​​Home Doctor System combines the latest medical information, individual medical histories, and communication data to realize health management tailored to the user. This allows users to efficiently manage their daily health and lead healthier lives. Thus, the AI ​​home doctor system efficiently supports users' health management and enables them to achieve a healthier lifestyle.

[0029] The AI ​​home doctor system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data. The data collection unit can collect, for example, the latest medical papers, industry information, public health data, individual medical histories, and communication data. For example, the data collection unit automatically collects the latest medical papers from the internet. The data collection unit can also collect news and reports from the medical industry. The data collection unit can also obtain public health data from government statistical databases. Furthermore, the data collection unit can collect individual medical histories from electronic medical records and medical records. For example, the data collection unit can obtain the user's past medical records from electronic medical records to understand their health status. The data collection unit can also collect communication data such as smartphone usage time and sleep patterns. For example, the data collection unit can record smartphone app usage time to identify excessive device use. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, evaluate the user's health status and health risks based on the collected data. For example, the analysis unit evaluates the current health status based on past medical records and test results. Furthermore, the analysis unit can evaluate the impact of excessive device use on health based on communication data. For example, the analysis unit analyzes smartphone usage time and sleep patterns to evaluate the impact of excessive device use on sleep. The provision unit provides appropriate advice to the user based on the analysis results obtained by the analysis unit. For example, the provision unit can provide information on new treatments and preventive measures based on the analysis results. For example, the provision unit can provide the user with information on the latest treatments and advise them to receive appropriate treatment. The provision unit can also provide advice on improving lifestyle habits based on the analysis results. For example, the provision unit can evaluate the impact of excessive device use on health and advise on reducing device usage time. As a result, the AI ​​home doctor system according to the embodiment can efficiently support the user's health management and enable a healthier life.

[0030] The data collection unit can collect the latest medical papers, industry information, public health data, individual medical histories, and communication data. For example, the data collection unit can automatically collect the latest medical papers from the internet. For example, the data collection unit can filter medical papers based on specific journals and publication years and collect relevant papers. The data collection unit can also collect news and reports from the medical industry. For example, the data collection unit can regularly check medical industry news sites and reports to collect the latest information. The data collection unit can also obtain public health data from government statistical databases. For example, the data collection unit can access government public health databases and collect epidemiological survey data and statistical data. Furthermore, the data collection unit can collect individual medical histories from electronic medical records and patient records. For example, the data collection unit can obtain a user's past patient records from electronic medical records to understand their health status. The data collection unit can also collect communication data such as smartphone usage time and sleep patterns. For example, the data collection unit can record smartphone app usage time to identify excessive device use. In this way, the data collection unit can collect information from a variety of data sources. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data collected from the internet into a generating AI and automatically extract relevant information.

[0031] The analysis unit can evaluate the user's health status and health risks based on the collected data. For example, the analysis unit can evaluate the user's health status based on the collected data. For example, the analysis unit can evaluate the user's current health status based on past medical records and test results. The analysis unit can also evaluate the user's health risks based on the collected data. For example, the analysis unit can evaluate the risk of disease based on the user's lifestyle and genetic information. The analysis unit can also evaluate the impact of excessive device use on health based on communication data. For example, the analysis unit can analyze smartphone usage time and sleep patterns to evaluate the impact of excessive device use on sleep. This allows the analysis unit to accurately evaluate the user's health status and risks. 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 evaluation of health status and risks.

[0032] The information provider can provide information on new treatments and preventive measures based on the analysis results. For example, the information provider can provide information on new treatments based on the analysis results. For example, the information provider can provide users with information on the latest medical technologies and drug therapies and advise them on how to receive appropriate treatment. The information provider can also provide information on preventive measures based on the analysis results. For example, the information provider can provide users with information on lifestyle improvements and vaccinations and advise them on how to maintain their health. In this way, the information provider can provide users with the latest treatments and preventive measures. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the analysis results into a generating AI and have the generating AI generate information on appropriate treatments and preventive measures.

[0033] The data collection unit can collect communication data such as smartphone usage time and sleep patterns. For example, the data collection unit can record smartphone usage time. For example, the data collection unit can record smartphone app usage time to identify excessive device use. The data collection unit can also record sleep patterns. For example, the data collection unit can use the smartphone's sensors to record the user's sleep duration and sleep quality. By collecting communication data, the data collection unit can understand the user's lifestyle patterns. 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 smartphone usage data into a generating AI and have the generating AI analyze the device usage patterns.

[0034] The analysis unit can evaluate the impact of excessive device use on health based on communication data. For example, the analysis unit analyzes smartphone usage time and sleep patterns. For example, the analysis unit can evaluate whether prolonged smartphone use may lead to a decrease in sleep quality. The analysis unit can also evaluate the impact of excessive device use on stress levels. For example, the analysis unit can analyze the relationship between smartphone usage time and stress levels and evaluate whether excessive device use may increase stress. In this way, the analysis unit can evaluate the impact of excessive device use on health. 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 communication data into a generating AI and have the generating AI evaluate the impact on health.

