system

Abstract

The system according to this embodiment aims to identify the necessary nutrients based on the user's body, exercise, and diet, and to provide customized supplements. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a supply unit. The data collection unit collects data on the user's body, exercise, and diet. The analysis unit analyzes the data collected by the data collection unit and identifies the nutrients necessary for the user. The supply unit provides customized supplements based on the nutrients identified by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to identify optimal nutrients for individual users and provide appropriate supplements.

[0005] The system according to the embodiment aims to identify necessary nutrients based on a user's body, exercise, and diet and provide customized supplements.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a supply unit. The data collection unit collects data on the user's body, exercise, and diet. The analysis unit analyzes the data collected by the data collection unit and identifies the nutrients necessary for the user. The supply unit provides customized supplements based on the nutrients identified by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify the necessary nutrients based on the user's body, exercise, and diet, and provide customized supplements. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health support system according to an embodiment of the present invention is a system that supports the creation of a healthy body effectively and effortlessly by understanding the individual user's body, exercise, and diet, and supplementing the necessary nutrients as supplements. The health support system collects data on the user's body, exercise, and diet, and the collected data is analyzed by AI to identify the nutrients the user needs. Furthermore, it provides customized supplements based on the identified nutrients. Through this mechanism, the user can effectively take in the nutrients they need, and the system can support the creation of a healthy body. For example, the health support system collects data on the user's body, exercise, and diet. At this time, detailed data such as the user's height, weight, age, gender, exercise habits, and dietary content is collected. For example, data such as what kind of meals the user eats every day and how much exercise they do is collected. This allows the system to understand the user's health status. Next, the health support system's AI analyzes the collected data. Based on the collected data, the AI ​​identifies the nutrients the user needs. For example, it can identify nutrients that are lacking from the user's diet and suggest supplements to compensate for those nutrients. This allows the user to effectively take in the nutrients they need. Furthermore, the health support system provides customized supplements based on identified nutrients. Specifically, it individually formulates and provides supplements containing the nutrients needed by the user. For example, if a user is deficient in vitamin D, a supplement containing vitamin D will be provided. In this way, users can effectively take in the nutrients they need. This mechanism allows users to support the creation of a healthy body effectively, without hassle or burden. Users can easily understand the nutrients they need and take supplements to compensate for them. This enables them to maintain a healthy body and live a healthy life in the era of 100-year lifespans. For example, if a user is deficient in vitamin C in their daily diet, the AI ​​will analyze that data and suggest a supplement containing vitamin C. By taking that supplement, the user can effectively replenish their vitamin C intake.Furthermore, if a user is not getting enough exercise, the AI ​​can analyze that data and provide advice to encourage exercise. This helps users build a healthy body. The health support system can effectively support the creation of a healthy body by identifying the necessary nutrients based on the user's body, exercise, and diet, and providing customized supplements.

[0029] The health support system according to this embodiment comprises a data collection unit, an analysis unit, and a provision unit. The data collection unit collects data about the user's body, exercise, and diet. For example, the data collection unit collects detailed data such as the user's height, weight, age, gender, exercise habits, and diet. For example, the data collection unit can collect data such as what the user eats every day and how much exercise they do. The data collection unit can also collect data periodically to understand the user's health status. The analysis unit analyzes the data collected by the data collection unit and identifies the nutrients the user needs. For example, the analysis unit uses AI to analyze the collected data and identify the nutrients the user needs. For example, the analysis unit can identify nutrients that are lacking from the user's diet and suggest supplements to compensate for those nutrients. For example, the analysis unit can analyze the user's exercise habits and provide advice to compensate for a lack of exercise. The provision unit provides customized supplements based on the nutrients identified by the analysis unit. For example, the provision unit individually prepares supplements containing the nutrients the user needs and provides them to the user. For example, if a user is deficient in vitamin D, the supply unit can provide a supplement containing vitamin D. The supply unit can also, for example, regularly provide the user with supplements containing the nutrients they need. In this way, the health support system according to the embodiment can effectively support the creation of a healthy body by identifying the necessary nutrients based on the user's body, exercise, and diet, and providing customized supplements.

[0030] The data collection unit collects data about the user's body, exercise, and diet. Specifically, it collects detailed data such as the user's height, weight, age, gender, exercise habits, and diet. This includes methods for automatically collecting records of the user's daily activities and meals using wearable devices and smartphone apps. For example, wearable devices can collect data such as the user's heart rate, steps taken, and calories burned in real time and synchronize it with a smartphone app. Dietary data can be collected by having the user take pictures of their meals and automatically analyzing them using image recognition technology. Furthermore, users can manually input their meal details, which allows for the acquisition of detailed nutritional information. The data collection unit collects this data regularly and continuously monitors the user's health status. For example, it can collect daily diet and exercise data and track weekly and monthly fluctuations. This allows the data collection unit to provide basic data for early detection of changes in the user's health status and for taking appropriate action.

