system

Abstract

We provide the system. [Solution] Information collection means for collecting user physiological information, An analysis means that performs analysis based on the aforementioned physiological information and visual data, A response means that provides the user with an exercise and meal plan, A modification means for updating the plan provided by the response means based on the analysis results of the analysis means, An autonomous machine to assist in collecting physiological information from users in a home environment, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult to provide users with individualized training and diet plans at an affordable price. Furthermore, it is difficult to provide a fitness plan that is dynamically adjusted based on the user's health status and lifestyle habits. Also, it is not easy to determine whether the form of training and the content of the diet are appropriate and to continuously provide feedback.

Means for Solving the Problems

[0005] The present invention provides a system comprising data collection means for collecting a user's physical data, analysis means for performing analysis based on the physical data, feedback means for providing the user with training and meal plans, and update means for updating the plans provided by the feedback means based on the analysis results. The system includes a camera function for acquiring image data of the user's meals and training, and further performs data analysis using machine learning algorithms to dynamically generate an optimal fitness plan for each individual user, thereby supporting effective health management while keeping costs down.

[0006] "Data collection means" refers to devices or methods that have the function of acquiring a user's physiological data or image data.

[0007] "Analysis means" refers to devices or methods for evaluating a user's health status and lifestyle based on collected data, and for generating appropriate training and meal plans.

[0008] A "feedback means" refers to a device or method that notifies the user of the plan obtained by the analysis means and provides advice and guidance regarding training and diet.

[0009] "Update means" refers to a device or method that has the function of appropriately adjusting and improving the training and meal plans provided based on new data obtained from the user.

[0010] The "camera function" refers to the means of taking pictures to acquire image data of the user's meals and training.

[0011] A "machine learning algorithm" is a mathematical method that analyzes large amounts of data to predict changes in a user's health status and generate the optimal plan. [Brief explanation of the drawing]

[0012] [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]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled 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.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system that supports user health management and consists of a wearable device, a terminal such as a smartphone or personal computer, and a cloud server.

[0034] First, users wear a wearable device to collect physiological data such as heart rate, steps taken, and blood pressure during their daily lives. The wearable device also has a camera function, which users can use to record their meals and workouts in photos and videos.

[0035] Next, the device receives physiological and image data from the wearable device. This data is sent to a cloud server at regular intervals and stored in a structured database.

[0036] The server uses machine learning algorithms to analyze the received data. The algorithms comprehensively evaluate the user's current health status, training history, and dietary content to generate an optimal training and meal plan for the user. This includes analyzing image data acquired by the camera function to determine whether the nutrients in the diet and the training form are appropriate.

[0037] The generated plan is sent from the server to the device. The device then provides this information back to the user through the application, offering specific training content and dietary recommendations.

[0038] Users receive this feedback and use it to improve their daily lives. New user behavioral data is collected again through wearable devices and terminals. The server then uses this new data to continuously update the generating AI. This makes it possible to continue providing more accurate fitness plans.

[0039] For example, this system provides users who regularly run with advice on adjusting exercise intensity based on heart rate data, and evaluates nutritional balance from images of meals taken, suggesting areas for improvement. By following these suggestions and improving their diet, users can effectively maintain their health.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user wears a wearable device and acquires physiological data and image data using the camera function. The device collects this data in real time.

[0043] Step 2:

[0044] The device receives physiological and image data from the wearable device via Bluetooth or Wi-Fi. This data is temporarily stored on the device.

[0045] Step 3:

[0046] The device sends data collected at regular intervals to a cloud server. This transmission is performed using a secure communication protocol.

[0047] Step 4:

[0048] The server has the functionality to store received data in a database and compare it with past data. A process is performed to verify the integrity of the data.

[0049] Step 5:

[0050] The server uses machine learning algorithms to analyze the data. This analysis assesses the user's health status and lifestyle, and generates training and meal plans.

[0051] Step 6:

[0052] The server generates feedback based on the analysis results and sends it to the terminal. This feedback includes specific training menus and meal suggestions.

[0053] Step 7:

[0054] The application notifies the user of feedback received by the device. The user can then refer to this information to help plan their next training session or meal plan.

[0055] Step 8:

[0056] Users adjust their lifestyle habits based on feedback. New behavioral data is then collected and used by the system for the next cycle.

[0057] Step 9:

[0058] Based on the new data collected by the server, the machine learning model is updated to improve the accuracy of subsequent analyses. Through continuous learning, the system provides users with increasingly appropriate plans.

[0059] (Example 1)

[0060] 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."

[0061] In modern times, it is important to understand an individual's health condition in detail and to adjust training and dietary habits to suit their actual lifestyle. However, providing a plan tailored to each user requires the analysis of diverse data, and there is a need for a means to effectively carry out this analysis. The present invention aims to provide a system that supports health maintenance by providing individually optimized exercise and nutrition plans using the user's physiological indicator data and image data.

[0062] 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.

[0063] In this invention, the server includes a device for acquiring the user's physiological indicator data, computing resources for analyzing the physiological indicator data and image data, and an information provider that presents an exercise and nutrition plan optimized for the user. This allows the user to receive a fitness plan tailored to their health condition and to make concrete and actionable improvements in their life.

[0064] "Device" refers to hardware or equipment used to acquire physiological indicator data from a user.

[0065] "Computational resources" refers to software or hardware for analyzing physiological indicator data and image data, and in particular, the computing power to perform the analysis.

[0066] An "information provision device" is a device or platform for presenting an exercise and nutrition plan optimized for the user.

[0067] An "imaging device" is a device equipped with a camera function to acquire the user's intake and exercise status as image data.

[0068] A "learning algorithm" is a machine learning technique that analyzes and processes user physiological indicator data to generate individually optimized fitness plans.

[0069] This invention is a system for efficiently supporting users' health management. The system is initiated by the user using a device to collect physiological indicator data. Specific hardware includes a wearable device capable of measuring heart rate, blood pressure, and steps. This device also includes a camera function for the user to take photos during daily life.

[0070] The user wears the aforementioned wearable device to acquire physiological indicator data. This data is then transmitted via the device to a server connected to the cloud. The device can be a smartphone or a personal computer.

[0071] The server possesses computing resources to analyze the received data. This analysis utilizes learning algorithms to analyze the user's health status and behavioral history. Furthermore, the acquired image data of meals and training is used with machine learning to evaluate dietary content and exercise performance.

[0072] Based on the analysis results, the server generates an exercise and nutrition plan optimized for the user. This information is presented to the user via a terminal as an information provider. Feedback is provided through the application, giving the user the opportunity to improve their daily life based on this feedback.

[0073] As a concrete example, this system suggests an ideal training pace for users aiming to run a marathon based on their heart rate data. It can also analyze nutritional balance from photos of meals and suggest ways to improve it. Based on this advice, users can adjust their daily exercise and manage their health more efficiently.

[0074] An example of a prompt message might be: "Create suggestions for the optimal exercise intensity and dietary improvements for a male user in his 30s who runs three times a week."

[0075] This system will enable users to manage their health in a specific and effective way.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users wear wearable devices that collect physiological data such as heart rate, steps taken, and blood pressure on a daily basis. Furthermore, they use the camera function to record meals and exercises. The input consists of collected physiological data and captured image data. This data serves as fundamental information for understanding the user's health status.

[0079] Step 2:

[0080] The terminal receives data collected from wearable devices. This received data includes physiological data and image data. The terminal then sends this data to a cloud server. The input is data from the wearable devices, and the output is the data sent to the server. This is a step in aggregating the data necessary for subsequent analysis processes.

[0081] Step 3:

[0082] The server stores received data in a cloud-based structured database. Input is data sent from the terminal, and output is stored in the structured database. Storing data in the database ensures data consistency and accessibility.

[0083] Step 4:

[0084] The server analyzes stored data using machine learning algorithms. The input consists of physiological and image data stored in a database. Data processing includes assessing the user's health status, identifying nutrients in their diet, and recognizing the effectiveness of training. The output is a training and meal plan tailored to the user. This generates user-specific health guidelines.

[0085] Step 5:

[0086] The plan generated by the server is provided to the user as feedback via the terminal. The output includes specific exercise instructions and suggestions for dietary improvements. The user then adjusts their daily life accordingly.

[0087] Step 6:

[0088] When a user generates new health data, it is collected again via a wearable device. The input includes newly collected physiological and image data, which the server receives to update the generating AI model. This enables continuous health management and improved plan accuracy.

[0089] (Application Example 1)

[0090] 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."

[0091] In modern society, personal health management is a crucial issue, and there is a particular need for a system that allows for easy and effective monitoring of health status, especially in a home environment, and provides appropriate exercise and dietary suggestions. However, there is currently a lack of simple and automated methods for users to continuously monitor their health data in their daily lives and receive appropriate advice. Therefore, there is a strong desire for such health management systems to be available for use at home.

[0092] 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.

[0093] In this invention, the server includes information gathering means for collecting the user's physiological information, analysis means for performing analysis based on the physiological information and visual data, and response means for providing the user with exercise and diet plans. This enables the automation of user health management in a home environment and effectively provides exercise and diet suggestions tailored to the individual's health condition.

[0094] "User physiological information" refers to data that indicates the user's health status, such as heart rate, steps taken, and blood pressure.

[0095] "Information gathering means" refers to devices or technologies that acquire physiological information and visual data from users.

[0096] "Analysis means" refers to a device or program for analyzing collected physiological information and visual data to evaluate the user's health status.

[0097] A "response means" is a technology or device that provides the user with an exercise and diet plan based on the analyzed data.

[0098] "Modification means" refers to a technique or device for updating the plan provided by the response means based on the analysis results.

[0099] "Visual information" refers to image or video data acquired using devices such as cameras, and includes information about eating and exercising.

[0100] An "autonomous machine" is a robot or mechanical device that automatically collects and supports a user's physiological information in a home environment.

[0101] The system for carrying out this invention is configured as follows to collect and analyze the user's physiological information and provide an appropriate exercise and meal plan. The system includes information collection means, including wearable sensors and cameras, for collecting the user's physiological information. This allows for the collection of image data such as heart rate, steps taken, blood pressure, and meals.

[0102] The collected data is sent to the terminal device and then transmitted to a cloud server. The cloud server is equipped with machine learning algorithms as an analysis tool and performs data analysis. Specifically, it uses platforms such as TENSORFLOW® and scikit-learn for analysis. Based on the analysis results, the user's current health status, appropriate training methods, and dietary balance are evaluated.

[0103] Subsequently, the analysis results are provided to the user through a response mechanism. This response mechanism is an autonomous machine in the user's home environment, namely a personal robot. Based on the analysis results, this robot proposes daily exercise and meal plans to the user using voice and on a screen. The robot has an image recognition function using OpenCV, which allows it to recognize the contents of meals from collected photographic data and evaluate nutritional balance.

[0104] As an example, consider a scenario where a robot instructs the user to "take a picture of today's meal and check its nutritional balance," and the user complies by taking a picture of the meal with a camera. The AI ​​then evaluates the meal and provides advice such as, "Your protein intake today is a little low. Let's be mindful of that at your next meal." In this way, users can receive support for maintaining their health based on instructions from an autonomous machine.

[0105] A concrete example of a generated AI prompt is, "Please evaluate what I ate today. If possible, please provide suggestions for improvement and health advice." This allows the user to receive necessary health management advice.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user wears a wearable sensor, which collects physiological information such as heart rate and steps. Furthermore, the user takes pictures of their meals using a capture device. The collected physiological and image data is input into a terminal. The terminal receives this data, prepares the data format, and verifies it.

[0109] Step 2:

[0110] The device sends the compiled physiological information and image data to a cloud server. The server uses machine learning algorithms to analyze the data. It determines the user's health status from the physiological information and evaluates the nutritional balance of the diet from the image data. As a result of this analysis, the server outputs the user's current health status and suggestions for necessary health management.

