System

Abstract

To provide a system for proposing exercise and a dietary program that can be continued reasonably by comprehensively taking into consideration medical checkup data and life cycle data of a user.SOLUTION: A system includes a collection part, a determination part, and a proposal part. The collection part collects a user's medical checkup data, attribute data, life cycle data, physical condition data, and meteorological data. The determination part determines a user's exercise content, exercise amount, and dietary menu, based on the medical checkup data collected by the collection part. The proposal part makes a proposal on the information determined by the determination part to the user.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 suggest exercise and dietary recommendations that comprehensively take into account a user's health checkup data, life cycle data, and the like, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an exercise and diet program that can be continued without difficulty by comprehensively taking into consideration the user's health checkup data, life cycle data, and the like. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a determination unit, and a suggestion unit. The collection unit collects health check data, attribute data, life cycle data, physical condition data, and weather data of a user. The decision unit decides an exercise content, an exercise amount, and a meal menu of the user based on the health check data collected by the collection unit. The suggestion unit suggests the information decided by the decision unit to the user. Effect of the Invention

[0007] The system according to the embodiment can propose an exercise and diet program that can be continued without difficulty by comprehensively taking into consideration the user's health check data, life cycle data, and the like. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The lifestyle-related disease prevention and improvement system according to the embodiment of the present invention is a system that collects a user's health check data, attribute data, life cycle data, physical condition data, and weather data, and proposes a reasonably sustainable exercise and meal program based on these data. The lifestyle-related disease prevention and improvement system collects a user's health check data, attribute data, life cycle data, physical condition data, and weather data, and proposes a reasonably sustainable exercise and meal program based on these data. For example, the lifestyle-related disease prevention and improvement system collects data such as the user's age, sex, occupation, lifestyle, weight, height, blood pressure, blood sugar level, temperature, and humidity. Next, based on the collected data, the system determines the exercise content, exercise amount, and meal menu suitable for the user. For example, the system determines the appropriate exercise intensity and exercise time, the calorie and nutritional balance of the meal from the user's health check data. In addition, the user's hobbies, life cycle, physical condition, weather conditions, etc. are also taken into consideration. Furthermore, the system proposes the determined exercise content, exercise amount, and meal menu to the user. The proposal is made through the user's smartphone or computer. For example, when the user opens the app, today's exercise program and meal menu are displayed. The suggestions also include links to purchase ingredients and health equipment in cooperation with an electronic commerce site. For example, links are provided so that the user can easily purchase ingredients necessary for the proposed meal menu. This system allows the user to practice an exercise and meal program that is easy to continue, and is expected to prevent and improve lifestyle-related diseases. For example, if the user practices the proposed exercise program and takes the proposed meal menu, it is expected that weight will decrease and blood pressure and blood sugar levels will return to normal ranges. In addition, the suggestions are made according to the user's life cycle and weather conditions, so the user can continue the program without difficulty. In this way, the lifestyle-related disease prevention and improvement system can suggest an exercise and meal program that is easy to continue based on the user's health checkup data.

[0029] The lifestyle-related disease prevention and improvement system according to the embodiment includes a collection unit, a determination unit, and a proposal unit. The collection unit collects health check data, attribute data, life cycle data, physical condition data, and weather data of a user. The health check data includes, for example, blood pressure, blood glucose level, weight, and the like, but is not limited to such examples. The attribute data includes, for example, age, sex, occupation, and the like, but is not limited to such examples. The life cycle data includes, for example, lifestyle habits, sleep patterns, and the like, but is not limited to such examples. The physical condition data includes, for example, athletic ability, physical strength level, and the like, but is not limited to such examples. The weather data includes, for example, temperature, humidity, precipitation, and the like, but is not limited to such examples. The collection unit collects data such as, for example, the user's age, sex, occupation, lifestyle habits, weight, height, blood pressure, blood glucose level, temperature, humidity, and the like. The determination unit determines the user's exercise content, exercise amount, and meal menu based on the health check data collected by the collection unit. The determination unit determines, for example, appropriate exercise intensity and exercise time, meal calories, and nutritional balance from the user's health check data. The determination unit also considers the user's hobbies, life cycle, physical condition, weather conditions, and the like. For example, if the user is a busy businessman, it proposes an effective exercise program in a short time. The suggestion unit proposes the information determined by the determination unit to the user. The suggestion unit makes the proposal, for example, through the user's smartphone or computer. For example, when the user opens the app, today's exercise program and meal menu are displayed. The suggestion unit also includes a link to purchase ingredients and health equipment in cooperation with an e-commerce site. For example, a link is provided so that the user can easily purchase ingredients necessary for the proposed meal menu. As a result, the lifestyle-related disease prevention and improvement system according to the embodiment can propose an exercise and meal program that can be continued without difficulty based on the user's health check data. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs the information determined by the determination unit and outputs a proposal.