[0031] The analysis unit analyzes the data collected by the data collection unit to identify the nutrients necessary for the user. Specifically, it uses AI to analyze the collected data and identify nutrients that are lacking in the user's diet. The AI ​​uses machine learning algorithms to analyze the user's eating patterns and exercise habits and calculate the optimal nutritional balance for each individual user. For example, based on data of the meals the user eats on a daily basis, it calculates the intake of nutrients such as vitamins, minerals, proteins, carbohydrates, and lipids, and identifies any deficient nutrients. It also analyzes the user's exercise habits and provides advice on supplementing necessary nutrients, taking into account nutrient depletion due to insufficient or excessive exercise. Furthermore, the analysis unit can identify nutrients with greater accuracy by considering individual factors such as the user's age, gender, weight, and health status. For example, for users with a specific medical history or allergies, it can recommend the intake of specific nutrients or suggest foods to avoid. In this way, the analysis unit can provide nutritional support tailored to the user's individual needs and help maintain or improve their health.

[0032] The supply department provides customized supplements based on the nutrients identified by the analysis department. Specifically, it individually formulates and provides supplements containing the nutrients needed by the user. For example, if a user is deficient in vitamin D, a supplement containing vitamin D can be provided. The supply department can propose and regularly provide the optimal combination of supplements according to the user's health condition and lifestyle. This includes the function of adjusting the contents of the supplements according to changes in the user's health condition. For example, supplements that enhance necessary nutrients can be provided for seasonal changes or specific events (such as a marathon). Furthermore, the supply department pays attention to the quality control and safety of the supplements, providing highly reliable products. This includes guaranteeing the safety and effectiveness of the supplements through manufacturing processes based on strict quality control standards and inspections by third-party organizations. The supply department provides users with information on how to take the supplements and their effects, encouraging correct intake to maximize their effectiveness. In this way, the supply department can support users in maintaining and improving their health and realize an effective health support system.

[0033] The data collection unit can collect data such as the user's height, weight, age, gender, exercise habits, and diet. For example, the data collection unit can measure the user's height in centimeters and collect it as data. The data collection unit can also measure the user's weight in kilograms and collect it as data. The data collection unit can also record the user's age in years and collect it as data. The data collection unit can also record the user's gender and collect it as data. The data collection unit can also record the user's exercise habits in weeks and collect it as data. The data collection unit can also record the user's diet on a daily basis and collect it as data. This allows the data collection unit to collect detailed user data, enabling more accurate identification of nutrients. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as the user's height, weight, age, gender, exercise habits, and diet into AI, which can then analyze and collect the data.

[0034] The analysis unit can identify the nutrients necessary for the user based on the collected data. For example, the analysis unit can statistically analyze the collected data to identify the nutrients necessary for the user. For example, the analysis unit can also analyze the collected data using a machine learning algorithm to identify the nutrients necessary for the user. For example, the analysis unit can analyze the collected data using AI to identify the nutrients necessary for the user. In this way, the analysis unit can accurately identify the nutrients necessary for the user by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can analyze the data to identify the nutrients necessary for the user.

[0035] The supply unit can individually formulate and provide supplements containing specified nutrients to users. For example, the supply unit can individually formulate and provide supplements containing specified nutrients to users. For example, if a user is deficient in vitamin D, the supply unit can provide a supplement containing vitamin D. This enables effective nutritional supplementation by individually providing supplements containing the nutrients that users need. Some or all of the above processing in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input supplements containing specified nutrients into AI, and the AI ​​can formulate and provide the supplements.

[0036] The analysis unit can identify deficient nutrients from the user's diet. For example, the analysis unit can analyze the user's diet and identify deficient nutrients. The analysis unit can also analyze the user's diet using AI and identify deficient nutrients. The analysis unit can also analyze the user's diet using a machine learning algorithm and identify deficient nutrients. As a result, the analysis unit can identify deficient nutrients by analyzing the user's diet and provide appropriate supplements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's diet into AI, and the AI ​​can analyze the data and identify deficient nutrients.

[0037] The supply unit can provide users with supplements containing the nutrients they need. For example, the supply unit can provide users with supplements containing the nutrients they need. The supply unit can also, for example, individually formulate and provide users with supplements containing the nutrients they need. For example, if a user is deficient in vitamin D, the supply unit can provide a supplement containing vitamin D. This enables effective nutritional supplementation by providing users with supplements containing the nutrients they need. Some or all of the above-described processes in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input the nutrients the user needs into an AI, and the AI ​​can formulate and provide the supplement.