[0035] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can collect data from the user's past health data at specific time periods. For example, the data collection unit can analyze the user's past medical records and test results and collect data at specific time periods. The data collection unit can also select the optimal data collection method (wearable device, smartphone, etc.) based on the user's past health data. For example, the data collection unit can analyze the user's past health data and collect data using a wearable device. The data collection unit can also analyze the user's past health data and focus on collecting data for specific health indicators. For example, the data collection unit can analyze the user's past health data and focus on collecting data for specific health indicators such as blood pressure and heart rate. This allows the data collection unit to select the optimal data collection method based on past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is currently on a diet, the data collection unit will prioritize collecting data related to diet and exercise. For example, the data collection unit will collect the user's food and exercise records and prioritize collecting data related to dieting. The data collection unit can also prioritize collecting data related to stress levels if the user is interested in stress management. For example, the data collection unit will measure the user's stress level and prioritize collecting data related to stress management. The data collection unit can also prioritize collecting data related to a specific disease if the user is interested in preventing that disease. For example, the data collection unit will monitor the user's health status and prioritize collecting data related to that disease. This allows the data collection unit to filter 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 related to the user's lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user lives at high altitude, the data collection unit can prioritize the collection of data related to health risks specific to high altitude. For example, the data collection unit can assess the health risks of the user living at high altitude and prioritize the collection of data related to health risks specific to high altitude. The data collection unit can also prioritize the collection of data related to health risks specific to urban areas if the user lives in an urban area. For example, the data collection unit can assess the health risks of the user living in an urban area and prioritize the collection of data related to health risks specific to urban areas. The data collection unit can also prioritize the collection of data related to health risks at the travel destination if the user is traveling. For example, the data collection unit can assess the health risks at the travel destination and prioritize the collection of data related to health risks at the travel destination. This allows the data collection unit to collect highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user is experiencing stress on social media, the data collection unit can prioritize collecting data related to their stress level. For example, the data collection unit can analyze the user's social media posts and prioritize collecting data related to their stress level. The data collection unit can also collect data based on information if a user frequently shares health-related information on social media. For example, the data collection unit can analyze the user's social media activity and collect data based on health-related information. The data collection unit can also prioritize collecting data related to a specific health issue if a user mentions that issue on social media. For example, the data collection unit can analyze the user's social media posts and prioritize collecting data related to that specific health issue. This allows the data collection unit to collect relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit will analyze data related to important health indicators in detail. For example, the analysis unit will analyze data related to important health indicators such as blood pressure and heart rate in detail. The analysis unit can also simplify the analysis of general health data. For example, the analysis unit will simplify the analysis of everyday health data (such as steps taken and calorie consumption). The analysis unit can also analyze data of high interest to the user in detail. For example, the analysis unit will analyze data related to health indicators of particular interest to the user in detail. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply medical analysis algorithms to health data. For instance, it can apply medical analysis algorithms to health data such as blood pressure and heart rate. The analysis unit can also apply behavioral analysis algorithms to communication data. For example, it can apply behavioral analysis algorithms to communication data such as smartphone usage time and sleep patterns. The analysis unit can also apply statistical analysis algorithms to public health data. For example, it can apply statistical analysis algorithms to government statistical data and epidemiological survey data. This allows the analysis unit to apply an appropriate analysis algorithm depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category 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 the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For instance, it may prioritize the analysis of recently collected data to understand the latest health status. The analysis unit can also analyze historical data as needed. For example, it may analyze past medical records and test results as needed to evaluate long-term health status. The analysis unit can also prioritize the analysis of data collected during a specific period. For example, it may prioritize the analysis of data related to a specific season or event to assess seasonal health risks. This allows the analysis unit to determine the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing 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 the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For instance, it can prioritize the analysis of data directly related to the user's health status to identify important health indicators. The analysis unit can also postpone the analysis of less relevant data. For example, it can postpone the analysis of general lifestyle data (such as steps taken or calorie consumption) and prioritize the analysis of important health data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. For example, it can evaluate the relevance of the data in real time and dynamically adjust the order of analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0043] The service provider can analyze the user's past health behaviors to select the most appropriate advice when providing it. For example, the service provider can propose an optimal exercise plan based on the user's past health behaviors. For example, the service provider can analyze the user's past exercise history and propose an optimal exercise plan. The service provider can also propose an optimal meal plan based on the user's past eating history. For example, the service provider can analyze the user's past eating history and propose an optimal meal plan. The service provider can also provide optimal sleep advice based on the user's past sleep patterns. For example, the service provider can analyze the user's past sleep patterns and provide optimal sleep advice. This allows the service provider to select the most appropriate advice based on past health behaviors. 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 past health behavior data into a generating AI and have the generating AI select the most appropriate advice.

