system

Abstract

We provide the system. [Solution] Methods for entering personal information, Means for storing entered personal information, A means of generating an optimal training plan based on stored personal information, A means of providing a generated training plan, A means of receiving user feedback and updating the training plan, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult to provide an optimal training and diet menu according to an individual's health condition and lifestyle. In particular, it is required to efficiently integrate and analyze various data, and individual correspondence has not been sufficiently realized by conventional methods. When a user starts exercising or dieting, it is unclear which plan is appropriate for oneself, resulting in problems such as a decrease in motivation and no results.

Means for Solving the Problems

[0005] This invention provides a system that includes means for inputting and storing personal information, means for generating an optimal training plan based on this information, and means for providing the generated plan to the user. Furthermore, by integrating health-related data from external devices and updating the plan based on user feedback, the system provides a constantly updated and personalized plan. In addition, by suggesting meal menus that take nutritional balance into consideration based on dietary information, the system achieves comprehensive health management.

[0006] A "means of entering personal information" refers to a device that provides an interface for users to register basic data about themselves in a system.

[0007] "Means of storing entered personal information" refers to a database or storage device for securely recording data obtained from users and keeping it accessible at a later date.

[0008] "Means for generating an optimal training plan" refers to an algorithm or program that analyzes a user's personal information and formulates the most effective exercise schedule for achieving their goals.

[0009] "Means for providing a training plan" refers to a display device or communication means for presenting a generated exercise schedule to the user and instructing them on how to carry it out.

[0010] "Means for receiving user feedback and updating training plans" refers to system components that incorporate user results and opinions and effectively adjust the plan accordingly.

[0011] "Means of acquiring and integrating data from external devices" refers to system functions that collect data from external devices such as fitness trackers and smart devices and utilize it effectively within the system.

[0012] "Methods for suggesting meal menus that consider nutritional balance" refers to algorithms or programs that plan and provide meals with appropriate nutritional value based on the user's health condition and goals. [Brief explanation of the drawing]

[0013] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] The 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.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to a system that automatically generates and provides optimal training and meal plans based on a user's personal health information and daily activity data. This system consists of a user terminal, a server, and software programs that link them. The embodiments of this invention will be described in detail below.

[0035] Users use their devices to enter basic personal information such as weight, height, age, gender, and target weight. This information is sent from the device to the server and securely recorded in a database. The server retrieves and integrates additional data from fitness trackers and other health-related devices as needed. In this way, the server has detailed information about the user's overall health status.

[0036] Based on this data, the server uses an AI algorithm to generate a training plan optimized for the user's individual needs. This plan includes exercise types, frequency, intensity, etc., and is tailored to the user's physical condition and goals. The generated training plan is displayed to the user via their device.

[0037] At the same time, the server also considers the user's dietary information and suggests healthy meals. The meal menu aims to balance the user's nutrition and can accommodate individual dietary restrictions and preferences. These meal suggestions can also be viewed on the device.

[0038] Based on these suggestions, users perform daily training and meals, and input the results via their device to provide feedback. This feedback is sent to the server and used to retrain the AI ​​model. The server constantly updates the plan based on the latest data and re-suggests it to the user. This system allows users to continue receiving personalized training and dietary management.

[0039] As a concrete example, User A sets a goal of losing weight and inputs their data into the system. The server analyzes this data and suggests a balanced exercise program five times a week and a low-calorie, high-protein meal plan for User A. User A follows this plan and provides feedback on their progress each week, which gradually optimizes the plan. This allows User A to effectively achieve their goal.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user uses their device to enter basic personal information such as weight, height, age, gender, and target weight. The device then sends the entered data to the server.

[0043] Step 2:

[0044] The server stores the user's personal information received in a database. It also collects additional information, such as steps taken and calories burned, from fitness trackers and other health-related devices and integrates it into the database.

[0045] Step 3:

[0046] The server uses AI algorithms based on stored data to generate a training plan optimized for the user. This plan is customized to the user's goals, health status, and schedule.

[0047] Step 4:

[0048] The terminal displays the training plan sent from the server to the user. The user reviews the plan items and starts their daily training.

[0049] Step 5:

[0050] The server simultaneously considers the user's dietary information and creates a meal menu that prioritizes nutritional balance. The server sends this information to the terminal, which then displays the menu to the user, including prohibited and recommended foods.

[0051] Step 6:

[0052] After completing their daily training and meal plans, users input their results and feedback via a device. The device then sends this information to the server as feedback data.

[0053] Step 7:

[0054] The server retrains the AI ​​model based on feedback data sent by the user and updates the training plan and meal menu as needed. The updated plan is then presented to the user again via the device.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In health management, there is a challenge in automatically suggesting and continuously updating individualized exercise plans and nutritionally balanced meal menus. Traditional methods have been problematic because the process of providing optimal plans tailored to individual health conditions and goals is complex and time-consuming.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for inputting an individual's biometric information, means for storing the input biometric information, and means for creating an optimized exercise plan based on the stored biometric information. This enables the efficient provision of optimal exercise plans and meal menus based on individual health data, allowing for health management tailored to the user's needs.

[0060] "Personal biometric information" refers to data that represents an individual's health status and characteristics, such as weight, height, age, gender, and target weight.

[0061] "Means of input" refers to interfaces or devices that allow users to provide biometric information to a system.

[0062] "Means of storage" refers to databases and storage devices used to securely store the entered biometric information.

[0063] An "optimized exercise plan" is an implementation plan that includes the type, frequency, and intensity of exercise, built based on the user's individual biometric information.

[0064] "Means of creation" refers to algorithms and system functions used to generate exercise plans and meal menus based on data.

[0065] "Means of delivery" refers to the interface or platform used to communicate the generated exercise plan and meal menu to the user.

[0066] "Means of improvement" refers to the processes and algorithms used to update and improve exercise plans and meal menus based on user feedback.

[0067] This invention is a system for individually optimizing user health management, and consists of a user terminal, a server, and a software program that links them together.

[0068] Users enter personal information such as weight, height, age, gender, and target weight using their own devices. This data is securely transmitted from the device to the server and recorded in a dedicated database. Privacy is protected by using secure communication protocols for data transmission.

[0069] The server collects additional data from fitness trackers and external health-related devices as needed, integrating the user's overall health profile. For example, it can import exercise history and heart rate data from fitness trackers via Bluetooth.

[0070] Next, the server inputs the integrated data into an AI algorithm to generate a training plan optimized for the user. The generative AI model used in this process employs the prompt, "Please suggest the optimal training plan based on the user's health data." Based on this prompt, the AI ​​customizes the exercise plan, including the type, frequency, and intensity of the exercises.

[0071] Furthermore, the server also takes the user's dietary information into consideration and suggests nutritionally balanced meal menus. These suggestions can accommodate the user's dietary restrictions and preferences, and are individually optimized.

[0072] Users view the generated exercise plan and meal menu on their device and carry out their daily activities. After completing the activities, they can provide feedback on their results and impressions to the server from their device, and this feedback is used to update the AI ​​model. The server then re-evaluates the exercise plan based on this data and continuously proposes new plans with the latest information. This allows users to continuously receive personalized health management.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] Users input health information using their own devices. Specifically, they enter their weight, height, age, gender, target weight, etc., into the application's form. This generates personalized health data, which is then prepared as an initial set of personal information. The entered data is then sent directly to the server.

[0076] Step 2:

[0077] The terminal transmits personal information to the server using a predetermined protocol. Upon receiving this data, the server stores it in a secure database. Encryption technology is used during data transmission to ensure data confidentiality. This information is stored in the database and referenced in subsequent processing.

[0078] Step 3:

[0079] The server retrieves additional data from fitness trackers and health devices as needed. For example, it collects daily step counts and heart rate via Bluetooth. This data is integrated with already stored information to create a more detailed health profile of the user.

[0080] Step 4:

[0081] The server uses integrated health data as input for an AI algorithm. This algorithm utilizes a generated AI model to create a training plan optimized for the user. For example, based on a prompt such as "Please suggest the optimal training plan based on the user's health data," it determines the type, frequency, and intensity of exercises. The output includes a personalized exercise plan.

[0082] Step 5:

[0083] Simultaneously, the server considers the user's nutritional information and suggests healthy meal menus. These suggestions reflect the user's dietary restrictions and preferences. This creates a nutritionally balanced plan, which is then output as a meal menu.

[0084] Step 6:

[0085] The server generates a training plan and meal menu, which are then sent to the user via their device. The user reviews these on their device and prepares to implement them.

[0086] Step 7:

[0087] Users perform daily training and meals based on the proposed plan. They then input the results and feedback back into their device and send it to the server. This allows the server to store the results as feedback.

[0088] Step 8:

[0089] The server utilizes accumulated feedback to retrain the AI ​​model. This process improves the accuracy and effectiveness of existing training and meal plans, which are then reflected in the next plan update. This allows for continuous optimization of health management as users receive updated suggestions.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In modern times, individual health management has become a crucial issue, and personalized exercise and diet plans tailored to each individual's physical condition and goals are required. However, the means to provide these efficiently and continuously are limited. Conventional systems have difficulty effectively utilizing user feedback to flexibly update plans, and also lack sufficient support functions to motivate users. Therefore, there is a need for a system that supports the achievement of individual health goals and enhances motivation.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes a function for inputting personal information, a function for storing the inputted personal information, a function for generating an optimal exercise plan based on the stored personal information, a function for presenting the generated exercise plan, a function for receiving user feedback and improving the exercise plan, a function for providing voice guidance to the user through a robot, and a function for providing voice feedback on the health management plan. This enables daily support for each user's health management, continuous improvement of the personalized plan, and maintenance and improvement of motivation.

[0095] "Personal information" refers to data that shows basic attribute information and health status about the user themselves.

[0096] "Function" refers to the actions or capabilities that a system or device possesses in order to achieve a specific purpose.

[0097] An "exercise plan" is a plan of regular physical activity designed based on the user's health goals.

[0098] "Opinions" are information that represents evaluations and impressions of what users have actually experienced and the results thereof.

[0099] "Voice guidance" is a communication method that uses voice to provide instructions and encouragement to users.

[0100] A "health management plan" is a plan that includes specific suggestions regarding exercise and diet in order to maintain and improve the user's health.

[0101] The system for realizing this invention incorporates the following program:

[0102] The server first receives personal information from the user's device and stores it in a database. This personal information includes weight, height, exercise history, and health goals. Based on the stored information, the server uses an AI algorithm to generate an optimal exercise plan for the user and provides it to the device. This exercise plan is customized according to the user's activity level and goals, and suggests the type and frequency of exercises.

[0103] Furthermore, the server receives user feedback and incorporates it into its AI model to continuously optimize the plan. This feedback process improves the user experience. In addition, the server acquires health-related data and integrates information with external measuring devices for more detailed analysis.

[0104] This system uses smartphones and consumer robots as hardware platforms and is implemented using mobile application development platforms. Specifically, Flutter® and React Native may be used. For AI services on the cloud, AWS® and Google® Cloud AI services are commonly applied.

[0105] When a user begins exercising, the robot provides voice guidance and real-time feedback on their progress and health management plan. This helps users maintain a high level of motivation and makes it easier to achieve their health goals.

[0106] As a concrete example, suppose a user sets a target weight and enters that information into the app. The server analyzes this data and suggests a walking plan of three times a week to help the user achieve their goal. Furthermore, once the robot starts walking, it provides voice guidance such as, "Only 500 meters left! Keep going!" to encourage the user.

[0107] An example of a prompt for a generative AI model could be text such as, "I would like to know about healthy meal options. Please recommend some recipes."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] Users input personal information such as weight, height, exercise history, and health goals using their devices. This input data is processed as a transmission request from the device to the server.

