system

Abstract

The system according to this embodiment aims to provide an optimal training menu and meal plan based on the user's vital data, lifestyle habits, and ideal body image. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a provision unit. The data collection unit collects the user's vital data, lifestyle habits, and ideal body image. The analysis unit analyzes the data collected by the data collection unit and generates an optimal training menu and meal plan for the user. The provision unit provides the training menu and meal plan generated by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 provide an optimal training menu and diet plan for each individual user and it is difficult to maintain continuous motivation.

[0005] The system according to the embodiment aims to provide an optimal training menu and diet plan based on the user's vital data, lifestyle, and ideal body image.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects the user's vital data, lifestyle habits, and ideal body image. The analysis unit analyzes the data collected by the data collection unit and generates an optimal training menu and meal plan for the user. The data provision unit provides the training menu and meal plan generated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal training menu and meal plan based on the user's vital data, lifestyle habits, and ideal body image. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 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 personal trainer service according to an embodiment of the present invention is a system that provides personalized training and meal plans based on the user's vital data, lifestyle, and ideal body image. This system takes the user's vital data, lifestyle, and ideal body image as input, and a generating AI analyzes this data to generate an optimal training menu and meal plan for the user. The generated menu reflects the user's physical condition and preferences, and is designed to be easy to follow. This allows the user to achieve their ideal body without undue strain. For example, the user inputs information such as their weight, height, age, gender, exercise habits, eating habits, and ideal body type. The user can set goals such as "I want to lose weight" or "I want to gain muscle." The generating AI generates an optimal training menu and meal plan based on the user's vital data, lifestyle, and ideal body image. For example, if the user wants to lose weight, the generating AI suggests a training menu that includes calorie restriction and aerobic exercise. If the user wants to gain muscle, it suggests strength training and a high-protein meal plan. The generated menu reflects the user's physical condition and preferences. For example, if the user likes a particular food, a meal plan including that food is suggested. Furthermore, if a user prefers a particular exercise, a training menu including that exercise will be suggested. This allows users to receive a plan that is easy to stick to. In addition, the generating AI provides feedback based on the user's health status. For example, if a user loses weight while continuing their training, the generating AI will provide feedback on that achievement and further motivate them. Also, if a user becomes unwell, the generating AI will adjust the training menu and meal plan to ensure that they can continue without difficulty. This system allows users to achieve their ideal body without undue strain. By utilizing the generating AI, users can receive a training menu and meal plan that is best suited to them, and maintain their motivation over time. For example, even in a busy daily life, users can continue training at their own pace and lead a healthy life.This allows personal trainer services to provide feedback based on the user's health condition, enabling them to achieve their ideal physique without undue strain.

[0029] The personal trainer service according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects the user's vital data, lifestyle habits, and ideal body image. For example, the data collection unit collects information such as the user's weight, height, age, gender, exercise habits, eating habits, and ideal body shape. For example, the data collection unit automatically collects data entered by the user and stores it in a database. The data collection unit can also collect vital data in real time through wearable devices or smartphone apps. For example, the data collection unit collects data such as the user's heart rate, blood pressure, and body temperature. The analysis unit analyzes the data collected by the data collection unit and generates an optimal training menu and meal plan for the user. For example, the analysis unit uses a generation AI to generate an optimal training menu and meal plan based on the user's vital data, lifestyle habits, and ideal body image. For example, if the user wants to lose weight, the analysis unit suggests a training menu that includes calorie restriction and aerobic exercise. Also, if the user wants to increase muscle mass, the analysis unit suggests strength training and a high-protein meal plan. The service provider provides training menus and meal plans generated by the analysis unit. The service provider provides training menus and meal plans that reflect the user's physical condition and preferences. For example, if the user likes a particular food, the service provider will suggest a meal plan that includes that food. Also, if the user likes a particular exercise, the service provider will suggest a training menu that includes that exercise. In this way, the personal trainer service according to the embodiment provides feedback based on the user's health condition and enables them to achieve their ideal body without strain. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider can take the training menus and meal plans generated by the analysis unit as input and provide training menus and meal plans using an AI model provided to the user.

[0030] The data collection unit collects users' vital data, lifestyle habits, and ideal body image. Specifically, it collects information such as the user's weight, height, age, gender, exercise habits, eating habits, and ideal body type. This data is managed by automatically collecting information entered by the user and storing it in a database. The data collection unit can also collect vital data in real time through wearable devices and smartphone apps. For example, a wearable device continuously monitors data such as the user's heart rate, blood pressure, body temperature, steps taken, and calories burned, and transmits this data to the data collection unit via a smartphone app. Furthermore, the data collection unit provides an application for users to record their meals, allowing them to understand their calorie intake and nutritional balance by entering their daily meals. This enables the data collection unit to centrally manage detailed data on the user's health status and lifestyle habits, making it available to the analysis and provision units. By adjusting the frequency and accuracy of data collection, the data collection unit can flexibly respond to user needs and circumstances. For example, by intensively collecting data over a specific period, it is possible to understand changes in physical condition and training effects in detail over a short period. This allows the data collection unit to build a foundation for understanding the user's health status in real time and providing appropriate feedback.