[0044] The service provider can customize the means of providing advice based on the user's current lifestyle. For example, if the user is busy, the service provider can provide advice that can be done in a short amount of time. For example, the service provider can analyze the user's schedule and provide advice on exercise and diet that can be done in a short amount of time. The service provider can also provide detailed advice if the user is relaxed. For example, the service provider can analyze the user's lifestyle and provide detailed health advice during their relaxation time. The service provider can also provide advice that can be done at the user's travel destination if the user is traveling. For example, the service provider can analyze the user's geographical location and provide advice on exercise and diet that can be done at the travel destination. This allows the service provider to customize the means of providing advice based on the user's current lifestyle. Some or all of the above processing 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 lifestyle data into a generating AI and have the generating AI customize the means of providing advice.

[0045] The service provider can select the most appropriate advice by considering the user's geographical location when providing advice. For example, if the user lives at high altitude, the service provider can provide advice on health risks specific to high altitude. For example, the service provider can assess the health risks of a user living at high altitude and provide advice on those risks. The service provider can also provide advice on health risks specific to urban areas if the user lives in an urban area. For example, the service provider can assess the health risks of a user living in an urban area and provide advice on those risks. The service provider can also provide advice on health risks at a travel destination if the user is traveling. For example, the service provider can assess the health risks at a travel destination and provide advice on those risks. This allows the service provider to select the most appropriate advice based on geographical location information. 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 geographical location information into a generating AI and have the generating AI select the most appropriate advice.

[0046] The service provider can analyze a user's social media activity and propose methods for providing advice. For example, if a user is experiencing stress on social media, the service provider can provide advice on stress management. For example, the service provider can analyze the user's social media posts and provide advice on stress management. The service provider can also provide advice based on information if a user frequently shares health-related information on social media. For example, the service provider can analyze the user's social media activity and provide advice based on health-related information. The service provider can also provide advice related to a specific health issue if a user mentions a specific health issue on social media. For example, the service provider can analyze the user's social media posts and provide advice related to a specific health issue. This allows the service provider to propose methods for providing advice based on social media activity. 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 social media activity data into a generating AI and have the generating AI propose methods for providing advice.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The analysis unit can also consider the user's genetic information when evaluating the user's health status. For example, the analysis unit can assess the risk of specific diseases based on the user's genetic information. Furthermore, the analysis unit can combine genetic information with lifestyle data to perform a more accurate health risk assessment. In addition, the analysis unit can provide personalized health management plans based on genetic information. This allows the analysis unit to perform health risk assessments that take the user's genetic information into account.

[0049] The service provider can propose an appropriate exercise plan based on the user's health condition. For example, the service provider can suggest an appropriate exercise intensity and frequency, taking into account the user's fitness level and health condition. Furthermore, the service provider can analyze the user's exercise history and propose an optimal exercise plan based on past exercise patterns. In addition, the service provider can propose a feasible exercise plan tailored to the user's lifestyle and schedule. This allows the service provider to provide an exercise plan that is truly tailored to the user's health condition.

[0050] The data collection unit can collect user meal data and evaluate nutritional balance. For example, it can record the user's meals and evaluate calorie intake and nutrient balance. It can also analyze the user's meal history and assess the risk of nutritional deficiencies or excesses. Furthermore, based on the user's meal data, the data collection unit can provide advice on improving nutritional balance. In this way, the data collection unit can collect user meal data and evaluate nutritional balance.

[0051] The data collection unit can collect user exercise data and analyze exercise patterns. For example, it can record the user's exercise time and intensity and analyze exercise patterns. It can also evaluate the effectiveness of exercise based on the user's exercise history. Furthermore, it can provide advice on improving exercise patterns based on the user's exercise data. In this way, the data collection unit can collect user exercise data and analyze exercise patterns.

[0052] The data collection unit can collect user sleep data and analyze sleep patterns. For example, it can record the user's sleep duration and quality and analyze sleep patterns. It can also evaluate sleep quality based on the user's sleep history. Furthermore, it can provide advice on improving sleep patterns based on the user's sleep data. In this way, the data collection unit can collect user sleep data and analyze sleep patterns.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The data collection unit collects data. The data collection unit can collect, for example, the latest medical papers, industry information, public health data, individual medical histories, and communication data. For example, the data collection unit automatically collects the latest medical papers from the internet. It can also collect news and reports from the medical industry. It can also obtain public health data from government statistical databases. Furthermore, the data collection unit can collect individual medical histories from electronic medical records and patient records. For example, the data collection unit can obtain a user's past medical records from electronic medical records to understand their health status. The data collection unit can also collect communication data such as smartphone usage time and sleep patterns. For example, the data collection unit can record smartphone app usage time to identify excessive device use. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, evaluate the user's health status and health risks based on the collected data. For example, the analysis unit can evaluate the current health status based on past medical records and test results. The analysis unit can also evaluate the impact of excessive device use on health based on communication data. For example, the analysis unit can analyze smartphone usage time and sleep patterns to evaluate the impact of excessive device use on sleep. Step 3: The service provider provides appropriate advice to the user based on the analysis results obtained by the analysis unit. For example, the service provider can provide information on new treatments and preventive measures based on the analysis results. For example, the service provider can provide the user with information on the latest treatments and advise them on how to receive appropriate treatment. The service provider can also provide advice on improving lifestyle habits based on the analysis results. For example, the service provider can assess the health impact of excessive device use and advise on reducing device usage time.