[0111] Step 2:

[0112] The server stores the received personal information in a database. Based on the stored data, an AI algorithm generates an optimal exercise plan tailored to the user's health goals. Specifically, data analysis is performed using an AI algorithm written in Python or R. The output of this process is the exercise plan proposed to the user.

[0113] Step 3:

[0114] The server sends the generated exercise plan to the device. The device displays the received exercise plan on its screen so that the user can review it. The displayed exercise plan includes the type of exercise, intensity, number of repetitions, etc.

[0115] Step 4:

[0116] The user begins an exercise based on a plan and receives real-time feedback from the robot's voice guidance. The robot uses sensors to collect data during the exercise (e.g., steps taken, calories burned) and uses that data to provide voice feedback such as, "You're on a good pace, keep going for another 5 minutes."

[0117] Step 5:

[0118] Users input feedback on exercise and diet into their devices and send this feedback data to a server. The server receives this feedback and uses it as training data to update its AI model. This improves the accuracy of fitness plans provided in the future.

[0119] Step 6:

[0120] The server acquires and integrates additional data from external health devices. This includes acquiring data from IoT devices via APIs. This integrated data is analyzed by AI to generate a more personalized health management plan. Possible prompts include, "How many minutes did you spend walking today?"

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention relates to a system that comprehensively analyzes an individual's health and emotional state and provides an optimal training and meal plan. This system consists of a user terminal, a server, and a software program including an emotion engine. Embodiments of the invention are described below in detail.

[0123] First, users enter their basic personal information using their device. Then, they can provide daily activity data through external devices such as fitness trackers. This information is transmitted from the device to the server and stored securely and efficiently in a database.

[0124] A key feature of this system is its emotion engine, which analyzes the user's emotional state and tracks its changes in real time. The emotion engine performs emotional analysis using user input, existing data, or biometric signals from wearable devices. Based on the analysis results, the server adjusts the training plan to suit the user's emotional state. For example, if the system determines that the user is stressed, relaxation-enhancing exercises will be suggested.

[0125] Furthermore, the server takes into account the analyzed emotional data to customize the meal menu. This dynamically adjusted menu considers not only nutritional balance but also the user's mental well-being. The server then sends these adjustments to the terminal and displays them to the user.

[0126] Users follow the suggested training and meal plans daily, providing further feedback by reporting their experiences and results via their devices. The server receives this feedback and uses it to optimize the plans.

[0127] For example, if the emotion engine determines that User B is experiencing recent work-related stress, the server will incorporate weekend outdoor activities and yoga sessions into the training plan. Additionally, the meal plan will include foods expected to reduce stress. This allows User B to receive support not only physically but also mentally, improving their overall quality of life.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The user uses their device to input basic personal information and goals. This includes weight, height, age, target weight, and preferred exercise. The device then sends the entered data to the server.

[0131] Step 2:

[0132] The server stores personal information submitted by users in a database. In addition, it acquires biometric data and activity information from compatible external devices such as fitness trackers and smartwatches and integrates it into the database.

[0133] Step 3:

[0134] The emotion engine activates, analyzing user input, biometric signals from external devices, and past behavioral data to evaluate the user's emotional state in real time.

[0135] Step 4:

[0136] Based on the evaluation results of the emotion engine, the server generates an appropriate training plan that corresponds to the user's emotional state. For example, if stress is detected, it will create a plan that includes exercises effective for stress reduction.

[0137] Step 5:

[0138] The server takes into account the user's nutritional and emotional state and customizes a nutritionally balanced meal menu. This menu may include ingredients that have a relaxing effect, for example.

[0139] Step 6:

[0140] The generated training plan and meal menu are sent from the server to the terminal, which then displays them to the user, providing them as daily guidance.

[0141] Step 7:

[0142] Users perform training and dietary activities, and then input their results and feelings via their device to provide feedback. This feedback includes changes in emotions and the degree of goal achievement.

[0143] Step 8:

[0144] The server receives user feedback and uses it to retrain the AI ​​model. Based on the learning results, the server updates the training plan and meal menu to help improve it in the next cycle.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] There is a lack of means to comprehensively assess an individual's health and emotional state and propose exercise and dietary plans based on that assessment. This makes it difficult to provide a lifestyle that balances improved health with mental well-being.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for inputting basic personal information, means for analyzing emotional state using acquired health-related information, and means for generating an exercise plan based on the analysis results. This makes it possible to provide an optimal exercise and diet plan tailored to the individual user's condition.

[0150] "Means for inputting personal basic information" refers to methods for receiving basic personal data such as the user's age, gender, height, and weight.

[0151] "Means of storing entered information" refers to storage devices or databases that hold information provided by the user and make it available for later use.

[0152] "Means for analyzing emotional states using acquired health-related information" refers to methods for evaluating a user's emotional state by analyzing health information obtained from external devices or user input.

[0153] "A means of generating an optimal exercise plan based on analysis results" refers to a process for creating an exercise program suitable for the user based on the results of an analysis of their emotional state and health data.

[0154] "Means of providing exercise plans" refers to interfaces and systems that provide users with generated exercise programs in an easily understandable format.

[0155] "Means of receiving user feedback and modifying exercise plans" refers to the process of receiving feedback from users and adjusting or updating exercise programs accordingly.

[0156] "Methods for adjusting meal plans" refer to methods for changing the content of meals in consideration of the user's health and emotional state.

[0157] "Means of monitoring emotional states and providing suggestions that consider mental health" refers to methods for tracking changes in a user's emotions and providing advice based on those changes to maintain their physical and mental health.

[0158] This invention relates to a system that comprehensively analyzes an individual's health and emotional state to provide an optimal exercise and diet plan. First, the user inputs basic personal information using a terminal. This includes inputting health-related information such as age, height, and weight via a dedicated application. The terminal functions as, for example, a smartphone or a personal computer. The user synchronizes an external device, such as a fitness tracker, and sends daily physical activity data to the server. This data is transferred to and stored on the server in a secure manner.

[0159] The server uses a generative AI model to analyze emotional states. This model assesses the user's stress levels, happiness, and fatigue based on user-provided input data and information from wearable devices. The analysis combines existing databases with real-time data streaming. Based on the generated emotional state assessment, the server creates a personalized exercise plan for the user. This may include suggesting relaxation-enhancing exercises, for example. The server also dynamically customizes meal plans, taking into account the user's emotional state and activity history. These plans consider nutritional balance and include considerations for mental health.

[0160] For example, if the system determines that a user is experiencing stress, it will suggest a program that includes weekend outdoor activities or yoga sessions. The meal plan will also include foods that are expected to reduce stress. An example of a prompt in this process would be: "Analyze user B's emotional state, assess their current stress level, and suggest training and meal plans to reduce stress." This allows the system to provide personalized health support and help users achieve a better quality of life.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The user enters personal information using a terminal. The terminal collects basic information such as the user's age, height, and weight through a dedicated application. The entered data is sent to a server as basic data for evaluating the user's health status.

[0164] Step 2:

[0165] The device synchronizes with external devices such as fitness trackers to collect the user's daily physical activity data. This data, including steps taken, heart rate, and calories burned, is sent to a server. The server stores this data for analysis.

[0166] Step 3:

[0167] The server uses a generative AI model to analyze the user's emotional state. Inputs include basic user information and health-related data obtained from external devices. The emotional state analysis involves data calculations to evaluate stress levels, happiness levels, and other factors. The output generates indicators related to the user's current emotional state.

[0168] Step 4:

[0169] The server generates an exercise plan based on the analysis of the user's emotional state. It uses emotional state indicators and user activity data as input. Data processing selects exercises expected to reduce stress and improve energy levels, and the resulting exercise plan is created as output.

[0170] Step 5:

[0171] The server adjusts meal plans considering the user's emotional state and activity history. It uses emotional state indicators and existing meal data as input. Through data calculations, a customized meal menu that considers nutritional balance and mental health is provided as output.

[0172] Step 6:

[0173] The server sends the generated exercise and meal plans to the device and provides them to the user. The device notifies the user of these plans and displays them in an easy-to-understand format. The user then incorporates them into their daily activities.

[0174] Step 7:

[0175] Users follow the suggested exercise and meal plans and report their experiences as feedback via their device. The server receives this feedback and uses it to modify the plans or for future analyses.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] Many existing health management systems focus solely on physical health, failing to adequately consider mental health and emotional states. This makes it difficult to achieve comprehensive health improvement for individuals. Furthermore, the suggested exercise and meal plans are often fixed and lack the flexibility to adapt to the user's emotional state and feedback, which is another challenge.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for inputting personal information, means for analyzing the user's emotional state and tracking its changes in real time, and means for adjusting the exercise plan based on the analysis results. This enables optimal health support that takes into account both the user's physical and mental health.

[0181] "Personal information" refers to basic data about a user, which is used to analyze their health and emotional state.

[0182] An "exercise plan" is an activity plan proposed based on the user's health and emotional state, and is adjusted according to individual needs.

[0183] "Feedback" refers to information provided by users regarding their impressions and results of exercise and meal plans, which the system uses to optimize the plans.

[0184] "Emotional state" refers to the user's current mental state, and is data that the system analyzes and incorporates into exercise plans and meal menus.

[0185] "External devices" refer to devices used to acquire the user's health-related information, such as wearable devices.

[0186] A "meal menu" is a nutritional plan suggested based on the user's dietary information and emotional state, and is designed with health and mental well-being in mind.

[0187] The system that implements this application is designed to provide multifaceted support for the health management of individual users. The server receives personal information and feedback from the user's terminal and analyzes their emotional state. A generative AI model using TENSORFLOW® is used for the analysis, and exercise plans and meal menus are adjusted according to the user's emotions.

[0188] Specifically, the system acquires health-related information such as activity levels and heart rate from external devices like fitness trackers and smartphones. This information is transmitted to a server in real time and stored in a database. The server utilizes Azure® to securely process the data. Qualitative data, including user-submitted self-reports and information about their situation, is also integrated. The emotion engine analyzes this data to provide exercise and dietary suggestions that take the user's mental health into consideration.

[0189] For example, if analysis reveals that a user has been experiencing recent stress, the server will incorporate relaxation-enhancing activities such as yoga or outdoor exercises into their exercise plan. Meal plans will include stress-reducing foods and herbal teas. In this way, users can receive support not only for their physical health but also for their mental well-being.

[0190] An example of a prompt message would be, "Predict the user's stress level based on their health and emotional data, and suggest the most suitable activity for relaxation." The system then provides a plan tailored to the user's state.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The terminal receives personal information from the user. This information includes age, gender, height, weight, and daily activity level. This information is sent to the server as basic health data.

[0194] Step 2:

[0195] The server stores personal information received from the terminal in a database. Based on this stored information, it prepares to generate an initial motor plan. The data is then organized for later analysis.

[0196] Step 3:

[0197] External devices acquire the user's health-related information in real time and transmit it to the server via the terminal. This includes activity levels, heart rate, and sleep patterns. The server integrates this new data with existing data and updates the database.

[0198] Step 4:

[0199] The server analyzes health-related information collected in real time and self-reported data from users. This analysis utilizes a generative AI model based on TensorFlow to predict the user's emotional state. The analysis results are output as an index indicating the individual's emotional state.

[0200] Step 5:

[0201] The server adjusts the exercise plan and meal menu based on the output of the generated AI model. For users predicted to be high in stress, it suggests exercises with relaxation effects and meals that are expected to reduce stress. The adjusted plan is created and sent to the device.

[0202] Step 6:

[0203] Users review the exercise plans and meal menus provided on their devices and incorporate them into their daily activities. After completing the activities, they input feedback and send this information to the server via their devices. This feedback is used to optimize future plans.

[0204] Step 7:

[0205] The server analyzes the received feedback and extracts areas for improvement in exercise plans and meal menus. The data obtained from this analysis is used to update the database for future plan generation. In this way, the system can provide dynamic plans tailored to the user's health and emotional state.

[0206] 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.