[0038] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can select the most effective data collection method from the user's past health data. The data collection unit can also adjust the frequency of data collection based on the user's past health data. For example, the data collection unit can analyze the user's past health data and collect data at specific time periods. This allows the data collection unit to select the optimal data collection method by analyzing past health data, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into AI, which can then analyze the data and select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current lifestyle and health goals during data collection. For example, the data collection unit can collect only the necessary data based on the user's current lifestyle. The data collection unit can also prioritize the collection of relevant data based on the user's health goals. The data collection unit can also adjust the scope of data collection according to the user's lifestyle and health goals. This allows the data collection unit to efficiently collect only the necessary data by filtering it according to the user's lifestyle and health goals. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle and health goals into AI, which can then filter and collect the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of health data related to that region. The data collection unit can also collect data that takes environmental factors into account based on the user's geographical location information. For example, the data collection unit can collect data related to region-specific health risks based on the user's geographical location information. This allows the data collection unit to collect more accurate data by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze health-related posts from users' social media activity and collect relevant data. The data collection unit can also identify health-related interests based on users' social media activity and collect data. For example, the data collection unit can analyze users' social media activity to understand health-related trends and collect data. This allows the data collection unit to efficiently collect relevant data by analyzing users' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input users' social media activity into AI, which can then analyze the data and collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the user's health goals. For example, if the user's health goal is weight loss, the analysis unit can perform a detailed analysis of calorie consumption. If the user's health goal is muscle strengthening, the analysis unit can also perform a detailed analysis of protein intake. If the user's health goal is overall health maintenance, the analysis unit can perform a balanced analysis. By adjusting the level of detail of the analysis according to the user's health goals, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health goals into the AI, which can then analyze the data and adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the user's lifestyle during analysis. For example, if the user is a night owl, the analysis unit can apply an analysis algorithm specialized for nighttime activities. For example, if the user has an exercise habit, the analysis unit can also apply an analysis algorithm specialized for exercise data. For example, if the user has specific dietary restrictions, the analysis unit can also apply an analysis algorithm tailored to those restrictions. In this way, the analysis unit can provide more accurate analysis results by applying an analysis algorithm tailored to the user's lifestyle. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into AI, which can analyze the data and apply an appropriate analysis algorithm.

[0044] The analysis unit can improve the accuracy of its analysis based on the user's past health data. For example, the analysis unit can more accurately analyze the user's current health status based on the user's past health data. The analysis unit can also, for example, analyze the user's past health data to grasp long-term health trends. The analysis unit can also, for example, predict future health risks based on the user's past health data. As a result, the analysis unit can more accurately analyze the user's current health status by analyzing based on past health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into AI, and the AI ​​can analyze the data to improve the accuracy of the analysis.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature for the user during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research papers related to the user's health status. The analysis unit can also improve the accuracy of its analysis by referring to past research data related to the user's health status. The analysis unit can also improve the accuracy of its analysis by referring to specialized books related to the user's health status. As a result, the analysis unit can improve the accuracy of its analysis by referring to relevant literature and provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input literature data related to the user's health status into AI, and the AI ​​can analyze the data to improve the accuracy of the analysis.

[0046] The delivery unit can analyze the user's past intake history to select the optimal delivery method when providing supplements. For example, the delivery unit can select the optimal supplement delivery method based on the user's past intake history. The delivery unit can also analyze the user's past intake history and adjust the timing of intake. The delivery unit can also select the type of supplement based on the user's past intake history. As a result, by analyzing past intake history, the delivery unit can select the optimal supplement delivery method and enable effective nutritional supplementation. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past intake history into AI, and the AI ​​can analyze the data to select the optimal supplement delivery method.

[0047] The delivery unit can customize the means of providing supplements based on the user's current health condition. For example, the delivery unit can select the optimal method of providing the supplement based on the user's current health condition. The delivery unit can also adjust the amount of supplement taken, taking into account the user's current health condition. The delivery unit can also customize the type of supplement, for example, according to the user's current health condition. This allows the delivery unit to provide supplements more effectively by customizing the means of provision based on the user's current health condition. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's current health condition data into AI, which can then analyze the data and customize the means of provision.

[0048] The delivery unit can select the optimal delivery method when providing supplements, taking into account the user's geographical location information. For example, if the user is in a specific region, the delivery unit can provide supplements based on health data related to that region. The delivery unit can also provide supplements that take environmental factors into account based on the user's geographical location information. For example, the delivery unit can provide supplements related to region-specific health risks based on the user's geographical location information. This allows the delivery unit to provide supplements more effectively by selecting the optimal delivery method based on geographical location information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's geographical location information into AI, and the AI ​​can analyze the data to select the optimal delivery method.

[0049] The service provider can analyze a user's social media activity and propose a means of providing supplements. For example, the service provider can analyze health-related posts from a user's social media activity and provide relevant supplements. The service provider can also identify health-related interests based on a user's social media activity and provide supplements accordingly. The service provider can also analyze a user's social media activity to understand health-related trends and provide supplements accordingly. In this way, the service provider can propose the most suitable means of providing supplements to a user by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input a user's social media activity into AI, which can then analyze the data and propose a means of providing supplements.

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

[0051] The health support system can analyze a user's past exercise data and suggest an optimal exercise program. For example, it can suggest an exercise program best suited to the user's current health condition based on the types and frequency of exercise the user has performed in the past. It can also suggest exercise programs that the user has previously succeeded with. Furthermore, it can provide advice to maximize the effects of exercise based on the user's past exercise data. In this way, it can provide a more effective exercise program by utilizing the user's past exercise data.

