system

Abstract

We provide the system. [Solution] A terminal means for acquiring health data from users, A server means for analyzing health data acquired from the terminal means, A proposal generation means that provides health management suggestions to the user based on the aforementioned analysis results, A display means for presenting the aforementioned proposal to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, 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] When an individual manages their own health, there are problems that monitoring daily vital data and providing advice on a healthy lifestyle based on the content of meals require specialized knowledge and take a lot of time and effort. In addition, there is also a problem that it is difficult to obtain information on how to receive appropriate medical services and insurance selection. The present invention aims to solve these problems and provide a system that allows an individual to easily manage their own health.

Means for Solving the Problems

[0005] This invention includes a terminal means for acquiring health data from users and a server means for analyzing the health data acquired by this terminal means. It also includes a proposal generation means for making health management suggestions based on the analysis results, and a display means for presenting these suggestions to the user. This configuration provides a system that efficiently offers health management, appropriate medical services, and insurance information tailored to individual needs, enabling users to easily understand their own health status and take appropriate measures.

[0006] A "user" refers to an individual who uses the system to manage their own health data and receive suggestions.

[0007] "Health data" refers to a collection of data that includes a user's vital data, dietary information, and other health-related information.

[0008] "Terminal means" refers to a device that has the function of acquiring health data from the user and transmitting it to the server means.

[0009] "Server means" refers to a computer system that has the function of analyzing health data acquired from terminal means and providing information that serves as the basis for generating suggestions.

[0010] "Analysis" refers to the process of performing calculations and evaluations on acquired health data to extract valuable insights.

[0011] "Suggestion generation means" refers to a system element that automatically generates suggestions to the user regarding health management and related services based on data analyzed by the server means.

[0012] "Display means" refers to an interface for visually presenting the generated suggestions to the user. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0016] In the following embodiments, 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.

[0017] 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.

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

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. 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), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0034] This invention is a system that provides individually customized health management suggestions using a user's health data. This system works by collecting health data using wearable devices or smartphones worn by the user and transmitting the data to a server via the internet.

[0035] Users first record their daily activity data through their smartphones or wearable devices. This includes heart rate, steps taken, body temperature, and photos of meals. This data is collected within the system by the device and sent to the server in real time or at specified intervals.

[0036] The server analyzes the received data to comprehensively assess the user's health status. This includes identifying abnormal patterns by comparing the data to normal values ​​and performing long-term trend analysis. Furthermore, it extracts nutritional information based on photos of meals to understand the user's nutrient intake trends.

[0037] Based on the analysis results, the server generates personalized health management advice tailored to the user's needs. For example, if the user is deficient in certain nutrients, it will suggest a meal plan to compensate for that deficiency. If the server determines that the user is not getting enough exercise, it can provide an exercise plan that is easy to incorporate into daily life. Furthermore, if necessary, it may recommend online medical consultations or encourage the user to consider specific insurance products.

[0038] The generated suggestions are notified to the user through a dedicated application on their smartphone. By referring to these suggestions, users can put the information they receive into practice in their daily lives and use it to help them lead a healthier lifestyle.

[0039] The implementation of this invention enables the utilization of a wide range of health data, facilitating users to easily understand their own health status and take appropriate actions. In this way, it is possible to improve the overall efficiency of health management.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device collects vital data and photos of meals through the user's wearable device or smartphone. This data includes heart rate, steps taken, calories burned, body temperature, and details of meals eaten.

[0043] Step 2:

[0044] The device encrypts the collected health data and transmits it to the server via the internet. Secure communication protocols are used to protect data privacy during this process.

[0045] Step 3:

[0046] The server receives health data sent from the terminal and stores it in a database. The stored data is then prepared for use in subsequent analysis processes.

[0047] Step 4:

[0048] The server analyzes stored health data using machine learning algorithms and image recognition technology. This allows it to evaluate the user's health patterns, diet, and nutrient intake.

[0049] Step 5:

[0050] The server evaluates the user's health status based on the analysis results and generates personalized health management advice. This advice includes suggestions for dietary improvements, exercise plans, and, if necessary, suggestions for online medical consultations.

[0051] Step 6:

[0052] The server sends the generated health management advice to the user's device.

[0053] Step 7:

[0054] The device notifies the user of advice received from the server. The user can then review the suggestions through the application and incorporate them into their daily life.

[0055] (Example 1)

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

[0057] In modern society, it is crucial for individuals to accurately understand their own health status and manage their health appropriately based on that understanding. However, there is a lack of means to efficiently collect diverse biometric information and provide personalized health recommendations based on that information. Conventional systems are limited to analyzing only a portion of the data, making it difficult to achieve comprehensive health promotion.

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

[0059] In this invention, the server includes a device means for acquiring biometric information from a user, an information processing device means for processing the biometric information acquired from the device means, and a proposal generation means for providing personalized health management suggestions to the user based on the processing results. This makes it possible to comprehensively analyze a wide range of the user's health data and quickly provide specific health suggestions that meet the individual needs of the user.

[0060] A "user" is an individual or organization that uses the system to record health information and receives suggestions based on the analysis results.

[0061] "Biometric information" refers to all data that represents the user's health status, including heart rate, steps taken, body temperature, and records of meals eaten.

[0062] "Device means" refers to equipment or devices used to acquire biometric information from a user, and includes wearable devices and smartphones.

[0063] "Information processing device means" refers to a central processing unit or server system used to analyze acquired biological information and evaluate health status.

[0064] "Proposal generation means" refers to a function or process that creates specific proposals for health management based on analysis results generated by an information processing device.

[0065] "Notification means" refers to methods or system functions for communicating proposals created by the proposal generation means to the user and prompting them to take necessary actions.

[0066] A "generative AI model" is an artificial intelligence system that utilizes machine learning technology to automatically analyze input data and generate suggestions.