[0207] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0213] 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.

[0214] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0215] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0216] 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.

[0217] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0218] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0219] The 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.

[0220] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0221] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0222] This invention relates to a system that automatically generates and provides optimal training and meal plans based on a user's personal health information and daily activity data. This system consists of a user terminal, a server, and software programs that link them. The embodiments of this invention will be described in detail below.

[0223] Users use their devices to enter basic personal information such as weight, height, age, gender, and target weight. This information is sent from the device to the server and securely recorded in a database. The server retrieves and integrates additional data from fitness trackers and other health-related devices as needed. In this way, the server has detailed information about the user's overall health status.

[0224] Based on this data, the server uses an AI algorithm to generate a training plan optimized for the user's individual needs. This plan includes exercise types, frequency, intensity, etc., and is tailored to the user's physical condition and goals. The generated training plan is displayed to the user via their device.

[0225] At the same time, the server also considers the user's dietary information and suggests healthy meals. The meal menu aims to balance the user's nutrition and can accommodate individual dietary restrictions and preferences. These meal suggestions can also be viewed on the device.

[0226] Based on these suggestions, users perform daily training and meals, and input the results via their device to provide feedback. This feedback is sent to the server and used to retrain the AI ​​model. The server constantly updates the plan based on the latest data and re-suggests it to the user. This system allows users to continue receiving personalized training and dietary management.

[0227] As a concrete example, User A sets a goal of losing weight and inputs their data into the system. The server analyzes this data and suggests a balanced exercise program five times a week and a low-calorie, high-protein meal plan for User A. User A follows this plan and provides feedback on their progress each week, which gradually optimizes the plan. This allows User A to effectively achieve their goal.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The user uses their device to enter basic personal information such as weight, height, age, gender, and target weight. The device then sends the entered data to the server.

[0231] Step 2:

[0232] The server stores the user's personal information received in a database. It also collects additional information, such as steps taken and calories burned, from fitness trackers and other health-related devices and integrates it into the database.

[0233] Step 3:

[0234] The server uses AI algorithms based on stored data to generate a training plan optimized for the user. This plan is customized to the user's goals, health status, and schedule.

[0235] Step 4:

[0236] The terminal displays the training plan sent from the server to the user. The user reviews the plan items and starts their daily training.

[0237] Step 5:

[0238] The server simultaneously considers the user's dietary information and creates a meal menu that prioritizes nutritional balance. The server sends this information to the terminal, which then displays the menu to the user, including prohibited and recommended foods.

[0239] Step 6:

[0240] After completing their daily training and meal plans, users input their results and feedback via a device. The device then sends this information to the server as feedback data.

[0241] Step 7:

[0242] The server retrains the AI ​​model based on feedback data sent by the user and updates the training plan and meal menu as needed. The updated plan is then presented to the user again via the device.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0245] In health management, there is a challenge in automatically suggesting and continuously updating individualized exercise plans and nutritionally balanced meal menus. Traditional methods have been problematic because the process of providing optimal plans tailored to individual health conditions and goals is complex and time-consuming.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes means for inputting an individual's biometric information, means for storing the input biometric information, and means for creating an optimized exercise plan based on the stored biometric information. This enables the efficient provision of optimal exercise plans and meal menus based on individual health data, allowing for health management tailored to the user's needs.

[0248] "Personal biometric information" refers to data that represents an individual's health status and characteristics, such as weight, height, age, gender, and target weight.

[0249] "Means of input" refers to interfaces or devices that allow users to provide biometric information to a system.

[0250] "Means of storage" refers to databases and storage devices used to securely store the entered biometric information.

[0251] An "optimized exercise plan" is an implementation plan that includes the type, frequency, and intensity of exercise, built based on the user's individual biometric information.

[0252] "Means of creation" refers to algorithms and system functions used to generate exercise plans and meal menus based on data.

[0253] "Means of delivery" refers to the interface or platform used to communicate the generated exercise plan and meal menu to the user.

[0254] "Means of improvement" refers to the processes and algorithms used to update and improve exercise plans and meal menus based on user feedback.

[0255] This invention is a system for individually optimizing user health management, and consists of a user terminal, a server, and a software program that links them together.

[0256] Users enter personal information such as weight, height, age, gender, and target weight using their own devices. This data is securely transmitted from the device to the server and recorded in a dedicated database. Privacy is protected by using secure communication protocols for data transmission.

[0257] The server collects additional data from fitness trackers and external health-related devices as needed, integrating the user's overall health profile. For example, it can import exercise history and heart rate data from fitness trackers via Bluetooth.

[0258] Next, the server inputs the integrated data into an AI algorithm to generate a training plan optimized for the user. The generative AI model used in this process employs the prompt, "Please suggest the optimal training plan based on the user's health data." Based on this prompt, the AI ​​customizes the exercise plan, including the type, frequency, and intensity of the exercises.

[0259] Furthermore, the server also takes the user's dietary information into consideration and suggests nutritionally balanced meal menus. These suggestions can accommodate the user's dietary restrictions and preferences, and are individually optimized.

[0260] Users view the generated exercise plan and meal menu on their device and carry out their daily activities. After completing the activities, they can provide feedback on their results and impressions to the server from their device, and this feedback is used to update the AI ​​model. The server then re-evaluates the exercise plan based on this data and continuously proposes new plans with the latest information. This allows users to continuously receive personalized health management.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] Users input health information using their own devices. Specifically, they enter their weight, height, age, gender, target weight, etc., into the application's form. This generates personalized health data, which is then prepared as an initial set of personal information. The entered data is then sent directly to the server.

[0264] Step 2:

[0265] The terminal transmits personal information to the server using a predetermined protocol. Upon receiving this data, the server stores it in a secure database. Encryption technology is used during data transmission to ensure data confidentiality. This information is stored in the database and referenced in subsequent processing.

[0266] Step 3:

[0267] The server retrieves additional data from fitness trackers and health devices as needed. For example, it collects daily step counts and heart rate via Bluetooth. This data is integrated with already stored information to create a more detailed health profile of the user.

[0268] Step 4:

[0269] The server uses integrated health data as input for an AI algorithm. This algorithm utilizes a generated AI model to create a training plan optimized for the user. For example, based on a prompt such as "Please suggest the optimal training plan based on the user's health data," it determines the type, frequency, and intensity of exercises. The output includes a personalized exercise plan.

[0270] Step 5:

[0271] Simultaneously, the server considers the user's nutritional information and suggests healthy meal menus. These suggestions reflect the user's dietary restrictions and preferences. This creates a nutritionally balanced plan, which is then output as a meal menu.

[0272] Step 6:

[0273] The server generates a training plan and meal menu, which are then sent to the user via their device. The user reviews these on their device and prepares to implement them.

[0274] Step 7:

[0275] Users perform daily training and meals based on the proposed plan. They then input the results and feedback back into their device and send it to the server. This allows the server to store the results as feedback.

[0276] Step 8:

[0277] The server utilizes accumulated feedback to retrain the AI ​​model. This process improves the accuracy and effectiveness of existing training and meal plans, which are then reflected in the next plan update. This allows for continuous optimization of health management as users receive updated suggestions.

[0278] (Application Example 1)

[0279] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0280] In modern times, individual health management has become a crucial issue, and personalized exercise and diet plans tailored to each individual's physical condition and goals are required. However, the means to provide these efficiently and continuously are limited. Conventional systems have difficulty effectively utilizing user feedback to flexibly update plans, and also lack sufficient support functions to motivate users. Therefore, there is a need for a system that supports the achievement of individual health goals and enhances motivation.

[0281] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0282] In this invention, the server includes a function for inputting personal information, a function for storing the inputted personal information, a function for generating an optimal exercise plan based on the stored personal information, a function for presenting the generated exercise plan, a function for receiving user feedback and improving the exercise plan, a function for providing voice guidance to the user through a robot, and a function for providing voice feedback on the health management plan. This enables daily support for each user's health management, continuous improvement of the personalized plan, and maintenance and improvement of motivation.

[0283] "Personal information" refers to basic attribute information about the user himself / herself and data indicating health status.

[0284] "Function" refers to the operations and operational capabilities that a system or device has to achieve a specific purpose.

[0285] "Exercise plan" refers to a plan of regular physical activities designed based on the user's health goals.

[0286] "Opinion" refers to information expressing the user's evaluations and feelings about what the user has actually experienced and its results.

[0287] "Voice guidance" refers to a communication method that provides instructions and encouragement to the user using voice.

[0288] "Health management plan" refers to a plan including specific proposals regarding exercise and diet in order to maintain and improve the user's health.

[0289] In the system for realizing this invention, a program is incorporated as follows.

[0290] First, the server receives personal information from the user's terminal and stores it in the database. This personal information includes weight, height, exercise history, health goals, etc., and based on the stored information, the server utilizes an AI algorithm to generate an optimal exercise plan for the user and provide it to the terminal. This exercise plan is customized according to the user's activity level and goals, and presents the types and frequencies of exercises.

[0291] Also, the server receives the user's feedback, incorporates it into the AI model, and thereby continuously optimizes the plan. Through this feedback process, the user experience can be improved. Furthermore, the server integrates information with external measuring devices to obtain health-related data and conducts more detailed analysis.

[0292] This system uses smartphones and consumer robots as hardware platforms and is implemented using mobile application development platforms. Specifically, Flutter and React Native may be used. For AI services on the cloud, AWS and Google Cloud's AI services are commonly applied.

[0293] When a user begins exercising, the robot provides voice guidance and real-time feedback on their progress and health management plan. This helps users maintain a high level of motivation and makes it easier to achieve their health goals.

[0294] As a concrete example, suppose a user sets a target weight and enters that information into the app. The server analyzes this data and suggests a walking plan of three times a week to help the user achieve their goal. Furthermore, once the robot starts walking, it provides voice guidance such as, "Only 500 meters left! Keep going!" to encourage the user.

[0295] An example of a prompt for a generative AI model could be text such as, "I would like to know about healthy meal options. Please recommend some recipes."

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] Users input personal information such as weight, height, exercise history, and health goals using their devices. This input data is processed as a transmission request from the device to the server.

[0299] Step 2:

[0300] The server stores the received personal information in a database. Based on the stored data, an AI algorithm generates an exercise plan that is optimal for the user's health goals. Specifically, data analysis is performed using an AI algorithm written in programs such as Python and R. The output of this process is the exercise plan proposed to the user.

[0301] Step 3:

[0302] The server sends the generated exercise plan to the terminal. The terminal displays the received exercise plan on the screen so that the user can view it. The displayed exercise plan includes the type of exercise, intensity, frequency, etc.

[0303] Step 4:

[0304] The user starts exercising based on the plan and receives real-time feedback from the robot's voice guidance. The robot uses sensors to obtain data during exercise (e.g., steps, calories burned) and provides voice feedback such as "Good pace, keep going for another 5 minutes" based on that data.

[0305] Step 5:

[0306] The user inputs feedback on exercise and diet into the terminal and sends the feedback data to the server. The server receives this feedback and uses it as learning data to update the AI model. This improves the accuracy of the fitness plan provided in the future.

[0307] Step 6:

[0308] The server obtains and integrates additional data from external health devices. This includes obtaining data from IoT devices via an API. This integrated data is analyzed by AI to generate a more personalized health management plan. Prompt sentences such as "How many minutes did you spend walking today?" can be considered.

[0309] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0310] This invention relates to a system that comprehensively analyzes an individual's health and emotional state and provides an optimal training and meal plan. This system consists of a user terminal, a server, and a software program including an emotion engine. Embodiments of the invention are described below in detail.

[0311] First, users enter their basic personal information using their device. Then, they can provide daily activity data through external devices such as fitness trackers. This information is transmitted from the device to the server and stored securely and efficiently in a database.