[0052] A health support system can provide support that addresses region-specific health risks by considering the user's geographical location. For example, if a user lives in a high-altitude area, it can provide advice to address health risks specific to that area. If a user lives in an urban area, it can also provide advice to address health risks specific to that urban area. Furthermore, if a user is traveling, it can provide advice to address health risks in their travel destination. This allows for the provision of more appropriate health support based on the user's geographical location.

[0053] A health support system can analyze users' social media activity to understand health trends and provide support. For example, it can analyze health-related posts users share on social media and suggest relevant supplements and exercise programs. It can also provide customized advice based on the health topics users are interested in. Furthermore, it can identify new health trends based on users' social media activity and provide the latest information. This allows for more effective health support by leveraging users' social media activity.

[0054] A health support system can predict future health risks and suggest preventative measures based on a user's past health data. For example, it can analyze a user's past health data to predict future health risks and suggest preventative measures for the user to address those risks. Furthermore, it can identify long-term health trends based on the user's past health data and provide appropriate advice. This allows for effective support in addressing future health risks by leveraging the user's past health data.

[0055] A health support system can provide different support plans tailored to the user's lifestyle. For example, if a user is a night owl, it can suggest exercise programs and meal plans suitable for nighttime. If a user has an exercise habit, it can also provide a support plan based on exercise data. Furthermore, if a user has specific dietary restrictions, it can provide a support plan that accommodates those restrictions. By providing support plans that match the user's lifestyle, more effective health support becomes possible.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects data about the user's body, exercise, and diet. For example, it collects detailed data such as the user's height, weight, age, gender, exercise habits, and diet. The data collection unit can collect data such as what the user eats each day and how much exercise they do. The data collection unit can also collect data periodically to understand the user's health status. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the nutrients the user needs. For example, it can use AI to analyze the collected data, identify nutrients that are lacking in the user's diet, and suggest supplements to make up for those deficiencies. It can also analyze the user's exercise habits and provide advice to address any lack of exercise. Step 3: The supply unit provides customized supplements based on the nutrients identified by the analysis unit. For example, it individually formulates and provides supplements containing the nutrients needed by the user. If the user is deficient in vitamin D, a supplement containing vitamin D can be provided. It is also possible to provide supplements containing the nutrients needed by the user on a regular basis.

[0058] (Example of form 2) The health support system according to an embodiment of the present invention is a system that supports the creation of a healthy body effectively and effortlessly by understanding the individual user's body, exercise, and diet, and supplementing the necessary nutrients as supplements. The health support system collects data on the user's body, exercise, and diet, and the collected data is analyzed by AI to identify the nutrients the user needs. Furthermore, it provides customized supplements based on the identified nutrients. Through this mechanism, the user can effectively take in the nutrients they need, and the system can support the creation of a healthy body. For example, the health support system collects data on the user's body, exercise, and diet. At this time, detailed data such as the user's height, weight, age, gender, exercise habits, and dietary content is collected. For example, data such as what kind of meals the user eats every day and how much exercise they do is collected. This allows the system to understand the user's health status. Next, the health support system's AI analyzes the collected data. Based on the collected data, the AI ​​identifies the nutrients the user needs. For example, it can identify nutrients that are lacking from the user's diet and suggest supplements to compensate for those nutrients. This allows the user to effectively take in the nutrients they need. Furthermore, the health support system provides customized supplements based on identified nutrients. Specifically, it individually formulates and provides supplements containing the nutrients needed by the user. For example, if a user is deficient in vitamin D, a supplement containing vitamin D will be provided. In this way, users can effectively take in the nutrients they need. This mechanism allows users to support the creation of a healthy body effectively, without hassle or burden. Users can easily understand the nutrients they need and take supplements to compensate for them. This enables them to maintain a healthy body and live a healthy life in the era of 100-year lifespans. For example, if a user is deficient in vitamin C in their daily diet, the AI ​​will analyze that data and suggest a supplement containing vitamin C. By taking that supplement, the user can effectively replenish their vitamin C intake.Furthermore, if a user is not getting enough exercise, the AI ​​can analyze that data and provide advice to encourage exercise. This helps users build a healthy body. The health support system can effectively support the creation of a healthy body by identifying the necessary nutrients based on the user's body, exercise, and diet, and providing customized supplements.