[0031] The analysis unit analyzes the data collected by the data collection unit to generate optimal training menus and meal plans for the user. The analysis uses a generative AI to generate optimal training menus and meal plans based on the user's vital data, lifestyle, and ideal body image. Specifically, the generative AI receives data such as the user's weight, height, age, gender, exercise habits, eating habits, and ideal body type as input, and generates optimal training menus and meal plans based on this data. For example, if the user wants to lose weight, the generative AI will suggest a training menu that includes calorie restriction and aerobic exercise. If the user wants to increase muscle mass, the generative AI will suggest strength training and a high-protein meal plan. The generative AI utilizes past data and statistical information to provide the plan best suited to the user's goals. Furthermore, the analysis unit can collect user feedback and continuously improve the accuracy and effectiveness of the plans provided by the generative AI. For example, it analyzes how the user reacted to the provided plan and adjusts the generative AI's algorithm based on the results. This allows the analysis unit to respond flexibly to the user's needs and circumstances, providing more effective training menus and meal plans. The analysis unit can perform detailed analyses of data related to the user's health status and lifestyle habits, and provide an optimal plan to support the user in achieving their goals.

[0032] The service provider provides training menus and meal plans generated by the analysis unit. Specifically, it provides training menus and meal plans that reflect the user's physical condition and preferences. For example, if a user likes a particular food, it will suggest a meal plan that includes that food. Also, if a user likes a particular exercise, it will suggest a training menu that includes that exercise. The service provider can collect user feedback and continuously improve the accuracy and effectiveness of the plans it provides. For example, it can analyze how users react to the provided plans and adjust the plans based on the results. Furthermore, the service provider can update training menus and meal plans in real time based on data on the user's health status and lifestyle. For example, if a user's physical condition changes, the service provider will immediately incorporate the new data and update the training menu and meal plan. The service provider will also regularly check the user's progress and adjust the plan as needed to support the user in achieving their goals. In this way, the service provider can provide users with the optimal training menu and meal plan, enabling them to achieve their ideal physique without difficulty. Some or all of the above-described processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use the training menu and meal plan generated by the analysis unit as input and provide the user with the training menu and meal plan using the AI ​​model provided to the user. This allows the service provider to respond flexibly to the user's needs and circumstances, and to provide more effective training menus and meal plans.

[0033] The data collection unit can collect information such as the user's weight, height, age, gender, exercise habits, eating habits, and ideal body type. For example, the data collection unit collects information such as the user's weight, height, age, and gender. The data collection unit can also collect information about the user's exercise habits, eating habits, and ideal body type. For example, the data collection unit collects information such as how many times a week the user exercises, what kind of exercise they do, how many meals they eat per day, what kind of meals they eat, their target weight, target body fat percentage, and ideal muscle mass. This allows the data collection unit to collect detailed information about the user, enabling more accurate personalization. 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 information entered by the user into a generating AI and have the generating AI perform the information collection.

[0034] The analysis unit can generate an optimal training menu and meal plan based on the user's vital data, lifestyle, and ideal body image. For example, the analysis unit uses a generation AI to generate an optimal training menu and meal plan based on the user's vital data, lifestyle, and ideal body image. For example, if the user wants to lose weight, the analysis unit will suggest a training menu that includes calorie restriction and aerobic exercise. Also, if the user wants to increase muscle mass, the analysis unit will suggest a strength training and high-protein meal plan. In this way, the analysis unit can provide the user with an optimal training menu and meal plan. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the user's vital data, lifestyle, and ideal body image into a generation AI and have the generation AI generate an optimal training menu and meal plan.

[0035] The service provider can provide training menus and meal plans that reflect the user's physical condition and preferences. For example, if the user likes a particular food, the service provider will suggest a meal plan that includes that food. Also, if the user likes a particular exercise, the service provider will suggest a training menu that includes that exercise. In this way, the service provider can provide a plan that is easy to continue by providing a menu that is tailored to the user's physical condition and preferences. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's physical condition and preferences into a generating AI and have the generating AI perform the provision of training menus and meal plans.

[0036] The service provider can provide feedback based on the user's health status. For example, if the user loses weight while continuing their training, the service provider can provide feedback on this achievement to further motivate them. Also, if the user becomes unwell, the service provider can adjust the training menu or meal plan to ensure they can continue without difficulty. In this way, the service provider makes it easier for users to maintain motivation by providing feedback based on their health status. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's health status into a generating AI and have the generating AI perform the task of providing feedback.

[0037] The analysis unit can generate training menus that include calorie restriction and aerobic exercise according to the user's goals. For example, if the user wants to lose weight, the analysis unit will suggest a training menu that includes calorie restriction and aerobic exercise. In this way, the analysis unit can provide a training menu that is tailored to the user's goals. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the analysis unit can input data about the user's goals into a generation AI and have the generation AI generate a training menu that includes calorie restriction and aerobic exercise.

[0038] The analysis unit can generate strength training and high-protein meal plans according to the user's goals. For example, if the user wants to increase muscle mass, the analysis unit will suggest strength training and high-protein meal plans. In this way, the analysis unit can provide meal plans that meet the user's goals. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input data about the user's goals into a generation AI and have the generation AI generate strength training and high-protein meal plans.