[0312] A key feature of this system is its emotion engine, which analyzes the user's emotional state and tracks its changes in real time. The emotion engine performs emotional analysis using user input, existing data, or biometric signals from wearable devices. Based on the analysis results, the server adjusts the training plan to suit the user's emotional state. For example, if the system determines that the user is stressed, relaxation-enhancing exercises will be suggested.

[0313] Furthermore, the server takes into account the analyzed emotional data to customize the meal menu. This dynamically adjusted menu considers not only nutritional balance but also the user's mental well-being. The server then sends these adjustments to the terminal and displays them to the user.

[0314] Users follow the suggested training and meal plans daily, providing further feedback by reporting their experiences and results via their devices. The server receives this feedback and uses it to optimize the plans.

[0315] For example, if the emotion engine determines that User B is experiencing recent work-related stress, the server will incorporate weekend outdoor activities and yoga sessions into the training plan. Additionally, the meal plan will include foods expected to reduce stress. This allows User B to receive support not only physically but also mentally, improving their overall quality of life.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The user uses their device to input basic personal information and goals. This includes weight, height, age, target weight, and preferred exercise. The device then sends the entered data to the server.

[0319] Step 2:

[0320] The server stores personal information submitted by users in a database. In addition, it acquires biometric data and activity information from compatible external devices such as fitness trackers and smartwatches and integrates it into the database.

[0321] Step 3:

[0322] The emotion engine activates, analyzing user input, biometric signals from external devices, and past behavioral data to evaluate the user's emotional state in real time.

[0323] Step 4:

[0324] Based on the evaluation results of the emotion engine, the server generates an appropriate training plan that corresponds to the user's emotional state. For example, if stress is detected, it will create a plan that includes exercises effective for stress reduction.

[0325] Step 5:

[0326] The server takes into account the user's nutritional and emotional state and customizes a nutritionally balanced meal menu. This menu may include ingredients that have a relaxing effect, for example.

[0327] Step 6:

[0328] The generated training plan and meal menu are sent from the server to the terminal, which then displays them to the user, providing them as daily guidance.

[0329] Step 7:

[0330] Users perform training and dietary activities, and then input their results and feelings via their device to provide feedback. This feedback includes changes in emotions and the degree of goal achievement.

[0331] Step 8:

[0332] The server receives user feedback and uses it to retrain the AI ​​model. Based on the learning results, the server updates the training plan and meal menu to help improve it in the next cycle.

[0333] (Example 2)

[0334] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0335] There is a lack of means to comprehensively assess an individual's health and emotional state and propose exercise and dietary plans based on that assessment. This makes it difficult to provide a lifestyle that balances improved health with mental well-being.

[0336] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0337] In this invention, the server includes means for inputting basic personal information, means for analyzing emotional state using acquired health-related information, and means for generating an exercise plan based on the analysis results. This makes it possible to provide an optimal exercise and diet plan tailored to the individual user's condition.

[0338] "Means for inputting personal basic information" refers to methods for receiving basic personal data such as the user's age, gender, height, and weight.

[0339] "Means of storing entered information" refers to storage devices or databases that hold information provided by the user and make it available for later use.

[0340] "Means for analyzing emotional states using acquired health-related information" refers to methods for evaluating a user's emotional state by analyzing health information obtained from external devices or user input.

[0341] "A means of generating an optimal exercise plan based on analysis results" refers to a process for creating an exercise program suitable for the user based on the results of an analysis of their emotional state and health data.

[0342] "Means of providing exercise plans" refers to interfaces and systems that provide users with generated exercise programs in an easily understandable format.

[0343] "Means of receiving user feedback and modifying exercise plans" refers to the process of receiving feedback from users and adjusting or updating exercise programs accordingly.

[0344] "Methods for adjusting meal plans" refer to methods for changing the content of meals in consideration of the user's health and emotional state.

[0345] "Means of monitoring emotional states and providing suggestions that consider mental health" refers to methods for tracking changes in a user's emotions and providing advice based on those changes to maintain their physical and mental health.

[0346] This invention relates to a system that comprehensively analyzes an individual's health and emotional state to provide an optimal exercise and diet plan. First, the user inputs basic personal information using a terminal. This includes inputting health-related information such as age, height, and weight via a dedicated application. The terminal functions as, for example, a smartphone or a personal computer. The user synchronizes an external device, such as a fitness tracker, and sends daily physical activity data to the server. This data is transferred to and stored on the server in a secure manner.

[0347] The server uses a generative AI model to analyze emotional states. This model assesses the user's stress levels, happiness, and fatigue based on user-provided input data and information from wearable devices. The analysis combines existing databases with real-time data streaming. Based on the generated emotional state assessment, the server creates a personalized exercise plan for the user. This may include suggesting relaxation-enhancing exercises, for example. The server also dynamically customizes meal plans, taking into account the user's emotional state and activity history. These plans consider nutritional balance and include considerations for mental health.

[0348] For example, if the system determines that a user is experiencing stress, it will suggest a program that includes weekend outdoor activities or yoga sessions. The meal plan will also include foods that are expected to reduce stress. An example of a prompt in this process would be: "Analyze user B's emotional state, assess their current stress level, and suggest training and meal plans to reduce stress." This allows the system to provide personalized health support and help users achieve a better quality of life.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The user enters personal information using a terminal. The terminal collects basic information such as the user's age, height, and weight through a dedicated application. The entered data is sent to a server as basic data for evaluating the user's health status.

[0352] Step 2:

[0353] The device synchronizes with external devices such as fitness trackers to collect the user's daily physical activity data. This data, including steps taken, heart rate, and calories burned, is sent to a server. The server stores this data for analysis.

[0354] Step 3:

[0355] The server uses a generative AI model to analyze the user's emotional state. Inputs include basic user information and health-related data obtained from external devices. The emotional state analysis involves data calculations to evaluate stress levels, happiness levels, and other factors. The output generates indicators related to the user's current emotional state.

[0356] Step 4:

[0357] The server generates an exercise plan based on the analysis of the user's emotional state. It uses emotional state indicators and user activity data as input. Data processing selects exercises expected to reduce stress and improve energy levels, and the resulting exercise plan is created as output.

[0358] Step 5:

[0359] The server adjusts meal plans considering the user's emotional state and activity history. It uses emotional state indicators and existing meal data as input. Through data calculations, a customized meal menu that considers nutritional balance and mental health is provided as output.

[0360] Step 6:

[0361] The server sends the generated exercise and meal plans to the device and provides them to the user. The device notifies the user of these plans and displays them in an easy-to-understand format. The user then incorporates them into their daily activities.

[0362] Step 7:

[0363] Users follow the suggested exercise and meal plans and report their experiences as feedback via their device. The server receives this feedback and uses it to modify the plans or for future analyses.

[0364] (Application Example 2)

[0365] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0366] Many existing health management systems focus solely on physical health, failing to adequately consider mental health and emotional states. This makes it difficult to achieve comprehensive health improvement for individuals. Furthermore, the suggested exercise and meal plans are often fixed and lack the flexibility to adapt to the user's emotional state and feedback, which is another challenge.

[0367] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0368] In this invention, the server includes means for inputting personal information, means for analyzing the user's emotional state and tracking its changes in real time, and means for adjusting the exercise plan based on the analysis results. This enables optimal health support that takes into account both the user's physical and mental health.

[0369] "Personal information" refers to basic data about a user, which is used to analyze their health and emotional state.

[0370] An "exercise plan" is an activity plan proposed based on the user's health and emotional state, and is adjusted according to individual needs.

[0371] "Feedback" refers to information provided by users regarding their impressions and results of exercise and meal plans, which the system uses to optimize the plans.

[0372] "Emotional state" refers to the user's current mental state, and is data that the system analyzes and incorporates into exercise plans and meal menus.

[0373] "External devices" refer to devices used to acquire the user's health-related information, such as wearable devices.

[0374] A "meal menu" is a nutritional plan suggested based on the user's dietary information and emotional state, and is designed with health and mental well-being in mind.

[0375] The system that implements this application is designed to provide comprehensive support for individual users' health management. The server receives personal information and feedback from the user's terminal and analyzes their emotional state. A generative AI model using TensorFlow is used for the analysis, and exercise plans and meal menus are adjusted according to the user's emotions.

[0376] Specifically, the system acquires health-related information such as activity levels and heart rate from external devices like fitness trackers and smartphones. This information is transmitted to a server in real time and stored in a database. The server utilizes Azure to securely process the data. Qualitative data, including user-submitted self-reports and information about their situation, is also integrated. The emotion engine analyzes this data and provides exercise and dietary suggestions that take the user's mental health into consideration.

[0377] For example, if analysis reveals that a user has been experiencing recent stress, the server will incorporate relaxation-enhancing activities such as yoga or outdoor exercises into their exercise plan. Meal plans will include stress-reducing foods and herbal teas. In this way, users can receive support not only for their physical health but also for their mental well-being.

[0378] An example of a prompt message would be, "Predict the user's stress level based on their health and emotional data, and suggest the most suitable activity for relaxation." The system then provides a plan tailored to the user's state.

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] The terminal receives personal information from the user. This information includes age, gender, height, weight, and daily activity level. This information is sent to the server as basic health data.

[0382] Step 2:

[0383] The server stores personal information received from the terminal in a database. Based on this stored information, it prepares to generate an initial motor plan. The data is then organized for later analysis.

[0384] Step 3:

[0385] External devices acquire the user's health-related information in real time and transmit it to the server via the terminal. This includes activity levels, heart rate, and sleep patterns. The server integrates this new data with existing data and updates the database.

[0386] Step 4:

[0387] The server analyzes health-related information collected in real time and self-reported data from users. This analysis utilizes a generative AI model based on TensorFlow to predict the user's emotional state. The analysis results are output as an index indicating the individual's emotional state.

[0388] Step 5:

[0389] The server adjusts the exercise plan and meal menu based on the output of the generated AI model. For users predicted to be high in stress, it suggests exercises with relaxation effects and meals that are expected to reduce stress. The adjusted plan is created and sent to the device.

[0390] Step 6:

[0391] Users review the exercise plans and meal menus provided on their devices and incorporate them into their daily activities. After completing the activities, they input feedback and send this information to the server via their devices. This feedback is used to optimize future plans.

[0392] Step 7:

[0393] The server analyzes the received feedback and extracts areas for improvement in exercise plans and meal menus. The data obtained from this analysis is used to update the database for future plan generation. In this way, the system can provide dynamic plans tailored to the user's health and emotional state.

[0394] 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.

[0395] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0396] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0397] [Third Embodiment]

[0398] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0399] 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.

[0400] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0401] 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.

[0402] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0403] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0404] 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.

[0405] 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.

[0406] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0407] The 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.

[0408] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0409] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0410] This invention relates to a system that automatically generates and provides optimal training and meal plans based on a user's personal health information and daily activity data. This system consists of a user terminal, a server, and software programs that link them. The embodiments of this invention will be described in detail below.

[0411] Users use their devices to enter basic personal information such as weight, height, age, gender, and target weight. This information is sent from the device to the server and securely recorded in a database. The server retrieves and integrates additional data from fitness trackers and other health-related devices as needed. In this way, the server has detailed information about the user's overall health status.

[0412] Based on this data, the server uses an AI algorithm to generate a training plan optimized for the user's individual needs. This plan includes exercise types, frequency, intensity, etc., and is tailored to the user's physical condition and goals. The generated training plan is displayed to the user via their device.

[0413] At the same time, the server also considers the user's dietary information and suggests healthy meals. The meal menu aims to balance the user's nutrition and can accommodate individual dietary restrictions and preferences. These meal suggestions can also be viewed on the device.

[0414] Based on these suggestions, users perform daily training and meals, and input the results via their device to provide feedback. This feedback is sent to the server and used to retrain the AI ​​model. The server constantly updates the plan based on the latest data and re-suggests it to the user. This system allows users to continue receiving personalized training and dietary management.