[0067] This invention is a system that provides personalized health management suggestions using a user's biometric information. This system primarily utilizes device means, information processing means, suggestion generation means, and notification means.

[0068] First, users collect daily biometric information using a device. This device includes wearable devices and smartphones, and acquires data such as heart rate, steps taken, body temperature, and photos of meals. Wearable devices can record data in real time using built-in sensors and store it in a dedicated application.

[0069] The collected data is transmitted from the terminal to an information processing device. This information processing device is located on a server system and performs data analysis. The analysis uses a generative AI model to compare the current data with past data, detect outliers, and evaluate long-term health trends. This analysis provides a comprehensive understanding of the user's health status.

[0070] Based on the analysis results, the suggestion generation mechanism is activated. The server creates personalized health management suggestions according to the user's current health status. This includes suggestions for dietary improvements and exercise plans. The generating AI model provides specific and practical advice by considering the user's preferences and lifestyle.

[0071] Ultimately, suggestions are communicated to the user through a notification system. Users can review the suggestions via a dedicated application and use them to improve their daily health management. These suggestions are delivered instantly via push notifications, allowing users to take action to improve their health based on the information.

[0072] For example, for a user diagnosed with a lack of exercise, the server can suggest an exercise plan that can be easily done indoors. Furthermore, if a user is lacking in nutrients, the server can provide a list of specific foods or recipes.

[0073] An example of a prompt for the generating AI model is: "Analyze user C's recent health data and suggest any particularly noteworthy abnormal patterns and health management advice based on them."

[0074] By implementing this system, users can effectively utilize their health data and achieve personalized health management.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] Users collect biometric information using wearable devices and smartphones. Inputs include data such as heart rate, steps taken, body temperature, and photos of meals. Specifically, users wear these devices during their daily lives and acquire data using a dedicated application. The output is biometric information stored within the device.

[0078] Step 2:

[0079] The terminal transmits the collected biometric information to the server. The input is the various biometric information recorded in step 1. Specifically, the terminal sends the data via an internet connection. The data is encrypted using a security protocol. The output is the biometric information received by the server.

[0080] Step 3:

[0081] The server analyzes the received biometric information. The input is the biometric information sent in step 2. Specifically, the server uses a generative AI model to detect anomalies and analyze health trends. Each data point is compared to the normal range using a machine learning algorithm. The output is the analysis results regarding the user's health status.

[0082] Step 4:

[0083] The server generates health management suggestions based on the analysis results. The input is the analysis results from step 3. Specifically, the server utilizes the suggestion generation mechanism to create personalized health management advice tailored to the user's lifecycle and preferences. The output is specific health suggestions for the user.

[0084] Step 5:

[0085] The device notifies the user of the generated health recommendations. The input is the health recommendations generated in step 4. Specifically, the device sends a push notification to the user through a dedicated application. The output is the user who received the notified health recommendations.

[0086] This process allows users to efficiently manage their own health.

[0087] (Application Example 1)

[0088] 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."

[0089] In modern life, it is crucial to provide health guidance tailored to individual users and health-promoting activities in their daily lives. However, many health management systems offer only standardized suggestions and struggle to address individual needs. Furthermore, users lack efficient means of monitoring and maintaining their health status within their homes. To address these challenges, there is a need for a system that operates autonomously and provides advanced analysis and suggestions based on individual health data.

[0090] 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.

[0091] In this invention, the server includes an information processing device means for acquiring biometric information from a user, a computing device means for analyzing the biometric information acquired from the information processing device means, and a suggestion generation means for providing health guidance suggestions to the user based on the analysis results. This enables the provision of customized health suggestions to each user, allowing for autonomous health management within the home.

[0092] "Information processing device means" refers to a device and its functions that acquire biometric information from a user.

[0093] "Computation device means" refers to a device and its functions that perform calculations for analyzing acquired biological information.

[0094] "Suggestion generation method" refers to a function that generates health guidance and suggestions for the user based on the analysis results.

[0095] "Output device means" refers to a device and its function for presenting the generated proposal to the user in audio or visual form.

[0096] "Autonomous mobility means" refers to a function that operates autonomously, moves around the user's surroundings, and collects information supplementarily.

[0097] "Plan generation means" refers to a function that proposes health promotion activities in daily life based on the analysis results.

[0098] This invention utilizes an information processing device to acquire the user's biometric information. This information processing device is incorporated into a wearable device worn by the user or into a consumer robot installed in the home. This device collects vital data such as heart rate, body temperature, and activity level in real time.

[0099] The server receives this data via the internet and analyzes it using computing devices. Machine learning models (e.g., TENSORFLOW®) are used for the analysis to evaluate the user's health status and long-term trends.

[0100] The suggestion generation means generates suggestions based on the analysis results that are useful for the user's health guidance and lifestyle improvement. These suggestions are presented to the user via an output device means, either by voice or display. The autonomous mobility means allows the robot to patrol the user's home, collecting additional environmental information to aid in health management.

[0101] For example, while a user is getting ready in the morning, the robot might suggest, "Why not add a little more protein to your breakfast?" and even provide specific food examples such as eggs and tofu. Furthermore, the generative AI model uses the user's daily activity records to offer motivational advice such as, "Let's continue with light jogging twice a week."

[0102] Examples of prompts when using the generative AI model include: "Based on the user's recent health data, please suggest an exercise plan for the weekend," and "Based on the user's nutritional intake data, please suggest a dinner menu for this week."

[0103] This system allows users to easily monitor their health status in their daily lives and achieve a healthier lifestyle through appropriate health-promoting activities.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The device collects the user's biometric information.

[0107] The input is sensor signals from the wearable device worn by the user, which are recorded as vital data by the acquisition device. The output provides biometric information such as heart rate, body temperature, and activity level. This data is stored in the terminal in real time.

[0108] Step 2:

[0109] The device sends the collected biometric information to the server.