[0039] 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 data. The data collection unit can also adjust the collection frequency based on the user's past data. Furthermore, the data collection unit can analyze the user's past data and collect data at specific time periods. This enables efficient data collection by allowing the data collection unit to select the optimal data collection method based on the user's past data. Some or all of the above processes 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 a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current lifestyle and health status during data collection. For example, if the user is unwell, the data collection unit can temporarily suspend data collection. The data collection unit can also reduce the amount of data collected if the user is busy. Furthermore, the data collection unit can change the type of data collected according to the user's health status. This enables the data collection unit to collect data that is tailored to the user's lifestyle and health status. 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 about the user's current lifestyle and health status into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data based on 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. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to the health risks of their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of data related to their daily life. This allows the data collection unit to obtain more useful data by collecting 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 a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on health information shared by the user on social media. The data collection unit can also identify health-related interests from the user's social media activity and collect relevant data. Furthermore, the data collection unit can collect relevant data based on information from health professionals followed by the user on social media. This allows the data collection unit to collect more comprehensive data by gathering relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's health data during the analysis. For example, the analysis unit will perform a detailed analysis on important health data. It can also perform a simplified analysis on general health data. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's health status. This allows the analysis unit to perform analysis according to the importance of the user's health data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms during analysis depending on the user's health goals. For example, the analysis unit can apply a calorie consumption analysis algorithm to a user aiming to lose weight. It can also apply a strength training analysis algorithm to a user aiming to increase muscle mass. Furthermore, it can apply a balanced analysis algorithm to a user aiming to maintain health. This allows the analysis unit to perform analysis tailored to the user's health goals. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the user's health goals into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the timing of user data collection. For example, the analysis unit may prioritize the analysis of recently collected data. It can also prioritize the analysis of data from periods when the user's health status changed. Furthermore, it can prioritize the analysis of data related to specific events in the user's life. This enables the analysis unit to perform analysis based on the timing of user data collection. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data about the timing of user data collection into a generative AI and have the generative AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on user relevance during the analysis process. For example, the analysis unit may prioritize the analysis of data related to the user's health goals. It can also prioritize the analysis of data related to the user's lifestyle. Furthermore, it can prioritize the analysis of data related to the user's vital data. This enables the analysis unit to perform analysis based on user relevance. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data related to user relevance into a generative AI and have the generative AI adjust the order of analysis.

[0047] The service provider can adjust the level of detail provided based on the importance of the user's health data at the time of provision. For example, the service provider can provide a detailed menu for important health data. It can also provide a concise menu for general health data. Furthermore, the service provider can adjust the level of detail provided according to the user's health status. This allows the service provider to provide a menu tailored to the importance of the user's health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0048] The service provider can apply different service provision algorithms depending on the user's health goals at the time of provision. For example, the service provider can provide a calorie-restricted menu to a user aiming for weight loss. It can also provide a strength training menu to a user aiming for muscle gain. Furthermore, it can provide a balanced menu to a user aiming for health maintenance. This enables the service provider to provide menus tailored to the user's health goals. 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 data on the user's health goals into a generating AI and have the generating AI execute the application of different service provision algorithms.

[0049] The service provider can determine the priority of services based on when the user's data was collected. For example, the service provider can provide a menu based on recently collected data. It can also provide a menu based on data from when the user's health status changed. Furthermore, it can provide a menu based on data related to specific events in the user's life. This enables the service provider to provide a menu based on when the user's data was collected. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data about when the user's data was collected into a generating AI and have the generating AI determine the priority of services.

[0050] The service provider can adjust the order of service delivery based on user relevance. For example, the service provider may prioritize providing menus related to the user's health goals. It may also prioritize providing menus related to the user's lifestyle. Furthermore, it may prioritize providing menus related to the user's vital data. This enables the service provider to deliver menus based on user relevance. 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 data on user relevance into a generating AI and have the generating AI adjust the order of service delivery.

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

[0052] The data collection unit can collect the user's vital data, lifestyle habits, and ideal body image, as well as monitor the user's sleep patterns and provide this data to the analysis unit. For example, the data collection unit can collect data such as the user's sleep duration, sleep quality, and the number of times they toss and turn during sleep. The data collection unit can also identify when the user is experiencing the deepest sleep. Furthermore, the data collection unit can adjust training and meal plans based on the user's sleep patterns. This enables the data collection unit to collect data that takes the user's sleep patterns into account, resulting in more accurate personalization.

[0053] In addition to providing training menus and meal plans that reflect the user's physical condition and preferences, the service provider can also propose menus that utilize local ingredients and exercise facilities based on the user's geographical location. For example, the service provider can propose meal plans that include local specialties from the area where the user lives. Furthermore, the service provider can propose training menus that utilize gyms or parks near the user. Moreover, if the user is traveling, the service provider can provide menus that address the health risks of their travel destination. This allows the service provider to offer menus that take the user's geographical location into consideration, providing more practical support.

