System

Abstract

To provide a system for making an optimal health proposal on the basis of a health state or life habits of each user.SOLUTION: A system includes a collection part, an analysis part, and a proposal part. The collection part collects data of a health state or life habits, and a health diagnosis or medical records of a user. The analysis part analyzes the data collected by the collection part. The proposal part makes a proposal on the basis of an analysis result obtained by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional technologies do not adequately provide optimal health recommendations based on individual health conditions and lifestyle habits, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal health suggestions based on an individual's health condition and lifestyle habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects data on a user's health condition or lifestyle, health checkup, or medical record. The analysis unit analyzes the data collected by the collection unit. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can provide optimal health suggestions based on an individual's health condition and lifestyle habits. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention is a system that collects data on a user's health condition, lifestyle, health checkup, and medical records, and utilizes a generating AI to make optimal suggestions for extending a healthy lifespan. The health management system collects data on a user's health condition, lifestyle, health checkup, and medical records, and utilizes a generating AI to make optimal suggestions for extending a healthy lifespan. For example, the health management system acquires data such as the number of steps, sleep time, and meal contents from a health care app used by the user. In addition, the system also collects data on the results of regular health checkups and medical records at medical institutions. This allows the user's health condition to be comprehensively understood. Next, the health management system uses a generating AI to analyze the collected data. The generating AI has previously learned the knowledge of various experts such as doctors, sports trainers, and nutritionists, and analyzes the data based on this. For example, the system analyzes data such as the number of steps, sleep time, and meal contents of the user to evaluate the health condition. In addition, the system analyzes the results of health checkups and data on medical records to identify potential health risks. Based on the analysis results, the health management system uses a generating AI to make optimal suggestions for extending a healthy lifespan. For example, if the user's number of steps is low, specific advice to increase the amount of daily exercise is provided. If the diet is unbalanced, a balanced meal menu is suggested. Furthermore, preventive measures against specific health risks are suggested based on the results of the health check. This allows the health management system to comprehensively grasp the user's health condition and make optimal suggestions to extend healthy lifespan. Since the user can manage their health on a daily basis without receiving advice from an expert, it is expected that their healthy lifespan will be extended. This allows the health management system to comprehensively grasp the user's health condition and make optimal suggestions to extend healthy lifespan.

[0029] The health management system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data on the user's health condition, lifestyle, health checkup, and medical record. The collection unit acquires data such as the number of steps, sleep time, and meal contents from a health care app used by the user. The collection unit can also collect data on the results of regular health checkups and medical records at medical institutions. For example, the collection unit automatically acquires data from a health care app installed on the user's smartphone. The collection unit can also collect data on health checkup results and medical records manually entered by the user. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis unit analyzes data such as the number of steps, sleep time, and meal contents of the user, and evaluates the health condition. The generation AI has previously learned the knowledge of various experts such as doctors, sports trainers, and nutritionists, and analyzes data based on this. For example, the generation AI evaluates the amount of daily exercise based on the user's step count data. The generation AI can also evaluate the nutritional balance based on the user's meal content data. Furthermore, the generation AI can also analyze the results of the health checkup and the data of the medical record to identify potential health risks. The suggestion unit uses the generation AI to make optimal suggestions based on the analysis results obtained by the analysis unit. For example, if the number of steps of the user is low, the suggestion unit provides specific advice to increase the amount of daily exercise. In addition, if the dietary content is unbalanced, the suggestion unit can also suggest a balanced meal menu. Furthermore, the suggestion unit can also suggest preventive measures against specific health risks based on the results of the health checkup. For example, the suggestion unit suggests improvement measures for lifestyle habits to reduce the risk of a specific disease based on the results of the user's health checkup. Thereby, the health management system according to the embodiment can comprehensively grasp the health condition of the user and make optimal suggestions to extend the healthy lifespan. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can make suggestions using a generation AI model that inputs the analysis results obtained by the analysis unit and outputs optimal suggestions.

[0030] The collection unit collects data on the user's health condition, lifestyle, health checkup, and medical record. The collection unit, for example, acquires data such as the number of steps, sleep time, and dietary contents from a health care app used by the user. Specifically, the collection unit collects data such as the number of steps, heart rate, and sleep patterns of the user in real time through sensors mounted on a smartphone or wearable device. These data are automatically transmitted to a cloud server via Bluetooth (registered trademark) or Wi-Fi and stored in a central database. The collection unit can also collect data on the results of regular health checkups and medical records at medical institutions. For example, electronic medical records and diagnostic result data issued when a user undergoes a health checkup can be directly acquired from the medical institution's system. Furthermore, data on health checkup results and medical records manually entered by the user can also be collected. This allows the collection unit to centrally manage various data related to the user's health condition and provide comprehensive health information. The collection unit can flexibly respond to the user's lifestyle and health condition by adjusting the frequency and accuracy of data collection. For example, for users with a high specific health risk, more detailed monitoring can be performed by increasing the frequency of data collection. In addition, the collection unit secures the trust of users by encrypting collected data and thoroughly protecting privacy. This allows the collection unit to collect data efficiently and safely, improving the performance and reliability of the entire system.

[0031] The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis unit analyzes data such as the user's steps, sleep time, and dietary content to evaluate the health condition. The generation AI has previously learned the knowledge of various experts such as doctors, sports trainers, and nutritionists, and analyzes the data based on this. Specifically, the generation AI evaluates the user's daily exercise amount based on the user's step count data. For example, it analyzes the average number of steps and frequency of exercise within a certain period of time to determine whether the user has achieved the recommended amount of exercise. The generation AI can also evaluate the nutritional balance based on the user's dietary content data. For example, it analyzes the intake of calories, protein, fat, and carbohydrates in meals to evaluate whether the user has a balanced diet. In addition, the generation AI can analyze the results of health checkups and chart data to identify potential health risks. For example, it can analyze health indicators such as blood pressure, blood sugar level, and cholesterol level, and issue an early warning if abnormal values ​​are found. The analysis unit comprehensively analyzes these data and comprehensively evaluates the user's health condition. Furthermore, the analysis unit can also analyze long-term health trends using past data and statistical information. For example, based on past health checkup results, it can track changes in the user's health and predict future risks. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and respond early. This allows the analysis unit to handle not only real-time health status evaluation, but also long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The suggestion unit uses the generation AI to make optimal suggestions based on the analysis results obtained by the analysis unit. For example, if the user's number of steps is low, the suggestion unit provides specific advice to increase the amount of daily exercise. Specifically, the suggestion unit proposes an exercise plan according to the user's lifestyle and exercise habits, and provides a specific exercise menu such as walking, jogging, and stretching. In addition, the suggestion unit can also propose a balanced meal menu if the dietary content is unbalanced. For example, based on the user's dietary data, the suggestion unit can propose recipes and ingredient selection methods that take nutritional balance into consideration, thereby supporting a healthy diet. Furthermore, the suggestion unit can also propose preventive measures against specific health risks based on the results of a health check. For example, based on the user's health check results, the suggestion unit can propose measures to improve lifestyle habits to reduce the risk of a specific disease. Specifically, the suggestion unit provides specific advice according to the user's health condition, such as improving diet, recommending exercise, and managing stress. The suggestion unit notifies the user of these suggestions to the user's smartphone or wearable device, and provides them in a form that is easy for the user to put into practice in their daily lives. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the results of the user putting the suggestions into practice are collected as feedback, and the generation AI learns from the data, allowing more personalized suggestions to be made. This allows the suggestion unit to comprehensively grasp the health condition of the user and make optimal suggestions to extend healthy lifespan. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit may make suggestions using a generation AI model that inputs the analysis results obtained by the analysis unit and outputs optimal suggestions.