[0110] The input is a dataset of biometric data obtained in Step 1, and this data is sent to the server via Wi-Fi. The output here is the comprehensive biometric data received by the server. This process ensures that data is collected on the server in a timely manner.

[0111] Step 3:

[0112] The server analyzes the biometric information it receives.

[0113] The input consists of all biometric data sent to the server in Step 2. A generative AI model (e.g., TensorFlow) is used to detect anomalies and perform trend analysis on the data. The output is a health status assessment derived from the analyzed data. This assessment provides analysis results tailored to the user's health condition.

[0114] Step 4:

[0115] The server generates health guidance suggestions based on the analysis results.

[0116] The input is the health status assessment analyzed in step 3. Based on this, the suggestion generation system plans individual health guidance for the user. The output includes specific dietary advice and exercise plans. The generated suggestions are tailored to the user's lifestyle.

[0117] Step 5:

[0118] The server generates suggestions which are then presented to the user via the terminal.

[0119] The input is the health guidance suggestion generated in step 4. The terminal notifies the user of this through voice guidance and display. The output is a presentation of the health suggestion in a format that the user can easily understand. This step allows the user to respond appropriately to the suggested actions.

[0120] Step 6:

[0121] Robots, as autonomous means of transportation, supplementarily collect environmental information.

[0122] The input consists of information from devices and existing home sensors. Based on this information, the robot continues to collect data while moving around the home. As output, additional information about the environment is sent to the server. This step ensures that environmental factors necessary for health management are taken into consideration.

[0123] 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.

[0124] This invention is a system that provides personalized health management suggestions by utilizing a user's health data and emotional information. This system uses an emotion engine installed in the user's terminal to collect and analyze daily emotional data, integrate it with health data, and comprehensively evaluate the user's health status.

[0125] Users utilize smartphones and wearable devices in their daily lives, and these devices collect vital data. Furthermore, these devices acquire user voice and text data through microphones and text input. This allows an emotion engine to analyze the user's emotional state and evaluate, for example, stress levels and happiness levels.

[0126] The device encrypts the collected health and emotional data and sends it to the server. The server stores the received data in a database and performs detailed analysis using machine learning algorithms. This analysis generates health management advice that takes into account the user's emotional state while maintaining healthy patterns. Specifically, if a high-stress state is detected, the advice can focus on relaxation exercises and dietary recommendations.

[0127] The generated health management suggestions are notified to the user's device. Users can review these suggestions through the application and use them as a reference when adjusting their daily activities. The system also regularly collects user feedback and uses it to improve the quality of the suggestions.

[0128] This invention combines sentiment analysis with conventional health data to enable more detailed and personalized health management. This allows users to take more appropriate actions, contributing to maintaining health and improving their quality of life.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The device collects vital data such as heart rate, steps taken, and body temperature in real time through the user's smartphone or wearable device.

[0132] Step 2:

[0133] The device acquires emotional data through the user's voice or text input. The emotion engine analyzes this data to identify the user's emotional state.

[0134] Step 3:

[0135] The device integrates collected health and emotional data, encrypts it, and then sends it to the server. Secure communication protocols are used to protect data privacy.

[0136] Step 4:

[0137] The server stores the received data in a database. Then, machine learning algorithms are used to analyze this data and evaluate the user's health status and emotional tendencies.

[0138] Step 5:

[0139] Based on the analysis results, the server generates personalized health management suggestions for each user. The suggestions can be adjusted according to the user's current emotional state, potentially including stress reduction techniques or dietary improvements.

[0140] Step 6:

[0141] The server sends the generated suggestions to the user's terminal.

[0142] Step 7:

[0143] The device notifies the user of received suggestions and displays details using a dedicated application.

[0144] Step 8:

[0145] Users improve their emotional and physical state by reviewing the suggested health management advice and putting it into practice in their daily lives.

[0146] (Example 2)

[0147] 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".

[0148] Conventional health management systems simply collect and present users' health information, making it difficult to provide personalized health management advice that takes into account mental states such as emotions and stress levels. This creates a challenge in obtaining specific and effective advice to improve users' quality of life.

[0149] 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.

[0150] In this invention, the server includes an information terminal means for acquiring health information from the user, an analysis means for analyzing the acquired voice and text information to evaluate the emotional state, and a computation means for analyzing health and emotional information using machine learning techniques. This makes it possible to provide personalized health management suggestions that take into account not only the user's physical state but also their mental state.

[0151] An "information terminal device" is a device that has the function of collecting health information, voice, and text information from users, and analyzing or transmitting that information.

[0152] "Analysis means" refers to a device or program used to analyze collected audio and text information and evaluate the user's emotional state.

[0153] "Transmission means" refers to a function used to securely transmit health information and emotional information acquired by an information terminal to a server.

[0154] "Computation means" refers to a device or function that stores health information and emotional information received on a server and analyzes it in detail using machine learning techniques.

[0155] The "proposal generation means" is a function that generates personalized health management suggestions for the user based on the analysis results.

[0156] "Communication means" refers to a device or function used to notify the user of the generated health management suggestions.

[0157] This invention is a system that integrates a user's health and emotional information to provide personalized health management suggestions. First, the user collects biometric information such as heart rate, steps taken, and sleep duration using a smartphone or wearable device. These information terminals then acquire voice and text data via microphones and text input, and evaluate the user's emotional state using built-in emotion analysis tools. This analysis uses a combination of natural language processing tools and speech processing algorithms to identify the user's stress level and well-being.

[0158] The device encrypts the analyzed emotional and health information using the SSL / TLS protocol and sends it to the server. The server stores the received data in a database and performs detailed data analysis using a Python®-based machine learning framework (e.g., TensorFlow, PyTorch). Specifically, it generates personalized health management suggestions based on the user's recent physical condition and mood through data pattern recognition and modeling of the relationship between emotions and health status.