[0054] The analysis unit can generate training menus, including calorie restriction and aerobic exercise, according to the user's goals, and can also analyze the user's dietary history to evaluate the balance of their meals. For example, the analysis unit can analyze the nutrient balance of meals the user has eaten in the past and identify any deficient nutrients. It can also identify nutrients the user is consuming in excess and suggest a balanced meal plan. Furthermore, the analysis unit can provide feedback on areas for improvement in the user's diet based on their dietary history. This allows the analysis unit to provide menus that take the user's dietary history into consideration, supporting a healthier lifestyle.

[0055] The analysis unit can generate strength training and high-protein meal plans according to the user's goals, as well as analyze the user's exercise history and evaluate the effectiveness of exercise. For example, the analysis unit can analyze the types and frequency of exercises the user has performed in the past and evaluate their effectiveness. It can also analyze changes in weight and muscle mass when the user performs specific exercises. Furthermore, the analysis unit can provide feedback on areas for improvement based on the user's exercise history. This allows the analysis unit to provide menus that take the user's exercise history into account, supporting more effective training.

[0056] The data collection unit analyzes the user's past health data and selects the optimal collection method. Furthermore, it can predict future health risks based on the user's past data. For example, the unit can predict future health risks from the user's past data and suggest preventative measures. It can also issue warnings about specific health risks based on the user's past data. Additionally, the unit can analyze the user's past data to support the early detection of health risks. This enables more effective health management by allowing the data collection unit to predict future health risks based on the user's past data.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects the user's vital data, lifestyle habits, and ideal body image. Specifically, it collects information such as the user's weight, height, age, gender, exercise habits, eating habits, and ideal body shape. The data collection unit automatically collects the data entered by the user and stores it in a database. It can also collect vital data such as heart rate, blood pressure, and body temperature in real time through wearable devices and smartphone apps. Step 2: The analysis unit analyzes the data collected by the data collection unit and generates an optimal training menu and meal plan for the user. Specifically, using a generation AI, it proposes a training menu including calorie restriction and aerobic exercise, as well as a high-protein meal plan, based on the user's vital data, lifestyle, and ideal body image. Step 3: The service provider provides the training menu and meal plan generated by the analysis unit. Specifically, it provides a training menu and meal plan that reflects the user's physical condition, hobbies, and preferences. For example, it proposes a plan tailored to the preferences of a user who likes certain foods or exercises. The processing by the service provider may or may not be performed using AI.

[0059] (Example of form 2) The personal trainer service according to an embodiment of the present invention is a system that provides personalized training and meal plans based on the user's vital data, lifestyle, and ideal body image. This system takes the user's vital data, lifestyle, and ideal body image as input, and a generating AI analyzes this data to generate an optimal training menu and meal plan for the user. The generated menu reflects the user's physical condition and preferences, and is designed to be easy to follow. This allows the user to achieve their ideal body without undue strain. For example, the user inputs information such as their weight, height, age, gender, exercise habits, eating habits, and ideal body type. The user can set goals such as "I want to lose weight" or "I want to gain muscle." The generating AI generates an optimal training menu and meal plan based on the user's vital data, lifestyle, and ideal body image. For example, if the user wants to lose weight, the generating AI suggests a training menu that includes calorie restriction and aerobic exercise. If the user wants to gain muscle, it suggests strength training and a high-protein meal plan. The generated menu reflects the user's physical condition and preferences. For example, if the user likes a particular food, a meal plan including that food is suggested. Furthermore, if a user prefers a particular exercise, a training menu including that exercise will be suggested. This allows users to receive a plan that is easy to stick to. In addition, the generating AI provides feedback based on the user's health status. For example, if a user loses weight while continuing their training, the generating AI will provide feedback on that achievement and further motivate them. Also, if a user becomes unwell, the generating AI will adjust the training menu and meal plan to ensure that they can continue without difficulty. This system allows users to achieve their ideal body without undue strain. By utilizing the generating AI, users can receive a training menu and meal plan that is best suited to them, and maintain their motivation over time. For example, even in a busy daily life, users can continue training at their own pace and lead a healthy life.This allows personal trainer services to provide feedback based on the user's health condition, enabling them to achieve their ideal physique without undue strain.

[0060] The personal trainer service according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects the user's vital data, lifestyle habits, and ideal body image. For example, the data collection unit collects information such as the user's weight, height, age, gender, exercise habits, eating habits, and ideal body shape. For example, the data collection unit automatically collects data entered by the user and stores it in a database. The data collection unit can also collect vital data in real time through wearable devices or smartphone apps. For example, the data collection unit collects data such as the user's heart rate, blood pressure, and body temperature. The analysis unit analyzes the data collected by the data collection unit and generates an optimal training menu and meal plan for the user. For example, the analysis unit uses a generation AI to generate an optimal training menu and meal plan based on the user's vital data, lifestyle habits, and ideal body image. For example, if the user wants to lose weight, the analysis unit suggests a training menu that includes calorie restriction and aerobic exercise. Also, if the user wants to increase muscle mass, the analysis unit suggests strength training and a high-protein meal plan. The service provider provides training menus and meal plans generated by the analysis unit. The service provider provides training menus and meal plans that reflect the user's physical condition and preferences. For example, if the user likes a particular food, the service provider will suggest a meal plan that includes that food. Also, if the user likes a particular exercise, the service provider will suggest a training menu that includes that exercise. In this way, the personal trainer service according to the embodiment provides feedback based on the user's health condition and enables them to achieve their ideal body without strain. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider can take the training menus and meal plans generated by the analysis unit as input and provide training menus and meal plans using an AI model provided to the user.