[0033] The collection unit can collect data on the number of steps, sleep time, meal contents, health check results, and medical records of the user. The collection unit, for example, acquires step count data from a health care app used by the user. For example, the collection unit can automatically record the number of steps of the user through a smartphone app. The collection unit can also acquire sleep time data of the user. For example, the collection unit can record the user's sleep time using a sleep tracker. Furthermore, the collection unit can acquire meal content data of the user. For example, the collection unit can record the user's meal contents through a food diary app. The collection unit can also acquire health check result data. For example, the collection unit can collect the results of the health check taken by the user in a digital format. Furthermore, the collection unit can acquire medical record data. For example, the collection unit can collect medical record data provided by a medical institution. As a result, the collection unit can collect data for comprehensively grasping the health condition of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's step count data to the generation AI and cause the generation AI to collect the data.

[0034] The analysis unit can analyze the collected data by the generation AI and evaluate the health condition. The analysis unit, for example, uses the generation AI to analyze the user's step count data. For example, the analysis unit can evaluate the daily exercise amount based on the generation AI's step count data. The analysis unit can also analyze the user's sleep time data using the generation AI. For example, the analysis unit can evaluate the quality of sleep based on the generation AI's sleep time data. Furthermore, the analysis unit can analyze the user's meal content data using the generation AI. For example, the analysis unit can evaluate the nutritional balance based on the generation AI's meal content data. The analysis unit can also analyze the health check result data using the generation AI. For example, the analysis unit can identify potential health risks based on the generation AI's health check result data. Furthermore, the analysis unit can analyze the medical record data using the generation AI. For example, the analysis unit can evaluate the user's health condition based on the medical record data provided by the generation AI from a medical institution. As a result, the analysis unit improves the accuracy of the evaluation of the health condition by using the generation AI. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the data collected by the collection unit to the generation AI and have the generation AI analyze the data.

[0035] The suggestion unit can provide specific suggestions for increasing the amount of exercise, a balanced meal menu, and preventive measures for health risks based on the analysis results using the generation AI. For example, the suggestion unit can use the generation AI to provide specific advice for increasing the amount of daily exercise when the user's number of steps is low. For example, the suggestion unit can recommend to the user that the generation AI walks 30 minutes every day. In addition, the suggestion unit can use the generation AI to suggest a balanced meal menu when the user's diet is unbalanced. For example, the suggestion unit can advise the user to consume a lot of vegetables and fruits. Furthermore, the suggestion unit can use the generation AI to suggest preventive measures for specific health risks based on the results of a health check. For example, the suggestion unit can recommend to the user that the generation AI undergo regular health checkups. As a result, the suggestion unit can use the generation AI to make specific and effective health suggestions. Part or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit can input the analysis results obtained by the analysis unit to the generation AI and have the generation AI execute the optimal suggestion.

[0036] The analysis unit can analyze data using machine learning or deep learning. The analysis unit, for example, uses a machine learning algorithm to analyze the user's health data. For example, the analysis unit can classify the user's health condition using a decision tree algorithm. The analysis unit can also predict the user's health risk using a support vector machine. Furthermore, the analysis unit can analyze the user's health data using a deep learning algorithm. For example, the analysis unit can extract features from the user's health data using a convolutional neural network. The analysis unit can also perform time series analysis of the user's health data using a recurrent neural network. As a result, the analysis unit uses machine learning or deep learning to improve the accuracy of data analysis. Part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the data collected by the collection unit to the generation AI and cause the generation AI to perform analysis of the data.

[0037] The suggestion unit can use the generation AI to comprehensively grasp the user's health condition and make optimal suggestions to extend the healthy lifespan. The suggestion unit uses, for example, the generation AI to comprehensively evaluate the user's health condition. For example, the suggestion unit can use the generation AI to comprehensively analyze the user's steps, sleep time, meal contents, health check results, and medical record data to comprehensively grasp the user's health condition. The suggestion unit can also use the generation AI to make optimal suggestions to extend the user's healthy lifespan. For example, the suggestion unit can provide the generation AI with specific advice to increase the amount of daily exercise. The suggestion unit can also suggest a balanced meal menu to the user. Furthermore, the suggestion unit can suggest preventive measures against specific health risks to the user. As a result, the suggestion unit can use the generation AI to make optimal suggestions to extend the user's healthy lifespan. Part or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit can input the analysis results obtained by the analysis unit to the generation AI and cause the generation AI to execute the optimal suggestions.

[0038] The collection unit can analyze the user's past health data and select the optimal data collection method. The collection unit selects the optimal data collection method, for example, based on data of a health care app used by the user in the past. For example, the collection unit can analyze the user's past step count data and determine the optimal data collection method. The collection unit can also analyze the user's past health check results and determine the necessary data collection items. For example, the collection unit can analyze the user's past blood test results and select data collection items based on specific health indicators. Furthermore, the collection unit can set the optimal data collection timing based on the user's lifestyle habit data. For example, the collection unit can analyze the user's past meal content data and determine the optimal data collection timing. As a result, the collection unit can select the optimal data collection method based on the past health data, thereby enabling efficient data collection. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's past health data into the generation AI and cause the generation AI to select the optimal data collection method.

[0039] The collection unit can perform filtering based on the user's current living situation and health goals when collecting data. For example, when the user is on a diet, the collection unit prioritizes collecting data on meal contents. For example, when the user is using a food diary app, the collection unit can prioritize collecting that data. In addition, when the user has just started exercising, the collection unit can also focus on collecting data on the number of steps and the amount of exercise. For example, when the user is using a fitness app, the collection unit can prioritize collecting that data. Furthermore, when the user is about to have a health checkup, the collection unit can also collect data related to the health checkup. For example, when the user has made a reservation for a health checkup, the collection unit can prioritize collecting that data. This enables the collection unit to collect data according to the user's living situation and health goals. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input data related to the user's living situation and health goals into the generation AI and cause the generation AI to perform filtering of data collection.