[0159] The generated suggestions are notified to the user's device via communication. Upon receiving this notification, the user can use the application to review the suggestions and utilize them to improve their daily behavior. For example, if the analysis results indicate that the user is experiencing excessive stress, the suggestion generation system might recommend "meditating for 10 minutes every morning." An example of a prompt to the generating AI model could be a question such as, "What actions would you suggest to reduce the user's stress?"

[0160] This invention enables users to receive more detailed and reliable advice regarding their physical and mental health, thereby contributing to an improved quality of life.

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

[0162] Step 1:

[0163] Users collect biometric information using smartphones and wearable devices. Specifically, information such as heart rate, steps taken, and sleep duration are acquired as input data. This data is collected by information terminals within the device and treated as basic biometric data to indicate health status.

[0164] Step 2:

[0165] The device acquires voice and text data through the microphone and text input. Input data includes voice messages such as "How are you feeling today?" and text entered into a memo app. Based on this, the device's emotion analysis system uses natural language processing tools to evaluate the emotional state and outputs stress levels and happiness levels. This output data is recorded as the user's emotional information.

[0166] Step 3:

[0167] The device encrypts the collected health and emotional information using the SSL / TLS protocol and sends it to the server. In this process, encrypted data is used as input and securely transferred to the server. The output is the encrypted data received on the server side.

[0168] Step 4:

[0169] The server stores the received data in a database. In this step, the raw data, which has been decrypted, becomes the input. The output is the stored data. After storage, the server analyzes the data using machine learning tools. Specifically, data pattern recognition is performed using TensorFlow. As a result of the analysis, feature data related to the user's health and emotional state is output.

[0170] Step 5:

[0171] The server generates personalized health management suggestions based on the analysis results. The input is the analyzed feature data, and the generating AI model inputs prompt sentences. For example, these might include questions such as, "What is the best relaxation method for a user experiencing high stress?" The output is specific health management suggestions.

[0172] Step 6:

[0173] The server sends the generated suggestions to the terminal via a communication method. The input is the generated health management suggestions, and the output is notification information displayed on the user's terminal. The user can review this information and incorporate the suggested actions into their daily life.

[0174] (Application Example 2)

[0175] 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".

[0176] In user health management, conventional systems are based on generalized health data and are unable to provide suggestions that adequately consider each individual's emotional state. Therefore, there is a need for a system that can provide more detailed and personalized health advice and improve quality of life.

[0177] 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.

[0178] In this invention, the server includes a communication device means for acquiring health information and emotional information from the user, an information processing means for analyzing the health information and emotional information acquired from the communication device means, and a proposal generation means for providing personalized health management proposals based on the analysis results. This makes it possible to provide personalized health management proposals that comprehensively consider the user's health and emotional state.

[0179] A "user" refers to an individual who uses a system.

[0180] "Health information" refers to data that includes physiological indicators such as heart rate and activity level, and represents the user's physical condition.

[0181] "Emotional information" refers to data such as stress levels and happiness levels, which are analyzed from the user's voice and text data.

[0182] "Communication device means" refers to a device or interface for collecting health information and emotional information from users.

[0183] "Information processing means" refers to technologies or software functions for analyzing collected health and emotional information and evaluating the user's health and emotional state.

[0184] The "proposal generation means" is a function that creates personalized health management proposals based on the analysis results of the information processing means.

[0185] "Display device means" refers to a screen or interface for notifying the user of the generated health management suggestions.

[0186] A "feedback collection method" is a function that obtains responses and opinions from users regarding suggestions and uses them to improve the quality of those suggestions.

[0187] "Physiological indicators" are data that constitute a part of health information, indicating the user's physical condition, such as heart rate and body temperature.

[0188] "Machine learning techniques" are algorithms and models used to effectively analyze health and emotional information, identifying patterns and trends from data.

[0189] The system implementing this invention comprehensively analyzes the user's health and emotional information and generates personalized health management suggestions. The system is configured as follows:

[0190] First, the user utilizes a communication device, specifically a wearable device such as a smartphone or smart glasses. This device collects health information representing physiological indicators such as heart rate and activity level. Furthermore, it collects the user's voice and text data using the device's built-in microphone and text input function, and an emotion engine analyzes this emotional information.

[0191] This data is encrypted by a communication device and then transmitted to a server. The server uses information processing to analyze health and emotional information. This process utilizes machine learning technology to quantify the user's emotional state and evaluate it in relation to their health state. Then, a suggestion generation device generates personalized health management suggestions based on these analysis results.

[0192] The generated health management suggestions are communicated to the user via a display device. The user can then incorporate the suggestions into their daily life. Furthermore, a feedback collection device allows the user to provide feedback to the system regarding their reactions to the suggestions. This feedback helps the system evolve and contributes to the generation of better suggestions in the future.

[0193] For example, the system can notify a user experiencing high stress levels that they need relaxation and suggest yoga stretches or relaxing music. An example of a prompt message used by the generative AI model in this case would be: "The user is experiencing high stress levels and needs relaxation. Please suggest appropriate exercises or relaxation methods."

[0194] In this way, users can receive suggestions that comprehensively consider their health and emotional state, thereby improving their quality of life.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The device collects health information from the user. At this stage, sensors in smartphones or wearable devices acquire physiological indicators such as heart rate and activity level, and input this data as health information. The output is the collected health information data.

[0198] Step 2:

[0199] The device collects user voice and text data as emotional information. It acquires voice and text data using a microphone and text input function, and an emotion engine analyzes this data to output the user's emotional information. The output is the analyzed emotional information.

[0200] Step 3:

[0201] The device encrypts the health information obtained in Step 1 and the emotional information obtained in Step 2 and sends them to the server. The output is the encrypted health information and emotional information.

[0202] Step 4:

[0203] The server decrypts the received encrypted data and analyzes it using information processing tools. This analysis utilizes machine learning techniques to assess the user's overall health and emotional state based on the input data. The output is evaluation data, including the analysis results.