[0055] (Example of form 2) The AI ​​Home Doctor System, according to an embodiment of the present invention, is a system that contributes to daily health management by providing physical and psychological health information from the latest medical papers, industry information, public health data, and individual medical histories. Furthermore, it supports a healthier lifestyle by analyzing not only individual medical histories but also communication data to identify physical and psychological health risks from excessive device use and unbalanced lifestyle patterns, and by providing optimal advice. For example, the AI ​​Home Doctor System collects the latest medical papers, industry information, and public health data and provides health information based on this information. For example, by providing information on new treatments and preventive measures, users can acquire the latest medical knowledge. Next, it analyzes individual medical histories to understand the user's health status. For example, it evaluates the current health status based on past medical records and test results and provides necessary health management advice. Furthermore, it analyzes communication data to identify health risks from excessive device use and unbalanced lifestyle patterns. For example, it analyzes smartphone usage time and sleep patterns to evaluate the impact of excessive device use on health. This allows the system to provide appropriate advice to the user and support a healthy lifestyle. In this way, the AI ​​Home Doctor System combines the latest medical information, individual medical histories, and communication data to realize health management tailored to the user. This allows users to efficiently manage their daily health and lead healthier lives. Thus, the AI ​​home doctor system efficiently supports users' health management and enables them to achieve a healthier lifestyle.

[0056] The AI ​​home doctor system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data. The data collection unit can collect, for example, the latest medical papers, industry information, public health data, individual medical histories, and communication data. For example, the data collection unit automatically collects the latest medical papers from the internet. The data collection unit can also collect news and reports from the medical industry. The data collection unit can also obtain public health data from government statistical databases. Furthermore, the data collection unit can collect individual medical histories from electronic medical records and medical records. For example, the data collection unit can obtain the user's past medical records from electronic medical records to understand their health status. The data collection unit can also collect communication data such as smartphone usage time and sleep patterns. For example, the data collection unit can record smartphone app usage time to identify excessive device use. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, evaluate the user's health status and health risks based on the collected data. For example, the analysis unit evaluates the current health status based on past medical records and test results. Furthermore, the analysis unit can evaluate the impact of excessive device use on health based on communication data. For example, the analysis unit analyzes smartphone usage time and sleep patterns to evaluate the impact of excessive device use on sleep. The provision unit provides appropriate advice to the user based on the analysis results obtained by the analysis unit. For example, the provision unit can provide information on new treatments and preventive measures based on the analysis results. For example, the provision unit can provide the user with information on the latest treatments and advise them to receive appropriate treatment. The provision unit can also provide advice on improving lifestyle habits based on the analysis results. For example, the provision unit can evaluate the impact of excessive device use on health and advise on reducing device usage time. As a result, the AI ​​home doctor system according to the embodiment can efficiently support the user's health management and enable a healthier life.

[0057] The data collection unit can collect the latest medical papers, industry information, public health data, individual medical histories, and communication data. For example, the data collection unit can automatically collect the latest medical papers from the internet. For example, the data collection unit can filter medical papers based on specific journals and publication years and collect relevant papers. The data collection unit can also collect news and reports from the medical industry. For example, the data collection unit can regularly check medical industry news sites and reports to collect the latest information. The data collection unit can also obtain public health data from government statistical databases. For example, the data collection unit can access government public health databases and collect epidemiological survey data and statistical data. Furthermore, the data collection unit can collect individual medical histories from electronic medical records and patient records. For example, the data collection unit can obtain a user's past patient records from electronic medical records to understand their health status. The data collection unit can also collect communication data such as smartphone usage time and sleep patterns. For example, the data collection unit can record smartphone app usage time to identify excessive device use. In this way, the data collection unit can collect information from a variety of data sources. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data collected from the internet into a generating AI and automatically extract relevant information.