[0415] As a concrete example, User A sets a goal of losing weight and inputs their data into the system. The server analyzes this data and suggests a balanced exercise program five times a week and a low-calorie, high-protein meal plan for User A. User A follows this plan and provides feedback on their progress each week, which gradually optimizes the plan. This allows User A to effectively achieve their goal.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The user uses their device to enter basic personal information such as weight, height, age, gender, and target weight. The device then sends the entered data to the server.

[0419] Step 2:

[0420] The server stores the user's personal information received in a database. It also collects additional information, such as steps taken and calories burned, from fitness trackers and other health-related devices and integrates it into the database.

[0421] Step 3:

[0422] The server uses AI algorithms based on stored data to generate a training plan optimized for the user. This plan is customized to the user's goals, health status, and schedule.

[0423] Step 4:

[0424] The terminal displays the training plan sent from the server to the user. The user reviews the plan items and starts their daily training.

[0425] Step 5:

[0426] The server simultaneously considers the user's dietary information and creates a meal menu that prioritizes nutritional balance. The server sends this information to the terminal, which then displays the menu to the user, including prohibited and recommended foods.

[0427] Step 6:

[0428] After completing their daily training and meal plans, users input their results and feedback via a device. The device then sends this information to the server as feedback data.

[0429] Step 7:

[0430] The server retrains the AI ​​model based on feedback data sent by the user and updates the training plan and meal menu as needed. The updated plan is then presented to the user again via the device.

[0431] (Example 1)

[0432] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0433] In health management, there is a challenge in automatically suggesting and continuously updating individualized exercise plans and nutritionally balanced meal menus. Traditional methods have been problematic because the process of providing optimal plans tailored to individual health conditions and goals is complex and time-consuming.

[0434] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0435] In this invention, the server includes means for inputting an individual's biometric information, means for storing the input biometric information, and means for creating an optimized exercise plan based on the stored biometric information. This enables the efficient provision of optimal exercise plans and meal menus based on individual health data, allowing for health management tailored to the user's needs.

[0436] "Personal biometric information" refers to data that represents an individual's health status and characteristics, such as weight, height, age, gender, and target weight.

[0437] "Means of input" refers to interfaces or devices that allow users to provide biometric information to a system.

[0438] "Means of storage" refers to databases and storage devices used to securely store the entered biometric information.

[0439] An "optimized exercise plan" is an implementation plan that includes the type, frequency, and intensity of exercise, built based on the user's individual biometric information.

[0440] "Means of creation" refers to algorithms and system functions used to generate exercise plans and meal menus based on data.

[0441] "Means of delivery" refers to the interface or platform used to communicate the generated exercise plan and meal menu to the user.

[0442] "Means of improvement" refers to the processes and algorithms used to update and improve exercise plans and meal menus based on user feedback.

[0443] This invention is a system for individually optimizing user health management, and consists of a user terminal, a server, and a software program that links them together.

[0444] Users enter personal information such as weight, height, age, gender, and target weight using their own devices. This data is securely transmitted from the device to the server and recorded in a dedicated database. Privacy is protected by using secure communication protocols for data transmission.

[0445] The server collects additional data from fitness trackers and external health-related devices as needed, integrating the user's overall health profile. For example, it can import exercise history and heart rate data from fitness trackers via Bluetooth.

[0446] Next, the server inputs the integrated data into an AI algorithm to generate a training plan optimized for the user. The generative AI model used in this process employs the prompt, "Please suggest the optimal training plan based on the user's health data." Based on this prompt, the AI ​​customizes the exercise plan, including the type, frequency, and intensity of the exercises.

[0447] Furthermore, the server also takes the user's dietary information into consideration and suggests nutritionally balanced meal menus. These suggestions can accommodate the user's dietary restrictions and preferences, and are individually optimized.

[0448] Users view the generated exercise plan and meal menu on their device and carry out their daily activities. After completing the activities, they can provide feedback on their results and impressions to the server from their device, and this feedback is used to update the AI ​​model. The server then re-evaluates the exercise plan based on this data and continuously proposes new plans with the latest information. This allows users to continuously receive personalized health management.

[0449] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0450] Step 1:

[0451] Users input health information using their own devices. Specifically, they enter their weight, height, age, gender, target weight, etc., into the application's form. This generates personalized health data, which is then prepared as an initial set of personal information. The entered data is then sent directly to the server.

[0452] Step 2:

[0453] The terminal transmits personal information to the server using a predetermined protocol. Upon receiving this data, the server stores it in a secure database. Encryption technology is used during data transmission to ensure data confidentiality. This information is stored in the database and referenced in subsequent processing.

[0454] Step 3:

[0455] The server retrieves additional data from fitness trackers and health devices as needed. For example, it collects daily step counts and heart rate via Bluetooth. This data is integrated with already stored information to create a more detailed health profile of the user.

[0456] Step 4:

[0457] The server uses integrated health data as input for an AI algorithm. This algorithm utilizes a generated AI model to create a training plan optimized for the user. For example, based on a prompt such as "Please suggest the optimal training plan based on the user's health data," it determines the type, frequency, and intensity of exercises. The output includes a personalized exercise plan.

[0458] Step 5:

[0459] Simultaneously, the server considers the user's nutritional information and suggests healthy meal menus. These suggestions reflect the user's dietary restrictions and preferences. This creates a nutritionally balanced plan, which is then output as a meal menu.

[0460] Step 6:

[0461] The server generates a training plan and meal menu, which are then sent to the user via their device. The user reviews these on their device and prepares to implement them.

[0462] Step 7:

[0463] Users perform daily training and meals based on the proposed plan. They then input the results and feedback back into their device and send it to the server. This allows the server to store the results as feedback.

[0464] Step 8:

[0465] The server utilizes accumulated feedback to retrain the AI ​​model. This process improves the accuracy and effectiveness of existing training and meal plans, which are then reflected in the next plan update. This allows for continuous optimization of health management as users receive updated suggestions.

[0466] (Application Example 1)

[0467] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0468] In modern times, individual health management has become a crucial issue, and personalized exercise and diet plans tailored to each individual's physical condition and goals are required. However, the means to provide these efficiently and continuously are limited. Conventional systems have difficulty effectively utilizing user feedback to flexibly update plans, and also lack sufficient support functions to motivate users. Therefore, there is a need for a system that supports the achievement of individual health goals and enhances motivation.

[0469] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0470] In this invention, the server includes a function for inputting personal information, a function for storing the inputted personal information, a function for generating an optimal exercise plan based on the stored personal information, a function for presenting the generated exercise plan, a function for receiving user feedback and improving the exercise plan, a function for providing voice guidance to the user through a robot, and a function for providing voice feedback on the health management plan. This enables daily support for each user's health management, continuous improvement of the personalized plan, and maintenance and improvement of motivation.

[0471] "Personal information" refers to data that shows basic attribute information and health status about the user themselves.

[0472] "Function" refers to the actions or capabilities that a system or device possesses in order to achieve a specific purpose.

[0473] An "exercise plan" is a plan of regular physical activity designed based on the user's health goals.

[0474] "Opinions" are information that represents evaluations and impressions of what users have actually experienced and the results thereof.

[0475] "Voice guidance" is a communication method that uses voice to provide instructions and encouragement to users.

[0476] A "health management plan" is a plan that includes specific suggestions regarding exercise and diet in order to maintain and improve the user's health.

[0477] The system for realizing this invention incorporates the following program:

[0478] The server first receives personal information from the user's device and stores it in a database. This personal information includes weight, height, exercise history, and health goals. Based on the stored information, the server uses an AI algorithm to generate an optimal exercise plan for the user and provides it to the device. This exercise plan is customized according to the user's activity level and goals, and suggests the type and frequency of exercises.

[0479] Furthermore, the server receives user feedback and incorporates it into its AI model to continuously optimize the plan. This feedback process improves the user experience. In addition, the server acquires health-related data and integrates information with external measuring devices for more detailed analysis.

[0480] This system uses smartphones and consumer robots as hardware platforms and is implemented using mobile application development platforms. Specifically, Flutter and React Native may be used. For AI services on the cloud, AWS and Google Cloud's AI services are commonly applied.

[0481] When a user begins exercising, the robot provides voice guidance and real-time feedback on their progress and health management plan. This helps users maintain a high level of motivation and makes it easier to achieve their health goals.

[0482] As a concrete example, suppose a user sets a target weight and enters that information into the app. The server analyzes this data and suggests a walking plan of three times a week to help the user achieve their goal. Furthermore, once the robot starts walking, it provides voice guidance such as, "Only 500 meters left! Keep going!" to encourage the user.

[0483] An example of a prompt for a generative AI model could be text such as, "I would like to know about healthy meal options. Please recommend some recipes."

[0484] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0485] Step 1:

[0486] Users input personal information such as weight, height, exercise history, and health goals using their devices. This input data is processed as a transmission request from the device to the server.

[0487] Step 2:

[0488] The server stores the received personal information in a database. Based on the stored data, an AI algorithm generates an optimal exercise plan tailored to the user's health goals. Specifically, data analysis is performed using an AI algorithm written in Python or R. The output of this process is the exercise plan proposed to the user.

[0489] Step 3:

[0490] The server sends the generated exercise plan to the device. The device displays the received exercise plan on its screen so that the user can review it. The displayed exercise plan includes the type of exercise, intensity, number of repetitions, etc.

[0491] Step 4:

[0492] The user begins an exercise based on a plan and receives real-time feedback from the robot's voice guidance. The robot uses sensors to collect data during the exercise (e.g., steps taken, calories burned) and uses that data to provide voice feedback such as, "You're on a good pace, keep going for another 5 minutes."

[0493] Step 5:

[0494] Users input feedback on exercise and diet into their devices and send this feedback data to a server. The server receives this feedback and uses it as training data to update its AI model. This improves the accuracy of fitness plans provided in the future.

[0495] Step 6:

[0496] The server acquires and integrates additional data from external health devices. This includes acquiring data from IoT devices via APIs. This integrated data is analyzed by AI to generate a more personalized health management plan. Possible prompts include, "How many minutes did you spend walking today?"

[0497] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0498] This invention relates to a system that comprehensively analyzes an individual's health and emotional state and provides an optimal training and meal plan. This system consists of a user terminal, a server, and a software program including an emotion engine. Embodiments of the invention are described below in detail.

[0499] First, users enter their basic personal information using their device. Then, they can provide daily activity data through external devices such as fitness trackers. This information is transmitted from the device to the server and stored securely and efficiently in a database.

[0500] A key feature of this system is its emotion engine, which analyzes the user's emotional state and tracks its changes in real time. The emotion engine performs emotional analysis using user input, existing data, or biometric signals from wearable devices. Based on the analysis results, the server adjusts the training plan to suit the user's emotional state. For example, if the system determines that the user is stressed, relaxation-enhancing exercises will be suggested.

[0501] Furthermore, the server takes into account the analyzed emotional data to customize the meal menu. This dynamically adjusted menu considers not only nutritional balance but also the user's mental well-being. The server then sends these adjustments to the terminal and displays them to the user.

[0502] Users follow the suggested training and meal plans daily, providing further feedback by reporting their experiences and results via their devices. The server receives this feedback and uses it to optimize the plans.

[0503] For example, if the emotion engine determines that User B is experiencing recent work-related stress, the server will incorporate weekend outdoor activities and yoga sessions into the training plan. Additionally, the meal plan will include foods expected to reduce stress. This allows User B to receive support not only physically but also mentally, improving their overall quality of life.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] The user uses their device to input basic personal information and goals. This includes weight, height, age, target weight, and preferred exercise. The device then sends the entered data to the server.

[0507] Step 2:

[0508] The server stores personal information submitted by users in a database. In addition, it acquires biometric data and activity information from compatible external devices such as fitness trackers and smartwatches and integrates it into the database.