[0204] Step 5:

[0205] The server generates personalized health management suggestions based on the analysis results. The suggestion generation mechanism takes evaluation data as input and outputs health suggestions tailored to the user's needs. The output is the generated health management suggestion.

[0206] Step 6:

[0207] The server sends the generated health management suggestions to the terminal, which then notifies the user using a display device. The user can then view these suggestions on the displayed screen. The output is the health management suggestions displayed to the user.

[0208] Step 7:

[0209] Users provide feedback to the system, including their opinions and reactions to the system's health management suggestions. The terminal collects this feedback as input and sends it to the server as data to improve the quality of the system's suggestions. The output is the feedback data.

[0210] 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.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] This invention is a system that provides individually customized health management suggestions using a user's health data. This system works by collecting health data using wearable devices or smartphones worn by the user and transmitting the data to a server via the internet.

[0227] Users first record their daily activity data through their smartphones or wearable devices. This includes heart rate, steps taken, body temperature, and photos of meals. This data is collected within the system by the device and sent to the server in real time or at specified intervals.

[0228] The server analyzes the received data to comprehensively assess the user's health status. This includes identifying abnormal patterns by comparing the data to normal values ​​and performing long-term trend analysis. Furthermore, it extracts nutritional information based on photos of meals to understand the user's nutrient intake trends.

[0229] Based on the analysis results, the server generates personalized health management advice tailored to the user's needs. For example, if the user is deficient in certain nutrients, it will suggest a meal plan to compensate for that deficiency. If the server determines that the user is not getting enough exercise, it can provide an exercise plan that is easy to incorporate into daily life. Furthermore, if necessary, it may recommend online medical consultations or encourage the user to consider specific insurance products.

[0230] The generated suggestions are notified to the user through a dedicated application on their smartphone. By referring to these suggestions, users can put the information they receive into practice in their daily lives and use it to help them lead a healthier lifestyle.

[0231] The implementation of this invention enables the utilization of a wide range of health data, facilitating users to easily understand their own health status and take appropriate actions. In this way, it is possible to improve the overall efficiency of health management.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The device collects vital data and photos of meals through the user's wearable device or smartphone. This data includes heart rate, steps taken, calories burned, body temperature, and details of meals eaten.

[0235] Step 2:

[0236] The device encrypts the collected health data and transmits it to the server via the internet. Secure communication protocols are used to protect data privacy during this process.

[0237] Step 3:

[0238] The server receives health data sent from the terminal and stores it in a database. The stored data is then prepared for use in subsequent analysis processes.

[0239] Step 4:

[0240] The server analyzes stored health data using machine learning algorithms and image recognition technology. This allows it to evaluate the user's health patterns, diet, and nutrient intake.

[0241] Step 5:

[0242] The server evaluates the user's health status based on the analysis results and generates personalized health management advice. This advice includes suggestions for dietary improvements, exercise plans, and, if necessary, suggestions for online medical consultations.

[0243] Step 6:

[0244] The server sends the generated health management advice to the user's device.

[0245] Step 7:

[0246] The device notifies the user of advice received from the server. The user can then review the suggestions through the application and incorporate them into their daily life.

[0247] (Example 1)

[0248] 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."

[0249] In modern society, it is crucial for individuals to accurately understand their own health status and manage their health appropriately based on that understanding. However, there is a lack of means to efficiently collect diverse biometric information and provide personalized health recommendations based on that information. Conventional systems are limited to analyzing only a portion of the data, making it difficult to achieve comprehensive health promotion.

[0250] 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.

[0251] In this invention, the server includes a device means for acquiring biometric information from a user, an information processing device means for processing the biometric information acquired from the device means, and a proposal generation means for providing personalized health management suggestions to the user based on the processing results. This makes it possible to comprehensively analyze a wide range of the user's health data and quickly provide specific health suggestions that meet the individual needs of the user.

[0252] A "user" is an individual or organization that uses the system to record health information and receives suggestions based on the analysis results.

[0253] "Biometric information" refers to all data that represents the user's health status, including heart rate, steps taken, body temperature, and records of meals eaten.

[0254] "Device means" refers to equipment or devices used to acquire biometric information from a user, and includes wearable devices and smartphones.

[0255] "Information processing device means" refers to a central processing unit or server system used to analyze acquired biological information and evaluate health status.

[0256] "Proposal generation means" refers to a function or process that creates specific proposals for health management based on analysis results generated by an information processing device.

[0257] "Notification means" refers to methods or system functions for communicating proposals created by the proposal generation means to the user and prompting them to take necessary actions.

[0258] A "generative AI model" is an artificial intelligence system that utilizes machine learning technology to automatically analyze input data and generate suggestions.

[0259] This invention is a system that provides personalized health management suggestions using a user's biometric information. This system primarily utilizes device means, information processing means, suggestion generation means, and notification means.

[0260] First, users collect daily biometric information using a device. This device includes wearable devices and smartphones, and acquires data such as heart rate, steps taken, body temperature, and photos of meals. Wearable devices can record data in real time using built-in sensors and store it in a dedicated application.

[0261] The collected data is transmitted from the terminal to an information processing device. This information processing device is located on a server system and performs data analysis. The analysis uses a generative AI model to compare the current data with past data, detect outliers, and evaluate long-term health trends. This analysis provides a comprehensive understanding of the user's health status.

[0262] Based on the analysis results, the suggestion generation mechanism is activated. The server creates personalized health management suggestions according to the user's current health status. This includes suggestions for dietary improvements and exercise plans. The generating AI model provides specific and practical advice by considering the user's preferences and lifestyle.

[0263] Ultimately, suggestions are communicated to the user through a notification system. Users can review the suggestions via a dedicated application and use them to improve their daily health management. These suggestions are delivered instantly via push notifications, allowing users to take action to improve their health based on the information.

[0264] For example, for a user diagnosed with a lack of exercise, the server can suggest an exercise plan that can be easily done indoors. Furthermore, if a user is lacking in nutrients, the server can provide a list of specific foods or recipes.

