system

Abstract

We provide the system. [Solution] Means of collecting health status information, A means for generating an individualized exercise plan using the aforementioned health status information, Means for presenting the individualized motion plan to a receiving device, A means of obtaining user feedback information and updating the analysis model, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Even if one tries to improve health through effective exercise plans and diet menus, it is difficult to receive specific guidance according to individual needs. In particular, in a diversified modern society, a personalized approach tailored to an individual's lifestyle is required. For this reason, there is a need for a system that allows users to obtain an optimal fitness plan at a low cost while daily grasping their health status. Furthermore, the mechanism for making more accurate proposals by continuously reflecting user feedback is not sufficient.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a system that includes means for collecting health status information from wearable devices and generates an individualized exercise plan based on that data. The generated exercise plan is presented to a receiving device and is easily usable by the user. Furthermore, by providing means for acquiring feedback information from the user and updating the analysis model, the proposed plan is always up-to-date and optimal. In addition, by including a personalized meal menu along with the exercise plan in this system, even more effective health improvement becomes possible.

[0006] "Health status information" refers to the user's physical and physiological data, including heart rate, steps taken, calories burned, and sleep patterns.

[0007] A "wearable device" refers to a device that is attached to the user's body and can measure and record health information in real time.

[0008] A "personalized exercise plan" refers to a customized fitness plan generated based on the user's health information and specific goals.

[0009] A "receiving device" refers to a device that displays an individualized exercise plan and allows the user to review it.

[0010] "Feedback information" refers to information obtained from users, such as their impressions, results, and physical changes, and is used for the continuous improvement of the system.

[0011] An "analysis model" refers to an AI algorithm that uses collected data to generate optimal exercise plans and meal menus for users.

[0012] A "meal menu" refers to a specific meal plan proposed in relation to the user's exercise plan, with the aim of improving their health. [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

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

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

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single 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, the 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, the 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 system provides users with effective fitness plans and meal menus by linking wearable devices, servers, and terminals.

[0035] System configuration:

[0036] 1. Functions of wearable devices

[0037] By wearing a wearable device on their body, users can obtain real-time health information such as heart rate, steps taken, calories burned, and sleep patterns. This device can record the user's movements in their daily life and transmit this data to a server.

[0038] 2. Server Functions

[0039] The server receives health information transmitted from wearable devices and stores it in a database. Using this data, an analysis model (AI algorithm) generates a personalized exercise plan and meal plan optimized for the user. The server also collects feedback from users to continuously improve the analysis model.

[0040] 3. Device functions

[0041] The device displays a personalized exercise plan and meal menu to the user. Users can view detailed exercise plans and meal recipes through the device. They can also send feedback via the device after completing their workout.

[0042] Specific example:

[0043] For example, if a user wears a wearable device throughout the day, their health status information for that day (e.g., 10,000 steps taken, 2,000 kcal burned) is recorded on the device. The server receives this data and, taking into account the user's health goals (e.g., 2kg weight loss or increased muscle mass), generates an exercise plan for the next day (e.g., 30 minutes of running, 10 minutes of stretching) and a meal plan (e.g., protein shake for breakfast, vegetable salad for lunch, chicken-centered menu for dinner).

[0044] The user reviews and executes the generated plan on their device. After execution, the user provides feedback through their device (e.g., "I felt less fatigued during the run"). The server then uses the provided feedback to update its analysis model and adjust the next training plan to better suit the user.

[0045] In this way, the present invention can continue to provide fitness plans optimized for individual users.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] Users wear wearable devices on their bodies while going about their daily lives. The wearable devices continuously record health information such as the user's heart rate, steps taken, calories burned, and activity time.

[0049] Step 2:

[0050] The device periodically receives data from wearable devices via Bluetooth or Wi-Fi. This received data includes health status information and measurement times.

[0051] Step 3:

[0052] The server receives health status information transmitted through the terminal and stores it in a database. This data is used as input data for the analysis model.

[0053] Step 4:

[0054] The server utilizes an AI model based on stored health information to generate personalized exercise plans and meal menus tailored to the user's current health status and goals.

[0055] Step 5:

[0056] The terminal visually displays the exercise plan and meal menu received from the server to the user. The user can then plan and execute their daily activities based on this information.

[0057] Step 6:

[0058] Users input the results of their training according to their exercise plan and their impressions of their meal plan on their device, and send this feedback to the server.

[0059] Step 7:

[0060] The server analyzes the feedback received from the user and adjusts the generated AI model to further improve the next exercise plan and meal menu. By repeating this process, the system continues to provide personalized suggestions.

[0061] (Example 1)

[0062] 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."

[0063] In modern society, many individuals seek to manage their own health and develop appropriate exercise and meal plans. However, existing systems on the market struggle to provide plans that accurately reflect an individual's health condition and goals, and they lack sufficient flexibility in responding to feedback. Therefore, there is a need to efficiently generate more precise and personalized exercise plans and meal menus to effectively support users' health management.

[0064] 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.

[0065] In this invention, the server includes means for using a device for collecting health status data, means for formulating an individualized exercise plan and meal menu using a generated AI model based on the health status data, and means for displaying the individualized exercise plan and meal menu on a display device. This makes it possible to provide a plan optimized for the user and to make adaptive improvements to it.

[0066] "Health status data" refers to information collected as health indicators, such as an individual's heart rate, steps taken, calories burned, and sleep patterns.

[0067] "Means using an apparatus" refers to a method of achieving an objective using a machine or device having a specific function.

[0068] A "generative AI model" is an artificial intelligence system established based on machine learning algorithms to identify specific patterns and make predictions based on data.

[0069] An "individualized exercise plan" refers to an exercise schedule or plan that is tailored to an individual's health condition and goals.

[0070] A "meal menu" is a list of meals that are put together with the aim of providing a specific nutritional value or calorie content.

[0071] A "display device" is a device used to visually present information and is used by users to check plans and data.

[0072] "Response data" refers to information obtained as feedback and evaluations from users, which is useful for improving the system.

[0073] This system is configured to function effectively using wearable devices, servers, and terminals to allow users to manage their health status and receive optimal exercise plans and meal menus.

[0074] The user first uses a wearable data collection device. This device acquires health data such as heart rate, steps taken, calories burned, and sleep patterns in real time. This collected data is transmitted to a server via wireless communication or other means.

[0075] The server stores the received health data in a dedicated database and analyzes it using a generative AI model. Specifically, it utilizes a server computer with high data processing capabilities, and the software includes machine learning algorithms and AI models. This AI model generates personalized exercise plans and meal plans based on the user's data. It is designed to provide the optimal plan by taking into account the user's health goals and past performance.

[0076] The device presents the user with a generated exercise plan and meal menu. The user can view specific exercise instructions and meal recipes through the device. For example, if the user's goal is weight loss, a plan including jogging three times a week and a low-calorie meal plan might be provided.

[0077] A distinctive feature of this system is the ability for users to provide feedback via their devices after completing their exercise plans and meal menus. For example, the server can acquire user feedback such as "The training felt easier than I expected" and use it to improve its analysis model.

[0078] For example, if a user completes a 5km run and provides feedback to the system stating that they felt less fatigued during the run, the system may adjust its plan to generate a slightly more intense workout next time. Another example of a prompt to input into the generating AI model is, "How should I plan my exercise to lose 2kg in the next week?"

[0079] In this way, this system can effectively support personalized health management for each user.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] Users collect health data such as heart rate, steps taken, calories burned, and sleep patterns using wearable devices. This data is recorded in real time, and the collected data is output. Specifically, the user wears the wearable device throughout the day while active.

[0083] Step 2:

[0084] The server receives health status data obtained from wearable devices. The server uses this data as input and stores it in a dedicated database. This storage process involves data format conversion and chronological organization. The output is the organized data from the database.

[0085] Step 3:

[0086] The server uses stored health status data to perform analysis with a generating AI model. Based on the input health status data, the AI ​​model generates an optimal, personalized exercise plan and meal menu for the user. Specifically, machine learning algorithms are utilized to perform data analysis and plan formulation calculations. As a result, the plan output is obtained.

[0087] Step 4:

[0088] The terminal displays an exercise plan and meal menu, which are output from the server, to the user. The user reviews this information through the terminal and then actually executes the plan. Specifically, the terminal's display shows detailed exercise instructions and meal recipes.

[0089] Step 5:

[0090] After completing their exercise and meal plans, users send feedback to the server via their device. This feedback serves as input, and the server retrieves this information to improve its analysis algorithms. For example, a user might input a comment such as "The training was not too strenuous" into their device. The output is then a revised plan for the next session.

[0091] (Application Example 1)

[0092] 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."

[0093] In today's world, where personalized health management is essential, there is a need to provide individualized fitness and meal plans tailored to each user's lifestyle and health condition. However, currently, general health management applications and devices are insufficient for individualization, and there are difficulties in providing effective feedback to users.

[0094] 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.

[0095] In this invention, the server includes means for collecting health status information, means for generating an individualized exercise plan, and means for providing the exercise plan and meal menu via voice or visual display using a home-use automated machine. This enables the suggestion of a fitness plan based on the user's health status and supports effective health management.

[0096] "Health status information" refers to data that indicates the user's physical health status, including daily activity information such as heart rate, steps taken, calories burned, and sleep patterns.

[0097] An "individualized exercise plan" is a plan that includes the type, duration, and intensity of exercise optimized for each user, based on collected health information.

[0098] A "presentation device" is a device that displays generated exercise plans and meal menus to users, providing information through sound and visual means.

[0099] "Feedback information" refers to opinions and comments provided by users after they have exercised or eaten, and is used as data to optimize their next plan.

[0100] An "analysis algorithm" is a computational method that combines collected health status information and feedback information to generate and update personalized plans.

[0101] A "household automated machine" is an automated device used in the home, a robot equipped with functions for health management and fitness support.

[0102] A "nutritional intake plan" is a plan that proposes the optimal diet and nutritional balance based on the user's health condition and goals.

[0103] This invention is a system that provides personalized health management by coordinating wearable devices, a server, a terminal, and home-use automated machines. The wearable devices have the function of monitoring health status information such as heart rate, steps, calories burned, and sleep patterns in real time and transmitting the data to the server. The server processes the received data using an analysis algorithm and generates personalized exercise plans and nutrition intake plans.

[0104] The home-use automated machine provides users with generated plans via voice and display. This allows users to intuitively understand and implement details of their daily fitness activities and diet. The device also collects feedback from users after exercise and meals are performed and sends it to a server. This feedback is used to improve the accuracy of future plans.

[0105] The primary hardware used includes household automated machines (e.g., typical household robots). The software utilizes Python and AI analytics platforms such as TENSORFLOW® for analyzing health status information, a Flask server for data transmission and reception, and Google® Speech-to-Text API for speech recognition. An intuitive interface for planning display and feedback collection is also a crucial element to ensure user convenience.

[0106] (Specific example)

[0107] For example, a user utilizes a health management system, and based on data acquired from a wearable device, a home-use automated exercise machine might suggest, via voice, "Today, let's do a 30-minute jog and 10 minutes of stretching." The user then completes the plan and sends feedback to the device afterward, such as, "The jogging was fine, but the stretching was a bit tough." The server receives this information and incorporates it into the next plan.

[0108] (Example of a prompt message)

[0109] "Based on the user's health data, please suggest the optimal fitness plan and meal menu for today."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user attaches a wearable device to their body and begins collecting health information. At this stage, the device records data such as heart rate, steps taken, calories burned, and sleep patterns in real time. The collected data is transmitted to a server via the network. The input to the server is the user's health information.

[0113] Step 2:

[0114] The server stores the received health status information in a database. Furthermore, it uses the stored data to perform analysis through an AI analysis platform. The input here is the health status information collected in step 1, and the output is the generation of a personalized exercise plan and nutrition intake plan.

[0115] Step 3:

[0116] The server transmits the generated exercise and nutrition plans to the home automated machine. The home automated machine receives this information and presents it to the user via voice or display. The input in this step is the plan information from the server, and the output is the plan presentation to the user.

[0117] Step 4:

[0118] The user follows the provided exercise and meal plan. Feedback during the plan execution (e.g., sensations during exercise and meal content) is recorded and sent to the server via the device. User input constitutes feedback information, which is then output to the server.