[0059] The health support system according to this embodiment comprises a data collection unit, an analysis unit, and a provision unit. The data collection unit collects data about the user's body, exercise, and diet. For example, the data collection unit collects detailed data such as the user's height, weight, age, gender, exercise habits, and diet. For example, the data collection unit can collect data such as what the user eats every day and how much exercise they do. The data collection unit can also collect data periodically to understand the user's health status. The analysis unit analyzes the data collected by the data collection unit and identifies the nutrients the user needs. For example, the analysis unit uses AI to analyze the collected data and identify the nutrients the user needs. For example, the analysis unit can identify nutrients that are lacking from the user's diet and suggest supplements to compensate for those nutrients. For example, the analysis unit can analyze the user's exercise habits and provide advice to compensate for a lack of exercise. The provision unit provides customized supplements based on the nutrients identified by the analysis unit. For example, the provision unit individually prepares supplements containing the nutrients the user needs and provides them to the user. For example, if a user is deficient in vitamin D, the supply unit can provide a supplement containing vitamin D. The supply unit can also, for example, regularly provide the user with supplements containing the nutrients they need. In this way, the health support system according to the embodiment can effectively support the creation of a healthy body by identifying the necessary nutrients based on the user's body, exercise, and diet, and providing customized supplements.

[0060] The data collection unit collects data about the user's body, exercise, and diet. Specifically, it collects detailed data such as the user's height, weight, age, gender, exercise habits, and diet. This includes methods for automatically collecting records of the user's daily activities and meals using wearable devices and smartphone apps. For example, wearable devices can collect data such as the user's heart rate, steps taken, and calories burned in real time and synchronize it with a smartphone app. Dietary data can be collected by having the user take pictures of their meals and automatically analyzing them using image recognition technology. Furthermore, users can manually input their meal details, which allows for the acquisition of detailed nutritional information. The data collection unit collects this data regularly and continuously monitors the user's health status. For example, it can collect daily diet and exercise data and track weekly and monthly fluctuations. This allows the data collection unit to provide basic data for early detection of changes in the user's health status and for taking appropriate action.

[0061] The analysis unit analyzes the data collected by the data collection unit to identify the nutrients necessary for the user. Specifically, it uses AI to analyze the collected data and identify nutrients that are lacking in the user's diet. The AI ​​uses machine learning algorithms to analyze the user's eating patterns and exercise habits and calculate the optimal nutritional balance for each individual user. For example, based on data of the meals the user eats on a daily basis, it calculates the intake of nutrients such as vitamins, minerals, proteins, carbohydrates, and lipids, and identifies any deficient nutrients. It also analyzes the user's exercise habits and provides advice on supplementing necessary nutrients, taking into account nutrient depletion due to insufficient or excessive exercise. Furthermore, the analysis unit can identify nutrients with greater accuracy by considering individual factors such as the user's age, gender, weight, and health status. For example, for users with a specific medical history or allergies, it can recommend the intake of specific nutrients or suggest foods to avoid. In this way, the analysis unit can provide nutritional support tailored to the user's individual needs and help maintain or improve their health.

[0062] The supply department provides customized supplements based on the nutrients identified by the analysis department. Specifically, it individually formulates and provides supplements containing the nutrients needed by the user. For example, if a user is deficient in vitamin D, a supplement containing vitamin D can be provided. The supply department can propose and regularly provide the optimal combination of supplements according to the user's health condition and lifestyle. This includes the function of adjusting the contents of the supplements according to changes in the user's health condition. For example, supplements that enhance necessary nutrients can be provided for seasonal changes or specific events (such as a marathon). Furthermore, the supply department pays attention to the quality control and safety of the supplements, providing highly reliable products. This includes guaranteeing the safety and effectiveness of the supplements through manufacturing processes based on strict quality control standards and inspections by third-party organizations. The supply department provides users with information on how to take the supplements and their effects, encouraging correct intake to maximize their effectiveness. In this way, the supply department can support users in maintaining and improving their health and realize an effective health support system.

[0063] The data collection unit can collect data such as the user's height, weight, age, gender, exercise habits, and diet. For example, the data collection unit can measure the user's height in centimeters and collect it as data. The data collection unit can also measure the user's weight in kilograms and collect it as data. The data collection unit can also record the user's age in years and collect it as data. The data collection unit can also record the user's gender and collect it as data. The data collection unit can also record the user's exercise habits in weeks and collect it as data. The data collection unit can also record the user's diet on a daily basis and collect it as data. This allows the data collection unit to collect detailed user data, enabling more accurate identification of nutrients. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as the user's height, weight, age, gender, exercise habits, and diet into AI, which can then analyze and collect the data.

[0064] The analysis unit can identify the nutrients necessary for the user based on the collected data. For example, the analysis unit can statistically analyze the collected data to identify the nutrients necessary for the user. For example, the analysis unit can also analyze the collected data using a machine learning algorithm to identify the nutrients necessary for the user. For example, the analysis unit can analyze the collected data using AI to identify the nutrients necessary for the user. In this way, the analysis unit can accurately identify the nutrients necessary for the user by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can analyze the data to identify the nutrients necessary for the user.

[0065] The supply unit can individually formulate and provide supplements containing specified nutrients to users. For example, the supply unit can individually formulate and provide supplements containing specified nutrients to users. For example, if a user is deficient in vitamin D, the supply unit can provide a supplement containing vitamin D. This enables effective nutritional supplementation by individually providing supplements containing the nutrients that users need. Some or all of the above processing in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input supplements containing specified nutrients into AI, and the AI ​​can formulate and provide the supplements.