[0265] An example of a prompt for the generating AI model is: "Analyze user C's recent health data and suggest any particularly noteworthy abnormal patterns and health management advice based on them."

[0266] By implementing this system, users can effectively utilize their health data and achieve personalized health management.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] Users collect biometric information using wearable devices and smartphones. Inputs include data such as heart rate, steps taken, body temperature, and photos of meals. Specifically, users wear these devices during their daily lives and acquire data using a dedicated application. The output is biometric information stored within the device.

[0270] Step 2:

[0271] The terminal transmits the collected biometric information to the server. The input is the various biometric information recorded in step 1. Specifically, the terminal sends the data via an internet connection. The data is encrypted using a security protocol. The output is the biometric information received by the server.

[0272] Step 3:

[0273] The server analyzes the received biometric information. The input is the biometric information sent in step 2. Specifically, the server uses a generative AI model to detect anomalies and analyze health trends. Each data point is compared to the normal range using a machine learning algorithm. The output is the analysis results regarding the user's health status.

[0274] Step 4:

[0275] The server generates health management suggestions based on the analysis results. The input is the analysis results from step 3. Specifically, the server utilizes the suggestion generation mechanism to create personalized health management advice tailored to the user's lifecycle and preferences. The output is specific health suggestions for the user.

[0276] Step 5:

[0277] The device notifies the user of the generated health recommendations. The input is the health recommendations generated in step 4. Specifically, the device sends a push notification to the user through a dedicated application. The output is the user who received the notified health recommendations.

[0278] This process allows users to efficiently manage their own health.

[0279] (Application Example 1)

[0280] 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."

[0281] In modern life, it is important to provide health guidance suitable for individual users and health promotion activities in daily life. However, many health management systems only offer standardized proposals and have difficulty meeting individual needs. There is also a problem that users lack means to efficiently grasp and maintain their health status at home. To solve these problems, a system that operates autonomously and performs advanced analysis and proposals based on individual health data is required.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0283] In this invention, the server includes an information processing device means for acquiring biological information from a user, a computing device means for analyzing the biological information acquired from the information processing device means, and a proposal generation means for providing a health guidance proposal to the user based on the analysis result. Thereby, customized health proposals can be provided to each user, enabling autonomous health management within the home.

[0284] The "information processing device means" refers to a device and its functions for acquiring biological information from a user.

[0285] The "computing device means" refers to a device and its functions for performing calculations to analyze the acquired biological information.

[0286] The "proposal generation means" refers to a function for generating health guidance and proposals for a user based on the analysis result.

[0287] The "output device means" refers to a device and its functions for presenting the generated proposal to the user audibly or visually.

[0288] The "autonomous movement means" refers to a function that operates autonomously and moves around the user to complementarily collect information.

[0289] "Plan generation means" refers to a function that proposes health promotion activities in daily life based on the analysis results.

[0290] This invention utilizes an information processing device to acquire the user's biometric information. This information processing device is incorporated into a wearable device worn by the user or into a consumer robot installed in the home. This device collects vital data such as heart rate, body temperature, and activity level in real time.

[0291] The server receives this data via the internet and analyzes it using computing devices. Machine learning models (e.g., TensorFlow) are used for the analysis to evaluate the user's health status and long-term trends.

[0292] The suggestion generation means generates suggestions based on the analysis results that are useful for the user's health guidance and lifestyle improvement. These suggestions are presented to the user via an output device means, either by voice or display. The autonomous mobility means allows the robot to patrol the user's home, collecting additional environmental information to aid in health management.

[0293] For example, while a user is getting ready in the morning, the robot might suggest, "Why not add a little more protein to your breakfast?" and even provide specific food examples such as eggs and tofu. Furthermore, the generative AI model uses the user's daily activity records to offer motivational advice such as, "Let's continue with light jogging twice a week."

[0294] Examples of prompts when using the generative AI model include: "Based on the user's recent health data, please suggest an exercise plan for the weekend," and "Based on the user's nutritional intake data, please suggest a dinner menu for this week."

[0295] This system allows users to easily monitor their health status in their daily lives and achieve a healthier lifestyle through appropriate health-promoting activities.

[0296] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0297] Step 1:

[0298] The terminal collects the user's biological information.

[0299] The input is a sensor signal from a wearable device worn by the user, which is recorded as vital data by the acquisition device. As output, biological information such as heart rate, body temperature, and activity level can be obtained. This data is accumulated in the terminal in real time.

[0300] Step 2:

[0301] The terminal transmits the biological information collected to the server.

[0302] The input is a dataset of the biological information obtained in Step 1, and this data is transmitted to the server via Wi-Fi. The output here is the comprehensive biological information received by the server. Through this process, data is collected in a timely manner at the server.

[0303] Step 3:

[0304] The server analyzes the biological information received.

[0305] The input includes all the biological information data transmitted to the server in Step 2. Using a generated AI model (e.g., TensorFlow), outlier detection and trend analysis of the data are performed. The output is a health status evaluation obtained from the analyzed data. Through this evaluation, analysis results corresponding to the user's health condition can be obtained.

[0306] Step 4:

[0307] The server generates health guidance suggestions based on the analysis results.

[0308] The input is the health status assessment analyzed in step 3. Based on this, the suggestion generation system plans individual health guidance for the user. The output includes specific dietary advice and exercise plans. The generated suggestions are tailored to the user's lifestyle.

[0309] Step 5:

[0310] The server generates suggestions which are then presented to the user via the terminal.

[0311] The input is the health guidance suggestion generated in step 4. The terminal notifies the user of this through voice guidance and display. The output is a presentation of the health suggestion in a format that the user can easily understand. This step allows the user to respond appropriately to the suggested actions.

[0312] Step 6:

[0313] Robots, as autonomous means of transportation, supplementarily collect environmental information.