[0030] The collection unit collects the user's health check data, attribute data, life cycle data, physical condition data, and weather data. The health check data includes, but is not limited to, blood pressure, blood glucose level, weight, and the like. These data are acquired from regular health checks and home health management devices. For example, data can be automatically collected from a blood pressure monitor, a weight scale, a blood glucose meter, and the like used by the user at home and transmitted to a cloud server. The attribute data includes, but is not limited to, age, sex, occupation, and the like. These data are basic information entered when the user registers with the system, and are important for evaluating the user's lifestyle and health risks. The life cycle data includes, but is not limited to, lifestyle habits, sleep patterns, and the like. These data are collected from smartphones and wearable devices, and are used to grasp the rhythm and activity level of the user's daily life. The physical condition data includes, but is not limited to, athletic ability, physical fitness level, and the like. These data are acquired from fitness trackers and exercise apps, and are used to monitor changes in the user's exercise habits and physical fitness. Weather data includes, but is not limited to, for example, temperature, humidity, precipitation, and the like. These data are obtained from a weather information providing service and are used to adjust the user's exercise program and meal menu. The collection unit collects, for example, data such as the user's age, sex, occupation, lifestyle, weight, height, blood pressure, blood sugar level, temperature, and humidity. This allows the collection unit to collect a wide range of information from various data sources and comprehensively evaluate the user's health condition. Furthermore, the collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the decision unit and the proposal unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The determination unit determines the user's exercise content, exercise amount, and meal menu based on the health check data collected by the collection unit. Specifically, the determination unit determines the appropriate exercise intensity, exercise time, meal calorie, and nutritional balance from the user's health check data. For example, if the user's blood pressure or blood sugar level is high, low-intensity aerobic exercise is recommended, and a low-calorie, high-nutritional menu is proposed for meals. The determination unit also takes into account the user's hobbies, life cycle, physical condition, weather conditions, etc. For example, if the user is a busy businessman, a short and effective exercise program is proposed. Specifically, stretching that can be done in the office and short-term high-intensity interval training (HIIT) are recommended. In addition, if outdoor exercise is difficult depending on the weather conditions, an exercise program that can be done indoors is proposed. The determination unit uses AI to analyze these data and determine the optimal exercise content and meal menu. The AI ​​generates an optimal program for the user's health condition and lifestyle based on past data and statistical information. For example, a machine learning algorithm is used to analyze the user's past exercise history and meal history, and the most effective exercise and meal are proposed. In addition, the AI ​​can continuously improve the accuracy and effectiveness of the program based on user feedback. This allows the decision unit to provide the user with an optimal exercise and diet program for their health condition and lifestyle, and to support the prevention and improvement of lifestyle-related diseases. Furthermore, the decision unit can flexibly adjust the program according to changes in the user's health condition. For example, if the user's blood pressure or blood sugar level improves, the exercise intensity and dietary content can be changed step by step to set higher goals. This allows the decision unit to continuously monitor the user's health condition and provide the optimal program.

[0032] The suggestion unit proposes the information determined by the determination unit to the user. Specifically, the suggestion unit makes the proposal through the user's smartphone or computer. For example, when the user opens the app, today's exercise program and meal menu are displayed. The suggestion unit also includes links to purchase ingredients and health equipment in cooperation with an e-commerce site. For example, links are provided so that the user can easily purchase ingredients necessary for the proposed meal menu. This allows the user to prepare the necessary ingredients and equipment without any hassle. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposal content. For example, after the user performs the proposed exercise program, the effect and impressions can be fed back to reflect the feedback in the next proposal. Furthermore, the suggestion unit can generate the proposal content using AI. For example, the suggestion unit can make a proposal using an AI model that inputs the information determined by the determination unit and outputs a proposal. The AI ​​generates optimal proposals based on the user's past data and feedback. For example, the proposal can be made more effective and easier to continue by taking into account the exercises and meal menus that the user has preferred in the past. As a result, the suggestion unit can provide personalized proposals to the user and support the prevention and improvement of lifestyle-related diseases. Furthermore, the suggestion unit can make suggestions that match the user's lifestyle and hobbies. For example, if the user likes outdoor activities, the suggestion unit can suggest an exercise program to be done in nature. If the user enjoys cooking, the suggestion unit can suggest healthy and delicious recipes to make eating more enjoyable. This allows the suggestion unit to increase the user's motivation and support a health management that can be continued without strain.

[0033] The suggestion unit cooperates with an electronic commerce site and can suggest at least one of ingredients and health equipment based on the information determined by the determination unit. The suggestion unit cooperates with an electronic commerce site, for example, so that the user can easily purchase ingredients and health equipment required for the proposed meal menu. For example, the suggestion unit provides a link so that the user can easily purchase ingredients required for the proposed meal menu. The suggestion unit also provides a link so that the user can easily purchase health equipment required for the proposed exercise program. This allows the user to easily purchase ingredients and health equipment required for the proposed meal menu. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that receives information determined by the determination unit as input and outputs a suggestion.

[0034] The collection unit can analyze the past data collection history and select the collection method. For example, the collection unit preferentially selects a data collection method that the user has used in the past. The collection unit can also select the most efficient collection method from the user's past data collection history. The collection unit can also analyze the user's past data collection history and optimize the collection frequency. This allows an efficient data collection method to be selected based on the past data collection history. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the past data collection history to the generation AI and cause the generation AI to select the optimal collection method.

[0035] The collection unit can perform filtering based on the user's current health condition and living situation when collecting data. For example, the collection unit refrains from collecting data when the user is in poor health. In addition, the collection unit can perform detailed data collection when the user is in good health. In addition, the collection unit can adjust the range of data collection according to the user's living situation. This makes it possible to collect data according to the user's health condition and living situation. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's health condition and living situation to the generation AI and cause the generation AI to perform filtering.

[0036] The collection unit can prioritize collecting highly relevant data in consideration of the geographical location information of the user when collecting data. For example, when the user is in a specific area, the collection unit prioritizes collecting weather data of the area. Also, when the user is traveling, the collection unit can prioritize collecting health data of the travel destination. Also, when the user is at home, the collection unit can prioritize collecting environmental data around the home. This makes it possible to collect highly relevant data based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to collect highly relevant data.

[0037] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit can collect, for example, health information shared by the user on social media. The collection unit can also collect data on exercise and diet of interest from the user's social media activity. The collection unit can also analyze the content of the user's social media posts and collect related health data. This makes it possible to collect related data based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or can be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related data.

[0038] The determination unit can adjust the level of detail of the exercise and diet based on the importance of the health data at the time of determination. For example, when the user's blood pressure is high, the determination unit can suggest in detail exercises and diets that lower blood pressure. Also, when the user's blood glucose level is high, the determination unit can suggest in detail exercises and diets that manage the blood glucose level. Also, when the user's weight is increasing, the determination unit can suggest in detail exercises and diets that are effective for weight management. This makes it possible to suggest detailed exercises and diets according to the importance of the user's health data. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's health data to the generation AI and cause the generation AI to adjust the level of detail of the exercise and diet.

[0039] The determination unit can apply an algorithm according to the life cycle and hobbies at the time of determination. For example, if the user lives a nocturnal lifestyle, the determination unit can suggest exercises and meals suitable for the night. In addition, if the user likes the outdoors, the determination unit can suggest exercises that can be done outdoors. In addition, if the user is an indoor person, the determination unit can suggest exercises that can be done indoors. This makes it possible to suggest exercises and meals according to the user's life cycle and hobbies. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on the user's life cycle and hobbies to the generation AI and cause the generation AI to apply the algorithm.