[0111] Step 3:

[0112] Based on the analysis results, the server generates an exercise and meal plan tailored to the user. This plan includes specific exercises and dietary recommendations customized based on the user's set health goals. This information is generated by a generative AI model, and it also provides advice based on user input using prompts. The generated plan is then sent to the device.

[0113] Step 4:

[0114] An autonomous device placed in the user's home environment presents the user with exercise and meal plans received from a terminal. The autonomous device has voice output capabilities and a display, showing analysis results and health plans verbally or visually. For example, advice such as "Let's include more protein in your next meal" is presented as a specific menu. It also provides support to the user when following instructions.

[0115] Step 5:

[0116] The user lives according to the presented health plan, acquiring new behavioral data. Wearable sensors and capture devices collect this new data again and send it to a server via the terminal. This data becomes input for the next analysis cycle. In addition, the generative AI model is improved by incorporating the new data, resulting in higher accuracy in subsequent plan generation.

[0117] 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.

[0118] This invention is a system for highly personalized user health management, and is comprised of a wearable device, a terminal, a cloud server, and an emotion engine.

[0119] First, users collect physiological data such as heart rate, steps taken, and blood pressure through a wearable device. In addition, the device's camera function is used to photograph the user's daily meals and exercise. This data is aggregated on the device and sent to a cloud server.

[0120] The server uses machine learning algorithms to analyze the user's health status based on received physiological and image data. The analysis results are used to generate optimal training and meal plans for the user. This process incorporates an emotion engine that analyzes the user's emotions from their facial expressions and voice tone. As a result, the health management plan is adjusted to take the user's emotional state into account.

[0121] The generated feedback is sent from the server to the terminal. The terminal notifies the user and provides specific advice. For example, if the user is feeling stressed, emotionally-based adjustments are made, such as suggesting relaxing exercise or meals.

[0122] Users adjust their daily behaviors based on the feedback. New behavioral and emotional data are collected again via wearable devices and terminals and used in the next analysis cycle. This allows the server to continuously monitor the user's state and further optimize the feedback.

[0123] For example, if a user feels fatigued from their daily training, the emotional engine will detect their stress level and suggest lighter exercise or recommend activities for relaxation. In this way, the system enables detailed health management tailored to the user's emotional state.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The user wears a wearable device that records physiological data, image data acquired using a camera, and audio data. This device collects this data throughout all aspects of daily life.

[0127] Step 2:

[0128] The terminal receives physiological data, image data, and audio data from wearable devices via Bluetooth or Wi-Fi. The terminal temporarily stores the data and prepares it for the next transfer to the cloud server.

[0129] Step 3:

[0130] The device sends data to the cloud server. A secure communication protocol is used to ensure data integrity during transmission.

[0131] Step 4:

[0132] The server saves received data to the database. During saving, it cross-references with past data to check the consistency of the user's state.

[0133] Step 5:

[0134] The server uses machine learning algorithms to analyze the received data. This analysis includes assessing the user's health status and generating exercise and meal plans.

[0135] Step 6:

[0136] The server uses an emotion engine to analyze image and audio data to recognize the user's emotional state. This analysis is then used to generate personalized feedback.

[0137] Step 7:

[0138] The server generates feedback based on analysis results and emotional states, and sends it to the terminal. This feedback includes training adjustments and dietary suggestions tailored to the user's emotional state.

[0139] Step 8:

[0140] The device receives feedback information, which is then communicated to the user via the application. The user then reviews the feedback and applies it to their daily training and diet.

[0141] Step 9:

[0142] Users incorporate behavioral changes based on feedback. New data is collected again through wearable devices and terminals and sent to the server.

[0143] Step 10:

[0144] The server updates its machine learning algorithms using new data, improving analysis accuracy for the next feedback cycle. In this way, the system continuously provides users with the most relevant information.

[0145] (Example 2)

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

[0147] Traditionally, health management systems have only provided general guidelines to users, and have faced the challenge of providing personalized health guidance that takes into account the physical condition and emotional state of individual users. Furthermore, the inability to adequately adjust real-time monitoring and feedback meant that advice tailored to the user's health condition could not be provided.

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

[0149] In this invention, the server includes information gathering means for collecting the user's physiological and visual data, information processing means for performing analysis based on the information, and response means for providing the user with exercise and nutritional intake guidelines. This makes it possible to continuously monitor the user's health status and emotions in real time and provide individually tailored guidelines.

[0150] "Information gathering means" refers to devices or processes that collect users' physiological and visual data.

[0151] "Information processing means" refers to devices and technologies used to analyze collected data and evaluate the user's health status.

[0152] A "response mechanism" refers to a device or process that provides users with guidance on exercise and nutritional intake based on the analysis results.

[0153] "Adjustment means" refers to devices or technologies that change the guidelines provided by the response means in real time based on the information processing results.

[0154] This invention is a system for personalizing user health management, in which the user collects physiological and visual data using a wearable device. Specifically, physiological data such as heart rate, steps taken, and blood pressure are acquired by sensors, and image data of meals and exercises are collected using a camera function.

[0155] The device temporarily stores this data and transfers it to a server in the cloud via a communication method. The device typically uses Bluetooth or Wi-Fi to collect data from wearable devices and transmits the data to the server using the internet.

[0156] The server analyzes the received physiological and visual data using information processing tools. This process utilizes machine learning algorithms and performs data analysis using software libraries such as TensorFlow and OpenCV. This allows the server to understand the user's health and emotional state and generate a personalized health plan.

[0157] The generated health plan is provided to the user via a response mechanism, based on the analysis results and the assessment of their emotional state. The device informs the user of specific exercise and nutritional guidelines through push notifications and in-app displays. For example, if the user is feeling stressed, the plan can be adjusted according to their emotional state, such as suggesting relaxing exercises or meals.

[0158] Users adjust their behavior based on the feedback they receive, and new physiological and emotional data are collected to aid in the next analysis cycle. This continuous process allows the server to continuously monitor the user's state and provide optimized guidance.

[0159] For example, a prompt such as "Suggest a suitable relaxation method when the user's stress level is high" can enable a generative AI model to create and provide personalized advice to the user.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The user wears a wearable device to collect physiological data (heart rate, steps, blood pressure, etc.) in real time. The device's camera function is also used to acquire image data of daily meals and exercise. The input consists of the user's physiological and visual data, which is transferred to the terminal. The output is a data package formed from the collected data. Specifically, the device transmits data to the terminal via Bluetooth.

[0163] Step 2:

[0164] The terminal temporarily stores physiological and image data received from wearable devices. Its input is a data package from the user, which it then processes to prepare for transmission to the cloud server. The output is data processed into a format suitable for transmission to the cloud server. Specifically, the terminal converts the data format and sends the data to the server via the HTTPS protocol.

[0165] Step 3:

[0166] The server receives data provided by the terminal and performs analysis using information processing tools. The input consists of the user's physiological data and image data, which are analyzed by machine learning algorithms. The output generates analysis results indicating the user's health status. Specifically, the server analyzes the data using tools such as TensorFlow and evaluates the health status.

[0167] Step 4:

[0168] The server uses response mechanisms based on the analysis results to generate exercise and nutritional guidelines tailored to the user. The input is analyzed health status data, which is used to form individualized feedback. The output is a recommended plan for the user. Specifically, the server creates a recommended plan by combining information, referencing pre-configured health plan templates.

[0169] Step 5:

[0170] The device receives recommended plans sent from the server and notifies the user. The input is recommended data from the server, and the output provides the user with specific action guidelines. Specifically, the device uses its notification function to send a push notification to the user.

[0171] Step 6:

[0172] Users adjust their behavior based on feedback from their device. The input is exercise and nutritional guidelines provided by the device, and the output is modifications to their daily behavior. Specific actions include performing suggested exercises or purchasing ingredients for suggested meals.

[0173] (Application Example 2)

[0174] 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".

[0175] Traditional health management systems often provide uniform plans without considering the emotional state of individual users, making it difficult to manage health in a way that takes users' emotional needs into account. As a result, there was a problem in that optimal health maintenance could not be fully achieved for each user.

[0176] 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.

[0177] In this invention, the server includes data acquisition means for collecting physiological information of the user, information processing means for performing analysis based on the physiological information and emotional information, and response generation means for supplying a health plan optimized for the user. This makes it possible to provide a personalized health plan that is tailored to the emotional state of each individual user.

[0178] "Physiological information" refers to data that indicates the user's physical condition, such as heart rate, steps taken, and blood pressure.

[0179] "Emotional information" refers to data about the user's emotional state, determined from factors such as facial expressions and tone of voice.

[0180] "Data acquisition means" refers to devices or methods equipped with functions for collecting physiological and emotional information from users.

[0181] "Information processing means" refers to a system or device that analyzes collected physiological and emotional information and performs computational processing to evaluate the user's health status.

[0182] A "response generation means" refers to a device or method for creating and providing an optimal health plan to a user based on analysis results.

[0183] "Adjustment means" refers to a method or apparatus for adapting the plan supplied by the response generation means to the analysis results of the information processing means.

[0184] "Emotional analysis means" refers to a technology or device for analyzing data such as a user's facial expressions and voice to evaluate their emotional state.

[0185] The embodiments for carrying out the invention are described below.

[0186] This system is designed to manage users' health in a personalized manner. The system includes a wearable device worn by the user to acquire biometric information, initially collecting data such as heart rate, steps taken, and blood pressure. This data is then transferred to the user's device via Bluetooth communication.

[0187] The device transmits the user's physiological information, along with images and video data of the user's daily life, diet, and exercise collected by the device's camera, to a cloud server. On the server, this data is analyzed using machine learning algorithms such as TensorFlow to evaluate the user's health status.

[0188] This analysis utilizes technologies such as OpenCV and PyAudio to analyze the user's emotional state from their facial expression and voice data. Emotional information is processed by an emotion analysis system and incorporated into the output of an information processing system. Based on this output, a response generation system generates an optimal health plan for the user. This plan is then fed back to the user's terminal and provided to the user through voice notifications and display information.

[0189] For example, if a user shows signs of fatigue, the system can suggest light exercise and rest. An example of a prompt might be, "If the user's heart rate is lower than normal and they have a tired expression, what kind of exercise and diet can you suggest?"

[0190] This system allows individual users to receive personalized health management based on their current physiological and emotional state.

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

[0192] Step 1:

[0193] The user wears a wearable device that continuously collects physiological information (heart rate, steps, blood pressure, etc.). The input is the data measured as physiological information, and the output is the raw data transferred to the terminal. The data is transmitted to the terminal via Bluetooth communication.

[0194] Step 2:

[0195] The terminal temporarily stores physiological information data received from the wearable device and acquires image and video data of the user's daily life, meals, and training captured by the built-in camera. Inputs are physiological information and image data, while output is data for transfer to the cloud server. The terminal prepares to upload the data to the cloud server.

[0196] Step 3:

[0197] The cloud server receives physiological and image data transmitted from the terminal and stores it in a database. Simultaneously, it analyzes the physiological and emotional information using machine learning algorithms (e.g., TensorFlow). The input is data on the cloud, and the output is the analysis results regarding the user's health and emotional state. The server then evaluates the health status based on the analysis.

[0198] Step 4:

[0199] The server determines the user's emotional state by analyzing the user's facial expressions from images using OpenCV and analyzing audio data using PyAudio. The inputs are image data and audio data, and the output is data indicating the user's emotional state.

[0200] Step 5:

[0201] The server uses a generative AI model based on the analysis results to create a health plan optimized for the user. The input is the analysis results and emotional information, and the output is the health plan and feedback message. For example, the model is run using the prompt statement "If the user's heart rate is lower than normal and their face looks tired, what kind of exercise and diet can be suggested?"