[0058] The analysis unit can evaluate the user's health status and health risks based on the collected data. For example, the analysis unit can evaluate the user's health status based on the collected data. For example, the analysis unit can evaluate the user's current health status based on past medical records and test results. The analysis unit can also evaluate the user's health risks based on the collected data. For example, the analysis unit can evaluate the risk of disease based on the user's lifestyle and genetic information. The analysis unit can also evaluate the impact of excessive device use on health based on communication data. For example, the analysis unit can analyze smartphone usage time and sleep patterns to evaluate the impact of excessive device use on sleep. This allows the analysis unit to accurately evaluate the user's health status and risks. 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 evaluation of health status and risks.

[0059] The information provider can provide information on new treatments and preventive measures based on the analysis results. For example, the information provider can provide information on new treatments based on the analysis results. For example, the information provider can provide users with information on the latest medical technologies and drug therapies and advise them on how to receive appropriate treatment. The information provider can also provide information on preventive measures based on the analysis results. For example, the information provider can provide users with information on lifestyle improvements and vaccinations and advise them on how to maintain their health. In this way, the information provider can provide users with the latest treatments and preventive measures. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the analysis results into a generating AI and have the generating AI generate information on appropriate treatments and preventive measures.

[0060] The data collection unit can collect communication data such as smartphone usage time and sleep patterns. For example, the data collection unit can record smartphone usage time. For example, the data collection unit can record smartphone app usage time to identify excessive device use. The data collection unit can also record sleep patterns. For example, the data collection unit can use the smartphone's sensors to record the user's sleep duration and sleep quality. By collecting communication data, the data collection unit can understand the user's lifestyle patterns. 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 smartphone usage data into a generating AI and have the generating AI analyze the device usage patterns.

[0061] The analysis unit can evaluate the impact of excessive device use on health based on communication data. For example, the analysis unit analyzes smartphone usage time and sleep patterns. For example, the analysis unit can evaluate whether prolonged smartphone use may lead to a decrease in sleep quality. The analysis unit can also evaluate the impact of excessive device use on stress levels. For example, the analysis unit can analyze the relationship between smartphone usage time and stress levels and evaluate whether excessive device use may increase stress. In this way, the analysis unit can evaluate the impact of excessive device use on health. 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 communication data into a generating AI and have the generating AI evaluate the impact on health.

[0062] The data collection unit can estimate the user's emotions and appropriately adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, the data collection unit can capture the user's facial expressions with a camera and estimate the stress level using an emotion estimation algorithm. The data collection unit can also increase the frequency of data collection and collect more detailed data if the user is relaxed. For example, the data collection unit can record the user's voice and estimate their emotions using voice analysis technology. The data collection unit can also adjust the timing of data collection to match the user's schedule if the user is in a hurry. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the data collection unit to adjust the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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, for example, or without AI. For example, the data collection unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0063] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can collect data from the user's past health data at specific time periods. For example, the data collection unit can analyze the user's past medical records and test results and collect data at specific time periods. The data collection unit can also select the optimal data collection method (wearable device, smartphone, etc.) based on the user's past health data. For example, the data collection unit can analyze the user's past health data and collect data using a wearable device. The data collection unit can also analyze the user's past health data and focus on collecting data for specific health indicators. For example, the data collection unit can analyze the user's past health data and focus on collecting data for specific health indicators such as blood pressure and heart rate. This allows the data collection unit to select the optimal data collection method based on past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal data collection method.