[0040] At the time of determination, the determination unit can adjust the timing of exercise and meals based on the user's life rhythm. For example, if the user is a morning person, the determination unit can suggest exercise and meals suitable for the morning. Also, if the user is a night person, the determination unit can suggest exercise and meals suitable for the evening. Also, the determination unit can adjust the timing of exercise and meals in accordance with the user's life rhythm. This makes it possible to suggest exercise and meals that match the user's life rhythm. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's life rhythm data to the generation AI and cause the generation AI to adjust the timing of exercise and meals.

[0041] The determination unit can select a program by referring to the past exercise history and meal history at the time of determination. The determination unit can, for example, analyze the effect of the exercise performed by the user in the past and select an optimal exercise program. The determination unit can also analyze the user's past meal history and select an optimal meal menu. The determination unit can also select a sustainable program based on the user's past exercise history and meal history. This makes it possible to suggest optimal exercise and meal programs based on the user's past history. Some or all of the above-mentioned processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input the user's past exercise history and meal history data to the generation AI and cause the generation AI to select an optimal program.

[0042] The suggestion unit can adjust the level of detail of the proposal based on the importance of the health data when making a proposal. For example, when the user's blood pressure is high, the suggestion unit can make a detailed proposal regarding blood pressure management. Also, when the user's blood glucose level is high, the suggestion unit can make a detailed proposal regarding blood glucose level management. Also, when the user's weight is increasing, the suggestion unit can make a detailed proposal regarding weight management. This enables detailed proposals to be made according to the importance of the user's health data. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's health data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0043] The suggestion unit can apply the suggestion algorithm according to the life cycle and hobbies at the time of suggestion. For example, if the user lives a nocturnal lifestyle, the suggestion unit makes suggestions suitable for the night. In addition, if the user likes the outdoors, the suggestion unit can suggest exercises that can be done outdoors. In addition, if the user is an indoor person, the suggestion unit can suggest exercises that can be done indoors. This makes it possible to make suggestions according to the user's life cycle and hobbies. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's life cycle and hobbies to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0044] The suggestion unit can adjust the timing of the suggestion based on the user's life rhythm when making a suggestion. For example, if the user is a morning person, the suggestion unit makes a suggestion suitable for the morning. Also, if the user is a night person, the suggestion unit can make a suggestion suitable for the evening. Also, the suggestion unit can adjust the timing of the suggestion in accordance with the user's life rhythm. This makes it possible to make a suggestion that matches the user's life rhythm. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's life rhythm data to the generation AI and cause the generation AI to adjust the timing of the suggestion.

[0045] The suggestion unit can make a suggestion by referring to the past exercise history and meal history when making a suggestion. For example, the suggestion unit can analyze the effect of the exercise performed by the user in the past and make an optimal suggestion. The suggestion unit can also analyze the user's past meal history and make an optimal suggestion. The suggestion unit can also make a sustainable suggestion based on the user's past exercise history and meal history. This makes it possible to make an optimal suggestion based on the user's past history. A part or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's past exercise history and meal history data to the generation AI and cause the generation AI to execute the optimal suggestion.

[0046] The suggestion unit cooperates with an electronic commerce site, and when suggesting at least one of ingredients and health equipment based on the information determined by the decision unit, the suggestion unit can select a product by referring to the past purchase history. For example, the suggestion unit suggests a product similar to a product previously purchased by the user. The suggestion unit can also suggest a highly relevant product based on the user's past purchase history. The suggestion unit can also analyze the user's past purchase history and suggest the most suitable product. This makes it possible to suggest an optimal product based on the user's past purchase history. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's past purchase history data to the generation AI and cause the generation AI to select a product.

[0047] The suggestion unit cooperates with an electronic commerce site, and when suggesting at least one of ingredients and health equipment based on the information determined by the decision unit, the suggestion unit can preferentially suggest highly relevant products taking into consideration the user's geographical location information. For example, when the user is in a specific area, the suggestion unit preferentially suggests products available in that area. Also, when the user is traveling, the suggestion unit can preferentially suggest products available at the travel destination. Also, the suggestion unit can suggest optimal products based on the user's geographical location information. This makes it possible to suggest highly relevant products based on the user's geographical location information. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the determination of the priority order of products.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The collection unit can collect the user's sleep data in addition to the user's health data. For example, the collection unit can collect data such as the user's sleep time, sleep quality, and heart rate during sleep. The collection unit can also collect physiological data such as the user's heart rate variability and electrodermal activity to measure the user's stress level. Furthermore, the collection unit can collect detailed records of the user's diet. This makes it possible to suggest exercise and diet programs that take into account the user's sleep patterns, stress level, and dietary content.

[0050] The collection unit can collect the user's exercise history in addition to the user's health data. For example, the collection unit collects data such as the type, frequency, and intensity of exercise performed by the user in the past. The collection unit can also collect data such as the user's heart rate and calories burned during exercise. Furthermore, the collection unit can collect the user's recovery status after exercise. This makes it possible to propose an exercise program that takes into account the user's exercise history and data during exercise.

[0051] The collection unit can collect the user's living environment data in addition to the user's health data. For example, the collection unit collects the user's residential environment data (temperature, humidity, air quality, etc.). The collection unit can also collect the user's workplace environment data. The collection unit can also collect data such as the user's commuting route and commuting time. This makes it possible to propose an exercise and diet program that takes the user's living environment into consideration.

[0052] The collection unit can collect data on the user's hobbies and interests in addition to the user's health data. For example, the collection unit collects data on the user's favorite sports, activities, foods, and the like. The collection unit can also collect data on communities and events in which the user participates. Furthermore, the collection unit can collect data on the user's hobbies, such as reading and watching movies. This makes it possible to suggest exercise and diet programs that take the user's hobbies and interests into consideration.

[0053] The process flow of the first embodiment will be briefly described below.