[0509] Step 3:

[0510] The emotion engine activates, analyzing user input, biometric signals from external devices, and past behavioral data to evaluate the user's emotional state in real time.

[0511] Step 4:

[0512] Based on the evaluation results of the emotion engine, the server generates an appropriate training plan that corresponds to the user's emotional state. For example, if stress is detected, it will create a plan that includes exercises effective for stress reduction.

[0513] Step 5:

[0514] The server takes into account the user's nutritional and emotional state and customizes a nutritionally balanced meal menu. This menu may include ingredients that have a relaxing effect, for example.

[0515] Step 6:

[0516] The generated training plan and meal menu are sent from the server to the terminal, which then displays them to the user, providing them as daily guidance.

[0517] Step 7:

[0518] Users perform training and dietary activities, and then input their results and feelings via their device to provide feedback. This feedback includes changes in emotions and the degree of goal achievement.

[0519] Step 8:

[0520] The server receives user feedback and uses it to retrain the AI ​​model. Based on the learning results, the server updates the training plan and meal menu to help improve it in the next cycle.

[0521] (Example 2)

[0522] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] There is a lack of means to comprehensively assess an individual's health and emotional state and propose exercise and dietary plans based on that assessment. This makes it difficult to provide a lifestyle that balances improved health with mental well-being.

[0524] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0525] In this invention, the server includes means for inputting basic personal information, means for analyzing emotional state using acquired health-related information, and means for generating an exercise plan based on the analysis results. This makes it possible to provide an optimal exercise and diet plan tailored to the individual user's condition.

[0526] "Means for inputting personal basic information" refers to methods for receiving basic personal data such as the user's age, gender, height, and weight.

[0527] "Means of storing entered information" refers to storage devices or databases that hold information provided by the user and make it available for later use.

[0528] "Means for analyzing emotional states using acquired health-related information" refers to methods for evaluating a user's emotional state by analyzing health information obtained from external devices or user input.

[0529] "A means of generating an optimal exercise plan based on analysis results" refers to a process for creating an exercise program suitable for the user based on the results of an analysis of their emotional state and health data.

[0530] "Means of providing exercise plans" refers to interfaces and systems that provide users with generated exercise programs in an easily understandable format.

[0531] "Means of receiving user feedback and modifying exercise plans" refers to the process of receiving feedback from users and adjusting or updating exercise programs accordingly.

[0532] "Methods for adjusting meal plans" refer to methods for changing the content of meals in consideration of the user's health and emotional state.

[0533] "Means of monitoring emotional states and providing suggestions that consider mental health" refers to methods for tracking changes in a user's emotions and providing advice based on those changes to maintain their physical and mental health.

[0534] This invention relates to a system that comprehensively analyzes an individual's health and emotional state to provide an optimal exercise and diet plan. First, the user inputs basic personal information using a terminal. This includes inputting health-related information such as age, height, and weight via a dedicated application. The terminal functions as, for example, a smartphone or a personal computer. The user synchronizes an external device, such as a fitness tracker, and sends daily physical activity data to the server. This data is transferred to and stored on the server in a secure manner.

[0535] The server uses a generative AI model to analyze emotional states. This model assesses the user's stress levels, happiness, and fatigue based on user-provided input data and information from wearable devices. The analysis combines existing databases with real-time data streaming. Based on the generated emotional state assessment, the server creates a personalized exercise plan for the user. This may include suggesting relaxation-enhancing exercises, for example. The server also dynamically customizes meal plans, taking into account the user's emotional state and activity history. These plans consider nutritional balance and include considerations for mental health.

[0536] For example, if the system determines that a user is experiencing stress, it will suggest a program that includes weekend outdoor activities or yoga sessions. The meal plan will also include foods that are expected to reduce stress. An example of a prompt in this process would be: "Analyze user B's emotional state, assess their current stress level, and suggest training and meal plans to reduce stress." This allows the system to provide personalized health support and help users achieve a better quality of life.

[0537] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0538] Step 1:

[0539] The user enters personal information using a terminal. The terminal collects basic information such as the user's age, height, and weight through a dedicated application. The entered data is sent to a server as basic data for evaluating the user's health status.

[0540] Step 2:

[0541] The device synchronizes with external devices such as fitness trackers to collect the user's daily physical activity data. This data, including steps taken, heart rate, and calories burned, is sent to a server. The server stores this data for analysis.

[0542] Step 3:

[0543] The server uses a generative AI model to analyze the user's emotional state. Inputs include basic user information and health-related data obtained from external devices. The emotional state analysis involves data calculations to evaluate stress levels, happiness levels, and other factors. The output generates indicators related to the user's current emotional state.

[0544] Step 4:

[0545] The server generates an exercise plan based on the analysis of the user's emotional state. It uses emotional state indicators and user activity data as input. Data processing selects exercises expected to reduce stress and improve energy levels, and the resulting exercise plan is created as output.

[0546] Step 5:

[0547] The server adjusts meal plans considering the user's emotional state and activity history. It uses emotional state indicators and existing meal data as input. Through data calculations, a customized meal menu that considers nutritional balance and mental health is provided as output.

[0548] Step 6:

[0549] The server sends the generated exercise and meal plans to the device and provides them to the user. The device notifies the user of these plans and displays them in an easy-to-understand format. The user then incorporates them into their daily activities.

[0550] Step 7:

[0551] Users follow the suggested exercise and meal plans and report their experiences as feedback via their device. The server receives this feedback and uses it to modify the plans or for future analyses.

[0552] (Application Example 2)

[0553] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0554] Many existing health management systems focus solely on physical health, failing to adequately consider mental health and emotional states. This makes it difficult to achieve comprehensive health improvement for individuals. Furthermore, the suggested exercise and meal plans are often fixed and lack the flexibility to adapt to the user's emotional state and feedback, which is another challenge.

[0555] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0556] In this invention, the server includes means for inputting personal information, means for analyzing the user's emotional state and tracking its changes in real time, and means for adjusting the exercise plan based on the analysis results. This enables optimal health support that takes into account both the user's physical and mental health.

[0557] "Personal information" refers to basic data about a user, which is used to analyze their health and emotional state.

[0558] An "exercise plan" is an activity plan proposed based on the user's health and emotional state, and is adjusted according to individual needs.

[0559] "Feedback" refers to information provided by users regarding their impressions and results of exercise and meal plans, which the system uses to optimize the plans.

[0560] "Emotional state" refers to the user's current mental state, and is data that the system analyzes and incorporates into exercise plans and meal menus.

[0561] "External devices" refer to devices used to acquire the user's health-related information, such as wearable devices.

[0562] A "meal menu" is a nutritional plan suggested based on the user's dietary information and emotional state, and is designed with health and mental well-being in mind.

[0563] The system that implements this application is designed to provide comprehensive support for individual users' health management. The server receives personal information and feedback from the user's terminal and analyzes their emotional state. A generative AI model using TensorFlow is used for the analysis, and exercise plans and meal menus are adjusted according to the user's emotions.

[0564] Specifically, the system acquires health-related information such as activity levels and heart rate from external devices like fitness trackers and smartphones. This information is transmitted to a server in real time and stored in a database. The server utilizes Azure to securely process the data. Qualitative data, including user-submitted self-reports and information about their situation, is also integrated. The emotion engine analyzes this data and provides exercise and dietary suggestions that take the user's mental health into consideration.

[0565] For example, if analysis reveals that a user has been experiencing recent stress, the server will incorporate relaxation-enhancing activities such as yoga or outdoor exercises into their exercise plan. Meal plans will include stress-reducing foods and herbal teas. In this way, users can receive support not only for their physical health but also for their mental well-being.

[0566] An example of a prompt message would be, "Predict the user's stress level based on their health and emotional data, and suggest the most suitable activity for relaxation." The system then provides a plan tailored to the user's state.

[0567] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0568] Step 1:

[0569] The terminal receives personal information from the user. This information includes age, gender, height, weight, and daily activity level. This information is sent to the server as basic health data.

[0570] Step 2:

[0571] The server stores personal information received from the terminal in a database. Based on this stored information, it prepares to generate an initial motor plan. The data is then organized for later analysis.

[0572] Step 3:

[0573] External devices acquire the user's health-related information in real time and transmit it to the server via the terminal. This includes activity levels, heart rate, and sleep patterns. The server integrates this new data with existing data and updates the database.

[0574] Step 4:

[0575] The server analyzes health-related information collected in real time and self-reported data from users. This analysis utilizes a generative AI model based on TensorFlow to predict the user's emotional state. The analysis results are output as an index indicating the individual's emotional state.

[0576] Step 5:

[0577] The server adjusts the exercise plan and meal menu based on the output of the generated AI model. For users predicted to be high in stress, it suggests exercises with relaxation effects and meals that are expected to reduce stress. The adjusted plan is created and sent to the device.

[0578] Step 6:

[0579] Users review the exercise plans and meal menus provided on their devices and incorporate them into their daily activities. After completing the activities, they input feedback and send this information to the server via their devices. This feedback is used to optimize future plans.

[0580] Step 7:

[0581] The server analyzes the received feedback and extracts areas for improvement in exercise plans and meal menus. The data obtained from this analysis is used to update the database for future plan generation. In this way, the system can provide dynamic plans tailored to the user's health and emotional state.

[0582] 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.

[0583] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0584] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0585] [Fourth Embodiment]

[0586] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0587] 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.

[0588] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0589] 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.

[0590] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0591] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0592] 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.

[0593] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0594] 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.

[0595] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0596] The 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.

[0597] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0598] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0599] This invention relates to a system that automatically generates and provides optimal training and meal plans based on a user's personal health information and daily activity data. This system consists of a user terminal, a server, and software programs that link them. The embodiments of this invention will be described in detail below.

[0600] Users use their devices to enter basic personal information such as weight, height, age, gender, and target weight. This information is sent from the device to the server and securely recorded in a database. The server retrieves and integrates additional data from fitness trackers and other health-related devices as needed. In this way, the server has detailed information about the user's overall health status.

[0601] Based on this data, the server uses an AI algorithm to generate a training plan optimized for the user's individual needs. This plan includes exercise types, frequency, intensity, etc., and is tailored to the user's physical condition and goals. The generated training plan is displayed to the user via their device.

[0602] At the same time, the server also considers the user's dietary information and suggests healthy meals. The meal menu aims to balance the user's nutrition and can accommodate individual dietary restrictions and preferences. These meal suggestions can also be viewed on the device.

[0603] Based on these suggestions, users perform daily training and meals, and input the results via their device to provide feedback. This feedback is sent to the server and used to retrain the AI ​​model. The server constantly updates the plan based on the latest data and re-suggests it to the user. This system allows users to continue receiving personalized training and dietary management.

[0604] As a concrete example, User A sets a goal of losing weight and inputs their data into the system. The server analyzes this data and suggests a balanced exercise program five times a week and a low-calorie, high-protein meal plan for User A. User A follows this plan and provides feedback on their progress each week, which gradually optimizes the plan. This allows User A to effectively achieve their goal.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] The user uses their device to enter basic personal information such as weight, height, age, gender, and target weight. The device then sends the entered data to the server.

[0608] Step 2:

[0609] The server stores the user's personal information received in a database. It also collects additional information, such as steps taken and calories burned, from fitness trackers and other health-related devices and integrates it into the database.

[0610] Step 3:

[0611] The server uses AI algorithms based on stored data to generate a training plan optimized for the user. This plan is customized to the user's goals, health status, and schedule.

[0612] Step 4:

[0613] The terminal displays the training plan sent from the server to the user. The user reviews the plan items and starts their daily training.

[0614] Step 5:

[0615] The server simultaneously considers the user's dietary information and creates a meal menu that prioritizes nutritional balance. The server sends this information to the terminal, which then displays the menu to the user, including prohibited and recommended foods.

[0616] Step 6:

[0617] After completing their daily training and meal plans, users input their results and feedback via a device. The device then sends this information to the server as feedback data.