[0119] Step 5:

[0120] The server receives and stores user feedback information and updates the analysis algorithm. This update allows the generative AI model to provide more individually optimized suggestions in the next plan generation. The input is feedback information, and the output is the next exercise and meal plan generated by the improved generative AI model.

[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 system integrates wearable devices, servers, terminals, and an emotion engine to comprehensively provide users with personalized exercise plans and meal menus.

[0123] System configuration:

[0124] 1. Functions of wearable devices

[0125] The user uses a wearable device to continuously measure health information such as heart rate, activity level, and calories burned. This allows the device to record the user's physiological data in real time and transfer it to the terminal.

[0126] 2. Function of the Emotion Engine

[0127] The server, via the terminal, analyzes data collected from the user's daily activities and activates an emotion engine to estimate the user's emotional state. This engine analyzes emotions based on the user's voice, facial expressions, and past behavioral history to understand the user's motivation and stress levels.

[0128] 3. Server Functions

[0129] The server uses data from wearable devices and an emotion engine to generate personalized exercise plans and meal plans optimized for the user's health and emotions. These generated plans are tailored to improve daily health and well-being.

[0130] 4. Device functions

[0131] The device displays personalized exercise plans and meal menus transmitted from the server to the user. Furthermore, an emotion engine adjusts the suggestions according to the user's emotions, presenting them in a comfortable and easy-to-follow format. Through the device, the user can review their exercise and meal plans and send feedback after completing them.

[0132] Specific example:

[0133] For example, if a user is facing a stressful daily life and this is detected through a wearable device and an emotion engine, the server will adjust the generated exercise plan to include relaxing yoga and deep breathing exercises. Similarly, the meal menu will feature dishes utilizing ingredients rich in vitamins and minerals to improve mood. Users can review these suggestions on their device and easily implement them.

[0134] Based on this feedback, the server utilizes the output of the emotion engine to continuously improve its analysis model, thereby providing users with optimal health maintenance support.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] Users wear wearable devices while engaging in daily activities, collecting real-time health information such as heart rate and activity levels. This allows for continuous monitoring of their physical condition.

[0138] Step 2:

[0139] The device periodically receives health status information from wearable devices via Bluetooth or Wi-Fi. The received data is then sent to a server.

[0140] Step 3:

[0141] The server receives health status information transmitted from the terminal and stores it in a database. At the same time, the server uses an emotion engine to analyze the user's voice and facial expression data to estimate their everyday emotional state.

[0142] Step 4:

[0143] The server utilizes a generative AI model based on health and emotional status to generate personalized exercise plans and meal plans optimized for the user. These plans may include exercises and dietary recommendations that help manage stress and improve emotional well-being.

[0144] Step 5:

[0145] The device visually displays personalized exercise plans and meal menus sent from the server to the user. Furthermore, it adjusts the plan content based on the analysis results of the emotion engine, presenting it in a way that is easy for the user to follow.

[0146] Step 6:

[0147] Users follow the provided plan for exercise and meals, and after completing it, they input feedback on the results and their impressions via their device.

[0148] Step 7:

[0149] The server receives user feedback and adjusts its analysis model and sentiment engine accordingly. This information is used to improve the accuracy of future suggestions. This allows the server to consistently provide the best possible support to the user.

[0150] (Example 2)

[0151] 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".

[0152] Many people in modern society face the problem of inadequate health management due to stress and irregular lifestyles. Traditional health management systems have difficulty effectively incorporating individual emotional states and feedback, resulting in insufficient provision of personalized health guidance and dietary plans.

[0153] 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.

[0154] In this invention, the server includes means for analyzing emotional states based on physiological information, means for generating personalized exercise guidance and meal plans based on the physiological information and emotional states, and means for acquiring feedback information from the user and updating the model using an analysis device. This enables personalized health management that comprehensively considers the user's health status and emotions.

[0155] "Physiological information" refers to data on health status acquired by wearable devices, such as heart rate, activity level, and calories burned.

[0156] "Emotional state" refers to information that indicates the user's psychological state, analyzed based on the user's voice, facial expressions, and past behavioral history.

[0157] "Personalized exercise instruction" refers to an exercise program customized to take into account the user's specific health and emotional state.

[0158] A "meal plan" is a menu with optimized nutrients and ingredients, provided with the aim of improving the user's health and emotional state.

[0159] A "portable measuring device" is a small electronic device that can be worn by the user to continuously collect health data.

[0160] A "visual display device" is a device equipped with a display or screen for providing users with generated exercise guidance and meal plans.

[0161] An "analysis system" is a configuration that includes hardware and software for performing data analysis and model updates based on collected physiological information and feedback.

[0162] To implement the invention, this system utilizes a series of hardware and software components for collecting and analyzing physiological information. Specifically, the user wears a portable measuring device that measures physiological information such as heart rate, activity level, and calories burned in real time and transmits it to a terminal. The terminal then transmits this data to a server.

[0163] The server is equipped with emotion analysis software to analyze received physiological information, estimating the user's emotional state by considering their voice, facial expressions, and past behavioral history. This emotion analysis utilizes advanced algorithms and generative AI models to enable more accurate analysis.

[0164] The server then integrates physiological information and emotional state to generate personalized exercise guidance and meal plans. This includes content based on the user's health goals and daily activities. The generated plans are presented to the user through the terminal's visual display.

[0165] The terminal visually presents the generation plan to the user while also having the function to receive user feedback. The user feedback information is then sent back to the server, which processes it with an analysis device and continuously updates the generated AI model.

[0166] As a concrete example, if a user is detected to be under high stress, the server will provide a plan that includes relaxation-enhancing exercises such as yoga. Similarly, a meal plan will suggest vitamin-rich menus. An example of a prompt using a generative AI model would be, "High stress level detected. Please suggest content effective for relaxation."

[0167] This system configuration allows users to receive plans that flexibly respond to their own health and emotional state, enabling them to manage their health comfortably and effectively.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The user wears a portable measuring device. The device measures physiological information such as heart rate, activity level, and calories burned in real time. This data serves as input. This data is automatically transferred to a terminal, which then sends it to a server as physiological data. The physiological data is stored on the server as output.

[0171] Step 2:

[0172] The server runs emotion analysis software to analyze physiological data received from wearable devices. The input is physiological data. Based on this, the server combines the user's voice, facial expressions, and past behavioral history to perform calculations that estimate the user's emotional state. The emotional state is obtained as output and used for subsequent processing.

[0173] Step 3:

[0174] The server integrates physiological data and emotional states, and utilizes a generative AI model to generate personalized exercise guidance and meal plans. The input for this step is the previously analyzed physiological data and emotional states. Data calculations create exercise and meal plans based on the user's characteristics. The resulting exercise guidance and meal plans include instructions tailored to the user's health condition.

[0175] Step 4:

[0176] The terminal receives personalized exercise plans and meal menus sent from the server and presents them to the user visually. The input consists of exercise guidance and meal plans from the server. The terminal displays this information clearly for the user, and the output allows the user to review their own health guidance.

[0177] Step 5:

[0178] Users implement the provided exercise and meal plans and provide feedback on the results and their opinions through the device. The input is the user's feedback information. The device sends this information to the server. The output is the user's feedback information, which is used for the next model update.

[0179] Step 6:

[0180] The server receives user feedback, and the analysis system updates the generated AI model to improve the accuracy of analysis and estimation. The input is user feedback information; data processing updates the generated AI model, resulting in an improved model as output. This update process is reflected in the subsequent provision of personalized plans.

[0181] (Application Example 2)

[0182] 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".

[0183] The problem that this invention aims to solve is to provide a system that provides comprehensive health management that takes into account not only the user's physical condition but also their emotional state. Specifically, it is required to understand the user's daily emotions and stress levels and provide personalized exercise plans and meal menus based on that information to achieve more effective health maintenance. Furthermore, it also includes the challenge of continuously improving the accuracy of the system by having users provide direct feedback.

[0184] 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.

[0185] In this invention, the server includes means for collecting physiological data, means for estimating the user's emotional state using the physiological data and voice / facial information, and means for generating a personalized exercise plan and meal menu based on the emotional state. This enables comprehensive and effective health management that takes into account both the user's physical and emotional state.

[0186] "Physiological data" refers to data that indicates the user's physical condition, such as heart rate, activity level, and energy expenditure.

[0187] A "portable device" is a device that a user wears or carries with them to measure their physical condition.

[0188] "Emotional state" refers to the user's psychological and emotional state, estimated based on voice and facial expression information.

[0189] A "personalized exercise plan" is an exercise program optimized based on the user's health and emotional state.

[0190] A "meal menu" is a nutritional plan that takes into account the user's health and emotional state.

[0191] A "humanoid assistant device" is a humanoid robot that can interact with users and provide guidance on exercise and diet.

[0192] "Feedback information" refers to information about the results and experiences provided by users.

[0193] A "learning model" is an information processing algorithm that generates optimized suggestions for the user based on accumulated data.

[0194] "Demonstrating physical exercise" means that a humanoid assistant device physically performs exercises together with the user to instruct them on how to do those exercises.

[0195] The system implementing this invention utilizes a portable device, an emotion analysis module, and a humanoid assistant device to comprehensively manage the user's health and emotional state.

[0196] First, the user uses a portable device to collect physiological data in real time. This device could be a data collection device such as a smartwatch. This data is sent to a server, where an emotion analysis module then analyzes the user's emotional state using a voice recognition system and facial expression analysis tools. Specifically, voice and facial expression data are captured and analyzed using a platform such as IBM Watson® or Microsoft® Azure® API to determine the user's psychological tendencies.

[0197] Based on this information, the server generates personalized exercise plans and meal plans for the user. These plans are optimized for maintaining daily health, taking into account the user's health and emotional state. In particular, users experiencing stress may be presented with plans for relaxing yoga or breathing exercises.

[0198] Furthermore, a humanoid assistant device will interact with the user, demonstrating exercises and providing dietary guidance. This device has a humanoid interface similar to SoftBank's robots, and can perform physical exercises together with the user and instruct them on correct form.

[0199] Users can execute the proposed plan through this system and provide feedback to the server. Based on this feedback, the server learns through its AI model and continuously improves the accuracy of its analysis.

[0200] For example, a suggestion for a particularly stressful day might be something like, "Today is a mentally demanding day, so please do a 30-minute relaxing yoga session. Afterwards, enjoy a vitamin-rich kiwi and grapefruit salad."

[0201] An example of a prompt message might be: "We've detected that the user is stressed based on their heart rate and tone of voice. Create a relaxing yoga routine and have the robot guide them according to their preferences."

[0202] Through this process, personal health management has been achieved, making it more convenient and highly efficient.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The user wears a portable device to collect physiological data. Inputs include physiological data such as heart rate, activity level, and calories burned, while output is an aggregate of this data. The portable device records this data in real time and transmits it wirelessly to a server.

[0206] Step 2:

[0207] The server prepares the received physiological data for sentiment analysis. The input is physiological data transmitted from a portable device, and the output is a dataset in a format suitable for machine learning models. The server preprocesses this data and transfers it to the sentiment analysis module.

[0208] Step 3:

[0209] The server activates the emotion analysis module and estimates the emotional state using a speech recognition system and facial expression analysis tool. The input is a dataset and speech and facial expression information obtained from the user, and the output is the user's emotional state. The emotion analysis module saves the analysis results and provides them for the next processing step.

[0210] Step 4:

[0211] The server uses a generative AI model to generate personalized exercise plans and meal plans based on emotional state and physiological data. The input is emotional state and physiological data, and the output is an optimized exercise plan and meal plan. This generated plan is then formatted in a way that is helpful for the user's health management.

[0212] Step 5:

[0213] The server transmits the generated exercise plan and meal menu to the humanoid assistant device, which then instructs it to display and execute the plan. The input is the generated plan data, and the output is visual and audio guidance for the user. The humanoid assistant device gives voice instructions and, if necessary, demonstrates the physical movements.

[0214] Step 6:

[0215] The user executes the provided plan and sends feedback information to the server via their device. The input is the user's feedback, and the output is the feedback data collected on the server. The user communicates their specific experiences and the challenges they encountered.