[0054] Step 1: The collection unit collects the user's health check data, attribute data, life cycle data, physical condition data, and weather data. The health check data includes, for example, blood pressure, blood glucose level, weight, etc., but is not limited to such examples. The attribute data includes, for example, age, sex, occupation, etc., but is not limited to such examples. The life cycle data includes, for example, lifestyle, sleep pattern, etc., but is not limited to such examples. The physical condition data includes, for example, athletic ability, physical strength level, etc., but is not limited to such examples. The weather data includes, for example, temperature, humidity, precipitation, etc., but is not limited to such examples. The collection unit collects, for example, data such as the user's age, sex, occupation, lifestyle, weight, height, blood pressure, blood glucose level, temperature, humidity, etc. Step 2: The determination unit determines the user's exercise content, exercise amount, and meal menu based on the health check data collected by the collection unit. The determination unit determines, for example, appropriate exercise intensity and exercise time, meal calories, and nutritional balance from the user's health check data. The determination unit also takes into account the user's hobbies, life cycle, physical condition, weather conditions, and the like. For example, if the user is a busy businessman, the determination unit suggests an effective exercise program in a short time. Step 3: The suggestion unit suggests the information decided by the decision unit to the user. The suggestion unit makes suggestions, for example, through the user's smartphone or computer. For example, when the user opens the app, today's exercise program and meal menu are displayed. The suggestion unit also includes links to purchase ingredients and health equipment in cooperation with an e-commerce site. For example, links are provided so that the user can easily purchase ingredients necessary for the proposed meal menu. In this way, the lifestyle-related disease prevention and improvement system according to the embodiment can suggest an exercise and meal program that can be continued without difficulty based on the user's health check data. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the information decided by the decision unit and outputs suggestions.

[0055] (Example 2) The lifestyle-related disease prevention and improvement system according to the embodiment of the present invention is a system that collects a user's health check data, attribute data, life cycle data, physical condition data, and weather data, and proposes a reasonably sustainable exercise and meal program based on these data. The lifestyle-related disease prevention and improvement system collects a user's health check data, attribute data, life cycle data, physical condition data, and weather data, and proposes a reasonably sustainable exercise and meal program based on these data. For example, the lifestyle-related disease prevention and improvement system collects data such as the user's age, sex, occupation, lifestyle, weight, height, blood pressure, blood sugar level, temperature, and humidity. Next, based on the collected data, the system determines the exercise content, exercise amount, and meal menu suitable for the user. For example, the system determines the appropriate exercise intensity and exercise time, the calorie and nutritional balance of the meal from the user's health check data. In addition, the user's hobbies, life cycle, physical condition, weather conditions, etc. are also taken into consideration. Furthermore, the system proposes the determined exercise content, exercise amount, and meal menu to the user. The proposal is made through the user's smartphone or computer. For example, when the user opens the app, today's exercise program and meal menu are displayed. The suggestions also include links to purchase ingredients and health equipment in cooperation with an electronic commerce site. For example, links are provided so that the user can easily purchase ingredients necessary for the proposed meal menu. This system allows the user to practice an exercise and meal program that is easy to continue, and is expected to prevent and improve lifestyle-related diseases. For example, if the user practices the proposed exercise program and takes the proposed meal menu, it is expected that weight will decrease and blood pressure and blood sugar levels will return to normal ranges. In addition, the suggestions are made according to the user's life cycle and weather conditions, so the user can continue the program without difficulty. In this way, the lifestyle-related disease prevention and improvement system can suggest an exercise and meal program that is easy to continue based on the user's health checkup data.

[0056] The lifestyle-related disease prevention and improvement system according to the embodiment includes a collection unit, a determination unit, and a proposal unit. The collection unit collects health check data, attribute data, life cycle data, physical condition data, and weather data of a user. The health check data includes, for example, blood pressure, blood glucose level, weight, and the like, but is not limited to such examples. The attribute data includes, for example, age, sex, occupation, and the like, but is not limited to such examples. The life cycle data includes, for example, lifestyle habits, sleep patterns, and the like, but is not limited to such examples. The physical condition data includes, for example, athletic ability, physical strength level, and the like, but is not limited to such examples. The weather data includes, for example, temperature, humidity, precipitation, and the like, but is not limited to such examples. The collection unit collects data such as, for example, the user's age, sex, occupation, lifestyle habits, weight, height, blood pressure, blood glucose level, temperature, humidity, and the like. The determination unit determines the user's exercise content, exercise amount, and meal menu based on the health check data collected by the collection unit. The determination unit determines, for example, appropriate exercise intensity and exercise time, meal calories, and nutritional balance from the user's health check data. The determination unit also considers the user's hobbies, life cycle, physical condition, weather conditions, and the like. For example, if the user is a busy businessman, it proposes an effective exercise program in a short time. The suggestion unit proposes the information determined by the determination unit to the user. The suggestion unit makes the proposal, for example, through the user's smartphone or computer. For example, when the user opens the app, today's exercise program and meal menu are displayed. The suggestion unit also includes a link to purchase ingredients and health equipment in cooperation with an e-commerce site. For example, a link is provided so that the user can easily purchase ingredients necessary for the proposed meal menu. As a result, the lifestyle-related disease prevention and improvement system according to the embodiment can propose an exercise and meal program that can be continued without difficulty based on the user's health check data. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs the information determined by the determination unit and outputs a proposal.

[0057] The collection unit collects the user's health check data, attribute data, life cycle data, physical condition data, and weather data. The health check data includes, but is not limited to, blood pressure, blood glucose level, weight, and the like. These data are acquired from regular health checks and home health management devices. For example, data can be automatically collected from a blood pressure monitor, a weight scale, a blood glucose meter, and the like used by the user at home and transmitted to a cloud server. The attribute data includes, but is not limited to, age, sex, occupation, and the like. These data are basic information entered when the user registers with the system, and are important for evaluating the user's lifestyle and health risks. The life cycle data includes, but is not limited to, lifestyle habits, sleep patterns, and the like. These data are collected from smartphones and wearable devices, and are used to grasp the rhythm and activity level of the user's daily life. The physical condition data includes, but is not limited to, athletic ability, physical fitness level, and the like. These data are acquired from fitness trackers and exercise apps, and are used to monitor changes in the user's exercise habits and physical fitness. Weather data includes, but is not limited to, for example, temperature, humidity, precipitation, and the like. These data are obtained from a weather information providing service and are used to adjust the user's exercise program and meal menu. The collection unit collects, for example, data such as the user's age, sex, occupation, lifestyle, weight, height, blood pressure, blood sugar level, temperature, and humidity. This allows the collection unit to collect a wide range of information from various data sources and comprehensively evaluate the user's health condition. Furthermore, the collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the decision unit and the proposal unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0058] The determination unit determines the user's exercise content, exercise amount, and meal menu based on the health check data collected by the collection unit. Specifically, the determination unit determines the appropriate exercise intensity, exercise time, meal calorie, and nutritional balance from the user's health check data. For example, if the user's blood pressure or blood sugar level is high, low-intensity aerobic exercise is recommended, and a low-calorie, high-nutritional menu is proposed for meals. The determination unit also takes into account the user's hobbies, life cycle, physical condition, weather conditions, etc. For example, if the user is a busy businessman, a short and effective exercise program is proposed. Specifically, stretching that can be done in the office and short-term high-intensity interval training (HIIT) are recommended. In addition, if outdoor exercise is difficult depending on the weather conditions, an exercise program that can be done indoors is proposed. The determination unit uses AI to analyze these data and determine the optimal exercise content and meal menu. The AI ​​generates an optimal program for the user's health condition and lifestyle based on past data and statistical information. For example, a machine learning algorithm is used to analyze the user's past exercise history and meal history, and the most effective exercise and meal are proposed. In addition, the AI ​​can continuously improve the accuracy and effectiveness of the program based on user feedback. This allows the decision unit to provide the user with an optimal exercise and diet program for their health condition and lifestyle, and to support the prevention and improvement of lifestyle-related diseases. Furthermore, the decision unit can flexibly adjust the program according to changes in the user's health condition. For example, if the user's blood pressure or blood sugar level improves, the exercise intensity and dietary content can be changed step by step to set higher goals. This allows the decision unit to continuously monitor the user's health condition and provide the optimal program.