[0040] When collecting data, the collection unit can prioritize collection of highly relevant data in consideration of the geographical location information of the user. For example, when the user is at a high altitude, the collection unit collects health data related to the altitude. For example, the collection unit can acquire the user's location information by GPS and collect data related to the altitude. In addition, when the user is in an urban area, the collection unit can also collect data related to air quality and noise levels. For example, the collection unit can collect environmental data of the urban area based on the user's location information. Furthermore, when the user is traveling, the collection unit can also collect data related to health risks at the travel destination. For example, the collection unit can collect health risk data at the travel destination based on the user's location information. As a result, the collection unit can collect highly relevant data based on the user's geographical location information, thereby enabling more accurate health management. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0041] The collection unit can analyze the user's social media activity and collect related health data at the time of data collection. For example, when the user posts about exercise on social media, the collection unit collects exercise data. For example, the collection unit can analyze the user's social media account and detect posts about exercise. In addition, when the user posts about food, the collection unit can also collect data about food content. For example, the collection unit can analyze the user's social media account and detect posts about food. In addition, when the user posts about stress, the collection unit can also collect stress-related data. For example, the collection unit can analyze the user's social media account and detect posts about stress. This allows the collection unit to collect related health data based on social media activity, thereby more accurately grasping the user's health condition. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's social media data to the generation AI and cause the generation AI to collect related health data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. The analysis unit performs a detailed analysis on important health data, for example. For example, the analysis unit can analyze the user's blood test results in detail and perform an evaluation based on a specific health index. The analysis unit can also perform a simplified analysis on general health data. For example, the analysis unit can simplify and analyze the user's step count data to evaluate the amount of daily exercise. Furthermore, the analysis unit can quickly analyze data with high urgency. For example, the analysis unit can quickly analyze the user's electrocardiogram data and detect abnormalities. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the health data, thereby enabling efficient data analysis. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the importance of the health data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms according to the category of health data during analysis. For example, the analysis unit applies an exercise analysis algorithm to the exercise data. For example, the analysis unit can input the user's step count data into the exercise analysis algorithm to evaluate the daily exercise amount. The analysis unit can also apply a nutrition analysis algorithm to the diet data. For example, the analysis unit can input the user's diet content data into the nutrition analysis algorithm to evaluate the nutritional balance. Furthermore, the analysis unit can apply a sleep analysis algorithm to the sleep data. For example, the analysis unit can input the user's sleep time data into the sleep analysis algorithm to evaluate the quality of sleep. Thereby, the analysis unit applies an analysis algorithm according to the category of health data, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the category of health data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0044] The analysis unit can determine the priority of the analysis based on the time of submission of the health data during the analysis. For example, the analysis unit prioritizes the analysis of the latest health data. For example, the analysis unit can prioritize the analysis of the health check results recently submitted by the user. The analysis unit can also prioritize the analysis of health data submitted periodically. For example, the analysis unit can prioritize the analysis of the blood test results submitted by the user every month. Furthermore, the analysis unit can also prioritize the analysis of health data with high urgency. For example, the analysis unit can prioritize the analysis of electrocardiogram data suddenly submitted by the user. As a result, the analysis unit determines the priority of the analysis based on the time of submission of the health data, thereby enabling efficient data analysis. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the time of submission of the health data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. The analysis unit, for example, prioritizes the analysis of highly relevant data. For example, the analysis unit can evaluate the relevance between the blood pressure data and the heart rate data of the user, and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can evaluate the relevance between the dietary content data and the exercise data of the user, and postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of data. For example, the analysis unit can evaluate the correlation of the health data of the user, and prioritize the analysis of highly relevant data. As a result, the analysis unit can adjust the order of analysis based on the relevance of the health data, thereby enabling efficient data analysis. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the relevance of the health data to the generation AI, and cause the generation AI to adjust the order of analysis.

[0046] The suggestion unit can adjust the level of detail of the proposal based on the importance of the health condition at the time of proposal. For example, the suggestion unit makes a detailed proposal for an important health condition. For example, the suggestion unit can propose a detailed meal menu based on the user's blood test results. The suggestion unit can also make a simplified proposal for a general health condition. For example, the suggestion unit can propose a simplified exercise plan based on the user's step count data. Furthermore, the suggestion unit can also make a quick proposal for a highly urgent health condition. For example, the suggestion unit can propose a quick countermeasure based on the user's electrocardiogram data. As a result, the suggestion unit can adjust the level of detail of the proposal according to the importance of the health condition, thereby enabling efficient proposal. Part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the importance of the health condition to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0047] The suggestion unit can apply different suggestion algorithms according to the category of the health condition at the time of suggestion. For example, the suggestion unit applies an exercise algorithm to a suggestion regarding exercise. For example, the suggestion unit can input the user's step count data into an exercise algorithm and propose a daily exercise plan. The suggestion unit can also apply a nutrition algorithm to a suggestion regarding meals. For example, the suggestion unit can input the user's meal content data into a nutrition algorithm and propose a balanced meal menu. Furthermore, the suggestion unit can apply a sleep algorithm to a suggestion regarding sleep. For example, the suggestion unit can input the user's sleep time data into a sleep algorithm and make a suggestion to promote high-quality sleep. Thereby, the suggestion unit applies a suggestion algorithm according to the category of the health condition, thereby improving the accuracy of the suggestion. Part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input a category of the health condition into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0048] The suggestion unit can determine the priority of the proposal based on the time of submission of the health data at the time of proposal. The suggestion unit, for example, makes a proposal based on the latest health data. For example, the suggestion unit can make a proposal regarding the latest health condition based on the health check result recently submitted by the user. The suggestion unit can also make a proposal based on health data submitted periodically. For example, the suggestion unit can make a proposal regarding regular health management based on the blood test result submitted by the user every month. Furthermore, the suggestion unit can also make a proposal based on health data with high urgency. For example, the suggestion unit can propose a quick countermeasure based on electrocardiogram data suddenly submitted by the user. As a result, the suggestion unit can determine the priority of the proposal based on the time of submission of the health data, thereby enabling efficient proposal. Part or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit can input the time of submission of the health data to the generation AI and cause the generation AI to execute the determination of the priority of the proposal.

[0049] The suggestion unit can adjust the order of the suggestions based on the relevance of the health data at the time of suggestion. The suggestion unit makes a suggestion based on, for example, highly relevant data. For example, the suggestion unit can evaluate the relevance between the blood pressure data and the heart rate data of the user, and make a suggestion based on the highly relevant data. The suggestion unit can also postpone the suggestion based on the less relevant data. For example, the suggestion unit can evaluate the relevance between the dietary content data and the exercise data of the user, and postpone the suggestion based on the less relevant data. Furthermore, the suggestion unit can dynamically adjust the order of the suggestions based on the relevance of the data. For example, the suggestion unit can evaluate the correlation of the health data of the user, and make a suggestion based on the more relevant data. As a result, the suggestion unit can adjust the order of the suggestions based on the relevance of the health data, thereby enabling efficient suggestions. A part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the relevance of the health data to the generation AI, and cause the generation AI to adjust the order of the suggestions.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The health management system can further anonymize the health data of users and analyze it as group data. This allows the health trends of the entire group to be understood while protecting the privacy of each individual user. For example, the collection unit can anonymize the individual data of users and collect it as group data. The analysis unit can analyze the anonymized group data and identify health risks in specific regions and age groups. The suggestion unit can suggest health improvement measures for each region and age group based on the group data. This allows the health management system to make suggestions to improve the health status of not only individual users but also the entire group.