[0061] The data collection unit collects users' vital data, lifestyle habits, and ideal body image. Specifically, it collects information such as the user's weight, height, age, gender, exercise habits, eating habits, and ideal body type. This data is managed by automatically collecting information entered by the user and storing it in a database. The data collection unit can also collect vital data in real time through wearable devices and smartphone apps. For example, a wearable device continuously monitors data such as the user's heart rate, blood pressure, body temperature, steps taken, and calories burned, and transmits this data to the data collection unit via a smartphone app. Furthermore, the data collection unit provides an application for users to record their meals, allowing them to understand their calorie intake and nutritional balance by entering their daily meals. This enables the data collection unit to centrally manage detailed data on the user's health status and lifestyle habits, making it available to the analysis and provision units. By adjusting the frequency and accuracy of data collection, the data collection unit can flexibly respond to user needs and circumstances. For example, by intensively collecting data over a specific period, it is possible to understand changes in physical condition and training effects in detail over a short period. This allows the data collection unit to build a foundation for understanding the user's health status in real time and providing appropriate feedback.

[0062] The analysis unit analyzes the data collected by the data collection unit to generate optimal training menus and meal plans for the user. The analysis uses a generative AI to generate optimal training menus and meal plans based on the user's vital data, lifestyle, and ideal body image. Specifically, the generative AI receives data such as the user's weight, height, age, gender, exercise habits, eating habits, and ideal body type as input, and generates optimal training menus and meal plans based on this data. For example, if the user wants to lose weight, the generative AI will suggest a training menu that includes calorie restriction and aerobic exercise. If the user wants to increase muscle mass, the generative AI will suggest strength training and a high-protein meal plan. The generative AI utilizes past data and statistical information to provide the plan best suited to the user's goals. Furthermore, the analysis unit can collect user feedback and continuously improve the accuracy and effectiveness of the plans provided by the generative AI. For example, it analyzes how the user reacted to the provided plan and adjusts the generative AI's algorithm based on the results. This allows the analysis unit to respond flexibly to the user's needs and circumstances, providing more effective training menus and meal plans. The analysis unit can perform detailed analyses of data related to the user's health status and lifestyle habits, and provide an optimal plan to support the user in achieving their goals.

[0063] The service provider provides training menus and meal plans generated by the analysis unit. Specifically, it provides training menus and meal plans that reflect the user's physical condition and preferences. For example, if a user likes a particular food, it will suggest a meal plan that includes that food. Also, if a user likes a particular exercise, it will suggest a training menu that includes that exercise. The service provider can collect user feedback and continuously improve the accuracy and effectiveness of the plans it provides. For example, it can analyze how users react to the provided plans and adjust the plans based on the results. Furthermore, the service provider can update training menus and meal plans in real time based on data on the user's health status and lifestyle. For example, if a user's physical condition changes, the service provider will immediately incorporate the new data and update the training menu and meal plan. The service provider will also regularly check the user's progress and adjust the plan as needed to support the user in achieving their goals. In this way, the service provider can provide users with the optimal training menu and meal plan, enabling them to achieve their ideal physique without difficulty. Some or all of the above-described processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use the training menu and meal plan generated by the analysis unit as input and provide the user with the training menu and meal plan using the AI ​​model provided to the user. This allows the service provider to respond flexibly to the user's needs and circumstances, and to provide more effective training menus and meal plans.

[0064] The data collection unit can collect information such as the user's weight, height, age, gender, exercise habits, eating habits, and ideal body type. For example, the data collection unit collects information such as the user's weight, height, age, and gender. The data collection unit can also collect information about the user's exercise habits, eating habits, and ideal body type. For example, the data collection unit collects information such as how many times a week the user exercises, what kind of exercise they do, how many meals they eat per day, what kind of meals they eat, their target weight, target body fat percentage, and ideal muscle mass. This allows the data collection unit to collect detailed information about the user, enabling more accurate personalization. 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 information entered by the user into a generating AI and have the generating AI perform the information collection.

[0065] The analysis unit can generate an optimal training menu and meal plan based on the user's vital data, lifestyle, and ideal body image. For example, the analysis unit uses a generation AI to generate an optimal training menu and meal plan based on the user's vital data, lifestyle, and ideal body image. For example, if the user wants to lose weight, the analysis unit will suggest a training menu that includes calorie restriction and aerobic exercise. Also, if the user wants to increase muscle mass, the analysis unit will suggest a strength training and high-protein meal plan. In this way, the analysis unit can provide the user with an optimal training menu and meal plan. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the user's vital data, lifestyle, and ideal body image into a generation AI and have the generation AI generate an optimal training menu and meal plan.

[0066] The service provider can provide training menus and meal plans that reflect the user's physical condition and preferences. For example, if the user likes a particular food, the service provider will suggest a meal plan that includes that food. Also, if the user likes a particular exercise, the service provider will suggest a training menu that includes that exercise. In this way, the service provider can provide a plan that is easy to continue by providing a menu that is tailored to the user's physical condition and preferences. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's physical condition and preferences into a generating AI and have the generating AI perform the provision of training menus and meal plans.