[0202] Step 6:

[0203] The device receives health plans and feedback messages sent from the server and notifies the user. Input is feedback data from the server, and output is audio or display-based feedback to the user. The device guides the user to implement the suggested exercises and dietary changes.

[0204] 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.

[0205] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] This invention is a system that supports user health management and consists of a wearable device, a terminal such as a smartphone or personal computer, and a cloud server.

[0221] First, users wear a wearable device to collect physiological data such as heart rate, steps taken, and blood pressure during their daily lives. The wearable device also has a camera function, which users can use to record their meals and workouts in photos and videos.

[0222] Next, the device receives physiological and image data from the wearable device. This data is sent to a cloud server at regular intervals and stored in a structured database.

[0223] The server uses machine learning algorithms to analyze the received data. The algorithms comprehensively evaluate the user's current health status, training history, and dietary content to generate an optimal training and meal plan for the user. This includes analyzing image data acquired by the camera function to determine whether the nutrients in the diet and the training form are appropriate.

[0224] The generated plan is sent from the server to the device. The device then provides this information back to the user through the application, offering specific training content and dietary recommendations.

[0225] Users receive this feedback and use it to improve their daily lives. New user behavioral data is collected again through wearable devices and terminals. The server then uses this new data to continuously update the generating AI. This makes it possible to continue providing more accurate fitness plans.

[0226] For example, this system provides users who regularly run with advice on adjusting exercise intensity based on heart rate data, and evaluates nutritional balance from images of meals taken, suggesting areas for improvement. By following these suggestions and improving their diet, users can effectively maintain their health.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The user wears a wearable device and acquires physiological data and image data using the camera function. The device collects this data in real time.

[0230] Step 2:

[0231] The device receives physiological and image data from the wearable device via Bluetooth or Wi-Fi. This data is temporarily stored on the device.

[0232] Step 3:

[0233] The device sends data collected at regular intervals to a cloud server. This transmission is performed using a secure communication protocol.

[0234] Step 4:

[0235] The server has the functionality to store received data in a database and compare it with past data. A process is performed to verify the integrity of the data.

[0236] Step 5:

[0237] The server uses machine learning algorithms to analyze the data. This analysis assesses the user's health status and lifestyle, and generates training and meal plans.

[0238] Step 6:

[0239] The server generates feedback based on the analysis results and sends it to the terminal. This feedback includes specific training menus and meal suggestions.

[0240] Step 7:

[0241] The application notifies the user of feedback received by the device. The user can then refer to this information to help plan their next training session or meal plan.

[0242] Step 8:

[0243] Users adjust their lifestyle habits based on feedback. New behavioral data is then collected and used by the system for the next cycle.

[0244] Step 9:

[0245] Based on the new data collected by the server, the machine learning model is updated to improve the accuracy of subsequent analyses. Through continuous learning, the system provides users with increasingly appropriate plans.

[0246] (Example 1)

[0247] 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."

[0248] In modern times, it is important to understand an individual's health condition in detail and to adjust training and dietary habits to suit their actual lifestyle. However, providing a plan tailored to each user requires the analysis of diverse data, and there is a need for a means to effectively carry out this analysis. The present invention aims to provide a system that supports health maintenance by providing individually optimized exercise and nutrition plans using the user's physiological indicator data and image data.

[0249] 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.

[0250] In this invention, the server includes a device for acquiring the user's physiological indicator data, computing resources for analyzing the physiological indicator data and image data, and an information provider that presents an exercise and nutrition plan optimized for the user. This allows the user to receive a fitness plan tailored to their health condition and to make concrete and actionable improvements in their life.

[0251] "Device" refers to hardware or equipment used to acquire physiological indicator data from a user.

[0252] "Computational resources" refers to software or hardware for analyzing physiological indicator data and image data, and in particular, the computing power to perform the analysis.

[0253] An "information provision device" is a device or platform for presenting an exercise and nutrition plan optimized for the user.

[0254] An "imaging device" is a device equipped with a camera function to acquire the user's intake and exercise status as image data.

[0255] A "learning algorithm" is a machine learning technique that analyzes and processes user physiological indicator data to generate individually optimized fitness plans.

[0256] This invention is a system for efficiently supporting users' health management. The system is initiated by the user using a device to collect physiological indicator data. Specific hardware includes a wearable device capable of measuring heart rate, blood pressure, and steps. This device also includes a camera function for the user to take photos during daily life.

[0257] The user wears the aforementioned wearable device to acquire physiological indicator data. This data is then transmitted via the device to a server connected to the cloud. The device can be a smartphone or a personal computer.

[0258] The server possesses computing resources to analyze the received data. This analysis utilizes learning algorithms to analyze the user's health status and behavioral history. Furthermore, the acquired image data of meals and training is used with machine learning to evaluate dietary content and exercise performance.

[0259] Based on the analysis results, the server generates an exercise and nutrition plan optimized for the user. This information is presented to the user via a terminal as an information provider. Feedback is provided through the application, giving the user the opportunity to improve their daily life based on this feedback.

[0260] As a concrete example, this system suggests an ideal training pace for users aiming to run a marathon based on their heart rate data. It can also analyze nutritional balance from photos of meals and suggest ways to improve it. Based on this advice, users can adjust their daily exercise and manage their health more efficiently.

[0261] An example of a prompt message might be: "Create suggestions for the optimal exercise intensity and dietary improvements for a male user in his 30s who runs three times a week."

[0262] This system will enable users to manage their health in a specific and effective way.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] Users wear wearable devices that collect physiological data such as heart rate, steps taken, and blood pressure on a daily basis. Furthermore, they use the camera function to record meals and exercises. The input consists of collected physiological data and captured image data. This data serves as fundamental information for understanding the user's health status.

[0266] Step 2:

[0267] The terminal receives data collected from wearable devices. This received data includes physiological data and image data. The terminal then sends this data to a cloud server. The input is data from the wearable devices, and the output is the data sent to the server. This is a step in aggregating the data necessary for subsequent analysis processes.

[0268] Step 3:

[0269] The server stores received data in a cloud-based structured database. Input is data sent from the terminal, and output is stored in the structured database. Storing data in the database ensures data consistency and accessibility.

[0270] Step 4:

[0271] The server analyzes stored data using machine learning algorithms. The input consists of physiological and image data stored in a database. Data processing includes assessing the user's health status, identifying nutrients in their diet, and recognizing the effectiveness of training. The output is a training and meal plan tailored to the user. This generates user-specific health guidelines.

[0272] Step 5:

[0273] The plan generated by the server is provided to the user as feedback via the terminal. The output includes specific exercise instructions and suggestions for dietary improvements. The user then adjusts their daily life accordingly.

[0274] Step 6:

[0275] When a user generates new health data, it is collected again via a wearable device. The input includes newly collected physiological and image data, which the server receives to update the generating AI model. This enables continuous health management and improved plan accuracy.

[0276] (Application Example 1)

[0277] 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."

[0278] In modern society, personal health management is a crucial issue, and there is a particular need for a system that allows for easy and effective monitoring of health status, especially in a home environment, and provides appropriate exercise and dietary suggestions. However, there is currently a lack of simple and automated methods for users to continuously monitor their health data in their daily lives and receive appropriate advice. Therefore, there is a strong desire for such health management systems to be available for use at home.

[0279] 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.

[0280] In this invention, the server includes information gathering means for collecting the user's physiological information, analysis means for performing analysis based on the physiological information and visual data, and response means for providing the user with exercise and diet plans. This enables the automation of user health management in a home environment and effectively provides exercise and diet suggestions tailored to the individual's health condition.

[0281] "User physiological information" refers to data that indicates the user's health status, such as heart rate, steps taken, and blood pressure.

[0282] "Information gathering means" refers to devices or technologies that acquire physiological information and visual data from users.

[0283] The "analysis means" is a device or program for analyzing the collected physiological information and visual data to evaluate the user's health status.

[0284] The "response means" is a technology or device that provides exercise and diet plans to the user based on the analyzed data.

[0285] The "modification means" is a technology or device for updating the plan provided by the response means based on the analysis results.

[0286] "Visual information" is image or video data obtained using devices such as cameras, and includes information such as the state of diet and exercise.

[0287] The "autonomous machine" is a robot or mechanical device that automatically collects and supports the user's physiological information in a home environment.

[0288] The system for implementing this invention is configured as follows to collect, analyze, and provide appropriate exercise and diet plans for the user's physiological information. The system includes information collection means such as wearable sensors and cameras for collecting the user's physiological information. As a result, heart rate, number of steps, blood pressure, image data of diet, etc. are collected.

[0289] The collected data is transmitted to the terminal device and further transmitted to the cloud server. The cloud server is equipped with a machine learning algorithm as the analysis means and performs data analysis. Specifically, the analysis is performed using platforms such as TensorFlow and scikit-learn. Based on the analysis results, the user's current health status, appropriate training methods, and diet balance are evaluated.

[0290] Subsequently, the analysis results are provided to the user through a response mechanism. This response mechanism is an autonomous machine in the user's home environment, namely a personal robot. Based on the analysis results, this robot proposes daily exercise and meal plans to the user using voice and on a screen. The robot has an image recognition function using OpenCV, which allows it to recognize the contents of meals from collected photographic data and evaluate nutritional balance.

[0291] As an example, consider a scenario where a robot instructs the user to "take a picture of today's meal and check its nutritional balance," and the user complies by taking a picture of the meal with a camera. The AI ​​then evaluates the meal and provides advice such as, "Your protein intake today is a little low. Let's be mindful of that at your next meal." In this way, users can receive support for maintaining their health based on instructions from an autonomous machine.

[0292] A concrete example of a generated AI prompt is, "Please evaluate what I ate today. If possible, please provide suggestions for improvement and health advice." This allows the user to receive necessary health management advice.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The user wears a wearable sensor, which collects physiological information such as heart rate and steps. Furthermore, the user takes pictures of their meals using a capture device. The collected physiological and image data is input into a terminal. The terminal receives this data, prepares the data format, and verifies it.

[0296] Step 2:

[0297] The device sends the compiled physiological information and image data to a cloud server. The server uses machine learning algorithms to analyze the data. It determines the user's health status from the physiological information and evaluates the nutritional balance of the diet from the image data. As a result of this analysis, the server outputs the user's current health status and suggestions for necessary health management.

[0298] Step 3:

[0299] Based on the analysis results, the server generates an exercise and meal plan tailored to the user. This plan includes specific exercises and dietary recommendations customized based on the user's set health goals. This information is generated by a generative AI model, and it also provides advice based on user input using prompts. The generated plan is then sent to the device.

[0300] Step 4:

[0301] An autonomous device placed in the user's home environment presents the user with exercise and meal plans received from a terminal. The autonomous device has voice output capabilities and a display, showing analysis results and health plans verbally or visually. For example, advice such as "Let's include more protein in your next meal" is presented as a specific menu. It also provides support to the user when following instructions.

[0302] Step 5:

[0303] The user lives according to the presented health plan, acquiring new behavioral data. Wearable sensors and capture devices collect this new data again and send it to a server via the terminal. This data becomes input for the next analysis cycle. In addition, the generative AI model is improved by incorporating the new data, resulting in higher accuracy in subsequent plan generation.

[0304] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion specific model 59 and perform specific processing using the user's emotions.

[0305] This invention is a system for highly individualized user health management, which is composed of a combination of a wearable device, a terminal, a cloud server, and an emotion engine.

[0306] First, the user collects physiological data such as heart rate, step count, and blood pressure through a wearable device. In addition to this, the camera function of the device is used to photograph the user's daily diet and training situation. These data are aggregated at the terminal and transmitted to the cloud server.