[0059] The suggestion unit proposes the information determined by the determination unit to the user. Specifically, the suggestion unit makes the proposal through the user's smartphone or computer. For example, when the user opens the app, today's exercise program and meal menu are displayed. The suggestion unit also includes links to purchase ingredients and health equipment in cooperation with an e-commerce site. For example, links are provided so that the user can easily purchase ingredients necessary for the proposed meal menu. This allows the user to prepare the necessary ingredients and equipment without any hassle. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposal content. For example, after the user performs the proposed exercise program, the effect and impressions can be fed back to reflect the feedback in the next proposal. Furthermore, the suggestion unit can generate the proposal content using AI. For example, the suggestion unit can make a proposal using an AI model that inputs the information determined by the determination unit and outputs a proposal. The AI ​​generates optimal proposals based on the user's past data and feedback. For example, the proposal can be made more effective and easier to continue by taking into account the exercises and meal menus that the user has preferred in the past. As a result, the suggestion unit can provide personalized proposals to the user and support the prevention and improvement of lifestyle-related diseases. Furthermore, the suggestion unit can make suggestions that match the user's lifestyle and hobbies. For example, if the user likes outdoor activities, the suggestion unit can suggest an exercise program to be done in nature. If the user enjoys cooking, the suggestion unit can suggest healthy and delicious recipes to make eating more enjoyable. This allows the suggestion unit to increase the user's motivation and support a health management that can be continued without strain.

[0060] The suggestion unit cooperates with an electronic commerce site and can suggest at least one of ingredients and health equipment based on the information determined by the determination unit. The suggestion unit cooperates with an electronic commerce site, for example, so that the user can easily purchase ingredients and health equipment required for the proposed meal menu. For example, the suggestion unit provides a link so that the user can easily purchase ingredients required for the proposed meal menu. The suggestion unit also provides a link so that the user can easily purchase health equipment required for the proposed exercise program. This allows the user to easily purchase ingredients and health equipment required for the proposed meal menu. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that receives information determined by the determination unit as input and outputs a suggestion.

[0061] The collection unit can estimate emotions and adjust the timing of data collection based on the estimated emotions. For example, when the user is stressed, the collection unit collects data during a relaxed time period. Also, when the user is relaxed, the collection unit can instantly collect data. Also, when the user is busy, the collection unit can postpone data collection and collect data during free time. This allows data collection at an optimal timing according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0062] The collection unit can analyze the past data collection history and select the collection method. For example, the collection unit preferentially selects a data collection method that the user has used in the past. The collection unit can also select the most efficient collection method from the user's past data collection history. The collection unit can also analyze the user's past data collection history and optimize the collection frequency. This allows an efficient data collection method to be selected based on the past data collection history. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the past data collection history to the generation AI and cause the generation AI to select the optimal collection method.

[0063] The collection unit can perform filtering based on the user's current health condition and living situation when collecting data. For example, the collection unit refrains from collecting data when the user is in poor health. In addition, the collection unit can perform detailed data collection when the user is in good health. In addition, the collection unit can adjust the range of data collection according to the user's living situation. This makes it possible to collect data according to the user's health condition and living situation. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's health condition and living situation to the generation AI and cause the generation AI to perform filtering.

[0064] 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. Also, when the user is relaxed, the collection unit can collect overall health data. Also, when the user is busy, the collection unit can preferentially collect only important data. This allows important data to be preferentially collected 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, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of data to be collected.

[0065] The collection unit can prioritize collecting highly relevant data in consideration of the geographical location information of the user when collecting data. For example, when the user is in a specific area, the collection unit prioritizes collecting weather data of the area. Also, when the user is traveling, the collection unit can prioritize collecting health data of the travel destination. Also, when the user is at home, the collection unit can prioritize collecting environmental data around the home. This makes it possible to collect highly relevant data based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to collect highly relevant data.

[0066] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit can collect, for example, health information shared by the user on social media. The collection unit can also collect data on exercise and diet of interest from the user's social media activity. The collection unit can also analyze the content of the user's social media posts and collect related health data. This makes it possible to collect related data based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or can be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related data.

[0067] The determination unit can estimate the user's emotion and adjust the expression method of the exercise content and the meal menu based on the estimated user's emotion. For example, when the user is stressed, the determination unit can suggest a relaxing exercise content and a meal menu. Also, when the user is relaxed, the determination unit can suggest a challenging exercise content and a meal menu. Also, when the user is busy, the determination unit can suggest an easy and convenient exercise content and a meal menu. This makes it possible to suggest exercise content and a meal menu in an appropriate expression method 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, 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 determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the exercise content and the meal menu.