[0216] Step 7:

[0217] The server updates its learning model using collected feedback information, improving the system so that it can provide more appropriate suggestions the next time it is used. The input is feedback data, and the output is the updated learning model. Through this process, the server improves the accuracy of its suggestions to the user.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] This system provides users with effective fitness plans and meal menus by linking wearable devices, servers, and terminals.

[0235] System configuration:

[0236] 1. Functions of wearable devices

[0237] By wearing a wearable device on their body, users can obtain real-time health information such as heart rate, steps taken, calories burned, and sleep patterns. This device can record the user's movements in their daily life and transmit this data to a server.

[0238] 2. Server Functions

[0239] The server receives health information transmitted from wearable devices and stores it in a database. Using this data, an analysis model (AI algorithm) generates a personalized exercise plan and meal plan optimized for the user. The server also collects feedback from users to continuously improve the analysis model.

[0240] 3. Device functions

[0241] The device displays a personalized exercise plan and meal menu to the user. Users can view detailed exercise plans and meal recipes through the device. They can also send feedback via the device after completing their workout.

[0242] Specific example:

[0243] For example, if a user wears a wearable device throughout the day, their health status information for that day (e.g., 10,000 steps taken, 2,000 kcal burned) is recorded on the device. The server receives this data and, taking into account the user's health goals (e.g., 2kg weight loss or increased muscle mass), generates an exercise plan for the next day (e.g., 30 minutes of running, 10 minutes of stretching) and a meal plan (e.g., protein shake for breakfast, vegetable salad for lunch, chicken-centered menu for dinner).

[0244] The user reviews and executes the generated plan on their device. After execution, the user provides feedback through their device (e.g., "I felt less fatigued during the run"). The server then uses the provided feedback to update its analysis model and adjust the next training plan to better suit the user.

[0245] In this way, the present invention can continue to provide fitness plans optimized for individual users.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] Users wear wearable devices on their bodies while going about their daily lives. The wearable devices continuously record health information such as the user's heart rate, steps taken, calories burned, and activity time.

[0249] Step 2:

[0250] The device periodically receives data from wearable devices via Bluetooth or Wi-Fi. This received data includes health status information and measurement times.

[0251] Step 3:

[0252] The server receives health status information transmitted through the terminal and stores it in a database. This data is used as input data for the analysis model.

[0253] Step 4:

[0254] The server utilizes an AI model based on stored health information to generate personalized exercise plans and meal menus tailored to the user's current health status and goals.

[0255] Step 5:

[0256] The terminal visually displays the exercise plan and meal menu received from the server to the user. The user can then plan and execute their daily activities based on this information.

[0257] Step 6:

[0258] Users input the results of their training according to their exercise plan and their impressions of their meal plan on their device, and send this feedback to the server.

[0259] Step 7:

[0260] The server analyzes the feedback received from the user and adjusts the generated AI model to further improve the next exercise plan and meal menu. By repeating this process, the system continues to provide personalized suggestions.

[0261] (Example 1)

[0262] 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."

[0263] In modern society, many individuals seek to manage their own health and develop appropriate exercise and meal plans. However, existing systems on the market struggle to provide plans that accurately reflect an individual's health condition and goals, and they lack sufficient flexibility in responding to feedback. Therefore, there is a need to efficiently generate more precise and personalized exercise plans and meal menus to effectively support users' health management.

[0264] 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.

[0265] In this invention, the server includes means for using a device for collecting health status data, means for formulating an individualized exercise plan and meal menu using a generated AI model based on the health status data, and means for displaying the individualized exercise plan and meal menu on a display device. This makes it possible to provide a plan optimized for the user and to make adaptive improvements to it.

[0266] "Health status data" refers to information collected as health indicators, such as an individual's heart rate, steps taken, calories burned, and sleep patterns.

[0267] "Means using an apparatus" refers to a method of achieving an objective using a machine or device having a specific function.

[0268] A "generative AI model" is an artificial intelligence system established based on machine learning algorithms to identify specific patterns and make predictions based on data.

[0269] An "individualized exercise plan" refers to an exercise schedule or plan that is tailored to an individual's health condition and goals.

[0270] A "meal menu" is a list of meals that are put together with the aim of providing a specific nutritional value or calorie content.

[0271] A "display device" is a device used to visually present information and is used by users to check plans and data.

[0272] "Response data" refers to information obtained as feedback and evaluations from users, which is useful for improving the system.

[0273] This system is configured to function effectively using wearable devices, servers, and terminals to allow users to manage their health status and receive optimal exercise plans and meal menus.

[0274] The user first uses a wearable data collection device. This device acquires health data such as heart rate, steps taken, calories burned, and sleep patterns in real time. This collected data is transmitted to a server via wireless communication or other means.

[0275] The server stores the received health data in a dedicated database and analyzes it using a generative AI model. Specifically, it utilizes a server computer with high data processing capabilities, and the software includes machine learning algorithms and AI models. This AI model generates personalized exercise plans and meal plans based on the user's data. It is designed to provide the optimal plan by taking into account the user's health goals and past performance.

[0276] The device presents the user with a generated exercise plan and meal menu. The user can view specific exercise instructions and meal recipes through the device. For example, if the user's goal is weight loss, a plan including jogging three times a week and a low-calorie meal plan might be provided.

[0277] A distinctive feature of this system is the ability for users to provide feedback via their devices after completing their exercise plans and meal menus. For example, the server can acquire user feedback such as "The training felt easier than I expected" and use it to improve its analysis model.

[0278] For example, if a user completes a 5km run and provides feedback to the system stating that they felt less fatigued during the run, the system may adjust its plan to generate a slightly more intense workout next time. Another example of a prompt to input into the generating AI model is, "How should I plan my exercise to lose 2kg in the next week?"

[0279] In this way, this system can effectively support personalized health management for each user.

[0280] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0281] Step 1:

[0282] The user uses a wearable device to collect health status data such as heart rate, number of steps, calories burned, sleep patterns, etc. These data are recorded in real time, and the collected data is output. As a specific operation, it is for the user to wear the wearable device throughout the day and be active.

[0283] Step 2:

[0284] The server receives the health status data obtained from the wearable device. The server uses this as input and stores it in a dedicated database. In this storage process, data format conversion and sorting based on time series are performed. As output, the data in the sorted database is obtained.

[0285] Step 3:

[0286] The server analyzes the stored health status data using a generated AI model. Based on the input health status data, the AI model generates an individualized exercise plan and diet menu that are optimal for the user. Specifically, machine learning algorithms are utilized, and data analysis and calculation processing for plan formulation are performed. As a result, the output of the plan is obtained.

[0287] Step 4:

[0288] The terminal presents the exercise plan and diet menu, which are the output from the server, to the user. The user checks this information through the terminal and actually executes the plan. As a specific operation, it includes displaying detailed exercise instructions and diet recipes through the display of the terminal.

[0289] Step 5:

[0290] After completing their exercise and meal plans, users send feedback to the server via their device. This feedback serves as input, and the server retrieves this information to improve its analysis algorithms. For example, a user might input a comment such as "The training was not too strenuous" into their device. The output is then a revised plan for the next session.

[0291] (Application Example 1)

[0292] 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."

[0293] In today's world, where personalized health management is essential, there is a need to provide individualized fitness and meal plans tailored to each user's lifestyle and health condition. However, currently, general health management applications and devices are insufficient for individualization, and there are difficulties in providing effective feedback to users.

[0294] 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.

[0295] In this invention, the server includes means for collecting health status information, means for generating an individualized exercise plan, and means for providing the exercise plan and meal menu via voice or visual display using a home-use automated machine. This enables the suggestion of a fitness plan based on the user's health status and supports effective health management.

[0296] "Health status information" refers to data that indicates the user's physical health status, including daily activity information such as heart rate, steps taken, calories burned, and sleep patterns.

[0297] An "individualized exercise plan" is a plan that includes the type, duration, and intensity of exercise optimized for each user, based on collected health information.

[0298] A "presentation device" is a device that displays generated exercise plans and meal menus to users, providing information through sound and visual means.

[0299] "Feedback information" refers to opinions and comments provided by users after they have exercised or eaten, and is used as data to optimize their next plan.

[0300] An "analysis algorithm" is a computational method that combines collected health status information and feedback information to generate and update personalized plans.

[0301] A "household automated machine" is an automated device used in the home, a robot equipped with functions for health management and fitness support.

[0302] A "nutritional intake plan" is a plan that proposes the optimal diet and nutritional balance based on the user's health condition and goals.

[0303] This invention is a system that provides personalized health management by coordinating wearable devices, a server, a terminal, and home-use automated machines. The wearable devices have the function of monitoring health status information such as heart rate, steps, calories burned, and sleep patterns in real time and transmitting the data to the server. The server processes the received data using an analysis algorithm and generates personalized exercise plans and nutrition intake plans.

[0304] The home-use automated machine provides users with generated plans via voice and display. This allows users to intuitively understand and implement details of their daily fitness activities and diet. The device also collects feedback from users after exercise and meals are performed and sends it to a server. This feedback is used to improve the accuracy of future plans.

[0305] The main hardware to be used includes household automatic machines (e.g., general household robots). For software, AI analysis platforms such as Python and TensorFlow are used for analyzing health status information, Flask server for data transmission and reception, and Google Speech-to-Text API for speech recognition. To enable users to use the system comfortably, an intuitive interface for plan display and feedback collection is also an important element.

[0306] (Specific example)

[0307] For example, when a user uses a health management system and the household automatic machine proposes in voice as an exercise plan generated based on the data obtained from the wearable device: "Let's do 30 minutes of jogging and 10 minutes of stretching today." The user executes the plan and sends the post-exercise impression to the terminal: "Jogging was fine, but the stretching was a bit too intense." The server receives this information and reflects it in the next plan.

[0308] (Example of prompt sentence)

[0309] "Please propose the optimal fitness plan and diet menu for today based on the user's health data."

[0310] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0311] Step 1:

[0312] The user wears the wearable device on the body and starts collecting health status information. At this stage, the device records data such as heart rate, number of steps, calories burned, and sleep pattern in real time. The collected data is sent to the server through the network. The input to the server is the user's health status information.

[0313] Step 2:

[0314] The server stores the received health status information in a database. Furthermore, it uses the stored data to perform analysis through an AI analysis platform. The input here is the health status information collected in step 1, and the output is the generation of a personalized exercise plan and nutrition intake plan.

[0315] Step 3:

[0316] The server transmits the generated exercise and nutrition plans to the home automated machine. The home automated machine receives this information and presents it to the user via voice or display. The input in this step is the plan information from the server, and the output is the plan presentation to the user.

[0317] Step 4:

[0318] The user follows the provided exercise and meal plan. Feedback during the plan execution (e.g., sensations during exercise and meal content) is recorded and sent to the server via the device. User input constitutes feedback information, which is then output to the server.

[0319] Step 5:

[0320] The server receives and stores user feedback information and updates the analysis algorithm. This update allows the generative AI model to provide more individually optimized suggestions in the next plan generation. The input is feedback information, and the output is the next exercise and meal plan generated by the improved generative AI model.

[0321] 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.

[0322] This system integrates wearable devices, servers, terminals, and an emotion engine to comprehensively provide users with personalized exercise plans and meal menus.

[0323] System configuration:

[0324] 1. Functions of wearable devices

[0325] The user uses a wearable device to continuously measure health information such as heart rate, activity level, and calories burned. This allows the device to record the user's physiological data in real time and transfer it to the terminal.

[0326] 2. Function of the Emotion Engine

[0327] The server, via the terminal, analyzes data collected from the user's daily activities and activates an emotion engine to estimate the user's emotional state. This engine analyzes emotions based on the user's voice, facial expressions, and past behavioral history to understand the user's motivation and stress levels.

[0328] 3. Server Functions

[0329] The server uses data from wearable devices and an emotion engine to generate personalized exercise plans and meal plans optimized for the user's health and emotions. These generated plans are tailored to improve daily health and well-being.