[0307] The server analyzes the user's health status using a machine learning algorithm based on the received physiological data and image data. The analysis results are used to generate an optimal training and diet plan for the user. An emotion engine is incorporated into this process, and a function for analyzing emotions from the user's facial expressions and voice tones is attached. As a result, the health management plan is adjusted considering the user's emotional state.

[0308] The generated feedback is transmitted from the server to the terminal. The terminal notifies the user of this and provides specific advice. For example, when the user is feeling stressed, adjustments based on emotions are made, such as proposing exercises and diets with a relaxing effect.

[0309] The user adjusts their actions in daily life referring to the feedback. New action data and emotion data are collected again via the wearable device and the terminal and used for the next analysis cycle. As a result, the server can continuously monitor the user's state and further optimize the feedback.

[0310] For example, if a user feels fatigued from their daily training, the emotional engine will detect their stress level and suggest lighter exercise or recommend activities for relaxation. In this way, the system enables detailed health management tailored to the user's emotional state.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The user wears a wearable device that records physiological data, image data acquired using a camera, and audio data. This device collects this data throughout all aspects of daily life.

[0314] Step 2:

[0315] The terminal receives physiological data, image data, and audio data from wearable devices via Bluetooth or Wi-Fi. The terminal temporarily stores the data and prepares it for the next transfer to the cloud server.

[0316] Step 3:

[0317] The device sends data to the cloud server. A secure communication protocol is used to ensure data integrity during transmission.

[0318] Step 4:

[0319] The server saves received data to the database. During saving, it cross-references with past data to check the consistency of the user's state.

[0320] Step 5:

[0321] The server uses machine learning algorithms to analyze the received data. This analysis includes assessing the user's health status and generating exercise and meal plans.

[0322] Step 6:

[0323] The server uses an emotion engine to analyze image and audio data to recognize the user's emotional state. This analysis is then used to generate personalized feedback.

[0324] Step 7:

[0325] The server generates feedback based on analysis results and emotional states, and sends it to the terminal. This feedback includes training adjustments and dietary suggestions tailored to the user's emotional state.

[0326] Step 8:

[0327] The device receives feedback information, which is then communicated to the user via the application. The user then reviews the feedback and applies it to their daily training and diet.

[0328] Step 9:

[0329] Users incorporate behavioral changes based on feedback. New data is collected again through wearable devices and terminals and sent to the server.

[0330] Step 10:

[0331] The server updates its machine learning algorithms using new data, improving analysis accuracy for the next feedback cycle. In this way, the system continuously provides users with the most relevant information.

[0332] (Example 2)

[0333] 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".

[0334] Traditionally, health management systems have only provided general guidelines to users, and have faced the challenge of providing personalized health guidance that takes into account the physical condition and emotional state of individual users. Furthermore, the inability to adequately adjust real-time monitoring and feedback meant that advice tailored to the user's health condition could not be provided.

[0335] 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.

[0336] In this invention, the server includes information gathering means for collecting the user's physiological and visual data, information processing means for performing analysis based on the information, and response means for providing the user with exercise and nutritional intake guidelines. This makes it possible to continuously monitor the user's health status and emotions in real time and provide individually tailored guidelines.

[0337] "Information gathering means" refers to devices or processes that collect users' physiological and visual data.

[0338] "Information processing means" refers to devices and technologies used to analyze collected data and evaluate the user's health status.

[0339] A "response mechanism" refers to a device or process that provides users with guidance on exercise and nutritional intake based on the analysis results.

[0340] "Adjustment means" refers to devices or technologies that change the guidelines provided by the response means in real time based on the information processing results.

[0341] This invention is a system for personalizing user health management, in which the user collects physiological and visual data using a wearable device. Specifically, physiological data such as heart rate, steps taken, and blood pressure are acquired by sensors, and image data of meals and exercises are collected using a camera function.

[0342] The device temporarily stores this data and transfers it to a server in the cloud via a communication method. The device typically uses Bluetooth or Wi-Fi to collect data from wearable devices and transmits the data to the server using the internet.

[0343] The server analyzes the received physiological and visual data using information processing tools. This process utilizes machine learning algorithms and performs data analysis using software libraries such as TensorFlow and OpenCV. This allows the server to understand the user's health and emotional state and generate a personalized health plan.

[0344] The generated health plan is provided to the user via a response mechanism, based on the analysis results and the assessment of their emotional state. The device informs the user of specific exercise and nutritional guidelines through push notifications and in-app displays. For example, if the user is feeling stressed, the plan can be adjusted according to their emotional state, such as suggesting relaxing exercises or meals.

[0345] Users adjust their behavior based on the feedback they receive, and new physiological and emotional data are collected to aid in the next analysis cycle. This continuous process allows the server to continuously monitor the user's state and provide optimized guidance.

[0346] For example, a prompt such as "Suggest a suitable relaxation method when the user's stress level is high" can enable a generative AI model to create and provide personalized advice to the user.

[0347] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0348] Step 1:

[0349] The user wears a wearable device to collect physiological data (heart rate, steps, blood pressure, etc.) in real time. The device's camera function is also used to acquire image data of daily meals and exercise. The input consists of the user's physiological and visual data, which is transferred to the terminal. The output is a data package formed from the collected data. Specifically, the device transmits data to the terminal via Bluetooth.

[0350] Step 2:

[0351] The terminal temporarily stores physiological and image data received from wearable devices. Its input is a data package from the user, which it then processes to prepare for transmission to the cloud server. The output is data processed into a format suitable for transmission to the cloud server. Specifically, the terminal converts the data format and sends the data to the server via the HTTPS protocol.

[0352] Step 3:

[0353] The server receives data provided by the terminal and performs analysis using information processing tools. The input consists of the user's physiological data and image data, which are analyzed by machine learning algorithms. The output generates analysis results indicating the user's health status. Specifically, the server analyzes the data using tools such as TensorFlow and evaluates the health status.

[0354] Step 4:

[0355] The server uses response mechanisms based on the analysis results to generate exercise and nutritional guidelines tailored to the user. The input is analyzed health status data, which is used to form individualized feedback. The output is a recommended plan for the user. Specifically, the server creates a recommended plan by combining information, referencing pre-configured health plan templates.

[0356] Step 5:

[0357] The device receives recommended plans sent from the server and notifies the user. The input is recommended data from the server, and the output provides the user with specific action guidelines. Specifically, the device uses its notification function to send a push notification to the user.

[0358] Step 6:

[0359] Users adjust their behavior based on feedback from their device. The input is exercise and nutritional guidelines provided by the device, and the output is modifications to their daily behavior. Specific actions include performing suggested exercises or purchasing ingredients for suggested meals.

[0360] (Application Example 2)

[0361] 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."

[0362] Traditional health management systems often provide uniform plans without considering the emotional state of individual users, making it difficult to manage health in a way that takes users' emotional needs into account. As a result, there was a problem in that optimal health maintenance could not be fully achieved for each user.

[0363] 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.

[0364] In this invention, the server includes data acquisition means for collecting physiological information of the user, information processing means for performing analysis based on the physiological information and emotional information, and response generation means for supplying a health plan optimized for the user. This makes it possible to provide a personalized health plan that is tailored to the emotional state of each individual user.

[0365] "Physiological information" refers to data that indicates the user's physical condition, such as heart rate, steps taken, and blood pressure.

[0366] "Emotional information" refers to data about the user's emotional state, determined from factors such as facial expressions and tone of voice.

[0367] "Data acquisition means" refers to devices or methods equipped with functions for collecting physiological and emotional information from users.

[0368] "Information processing means" refers to a system or device that analyzes collected physiological and emotional information and performs computational processing to evaluate the user's health status.

[0369] A "response generation means" refers to a device or method for creating and providing an optimal health plan to a user based on analysis results.

[0370] "Adjustment means" refers to a method or apparatus for adapting the plan supplied by the response generation means to the analysis results of the information processing means.

[0371] "Emotional analysis means" refers to a technology or device for analyzing data such as a user's facial expressions and voice to evaluate their emotional state.

[0372] The embodiments for carrying out the invention are described below.

[0373] This system is designed to manage users' health in a personalized manner. The system includes a wearable device worn by the user to acquire biometric information, initially collecting data such as heart rate, steps taken, and blood pressure. This data is then transferred to the user's device via Bluetooth communication.

[0374] The device transmits the user's physiological information, along with images and video data of the user's daily life, diet, and exercise collected by the device's camera, to a cloud server. On the server, this data is analyzed using machine learning algorithms such as TensorFlow to evaluate the user's health status.

[0375] This analysis utilizes technologies such as OpenCV and PyAudio to analyze the user's emotional state from their facial expression and voice data. Emotional information is processed by an emotion analysis system and incorporated into the output of an information processing system. Based on this output, a response generation system generates an optimal health plan for the user. This plan is then fed back to the user's terminal and provided to the user through voice notifications and display information.

[0376] For example, if a user shows signs of fatigue, the system can suggest light exercise and rest. An example of a prompt might be, "If the user's heart rate is lower than normal and they have a tired expression, what kind of exercise and diet can you suggest?"

[0377] This system allows individual users to receive personalized health management based on their current physiological and emotional state.

[0378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0379] Step 1:

[0380] The user wears a wearable device that continuously collects physiological information (heart rate, steps, blood pressure, etc.). The input is the data measured as physiological information, and the output is the raw data transferred to the terminal. The data is transmitted to the terminal via Bluetooth communication.

[0381] Step 2:

[0382] The terminal temporarily stores physiological information data received from the wearable device and acquires image and video data of the user's daily life, meals, and training captured by the built-in camera. Inputs are physiological information and image data, while output is data for transfer to the cloud server. The terminal prepares to upload the data to the cloud server.

[0383] Step 3:

[0384] The cloud server receives physiological and image data transmitted from the terminal and stores it in a database. Simultaneously, it analyzes the physiological and emotional information using machine learning algorithms (e.g., TensorFlow). The input is data on the cloud, and the output is the analysis results regarding the user's health and emotional state. The server then evaluates the health status based on the analysis.

[0385] Step 4:

[0386] The server determines the user's emotional state by analyzing the user's facial expressions from images using OpenCV and analyzing audio data using PyAudio. The inputs are image data and audio data, and the output is data indicating the user's emotional state.

[0387] Step 5:

[0388] The server uses a generative AI model based on the analysis results to create a health plan optimized for the user. The input is the analysis results and emotional information, and the output is the health plan and feedback message. For example, the model is run using the prompt statement "If the user's heart rate is lower than normal and their face looks tired, what kind of exercise and diet can be suggested?"

[0389] Step 6:

[0390] The device receives health plans and feedback messages sent from the server and notifies the user. Input is feedback data from the server, and output is audio or display-based feedback to the user. The device guides the user to implement the suggested exercises and dietary changes.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] [Third Embodiment]

[0395] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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".

[0407] This invention is a system that supports user health management and consists of a wearable device, a terminal such as a smartphone or personal computer, and a cloud server.

[0408] First, users wear a wearable device to collect physiological data such as heart rate, steps taken, and blood pressure during their daily lives. The wearable device also has a camera function, which users can use to record their meals and workouts in photos and videos.

[0409] Next, the device receives physiological and image data from the wearable device. This data is sent to a cloud server at regular intervals and stored in a structured database.

[0410] The server uses machine learning algorithms to analyze the received data. The algorithms comprehensively evaluate the user's current health status, training history, and dietary content to generate an optimal training and meal plan for the user. This includes analyzing image data acquired by the camera function to determine whether the nutrients in the diet and the training form are appropriate.

[0411] The generated plan is sent from the server to the device. The device then provides this information back to the user through the application, offering specific training content and dietary recommendations.

[0412] Users receive this feedback and use it to improve their daily lives. New user behavioral data is collected again through wearable devices and terminals. The server then uses this new data to continuously update the generating AI. This makes it possible to continue providing more accurate fitness plans.