[0067] The service provider can provide feedback based on the user's health status. For example, if the user loses weight while continuing their training, the service provider can provide feedback on this achievement to further motivate them. Also, if the user becomes unwell, the service provider can adjust the training menu or meal plan to ensure they can continue without difficulty. In this way, the service provider makes it easier for users to maintain motivation by providing feedback based on their health status. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's health status into a generating AI and have the generating AI perform the task of providing feedback.

[0068] The analysis unit can generate training menus that include calorie restriction and aerobic exercise according to the user's goals. For example, if the user wants to lose weight, the analysis unit will suggest a training menu that includes calorie restriction and aerobic exercise. In this way, the analysis unit can provide a training menu that is tailored to the user's goals. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the analysis unit can input data about the user's goals into a generation AI and have the generation AI generate a training menu that includes calorie restriction and aerobic exercise.

[0069] The analysis unit can generate strength training and high-protein meal plans according to the user's goals. For example, if the user wants to increase muscle mass, the analysis unit will suggest strength training and high-protein meal plans. In this way, the analysis unit can provide meal plans that meet the user's goals. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input data about the user's goals into a generation AI and have the generation AI generate strength training and high-protein meal plans.

[0070] 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. Furthermore, if the user is relaxed, the data collection unit can collect detailed data. Additionally, if the user is busy, the data collection unit can collect the necessary data in a short amount of time. This allows the data collection unit to adjust the timing of data collection according to the user's emotions, enabling more appropriate data collection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0071] 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 data. The data collection unit can also adjust the collection frequency based on the user's past data. Furthermore, the data collection unit can analyze the user's past data and collect data at specific time periods. This enables efficient data collection by allowing the data collection unit to select the optimal data collection method based on the user's past data. Some or all of the above processes 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 a generating AI and have the generating AI select the optimal data collection method.

[0072] The data collection unit can filter data based on the user's current lifestyle and health status during data collection. For example, if the user is unwell, the data collection unit can temporarily suspend data collection. The data collection unit can also reduce the amount of data collected if the user is busy. Furthermore, the data collection unit can change the type of data collected according to the user's health status. This enables the data collection unit to collect data that is tailored to the user's lifestyle and health status. 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 about the user's current lifestyle and health status into a generating AI and have the generating AI perform the filtering.

[0073] 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. If the user is relaxed, the data collection unit can also prioritize collecting detailed health data. Furthermore, if the user is busy, the data collection unit can prioritize collecting only important data. This allows the data collection unit to prioritize important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data prioritization.

[0074] The data collection unit can prioritize the collection of highly relevant data based on 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. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to the health risks of their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of data related to their daily life. This allows the data collection unit to obtain more useful data by collecting 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 a generating AI and have the generating AI perform the collection of highly relevant data.

[0075] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on health information shared by the user on social media. The data collection unit can also identify health-related interests from the user's social media activity and collect relevant data. Furthermore, the data collection unit can collect relevant data based on information from health professionals followed by the user on social media. This allows the data collection unit to collect more comprehensive data by gathering relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0076] 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 relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, the analysis unit can provide more appropriate 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. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's health data during the analysis. For example, the analysis unit will perform a detailed analysis on important health data. It can also perform a simplified analysis on general health data. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's health status. This allows the analysis unit to perform analysis according to the importance of the user's health data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms during analysis depending on the user's health goals. For example, the analysis unit can apply a calorie consumption analysis algorithm to a user aiming to lose weight. It can also apply a strength training analysis algorithm to a user aiming to increase muscle mass. Furthermore, it can apply a balanced analysis algorithm to a user aiming to maintain health. This allows the analysis unit to perform analysis tailored to the user's health goals. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the user's health goals into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length 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 a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0080] The analysis unit can determine the priority of analysis based on the timing of user data collection. For example, the analysis unit may prioritize the analysis of recently collected data. It can also prioritize the analysis of data from periods when the user's health status changed. Furthermore, it can prioritize the analysis of data related to specific events in the user's life. This enables the analysis unit to perform analysis based on the timing of user data collection. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data about the timing of user data collection into a generative AI and have the generative AI determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on user relevance during the analysis process. For example, the analysis unit may prioritize the analysis of data related to the user's health goals. It can also prioritize the analysis of data related to the user's lifestyle. Furthermore, it can prioritize the analysis of data related to the user's vital data. This enables the analysis unit to perform analysis based on user relevance. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data related to user relevance into a generative AI and have the generative AI adjust the order of analysis.

[0082] The service provider can estimate the user's emotions and adjust the presentation of the menu based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed menu. If the user is stressed, the service provider can provide a concise menu. Furthermore, if the user is excited, the service provider can provide a visually appealing menu. In this way, the service provider can provide a more appropriate menu by providing a menu presentation that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI adjust the presentation of the menu.