[0330] 4. Device functions

[0331] The device displays personalized exercise plans and meal menus transmitted from the server to the user. Furthermore, an emotion engine adjusts the suggestions according to the user's emotions, presenting them in a comfortable and easy-to-follow format. Through the device, the user can review their exercise and meal plans and send feedback after completing them.

[0332] Specific example:

[0333] For example, if a user is facing a stressful daily life and this is detected through a wearable device and an emotion engine, the server will adjust the generated exercise plan to include relaxing yoga and deep breathing exercises. Similarly, the meal menu will feature dishes utilizing ingredients rich in vitamins and minerals to improve mood. Users can review these suggestions on their device and easily implement them.

[0334] Based on this feedback, the server utilizes the output of the emotion engine to continuously improve its analysis model, thereby providing users with optimal health maintenance support.

[0335] The following describes the processing flow.

[0336] Step 1:

[0337] Users wear wearable devices while engaging in daily activities, collecting real-time health information such as heart rate and activity levels. This allows for continuous monitoring of their physical condition.

[0338] Step 2:

[0339] The device periodically receives health status information from wearable devices via Bluetooth or Wi-Fi. The received data is then sent to a server.

[0340] Step 3:

[0341] The server receives health status information transmitted from the terminal and stores it in a database. At the same time, the server uses an emotion engine to analyze the user's voice and facial expression data to estimate their everyday emotional state.

[0342] Step 4:

[0343] The server utilizes a generative AI model based on health and emotional status to generate personalized exercise plans and meal plans optimized for the user. These plans may include exercises and dietary recommendations that help manage stress and improve emotional well-being.

[0344] Step 5:

[0345] The device visually displays personalized exercise plans and meal menus sent from the server to the user. Furthermore, it adjusts the plan content based on the analysis results of the emotion engine, presenting it in a way that is easy for the user to follow.

[0346] Step 6:

[0347] Users follow the provided plan for exercise and meals, and after completing it, they input feedback on the results and their impressions via their device.

[0348] Step 7:

[0349] The server receives user feedback and adjusts its analysis model and sentiment engine accordingly. This information is used to improve the accuracy of future suggestions. This allows the server to consistently provide the best possible support to the user.

[0350] (Example 2)

[0351] 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".

[0352] Many people in modern society face the problem of inadequate health management due to stress and irregular lifestyles. Traditional health management systems have difficulty effectively incorporating individual emotional states and feedback, resulting in insufficient provision of personalized health guidance and dietary plans.

[0353] 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.

[0354] In this invention, the server includes means for analyzing emotional states based on physiological information, means for generating personalized exercise guidance and meal plans based on the physiological information and emotional states, and means for acquiring feedback information from the user and updating the model using an analysis device. This enables personalized health management that comprehensively considers the user's health status and emotions.

[0355] "Physiological information" refers to data on health status acquired by wearable devices, such as heart rate, activity level, and calories burned.

[0356] "Emotional state" refers to information that indicates the user's psychological state, analyzed based on the user's voice, facial expressions, and past behavioral history.

[0357] "Personalized exercise instruction" refers to an exercise program customized to take into account the user's specific health and emotional state.

[0358] A "meal plan" is a menu with optimized nutrients and ingredients, provided with the aim of improving the user's health and emotional state.

[0359] A "portable measuring device" is a small electronic device that can be worn by the user to continuously collect health data.

[0360] A "visual display device" is a device equipped with a display or screen for providing users with generated exercise guidance and meal plans.

[0361] An "analysis system" is a configuration that includes hardware and software for performing data analysis and model updates based on collected physiological information and feedback.

[0362] To implement the invention, this system utilizes a series of hardware and software components for collecting and analyzing physiological information. Specifically, the user wears a portable measuring device that measures physiological information such as heart rate, activity level, and calories burned in real time and transmits it to a terminal. The terminal then transmits this data to a server.

[0363] The server is equipped with emotion analysis software to analyze received physiological information, estimating the user's emotional state by considering their voice, facial expressions, and past behavioral history. This emotion analysis utilizes advanced algorithms and generative AI models to enable more accurate analysis.

[0364] The server then integrates physiological information and emotional state to generate personalized exercise guidance and meal plans. This includes content based on the user's health goals and daily activities. The generated plans are presented to the user through the terminal's visual display.

[0365] The terminal visually presents the generation plan to the user while also having the function to receive user feedback. The user feedback information is then sent back to the server, which processes it with an analysis device and continuously updates the generated AI model.

[0366] As a concrete example, if a user is detected to be under high stress, the server will provide a plan that includes relaxation-enhancing exercises such as yoga. Similarly, a meal plan will suggest vitamin-rich menus. An example of a prompt using a generative AI model would be, "High stress level detected. Please suggest content effective for relaxation."

[0367] This system configuration allows users to receive plans that flexibly respond to their own health and emotional state, enabling them to manage their health comfortably and effectively.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The user wears a portable measuring device. The device measures physiological information such as heart rate, activity level, and calories burned in real time. This data serves as input. This data is automatically transferred to a terminal, which then sends it to a server as physiological data. The physiological data is stored on the server as output.

[0371] Step 2:

[0372] The server runs emotion analysis software to analyze physiological data received from wearable devices. The input is physiological data. Based on this, the server combines the user's voice, facial expressions, and past behavioral history to perform calculations that estimate the user's emotional state. The emotional state is obtained as output and used for subsequent processing.

[0373] Step 3:

[0374] The server integrates physiological data and emotional states, and utilizes a generative AI model to generate personalized exercise guidance and meal plans. The input for this step is the previously analyzed physiological data and emotional states. Data calculations create exercise and meal plans based on the user's characteristics. The resulting exercise guidance and meal plans include instructions tailored to the user's health condition.

[0375] Step 4:

[0376] The terminal receives personalized exercise plans and meal menus sent from the server and presents them to the user visually. The input consists of exercise guidance and meal plans from the server. The terminal displays this information clearly for the user, and the output allows the user to review their own health guidance.

[0377] Step 5:

[0378] Users implement the provided exercise and meal plans and provide feedback on the results and their opinions through the device. The input is the user's feedback information. The device sends this information to the server. The output is the user's feedback information, which is used for the next model update.

[0379] Step 6:

[0380] The server receives user feedback, and the analysis system updates the generated AI model to improve the accuracy of analysis and estimation. The input is user feedback information; data processing updates the generated AI model, resulting in an improved model as output. This update process is reflected in the subsequent provision of personalized plans.

[0381] (Application Example 2)

[0382] 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."

[0383] The problem that this invention aims to solve is to provide a system that provides comprehensive health management that takes into account not only the user's physical condition but also their emotional state. Specifically, it is required to understand the user's daily emotions and stress levels and provide personalized exercise plans and meal menus based on that information to achieve more effective health maintenance. Furthermore, it also includes the challenge of continuously improving the accuracy of the system by having users provide direct feedback.

[0384] 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.

[0385] In this invention, the server includes means for collecting physiological data, means for estimating the user's emotional state using the physiological data and voice / facial information, and means for generating a personalized exercise plan and meal menu based on the emotional state. This enables comprehensive and effective health management that takes into account both the user's physical and emotional state.

[0386] "Physiological data" refers to data that indicates the user's physical condition, such as heart rate, activity level, and energy expenditure.

[0387] A "portable device" is a device that a user wears or carries with them to measure their physical condition.

[0388] "Emotional state" refers to the user's psychological and emotional state, estimated based on voice and facial expression information.

[0389] A "personalized exercise plan" is an exercise program optimized based on the user's health and emotional state.

[0390] A "meal menu" is a nutritional plan that takes into account the user's health and emotional state.

[0391] A "humanoid assistant device" is a humanoid robot that can interact with users and provide guidance on exercise and diet.

[0392] "Feedback information" refers to information about the results and experiences provided by users.

[0393] A "learning model" is an information processing algorithm that generates optimized suggestions for the user based on accumulated data.

[0394] "Demonstrating physical exercise" means that a humanoid assistant device physically performs exercises together with the user to instruct them on how to do those exercises.

[0395] The system implementing this invention utilizes a portable device, an emotion analysis module, and a humanoid assistant device to comprehensively manage the user's health and emotional state.

[0396] First, the user uses a portable device to collect physiological data in real time. This device could be a data collection device such as a smartwatch. This data is sent to a server, where an emotion analysis module then analyzes the user's emotional state using a speech recognition system and facial expression analysis tools. Specifically, it takes in voice and facial expression data and analyzes the user's psychological tendencies using a platform such as IBM Watson or Microsoft Azure API.

[0397] Based on this information, the server generates personalized exercise plans and meal plans for the user. These plans are optimized for maintaining daily health, taking into account the user's health and emotional state. In particular, users experiencing stress may be presented with plans for relaxing yoga or breathing exercises.

[0398] Furthermore, a humanoid assistant device will interact with the user, demonstrating exercises and providing dietary guidance. This device has a humanoid interface similar to SoftBank's robots, and can perform physical exercises together with the user and instruct them on correct form.

[0399] Users can execute the proposed plan through this system and provide feedback to the server. Based on this feedback, the server learns through its AI model and continuously improves the accuracy of its analysis.

[0400] For example, a suggestion for a particularly stressful day might be something like, "Today is a mentally demanding day, so please do a 30-minute relaxing yoga session. Afterwards, enjoy a vitamin-rich kiwi and grapefruit salad."

[0401] An example of a prompt message might be: "We've detected that the user is stressed based on their heart rate and tone of voice. Create a relaxing yoga routine and have the robot guide them according to their preferences."

[0402] Through this process, personal health management has been achieved, making it more convenient and highly efficient.

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] The user wears a portable device to collect physiological data. Inputs include physiological data such as heart rate, activity level, and calories burned, while output is an aggregate of this data. The portable device records this data in real time and transmits it wirelessly to a server.

[0406] Step 2:

[0407] The server prepares the received physiological data for sentiment analysis. The input is physiological data transmitted from a portable device, and the output is a dataset in a format suitable for machine learning models. The server preprocesses this data and transfers it to the sentiment analysis module.

[0408] Step 3:

[0409] The server activates the emotion analysis module and estimates the emotional state using a speech recognition system and facial expression analysis tool. The input is a dataset and speech and facial expression information obtained from the user, and the output is the user's emotional state. The emotion analysis module saves the analysis results and provides them for the next processing step.

[0410] Step 4:

[0411] The server uses a generative AI model to generate personalized exercise plans and meal plans based on emotional state and physiological data. The input is emotional state and physiological data, and the output is an optimized exercise plan and meal plan. This generated plan is then formatted in a way that is helpful for the user's health management.

[0412] Step 5:

[0413] The server transmits the generated exercise plan and meal menu to the humanoid assistant device, which then instructs it to display and execute the plan. The input is the generated plan data, and the output is visual and audio guidance for the user. The humanoid assistant device gives voice instructions and, if necessary, demonstrates the physical movements.

[0414] Step 6:

[0415] The user executes the provided plan and sends feedback information to the server via their device. The input is the user's feedback, and the output is the feedback data collected on the server. The user communicates their specific experiences and the challenges they encountered.

[0416] Step 7:

[0417] The server updates its learning model using collected feedback information, improving the system so that it can provide more appropriate suggestions the next time it is used. The input is feedback data, and the output is the updated learning model. Through this process, the server improves the accuracy of its suggestions to the user.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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".

[0434] This system provides users with effective fitness plans and meal menus by linking wearable devices, servers, and terminals.

[0435] System configuration:

[0436] 1. Functions of wearable devices

[0437] By wearing a wearable device on their body, users can obtain real-time health information such as heart rate, steps taken, calories burned, and sleep patterns. This device can record the user's movements in their daily life and transmit this data to a server.

[0438] 2. Server Functions

[0439] The server receives health information transmitted from wearable devices and stores it in a database. Using this data, an analysis model (AI algorithm) generates a personalized exercise plan and meal plan optimized for the user. The server also collects feedback from users to continuously improve the analysis model.

[0440] 3. Device functions

[0441] The device displays a personalized exercise plan and meal menu to the user. Users can view detailed exercise plans and meal recipes through the device. They can also send feedback via the device after completing their workout.