[0052] The collection unit can monitor the user's health data in real time and issue an alert when an abnormality is detected. For example, the collection unit can monitor the user's heart rate and blood pressure in real time and notify the user when an abnormal value is detected. The collection unit can also monitor the user's sleep pattern in real time and issue an alert when an abnormal sleep pattern is detected. Furthermore, the collection unit can monitor the user's exercise data in real time and issue an alert when excessive exercise or lack of exercise is detected. In this way, the collection unit can monitor the user's health condition in real time and detect abnormalities early.

[0053] The analysis unit can take into account the influence of seasons and weather when analyzing the health data of the user. For example, the analysis unit can perform the analysis taking into account a decrease in the amount of exercise and vitamin D deficiency in winter. The analysis unit can also perform the analysis taking into account the risk of dehydration in summer. Furthermore, the analysis unit can also perform the analysis taking into account a lack of exercise during rainy weather and changes in physical condition due to changes in atmospheric pressure. This allows the analysis unit to perform a highly accurate evaluation of the health condition taking into account the influence of seasons and weather.

[0054] The suggestion unit can provide educational content related to health based on the health data of the user. For example, the suggestion unit can provide an educational video related to nutritional balance based on the dietary data of the user. The suggestion unit can also provide a guide on a correct exercise method based on the exercise data of the user. Furthermore, the suggestion unit can provide a hint for getting a good night's sleep based on the sleep data of the user. In this way, the suggestion unit can support the user in deepening his / her knowledge about health and improving his / her self-management ability.

[0055] The analysis unit can take the genetic information into consideration when analyzing the user's health data. For example, the analysis unit can evaluate the risk of a particular disease based on the user's genetic information. The analysis unit can also suggest optimal diet and exercise plans based on the user's genetic information. Furthermore, the analysis unit can predict the effectiveness and side effects of medicine based on the user's genetic information. This enables the analysis unit to provide personalized medicine that takes the user's genetic information into consideration.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The collection unit collects data on the user's health condition, lifestyle, health checkups, and medical records. For example, the collection unit obtains data such as the number of steps, sleep time, and dietary contents from a health care app used by the user. It can also collect results of regular health checkups and medical records from medical institutions. The collection unit automatically obtains data from health care apps installed on the user's smartphone, and also collects health checkup results and medical record data manually entered by the user. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. For example, it analyzes data such as the user's number of steps, sleep time, and dietary content to evaluate their health condition. The generation AI has previously learned the knowledge of various experts, such as doctors, sports trainers, and nutritionists, and analyzes the data based on this. The generation AI can also evaluate the user's daily exercise volume based on the user's step count data, and evaluate nutritional balance based on dietary content data. It can also analyze health checkup results and medical record data to identify potential health risks. Step 3: The suggestion unit uses the generation AI to make optimal suggestions based on the analysis results obtained by the analysis unit. For example, if the user's number of steps is low, specific advice on increasing the amount of daily exercise is provided. Also, if the user's diet is unbalanced, a balanced meal menu can be suggested. Furthermore, preventive measures against specific health risks can be suggested based on the results of a health check. For example, based on the user's health check results, measures to improve lifestyle habits to reduce the risk of a specific disease are suggested.

[0058] (Example 2) A health management system according to an embodiment of the present invention is a system that collects data on a user's health condition, lifestyle, health checkup, and medical records, and utilizes a generating AI to make optimal suggestions for extending a healthy lifespan. The health management system collects data on a user's health condition, lifestyle, health checkup, and medical records, and utilizes a generating AI to make optimal suggestions for extending a healthy lifespan. For example, the health management system acquires data such as the number of steps, sleep time, and meal contents from a health care app used by the user. In addition, the system also collects data on the results of regular health checkups and medical records at medical institutions. This allows the user's health condition to be comprehensively understood. Next, the health management system uses a generating AI to analyze the collected data. The generating AI has previously learned the knowledge of various experts such as doctors, sports trainers, and nutritionists, and analyzes the data based on this. For example, the system analyzes data such as the number of steps, sleep time, and meal contents of the user to evaluate the health condition. In addition, the system analyzes the results of health checkups and data on medical records to identify potential health risks. Based on the analysis results, the health management system uses a generating AI to make optimal suggestions for extending a healthy lifespan. For example, if the user's number of steps is low, specific advice to increase the amount of daily exercise is provided. If the diet is unbalanced, a balanced meal menu is suggested. Furthermore, preventive measures against specific health risks are suggested based on the results of the health check. This allows the health management system to comprehensively grasp the user's health condition and make optimal suggestions to extend healthy lifespan. Since the user can manage their health on a daily basis without receiving advice from an expert, it is expected that their healthy lifespan will be extended. This allows the health management system to comprehensively grasp the user's health condition and make optimal suggestions to extend healthy lifespan.

[0059] The health management system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data on the user's health condition, lifestyle, health checkup, and medical record. The collection unit acquires data such as the number of steps, sleep time, and meal contents from a health care app used by the user. The collection unit can also collect data on the results of regular health checkups and medical records at medical institutions. For example, the collection unit automatically acquires data from a health care app installed on the user's smartphone. The collection unit can also collect data on health checkup results and medical records manually entered by the user. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis unit analyzes data such as the number of steps, sleep time, and meal contents of the user, and evaluates the health condition. The generation AI has previously learned the knowledge of various experts such as doctors, sports trainers, and nutritionists, and analyzes data based on this. For example, the generation AI evaluates the amount of daily exercise based on the user's step count data. The generation AI can also evaluate the nutritional balance based on the user's meal content data. Furthermore, the generation AI can also analyze the results of the health checkup and the data of the medical record to identify potential health risks. The suggestion unit uses the generation AI to make optimal suggestions based on the analysis results obtained by the analysis unit. For example, if the number of steps of the user is low, the suggestion unit provides specific advice to increase the amount of daily exercise. In addition, if the dietary content is unbalanced, the suggestion unit can also suggest a balanced meal menu. Furthermore, the suggestion unit can also suggest preventive measures against specific health risks based on the results of the health checkup. For example, the suggestion unit suggests improvement measures for lifestyle habits to reduce the risk of a specific disease based on the results of the user's health checkup. Thereby, the health management system according to the embodiment can comprehensively grasp the health condition of the user and make optimal suggestions to extend the healthy lifespan. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can make suggestions using a generation AI model that inputs the analysis results obtained by the analysis unit and outputs optimal suggestions.