[0064] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is currently on a diet, the data collection unit will prioritize collecting data related to diet and exercise. For example, the data collection unit will collect the user's food and exercise records and prioritize collecting data related to dieting. The data collection unit can also prioritize collecting data related to stress levels if the user is interested in stress management. For example, the data collection unit will measure the user's stress level and prioritize collecting data related to stress management. The data collection unit can also prioritize collecting data related to a specific disease if the user is interested in preventing that disease. For example, the data collection unit will monitor the user's health status and prioritize collecting data related to that disease. This allows the data collection unit to filter 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 related to the user's lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0065] The data collection unit can estimate the user's emotions and appropriately prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to stress levels. For instance, the data collection unit may capture the user's facial expressions with a camera, estimate the stress level using an emotion estimation algorithm, and prioritize collecting data related to stress levels. Similarly, if the user is relaxed, the data collection unit may prioritize collecting data related to their overall health. For example, the data collection unit may record the user's voice, estimate their emotions using voice analysis technology, and prioritize collecting data related to their overall health. Furthermore, if the user is in a hurry, the data collection unit may prioritize collecting data related to important health indicators. For example, the data collection unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and prioritize collecting data related to important health indicators. This allows the data collection unit to prioritize the data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI is, 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 collection unit may be performed using AI, or not using AI. For example, the collection unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0066] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user lives at high altitude, the data collection unit can prioritize the collection of data related to health risks specific to high altitude. For example, the data collection unit can assess the health risks of the user living at high altitude and prioritize the collection of data related to health risks specific to high altitude. The data collection unit can also prioritize the collection of data related to health risks specific to urban areas if the user lives in an urban area. For example, the data collection unit can assess the health risks of the user living in an urban area and prioritize the collection of data related to health risks specific to urban areas. The data collection unit can also prioritize the collection of data related to health risks at the travel destination if the user is traveling. For example, the data collection unit can assess the health risks at the travel destination and prioritize the collection of data related to health risks at the travel destination. This allows the data collection unit to collect highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0067] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user is experiencing stress on social media, the data collection unit can prioritize collecting data related to their stress level. For example, the data collection unit can analyze the user's social media posts and prioritize collecting data related to their stress level. The data collection unit can also collect data based on information if a user frequently shares health-related information on social media. For example, the data collection unit can analyze the user's social media activity and collect data based on health-related information. The data collection unit can also prioritize collecting data related to a specific health issue if a user mentions that issue on social media. For example, the data collection unit can analyze the user's social media posts and prioritize collecting data related to that specific health issue. This allows the data collection unit to collect relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0068] The analysis unit can estimate the user's emotions and appropriately adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, the analysis unit can capture the user's facial expression with a camera, estimate the tension level using an emotion estimation algorithm, and provide simple and easy-to-understand analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit can record the user's voice, estimate their emotions using voice analysis technology, and provide detailed analysis results. The analysis unit can also provide concise analysis results if the user is in a hurry. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and provide concise analysis results. This allows the analysis unit to adjust the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit will analyze data related to important health indicators in detail. For example, the analysis unit will analyze data related to important health indicators such as blood pressure and heart rate in detail. The analysis unit can also simplify the analysis of general health data. For example, the analysis unit will simplify the analysis of everyday health data (such as steps taken and calorie consumption). The analysis unit can also analyze data of high interest to the user in detail. For example, the analysis unit will analyze data related to health indicators of particular interest to the user in detail. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply medical analysis algorithms to health data. For instance, it can apply medical analysis algorithms to health data such as blood pressure and heart rate. The analysis unit can also apply behavioral analysis algorithms to communication data. For example, it can apply behavioral analysis algorithms to communication data such as smartphone usage time and sleep patterns. The analysis unit can also apply statistical analysis algorithms to public health data. For example, it can apply statistical analysis algorithms to government statistical data and epidemiological survey data. This allows the analysis unit to apply an appropriate analysis algorithm depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0071] The analysis unit can estimate the user's emotions and appropriately adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For instance, the analysis unit can capture the user's facial expression with a camera, estimate that the user is in a hurry using an emotion estimation algorithm, and provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can record the user's voice, estimate their emotions using voice analysis technology, and provide a detailed analysis result. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and provide a visually stimulating analysis result. This allows the analysis unit to adjust the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0072] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For instance, it may prioritize the analysis of recently collected data to understand the latest health status. The analysis unit can also analyze historical data as needed. For example, it may analyze past medical records and test results as needed to evaluate long-term health status. The analysis unit can also prioritize the analysis of data collected during a specific period. For example, it may prioritize the analysis of data related to a specific season or event to assess seasonal health risks. This allows the analysis unit to determine the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0073] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For instance, it can prioritize the analysis of data directly related to the user's health status to identify important health indicators. The analysis unit can also postpone the analysis of less relevant data. For example, it can postpone the analysis of general lifestyle data (such as steps taken or calorie consumption) and prioritize the analysis of important health data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. For example, it can evaluate the relevance of the data in real time and dynamically adjust the order of analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0074] The service provider can estimate the user's emotions and appropriately adjust the way advice is expressed based on the estimated emotions. For example, if the user is nervous, the service provider can provide simple and easy-to-understand advice. For example, the service provider can capture the user's facial expression with a camera, estimate the level of tension using an emotion estimation algorithm, and provide simple and easy-to-understand advice. The service provider can also provide detailed advice if the user is relaxed. For example, the service provider can record the user's voice, estimate their emotions using voice analysis technology, and provide detailed advice. The service provider can also provide concise advice if the user is in a hurry. For example, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and provide concise advice. This allows the service provider to adjust the way advice is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0075] The service provider can analyze the user's past health behaviors to select the most appropriate advice when providing it. For example, the service provider can propose an optimal exercise plan based on the user's past health behaviors. For example, the service provider can analyze the user's past exercise history and propose an optimal exercise plan. The service provider can also propose an optimal meal plan based on the user's past eating history. For example, the service provider can analyze the user's past eating history and propose an optimal meal plan. The service provider can also provide optimal sleep advice based on the user's past sleep patterns. For example, the service provider can analyze the user's past sleep patterns and provide optimal sleep advice. This allows the service provider to select the most appropriate advice based on past health behaviors. 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 past health behavior data into a generating AI and have the generating AI select the most appropriate advice.