[0442] Specific example:

[0443] For example, if a user wears a wearable device throughout the day, their health status information for that day (e.g., 10,000 steps taken, 2,000 kcal burned) is recorded on the device. The server receives this data and, taking into account the user's health goals (e.g., 2kg weight loss or increased muscle mass), generates an exercise plan for the next day (e.g., 30 minutes of running, 10 minutes of stretching) and a meal plan (e.g., protein shake for breakfast, vegetable salad for lunch, chicken-centered menu for dinner).

[0444] The user reviews and executes the generated plan on their device. After execution, the user provides feedback through their device (e.g., "I felt less fatigued during the run"). The server then uses the provided feedback to update its analysis model and adjust the next training plan to better suit the user.

[0445] In this way, the present invention can continue to provide fitness plans optimized for individual users.

[0446] The following describes the processing flow.

[0447] Step 1:

[0448] Users wear wearable devices on their bodies while going about their daily lives. The wearable devices continuously record health information such as the user's heart rate, steps taken, calories burned, and activity time.

[0449] Step 2:

[0450] The device periodically receives data from wearable devices via Bluetooth or Wi-Fi. This received data includes health status information and measurement times.

[0451] Step 3:

[0452] The server receives health status information transmitted through the terminal and stores it in a database. This data is used as input data for the analysis model.

[0453] Step 4:

[0454] The server utilizes an AI model based on stored health information to generate personalized exercise plans and meal menus tailored to the user's current health status and goals.

[0455] Step 5:

[0456] The terminal visually displays the exercise plan and meal menu received from the server to the user. The user can then plan and execute their daily activities based on this information.

[0457] Step 6:

[0458] Users input the results of their training according to their exercise plan and their impressions of their meal plan on their device, and send this feedback to the server.

[0459] Step 7:

[0460] The server analyzes the feedback received from the user and adjusts the generated AI model to further improve the next exercise plan and meal menu. By repeating this process, the system continues to provide personalized suggestions.

[0461] (Example 1)

[0462] 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."

[0463] In modern society, many individuals seek to manage their own health and develop appropriate exercise and meal plans. However, existing systems on the market struggle to provide plans that accurately reflect an individual's health condition and goals, and they lack sufficient flexibility in responding to feedback. Therefore, there is a need to efficiently generate more precise and personalized exercise plans and meal menus to effectively support users' health management.

[0464] 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.

[0465] In this invention, the server includes means for using a device for collecting health status data, means for formulating an individualized exercise plan and meal menu using a generated AI model based on the health status data, and means for displaying the individualized exercise plan and meal menu on a display device. This makes it possible to provide a plan optimized for the user and to make adaptive improvements to it.

[0466] "Health status data" refers to information collected as health indicators, such as an individual's heart rate, steps taken, calories burned, and sleep patterns.

[0467] "Means using an apparatus" refers to a method of achieving an objective using a machine or device having a specific function.

[0468] A "generative AI model" is an artificial intelligence system established based on machine learning algorithms to identify specific patterns and make predictions based on data.

[0469] An "individualized exercise plan" refers to an exercise schedule or plan that is tailored to an individual's health condition and goals.

[0470] A "meal menu" is a list of meals that are put together with the aim of providing a specific nutritional value or calorie content.

[0471] A "display device" is a device used to visually present information and is used by users to check plans and data.

[0472] "Response data" refers to information obtained as feedback and evaluations from users, which is useful for improving the system.

[0473] This system is configured to function effectively using wearable devices, servers, and terminals to allow users to manage their health status and receive optimal exercise plans and meal menus.

[0474] The user first uses a wearable data collection device. This device acquires health data such as heart rate, steps taken, calories burned, and sleep patterns in real time. This collected data is transmitted to a server via wireless communication or other means.

[0475] The server stores the received health data in a dedicated database and analyzes it using a generative AI model. Specifically, it utilizes a server computer with high data processing capabilities, and the software includes machine learning algorithms and AI models. This AI model generates personalized exercise plans and meal plans based on the user's data. It is designed to provide the optimal plan by taking into account the user's health goals and past performance.

[0476] The device presents the user with a generated exercise plan and meal menu. The user can view specific exercise instructions and meal recipes through the device. For example, if the user's goal is weight loss, a plan including jogging three times a week and a low-calorie meal plan might be provided.

[0477] A distinctive feature of this system is the ability for users to provide feedback via their devices after completing their exercise plans and meal menus. For example, the server can acquire user feedback such as "The training felt easier than I expected" and use it to improve its analysis model.

[0478] For example, if a user completes a 5km run and provides feedback to the system stating that they felt less fatigued during the run, the system may adjust its plan to generate a slightly more intense workout next time. Another example of a prompt to input into the generating AI model is, "How should I plan my exercise to lose 2kg in the next week?"

[0479] In this way, this system can effectively support personalized health management for each user.

[0480] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0481] Step 1:

[0482] Users collect health data such as heart rate, steps taken, calories burned, and sleep patterns using wearable devices. This data is recorded in real time, and the collected data is output. Specifically, the user wears the wearable device throughout the day while active.

[0483] Step 2:

[0484] The server receives health status data obtained from wearable devices. The server uses this data as input and stores it in a dedicated database. This storage process involves data format conversion and chronological organization. The output is the organized data from the database.

[0485] Step 3:

[0486] The server uses stored health status data to perform analysis with a generating AI model. Based on the input health status data, the AI ​​model generates an optimal, personalized exercise plan and meal menu for the user. Specifically, machine learning algorithms are utilized to perform data analysis and plan formulation calculations. As a result, the plan output is obtained.

[0487] Step 4:

[0488] The terminal displays an exercise plan and meal menu, which are output from the server, to the user. The user reviews this information through the terminal and then actually executes the plan. Specifically, the terminal's display shows detailed exercise instructions and meal recipes.

[0489] Step 5:

[0490] After completing their exercise and meal plans, users send feedback to the server via their device. This feedback serves as input, and the server retrieves this information to improve its analysis algorithms. For example, a user might input a comment such as "The training was not too strenuous" into their device. The output is then a revised plan for the next session.

[0491] (Application Example 1)

[0492] 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."

[0493] In today's world, where personalized health management is essential, there is a need to provide individualized fitness and meal plans tailored to each user's lifestyle and health condition. However, currently, general health management applications and devices are insufficient for individualization, and there are difficulties in providing effective feedback to users.

[0494] 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.

[0495] In this invention, the server includes means for collecting health status information, means for generating an individualized exercise plan, and means for providing the exercise plan and meal menu via voice or visual display using a home-use automated machine. This enables the suggestion of a fitness plan based on the user's health status and supports effective health management.

[0496] "Health status information" refers to data that indicates the user's physical health status, including daily activity information such as heart rate, steps taken, calories burned, and sleep patterns.

[0497] An "individualized exercise plan" is a plan that includes the type, duration, and intensity of exercise optimized for each user, based on collected health information.

[0498] A "presentation device" is a device that displays generated exercise plans and meal menus to users, providing information through sound and visual means.

[0499] "Feedback information" refers to opinions and comments provided by users after they have exercised or eaten, and is used as data to optimize their next plan.

[0500] An "analysis algorithm" is a computational method that combines collected health status information and feedback information to generate and update personalized plans.

[0501] A "household automated machine" is an automated device used in the home, a robot equipped with functions for health management and fitness support.

[0502] A "nutritional intake plan" is a plan that proposes the optimal diet and nutritional balance based on the user's health condition and goals.

[0503] This invention is a system that provides personalized health management by coordinating wearable devices, a server, a terminal, and home-use automated machines. The wearable devices have the function of monitoring health status information such as heart rate, steps, calories burned, and sleep patterns in real time and transmitting the data to the server. The server processes the received data using an analysis algorithm and generates personalized exercise plans and nutrition intake plans.

[0504] The home-use automated machine provides users with generated plans via voice and display. This allows users to intuitively understand and implement details of their daily fitness activities and diet. The device also collects feedback from users after exercise and meals are performed and sends it to a server. This feedback is used to improve the accuracy of future plans.

[0505] The primary hardware used includes home automation devices (e.g., typical home robots). The software utilizes AI analytics platforms such as Python and TensorFlow for analyzing health status information, a Flask server for data transmission and reception, and the Google Speech-to-Text API for speech recognition. An intuitive interface for plan display and feedback collection is also a crucial element to ensure user convenience.

[0506] (Specific example)

[0507] For example, a user utilizes a health management system, and based on data acquired from a wearable device, a home-use automated exercise machine might suggest, via voice, "Today, let's do a 30-minute jog and 10 minutes of stretching." The user then completes the plan and sends feedback to the device afterward, such as, "The jogging was fine, but the stretching was a bit tough." The server receives this information and incorporates it into the next plan.

[0508] (Example of a prompt message)

[0509] "Based on the user's health data, please suggest the optimal fitness plan and meal menu for today."

[0510] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0511] Step 1:

[0512] The user attaches a wearable device to their body and begins collecting health information. At this stage, the device records data such as heart rate, steps taken, calories burned, and sleep patterns in real time. The collected data is transmitted to a server via the network. The input to the server is the user's health information.

[0513] Step 2:

[0514] The server stores the received health status information in a database. Furthermore, it uses the stored data to perform analysis through an AI analysis platform. The input here is the health status information collected in step 1, and the output is the generation of a personalized exercise plan and nutrition intake plan.

[0515] Step 3:

[0516] The server transmits the generated exercise and nutrition plans to the home automated machine. The home automated machine receives this information and presents it to the user via voice or display. The input in this step is the plan information from the server, and the output is the plan presentation to the user.

[0517] Step 4:

[0518] The user follows the provided exercise and meal plan. Feedback during the plan execution (e.g., sensations during exercise and meal content) is recorded and sent to the server via the device. User input constitutes feedback information, which is then output to the server.

[0519] Step 5:

[0520] The server receives and stores user feedback information and updates the analysis algorithm. This update allows the generative AI model to provide more individually optimized suggestions in the next plan generation. The input is feedback information, and the output is the next exercise and meal plan generated by the improved generative AI model.

[0521] 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.

[0522] This system integrates wearable devices, servers, terminals, and an emotion engine to comprehensively provide users with personalized exercise plans and meal menus.

[0523] System configuration:

[0524] 1. Functions of wearable devices

[0525] The user uses a wearable device to continuously measure health information such as heart rate, activity level, and calories burned. This allows the device to record the user's physiological data in real time and transfer it to the terminal.

[0526] 2. Function of the Emotion Engine

[0527] The server, via the terminal, analyzes data collected from the user's daily activities and activates an emotion engine to estimate the user's emotional state. This engine analyzes emotions based on the user's voice, facial expressions, and past behavioral history to understand the user's motivation and stress levels.

[0528] 3. Server Functions

[0529] The server uses data from wearable devices and an emotion engine to generate personalized exercise plans and meal plans optimized for the user's health and emotions. These generated plans are tailored to improve daily health and well-being.

[0530] 4. Device functions

[0531] The device displays personalized exercise plans and meal menus transmitted from the server to the user. Furthermore, an emotion engine adjusts the suggestions according to the user's emotions, presenting them in a comfortable and easy-to-follow format. Through the device, the user can review their exercise and meal plans and send feedback after completing them.

[0532] Specific example:

[0533] For example, if a user is facing a stressful daily life and this is detected through a wearable device and an emotion engine, the server will adjust the generated exercise plan to include relaxing yoga and deep breathing exercises. Similarly, the meal menu will feature dishes utilizing ingredients rich in vitamins and minerals to improve mood. Users can review these suggestions on their device and easily implement them.

[0534] Based on this feedback, the server utilizes the output of the emotion engine to continuously improve its analysis model, thereby providing users with optimal health maintenance support.

[0535] The following describes the processing flow.

[0536] Step 1:

[0537] Users wear wearable devices while engaging in daily activities, collecting real-time health information such as heart rate and activity levels. This allows for continuous monitoring of their physical condition.

[0538] Step 2:

[0539] The device periodically receives health status information from wearable devices via Bluetooth or Wi-Fi. The received data is then sent to a server.

[0540] Step 3:

[0541] The server receives health status information transmitted from the terminal and stores it in a database. At the same time, the server uses an emotion engine to analyze the user's voice and facial expression data to estimate their everyday emotional state.