[0413] For example, this system provides users who regularly run with advice on adjusting exercise intensity based on heart rate data, and evaluates nutritional balance from images of meals taken, suggesting areas for improvement. By following these suggestions and improving their diet, users can effectively maintain their health.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The user wears a wearable device and acquires physiological data and image data using the camera function. The device collects this data in real time.

[0417] Step 2:

[0418] The device receives physiological and image data from the wearable device via Bluetooth or Wi-Fi. This data is temporarily stored on the device.

[0419] Step 3:

[0420] The device sends data collected at regular intervals to a cloud server. This transmission is performed using a secure communication protocol.

[0421] Step 4:

[0422] The server has the functionality to store received data in a database and compare it with past data. A process is performed to verify the integrity of the data.

[0423] Step 5:

[0424] The server uses machine learning algorithms to analyze the data. This analysis assesses the user's health status and lifestyle, and generates training and meal plans.

[0425] Step 6:

[0426] The server generates feedback based on the analysis results and sends it to the terminal. This feedback includes specific training menus and meal suggestions.

[0427] Step 7:

[0428] The application notifies the user of feedback received by the device. The user can then refer to this information to help plan their next training session or meal plan.

[0429] Step 8:

[0430] Users adjust their lifestyle habits based on feedback. New behavioral data is then collected and used by the system for the next cycle.

[0431] Step 9:

[0432] Based on the new data collected by the server, the machine learning model is updated to improve the accuracy of subsequent analyses. Through continuous learning, the system provides users with increasingly appropriate plans.

[0433] (Example 1)

[0434] 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."

[0435] In modern times, it is important to understand an individual's health condition in detail and to adjust training and dietary habits to suit their actual lifestyle. However, providing a plan tailored to each user requires the analysis of diverse data, and there is a need for a means to effectively carry out this analysis. The present invention aims to provide a system that supports health maintenance by providing individually optimized exercise and nutrition plans using the user's physiological indicator data and image data.

[0436] 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.

[0437] In this invention, the server includes a device for acquiring the user's physiological indicator data, computing resources for analyzing the physiological indicator data and image data, and an information provider that presents an exercise and nutrition plan optimized for the user. This allows the user to receive a fitness plan tailored to their health condition and to make concrete and actionable improvements in their life.

[0438] "Device" refers to hardware or equipment used to acquire physiological indicator data from a user.

[0439] "Computational resources" refers to software or hardware for analyzing physiological indicator data and image data, and in particular, the computing power to perform the analysis.

[0440] An "information provision device" is a device or platform for presenting an exercise and nutrition plan optimized for the user.

[0441] An "imaging device" is a device equipped with a camera function to acquire the user's intake and exercise status as image data.

[0442] A "learning algorithm" is a machine learning technique that analyzes and processes user physiological indicator data to generate individually optimized fitness plans.

[0443] This invention is a system for efficiently supporting users' health management. The system is initiated by the user using a device to collect physiological indicator data. Specific hardware includes a wearable device capable of measuring heart rate, blood pressure, and steps. This device also includes a camera function for the user to take photos during daily life.

[0444] The user wears the aforementioned wearable device to acquire physiological indicator data. This data is then transmitted via the device to a server connected to the cloud. The device can be a smartphone or a personal computer.

[0445] The server possesses computing resources to analyze the received data. This analysis utilizes learning algorithms to analyze the user's health status and behavioral history. Furthermore, the acquired image data of meals and training is used with machine learning to evaluate dietary content and exercise performance.

[0446] Based on the analysis results, the server generates an exercise and nutrition plan optimized for the user. This information is presented to the user via a terminal as an information provider. Feedback is provided through the application, giving the user the opportunity to improve their daily life based on this feedback.

[0447] As a concrete example, this system suggests an ideal training pace for users aiming to run a marathon based on their heart rate data. It can also analyze nutritional balance from photos of meals and suggest ways to improve it. Based on this advice, users can adjust their daily exercise and manage their health more efficiently.

[0448] An example of a prompt message might be: "Create suggestions for the optimal exercise intensity and dietary improvements for a male user in his 30s who runs three times a week."

[0449] This system will enable users to manage their health in a specific and effective way.

[0450] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0451] Step 1:

[0452] Users wear wearable devices that collect physiological data such as heart rate, steps taken, and blood pressure on a daily basis. Furthermore, they use the camera function to record meals and exercises. The input consists of collected physiological data and captured image data. This data serves as fundamental information for understanding the user's health status.

[0453] Step 2:

[0454] The terminal receives data collected from wearable devices. This received data includes physiological data and image data. The terminal then sends this data to a cloud server. The input is data from the wearable devices, and the output is the data sent to the server. This is a step in aggregating the data necessary for subsequent analysis processes.

[0455] Step 3:

[0456] The server stores received data in a cloud-based structured database. Input is data sent from the terminal, and output is stored in the structured database. Storing data in the database ensures data consistency and accessibility.

[0457] Step 4:

[0458] The server analyzes stored data using machine learning algorithms. The input consists of physiological and image data stored in a database. Data processing includes assessing the user's health status, identifying nutrients in their diet, and recognizing the effectiveness of training. The output is a training and meal plan tailored to the user. This generates user-specific health guidelines.

[0459] Step 5:

[0460] The plan generated by the server is provided to the user as feedback via the terminal. The output includes specific exercise instructions and suggestions for dietary improvements. The user then adjusts their daily life accordingly.

[0461] Step 6:

[0462] When a user generates new health data, it is collected again via a wearable device. The input includes newly collected physiological and image data, which the server receives to update the generating AI model. This enables continuous health management and improved plan accuracy.

[0463] (Application Example 1)

[0464] 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."

[0465] In modern society, personal health management is a crucial issue, and there is a particular need for a system that allows for easy and effective monitoring of health status, especially in a home environment, and provides appropriate exercise and dietary suggestions. However, there is currently a lack of simple and automated methods for users to continuously monitor their health data in their daily lives and receive appropriate advice. Therefore, there is a strong desire for such health management systems to be available for use at home.

[0466] 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.

[0467] In this invention, the server includes information gathering means for collecting the user's physiological information, analysis means for performing analysis based on the physiological information and visual data, and response means for providing the user with exercise and diet plans. This enables the automation of user health management in a home environment and effectively provides exercise and diet suggestions tailored to the individual's health condition.

[0468] "User physiological information" refers to data that indicates the user's health status, such as heart rate, steps taken, and blood pressure.

[0469] "Information gathering means" refers to devices or technologies that acquire physiological information and visual data from users.

[0470] "Analysis means" refers to a device or program for analyzing collected physiological information and visual data to evaluate the user's health status.

[0471] A "response means" is a technology or device that provides the user with an exercise and diet plan based on the analyzed data.

[0472] "Modification means" refers to a technique or device for updating the plan provided by the response means based on the analysis results.

[0473] "Visual information" refers to image or video data acquired using devices such as cameras, and includes information about eating and exercising.

[0474] An "autonomous machine" is a robot or mechanical device that automatically collects and supports a user's physiological information in a home environment.

[0475] The system for carrying out this invention is configured as follows to collect and analyze the user's physiological information and provide an appropriate exercise and meal plan. The system includes information collection means, including wearable sensors and cameras, for collecting the user's physiological information. This allows for the collection of image data such as heart rate, steps taken, blood pressure, and meals.

[0476] The collected data is sent to the terminal device and then transmitted to a cloud server. The cloud server is equipped with machine learning algorithms as an analysis tool and performs data analysis. Specifically, it uses platforms such as TensorFlow and scikit-learn for analysis. Based on the analysis results, the user's current health status, appropriate training methods, and dietary balance are evaluated.

[0477] Subsequently, the analysis results are provided to the user through a response mechanism. This response mechanism is an autonomous machine in the user's home environment, namely a personal robot. Based on the analysis results, this robot proposes daily exercise and meal plans to the user using voice and on a screen. The robot has an image recognition function using OpenCV, which allows it to recognize the contents of meals from collected photographic data and evaluate nutritional balance.

[0478] As an example, consider a scenario where a robot instructs the user to "take a picture of today's meal and check its nutritional balance," and the user complies by taking a picture of the meal with a camera. The AI ​​then evaluates the meal and provides advice such as, "Your protein intake today is a little low. Let's be mindful of that at your next meal." In this way, users can receive support for maintaining their health based on instructions from an autonomous machine.

[0479] A concrete example of a generated AI prompt is, "Please evaluate what I ate today. If possible, please provide suggestions for improvement and health advice." This allows the user to receive necessary health management advice.

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] The user wears a wearable sensor, which collects physiological information such as heart rate and steps. Furthermore, the user takes pictures of their meals using a capture device. The collected physiological and image data is input into a terminal. The terminal receives this data, prepares the data format, and verifies it.

[0483] Step 2:

[0484] The device sends the compiled physiological information and image data to a cloud server. The server uses machine learning algorithms to analyze the data. It determines the user's health status from the physiological information and evaluates the nutritional balance of the diet from the image data. As a result of this analysis, the server outputs the user's current health status and suggestions for necessary health management.

[0485] Step 3:

[0486] Based on the analysis results, the server generates an exercise and meal plan tailored to the user. This plan includes specific exercises and dietary recommendations customized based on the user's set health goals. This information is generated by a generative AI model, and it also provides advice based on user input using prompts. The generated plan is then sent to the device.

[0487] Step 4:

[0488] An autonomous device placed in the user's home environment presents the user with exercise and meal plans received from a terminal. The autonomous device has voice output capabilities and a display, showing analysis results and health plans verbally or visually. For example, advice such as "Let's include more protein in your next meal" is presented as a specific menu. It also provides support to the user when following instructions.

[0489] Step 5:

[0490] The user lives according to the presented health plan, acquiring new behavioral data. Wearable sensors and capture devices collect this new data again and send it to a server via the terminal. This data becomes input for the next analysis cycle. In addition, the generative AI model is improved by incorporating the new data, resulting in higher accuracy in subsequent plan generation.

[0491] 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.

[0492] This invention is a system for highly personalized user health management, and is comprised of a wearable device, a terminal, a cloud server, and an emotion engine.

[0493] First, users collect physiological data such as heart rate, steps taken, and blood pressure through a wearable device. In addition, the device's camera function is used to photograph the user's daily meals and exercise. This data is aggregated on the device and sent to a cloud server.

[0494] The server uses machine learning algorithms to analyze the user's health status based on received physiological and image data. The analysis results are used to generate optimal training and meal plans for the user. This process incorporates an emotion engine that analyzes the user's emotions from their facial expressions and voice tone. As a result, the health management plan is adjusted to take the user's emotional state into account.

[0495] The generated feedback is sent from the server to the terminal. The terminal notifies the user and provides specific advice. For example, if the user is feeling stressed, emotionally-based adjustments are made, such as suggesting relaxing exercise or meals.

[0496] Users adjust their daily behaviors based on the feedback. New behavioral and emotional data are collected again via wearable devices and terminals and used in the next analysis cycle. This allows the server to continuously monitor the user's state and further optimize the feedback.

[0497] For example, if a user feels fatigued from their daily training, the emotional engine will detect their stress level and suggest lighter exercise or recommend activities for relaxation. In this way, the system enables detailed health management tailored to the user's emotional state.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The user wears a wearable device that records physiological data, image data acquired using a camera, and audio data. This device collects this data throughout all aspects of daily life.

[0501] Step 2:

[0502] The terminal receives physiological data, image data, and audio data from wearable devices via Bluetooth or Wi-Fi. The terminal temporarily stores the data and prepares it for the next transfer to the cloud server.

[0503] Step 3:

[0504] The device sends data to the cloud server. A secure communication protocol is used to ensure data integrity during transmission.