[0076] The service provider can customize the means of providing advice based on the user's current lifestyle. For example, if the user is busy, the service provider can provide advice that can be done in a short amount of time. For example, the service provider can analyze the user's schedule and provide advice on exercise and diet that can be done in a short amount of time. The service provider can also provide detailed advice if the user is relaxed. For example, the service provider can analyze the user's lifestyle and provide detailed health advice during their relaxation time. The service provider can also provide advice that can be done at the user's travel destination if the user is traveling. For example, the service provider can analyze the user's geographical location and provide advice on exercise and diet that can be done at the travel destination. This allows the service provider to customize the means of providing advice based on the user's current lifestyle. Some or all of the above processing 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 lifestyle data into a generating AI and have the generating AI customize the means of providing advice.

[0077] The service provider can estimate the user's emotions and appropriately prioritize advice based on those emotions. For example, if the user is stressed, the service provider will prioritize stress management advice. For instance, the service provider could capture the user's facial expressions with a camera, estimate their stress level using an emotion estimation algorithm, and prioritize stress management advice. The service provider can also provide overall health management advice if the user is relaxed. For example, the service provider could record the user's voice, estimate their emotions using voice analysis technology, and provide overall health management advice. Furthermore, if the user is in a hurry, the service provider can prioritize advice on important health indicators. For example, the service provider could collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and prioritize advice on important health indicators. This allows the service provider to prioritize advice according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the service provider may be performed using AI, or not using AI. For example, the service provider can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0078] The service provider can select the most appropriate advice by considering the user's geographical location when providing advice. For example, if the user lives at high altitude, the service provider can provide advice on health risks specific to high altitude. For example, the service provider can assess the health risks of a user living at high altitude and provide advice on those risks. The service provider can also provide advice on health risks specific to urban areas if the user lives in an urban area. For example, the service provider can assess the health risks of a user living in an urban area and provide advice on those risks. The service provider can also provide advice on health risks at a travel destination if the user is traveling. For example, the service provider can assess the health risks at a travel destination and provide advice on those risks. This allows the service provider to select the most appropriate advice based on geographical location information. 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 geographical location information into a generating AI and have the generating AI select the most appropriate advice.

[0079] The service provider can analyze a user's social media activity and propose methods for providing advice. For example, if a user is experiencing stress on social media, the service provider can provide advice on stress management. For example, the service provider can analyze the user's social media posts and provide advice on stress management. The service provider can also provide advice based on information if a user frequently shares health-related information on social media. For example, the service provider can analyze the user's social media activity and provide advice based on health-related information. The service provider can also provide advice related to a specific health issue if a user mentions a specific health issue on social media. For example, the service provider can analyze the user's social media posts and provide advice related to a specific health issue. This allows the service provider to propose methods for providing advice based on social media activity. 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 social media activity data into a generating AI and have the generating AI propose methods for providing advice. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects the data. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to evaluate the user's health status and health risks. The provision unit generates appropriate advice based on the analysis results using the identification processing unit 290 of the data processing unit 12 and provides it to the user through the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, and data 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 uses the camera 42 and microphone 238 of the smart glasses 214 to collect the user's facial expressions and voice, and the control unit 46A collects the data. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to evaluate the user's health status and health risks. The data provision unit generates appropriate advice based on the analysis results using the identification processing unit 290 of the data processing unit 12 and provides it to the user through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect the user's facial expressions and voice, and the control unit 46A collects the data. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to evaluate the user's health status and health risks. The provision unit generates appropriate advice based on the analysis results using the identification processing unit 290 of the data processing unit 12 and provides it to the user through the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect the user's facial expressions and voice, and the control unit 46A collects the data. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to evaluate the user's health status and health risks. The provision unit generates appropriate advice based on the analysis results using the identification processing unit 290 of the data processing unit 12 and provides it to the user through the speaker 240 of the robot 414.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The analysis unit can also consider the user's genetic information when evaluating the user's health status. For example, the analysis unit can assess the risk of specific diseases based on the user's genetic information. Furthermore, the analysis unit can combine genetic information with lifestyle data to perform a more accurate health risk assessment. In addition, the analysis unit can provide personalized health management plans based on genetic information. This allows the analysis unit to perform health risk assessments that take the user's genetic information into account.

[0082] The service provider can propose an appropriate exercise plan based on the user's health condition. For example, the service provider can suggest an appropriate exercise intensity and frequency, taking into account the user's fitness level and health condition. Furthermore, the service provider can analyze the user's exercise history and propose an optimal exercise plan based on past exercise patterns. In addition, the service provider can propose a feasible exercise plan tailored to the user's lifestyle and schedule. This allows the service provider to provide an exercise plan that is truly tailored to the user's health condition.