[0542] Step 4:

[0543] The server utilizes a generative AI model based on health and emotional status to generate personalized exercise plans and meal plans optimized for the user. These plans may include exercises and dietary recommendations that help manage stress and improve emotional well-being.

[0544] Step 5:

[0545] The device visually displays personalized exercise plans and meal menus sent from the server to the user. Furthermore, it adjusts the plan content based on the analysis results of the emotion engine, presenting it in a way that is easy for the user to follow.

[0546] Step 6:

[0547] Users follow the provided plan for exercise and meals, and after completing it, they input feedback on the results and their impressions via their device.

[0548] Step 7:

[0549] The server receives user feedback and adjusts its analysis model and sentiment engine accordingly. This information is used to improve the accuracy of future suggestions. This allows the server to consistently provide the best possible support to the user.

[0550] (Example 2)

[0551] 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."

[0552] Many people in modern society face the problem of inadequate health management due to stress and irregular lifestyles. Traditional health management systems have difficulty effectively incorporating individual emotional states and feedback, resulting in insufficient provision of personalized health guidance and dietary plans.

[0553] 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.

[0554] In this invention, the server includes means for analyzing emotional states based on physiological information, means for generating personalized exercise guidance and meal plans based on the physiological information and emotional states, and means for acquiring feedback information from the user and updating the model using an analysis device. This enables personalized health management that comprehensively considers the user's health status and emotions.

[0555] "Physiological information" refers to data on health status acquired by wearable devices, such as heart rate, activity level, and calories burned.

[0556] "Emotional state" refers to information that indicates the user's psychological state, analyzed based on the user's voice, facial expressions, and past behavioral history.

[0557] "Personalized exercise instruction" refers to an exercise program customized to take into account the user's specific health and emotional state.

[0558] A "meal plan" is a menu with optimized nutrients and ingredients, provided with the aim of improving the user's health and emotional state.

[0559] A "portable measuring device" is a small electronic device that can be worn by the user to continuously collect health data.

[0560] A "visual display device" is a device equipped with a display or screen for providing users with generated exercise guidance and meal plans.

[0561] An "analysis system" is a configuration that includes hardware and software for performing data analysis and model updates based on collected physiological information and feedback.

[0562] To implement the invention, this system utilizes a series of hardware and software components for collecting and analyzing physiological information. Specifically, the user wears a portable measuring device that measures physiological information such as heart rate, activity level, and calories burned in real time and transmits it to a terminal. The terminal then transmits this data to a server.

[0563] The server is equipped with emotion analysis software to analyze received physiological information, estimating the user's emotional state by considering their voice, facial expressions, and past behavioral history. This emotion analysis utilizes advanced algorithms and generative AI models to enable more accurate analysis.

[0564] The server then integrates physiological information and emotional state to generate personalized exercise guidance and meal plans. This includes content based on the user's health goals and daily activities. The generated plans are presented to the user through the terminal's visual display.

[0565] The terminal visually presents the generation plan to the user while also having the function to receive user feedback. The user feedback information is then sent back to the server, which processes it with an analysis device and continuously updates the generated AI model.

[0566] As a concrete example, if a user is detected to be under high stress, the server will provide a plan that includes relaxation-enhancing exercises such as yoga. Similarly, a meal plan will suggest vitamin-rich menus. An example of a prompt using a generative AI model would be, "High stress level detected. Please suggest content effective for relaxation."

[0567] This system configuration allows users to receive plans that flexibly respond to their own health and emotional state, enabling them to manage their health comfortably and effectively.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] The user wears a portable measuring device. The device measures physiological information such as heart rate, activity level, and calories burned in real time. This data serves as input. This data is automatically transferred to a terminal, which then sends it to a server as physiological data. The physiological data is stored on the server as output.

[0571] Step 2:

[0572] The server runs emotion analysis software to analyze physiological data received from wearable devices. The input is physiological data. Based on this, the server combines the user's voice, facial expressions, and past behavioral history to perform calculations that estimate the user's emotional state. The emotional state is obtained as output and used for subsequent processing.

[0573] Step 3:

[0574] The server integrates physiological data and emotional states, and utilizes a generative AI model to generate personalized exercise guidance and meal plans. The input for this step is the previously analyzed physiological data and emotional states. Data calculations create exercise and meal plans based on the user's characteristics. The resulting exercise guidance and meal plans include instructions tailored to the user's health condition.

[0575] Step 4:

[0576] The terminal receives personalized exercise plans and meal menus sent from the server and presents them to the user visually. The input consists of exercise guidance and meal plans from the server. The terminal displays this information clearly for the user, and the output allows the user to review their own health guidance.

[0577] Step 5:

[0578] Users implement the provided exercise and meal plans and provide feedback on the results and their opinions through the device. The input is the user's feedback information. The device sends this information to the server. The output is the user's feedback information, which is used for the next model update.

[0579] Step 6:

[0580] The server receives user feedback, and the analysis system updates the generated AI model to improve the accuracy of analysis and estimation. The input is user feedback information; data processing updates the generated AI model, resulting in an improved model as output. This update process is reflected in the subsequent provision of personalized plans.

[0581] (Application Example 2)

[0582] 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."

[0583] The problem that this invention aims to solve is to provide a system that provides comprehensive health management that takes into account not only the user's physical condition but also their emotional state. Specifically, it is required to understand the user's daily emotions and stress levels and provide personalized exercise plans and meal menus based on that information to achieve more effective health maintenance. Furthermore, it also includes the challenge of continuously improving the accuracy of the system by having users provide direct feedback.

[0584] 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.

[0585] In this invention, the server includes means for collecting physiological data, means for estimating the user's emotional state using the physiological data and voice / facial information, and means for generating a personalized exercise plan and meal menu based on the emotional state. This enables comprehensive and effective health management that takes into account both the user's physical and emotional state.

[0586] "Physiological data" refers to data that indicates the user's physical condition, such as heart rate, activity level, and energy expenditure.

[0587] A "portable device" is a device that a user wears or carries with them to measure their physical condition.

[0588] "Emotional state" refers to the user's psychological and emotional state, estimated based on voice and facial expression information.

[0589] A "personalized exercise plan" is an exercise program optimized based on the user's health and emotional state.

[0590] A "meal menu" is a nutritional plan that takes into account the user's health and emotional state.

[0591] A "humanoid assistant device" is a humanoid robot that can interact with users and provide guidance on exercise and diet.

[0592] "Feedback information" refers to information about the results and experiences provided by users.

[0593] A "learning model" is an information processing algorithm that generates optimized suggestions for the user based on accumulated data.

[0594] "Demonstrating physical exercise" means that a humanoid assistant device physically performs exercises together with the user to instruct them on how to do those exercises.

[0595] The system implementing this invention utilizes a portable device, an emotion analysis module, and a humanoid assistant device to comprehensively manage the user's health and emotional state.

[0596] First, the user uses a portable device to collect physiological data in real time. This device could be a data collection device such as a smartwatch. This data is sent to a server, where an emotion analysis module then analyzes the user's emotional state using a speech recognition system and facial expression analysis tools. Specifically, it takes in voice and facial expression data and analyzes the user's psychological tendencies using a platform such as IBM Watson or Microsoft Azure API.

[0597] Based on this information, the server generates personalized exercise plans and meal plans for the user. These plans are optimized for maintaining daily health, taking into account the user's health and emotional state. In particular, users experiencing stress may be presented with plans for relaxing yoga or breathing exercises.

[0598] Furthermore, a humanoid assistant device will interact with the user, demonstrating exercises and providing dietary guidance. This device has a humanoid interface similar to SoftBank's robots, and can perform physical exercises together with the user and instruct them on correct form.

[0599] Users can execute the proposed plan through this system and provide feedback to the server. Based on this feedback, the server learns through its AI model and continuously improves the accuracy of its analysis.

[0600] For example, a suggestion for a particularly stressful day might be something like, "Today is a mentally demanding day, so please do a 30-minute relaxing yoga session. Afterwards, enjoy a vitamin-rich kiwi and grapefruit salad."

[0601] An example of a prompt message might be: "We've detected that the user is stressed based on their heart rate and tone of voice. Create a relaxing yoga routine and have the robot guide them according to their preferences."

[0602] Through this process, personal health management has been achieved, making it more convenient and highly efficient.

[0603] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0604] Step 1:

[0605] The user wears a portable device to collect physiological data. Inputs include physiological data such as heart rate, activity level, and calories burned, while output is an aggregate of this data. The portable device records this data in real time and transmits it wirelessly to a server.

[0606] Step 2:

[0607] The server prepares the received physiological data for sentiment analysis. The input is physiological data transmitted from a portable device, and the output is a dataset in a format suitable for machine learning models. The server preprocesses this data and transfers it to the sentiment analysis module.

[0608] Step 3:

[0609] The server activates the emotion analysis module and estimates the emotional state using a speech recognition system and facial expression analysis tool. The input is a dataset and speech and facial expression information obtained from the user, and the output is the user's emotional state. The emotion analysis module saves the analysis results and provides them for the next processing step.

[0610] Step 4:

[0611] The server uses a generative AI model to generate personalized exercise plans and meal plans based on emotional state and physiological data. The input is emotional state and physiological data, and the output is an optimized exercise plan and meal plan. This generated plan is then formatted in a way that is helpful for the user's health management.

[0612] Step 5:

[0613] The server transmits the generated exercise plan and meal menu to the humanoid assistant device, which then instructs it to display and execute the plan. The input is the generated plan data, and the output is visual and audio guidance for the user. The humanoid assistant device gives voice instructions and, if necessary, demonstrates the physical movements.

[0614] Step 6:

[0615] The user executes the provided plan and sends feedback information to the server via their device. The input is the user's feedback, and the output is the feedback data collected on the server. The user communicates their specific experiences and the challenges they encountered.

[0616] Step 7:

[0617] The server updates its learning model using collected feedback information, improving the system so that it can provide more appropriate suggestions the next time it is used. The input is feedback data, and the output is the updated learning model. Through this process, the server improves the accuracy of its suggestions to the user.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] [Fourth Embodiment]

[0622] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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".

[0635] This system provides users with effective fitness plans and meal menus by linking wearable devices, servers, and terminals.

[0636] System configuration:

[0637] 1. Functions of wearable devices

[0638] By wearing a wearable device on their body, users can obtain real-time health information such as heart rate, steps taken, calories burned, and sleep patterns. This device can record the user's movements in their daily life and transmit this data to a server.

[0639] 2. Server Functions

[0640] The server receives health information transmitted from wearable devices and stores it in a database. Using this data, an analysis model (AI algorithm) generates a personalized exercise plan and meal plan optimized for the user. The server also collects feedback from users to continuously improve the analysis model.

[0641] 3. Device functions

[0642] The device displays a personalized exercise plan and meal menu to the user. Users can view detailed exercise plans and meal recipes through the device. They can also send feedback via the device after completing their workout.

[0643] Specific example:

[0644] For example, if a user wears a wearable device throughout the day, their health status information for that day (e.g., 10,000 steps taken, 2,000 kcal burned) is recorded on the device. The server receives this data and, taking into account the user's health goals (e.g., 2kg weight loss or increased muscle mass), generates an exercise plan for the next day (e.g., 30 minutes of running, 10 minutes of stretching) and a meal plan (e.g., protein shake for breakfast, vegetable salad for lunch, chicken-centered menu for dinner).

[0645] The user reviews and executes the generated plan on their device. After execution, the user provides feedback through their device (e.g., "I felt less fatigued during the run"). The server then uses the provided feedback to update its analysis model and adjust the next training plan to better suit the user.

[0646] In this way, the present invention can continue to provide fitness plans optimized for individual users.

[0647] The following describes the processing flow.

[0648] Step 1:

[0649] Users wear wearable devices on their bodies while going about their daily lives. The wearable devices continuously record health information such as the user's heart rate, steps taken, calories burned, and activity time.

[0650] Step 2:

[0651] The device periodically receives data from wearable devices via Bluetooth or Wi-Fi. This received data includes health status information and measurement times.

[0652] Step 3:

[0653] The server receives health status information transmitted through the terminal and stores it in a database. This data is used as input data for the analysis model.