[0083] The service provider can adjust the level of detail provided based on the importance of the user's health data at the time of provision. For example, the service provider can provide a detailed menu for important health data. It can also provide a concise menu for general health data. Furthermore, the service provider can adjust the level of detail provided according to the user's health status. This allows the service provider to provide a menu tailored to the importance of the user's health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0084] The service provider can apply different service provision algorithms depending on the user's health goals at the time of provision. For example, the service provider can provide a calorie-restricted menu to a user aiming for weight loss. It can also provide a strength training menu to a user aiming for muscle gain. Furthermore, it can provide a balanced menu to a user aiming for health maintenance. This enables the service provider to provide menus tailored to the user's health goals. 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 data on the user's health goals into a generating AI and have the generating AI execute the application of different service provision algorithms.

[0085] The service provider can estimate the user's emotions and adjust the length of the menu based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide a short, concise menu. If the user is relaxed, the service provider can provide a more detailed menu. Furthermore, if the user is excited, the service provider can provide a visually appealing menu. In this way, the service provider can provide a more appropriate menu by providing menu lengths that match 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of the menu.

[0086] The service provider can determine the priority of services based on when the user's data was collected. For example, the service provider can provide a menu based on recently collected data. It can also provide a menu based on data from when the user's health status changed. Furthermore, it can provide a menu based on data related to specific events in the user's life. This enables the service provider to provide a menu based on when the user's data was collected. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data about when the user's data was collected into a generating AI and have the generating AI determine the priority of services.

[0087] The service provider can adjust the order of service delivery based on user relevance. For example, the service provider may prioritize providing menus related to the user's health goals. It may also prioritize providing menus related to the user's lifestyle. Furthermore, it may prioritize providing menus related to the user's vital data. This enables the service provider to deliver menus based on user relevance. 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 data on user relevance into a generating AI and have the generating AI adjust the order of service delivery.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The data collection unit can collect the user's vital data, lifestyle habits, and ideal body image, as well as monitor the user's sleep patterns and provide this data to the analysis unit. For example, the data collection unit can collect data such as the user's sleep duration, sleep quality, and the number of times they toss and turn during sleep. The data collection unit can also identify when the user is experiencing the deepest sleep. Furthermore, the data collection unit can adjust training and meal plans based on the user's sleep patterns. This enables the data collection unit to collect data that takes the user's sleep patterns into account, resulting in more accurate personalization.

[0090] The analysis unit generates optimal training and meal plans based on the user's vital data, lifestyle, and ideal body image. In addition, it can analyze the user's stress level and suggest stress-reducing menus. For example, the analysis unit can analyze fluctuations in the user's heart rate and blood pressure to estimate their stress level. It can also suggest exercise and meal plans that help the user relax. Furthermore, the analysis unit can adjust the intensity of training and the content of meals according to the user's stress level. This allows the analysis unit to provide menus that take the user's stress level into consideration, supporting a healthier lifestyle.

[0091] In addition to providing training menus and meal plans that reflect the user's physical condition and preferences, the service provider can also propose menus that utilize local ingredients and exercise facilities based on the user's geographical location. For example, the service provider can propose meal plans that include local specialties from the area where the user lives. Furthermore, the service provider can propose training menus that utilize gyms or parks near the user. Moreover, if the user is traveling, the service provider can provide menus that address the health risks of their travel destination. This allows the service provider to offer menus that take the user's geographical location into consideration, providing more practical support.

[0092] In addition to providing feedback based on the user's health status, the service provider can estimate the user's emotions and deliver motivational messages tailored to those emotions. For example, if the user's motivation declines while continuing their training, the service provider can send an encouraging message. It can also send a congratulatory message when the user achieves their goals. Furthermore, if the user is feeling stressed, the service provider can offer advice on how to relax. This allows the service provider to maintain continuous motivation by providing feedback that is tailored to the user's emotions.

[0093] The analysis unit can generate training menus, including calorie restriction and aerobic exercise, according to the user's goals, and can also analyze the user's dietary history to evaluate the balance of their meals. For example, the analysis unit can analyze the nutrient balance of meals the user has eaten in the past and identify any deficient nutrients. It can also identify nutrients the user is consuming in excess and suggest a balanced meal plan. Furthermore, the analysis unit can provide feedback on areas for improvement in the user's diet based on their dietary history. This allows the analysis unit to provide menus that take the user's dietary history into consideration, supporting a healthier lifestyle.

[0094] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. Furthermore, it can change the data collection method according to the user's emotions. For example, if the user is stressed, the unit can collect data through simple questions. If the user is relaxed, it can collect data through a detailed questionnaire. Additionally, if the user is busy, the unit can choose a method to collect the necessary data quickly. This allows the data collection unit to provide data collection methods tailored to the user's emotions, enabling more appropriate data collection.

[0095] The analysis unit can generate strength training and high-protein meal plans according to the user's goals, as well as analyze the user's exercise history and evaluate the effectiveness of exercise. For example, the analysis unit can analyze the types and frequency of exercises the user has performed in the past and evaluate their effectiveness. It can also analyze changes in weight and muscle mass when the user performs specific exercises. Furthermore, the analysis unit can provide feedback on areas for improvement based on the user's exercise history. This allows the analysis unit to provide menus that take the user's exercise history into account, supporting more effective training.