[0314] The input consists of information from devices and existing home sensors. Based on this information, the robot continues to collect data while moving around the home. As output, additional information about the environment is sent to the server. This step ensures that environmental factors necessary for health management are taken into consideration.

[0315] 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.

[0316] This invention is a system that provides personalized health management suggestions by utilizing a user's health data and emotional information. This system uses an emotion engine installed in the user's terminal to collect and analyze daily emotional data, integrate it with health data, and comprehensively evaluate the user's health status.

[0317] Users utilize smartphones and wearable devices in their daily lives, and these devices collect vital data. Furthermore, these devices acquire user voice and text data through microphones and text input. This allows an emotion engine to analyze the user's emotional state and evaluate, for example, stress levels and happiness levels.

[0318] The device encrypts the collected health and emotional data and sends it to the server. The server stores the received data in a database and performs detailed analysis using machine learning algorithms. This analysis generates health management advice that takes into account the user's emotional state while maintaining healthy patterns. Specifically, if a high-stress state is detected, the advice can focus on relaxation exercises and dietary recommendations.

[0319] The generated health management suggestions are notified to the user's device. Users can review these suggestions through the application and use them as a reference when adjusting their daily activities. The system also regularly collects user feedback and uses it to improve the quality of the suggestions.

[0320] This invention combines sentiment analysis with conventional health data to enable more detailed and personalized health management. This allows users to take more appropriate actions, contributing to maintaining health and improving their quality of life.

[0321] The following describes the processing flow.

[0322] Step 1:

[0323] The device collects vital data such as heart rate, steps taken, and body temperature in real time through the user's smartphone or wearable device.

[0324] Step 2:

[0325] The device acquires emotional data through the user's voice or text input. The emotion engine analyzes this data to identify the user's emotional state.

[0326] Step 3:

[0327] The device integrates collected health and emotional data, encrypts it, and then sends it to the server. Secure communication protocols are used to protect data privacy.

[0328] Step 4:

[0329] The server stores the received data in a database. Then, machine learning algorithms are used to analyze this data and evaluate the user's health status and emotional tendencies.

[0330] Step 5:

[0331] Based on the analysis results, the server generates personalized health management suggestions for each user. The suggestions can be adjusted according to the user's current emotional state, potentially including stress reduction techniques or dietary improvements.

[0332] Step 6:

[0333] The server sends the generated suggestions to the user's terminal.

[0334] Step 7:

[0335] The device notifies the user of received suggestions and displays details using a dedicated application.

[0336] Step 8:

[0337] Users improve their emotional and physical state by reviewing the suggested health management advice and putting it into practice in their daily lives.

[0338] (Example 2)

[0339] 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".

[0340] Conventional health management systems simply collect and present users' health information, making it difficult to provide personalized health management advice that takes into account mental states such as emotions and stress levels. This creates a challenge in obtaining specific and effective advice to improve users' quality of life.

[0341] 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.

[0342] In this invention, the server includes an information terminal means for acquiring health information from the user, an analysis means for analyzing the acquired voice and text information to evaluate the emotional state, and a computation means for analyzing health and emotional information using machine learning techniques. This makes it possible to provide personalized health management suggestions that take into account not only the user's physical state but also their mental state.

[0343] An "information terminal device" is a device that has the function of collecting health information, voice, and text information from users, and analyzing or transmitting that information.

[0344] "Analysis means" refers to a device or program used to analyze collected audio and text information and evaluate the user's emotional state.

[0345] "Transmission means" refers to a function used to securely transmit health information and emotional information acquired by an information terminal to a server.

[0346] "Computation means" refers to a device or function that stores health information and emotional information received on a server and analyzes it in detail using machine learning techniques.

[0347] The "proposal generation means" is a function that generates personalized health management suggestions for the user based on the analysis results.

[0348] "Communication means" refers to a device or function used to notify the user of the generated health management suggestions.

[0349] This invention is a system that integrates a user's health and emotional information to provide personalized health management suggestions. First, the user collects biometric information such as heart rate, steps taken, and sleep duration using a smartphone or wearable device. These information terminals then acquire voice and text data via microphones and text input, and evaluate the user's emotional state using built-in emotion analysis tools. This analysis uses a combination of natural language processing tools and speech processing algorithms to identify the user's stress level and well-being.

[0350] The device encrypts the analyzed emotional and health information using the SSL / TLS protocol and sends it to the server. The server stores the received data in a database and performs detailed data analysis using Python-based machine learning frameworks (e.g., TensorFlow, PyTorch). Specifically, it generates personalized health management suggestions based on the user's recent physical condition and mood through data pattern recognition and modeling of the relationship between emotions and health status.

[0351] The generated suggestions are notified to the user's device via communication. Upon receiving this notification, the user can use the application to review the suggestions and utilize them to improve their daily behavior. For example, if the analysis results indicate that the user is experiencing excessive stress, the suggestion generation system might recommend "meditating for 10 minutes every morning." An example of a prompt to the generating AI model could be a question such as, "What actions would you suggest to reduce the user's stress?"

[0352] This invention enables users to receive more detailed and reliable advice regarding their physical and mental health, thereby contributing to an improved quality of life.

[0353] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0354] Step 1:

[0355] Users collect biometric information using smartphones and wearable devices. Specifically, information such as heart rate, steps taken, and sleep duration are acquired as input data. This data is collected by information terminals within the device and treated as basic biometric data to indicate health status.

[0356] Step 2:

[0357] The device acquires voice and text data through the microphone and text input. Input data includes voice messages such as "How are you feeling today?" and text entered into a memo app. Based on this, the device's emotion analysis system uses natural language processing tools to evaluate the emotional state and outputs stress levels and happiness levels. This output data is recorded as the user's emotional information.

[0358] Step 3:

[0359] The device encrypts the collected health and emotional information using the SSL / TLS protocol and sends it to the server. In this process, encrypted data is used as input and securely transferred to the server. The output is the encrypted data received on the server side.