[0654] Step 4:

[0655] The server utilizes an AI model based on stored health information to generate personalized exercise plans and meal menus tailored to the user's current health status and goals.

[0656] Step 5:

[0657] The terminal visually displays the exercise plan and meal menu received from the server to the user. The user can then plan and execute their daily activities based on this information.

[0658] Step 6:

[0659] Users input the results of their training according to their exercise plan and their impressions of their meal plan on their device, and send this feedback to the server.

[0660] Step 7:

[0661] The server analyzes the feedback received from the user and adjusts the generated AI model to further improve the next exercise plan and meal menu. By repeating this process, the system continues to provide personalized suggestions.

[0662] (Example 1)

[0663] 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".

[0664] In modern society, many individuals seek to manage their own health and develop appropriate exercise and meal plans. However, existing systems on the market struggle to provide plans that accurately reflect an individual's health condition and goals, and they lack sufficient flexibility in responding to feedback. Therefore, there is a need to efficiently generate more precise and personalized exercise plans and meal menus to effectively support users' health management.

[0665] 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.

[0666] In this invention, the server includes means for using a device for collecting health status data, means for formulating an individualized exercise plan and meal menu using a generated AI model based on the health status data, and means for displaying the individualized exercise plan and meal menu on a display device. This makes it possible to provide a plan optimized for the user and to make adaptive improvements to it.

[0667] "Health status data" refers to information collected as health indicators, such as an individual's heart rate, steps taken, calories burned, and sleep patterns.

[0668] "Means using an apparatus" refers to a method of achieving an objective using a machine or device having a specific function.

[0669] A "generative AI model" is an artificial intelligence system established based on machine learning algorithms to identify specific patterns and make predictions based on data.

[0670] An "individualized exercise plan" refers to an exercise schedule or plan that is tailored to an individual's health condition and goals.

[0671] A "meal menu" is a list of meals that are put together with the aim of providing a specific nutritional value or calorie content.

[0672] A "display device" is a device used to visually present information and is used by users to check plans and data.

[0673] "Response data" refers to information obtained as feedback and evaluations from users, which is useful for improving the system.

[0674] This system is configured to function effectively using wearable devices, servers, and terminals to allow users to manage their health status and receive optimal exercise plans and meal menus.

[0675] The user first uses a wearable data collection device. This device acquires health data such as heart rate, steps taken, calories burned, and sleep patterns in real time. This collected data is transmitted to a server via wireless communication or other means.

[0676] The server stores the received health data in a dedicated database and analyzes it using a generative AI model. Specifically, it utilizes a server computer with high data processing capabilities, and the software includes machine learning algorithms and AI models. This AI model generates personalized exercise plans and meal plans based on the user's data. It is designed to provide the optimal plan by taking into account the user's health goals and past performance.

[0677] The device presents the user with a generated exercise plan and meal menu. The user can view specific exercise instructions and meal recipes through the device. For example, if the user's goal is weight loss, a plan including jogging three times a week and a low-calorie meal plan might be provided.

[0678] A distinctive feature of this system is the ability for users to provide feedback via their devices after completing their exercise plans and meal menus. For example, the server can acquire user feedback such as "The training felt easier than I expected" and use it to improve its analysis model.

[0679] For example, if a user completes a 5km run and provides feedback to the system stating that they felt less fatigued during the run, the system may adjust its plan to generate a slightly more intense workout next time. Another example of a prompt to input into the generating AI model is, "How should I plan my exercise to lose 2kg in the next week?"

[0680] In this way, this system can effectively support personalized health management for each user.

[0681] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0682] Step 1:

[0683] Users collect health data such as heart rate, steps taken, calories burned, and sleep patterns using wearable devices. This data is recorded in real time, and the collected data is output. Specifically, the user wears the wearable device throughout the day while active.

[0684] Step 2:

[0685] The server receives health status data obtained from wearable devices. The server uses this data as input and stores it in a dedicated database. This storage process involves data format conversion and chronological organization. The output is the organized data from the database.

[0686] Step 3:

[0687] The server uses stored health status data to perform analysis with a generating AI model. Based on the input health status data, the AI ​​model generates an optimal, personalized exercise plan and meal menu for the user. Specifically, machine learning algorithms are utilized to perform data analysis and plan formulation calculations. As a result, the plan output is obtained.

[0688] Step 4:

[0689] The terminal displays an exercise plan and meal menu, which are output from the server, to the user. The user reviews this information through the terminal and then actually executes the plan. Specifically, the terminal's display shows detailed exercise instructions and meal recipes.

[0690] Step 5:

[0691] After completing their exercise and meal plans, users send feedback to the server via their device. This feedback serves as input, and the server retrieves this information to improve its analysis algorithms. For example, a user might input a comment such as "The training was not too strenuous" into their device. The output is then a revised plan for the next session.

[0692] (Application Example 1)

[0693] 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".

[0694] In today's world, where personalized health management is essential, there is a need to provide individualized fitness and meal plans tailored to each user's lifestyle and health condition. However, currently, general health management applications and devices are insufficient for individualization, and there are difficulties in providing effective feedback to users.

[0695] 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.

[0696] In this invention, the server includes means for collecting health status information, means for generating an individualized exercise plan, and means for providing the exercise plan and meal menu via voice or visual display using a home-use automated machine. This enables the suggestion of a fitness plan based on the user's health status and supports effective health management.

[0697] "Health status information" refers to data that indicates the user's physical health status, including daily activity information such as heart rate, steps taken, calories burned, and sleep patterns.

[0698] An "individualized exercise plan" is a plan that includes the type, duration, and intensity of exercise optimized for each user, based on collected health information.

[0699] A "presentation device" is a device that displays generated exercise plans and meal menus to users, providing information through sound and visual means.

[0700] "Feedback information" refers to opinions and comments provided by users after they have exercised or eaten, and is used as data to optimize their next plan.

[0701] An "analysis algorithm" is a computational method that combines collected health status information and feedback information to generate and update personalized plans.

[0702] A "household automated machine" is an automated device used in the home, a robot equipped with functions for health management and fitness support.

[0703] A "nutritional intake plan" is a plan that proposes the optimal diet and nutritional balance based on the user's health condition and goals.

[0704] This invention is a system that provides personalized health management by coordinating wearable devices, a server, a terminal, and home-use automated machines. The wearable devices have the function of monitoring health status information such as heart rate, steps, calories burned, and sleep patterns in real time and transmitting the data to the server. The server processes the received data using an analysis algorithm and generates personalized exercise plans and nutrition intake plans.

[0705] The home-use automated machine provides users with generated plans via voice and display. This allows users to intuitively understand and implement details of their daily fitness activities and diet. The device also collects feedback from users after exercise and meals are performed and sends it to a server. This feedback is used to improve the accuracy of future plans.

[0706] The primary hardware used includes home automation devices (e.g., typical home robots). The software utilizes AI analytics platforms such as Python and TensorFlow for analyzing health status information, a Flask server for data transmission and reception, and the Google Speech-to-Text API for speech recognition. An intuitive interface for plan display and feedback collection is also a crucial element to ensure user convenience.

[0707] (Specific example)

[0708] For example, a user utilizes a health management system, and based on data acquired from a wearable device, a home-use automated exercise machine might suggest, via voice, "Today, let's do a 30-minute jog and 10 minutes of stretching." The user then completes the plan and sends feedback to the device afterward, such as, "The jogging was fine, but the stretching was a bit tough." The server receives this information and incorporates it into the next plan.

[0709] (Example of a prompt message)

[0710] "Based on the user's health data, please suggest the optimal fitness plan and meal menu for today."

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The user attaches a wearable device to their body and begins collecting health information. At this stage, the device records data such as heart rate, steps taken, calories burned, and sleep patterns in real time. The collected data is transmitted to a server via the network. The input to the server is the user's health information.

[0714] Step 2:

[0715] The server stores the received health status information in a database. Furthermore, it uses the stored data to perform analysis through an AI analysis platform. The input here is the health status information collected in step 1, and the output is the generation of a personalized exercise plan and nutrition intake plan.

[0716] Step 3:

[0717] The server transmits the generated exercise and nutrition plans to the home automated machine. The home automated machine receives this information and presents it to the user via voice or display. The input in this step is the plan information from the server, and the output is the plan presentation to the user.

[0718] Step 4:

[0719] The user follows the provided exercise and meal plan. Feedback during the plan execution (e.g., sensations during exercise and meal content) is recorded and sent to the server via the device. User input constitutes feedback information, which is then output to the server.

[0720] Step 5:

[0721] The server receives and stores user feedback information and updates the analysis algorithm. This update allows the generative AI model to provide more individually optimized suggestions in the next plan generation. The input is feedback information, and the output is the next exercise and meal plan generated by the improved generative AI model.

[0722] 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.

[0723] This system integrates wearable devices, servers, terminals, and an emotion engine to comprehensively provide users with personalized exercise plans and meal menus.

[0724] System configuration:

[0725] 1. Functions of wearable devices

[0726] The user uses a wearable device to continuously measure health information such as heart rate, activity level, and calories burned. This allows the device to record the user's physiological data in real time and transfer it to the terminal.

[0727] 2. Function of the Emotion Engine

[0728] The server, via the terminal, analyzes data collected from the user's daily activities and activates an emotion engine to estimate the user's emotional state. This engine analyzes emotions based on the user's voice, facial expressions, and past behavioral history to understand the user's motivation and stress levels.

[0729] 3. Server Functions

[0730] The server uses data from wearable devices and an emotion engine to generate personalized exercise plans and meal plans optimized for the user's health and emotions. These generated plans are tailored to improve daily health and well-being.

[0731] 4. Device functions

[0732] The device displays personalized exercise plans and meal menus transmitted from the server to the user. Furthermore, an emotion engine adjusts the suggestions according to the user's emotions, presenting them in a comfortable and easy-to-follow format. Through the device, the user can review their exercise and meal plans and send feedback after completing them.

[0733] Specific example:

[0734] For example, if a user is facing a stressful daily life and this is detected through a wearable device and an emotion engine, the server will adjust the generated exercise plan to include relaxing yoga and deep breathing exercises. Similarly, the meal menu will feature dishes utilizing ingredients rich in vitamins and minerals to improve mood. Users can review these suggestions on their device and easily implement them.

[0735] Based on this feedback, the server utilizes the output of the emotion engine to continuously improve its analysis model, thereby providing users with optimal health maintenance support.

[0736] The following describes the processing flow.

[0737] Step 1:

[0738] Users wear wearable devices while engaging in daily activities, collecting real-time health information such as heart rate and activity levels. This allows for continuous monitoring of their physical condition.

[0739] Step 2:

[0740] The device periodically receives health status information from wearable devices via Bluetooth or Wi-Fi. The received data is then sent to a server.

[0741] Step 3:

[0742] The server receives health status information transmitted from the terminal and stores it in a database. At the same time, the server uses an emotion engine to analyze the user's voice and facial expression data to estimate their everyday emotional state.

[0743] Step 4:

[0744] The server utilizes a generative AI model based on health and emotional status to generate personalized exercise plans and meal plans optimized for the user. These plans may include exercises and dietary recommendations that help manage stress and improve emotional well-being.

[0745] Step 5:

[0746] The device visually displays personalized exercise plans and meal menus sent from the server to the user. Furthermore, it adjusts the plan content based on the analysis results of the emotion engine, presenting it in a way that is easy for the user to follow.

[0747] Step 6:

[0748] Users follow the provided plan for exercise and meals, and after completing it, they input feedback on the results and their impressions via their device.

[0749] Step 7:

[0750] The server receives user feedback and adjusts its analysis model and sentiment engine accordingly. This information is used to improve the accuracy of future suggestions. This allows the server to consistently provide the best possible support to the user.

[0751] (Example 2)

[0752] 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".

[0753] Many people in modern society face the problem of inadequate health management due to stress and irregular lifestyles. Traditional health management systems have difficulty effectively incorporating individual emotional states and feedback, resulting in insufficient provision of personalized health guidance and dietary plans.

[0754] 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.

[0755] In this invention, the server includes means for analyzing emotional states based on physiological information, means for generating personalized exercise guidance and meal plans based on the physiological information and emotional states, and means for acquiring feedback information from the user and updating the model using an analysis device. This enables personalized health management that comprehensively considers the user's health status and emotions.