[0505] Step 4:

[0506] The server saves received data to the database. During saving, it cross-references with past data to check the consistency of the user's state.

[0507] Step 5:

[0508] The server uses machine learning algorithms to analyze the received data. This analysis includes assessing the user's health status and generating exercise and meal plans.

[0509] Step 6:

[0510] The server uses an emotion engine to analyze image and audio data to recognize the user's emotional state. This analysis is then used to generate personalized feedback.

[0511] Step 7:

[0512] The server generates feedback based on analysis results and emotional states, and sends it to the terminal. This feedback includes training adjustments and dietary suggestions tailored to the user's emotional state.

[0513] Step 8:

[0514] The device receives feedback information, which is then communicated to the user via the application. The user then reviews the feedback and applies it to their daily training and diet.

[0515] Step 9:

[0516] Users incorporate behavioral changes based on feedback. New data is collected again through wearable devices and terminals and sent to the server.

[0517] Step 10:

[0518] The server updates its machine learning algorithms using new data, improving analysis accuracy for the next feedback cycle. In this way, the system continuously provides users with the most relevant information.

[0519] (Example 2)

[0520] 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."

[0521] Traditionally, health management systems have only provided general guidelines to users, and have faced the challenge of providing personalized health guidance that takes into account the physical condition and emotional state of individual users. Furthermore, the inability to adequately adjust real-time monitoring and feedback meant that advice tailored to the user's health condition could not be provided.

[0522] 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.

[0523] In this invention, the server includes information gathering means for collecting the user's physiological and visual data, information processing means for performing analysis based on the information, and response means for providing the user with exercise and nutritional intake guidelines. This makes it possible to continuously monitor the user's health status and emotions in real time and provide individually tailored guidelines.

[0524] "Information gathering means" refers to devices or processes that collect users' physiological and visual data.

[0525] "Information processing means" refers to devices and technologies used to analyze collected data and evaluate the user's health status.

[0526] A "response mechanism" refers to a device or process that provides users with guidance on exercise and nutritional intake based on the analysis results.

[0527] "Adjustment means" refers to devices or technologies that change the guidelines provided by the response means in real time based on the information processing results.

[0528] This invention is a system for personalizing user health management, in which the user collects physiological and visual data using a wearable device. Specifically, physiological data such as heart rate, steps taken, and blood pressure are acquired by sensors, and image data of meals and exercises are collected using a camera function.

[0529] The device temporarily stores this data and transfers it to a server in the cloud via a communication method. The device typically uses Bluetooth or Wi-Fi to collect data from wearable devices and transmits the data to the server using the internet.

[0530] The server analyzes the received physiological and visual data using information processing tools. This process utilizes machine learning algorithms and performs data analysis using software libraries such as TensorFlow and OpenCV. This allows the server to understand the user's health and emotional state and generate a personalized health plan.

[0531] The generated health plan is provided to the user via a response mechanism, based on the analysis results and the assessment of their emotional state. The device informs the user of specific exercise and nutritional guidelines through push notifications and in-app displays. For example, if the user is feeling stressed, the plan can be adjusted according to their emotional state, such as suggesting relaxing exercises or meals.

[0532] Users adjust their behavior based on the feedback they receive, and new physiological and emotional data are collected to aid in the next analysis cycle. This continuous process allows the server to continuously monitor the user's state and provide optimized guidance.

[0533] For example, a prompt such as "Suggest a suitable relaxation method when the user's stress level is high" can enable a generative AI model to create and provide personalized advice to the user.

[0534] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0535] Step 1:

[0536] The user wears a wearable device to collect physiological data (heart rate, steps, blood pressure, etc.) in real time. The device's camera function is also used to acquire image data of daily meals and exercise. The input consists of the user's physiological and visual data, which is transferred to the terminal. The output is a data package formed from the collected data. Specifically, the device transmits data to the terminal via Bluetooth.

[0537] Step 2:

[0538] The terminal temporarily stores physiological and image data received from wearable devices. Its input is a data package from the user, which it then processes to prepare for transmission to the cloud server. The output is data processed into a format suitable for transmission to the cloud server. Specifically, the terminal converts the data format and sends the data to the server via the HTTPS protocol.

[0539] Step 3:

[0540] The server receives data provided by the terminal and performs analysis using information processing tools. The input consists of the user's physiological data and image data, which are analyzed by machine learning algorithms. The output generates analysis results indicating the user's health status. Specifically, the server analyzes the data using tools such as TensorFlow and evaluates the health status.

[0541] Step 4:

[0542] The server uses response mechanisms based on the analysis results to generate exercise and nutritional guidelines tailored to the user. The input is analyzed health status data, which is used to form individualized feedback. The output is a recommended plan for the user. Specifically, the server creates a recommended plan by combining information, referencing pre-configured health plan templates.

[0543] Step 5:

[0544] The device receives recommended plans sent from the server and notifies the user. The input is recommended data from the server, and the output provides the user with specific action guidelines. Specifically, the device uses its notification function to send a push notification to the user.

[0545] Step 6:

[0546] Users adjust their behavior based on feedback from their device. The input is exercise and nutritional guidelines provided by the device, and the output is modifications to their daily behavior. Specific actions include performing suggested exercises or purchasing ingredients for suggested meals.

[0547] (Application Example 2)

[0548] 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."

[0549] Traditional health management systems often provide uniform plans without considering the emotional state of individual users, making it difficult to manage health in a way that takes users' emotional needs into account. As a result, there was a problem in that optimal health maintenance could not be fully achieved for each user.

[0550] 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.

[0551] In this invention, the server includes data acquisition means for collecting physiological information of the user, information processing means for performing analysis based on the physiological information and emotional information, and response generation means for supplying a health plan optimized for the user. This makes it possible to provide a personalized health plan that is tailored to the emotional state of each individual user.

[0552] "Physiological information" refers to data that indicates the user's physical condition, such as heart rate, steps taken, and blood pressure.

[0553] "Emotional information" refers to data about the user's emotional state, determined from factors such as facial expressions and tone of voice.

[0554] "Data acquisition means" refers to devices or methods equipped with functions for collecting physiological and emotional information from users.

[0555] "Information processing means" refers to a system or device that analyzes collected physiological and emotional information and performs computational processing to evaluate the user's health status.

[0556] A "response generation means" refers to a device or method for creating and providing an optimal health plan to a user based on analysis results.

[0557] "Adjustment means" refers to a method or apparatus for adapting the plan supplied by the response generation means to the analysis results of the information processing means.

[0558] "Emotional analysis means" refers to a technology or device for analyzing data such as a user's facial expressions and voice to evaluate their emotional state.

[0559] The embodiments for carrying out the invention are described below.

[0560] This system is designed to manage users' health in a personalized manner. The system includes a wearable device worn by the user to acquire biometric information, initially collecting data such as heart rate, steps taken, and blood pressure. This data is then transferred to the user's device via Bluetooth communication.

[0561] The device transmits the user's physiological information, along with images and video data of the user's daily life, diet, and exercise collected by the device's camera, to a cloud server. On the server, this data is analyzed using machine learning algorithms such as TensorFlow to evaluate the user's health status.

[0562] This analysis utilizes technologies such as OpenCV and PyAudio to analyze the user's emotional state from their facial expression and voice data. Emotional information is processed by an emotion analysis system and incorporated into the output of an information processing system. Based on this output, a response generation system generates an optimal health plan for the user. This plan is then fed back to the user's terminal and provided to the user through voice notifications and display information.

[0563] For example, if a user shows signs of fatigue, the system can suggest light exercise and rest. An example of a prompt might be, "If the user's heart rate is lower than normal and they have a tired expression, what kind of exercise and diet can you suggest?"

[0564] This system allows individual users to receive personalized health management based on their current physiological and emotional state.

[0565] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0566] Step 1:

[0567] The user wears a wearable device that continuously collects physiological information (heart rate, steps, blood pressure, etc.). The input is the data measured as physiological information, and the output is the raw data transferred to the terminal. The data is transmitted to the terminal via Bluetooth communication.

[0568] Step 2:

[0569] The terminal temporarily stores physiological information data received from the wearable device and acquires image and video data of the user's daily life, meals, and training captured by the built-in camera. Inputs are physiological information and image data, while output is data for transfer to the cloud server. The terminal prepares to upload the data to the cloud server.

[0570] Step 3:

[0571] The cloud server receives physiological and image data transmitted from the terminal and stores it in a database. Simultaneously, it analyzes the physiological and emotional information using machine learning algorithms (e.g., TensorFlow). The input is data on the cloud, and the output is the analysis results regarding the user's health and emotional state. The server then evaluates the health status based on the analysis.

[0572] Step 4:

[0573] The server determines the user's emotional state by analyzing the user's facial expressions from images using OpenCV and analyzing audio data using PyAudio. The inputs are image data and audio data, and the output is data indicating the user's emotional state.

[0574] Step 5:

[0575] The server uses a generative AI model based on the analysis results to create a health plan optimized for the user. The input is the analysis results and emotional information, and the output is the health plan and feedback message. For example, the model is run using the prompt statement "If the user's heart rate is lower than normal and their face looks tired, what kind of exercise and diet can be suggested?"

[0576] Step 6:

[0577] The device receives health plans and feedback messages sent from the server and notifies the user. Input is feedback data from the server, and output is audio or display-based feedback to the user. The device guides the user to implement the suggested exercises and dietary changes.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] [Fourth Embodiment]

[0582] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0583] 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.

[0584] 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).

[0585] 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.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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".

[0595] This invention is a system that supports user health management and consists of a wearable device, a terminal such as a smartphone or personal computer, and a cloud server.

[0596] First, users wear a wearable device to collect physiological data such as heart rate, steps taken, and blood pressure during their daily lives. The wearable device also has a camera function, which users can use to record their meals and workouts in photos and videos.

[0597] Next, the device receives physiological and image data from the wearable device. This data is sent to a cloud server at regular intervals and stored in a structured database.

[0598] The server uses machine learning algorithms to analyze the received data. The algorithms comprehensively evaluate the user's current health status, training history, and dietary content to generate an optimal training and meal plan for the user. This includes analyzing image data acquired by the camera function to determine whether the nutrients in the diet and the training form are appropriate.

[0599] The generated plan is sent from the server to the device. The device then provides this information back to the user through the application, offering specific training content and dietary recommendations.

[0600] Users receive this feedback and use it to improve their daily lives. New user behavioral data is collected again through wearable devices and terminals. The server then uses this new data to continuously update the generating AI. This makes it possible to continue providing more accurate fitness plans.

[0601] For example, this system provides users who regularly run with advice on adjusting exercise intensity based on heart rate data, and evaluates nutritional balance from images of meals taken, suggesting areas for improvement. By following these suggestions and improving their diet, users can effectively maintain their health.

[0602] The following describes the processing flow.

[0603] Step 1:

[0604] The user wears a wearable device and acquires physiological data and image data using the camera function. The device collects this data in real time.

[0605] Step 2:

[0606] The device receives physiological and image data from the wearable device via Bluetooth or Wi-Fi. This data is temporarily stored on the device.

[0607] Step 3:

[0608] The device sends data collected at regular intervals to a cloud server. This transmission is performed using a secure communication protocol.

[0609] Step 4:

[0610] The server has the functionality to store received data in a database and compare it with past data. A process is performed to verify the integrity of the data.

[0611] Step 5:

[0612] The server uses machine learning algorithms to analyze the data. This analysis assesses the user's health status and lifestyle, and generates training and meal plans.

[0613] Step 6:

[0614] The server generates feedback based on the analysis results and sends it to the terminal. This feedback includes specific training menus and meal suggestions.

[0615] Step 7:

[0616] The application notifies the user of feedback received by the device. The user can then refer to this information to help plan their next training session or meal plan.