[0068] The determination unit can adjust the level of detail of the exercise and diet based on the importance of the health data at the time of determination. For example, when the user's blood pressure is high, the determination unit can suggest in detail exercises and diets that lower blood pressure. Also, when the user's blood glucose level is high, the determination unit can suggest in detail exercises and diets that manage the blood glucose level. Also, when the user's weight is increasing, the determination unit can suggest in detail exercises and diets that are effective for weight management. This makes it possible to suggest detailed exercises and diets according to the importance of the user's health data. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's health data to the generation AI and cause the generation AI to adjust the level of detail of the exercise and diet.

[0069] The determination unit can apply an algorithm according to the life cycle and hobbies at the time of determination. For example, if the user lives a nocturnal lifestyle, the determination unit can suggest exercises and meals suitable for the night. In addition, if the user likes the outdoors, the determination unit can suggest exercises that can be done outdoors. In addition, if the user is an indoor person, the determination unit can suggest exercises that can be done indoors. This makes it possible to suggest exercises and meals according to the user's life cycle and hobbies. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on the user's life cycle and hobbies to the generation AI and cause the generation AI to apply the algorithm.

[0070] The determination unit can estimate the user's emotion and determine the priority of exercise and meals based on the estimated user's emotion. For example, when the user is stressed, the determination unit preferentially suggests relaxing exercise. Also, when the user is relaxed, the determination unit can preferentially suggest challenging exercise. Also, when the user is busy, the determination unit can preferentially suggest easy exercise. This allows exercise and meals to be preferentially suggested 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, 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 determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of exercise and meals.

[0071] At the time of determination, the determination unit can adjust the timing of exercise and meals based on the user's life rhythm. For example, if the user is a morning person, the determination unit can suggest exercise and meals suitable for the morning. Also, if the user is a night person, the determination unit can suggest exercise and meals suitable for the evening. Also, the determination unit can adjust the timing of exercise and meals in accordance with the user's life rhythm. This makes it possible to suggest exercise and meals that match the user's life rhythm. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's life rhythm data to the generation AI and cause the generation AI to adjust the timing of exercise and meals.

[0072] The determination unit can select a program by referring to the past exercise history and meal history at the time of determination. The determination unit can, for example, analyze the effect of the exercise performed by the user in the past and select an optimal exercise program. The determination unit can also analyze the user's past meal history and select an optimal meal menu. The determination unit can also select a sustainable program based on the user's past exercise history and meal history. This makes it possible to suggest optimal exercise and meal programs based on the user's past history. Some or all of the above-mentioned processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input the user's past exercise history and meal history data to the generation AI and cause the generation AI to select an optimal program.

[0073] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit suggests an expression method that relaxes the user. Also, when the user is relaxed, the suggestion unit can suggest an expression method including detailed information. Also, when the user is busy, the suggestion unit can suggest an expression method that is concise and to the point. This allows the suggestion to be made in an appropriate expression method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the proposal.

[0074] The suggestion unit can adjust the level of detail of the proposal based on the importance of the health data when making a proposal. For example, when the user's blood pressure is high, the suggestion unit can make a detailed proposal regarding blood pressure management. Also, when the user's blood glucose level is high, the suggestion unit can make a detailed proposal regarding blood glucose level management. Also, when the user's weight is increasing, the suggestion unit can make a detailed proposal regarding weight management. This enables detailed proposals to be made according to the importance of the user's health data. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's health data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0075] The suggestion unit can apply the suggestion algorithm according to the life cycle and hobbies at the time of suggestion. For example, if the user lives a nocturnal lifestyle, the suggestion unit makes suggestions suitable for the night. In addition, if the user likes the outdoors, the suggestion unit can suggest exercises that can be done outdoors. In addition, if the user is an indoor person, the suggestion unit can suggest exercises that can be done indoors. This makes it possible to make suggestions according to the user's life cycle and hobbies. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's life cycle and hobbies to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0076] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit can prioritize suggestions that will relax the user. Also, when the user is relaxed, the suggestion unit can prioritize challenging suggestions. Also, when the user is busy, the suggestion unit can prioritize suggestions that can be easily made. This allows suggestions to be made preferentially according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of suggestions.

[0077] The suggestion unit can adjust the timing of the suggestion based on the user's life rhythm when making a suggestion. For example, if the user is a morning person, the suggestion unit makes a suggestion suitable for the morning. Also, if the user is a night person, the suggestion unit can make a suggestion suitable for the evening. Also, the suggestion unit can adjust the timing of the suggestion in accordance with the user's life rhythm. This makes it possible to make a suggestion that matches the user's life rhythm. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's life rhythm data to the generation AI and cause the generation AI to adjust the timing of the suggestion.

[0078] The suggestion unit can make a suggestion by referring to the past exercise history and meal history when making a suggestion. For example, the suggestion unit can analyze the effect of the exercise performed by the user in the past and make an optimal suggestion. The suggestion unit can also analyze the user's past meal history and make an optimal suggestion. The suggestion unit can also make a sustainable suggestion based on the user's past exercise history and meal history. This makes it possible to make an optimal suggestion based on the user's past history. A part or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's past exercise history and meal history data to the generation AI and cause the generation AI to execute the optimal suggestion.

[0079] The suggestion unit cooperates with an electronic commerce site, and when suggesting at least one of ingredients and health tools based on the information determined by the decision unit, the suggestion unit can estimate the user's emotions and determine the priority order of products to be suggested based on the estimated user's emotions. For example, when the user is stressed, the suggestion unit preferentially suggests ingredients and health tools that have a relaxing effect. Also, when the user is relaxed, the suggestion unit can preferentially suggest products that are useful for maintaining health. Also, when the user is busy, the suggestion unit can preferentially suggest products that can be used easily. This makes it possible to preferentially suggest optimal products according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority order of products.

[0080] The suggestion unit cooperates with an electronic commerce site, and when suggesting at least one of ingredients and health equipment based on the information determined by the decision unit, the suggestion unit can select a product by referring to the past purchase history. For example, the suggestion unit suggests a product similar to a product previously purchased by the user. The suggestion unit can also suggest a highly relevant product based on the user's past purchase history. The suggestion unit can also analyze the user's past purchase history and suggest the most suitable product. This makes it possible to suggest an optimal product based on the user's past purchase history. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's past purchase history data to the generation AI and cause the generation AI to select a product.