[0060] The collection unit collects data on the user's health condition, lifestyle, health checkup, and medical record. The collection unit, for example, acquires data such as the number of steps, sleep time, and dietary contents from a health care app used by the user. Specifically, the collection unit collects data such as the number of steps, heart rate, and sleep patterns of the user in real time through sensors installed in a smartphone or wearable device. These data are automatically transmitted to a cloud server via Bluetooth or Wi-Fi and stored in a central database. The collection unit can also collect data on the results of regular health checkups and medical records at medical institutions. For example, electronic medical records and diagnostic result data issued when a user undergoes a health checkup can be directly acquired from the medical institution's system. In addition, data on health checkup results and medical records manually entered by the user can also be collected. This allows the collection unit to centrally manage various data related to the user's health condition and provide comprehensive health information. The collection unit can flexibly respond to the user's lifestyle and health condition by adjusting the frequency and accuracy of data collection. For example, for users with a high specific health risk, more detailed monitoring can be performed by increasing the frequency of data collection. In addition, the collection unit secures the trust of users by encrypting collected data and thoroughly protecting privacy. This allows the collection unit to collect data efficiently and safely, improving the performance and reliability of the entire system.

[0061] The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis unit analyzes data such as the user's steps, sleep time, and dietary content to evaluate the health condition. The generation AI has previously learned the knowledge of various experts such as doctors, sports trainers, and nutritionists, and analyzes the data based on this. Specifically, the generation AI evaluates the user's daily exercise amount based on the user's step count data. For example, it analyzes the average number of steps and frequency of exercise within a certain period of time to determine whether the user has achieved the recommended amount of exercise. The generation AI can also evaluate the nutritional balance based on the user's dietary content data. For example, it analyzes the intake of calories, protein, fat, and carbohydrates in meals to evaluate whether the user has a balanced diet. In addition, the generation AI can analyze the results of health checkups and chart data to identify potential health risks. For example, it can analyze health indicators such as blood pressure, blood sugar level, and cholesterol level, and issue an early warning if abnormal values ​​are found. The analysis unit comprehensively analyzes these data and comprehensively evaluates the user's health condition. Furthermore, the analysis unit can also analyze long-term health trends using past data and statistical information. For example, based on past health checkup results, it can track changes in the user's health and predict future risks. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and respond early. This allows the analysis unit to handle not only real-time health status evaluation, but also long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0062] The suggestion unit uses the generation AI to make optimal suggestions based on the analysis results obtained by the analysis unit. For example, if the user's number of steps is low, the suggestion unit provides specific advice to increase the amount of daily exercise. Specifically, the suggestion unit proposes an exercise plan according to the user's lifestyle and exercise habits, and provides a specific exercise menu such as walking, jogging, and stretching. In addition, the suggestion unit can also propose a balanced meal menu if the dietary content is unbalanced. For example, based on the user's dietary data, the suggestion unit can propose recipes and ingredient selection methods that take nutritional balance into consideration, thereby supporting a healthy diet. Furthermore, the suggestion unit can also propose preventive measures against specific health risks based on the results of a health check. For example, based on the user's health check results, the suggestion unit can propose measures to improve lifestyle habits to reduce the risk of a specific disease. Specifically, the suggestion unit provides specific advice according to the user's health condition, such as improving diet, recommending exercise, and managing stress. The suggestion unit notifies the user of these suggestions to the user's smartphone or wearable device, and provides them in a form that is easy for the user to put into practice in their daily lives. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, the results of the user putting the suggestions into practice are collected as feedback, and the generation AI learns from the data, allowing more personalized suggestions to be made. This allows the suggestion unit to comprehensively grasp the health condition of the user and make optimal suggestions to extend healthy lifespan. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit may make suggestions using a generation AI model that inputs the analysis results obtained by the analysis unit and outputs optimal suggestions.

[0063] The collection unit can collect data on the number of steps, sleep time, meal contents, health check results, and medical records of the user. The collection unit, for example, acquires step count data from a health care app used by the user. For example, the collection unit can automatically record the number of steps of the user through a smartphone app. The collection unit can also acquire sleep time data of the user. For example, the collection unit can record the user's sleep time using a sleep tracker. Furthermore, the collection unit can acquire meal content data of the user. For example, the collection unit can record the user's meal contents through a food diary app. The collection unit can also acquire health check result data. For example, the collection unit can collect the results of the health check taken by the user in a digital format. Furthermore, the collection unit can acquire medical record data. For example, the collection unit can collect medical record data provided by a medical institution. As a result, the collection unit can collect data for comprehensively grasping the health condition of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's step count data to the generation AI and cause the generation AI to collect the data.

[0064] The analysis unit can analyze the collected data by the generation AI and evaluate the health condition. The analysis unit, for example, uses the generation AI to analyze the user's step count data. For example, the analysis unit can evaluate the daily exercise amount based on the generation AI's step count data. The analysis unit can also analyze the user's sleep time data using the generation AI. For example, the analysis unit can evaluate the quality of sleep based on the generation AI's sleep time data. Furthermore, the analysis unit can analyze the user's meal content data using the generation AI. For example, the analysis unit can evaluate the nutritional balance based on the generation AI's meal content data. The analysis unit can also analyze the health check result data using the generation AI. For example, the analysis unit can identify potential health risks based on the generation AI's health check result data. Furthermore, the analysis unit can analyze the medical record data using the generation AI. For example, the analysis unit can evaluate the user's health condition based on the medical record data provided by the generation AI from a medical institution. As a result, the analysis unit improves the accuracy of the evaluation of the health condition by using the generation AI. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the data collected by the collection unit to the generation AI and have the generation AI analyze the data.

[0065] The suggestion unit can provide specific suggestions for increasing the amount of exercise, a balanced meal menu, and preventive measures for health risks based on the analysis results using the generation AI. For example, the suggestion unit can use the generation AI to provide specific advice for increasing the amount of daily exercise when the user's number of steps is low. For example, the suggestion unit can recommend to the user that the generation AI walks 30 minutes every day. In addition, the suggestion unit can use the generation AI to suggest a balanced meal menu when the user's diet is unbalanced. For example, the suggestion unit can advise the user to consume a lot of vegetables and fruits. Furthermore, the suggestion unit can use the generation AI to suggest preventive measures for specific health risks based on the results of a health check. For example, the suggestion unit can recommend to the user that the generation AI undergo regular health checkups. As a result, the suggestion unit can use the generation AI to make specific and effective health suggestions. Part or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit can input the analysis results obtained by the analysis unit to the generation AI and have the generation AI execute the optimal suggestion.

[0066] The analysis unit can analyze data using machine learning or deep learning. The analysis unit can analyze the user's health data using, for example, a machine learning algorithm. For example, the analysis unit can classify the user's health condition using a decision tree algorithm. The analysis unit can also predict the user's health risk using a support vector machine. Furthermore, the analysis unit can analyze the user's health data using a deep learning algorithm. For example, the analysis unit can extract features from the user's health data using a convolutional neural network. The analysis unit can also perform time series analysis of the user's health data using a recurrent neural network. As a result, the analysis unit uses machine learning or deep learning to improve the accuracy of data analysis. Part or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input the data collected by the collection unit to the generation AI and cause the generation AI to perform analysis of the data.