[0618] Step 7:

[0619] The server retrains the AI ​​model based on feedback data sent by the user and updates the training plan and meal menu as needed. The updated plan is then presented to the user again via the device.

[0620] (Example 1)

[0621] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0622] In health management, there is a challenge in automatically suggesting and continuously updating individualized exercise plans and nutritionally balanced meal menus. Traditional methods have been problematic because the process of providing optimal plans tailored to individual health conditions and goals is complex and time-consuming.

[0623] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0624] In this invention, the server includes means for inputting an individual's biometric information, means for storing the input biometric information, and means for creating an optimized exercise plan based on the stored biometric information. This enables the efficient provision of optimal exercise plans and meal menus based on individual health data, allowing for health management tailored to the user's needs.

[0625] "Personal biometric information" refers to data that represents an individual's health status and characteristics, such as weight, height, age, gender, and target weight.

[0626] "Means of input" refers to interfaces or devices that allow users to provide biometric information to a system.

[0627] "Means of storage" refers to databases and storage devices used to securely store the entered biometric information.

[0628] An "optimized exercise plan" is an implementation plan that includes the type, frequency, and intensity of exercise, built based on the user's individual biometric information.

[0629] "Means of creation" refers to algorithms and system functions used to generate exercise plans and meal menus based on data.

[0630] "Means of delivery" refers to the interface or platform used to communicate the generated exercise plan and meal menu to the user.

[0631] "Means of improvement" refers to the processes and algorithms used to update and improve exercise plans and meal menus based on user feedback.

[0632] This invention is a system for individually optimizing user health management, and consists of a user terminal, a server, and a software program that links them together.

[0633] Users enter personal information such as weight, height, age, gender, and target weight using their own devices. This data is securely transmitted from the device to the server and recorded in a dedicated database. Privacy is protected by using secure communication protocols for data transmission.

[0634] The server collects additional data from fitness trackers and external health-related devices as needed, integrating the user's overall health profile. For example, it can import exercise history and heart rate data from fitness trackers via Bluetooth.

[0635] Next, the server inputs the integrated data into an AI algorithm to generate a training plan optimized for the user. The generative AI model used in this process employs the prompt, "Please suggest the optimal training plan based on the user's health data." Based on this prompt, the AI ​​customizes the exercise plan, including the type, frequency, and intensity of the exercises.

[0636] Furthermore, the server also takes the user's dietary information into consideration and suggests nutritionally balanced meal menus. These suggestions can accommodate the user's dietary restrictions and preferences, and are individually optimized.

[0637] Users view the generated exercise plan and meal menu on their device and carry out their daily activities. After completing the activities, they can provide feedback on their results and impressions to the server from their device, and this feedback is used to update the AI ​​model. The server then re-evaluates the exercise plan based on this data and continuously proposes new plans with the latest information. This allows users to continuously receive personalized health management.

[0638] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0639] Step 1:

[0640] Users input health information using their own devices. Specifically, they enter their weight, height, age, gender, target weight, etc., into the application's form. This generates personalized health data, which is then prepared as an initial set of personal information. The entered data is then sent directly to the server.

[0641] Step 2:

[0642] The terminal transmits personal information to the server using a predetermined protocol. Upon receiving this data, the server stores it in a secure database. Encryption technology is used during data transmission to ensure data confidentiality. This information is stored in the database and referenced in subsequent processing.

[0643] Step 3:

[0644] The server retrieves additional data from fitness trackers and health devices as needed. For example, it collects daily step counts and heart rate via Bluetooth. This data is integrated with already stored information to create a more detailed health profile of the user.

[0645] Step 4:

[0646] The server uses integrated health data as input for an AI algorithm. This algorithm utilizes a generated AI model to create a training plan optimized for the user. For example, based on a prompt such as "Please suggest the optimal training plan based on the user's health data," it determines the type, frequency, and intensity of exercises. The output includes a personalized exercise plan.

[0647] Step 5:

[0648] Simultaneously, the server considers the user's nutritional information and suggests healthy meal menus. These suggestions reflect the user's dietary restrictions and preferences. This creates a nutritionally balanced plan, which is then output as a meal menu.

[0649] Step 6:

[0650] The server generates a training plan and meal menu, which are then sent to the user via their device. The user reviews these on their device and prepares to implement them.

[0651] Step 7:

[0652] Users perform daily training and meals based on the proposed plan. They then input the results and feedback back into their device and send it to the server. This allows the server to store the results as feedback.

[0653] Step 8:

[0654] The server utilizes accumulated feedback to retrain the AI ​​model. This process improves the accuracy and effectiveness of existing training and meal plans, which are then reflected in the next plan update. This allows for continuous optimization of health management as users receive updated suggestions.

[0655] (Application Example 1)

[0656] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0657] In modern times, individual health management has become a crucial issue, and personalized exercise and diet plans tailored to each individual's physical condition and goals are required. However, the means to provide these efficiently and continuously are limited. Conventional systems have difficulty effectively utilizing user feedback to flexibly update plans, and also lack sufficient support functions to motivate users. Therefore, there is a need for a system that supports the achievement of individual health goals and enhances motivation.

[0658] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0659] In this invention, the server includes a function for inputting personal information, a function for storing the inputted personal information, a function for generating an optimal exercise plan based on the stored personal information, a function for presenting the generated exercise plan, a function for receiving user feedback and improving the exercise plan, a function for providing voice guidance to the user through a robot, and a function for providing voice feedback on the health management plan. This enables daily support for each user's health management, continuous improvement of the personalized plan, and maintenance and improvement of motivation.

[0660] "Personal information" refers to data that shows basic attribute information and health status about the user themselves.

[0661] "Function" refers to the actions or capabilities that a system or device possesses in order to achieve a specific purpose.

[0662] An "exercise plan" is a plan of regular physical activity designed based on the user's health goals.

[0663] "Opinions" are information that represents evaluations and impressions of what users have actually experienced and the results thereof.

[0664] "Voice guidance" is a communication method that uses voice to provide instructions and encouragement to users.

[0665] A "health management plan" is a plan that includes specific suggestions regarding exercise and diet in order to maintain and improve the user's health.

[0666] The system for realizing this invention incorporates the following program:

[0667] The server first receives personal information from the user's device and stores it in a database. This personal information includes weight, height, exercise history, and health goals. Based on the stored information, the server uses an AI algorithm to generate an optimal exercise plan for the user and provides it to the device. This exercise plan is customized according to the user's activity level and goals, and suggests the type and frequency of exercises.

[0668] Furthermore, the server receives user feedback and incorporates it into its AI model to continuously optimize the plan. This feedback process improves the user experience. In addition, the server acquires health-related data and integrates information with external measuring devices for more detailed analysis.

[0669] This system uses smartphones and consumer robots as hardware platforms and is implemented using mobile application development platforms. Specifically, Flutter and React Native may be used. For AI services on the cloud, AWS and Google Cloud's AI services are commonly applied.

[0670] When a user begins exercising, the robot provides voice guidance and real-time feedback on their progress and health management plan. This helps users maintain a high level of motivation and makes it easier to achieve their health goals.

[0671] As a concrete example, suppose a user sets a target weight and enters that information into the app. The server analyzes this data and suggests a walking plan of three times a week to help the user achieve their goal. Furthermore, once the robot starts walking, it provides voice guidance such as, "Only 500 meters left! Keep going!" to encourage the user.

[0672] An example of a prompt for a generative AI model could be text such as, "I would like to know about healthy meal options. Please recommend some recipes."

[0673] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0674] Step 1:

[0675] Users input personal information such as weight, height, exercise history, and health goals using their devices. This input data is processed as a transmission request from the device to the server.

[0676] Step 2:

[0677] The server stores the received personal information in a database. Based on the stored data, an AI algorithm generates an optimal exercise plan tailored to the user's health goals. Specifically, data analysis is performed using an AI algorithm written in Python or R. The output of this process is the exercise plan proposed to the user.

[0678] Step 3:

[0679] The server sends the generated exercise plan to the device. The device displays the received exercise plan on its screen so that the user can review it. The displayed exercise plan includes the type of exercise, intensity, number of repetitions, etc.

[0680] Step 4:

[0681] The user begins an exercise based on a plan and receives real-time feedback from the robot's voice guidance. The robot uses sensors to collect data during the exercise (e.g., steps taken, calories burned) and uses that data to provide voice feedback such as, "You're on a good pace, keep going for another 5 minutes."

[0682] Step 5:

[0683] Users input feedback on exercise and diet into their devices and send this feedback data to a server. The server receives this feedback and uses it as training data to update its AI model. This improves the accuracy of fitness plans provided in the future.

[0684] Step 6:

[0685] The server acquires and integrates additional data from external health devices. This includes acquiring data from IoT devices via APIs. This integrated data is analyzed by AI to generate a more personalized health management plan. Possible prompts include, "How many minutes did you spend walking today?"

[0686] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0687] This invention relates to a system that comprehensively analyzes an individual's health and emotional state and provides an optimal training and meal plan. This system consists of a user terminal, a server, and a software program including an emotion engine. Embodiments of the invention are described below in detail.

[0688] First, users enter their basic personal information using their device. Then, they can provide daily activity data through external devices such as fitness trackers. This information is transmitted from the device to the server and stored securely and efficiently in a database.

[0689] A key feature of this system is its emotion engine, which analyzes the user's emotional state and tracks its changes in real time. The emotion engine performs emotional analysis using user input, existing data, or biometric signals from wearable devices. Based on the analysis results, the server adjusts the training plan to suit the user's emotional state. For example, if the system determines that the user is stressed, relaxation-enhancing exercises will be suggested.

[0690] Furthermore, the server takes into account the analyzed emotional data to customize the meal menu. This dynamically adjusted menu considers not only nutritional balance but also the user's mental well-being. The server then sends these adjustments to the terminal and displays them to the user.

[0691] Users follow the suggested training and meal plans daily, providing further feedback by reporting their experiences and results via their devices. The server receives this feedback and uses it to optimize the plans.

[0692] For example, if the emotion engine determines that User B is experiencing recent work-related stress, the server will incorporate weekend outdoor activities and yoga sessions into the training plan. Additionally, the meal plan will include foods expected to reduce stress. This allows User B to receive support not only physically but also mentally, improving their overall quality of life.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] The user uses their device to input basic personal information and goals. This includes weight, height, age, target weight, and preferred exercise. The device then sends the entered data to the server.

[0696] Step 2:

[0697] The server stores personal information submitted by users in a database. In addition, it acquires biometric data and activity information from compatible external devices such as fitness trackers and smartwatches and integrates it into the database.

[0698] Step 3:

[0699] The emotion engine activates, analyzing user input, biometric signals from external devices, and past behavioral data to evaluate the user's emotional state in real time.

[0700] Step 4:

[0701] Based on the evaluation results of the emotion engine, the server generates an appropriate training plan that corresponds to the user's emotional state. For example, if stress is detected, it will create a plan that includes exercises effective for stress reduction.

[0702] Step 5:

[0703] The server takes into account the user's nutritional and emotional state and customizes a nutritionally balanced meal menu. This menu may include ingredients that have a relaxing effect, for example.

[0704] Step 6:

[0705] The generated training plan and meal menu are sent from the server to the terminal, which then displays them to the user, providing them as daily guidance.

[0706] Step 7:

[0707] Users perform training and dietary activities, and then input their results and feelings via their device to provide feedback. This feedback includes changes in emotions and the degree of goal achievement.

[0708] Step 8:

[0709] The server receives user feedback and uses it to retrain the AI ​​model. Based on the learning results, the server updates the training plan and meal menu to help improve it in the next cycle.

[0710] (Example 2)

[0711] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] There is a lack of means to comprehensively assess an individual's health and emotional state and propose exercise and dietary plans based on that assessment. This makes it difficult to provide a lifestyle that balances improved health with mental well-being.