[0617] Step 8:

[0618] Users adjust their lifestyle habits based on feedback. New behavioral data is then collected and used by the system for the next cycle.

[0619] Step 9:

[0620] Based on the new data collected by the server, the machine learning model is updated to improve the accuracy of subsequent analyses. Through continuous learning, the system provides users with increasingly appropriate plans.

[0621] (Example 1)

[0622] 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".

[0623] In modern times, it is important to understand an individual's health condition in detail and to adjust training and dietary habits to suit their actual lifestyle. However, providing a plan tailored to each user requires the analysis of diverse data, and there is a need for a means to effectively carry out this analysis. The present invention aims to provide a system that supports health maintenance by providing individually optimized exercise and nutrition plans using the user's physiological indicator data and image data.

[0624] 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.

[0625] In this invention, the server includes a device for acquiring the user's physiological indicator data, computing resources for analyzing the physiological indicator data and image data, and an information provider that presents an exercise and nutrition plan optimized for the user. This allows the user to receive a fitness plan tailored to their health condition and to make concrete and actionable improvements in their life.

[0626] "Device" refers to hardware or equipment used to acquire physiological indicator data from a user.

[0627] "Computational resources" refers to software or hardware for analyzing physiological indicator data and image data, and in particular, the computing power to perform the analysis.

[0628] An "information provision device" is a device or platform for presenting an exercise and nutrition plan optimized for the user.

[0629] An "imaging device" is a device equipped with a camera function to acquire the user's intake and exercise status as image data.

[0630] A "learning algorithm" is a machine learning technique that analyzes and processes user physiological indicator data to generate individually optimized fitness plans.

[0631] This invention is a system for efficiently supporting users' health management. The system is initiated by the user using a device to collect physiological indicator data. Specific hardware includes a wearable device capable of measuring heart rate, blood pressure, and steps. This device also includes a camera function for the user to take photos during daily life.

[0632] The user wears the aforementioned wearable device to acquire physiological indicator data. This data is then transmitted via the device to a server connected to the cloud. The device can be a smartphone or a personal computer.

[0633] The server possesses computing resources to analyze the received data. This analysis utilizes learning algorithms to analyze the user's health status and behavioral history. Furthermore, the acquired image data of meals and training is used with machine learning to evaluate dietary content and exercise performance.

[0634] Based on the analysis results, the server generates an exercise and nutrition plan optimized for the user. This information is presented to the user via a terminal as an information provider. Feedback is provided through the application, giving the user the opportunity to improve their daily life based on this feedback.

[0635] As a concrete example, this system suggests an ideal training pace for users aiming to run a marathon based on their heart rate data. It can also analyze nutritional balance from photos of meals and suggest ways to improve it. Based on this advice, users can adjust their daily exercise and manage their health more efficiently.

[0636] An example of a prompt message might be: "Create suggestions for the optimal exercise intensity and dietary improvements for a male user in his 30s who runs three times a week."

[0637] This system will enable users to manage their health in a specific and effective way.

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

[0639] Step 1:

[0640] Users wear wearable devices that collect physiological data such as heart rate, steps taken, and blood pressure on a daily basis. Furthermore, they use the camera function to record meals and exercises. The input consists of collected physiological data and captured image data. This data serves as fundamental information for understanding the user's health status.

[0641] Step 2:

[0642] The terminal receives data collected from wearable devices. This received data includes physiological data and image data. The terminal then sends this data to a cloud server. The input is data from the wearable devices, and the output is the data sent to the server. This is a step in aggregating the data necessary for subsequent analysis processes.

[0643] Step 3:

[0644] The server stores received data in a cloud-based structured database. Input is data sent from the terminal, and output is stored in the structured database. Storing data in the database ensures data consistency and accessibility.

[0645] Step 4:

[0646] The server analyzes stored data using machine learning algorithms. The input consists of physiological and image data stored in a database. Data processing includes assessing the user's health status, identifying nutrients in their diet, and recognizing the effectiveness of training. The output is a training and meal plan tailored to the user. This generates user-specific health guidelines.

[0647] Step 5:

[0648] The plan generated by the server is provided to the user as feedback via the terminal. The output includes specific exercise instructions and suggestions for dietary improvements. The user then adjusts their daily life accordingly.

[0649] Step 6:

[0650] When a user generates new health data, it is collected again via a wearable device. The input includes newly collected physiological and image data, which the server receives to update the generating AI model. This enables continuous health management and improved plan accuracy.

[0651] (Application Example 1)

[0652] 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".

[0653] In modern society, personal health management is a crucial issue, and there is a particular need for a system that allows for easy and effective monitoring of health status, especially in a home environment, and provides appropriate exercise and dietary suggestions. However, there is currently a lack of simple and automated methods for users to continuously monitor their health data in their daily lives and receive appropriate advice. Therefore, there is a strong desire for such health management systems to be available for use at home.

[0654] 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.

[0655] In this invention, the server includes information gathering means for collecting the user's physiological information, analysis means for performing analysis based on the physiological information and visual data, and response means for providing the user with exercise and diet plans. This enables the automation of user health management in a home environment and effectively provides exercise and diet suggestions tailored to the individual's health condition.

[0656] "User physiological information" refers to data that indicates the user's health status, such as heart rate, steps taken, and blood pressure.

[0657] "Information gathering means" refers to devices or technologies that acquire physiological information and visual data from users.

[0658] "Analysis means" refers to a device or program for analyzing collected physiological information and visual data to evaluate the user's health status.

[0659] A "response means" is a technology or device that provides the user with an exercise and diet plan based on the analyzed data.

[0660] "Modification means" refers to a technique or device for updating the plan provided by the response means based on the analysis results.

[0661] "Visual information" refers to image or video data acquired using devices such as cameras, and includes information about eating and exercising.

[0662] An "autonomous machine" is a robot or mechanical device that automatically collects and supports a user's physiological information in a home environment.

[0663] The system for carrying out this invention is configured as follows to collect and analyze the user's physiological information and provide an appropriate exercise and meal plan. The system includes information collection means, including wearable sensors and cameras, for collecting the user's physiological information. This allows for the collection of image data such as heart rate, steps taken, blood pressure, and meals.

[0664] The collected data is sent to the terminal device and then transmitted to a cloud server. The cloud server is equipped with machine learning algorithms as an analysis tool and performs data analysis. Specifically, it uses platforms such as TensorFlow and scikit-learn for analysis. Based on the analysis results, the user's current health status, appropriate training methods, and dietary balance are evaluated.

[0665] Subsequently, the analysis results are provided to the user through a response mechanism. This response mechanism is an autonomous machine in the user's home environment, namely a personal robot. Based on the analysis results, this robot proposes daily exercise and meal plans to the user using voice and on a screen. The robot has an image recognition function using OpenCV, which allows it to recognize the contents of meals from collected photographic data and evaluate nutritional balance.

[0666] As an example, consider a scenario where a robot instructs the user to "take a picture of today's meal and check its nutritional balance," and the user complies by taking a picture of the meal with a camera. The AI ​​then evaluates the meal and provides advice such as, "Your protein intake today is a little low. Let's be mindful of that at your next meal." In this way, users can receive support for maintaining their health based on instructions from an autonomous machine.

[0667] A concrete example of a generated AI prompt is, "Please evaluate what I ate today. If possible, please provide suggestions for improvement and health advice." This allows the user to receive necessary health management advice.

[0668] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0669] Step 1:

[0670] The user wears a wearable sensor, which collects physiological information such as heart rate and steps. Furthermore, the user takes pictures of their meals using a capture device. The collected physiological and image data is input into a terminal. The terminal receives this data, prepares the data format, and verifies it.

[0671] Step 2:

[0672] The device sends the compiled physiological information and image data to a cloud server. The server uses machine learning algorithms to analyze the data. It determines the user's health status from the physiological information and evaluates the nutritional balance of the diet from the image data. As a result of this analysis, the server outputs the user's current health status and suggestions for necessary health management.

[0673] Step 3:

[0674] Based on the analysis results, the server generates an exercise and meal plan tailored to the user. This plan includes specific exercises and dietary recommendations customized based on the user's set health goals. This information is generated by a generative AI model, and it also provides advice based on user input using prompts. The generated plan is then sent to the device.

[0675] Step 4:

[0676] An autonomous device placed in the user's home environment presents the user with exercise and meal plans received from a terminal. The autonomous device has voice output capabilities and a display, showing analysis results and health plans verbally or visually. For example, advice such as "Let's include more protein in your next meal" is presented as a specific menu. It also provides support to the user when following instructions.

[0677] Step 5:

[0678] The user lives according to the presented health plan, acquiring new behavioral data. Wearable sensors and capture devices collect this new data again and send it to a server via the terminal. This data becomes input for the next analysis cycle. In addition, the generative AI model is improved by incorporating the new data, resulting in higher accuracy in subsequent plan generation.

[0679] 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.

[0680] This invention is a system for highly personalized user health management, and is comprised of a wearable device, a terminal, a cloud server, and an emotion engine.

[0681] First, users collect physiological data such as heart rate, steps taken, and blood pressure through a wearable device. In addition, the device's camera function is used to photograph the user's daily meals and exercise. This data is aggregated on the device and sent to a cloud server.

[0682] The server uses machine learning algorithms to analyze the user's health status based on received physiological and image data. The analysis results are used to generate optimal training and meal plans for the user. This process incorporates an emotion engine that analyzes the user's emotions from their facial expressions and voice tone. As a result, the health management plan is adjusted to take the user's emotional state into account.

[0683] The generated feedback is sent from the server to the terminal. The terminal notifies the user and provides specific advice. For example, if the user is feeling stressed, emotionally-based adjustments are made, such as suggesting relaxing exercise or meals.

[0684] Users adjust their daily behaviors based on the feedback. New behavioral and emotional data are collected again via wearable devices and terminals and used in the next analysis cycle. This allows the server to continuously monitor the user's state and further optimize the feedback.

[0685] For example, if a user feels fatigued from their daily training, the emotional engine will detect their stress level and suggest lighter exercise or recommend activities for relaxation. In this way, the system enables detailed health management tailored to the user's emotional state.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] The user wears a wearable device that records physiological data, image data acquired using a camera, and audio data. This device collects this data throughout all aspects of daily life.

[0689] Step 2:

[0690] The terminal receives physiological data, image data, and audio data from wearable devices via Bluetooth or Wi-Fi. The terminal temporarily stores the data and prepares it for the next transfer to the cloud server.

[0691] Step 3:

[0692] The device sends data to the cloud server. A secure communication protocol is used to ensure data integrity during transmission.

[0693] Step 4:

[0694] The server saves received data to the database. During saving, it cross-references with past data to check the consistency of the user's state.

[0695] Step 5:

[0696] The server uses machine learning algorithms to analyze the received data. This analysis includes assessing the user's health status and generating exercise and meal plans.

[0697] Step 6:

[0698] The server uses an emotion engine to analyze image and audio data to recognize the user's emotional state. This analysis is then used to generate personalized feedback.

[0699] Step 7:

[0700] The server generates feedback based on analysis results and emotional states, and sends it to the terminal. This feedback includes training adjustments and dietary suggestions tailored to the user's emotional state.

[0701] Step 8:

[0702] The device receives feedback information, which is then communicated to the user via the application. The user then reviews the feedback and applies it to their daily training and diet.

[0703] Step 9:

[0704] Users incorporate behavioral changes based on feedback. New data is collected again through wearable devices and terminals and sent to the server.

[0705] Step 10:

[0706] The server updates its machine learning algorithms using new data, improving analysis accuracy for the next feedback cycle. In this way, the system continuously provides users with the most relevant information.

[0707] (Example 2)

[0708] 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".