[0067] The suggestion unit can use the generation AI to comprehensively grasp the user's health condition and make optimal suggestions to extend the healthy lifespan. The suggestion unit uses, for example, the generation AI to comprehensively evaluate the user's health condition. For example, the suggestion unit can use the generation AI to comprehensively analyze the user's steps, sleep time, meal contents, health check results, and medical record data to comprehensively grasp the user's health condition. The suggestion unit can also use the generation AI to make optimal suggestions to extend the user's healthy lifespan. For example, the suggestion unit can provide the generation AI with specific advice to increase the amount of daily exercise. The suggestion unit can also suggest a balanced meal menu to the user. Furthermore, the suggestion unit can suggest preventive measures against specific health risks to the user. As a result, the suggestion unit can use the generation AI to make optimal suggestions to extend the user's healthy lifespan. Part or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit can input the analysis results obtained by the analysis unit to the generation AI and cause the generation AI to execute the optimal suggestions.

[0068] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. In addition, the collection unit can increase the frequency of data collection and collect detailed data when the user is relaxed. For example, the collection unit can record the user's voice and estimate the emotion using a voice analysis technique. Furthermore, the collection unit can temporarily stop data collection and resume it later when the user is in a hurry. For example, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Thereby, the collection unit can reduce the burden on the user by adjusting the timing of data collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processes in the collection unit are performed using the generation AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of data collection.

[0069] The collection unit can analyze the user's past health data and select the optimal data collection method. The collection unit selects the optimal data collection method, for example, based on data of a health care app used by the user in the past. For example, the collection unit can analyze the user's past step count data and determine the optimal data collection method. The collection unit can also analyze the user's past health check results and determine the necessary data collection items. For example, the collection unit can analyze the user's past blood test results and select data collection items based on specific health indicators. Furthermore, the collection unit can set the optimal data collection timing based on the user's lifestyle habit data. For example, the collection unit can analyze the user's past meal content data and determine the optimal data collection timing. As a result, the collection unit can select the optimal data collection method based on the past health data, thereby enabling efficient data collection. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's past health data into the generation AI and cause the generation AI to select the optimal data collection method.

[0070] The collection unit can perform filtering based on the user's current living situation and health goals when collecting data. For example, when the user is on a diet, the collection unit prioritizes collecting data on meal contents. For example, when the user is using a food diary app, the collection unit can prioritize collecting that data. In addition, when the user has just started exercising, the collection unit can also focus on collecting data on the number of steps and the amount of exercise. For example, when the user is using a fitness app, the collection unit can prioritize collecting that data. Furthermore, when the user is about to have a health checkup, the collection unit can also collect data related to the health checkup. For example, when the user has made a reservation for a health checkup, the collection unit can prioritize collecting that data. This enables the collection unit to collect data according to the user's living situation and health goals. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input data related to the user's living situation and health goals into the generation AI and cause the generation AI to perform filtering of data collection.

[0071] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects stress-related data. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the collection unit can also collect overall health data in a balanced manner. For example, the collection unit can record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, when the user is in a hurry, the collection unit can preferentially collect only important data. For example, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Thereby, the collection unit can preferentially collect important data by determining the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's emotion data to the generation AI and have the generation AI determine the priority order of the data.

[0072] When collecting data, the collection unit can prioritize collection of highly relevant data in consideration of the geographical location information of the user. For example, when the user is at a high altitude, the collection unit collects health data related to the altitude. For example, the collection unit can acquire the user's location information by GPS and collect data related to the altitude. In addition, when the user is in an urban area, the collection unit can also collect data related to air quality and noise levels. For example, the collection unit can collect environmental data of the urban area based on the user's location information. Furthermore, when the user is traveling, the collection unit can also collect data related to health risks at the travel destination. For example, the collection unit can collect health risk data at the travel destination based on the user's location information. As a result, the collection unit can collect highly relevant data based on the user's geographical location information, thereby enabling more accurate health management. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0073] The collection unit can analyze the user's social media activity and collect related health data at the time of data collection. For example, when the user posts about exercise on social media, the collection unit collects exercise data. For example, the collection unit can analyze the user's social media account and detect posts about exercise. In addition, when the user posts about food, the collection unit can also collect data about food content. For example, the collection unit can analyze the user's social media account and detect posts about food. In addition, when the user posts about stress, the collection unit can also collect stress-related data. For example, the collection unit can analyze the user's social media account and detect posts about stress. This allows the collection unit to collect related health data based on social media activity, thereby more accurately grasping the user's health condition. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's social media data to the generation AI and cause the generation AI to collect related health data.

[0074] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit provides a simple and easy-to-understand analysis result. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. In addition, the analysis unit can provide a detailed analysis result when the user is relaxed. For example, the analysis unit can record the user's voice and estimate the emotion using a voice analysis technique. Furthermore, when the user is in a hurry, the analysis unit can provide a concise analysis result that covers the main points. For example, the analysis unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Thereby, the analysis unit can provide an analysis result that is easy for the user to understand by adjusting the expression method of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the analysis.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. The analysis unit performs a detailed analysis on important health data, for example. For example, the analysis unit can analyze the user's blood test results in detail and perform an evaluation based on a specific health index. The analysis unit can also perform a simplified analysis on general health data. For example, the analysis unit can simplify and analyze the user's step count data to evaluate the amount of daily exercise. Furthermore, the analysis unit can quickly analyze data with high urgency. For example, the analysis unit can quickly analyze the user's electrocardiogram data and detect abnormalities. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the health data, thereby enabling efficient data analysis. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the importance of the health data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms according to the category of health data during analysis. For example, the analysis unit applies an exercise analysis algorithm to the exercise data. For example, the analysis unit can input the user's step count data into the exercise analysis algorithm to evaluate the daily exercise amount. The analysis unit can also apply a nutrition analysis algorithm to the diet data. For example, the analysis unit can input the user's diet content data into the nutrition analysis algorithm to evaluate the nutritional balance. Furthermore, the analysis unit can apply a sleep analysis algorithm to the sleep data. For example, the analysis unit can input the user's sleep time data into the sleep analysis algorithm to evaluate the quality of sleep. Thereby, the analysis unit applies an analysis algorithm according to the category of health data, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the category of health data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0077] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit provides a short and to the point analysis. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. In addition, the analysis unit can provide a detailed analysis when the user is relaxed. For example, the analysis unit can record the user's voice and estimate the emotion using a voice analysis technique. In addition, the analysis unit can provide a concise analysis when the user is in a hurry. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Thereby, the analysis unit can provide an optimal analysis result for the user by adjusting the length of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input a user's emotion data to the generation AI and have the generation AI adjust the length of the analysis.