[0081] The suggestion unit cooperates with an electronic commerce site, and when suggesting at least one of ingredients and health equipment based on the information determined by the decision unit, the suggestion unit can estimate the user's emotions and adjust the display method of the proposed product based on the estimated user's emotions. For example, when the user is stressed, the suggestion unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the suggestion unit can provide a display method including detailed information. Furthermore, when the user is busy, the suggestion unit can provide a simple display method that focuses on the main points. This makes it possible to suggest products in an optimal display method according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the product.

[0082] The suggestion unit cooperates with an electronic commerce site, and when suggesting at least one of ingredients and health equipment based on the information determined by the decision unit, the suggestion unit can preferentially suggest highly relevant products taking into consideration the user's geographical location information. For example, when the user is in a specific area, the suggestion unit preferentially suggests products available in that area. Also, when the user is traveling, the suggestion unit can preferentially suggest products available at the travel destination. Also, the suggestion unit can suggest optimal products based on the user's geographical location information. This makes it possible to suggest highly relevant products based on the user's geographical location information. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the determination of the priority order of products.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The collection unit can collect the user's sleep data in addition to the user's health data. For example, the collection unit can collect data such as the user's sleep time, sleep quality, and heart rate during sleep. The collection unit can also collect physiological data such as the user's heart rate variability and electrodermal activity to measure the user's stress level. Furthermore, the collection unit can collect detailed records of the user's diet. This makes it possible to suggest exercise and diet programs that take into account the user's sleep patterns, stress level, and dietary content.

[0085] The determination unit can estimate the user's emotions and adjust the difficulty of the exercise program based on the estimated emotions. For example, if the user is stressed, a light exercise that is relaxing can be suggested. Also, if the user is relaxed, a challenging exercise can be suggested. Furthermore, if the user is tired, stretching or yoga that promotes recovery can be suggested. This makes it possible to suggest an optimal exercise program according to the user's emotions.

[0086] The suggestion unit can estimate the user's emotions and suggest a meal menu based on the estimated emotions. For example, if the user is feeling stressed, a menu using ingredients that have a relaxing effect can be suggested. Also, if the user is relaxed, a nutritionally balanced menu can be suggested. Furthermore, if the user is tired, a menu suitable for replenishing energy can be suggested. This makes it possible to suggest an optimal meal menu according to the user's emotions.

[0087] The collection unit can collect the user's exercise history in addition to the user's health data. For example, the collection unit collects data such as the type, frequency, and intensity of exercise performed by the user in the past. The collection unit can also collect data such as the user's heart rate and calories burned during exercise. Furthermore, the collection unit can collect the user's recovery status after exercise. This makes it possible to propose an exercise program that takes into account the user's exercise history and data during exercise.

[0088] The determination unit can estimate the user's emotions and customize the meal menu based on the estimated emotions. For example, if the user is stressed, it can suggest relaxing herbal tea or smoothie. If the user is relaxed, it can suggest nutritionally balanced salads or soups. Furthermore, if the user is tired, it can suggest protein bars or nuts suitable for energy replenishment. This makes it possible to customize the optimal meal menu according to the user's emotions.

[0089] The suggestion unit can estimate the user's emotions and suggest an exercise program based on the estimated emotions. For example, if the user is feeling stressed, yoga or meditation, which has a relaxing effect, can be suggested. If the user is relaxed, strength training or aerobic exercise can be suggested. Furthermore, if the user is tired, light stretching or walking can be suggested. This makes it possible to suggest the optimal exercise program according to the user's emotions.

[0090] The collection unit can collect the user's living environment data in addition to the user's health data. For example, the collection unit collects the user's residential environment data (temperature, humidity, air quality, etc.). The collection unit can also collect the user's workplace environment data. The collection unit can also collect data such as the user's commuting route and commuting time. This makes it possible to propose an exercise and diet program that takes the user's living environment into consideration.

[0091] The determination unit can estimate the user's emotions and adjust the frequency of the exercise program based on the estimated emotions. For example, if the user is stressed, the determination unit can reduce the frequency of relaxing exercises and suggest more challenging exercises. If the user is relaxed, the determination unit can increase the frequency of challenging exercises and suggest more challenging exercises. If the user is tired, the determination unit can adjust the frequency of the exercise program to suit the user's emotions.

[0092] The collection unit can collect data on the user's hobbies and interests in addition to the user's health data. For example, the collection unit collects data on the user's favorite sports, activities, foods, and the like. The collection unit can also collect data on communities and events in which the user participates. Furthermore, the collection unit can collect data on the user's hobbies, such as reading and watching movies. This makes it possible to suggest exercise and diet programs that take the user's hobbies and interests into consideration.

[0093] The suggestion unit can estimate the user's emotions and suggest an exercise program based on the estimated emotions. For example, if the user is feeling stressed, yoga or meditation, which has a relaxing effect, can be suggested. If the user is relaxed, strength training or aerobic exercise can be suggested. Furthermore, if the user is tired, light stretching or walking can be suggested. This makes it possible to suggest the optimal exercise program according to the user's emotions.

[0094] The process flow of the second embodiment will be briefly described below.