[0066] The analysis unit can identify deficient nutrients from the user's diet. For example, the analysis unit can analyze the user's diet and identify deficient nutrients. The analysis unit can also analyze the user's diet using AI and identify deficient nutrients. The analysis unit can also analyze the user's diet using a machine learning algorithm and identify deficient nutrients. As a result, the analysis unit can identify deficient nutrients by analyzing the user's diet and provide appropriate supplements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's diet into AI, and the AI ​​can analyze the data and identify deficient nutrients.

[0067] The supply unit can provide users with supplements containing the nutrients they need. For example, the supply unit can provide users with supplements containing the nutrients they need. The supply unit can also, for example, individually formulate and provide users with supplements containing the nutrients they need. For example, if a user is deficient in vitamin D, the supply unit can provide a supplement containing vitamin D. This enables effective nutritional supplementation by providing users with supplements containing the nutrients they need. Some or all of the above-described processes in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input the nutrients the user needs into an AI, and the AI ​​can formulate and provide the supplement.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect data during relaxed periods. For example, if the user is relaxed, the data collection unit can also perform detailed data collection. For example, if the user is in a hurry, the data collection unit can perform simplified data collection. This allows the data collection unit to perform more accurate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of data collection.

[0069] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can select the most effective data collection method from the user's past health data. The data collection unit can also adjust the frequency of data collection based on the user's past health data. For example, the data collection unit can analyze the user's past health data and collect data at specific time periods. This allows the data collection unit to select the optimal data collection method by analyzing past health data, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into AI, which can then analyze the data and select the optimal data collection method.

[0070] The data collection unit can filter data based on the user's current lifestyle and health goals during data collection. For example, the data collection unit can collect only the necessary data based on the user's current lifestyle. The data collection unit can also prioritize the collection of relevant data based on the user's health goals. The data collection unit can also adjust the scope of data collection according to the user's lifestyle and health goals. This allows the data collection unit to efficiently collect only the necessary data by filtering it according to the user's lifestyle and health goals. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle and health goals into AI, which can then filter and collect the data.

[0071] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed health data. For example, if the user is in a hurry, the data collection unit may prioritize collecting basic health data. In this way, the data collection unit can prioritize collecting important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate emotions and determine data priority.

[0072] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of health data related to that region. The data collection unit can also collect data that takes environmental factors into account based on the user's geographical location information. For example, the data collection unit can collect data related to region-specific health risks based on the user's geographical location information. This allows the data collection unit to collect more accurate data by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0073] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze health-related posts from users' social media activity and collect relevant data. The data collection unit can also identify health-related interests based on users' social media activity and collect data. For example, the data collection unit can analyze users' social media activity to understand health-related trends and collect data. This allows the data collection unit to efficiently collect relevant data by analyzing users' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input users' social media activity into AI, which can then analyze the data and collect relevant data.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result. In this way, the analysis unit can provide more easily understandable analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0075] The analysis unit can adjust the level of detail of the analysis based on the user's health goals. For example, if the user's health goal is weight loss, the analysis unit can perform a detailed analysis of calorie consumption. If the user's health goal is muscle strengthening, the analysis unit can also perform a detailed analysis of protein intake. If the user's health goal is overall health maintenance, the analysis unit can perform a balanced analysis. By adjusting the level of detail of the analysis according to the user's health goals, the analysis unit can provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health goals into the AI, which can then analyze the data and adjust the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the user's lifestyle during analysis. For example, if the user is a night owl, the analysis unit can apply an analysis algorithm specialized for nighttime activities. For example, if the user has an exercise habit, the analysis unit can also apply an analysis algorithm specialized for exercise data. For example, if the user has specific dietary restrictions, the analysis unit can also apply an analysis algorithm tailored to those restrictions. In this way, the analysis unit can provide more accurate analysis results by applying an analysis algorithm tailored to the user's lifestyle. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into AI, which can analyze the data and apply an appropriate analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing stress-related data. For example, if the user is relaxed, the analysis unit may also prioritize analyzing detailed health data. For example, if the user is in a hurry, the analysis unit may also prioritize analyzing basic health data. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of analysis.

[0078] The analysis unit can improve the accuracy of its analysis based on the user's past health data. For example, the analysis unit can more accurately analyze the user's current health status based on the user's past health data. The analysis unit can also, for example, analyze the user's past health data to grasp long-term health trends. The analysis unit can also, for example, predict future health risks based on the user's past health data. As a result, the analysis unit can more accurately analyze the user's current health status by analyzing based on past health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into AI, and the AI ​​can analyze the data to improve the accuracy of the analysis.

[0079] The analysis unit can improve the accuracy of its analysis by referring to relevant literature for the user during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research papers related to the user's health status. The analysis unit can also improve the accuracy of its analysis by referring to past research data related to the user's health status. The analysis unit can also improve the accuracy of its analysis by referring to specialized books related to the user's health status. As a result, the analysis unit can improve the accuracy of its analysis by referring to relevant literature and provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input literature data related to the user's health status into AI, and the AI ​​can analyze the data to improve the accuracy of the analysis.