[0078] The analysis unit can determine the priority of the analysis based on the time of submission of the health data during the analysis. For example, the analysis unit prioritizes the analysis of the latest health data. For example, the analysis unit can prioritize the analysis of the health check results recently submitted by the user. The analysis unit can also prioritize the analysis of health data submitted periodically. For example, the analysis unit can prioritize the analysis of the blood test results submitted by the user every month. Furthermore, the analysis unit can also prioritize the analysis of health data with high urgency. For example, the analysis unit can prioritize the analysis of electrocardiogram data suddenly submitted by the user. As a result, the analysis unit determines the priority of the analysis based on the time of submission of the health data, thereby enabling efficient data analysis. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the time of submission of the health data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. The analysis unit, for example, prioritizes the analysis of highly relevant data. For example, the analysis unit can evaluate the relevance between the blood pressure data and the heart rate data of the user, and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can evaluate the relevance between the dietary content data and the exercise data of the user, and postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of data. For example, the analysis unit can evaluate the correlation of the health data of the user, and prioritize the analysis of highly relevant data. As a result, the analysis unit can adjust the order of analysis based on the relevance of the health data, thereby enabling efficient data analysis. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the relevance of the health data to the generation AI, and cause the generation AI to adjust the order of analysis.

[0080] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the user. For example, when the user is feeling stressed, the suggestion unit provides a simple and easy-to-understand suggestion. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. In addition, the suggestion unit can provide a detailed suggestion when the user is relaxed. For example, the suggestion unit can record the user's voice and estimate the emotion using a voice analysis technology. In addition, when the user is in a hurry, the suggestion unit can provide a concise suggestion that covers the main points. For example, the suggestion unit can collect the user's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Thereby, the suggestion unit can provide a suggestion that is easy for the user to understand by adjusting the way of expressing the suggestion according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the suggestion.

[0081] The suggestion unit can adjust the level of detail of the proposal based on the importance of the health condition at the time of proposal. For example, the suggestion unit makes a detailed proposal for an important health condition. For example, the suggestion unit can propose a detailed meal menu based on the user's blood test results. The suggestion unit can also make a simplified proposal for a general health condition. For example, the suggestion unit can propose a simplified exercise plan based on the user's step count data. Furthermore, the suggestion unit can also make a quick proposal for a highly urgent health condition. For example, the suggestion unit can propose a quick countermeasure based on the user's electrocardiogram data. As a result, the suggestion unit can adjust the level of detail of the proposal according to the importance of the health condition, thereby enabling efficient proposal. Part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the importance of the health condition to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0082] The suggestion unit can apply different suggestion algorithms according to the category of the health condition at the time of suggestion. For example, the suggestion unit applies an exercise algorithm to a suggestion regarding exercise. For example, the suggestion unit can input the user's step count data into an exercise algorithm and propose a daily exercise plan. The suggestion unit can also apply a nutrition algorithm to a suggestion regarding meals. For example, the suggestion unit can input the user's meal content data into a nutrition algorithm and propose a balanced meal menu. Furthermore, the suggestion unit can apply a sleep algorithm to a suggestion regarding sleep. For example, the suggestion unit can input the user's sleep time data into a sleep algorithm and make a suggestion to promote high-quality sleep. Thereby, the suggestion unit applies a suggestion algorithm according to the category of the health condition, thereby improving the accuracy of the suggestion. Part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input a category of the health condition into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0083] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit provides a short and to the point suggestion. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. In addition, the suggestion unit can provide a detailed suggestion when the user is relaxed. For example, the suggestion unit can record the user's voice and estimate the emotion using a voice analysis technique. In addition, the suggestion unit can provide a concise suggestion when the user is in a hurry. For example, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Thereby, the suggestion unit can provide an optimal suggestion for the user by adjusting the length of the suggestion according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit can input user emotion data to the generation AI and cause the generation AI to adjust the length of the suggestions.

[0084] The suggestion unit can determine the priority of the proposal based on the time of submission of the health data at the time of proposal. The suggestion unit, for example, makes a proposal based on the latest health data. For example, the suggestion unit can make a proposal regarding the latest health condition based on the health check result recently submitted by the user. The suggestion unit can also make a proposal based on health data submitted periodically. For example, the suggestion unit can make a proposal regarding regular health management based on the blood test result submitted by the user every month. Furthermore, the suggestion unit can also make a proposal based on health data with high urgency. For example, the suggestion unit can propose a quick countermeasure based on electrocardiogram data suddenly submitted by the user. As a result, the suggestion unit can determine the priority of the proposal based on the time of submission of the health data, thereby enabling efficient proposal. Part or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit can input the time of submission of the health data to the generation AI and cause the generation AI to execute the determination of the priority of the proposal.

[0085] The suggestion unit can adjust the order of the suggestions based on the relevance of the health data at the time of suggestion. The suggestion unit makes a suggestion based on, for example, highly relevant data. For example, the suggestion unit can evaluate the relevance between the blood pressure data and the heart rate data of the user, and make a suggestion based on the highly relevant data. The suggestion unit can also postpone the suggestion based on the less relevant data. For example, the suggestion unit can evaluate the relevance between the dietary content data and the exercise data of the user, and postpone the suggestion based on the less relevant data. Furthermore, the suggestion unit can dynamically adjust the order of the suggestions based on the relevance of the data. For example, the suggestion unit can evaluate the correlation of the health data of the user, and make a suggestion based on the more relevant data. As a result, the suggestion unit can adjust the order of the suggestions based on the relevance of the health data, thereby enabling efficient suggestions. A part or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the relevance of the health data to the generation AI, and cause the generation AI to adjust the order of the suggestions.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The health management system can further anonymize the health data of users and analyze it as group data. This allows the health trends of the entire group to be understood while protecting the privacy of each individual user. For example, the collection unit can anonymize the individual data of users and collect it as group data. The analysis unit can analyze the anonymized group data and identify health risks in specific regions and age groups. The suggestion unit can suggest health improvement measures for each region and age group based on the group data. This allows the health management system to make suggestions to improve the health status of not only individual users but also the entire group.

[0088] The collection unit can monitor the user's health data in real time and issue an alert when an abnormality is detected. For example, the collection unit can monitor the user's heart rate and blood pressure in real time and notify the user when an abnormal value is detected. The collection unit can also monitor the user's sleep pattern in real time and issue an alert when an abnormal sleep pattern is detected. Furthermore, the collection unit can monitor the user's exercise data in real time and issue an alert when excessive exercise or lack of exercise is detected. In this way, the collection unit can monitor the user's health condition in real time and detect abnormalities early.

[0089] The analysis unit can take into account the influence of seasons and weather when analyzing the health data of the user. For example, the analysis unit can perform the analysis taking into account a decrease in the amount of exercise and vitamin D deficiency in winter. The analysis unit can also perform the analysis taking into account the risk of dehydration in summer. Furthermore, the analysis unit can also perform the analysis taking into account a lack of exercise during rainy weather and changes in physical condition due to changes in atmospheric pressure. This allows the analysis unit to perform a highly accurate evaluation of the health condition taking into account the influence of seasons and weather.

[0090] The suggestion unit can provide educational content related to health based on the health data of the user. For example, the suggestion unit can provide an educational video related to nutritional balance based on the dietary data of the user. The suggestion unit can also provide a guide on a correct exercise method based on the exercise data of the user. Furthermore, the suggestion unit can provide a hint for getting a good night's sleep based on the sleep data of the user. In this way, the suggestion unit can support the user in deepening his / her knowledge about health and improving his / her self-management ability.