[0713] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0714] In this invention, the server includes means for inputting basic personal information, means for analyzing emotional state using acquired health-related information, and means for generating an exercise plan based on the analysis results. This makes it possible to provide an optimal exercise and diet plan tailored to the individual user's condition.

[0715] "Means for inputting personal basic information" refers to methods for receiving basic personal data such as the user's age, gender, height, and weight.

[0716] "Means of storing entered information" refers to storage devices or databases that hold information provided by the user and make it available for later use.

[0717] "Means for analyzing emotional states using acquired health-related information" refers to methods for evaluating a user's emotional state by analyzing health information obtained from external devices or user input.

[0718] "A means of generating an optimal exercise plan based on analysis results" refers to a process for creating an exercise program suitable for the user based on the results of an analysis of their emotional state and health data.

[0719] "Means of providing exercise plans" refers to interfaces and systems that provide users with generated exercise programs in an easily understandable format.

[0720] "Means of receiving user feedback and modifying exercise plans" refers to the process of receiving feedback from users and adjusting or updating exercise programs accordingly.

[0721] "Methods for adjusting meal plans" refer to methods for changing the content of meals in consideration of the user's health and emotional state.

[0722] "Means of monitoring emotional states and providing suggestions that consider mental health" refers to methods for tracking changes in a user's emotions and providing advice based on those changes to maintain their physical and mental health.

[0723] This invention relates to a system that comprehensively analyzes an individual's health and emotional state to provide an optimal exercise and diet plan. First, the user inputs basic personal information using a terminal. This includes inputting health-related information such as age, height, and weight via a dedicated application. The terminal functions as, for example, a smartphone or a personal computer. The user synchronizes an external device, such as a fitness tracker, and sends daily physical activity data to the server. This data is transferred to and stored on the server in a secure manner.

[0724] The server uses a generative AI model to analyze emotional states. This model assesses the user's stress levels, happiness, and fatigue based on user-provided input data and information from wearable devices. The analysis combines existing databases with real-time data streaming. Based on the generated emotional state assessment, the server creates a personalized exercise plan for the user. This may include suggesting relaxation-enhancing exercises, for example. The server also dynamically customizes meal plans, taking into account the user's emotional state and activity history. These plans consider nutritional balance and include considerations for mental health.

[0725] For example, if the system determines that a user is experiencing stress, it will suggest a program that includes weekend outdoor activities or yoga sessions. The meal plan will also include foods that are expected to reduce stress. An example of a prompt in this process would be: "Analyze user B's emotional state, assess their current stress level, and suggest training and meal plans to reduce stress." This allows the system to provide personalized health support and help users achieve a better quality of life.

[0726] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0727] Step 1:

[0728] The user enters personal information using a terminal. The terminal collects basic information such as the user's age, height, and weight through a dedicated application. The entered data is sent to a server as basic data for evaluating the user's health status.

[0729] Step 2:

[0730] The device synchronizes with external devices such as fitness trackers to collect the user's daily physical activity data. This data, including steps taken, heart rate, and calories burned, is sent to a server. The server stores this data for analysis.

[0731] Step 3:

[0732] The server uses a generative AI model to analyze the user's emotional state. Inputs include basic user information and health-related data obtained from external devices. The emotional state analysis involves data calculations to evaluate stress levels, happiness levels, and other factors. The output generates indicators related to the user's current emotional state.

[0733] Step 4:

[0734] The server generates an exercise plan based on the analysis of the user's emotional state. It uses emotional state indicators and user activity data as input. Data processing selects exercises expected to reduce stress and improve energy levels, and the resulting exercise plan is created as output.

[0735] Step 5:

[0736] The server adjusts meal plans considering the user's emotional state and activity history. It uses emotional state indicators and existing meal data as input. Through data calculations, a customized meal menu that considers nutritional balance and mental health is provided as output.

[0737] Step 6:

[0738] The server sends the generated exercise and meal plans to the device and provides them to the user. The device notifies the user of these plans and displays them in an easy-to-understand format. The user then incorporates them into their daily activities.

[0739] Step 7:

[0740] Users follow the suggested exercise and meal plans and report their experiences as feedback via their device. The server receives this feedback and uses it to modify the plans or for future analyses.

[0741] (Application Example 2)

[0742] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0743] Many existing health management systems focus solely on physical health, failing to adequately consider mental health and emotional states. This makes it difficult to achieve comprehensive health improvement for individuals. Furthermore, the suggested exercise and meal plans are often fixed and lack the flexibility to adapt to the user's emotional state and feedback, which is another challenge.

[0744] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0745] In this invention, the server includes means for inputting personal information, means for analyzing the user's emotional state and tracking its changes in real time, and means for adjusting the exercise plan based on the analysis results. This enables optimal health support that takes into account both the user's physical and mental health.

[0746] "Personal information" refers to basic data about a user, which is used to analyze their health and emotional state.

[0747] An "exercise plan" is an activity plan proposed based on the user's health and emotional state, and is adjusted according to individual needs.

[0748] "Feedback" refers to information provided by users regarding their impressions and results of exercise and meal plans, which the system uses to optimize the plans.

[0749] "Emotional state" refers to the user's current mental state, and is data that the system analyzes and incorporates into exercise plans and meal menus.

[0750] "External devices" refer to devices used to acquire the user's health-related information, such as wearable devices.

[0751] A "meal menu" is a nutritional plan suggested based on the user's dietary information and emotional state, and is designed with health and mental well-being in mind.

[0752] The system that implements this application is designed to provide comprehensive support for individual users' health management. The server receives personal information and feedback from the user's terminal and analyzes their emotional state. A generative AI model using TensorFlow is used for the analysis, and exercise plans and meal menus are adjusted according to the user's emotions.

[0753] Specifically, the system acquires health-related information such as activity levels and heart rate from external devices like fitness trackers and smartphones. This information is transmitted to a server in real time and stored in a database. The server utilizes Azure to securely process the data. Qualitative data, including user-submitted self-reports and information about their situation, is also integrated. The emotion engine analyzes this data and provides exercise and dietary suggestions that take the user's mental health into consideration.

[0754] For example, if analysis reveals that a user has been experiencing recent stress, the server will incorporate relaxation-enhancing activities such as yoga or outdoor exercises into their exercise plan. Meal plans will include stress-reducing foods and herbal teas. In this way, users can receive support not only for their physical health but also for their mental well-being.

[0755] An example of a prompt message would be, "Predict the user's stress level based on their health and emotional data, and suggest the most suitable activity for relaxation." The system then provides a plan tailored to the user's state.

[0756] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0757] Step 1:

[0758] The terminal receives personal information from the user. This information includes age, gender, height, weight, and daily activity level. This information is sent to the server as basic health data.

[0759] Step 2:

[0760] The server stores personal information received from the terminal in a database. Based on this stored information, it prepares to generate an initial motor plan. The data is then organized for later analysis.

[0761] Step 3:

[0762] External devices acquire the user's health-related information in real time and transmit it to the server via the terminal. This includes activity levels, heart rate, and sleep patterns. The server integrates this new data with existing data and updates the database.

[0763] Step 4:

[0764] The server analyzes health-related information collected in real time and self-reported data from users. This analysis utilizes a generative AI model based on TensorFlow to predict the user's emotional state. The analysis results are output as an index indicating the individual's emotional state.

[0765] Step 5:

[0766] The server adjusts the exercise plan and meal menu based on the output of the generated AI model. For users predicted to be high in stress, it suggests exercises with relaxation effects and meals that are expected to reduce stress. The adjusted plan is created and sent to the device.

[0767] Step 6:

[0768] Users review the exercise plans and meal menus provided on their devices and incorporate them into their daily activities. After completing the activities, they input feedback and send this information to the server via their devices. This feedback is used to optimize future plans.

[0769] Step 7:

[0770] The server analyzes the received feedback and extracts areas for improvement in exercise plans and meal menus. The data obtained from this analysis is used to update the database for future plan generation. In this way, the system can provide dynamic plans tailored to the user's health and emotional state.

[0771] 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.

[0772] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0773] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0774] 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.

[0775] Figure 9 shows an 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.

[0776] 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.

[0777] 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.

[0778] 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, motorcycles, etc., 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, for example, based 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.

[0779] 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."

[0780] 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.

[0781] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0782] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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 the like 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.

[0791] 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.

[0792] The following is further disclosed regarding the embodiments described above.

[0793] (Claim 1)

[0794] Methods for entering personal information,

[0795] Means for storing entered personal information,

[0796] A means of generating an optimal training plan based on stored personal information,

[0797] A means of providing a generated training plan,

[0798] A means of receiving user feedback and updating the training plan,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, comprising means for acquiring and integrating user health-related data from an external device.

[0802] (Claim 3)

[0803] The system according to claim 1, comprising means for suggesting a meal menu that takes nutritional balance into consideration based on the user's dietary information.

[0804] "Example 1"

[0805] (Claim 1)

[0806] A means of inputting personal biometric information,

[0807] A means for storing the input biometric information,

[0808] A means for creating an optimized exercise plan based on stored biological information,

[0809] Means for providing the created exercise plan,

[0810] A means of receiving user feedback and improving the exercise plan,

[0811] A method for analyzing biological information and proposing meal menus that take nutritional balance into consideration,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, comprising means for acquiring and integrating user health data from an external device.

[0815] (Claim 3)

[0816] The system according to claim 1, comprising means for transmitting a generated exercise plan and a suggested meal menu to the user's terminal.

[0817] "Application Example 1"

[0818] (Claim 1)

[0819] A function for entering personal information,

[0820] A function to save the entered personal information,

[0821] A function that generates an optimal exercise plan based on saved personal information,

[0822] Features that present the generated exercise plan,

[0823] Features that receive user feedback and improve exercise plans,

[0824] A function that provides voice guidance to the user through a robot,

[0825] A feature that provides voice feedback on your health management plan,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, comprising a function for acquiring and integrating user health-related information from an external measuring device.

[0829] (Claim 3)

[0830] The system according to claim 1, which has a function to propose a meal plan that takes nutritional balance into consideration based on the user's meal information.

[0831] "Example 2 of combining an emotion engine"

[0832] (Claim 1)

[0833] A means of entering personal basic information,

[0834] A means for saving the entered information,

[0835] A means of analyzing emotional states using acquired health-related information,

[0836] A means for generating an optimal movement plan based on the analysis results,

[0837] Means for providing an exercise plan,

[0838] A means of receiving user feedback and modifying the exercise plan,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, comprising means for adjusting the meal plan based on the user's health condition.

[0842] (Claim 3)

[0843] The system according to claim 1, comprising means for monitoring the user's emotional state and making suggestions that take mental health into consideration.

[0844] "Application example 2 when combining with an emotional engine"

[0845] (Claim 1)

[0846] Methods for entering personal information,

[0847] Means for storing entered personal information,

[0848] A means of generating an optimal exercise plan based on stored personal information,

[0849] Means for providing a generated motion plan,

[0850] A means of receiving user feedback and updating the exercise plan,

[0851] A means to analyze the user's emotional state and track its changes in real time,

[0852] A means of adjusting the exercise plan based on the analysis results,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, comprising means for acquiring and integrating user health-related information from external devices.

[0856] (Claim 3)

[0857] The system according to claim 1, comprising means for suggesting meal menus that take into account nutritional balance and mental health based on the user's dietary information. [Explanation of Symbols]

[0858] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Methods for entering personal information, Means for storing entered personal information, A means of generating an optimal training plan based on stored personal information, A means of providing a generated training plan, A means of receiving user feedback and updating the training plan, A system that includes this.

2. The system according to claim 1, comprising means for acquiring and integrating user health-related data from an external device.

3. The system according to claim 1, comprising means for suggesting a meal menu that takes nutritional balance into consideration based on the user's dietary information.

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