[0080] The delivery unit can estimate the user's emotions and adjust the method of providing supplements based on the estimated emotions. For example, if the user is stressed, the delivery unit can provide a supplement with relaxing effects. For example, if the user is relaxed, the delivery unit can also provide detailed supplement information. For example, if the user is in a hurry, the delivery unit can provide a supplement that is easy to take. This allows the delivery unit to provide supplements more effectively by adjusting the method of providing supplements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the method of providing supplements.

[0081] The delivery unit can analyze the user's past intake history to select the optimal delivery method when providing supplements. For example, the delivery unit can select the optimal supplement delivery method based on the user's past intake history. The delivery unit can also analyze the user's past intake history and adjust the timing of intake. The delivery unit can also select the type of supplement based on the user's past intake history. As a result, by analyzing past intake history, the delivery unit can select the optimal supplement delivery method and enable effective nutritional supplementation. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past intake history into AI, and the AI ​​can analyze the data to select the optimal supplement delivery method.

[0082] The delivery unit can customize the means of providing supplements based on the user's current health condition. For example, the delivery unit can select the optimal method of providing the supplement based on the user's current health condition. The delivery unit can also adjust the amount of supplement taken, taking into account the user's current health condition. The delivery unit can also customize the type of supplement, for example, according to the user's current health condition. This allows the delivery unit to provide supplements more effectively by customizing the means of provision based on the user's current health condition. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's current health condition data into AI, which can then analyze the data and customize the means of provision.

[0083] The delivery unit can estimate the user's emotions and determine the order in which supplements are provided based on the estimated emotions. For example, if the user is stressed, the delivery unit may prioritize providing supplements with relaxing effects. For example, if the user is relaxed, the delivery unit may also provide detailed supplement information. For example, if the user is in a hurry, the delivery unit may prioritize providing supplements that are easy to take. This allows the delivery unit to provide supplements more effectively by determining the order in which supplements are provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI, which can estimate the emotions and determine the order in which supplements are provided.

[0084] The delivery unit can select the optimal delivery method when providing supplements, taking into account the user's geographical location information. For example, if the user is in a specific region, the delivery unit can provide supplements based on health data related to that region. The delivery unit can also provide supplements that take environmental factors into account based on the user's geographical location information. For example, the delivery unit can provide supplements related to region-specific health risks based on the user's geographical location information. This allows the delivery unit to provide supplements more effectively by selecting the optimal delivery method based on geographical location information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's geographical location information into AI, and the AI ​​can analyze the data to select the optimal delivery method.

[0085] The service provider can analyze a user's social media activity and propose a means of providing supplements. For example, the service provider can analyze health-related posts from a user's social media activity and provide relevant supplements. The service provider can also identify health-related interests based on a user's social media activity and provide supplements accordingly. The service provider can also analyze a user's social media activity to understand health-related trends and provide supplements accordingly. In this way, the service provider can propose the most suitable means of providing supplements to a user by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input a user's social media activity into AI, which can then analyze the data and propose a means of providing supplements.

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

[0087] The health support system can estimate the user's emotions and customize exercise programs based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing yoga or stretching. If the user is relaxed, it can suggest a higher-intensity exercise program. Furthermore, if the user is in a hurry, it can suggest a short, effective exercise program. This allows for more effective health support by providing exercise programs tailored to the user's emotions.

[0088] The health support system can analyze a user's past exercise data and suggest an optimal exercise program. For example, it can suggest an exercise program best suited to the user's current health condition based on the types and frequency of exercise the user has performed in the past. It can also suggest exercise programs that the user has previously succeeded with. Furthermore, it can provide advice to maximize the effects of exercise based on the user's past exercise data. In this way, it can provide a more effective exercise program by utilizing the user's past exercise data.

[0089] The health support system can estimate the user's emotions and customize meal plans based on those emotions. For example, if the user is stressed, it can suggest a meal plan using ingredients that promote relaxation. If the user is relaxed, it can suggest a nutritionally balanced meal plan. Furthermore, if the user is in a hurry, it can suggest a meal plan that is easy to prepare. By providing meal plans tailored to the user's emotions, it enables more effective health support.

[0090] A health support system can provide support that addresses region-specific health risks by considering the user's geographical location. For example, if a user lives in a high-altitude area, it can provide advice to address health risks specific to that area. If a user lives in an urban area, it can also provide advice to address health risks specific to that urban area. Furthermore, if a user is traveling, it can provide advice to address health risks in their travel destination. This allows for the provision of more appropriate health support based on the user's geographical location.

[0091] The health support system can estimate the user's emotions and adjust the timing of supplement intake based on those emotions. For example, if the user is feeling stressed, it can suggest taking a relaxing supplement at night. If the user is relaxed, it can suggest taking a nutritional supplement in the morning. Furthermore, if the user is in a hurry, it can suggest a supplement that is easy to take. By providing supplement intake timing tailored to the user's emotions, this enables more effective health support.