[0756] "Physiological information" refers to data on health status acquired by wearable devices, such as heart rate, activity level, and calories burned.

[0757] "Emotional state" refers to information that indicates the user's psychological state, analyzed based on the user's voice, facial expressions, and past behavioral history.

[0758] "Personalized exercise instruction" refers to an exercise program customized to take into account the user's specific health and emotional state.

[0759] A "meal plan" is a menu with optimized nutrients and ingredients, provided with the aim of improving the user's health and emotional state.

[0760] A "portable measuring device" is a small electronic device that can be worn by the user to continuously collect health data.

[0761] A "visual display device" is a device equipped with a display or screen for providing users with generated exercise guidance and meal plans.

[0762] An "analysis system" is a configuration that includes hardware and software for performing data analysis and model updates based on collected physiological information and feedback.

[0763] To implement the invention, this system utilizes a series of hardware and software components for collecting and analyzing physiological information. Specifically, the user wears a portable measuring device that measures physiological information such as heart rate, activity level, and calories burned in real time and transmits it to a terminal. The terminal then transmits this data to a server.

[0764] The server is equipped with emotion analysis software to analyze received physiological information, estimating the user's emotional state by considering their voice, facial expressions, and past behavioral history. This emotion analysis utilizes advanced algorithms and generative AI models to enable more accurate analysis.

[0765] The server then integrates physiological information and emotional state to generate personalized exercise guidance and meal plans. This includes content based on the user's health goals and daily activities. The generated plans are presented to the user through the terminal's visual display.

[0766] The terminal visually presents the generation plan to the user while also having the function to receive user feedback. The user feedback information is then sent back to the server, which processes it with an analysis device and continuously updates the generated AI model.

[0767] As a concrete example, if a user is detected to be under high stress, the server will provide a plan that includes relaxation-enhancing exercises such as yoga. Similarly, a meal plan will suggest vitamin-rich menus. An example of a prompt using a generative AI model would be, "High stress level detected. Please suggest content effective for relaxation."

[0768] This system configuration allows users to receive plans that flexibly respond to their own health and emotional state, enabling them to manage their health comfortably and effectively.

[0769] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0770] Step 1:

[0771] The user wears a portable measuring device. The device measures physiological information such as heart rate, activity level, and calories burned in real time. This data serves as input. This data is automatically transferred to a terminal, which then sends it to a server as physiological data. The physiological data is stored on the server as output.

[0772] Step 2:

[0773] The server runs emotion analysis software to analyze physiological data received from wearable devices. The input is physiological data. Based on this, the server combines the user's voice, facial expressions, and past behavioral history to perform calculations that estimate the user's emotional state. The emotional state is obtained as output and used for subsequent processing.

[0774] Step 3:

[0775] The server integrates physiological data and emotional states, and utilizes a generative AI model to generate personalized exercise guidance and meal plans. The input for this step is the previously analyzed physiological data and emotional states. Data calculations create exercise and meal plans based on the user's characteristics. The resulting exercise guidance and meal plans include instructions tailored to the user's health condition.

[0776] Step 4:

[0777] The terminal receives personalized exercise plans and meal menus sent from the server and presents them to the user visually. The input consists of exercise guidance and meal plans from the server. The terminal displays this information clearly for the user, and the output allows the user to review their own health guidance.

[0778] Step 5:

[0779] Users implement the provided exercise and meal plans and provide feedback on the results and their opinions through the device. The input is the user's feedback information. The device sends this information to the server. The output is the user's feedback information, which is used for the next model update.

[0780] Step 6:

[0781] The server receives user feedback, and the analysis system updates the generated AI model to improve the accuracy of analysis and estimation. The input is user feedback information; data processing updates the generated AI model, resulting in an improved model as output. This update process is reflected in the subsequent provision of personalized plans.

[0782] (Application Example 2)

[0783] 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".

[0784] The problem that this invention aims to solve is to provide a system that provides comprehensive health management that takes into account not only the user's physical condition but also their emotional state. Specifically, it is required to understand the user's daily emotions and stress levels and provide personalized exercise plans and meal menus based on that information to achieve more effective health maintenance. Furthermore, it also includes the challenge of continuously improving the accuracy of the system by having users provide direct feedback.

[0785] 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.

[0786] In this invention, the server includes means for collecting physiological data, means for estimating the user's emotional state using the physiological data and voice / facial information, and means for generating a personalized exercise plan and meal menu based on the emotional state. This enables comprehensive and effective health management that takes into account both the user's physical and emotional state.

[0787] "Physiological data" refers to data that indicates the user's physical condition, such as heart rate, activity level, and energy expenditure.

[0788] A "portable device" is a device that a user wears or carries with them to measure their physical condition.

[0789] "Emotional state" refers to the user's psychological and emotional state, estimated based on voice and facial expression information.

[0790] A "personalized exercise plan" is an exercise program optimized based on the user's health and emotional state.

[0791] A "meal menu" is a nutritional plan that takes into account the user's health and emotional state.

[0792] A "humanoid assistant device" is a humanoid robot that can interact with users and provide guidance on exercise and diet.

[0793] "Feedback information" refers to information about the results and experiences provided by users.

[0794] A "learning model" is an information processing algorithm that generates optimized suggestions for the user based on accumulated data.

[0795] "Demonstrating physical exercise" means that a humanoid assistant device physically performs exercises together with the user to instruct them on how to do those exercises.

[0796] The system implementing this invention utilizes a portable device, an emotion analysis module, and a humanoid assistant device to comprehensively manage the user's health and emotional state.

[0797] First, the user uses a portable device to collect physiological data in real time. This device could be a data collection device such as a smartwatch. This data is sent to a server, where an emotion analysis module then analyzes the user's emotional state using a speech recognition system and facial expression analysis tools. Specifically, it takes in voice and facial expression data and analyzes the user's psychological tendencies using a platform such as IBM Watson or Microsoft Azure API.

[0798] Based on this information, the server generates personalized exercise plans and meal plans for the user. These plans are optimized for maintaining daily health, taking into account the user's health and emotional state. In particular, users experiencing stress may be presented with plans for relaxing yoga or breathing exercises.

[0799] Furthermore, a humanoid assistant device will interact with the user, demonstrating exercises and providing dietary guidance. This device has a humanoid interface similar to SoftBank's robots, and can perform physical exercises together with the user and instruct them on correct form.

[0800] Users can execute the proposed plan through this system and provide feedback to the server. Based on this feedback, the server learns through its AI model and continuously improves the accuracy of its analysis.

[0801] For example, as a proposal for a particularly stressful day, it would be something like "Since you're under a lot of mental stress today, please do a 30-minute relaxation yoga session. Then, please enjoy a salad of vitamin-rich kiwis and grapefruit."

[0802] Examples of prompt sentences include "It has been found that the user is feeling stress from their heart rate and voice tone. Assemble a yoga menu for relaxation so that the robot can guide according to their preferences."

[0803] Through this system, personal health management has been achieved more conveniently and efficiently.

[0804] The flow of the specific process in Application Example 2 will be described using FIG. 14.

[0805] Step 1:

[0806] The user wears a portable device and collects physiological data. The input is physiological data such as heart rate, activity level, and calories consumed, and the output is the accumulation of this data. The portable device records these data in real time and transmits them to the server via wireless communication.

[0807] Step 2:

[0808] The server prepares the received physiological data for sentiment analysis. The input is the physiological data transmitted from the portable device, and the output is a dataset in a format suitable for the machine learning model. The server preprocesses this data and transfers it to the sentiment analysis module.

[0809] Step 3:

[0810] The server activates the sentiment analysis module and estimates the emotional state using the speech recognition system and the facial expression analysis tool. The input is the dataset and the speech and facial expression information obtained from the user, and the output is the emotional state of the user. The sentiment analysis module saves the analysis result and provides it for the next process.

[0811] Step 4:

[0812] The server uses a generative AI model to generate personalized exercise plans and meal plans based on emotional state and physiological data. The input is emotional state and physiological data, and the output is an optimized exercise plan and meal plan. This generated plan is then formatted in a way that is helpful for the user's health management.

[0813] Step 5:

[0814] The server transmits the generated exercise plan and meal menu to the humanoid assistant device, which then instructs it to display and execute the plan. The input is the generated plan data, and the output is visual and audio guidance for the user. The humanoid assistant device gives voice instructions and, if necessary, demonstrates the physical movements.

[0815] Step 6:

[0816] The user executes the provided plan and sends feedback information to the server via their device. The input is the user's feedback, and the output is the feedback data collected on the server. The user communicates their specific experiences and the challenges they encountered.

[0817] Step 7:

[0818] The server updates its learning model using collected feedback information, improving the system so that it can provide more appropriate suggestions the next time it is used. The input is feedback data, and the output is the updated learning model. Through this process, the server improves the accuracy of its suggestions to the user.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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."

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] The following is further disclosed regarding the embodiments described above.

[0841] (Claim 1)

[0842] Means of collecting health status information,

[0843] A means for generating an individualized exercise plan using the aforementioned health status information,

[0844] Means for presenting the individualized motion plan to a receiving device,

[0845] A means of obtaining user feedback information and updating the analysis model,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, wherein the aforementioned health status information is obtained from a wearable device.

[0849] (Claim 3)

[0850] The system according to claim 1, which includes a meal menu in the exercise plan.

[0851] "Example 1"

[0852] (Claim 1)

[0853] A means of using a device for collecting health status data,

[0854] A means for formulating an individualized exercise plan and meal menu using a generated AI model based on the aforementioned health status data,

[0855] A means for displaying the personalized exercise plan and meal menu on a display device,

[0856] A means of acquiring user response data and improving the analysis algorithm,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, wherein the aforementioned health status data is acquired from a portable device.

[0860] (Claim 3)

[0861] The system according to claim 1, which includes a nutrition plan in the exercise plan.

[0862] "Application Example 1"

[0863] (Claim 1)

[0864] Means of collecting health status information,

[0865] A means for generating an individualized exercise plan using the aforementioned health status information,

[0866] Means for presenting the individualized movement plan to a presentation device,

[0867] A means of obtaining user feedback information and updating the analysis algorithm,

[0868] A means for providing the exercise plan and meal menu via voice or visual display using a home-use automated machine,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, wherein the aforementioned health status information is obtained from a wearable device.

[0872] (Claim 3)

[0873] The system according to claim 1, which includes a nutritional intake plan in the exercise plan.

[0874] "Example 2 of combining an emotion engine"

[0875] (Claim 1)

[0876] Means for collecting physiological information,

[0877] A means for analyzing emotional states based on the aforementioned physiological information,

[0878] A means for generating personalized exercise guidance and meal plans based on the aforementioned physiological information and emotional state,

[0879] Means for displaying the individualized exercise guidance and meal plan on a visual display device,

[0880] A means of obtaining feedback information from users and updating the model using an analysis device,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, wherein the physiological information is obtained from a portable measuring device.

[0884] (Claim 3)

[0885] The system according to claim 1, which includes a meal plan in the exercise instruction.

[0886] "Application example 2 when combining with an emotional engine"

[0887] (Claim 1)

[0888] Means for collecting physiological data,

[0889] A means for estimating the user's emotional state using the aforementioned physiological data and voice / facial information,

[0890] Means for generating a personalized exercise plan and meal menu based on the aforementioned emotional state,

[0891] Means having a humanoid assistant device for presenting the generated exercise plan and meal menu,

[0892] A means of obtaining feedback information from users and updating the corresponding learning model,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, wherein the physiological data is acquired from a portable device.

[0896] (Claim 3)

[0897] The system according to claim 1, wherein the humanoid assistant device demonstrates physical movements together with the user. [Explanation of symbols]

[0898] 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. Means of collecting health status information, A means for generating an individualized exercise plan using the aforementioned health status information, Means for presenting the individualized motion plan to a receiving device, A means of obtaining user feedback information and updating the analysis model, A system that includes this.

2. The system according to claim 1, wherein the aforementioned health status information is obtained from a wearable device.

3. The system according to claim 1, which includes a meal menu in the exercise plan.

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