[0083] The data collection unit can collect user meal data and evaluate nutritional balance. For example, it can record the user's meals and evaluate calorie intake and nutrient balance. It can also analyze the user's meal history and assess the risk of nutritional deficiencies or excesses. Furthermore, based on the user's meal data, the data collection unit can provide advice on improving nutritional balance. In this way, the data collection unit can collect user meal data and evaluate nutritional balance.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on those estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand results. If the user is relaxed, it can provide detailed results. Furthermore, if the user is in a hurry, it can provide concise results. In this way, the analysis unit can adjust the presentation of the analysis results according to the user's emotions.

[0085] The service provider can estimate the user's emotions and adjust the content of the advice based on those emotions. For example, if the user is feeling stressed, the service provider can offer advice on how to relax. If the user is relaxed, the service provider can also offer advice on maintaining good health. Furthermore, if the user is in a hurry, the service provider can offer advice that can be implemented in a short amount of time. In this way, the service provider can adjust the content of the advice according to the user's emotions.

[0086] The data collection unit can collect user exercise data and analyze exercise patterns. For example, it can record the user's exercise time and intensity and analyze exercise patterns. It can also evaluate the effectiveness of exercise based on the user's exercise history. Furthermore, it can provide advice on improving exercise patterns based on the user's exercise data. In this way, the data collection unit can collect user exercise data and analyze exercise patterns.

[0087] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing data related to stress management. Similarly, if the user is relaxed, the analysis unit can prioritize analyzing data related to overall health status. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing data related to important health indicators. This allows the analysis unit to determine the priority of analysis according to the user's emotions.

[0088] The service provider can estimate the user's emotions and adjust the timing of advice based on those emotions. For example, if the user is feeling stressed, the service provider can adjust the timing of providing advice to help them relax. Similarly, if the user is relaxed, the service provider can adjust the timing of providing advice to help them maintain their health. Furthermore, if the user is in a hurry, the service provider can adjust the timing of providing advice that can be implemented quickly. In this way, the service provider can adjust the timing of advice according to the user's emotions.

[0089] The data collection unit can collect user sleep data and analyze sleep patterns. For example, it can record the user's sleep duration and quality and analyze sleep patterns. It can also evaluate sleep quality based on the user's sleep history. Furthermore, it can provide advice on improving sleep patterns based on the user's sleep data. In this way, the data collection unit can collect user sleep data and analyze sleep patterns.

[0090] The analysis unit can estimate the user's emotions and adjust the level of detail in 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. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can adjust the level of detail in the analysis according to the user's emotions.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The data collection unit collects data. The data collection unit can collect, for example, the latest medical papers, industry information, public health data, individual medical histories, and communication data. For example, the data collection unit automatically collects the latest medical papers from the internet. It can also collect news and reports from the medical industry. It can also obtain public health data from government statistical databases. Furthermore, the data collection unit can collect individual medical histories from electronic medical records and patient records. For example, the data collection unit can obtain a user's past medical records from electronic medical records to understand their health status. The data collection unit can also collect communication data such as smartphone usage time and sleep patterns. For example, the data collection unit can record smartphone app usage time to identify excessive device use. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, evaluate the user's health status and health risks based on the collected data. For example, the analysis unit can evaluate the current health status based on past medical records and test results. The analysis unit can also evaluate the impact of excessive device use on health based on communication data. For example, the analysis unit can analyze smartphone usage time and sleep patterns to evaluate the impact of excessive device use on sleep. Step 3: The service provider provides appropriate advice to the user based on the analysis results obtained by the analysis unit. For example, the service provider can provide information on new treatments and preventive measures based on the analysis results. For example, the service provider can provide the user with information on the latest treatments and advise them on how to receive appropriate treatment. The service provider can also provide advice on improving lifestyle habits based on the analysis results. For example, the service provider can assess the health impact of excessive device use and advise on reducing device usage time.

[0093] 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.

[0094] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.).

[0109] 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.

[0110] 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. 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.

[0111] 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.

[0112] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] 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.

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

[0116] The 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.

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

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

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

[0120] Figure 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.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0123] In the 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.

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

[0125] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0127] The data processing system 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.

[0128] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] 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.

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

[0132] The 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.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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. 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.

[0144] 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.

[0145] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

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

2. The aforementioned collection unit is Collect the latest medical papers, industry information, public health data, individual medical histories, and communication data. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we evaluate the user's health status and health risks. The system according to feature 1.

4. The aforementioned supply unit is, Based on the analysis results, we will provide information on new treatments and preventive measures. The system according to feature 1.

5. The aforementioned collection unit is It collects communication data such as smartphone usage time and sleep patterns. The system according to feature 1.

6. The aforementioned analysis unit, Based on communication data, we will assess the health impacts of excessive device use. The system according to feature 1.

7. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of data collection appropriately based on the estimated user sentiment. The system according to feature 1.

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

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is Estimate user sentiment and appropriately prioritize the data to collect based on the estimated user sentiment. The system according to feature 1.

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