[0095] Step 1: The collection unit collects the user's health check data, attribute data, life cycle data, physical condition data, and weather data. The health check data includes, for example, blood pressure, blood glucose level, weight, etc., but is not limited to such examples. The attribute data includes, for example, age, sex, occupation, etc., but is not limited to such examples. The life cycle data includes, for example, lifestyle, sleep pattern, etc., but is not limited to such examples. The physical condition data includes, for example, athletic ability, physical strength level, etc., but is not limited to such examples. The weather data includes, for example, temperature, humidity, precipitation, etc., but is not limited to such examples. The collection unit collects, for example, data such as the user's age, sex, occupation, lifestyle, weight, height, blood pressure, blood glucose level, temperature, humidity, etc. Step 2: The determination unit determines the user's exercise content, exercise amount, and meal menu based on the health check data collected by the collection unit. The determination unit determines, for example, appropriate exercise intensity and exercise time, meal calories, and nutritional balance from the user's health check data. The determination unit also takes into account the user's hobbies, life cycle, physical condition, weather conditions, and the like. For example, if the user is a busy businessman, the determination unit suggests an effective exercise program in a short time. Step 3: The suggestion unit suggests the information decided by the decision unit to the user. The suggestion unit makes suggestions, for example, through the user's smartphone or computer. For example, when the user opens the app, today's exercise program and meal menu are displayed. The suggestion unit also includes links to purchase ingredients and health equipment in cooperation with an e-commerce site. For example, links are provided so that the user can easily purchase ingredients necessary for the proposed meal menu. In this way, the lifestyle-related disease prevention and improvement system according to the embodiment can suggest an exercise and meal program that can be continued without difficulty based on the user's health check data. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the information decided by the decision unit and outputs suggestions.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] For example, the collection unit can collect the user's health check data and attribute data using the camera 42 and microphone 38B of the smart device 14. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and can collect data via the network 54. The determination unit can be realized by the specific processing unit 290 of the data processing device 12, and determines the exercise content and meal menu based on the collected data. The suggestion unit can be realized by the control unit 46A of the smart device 14, and makes suggestions through the user's smartphone or personal computer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] For example, the collection unit can collect the user's health check data and attribute data using the camera 42 and microphone 238 of the smart glasses 214. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and collect data via the network 54. The determination unit can be realized by, for example, the specific processing unit 290 of the data processing device 12 and determines the exercise content and meal menu based on the collected data. The suggestion unit can be realized by, for example, the control unit 46A of the smart glasses 214 and makes suggestions through the user's smartphone or personal computer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] For example, the collecting unit can collect the health check data and attribute data of the user using the camera 42 and microphone 238 of the headset type terminal 314. For example, the collecting unit can also be realized by the specific processing unit 290 of the data processing device 12, and can collect data via the network 54. The deciding unit can be realized by the specific processing unit 290 of the data processing device 12, and decides the exercise content and meal menu based on the collected data. The suggesting unit can be realized by the control unit 46A of the headset type terminal 314, and makes suggestions through the user's smartphone or personal computer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

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

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

[0143] In the 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.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0147] The data processing system 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.

[0148] For example, the collecting unit can collect the health check data and attribute data of the user using the camera 42 and microphone 238 of the robot 414. For example, the collecting unit can also be realized by the specific processing unit 290 of the data processing device 12, and can collect data via the network 54. The deciding unit can be realized by the specific processing unit 290 of the data processing device 12, and decides the exercise content and meal menu based on the collected data. The suggesting unit can be realized by the control unit 46A of the robot 414, and makes suggestions through the user's smartphone or personal computer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Appendix 1) A collection unit that collects health checkup data, attribute data, life cycle data, physical condition data, and weather data of a user; A determination unit that determines the user's exercise content, exercise amount, and meal menu based on the health check data collected by the collection unit; A suggestion unit that suggests the information determined by the determination unit to a user. A system characterized by: (Appendix 2) The suggestion unit, In cooperation with an electronic commerce site, at least one of food ingredients and health equipment is proposed based on the information determined by the determination unit. 2. The system of claim 1, (Appendix 3) The collection department: Infer emotions and adjust the timing of data collection based on the inferred emotions 2. The system of claim 1, (Appendix 4) The collection department: Analyze past data collection history and select collection methods 2. The system of claim 1, (Appendix 5) The collecting unit includes: Filter data collection based on the user's current health and living conditions 2. The system of claim 1, (Appendix 6) 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 7) 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 8) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 9) The determination unit is Estimate the user's emotions and adjust the presentation of exercise content and meal menus based on the estimated user emotions 2. The system of claim 1, (Appendix 10) The determination unit: When making decisions, adjust the details of your exercise and diet based on the importance of your health data 2. The system of claim 1, (Appendix 11) The determination unit: When making decisions, apply algorithms based on life cycles and interests 2. The system of claim 1, (Appendix 12) The determination unit is Estimate the user's emotions and prioritize exercise and meals based on the estimated user emotions 2. The system of claim 1, (Appendix 13) The determination unit is When a decision is made, the timing of exercise and meals is adjusted based on the user's daily rhythm. 2. The system of claim 1, (Appendix 14) The determination unit: When deciding, select a program based on your past exercise and diet history. 2. The system of claim 1, (Appendix 15) 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 16) The proposal section is When making suggestions, adjust the detail of the suggestions based on the importance of the health data 2. The system of claim 1, (Appendix 17) The proposal section is When making suggestions, we apply a suggestion algorithm based on life cycle and hobbies. 2. The system of claim 1, (Appendix 18) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 19) The suggestion unit, When making suggestions, the timing of the suggestions is adjusted based on the user's daily rhythm 2. The system of claim 1, (Appendix 20) The proposal section is When making suggestions, past exercise and dietary history is referenced 2. The system of claim 1, (Appendix 21) The suggestion unit, In cooperation with an electronic commerce site, when suggesting at least one of food ingredients and health equipment based on the information determined by the determination unit, the user's emotion is estimated, and a priority order of the proposed products is determined based on the estimated user's emotion. 3. The system of claim 2, (Appendix 22) The proposal section is In cooperation with an electronic commerce site, when proposing at least one of food ingredients and health equipment based on the information determined by the determination unit, a product is selected by referring to the past purchase history. 3. The system of claim 2, (Appendix 23) The suggestion unit, In cooperation with an electronic commerce site, when suggesting at least one of food ingredients and health equipment based on the information determined by the determination unit, the user's emotion is estimated, and a display method of the suggested product is adjusted based on the estimated user's emotion. 3. The system of claim 2, (Appendix 24) The suggestion unit, In cooperation with an electronic commerce site, when suggesting at least one of food ingredients and health equipment based on the information determined by the determination unit, highly related products are preferentially suggested in consideration of the geographical location information of the user. 3. The system of claim 2, [Explanation of symbols]

[0168] 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 health checkup data, attribute data, life cycle data, physical condition data, and weather data of a user; A determination unit that determines the user's exercise content, exercise amount, and meal menu based on the health check data collected by the collection unit; A suggestion unit that suggests the information determined by the determination unit to a user. A system characterized by:

2. The suggestion unit, In cooperation with an electronic commerce site, at least one of food ingredients and health equipment is proposed based on the information determined by the determination unit.

2. The system of claim 1 .

3. The collecting unit includes: Infer emotions and adjust the timing of data collection based on the inferred emotions 2. The system of claim 1 .

4. The collecting unit includes: Analyze past data collection history and select collection methods 2. The system of claim 1 .

5. The collecting unit includes: Filter data collection based on the user's current health and living conditions 2. The system of claim 1 .

6. 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 .

7. 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 .

8. The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1 .

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