[0091] The analysis unit can take the genetic information into consideration when analyzing the user's health data. For example, the analysis unit can evaluate the risk of a particular disease based on the user's genetic information. The analysis unit can also suggest optimal diet and exercise plans based on the user's genetic information. Furthermore, the analysis unit can predict the effectiveness and side effects of medicine based on the user's genetic information. This enables the analysis unit to provide personalized medicine that takes the user's genetic information into consideration.

[0092] The collection unit can estimate the user's emotions and adjust the method of data collection based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, the collection unit can collect detailed data when the user is relaxed. Furthermore, the collection unit can temporarily stop data collection and resume it later when the user is in a hurry. In this way, the collection unit can reduce the burden on the user by adjusting the method of data collection according to the user's emotions.

[0093] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user's emotions. For example, the analysis unit can temporarily delay the analysis when the user is feeling stressed. The analysis unit can also perform the analysis quickly when the user is relaxed. Furthermore, the analysis unit can prioritize analysis of only important data when the user is in a hurry. In this way, the analysis unit can provide the analysis results at the optimal timing for the user by adjusting the timing of the analysis according to the user's emotions.

[0094] The suggestion unit can estimate the user's emotion and adjust the content of the suggestion based on the estimated user's emotion. For example, when the user is feeling stressed, the suggestion unit can make a suggestion to relax. Also, when the user is relaxed, the suggestion unit can suggest a proactive health improvement measure. Furthermore, when the user is in a hurry, the suggestion unit can make a simple and easy-to-implement suggestion. In this way, the suggestion unit can provide the most suitable suggestion for the user by adjusting the content of the suggestion according to the user's emotion.

[0095] The collection unit can estimate the user's emotion and adjust the type of data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit can preferentially collect stress-related data. Also, when the user is relaxed, the collection unit can collect overall health data in a balanced manner. Furthermore, when the user is in a hurry, the collection unit can preferentially collect only important data. In this way, the collection unit can preferentially collect important data by adjusting the type of data to be collected according to the user's emotion.

[0096] The suggestion unit can estimate the user's emotion and adjust the frequency of suggestions based on the estimated user's emotion. For example, when the user is feeling stressed, the suggestion unit can reduce the frequency of suggestions to reduce the burden on the user. Also, when the user is relaxed, the suggestion unit can increase the frequency of suggestions and provide detailed advice. Furthermore, when the user is in a hurry, the suggestion unit can temporarily reduce the frequency of suggestions and resume them later. In this way, the suggestion unit can reduce the burden on the user by adjusting the frequency of suggestions according to the user's emotion.

[0097] The process flow of the second embodiment will be briefly described below.

[0098] Step 1: The collection unit collects data on the user's health condition, lifestyle, health checkups, and medical records. For example, the collection unit obtains data such as the number of steps, sleep time, and dietary contents from a health care app used by the user. It can also collect results of regular health checkups and medical records from medical institutions. The collection unit automatically obtains data from health care apps installed on the user's smartphone, and also collects health checkup results and medical record data manually entered by the user. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. For example, it analyzes data such as the user's number of steps, sleep time, and dietary content to evaluate their health condition. The generation AI has previously learned the knowledge of various experts, such as doctors, sports trainers, and nutritionists, and analyzes the data based on this. The generation AI can also evaluate the user's daily exercise volume based on the user's step count data, and evaluate nutritional balance based on dietary content data. It can also analyze health checkup results and medical record data to identify potential health risks. Step 3: The suggestion unit uses the generation AI to make optimal suggestions based on the analysis results obtained by the analysis unit. For example, if the user's number of steps is low, specific advice on increasing the amount of daily exercise is provided. Also, if the user's diet is unbalanced, a balanced meal menu can be suggested. Furthermore, preventive measures against specific health risks can be suggested based on the results of a health check. For example, based on the user's health check results, measures to improve lifestyle habits to reduce the risk of a specific disease are suggested.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0101] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects health data of the user using the computer 36 and the camera 42 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimal suggestions for extending healthy life expectancy based on the analysis results. Part or all of the collection unit, analysis unit, and suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0110] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects health data of the user using the computer 36 and the camera 42 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimal suggestions for extending healthy life expectancy based on the analysis results. Part or all of the collection unit, analysis unit, and suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and proposal unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the user's health data using the computer 36 and the camera 42 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimal proposals for extending healthy life expectancy based on the analysis results. Part or all of the collection unit, analysis unit, and proposal unit may be realized, for example, by the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and proposal unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects health data of the user using the computer 36 and the camera 42 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimal proposals for extending healthy life expectancy based on the analysis results. Part or all of the collection unit, analysis unit, and proposal unit may be realized, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0153] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0157] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0164] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0168] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0170] (Appendix 1) A collection unit that collects data on the user's health condition or lifestyle, health checkup, or medical record; an analysis unit that analyzes the data collected by the collection unit; A proposal unit that makes a proposal based on the analysis result obtained by the analysis unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect data on the user's steps, sleep time, diet, health check results, and medical records 2. The system of claim 1, (Appendix 3) The analysis unit is Generative AI will analyze collected data and assess health status 2. The system of claim 1, (Appendix 4) The suggestion unit, Based on the analysis results, generative AI will provide specific suggestions on increasing exercise, choosing a balanced diet, and taking preventative measures against health risks. 2. The system of claim 1, (Appendix 5) The analysis unit is Analyze data using machine learning and deep learning 2. The system of claim 1, (Appendix 6) The suggestion unit, Generative AI will comprehensively understand the user's health condition and provide optimal suggestions to extend their healthy lifespan. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze users' past health data and select the optimal data collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: Filter data collection based on the user's current lifestyle and health goals 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 12) The collecting unit includes: During data collection, users' social media activity is analyzed to gather relevant health data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the health data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of health data. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when health data was submitted 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of health data 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of the health condition 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the health category. 2. The system of claim 1, (Appendix 22) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 23) The suggestion unit, When submitting proposals, prioritize proposals based on when the health data was submitted 2. The system of claim 1, (Appendix 24) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of your health data 2. The system of claim 1, [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects data on the user's health condition or lifestyle, health checkup, or medical record; an analysis unit that analyzes the data collected by the collection unit; A proposal unit that makes a proposal based on the analysis result obtained by the analysis unit. A system characterized by:

2. The collecting unit includes: Collect data on the user's steps, sleep time, diet, health check results, and medical records 2. The system of claim 1 .

3. The analysis unit is Generative AI will analyze the collected data and evaluate the health status.

2. The system of claim 1 .

4. The suggestion unit, Based on the analysis results, generative AI will provide specific suggestions for increasing exercise, balanced meal menus, and preventive measures against health risks.

2. The system of claim 1 .

5. The analysis unit is Analyze data using machine learning and deep learning 2. The system of claim 1 .

6. The suggestion unit, Generative AI will comprehensively understand the user's health condition and provide optimal suggestions for extending their healthy lifespan.

2. The system of claim 1 .

7. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

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

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