[0092] A health support system can analyze users' social media activity to understand health trends and provide support. For example, it can analyze health-related posts users share on social media and suggest relevant supplements and exercise programs. It can also provide customized advice based on the health topics users are interested in. Furthermore, it can identify new health trends based on users' social media activity and provide the latest information. This allows for more effective health support by leveraging users' social media activity.

[0093] A health support system can estimate a user's emotions and adjust the way health advice is presented based on those emotions. For example, if a user is stressed, it can provide simple and easy-to-understand advice. If the user is relaxed, it can provide more detailed advice. Furthermore, if the user is in a hurry, it can provide concise and to-the-point advice. By providing advice tailored to the user's emotions, this enables more effective health support.

[0094] A health support system can predict future health risks and suggest preventative measures based on a user's past health data. For example, it can analyze a user's past health data to predict future health risks and suggest preventative measures for the user to address those risks. Furthermore, it can identify long-term health trends based on the user's past health data and provide appropriate advice. This allows for effective support in addressing future health risks by leveraging the user's past health data.

[0095] A health support system can estimate a user's emotions and set health goals based on those emotions. For example, if a user is feeling stressed, it can suggest a health plan aimed at stress reduction. If a user is relaxed, it can suggest a health plan aimed at improving physical fitness. Furthermore, if a user is in a hurry, it can suggest health goals that can be achieved in a short period of time. By setting health goals that are tailored to the user's emotions, more effective health support becomes possible.

[0096] A health support system can provide different support plans tailored to the user's lifestyle. For example, if a user is a night owl, it can suggest exercise programs and meal plans suitable for nighttime. If a user has an exercise habit, it can also provide a support plan based on exercise data. Furthermore, if a user has specific dietary restrictions, it can provide a support plan that accommodates those restrictions. By providing support plans that match the user's lifestyle, more effective health support becomes possible.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects data about the user's body, exercise, and diet. For example, it collects detailed data such as the user's height, weight, age, gender, exercise habits, and diet. The data collection unit can collect data such as what the user eats each day and how much exercise they do. The data collection unit can also collect data periodically to understand the user's health status. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the nutrients the user needs. For example, it can use AI to analyze the collected data, identify nutrients that are lacking in the user's diet, and suggest supplements to make up for those deficiencies. It can also analyze the user's exercise habits and provide advice to address any lack of exercise. Step 3: The supply unit provides customized supplements based on the nutrients identified by the analysis unit. For example, it individually formulates and provides supplements containing the nutrients needed by the user. If the user is deficient in vitamin D, a supplement containing vitamin D can be provided. It is also possible to provide supplements containing the nutrients needed by the user on a regular basis.

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

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data on the user's body, exercise, and diet using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, where AI analyzes the collected data to identify the nutrients the user needs. The provision unit is implemented in the identification processing unit 290 of the data processing unit 12, where customized supplements are provided based on the identified nutrients. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and supply unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data on the user's body, exercise, and diet using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where AI analyzes the collected data to identify the nutrients the user needs. The supply unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and provides a customized supplement based on the identified nutrients. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data on the user's body, exercise, and diet using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, where AI analyzes the collected data to identify the nutrients the user needs. The provision unit is implemented in the identification processing unit 290 of the data processing unit 12, where customized supplements are provided based on the identified nutrients. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and supply unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data on the user's body, exercise, and diet using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where AI analyzes the collected data to identify the nutrients the user needs. The supply unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and provides customized supplements based on the identified nutrients. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

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

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

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

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

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

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A data collection unit that collects data on the user's body, exercise, and diet, An analysis unit analyzes the data collected by the aforementioned collection unit and identifies the nutrients necessary for the user, The system includes a supply unit that provides customized supplements based on the nutrients identified by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data such as the user's height, weight, age, gender, exercise habits, and diet. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, identify the nutrients that the user needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We individually formulate and provide users with supplements containing specific nutrients. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Identify nutritional deficiencies from the user's diet. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide supplements containing the nutrients that users need. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and health goals. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the system references relevant literature from the user to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way supplements are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing supplements, we analyze the user's past intake history to select the optimal method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing supplements, customize the delivery method based on the user's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which supplements are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing supplements, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing supplements, we analyze users' social media activity and propose methods for delivery. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A data collection unit that collects data on the user's body, exercise, and diet, An analysis unit analyzes the data collected by the aforementioned collection unit and identifies the nutrients necessary for the user, The system includes a supply unit that provides customized supplements based on the nutrients identified by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data such as the user's height, weight, age, gender, exercise habits, and diet. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, identify the nutrients that the user needs. The system according to feature 1.

4. The aforementioned supply unit is, We individually formulate and provide users with supplements containing specific nutrients. The system according to feature 1.

5. The aforementioned analysis unit, Identify nutritional deficiencies from the user's diet. The system according to feature 1.

6. The aforementioned supply unit is, We provide supplements containing the nutrients that users need. The system according to feature 1.

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

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

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

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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