[0709] Traditionally, health management systems have only provided general guidelines to users, and have faced the challenge of providing personalized health guidance that takes into account the physical condition and emotional state of individual users. Furthermore, the inability to adequately adjust real-time monitoring and feedback meant that advice tailored to the user's health condition could not be provided.

[0710] 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.

[0711] In this invention, the server includes information gathering means for collecting the user's physiological and visual data, information processing means for performing analysis based on the information, and response means for providing the user with exercise and nutritional intake guidelines. This makes it possible to continuously monitor the user's health status and emotions in real time and provide individually tailored guidelines.

[0712] "Information gathering means" refers to devices or processes that collect users' physiological and visual data.

[0713] "Information processing means" refers to devices and technologies used to analyze collected data and evaluate the user's health status.

[0714] A "response mechanism" refers to a device or process that provides users with guidance on exercise and nutritional intake based on the analysis results.

[0715] "Adjustment means" refers to devices or technologies that change the guidelines provided by the response means in real time based on the information processing results.

[0716] This invention is a system for personalizing user health management, in which the user collects physiological and visual data using a wearable device. Specifically, physiological data such as heart rate, steps taken, and blood pressure are acquired by sensors, and image data of meals and exercises are collected using a camera function.

[0717] The device temporarily stores this data and transfers it to a server in the cloud via a communication method. The device typically uses Bluetooth or Wi-Fi to collect data from wearable devices and transmits the data to the server using the internet.

[0718] The server analyzes the received physiological and visual data using information processing tools. This process utilizes machine learning algorithms and performs data analysis using software libraries such as TensorFlow and OpenCV. This allows the server to understand the user's health and emotional state and generate a personalized health plan.

[0719] The generated health plan is provided to the user via a response mechanism, based on the analysis results and the assessment of their emotional state. The device informs the user of specific exercise and nutritional guidelines through push notifications and in-app displays. For example, if the user is feeling stressed, the plan can be adjusted according to their emotional state, such as suggesting relaxing exercises or meals.

[0720] Users adjust their behavior based on the feedback they receive, and new physiological and emotional data are collected to aid in the next analysis cycle. This continuous process allows the server to continuously monitor the user's state and provide optimized guidance.

[0721] For example, a prompt such as "Suggest a suitable relaxation method when the user's stress level is high" can enable a generative AI model to create and provide personalized advice to the user.

[0722] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0723] Step 1:

[0724] The user wears a wearable device to collect physiological data (heart rate, steps, blood pressure, etc.) in real time. The device's camera function is also used to acquire image data of daily meals and exercise. The input consists of the user's physiological and visual data, which is transferred to the terminal. The output is a data package formed from the collected data. Specifically, the device transmits data to the terminal via Bluetooth.

[0725] Step 2:

[0726] The terminal temporarily stores physiological and image data received from wearable devices. Its input is a data package from the user, which it then processes to prepare for transmission to the cloud server. The output is data processed into a format suitable for transmission to the cloud server. Specifically, the terminal converts the data format and sends the data to the server via the HTTPS protocol.

[0727] Step 3:

[0728] The server receives data provided by the terminal and performs analysis using information processing tools. The input consists of the user's physiological data and image data, which are analyzed by machine learning algorithms. The output generates analysis results indicating the user's health status. Specifically, the server analyzes the data using tools such as TensorFlow and evaluates the health status.

[0729] Step 4:

[0730] The server uses response mechanisms based on the analysis results to generate exercise and nutritional guidelines tailored to the user. The input is analyzed health status data, which is used to form individualized feedback. The output is a recommended plan for the user. Specifically, the server creates a recommended plan by combining information, referencing pre-configured health plan templates.

[0731] Step 5:

[0732] The device receives recommended plans sent from the server and notifies the user. The input is recommended data from the server, and the output provides the user with specific action guidelines. Specifically, the device uses its notification function to send a push notification to the user.

[0733] Step 6:

[0734] Users adjust their behavior based on feedback from their device. The input is exercise and nutritional guidelines provided by the device, and the output is modifications to their daily behavior. Specific actions include performing suggested exercises or purchasing ingredients for suggested meals.

[0735] (Application Example 2)

[0736] 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".

[0737] Traditional health management systems often provide uniform plans without considering the emotional state of individual users, making it difficult to manage health in a way that takes users' emotional needs into account. As a result, there was a problem in that optimal health maintenance could not be fully achieved for each user.

[0738] 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.

[0739] In this invention, the server includes data acquisition means for collecting physiological information of the user, information processing means for performing analysis based on the physiological information and emotional information, and response generation means for supplying a health plan optimized for the user. This makes it possible to provide a personalized health plan that is tailored to the emotional state of each individual user.

[0740] "Physiological information" refers to data that indicates the user's physical condition, such as heart rate, steps taken, and blood pressure.

[0741] "Emotional information" refers to data about the user's emotional state, determined from factors such as facial expressions and tone of voice.

[0742] "Data acquisition means" refers to devices or methods equipped with functions for collecting physiological and emotional information from users.

[0743] "Information processing means" refers to a system or device that analyzes collected physiological and emotional information and performs computational processing to evaluate the user's health status.

[0744] A "response generation means" refers to a device or method for creating and providing an optimal health plan to a user based on analysis results.

[0745] "Adjustment means" refers to a method or apparatus for adapting the plan supplied by the response generation means to the analysis results of the information processing means.

[0746] "Emotional analysis means" refers to a technology or device for analyzing data such as a user's facial expressions and voice to evaluate their emotional state.

[0747] The embodiments for carrying out the invention are described below.

[0748] This system is designed to manage users' health in a personalized manner. The system includes a wearable device worn by the user to acquire biometric information, initially collecting data such as heart rate, steps taken, and blood pressure. This data is then transferred to the user's device via Bluetooth communication.

[0749] The device transmits the user's physiological information, along with images and video data of the user's daily life, diet, and exercise collected by the device's camera, to a cloud server. On the server, this data is analyzed using machine learning algorithms such as TensorFlow to evaluate the user's health status.

[0750] This analysis utilizes technologies such as OpenCV and PyAudio to analyze the user's emotional state from their facial expression and voice data. Emotional information is processed by an emotion analysis system and incorporated into the output of an information processing system. Based on this output, a response generation system generates an optimal health plan for the user. This plan is then fed back to the user's terminal and provided to the user through voice notifications and display information.

[0751] For example, if a user shows signs of fatigue, the system can suggest light exercise and rest. An example of a prompt might be, "If the user's heart rate is lower than normal and they have a tired expression, what kind of exercise and diet can you suggest?"

[0752] This system allows individual users to receive personalized health management based on their current physiological and emotional state.

[0753] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0754] Step 1:

[0755] The user wears a wearable device that continuously collects physiological information (heart rate, steps, blood pressure, etc.). The input is the data measured as physiological information, and the output is the raw data transferred to the terminal. The data is transmitted to the terminal via Bluetooth communication.

[0756] Step 2:

[0757] The terminal temporarily stores physiological information data received from the wearable device and acquires image and video data of the user's daily life, meals, and training captured by the built-in camera. Inputs are physiological information and image data, while output is data for transfer to the cloud server. The terminal prepares to upload the data to the cloud server.

[0758] Step 3:

[0759] The cloud server receives physiological and image data transmitted from the terminal and stores it in a database. Simultaneously, it analyzes the physiological and emotional information using machine learning algorithms (e.g., TensorFlow). The input is data on the cloud, and the output is the analysis results regarding the user's health and emotional state. The server then evaluates the health status based on the analysis.

[0760] Step 4:

[0761] The server determines the user's emotional state by analyzing the user's facial expressions from images using OpenCV and analyzing audio data using PyAudio. The inputs are image data and audio data, and the output is data indicating the user's emotional state.

[0762] Step 5:

[0763] The server uses a generative AI model based on the analysis results to create a health plan optimized for the user. The input is the analysis results and emotional information, and the output is the health plan and feedback message. For example, the model is run using the prompt statement "If the user's heart rate is lower than normal and their face looks tired, what kind of exercise and diet can be suggested?"

[0764] Step 6:

[0765] The device receives health plans and feedback messages sent from the server and notifies the user. Input is feedback data from the server, and output is audio or display-based feedback to the user. The device guides the user to implement the suggested exercises and dietary changes.

[0766] 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.

[0767] 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.

[0768] 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 robot 414.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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."

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] The following is further disclosed regarding the embodiments described above.

[0788] (Claim 1)

[0789] A data collection method for collecting user physical data,

[0790] An analysis means that performs analysis based on the aforementioned physical data,

[0791] A feedback mechanism that provides users with training and meal plans,

[0792] An update means that updates the plan provided by the feedback means based on the analysis results of the analysis means,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, wherein the data collection means includes a camera function for acquiring image data of the user's meals and training.

[0796] (Claim 3)

[0797] The system according to claim 1, wherein the analysis means analyzes the user's physical data using a machine learning algorithm.

[0798] "Example 1"

[0799] (Claim 1)

[0800] A device for acquiring physiological indicator data of users,

[0801] Computational resources for analyzing the aforementioned physiological indicator data and image data,

[0802] An information-providing device that presents an exercise and nutrition plan optimized for the user,

[0803] An improvement means for updating the plan proposed by the information providing device based on the analysis results of the computing resources,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, wherein the device includes an imaging device that acquires image data of the user's intake and exercise status.

[0807] (Claim 3)

[0808] The system according to claim 1, wherein the computing resources process the user's physiological indicator data using a learning algorithm.

[0809] "Application Example 1"

[0810] (Claim 1)

[0811] Information collection means for collecting user physiological information,

[0812] An analysis means that performs analysis based on the aforementioned physiological information and visual data,

[0813] A response means that provides the user with an exercise and meal plan,

[0814] A modification means for updating the plan provided by the response means based on the analysis results of the analysis means,

[0815] An autonomous machine to assist in collecting physiological information from users in a home environment,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the information gathering means includes a visual function for acquiring visual information of the user's meals and exercise.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the analysis means analyzes the user's physiological information using a machine learning method.

[0821] "Example 2 of combining an emotion engine"

[0822] (Claim 1)

[0823] Information collection means for collecting user physiological and visual data,

[0824] Information processing means that performs analysis based on the aforementioned information,

[0825] A response mechanism that provides users with guidance on exercise and nutritional intake,

[0826] An adjustment means for adjusting the guidelines provided by the response means based on the analysis results of the information processing means,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, wherein the information gathering means includes a shooting function for acquiring visual data of the user's nutritional intake and exercise status.

[0830] (Claim 3)

[0831] The system according to claim 1, wherein the information processing means analyzes the user's physiological data using machine learning technology.

[0832] "Application example 2 when combining with an emotional engine"

[0833] (Claim 1)

[0834] A means for acquiring data to collect physiological information from users,

[0835] Information processing means that performs analysis based on the aforementioned physiological information and emotional information,

[0836] A response generation means for supplying a health plan optimized for the user,

[0837] An adjustment means for adapting the plan supplied by the response generation means based on the analysis results of the information processing means,

[0838] A means of sentiment analysis for evaluating the emotional state of a user,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, wherein the data acquisition means includes an image acquisition function that acquires visual information of the user's daily activities and meals.

[0842] (Claim 3)

[0843] The system according to claim 1, wherein the information processing means analyzes the user's physiological and emotional information using a machine learning algorithm. [Explanation of Symbols]

[0844] 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. Information collection means for collecting user physiological information, An analysis means that performs analysis based on the aforementioned physiological information and visual data, A response means that provides the user with an exercise and meal plan, A modification means for updating the plan provided by the response means based on the analysis results of the analysis means, An autonomous machine to assist in collecting physiological information from users in a home environment, A system that includes this.

2. The system according to claim 1, wherein the information gathering means includes a visual function for acquiring visual information of the user's meals and exercise.

3. The system according to claim 1, wherein the analysis means analyzes the user's physiological information using a machine learning method.

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