[0096] The service provider can estimate the user's emotions and adjust the presentation of the menu based on those emotions, as well as modify the menu content to match the user's mood. For example, if the user is stressed, the service provider can offer a meal plan that includes ingredients with relaxing properties. If the user is relaxed, the service provider can offer a nutritionally balanced meal plan. Furthermore, if the user is excited, the service provider can offer a meal plan suitable for energy replenishment. In this way, the service provider can provide more appropriate support by offering menu content tailored to the user's emotions.

[0097] The data collection unit analyzes the user's past health data and selects the optimal collection method. Furthermore, it can predict future health risks based on the user's past data. For example, the unit can predict future health risks from the user's past data and suggest preventative measures. It can also issue warnings about specific health risks based on the user's past data. Additionally, the unit can analyze the user's past data to support the early detection of health risks. This enables more effective health management by allowing the data collection unit to predict future health risks based on the user's past data.

[0098] The service provider can estimate the user's emotions and adjust the length of the menu items offered based on those emotions, as well as adjust the frequency of menu items according to the user's emotions. For example, if the user is feeling stressed, the service provider can reduce the frequency of menu items. Conversely, if the user is relaxed, the service provider can increase the frequency. Furthermore, if the user is excited, the service provider can offer menu items at an appropriate frequency. In this way, the service provider can provide more appropriate support by offering menu items at a frequency that matches the user's emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects the user's vital data, lifestyle habits, and ideal body image. Specifically, it collects information such as the user's weight, height, age, gender, exercise habits, eating habits, and ideal body shape. The data collection unit automatically collects the data entered by the user and stores it in a database. It can also collect vital data such as heart rate, blood pressure, and body temperature in real time through wearable devices and smartphone apps. Step 2: The analysis unit analyzes the data collected by the data collection unit and generates an optimal training menu and meal plan for the user. Specifically, using a generation AI, it proposes a training menu including calorie restriction and aerobic exercise, as well as a high-protein meal plan, based on the user's vital data, lifestyle, and ideal body image. Step 3: The service provider provides the training menu and meal plan generated by the analysis unit. Specifically, it provides a training menu and meal plan that reflects the user's physical condition, hobbies, and preferences. For example, it proposes a plan tailored to the preferences of a user who likes certain foods or exercises. The processing by the service provider may or may not be performed using AI.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's vital data and lifestyle habits using the camera 42 and microphone 38B of the smart device 14, and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to generate an optimal training menu and meal plan. The provision unit is implemented in the control unit 46A of the smart device 14, and provides the generated menu to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's vital data and lifestyle habits using the camera 42 and microphone 238 of the smart glasses 214, and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to generate an optimal training menu and meal plan. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides the generated menu to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 the user's vital data and lifestyle habits using the camera 42 and microphone 238 of the headset terminal 314, and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to generate an optimal training menu and meal plan. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, which provides the generated menu to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the user's vital data and lifestyle habits using the camera 42 and microphone 238 of the robot 414, and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to generate an optimal training menu and meal plan. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides the generated menu to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A data collection unit that collects the user's vital data, lifestyle habits, and ideal body image, An analysis unit analyzes the data collected by the aforementioned collection unit and generates an optimal training menu and meal plan for the user. The system includes a provisioning unit that provides training menus and meal plans generated by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects information such as the user's weight, height, age, gender, exercise habits, eating habits, and ideal body type. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the user's vital data, lifestyle, and ideal body image, the system generates an optimal training menu and meal plan. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide training menus and meal plans that reflect the user's physical condition, hobbies, and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides feedback based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, It generates training menus, including calorie restriction and aerobic exercise, according to the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It generates strength training and high-protein meal plans according to the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) 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 10) The aforementioned collection unit is During data collection, filtering is performed based on the user's current living situation and health status. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) 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 15) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the user's health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the user's data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the menu is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the data, the level of detail provided will be adjusted based on the importance of the user's health data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the product, different delivery algorithms are applied depending on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the menu items provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we will determine the priority of provision based on when the user's data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing content, the order of delivery will be adjusted based on the user's relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 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 the user's vital data, lifestyle habits, and ideal body image, An analysis unit analyzes the data collected by the aforementioned collection unit and generates an optimal training menu and meal plan for the user. The system includes a provisioning unit that provides training menus and meal plans generated by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects information such as the user's weight, height, age, gender, exercise habits, eating habits, and ideal body type. The system according to feature 1.

3. The aforementioned analysis unit, Based on the user's vital data, lifestyle, and ideal body image, the system generates an optimal training menu and meal plan. The system according to feature 1.

4. The aforementioned supply unit is, We provide training menus and meal plans that reflect the user's physical condition, hobbies, and preferences. The system according to feature 1.

5. The aforementioned supply unit is, Provides feedback based on the user's health status. The system according to feature 1.

6. The aforementioned analysis unit, It generates training menus, including calorie restriction and aerobic exercise, according to the user's goals. The system according to feature 1.

7. The aforementioned analysis unit, It generates strength training and high-protein meal plans according to the user's goals. The system according to feature 1.

8. 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.

9. 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.

10. The aforementioned collection unit is During data collection, filtering is performed based on the user's current living situation and health status. The system according to feature 1.

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