[0360] Step 4:

[0361] The server stores the received data in a database. In this step, the raw data, which has been decrypted, becomes the input. The output is the stored data. After storage, the server analyzes the data using machine learning tools. Specifically, data pattern recognition is performed using TensorFlow. As a result of the analysis, feature data related to the user's health and emotional state is output.

[0362] Step 5:

[0363] The server generates personalized health management suggestions based on the analysis results. The input is the analyzed feature data, and the generating AI model inputs prompt sentences. For example, these might include questions such as, "What is the best relaxation method for a user experiencing high stress?" The output is specific health management suggestions.

[0364] Step 6:

[0365] The server sends the generated suggestions to the terminal via a communication method. The input is the generated health management suggestions, and the output is notification information displayed on the user's terminal. The user can review this information and incorporate the suggested actions into their daily life.

[0366] (Application Example 2)

[0367] 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."

[0368] In user health management, conventional systems are based on generalized health data and are unable to provide suggestions that adequately consider each individual's emotional state. Therefore, there is a need for a system that can provide more detailed and personalized health advice and improve quality of life.

[0369] 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.

[0370] In this invention, the server includes a communication device means for acquiring health information and emotional information from the user, an information processing means for analyzing the health information and emotional information acquired from the communication device means, and a proposal generation means for providing personalized health management proposals based on the analysis results. This makes it possible to provide personalized health management proposals that comprehensively consider the user's health and emotional state.

[0371] A "user" refers to an individual who uses a system.

[0372] "Health information" refers to data that includes physiological indicators such as heart rate and activity level, and represents the user's physical condition.

[0373] "Emotional information" refers to data such as stress levels and happiness levels, which are analyzed from the user's voice and text data.

[0374] "Communication device means" refers to a device or interface for collecting health information and emotional information from users.

[0375] "Information processing means" refers to technologies or software functions for analyzing collected health and emotional information and evaluating the user's health and emotional state.

[0376] The "proposal generation means" is a function that creates personalized health management proposals based on the analysis results of the information processing means.

[0377] "Display device means" refers to a screen or interface for notifying the user of the generated health management suggestions.

[0378] A "feedback collection method" is a function that obtains responses and opinions from users regarding suggestions and uses them to improve the quality of those suggestions.

[0379] "Physiological indicators" are data that constitute a part of health information, indicating the user's physical condition, such as heart rate and body temperature.

[0380] "Machine learning techniques" are algorithms and models used to effectively analyze health and emotional information, identifying patterns and trends from data.

[0381] The system implementing this invention comprehensively analyzes the user's health and emotional information and generates personalized health management suggestions. The system is configured as follows:

[0382] First, the user utilizes a communication device, specifically a wearable device such as a smartphone or smart glasses. This device collects health information representing physiological indicators such as heart rate and activity level. Furthermore, it collects the user's voice and text data using the device's built-in microphone and text input function, and an emotion engine analyzes this emotional information.

[0383] This data is encrypted by a communication device and then transmitted to a server. The server uses information processing to analyze health and emotional information. This process utilizes machine learning technology to quantify the user's emotional state and evaluate it in relation to their health state. Then, a suggestion generation device generates personalized health management suggestions based on these analysis results.

[0384] The generated health management suggestions are communicated to the user via a display device. The user can then incorporate the suggestions into their daily life. Furthermore, a feedback collection device allows the user to provide feedback to the system regarding their reactions to the suggestions. This feedback helps the system evolve and contributes to the generation of better suggestions in the future.

[0385] For example, the system can notify a user experiencing high stress levels that they need relaxation and suggest yoga stretches or relaxing music. An example of a prompt message used by the generative AI model in this case would be: "The user is experiencing high stress levels and needs relaxation. Please suggest appropriate exercises or relaxation methods."

[0386] In this way, users can receive suggestions that comprehensively consider their health and emotional state, thereby improving their quality of life.

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] The device collects health information from the user. At this stage, sensors in smartphones or wearable devices acquire physiological indicators such as heart rate and activity level, and input this data as health information. The output is the collected health information data.

[0390] Step 2:

[0391] The device collects user voice and text data as emotional information. It acquires voice and text data using a microphone and text input function, and an emotion engine analyzes this data to output the user's emotional information. The output is the analyzed emotional information.

[0392] Step 3:

[0393] The device encrypts the health information obtained in Step 1 and the emotional information obtained in Step 2 and sends them to the server. The output is the encrypted health information and emotional information.

[0394] Step 4:

[0395] The server decrypts the received encrypted data and analyzes it using information processing tools. This analysis utilizes machine learning techniques to assess the user's overall health and emotional state based on the input data. The output is evaluation data, including the analysis results.

[0396] Step 5:

[0397] The server generates personalized health management suggestions based on the analysis results. The suggestion generation mechanism takes evaluation data as input and outputs health suggestions tailored to the user's needs. The output is the generated health management suggestion.

[0398] Step 6:

[0399] The server sends the generated health management suggestions to the terminal, which then notifies the user using a display device. The user can then view these suggestions on the displayed screen. The output is the health management suggestions displayed to the user.

[0400] Step 7:

[0401] Users provide feedback to the system, including their opinions and reactions to the system's health management suggestions. The terminal collects this feedback as input and sends it to the server as data to improve the quality of the system's suggestions. The output is the feedback data.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] This invention is a system that provides individually customized health management suggestions using a user's health data. This system works by collecting health data using wearable devices or smartphones worn by the user and transmitting the data to a server via the internet.

[0419] Users first record their daily activity data through their smartphones or wearable devices. This includes heart rate, steps taken, body temperature, and photos of meals. This data is collected within the system by the device and sent to the server in real time or at specified intervals.

[0420] The server analyzes the received data to comprehensively assess the user's health status. This includes identifying abnormal patterns by comparing the data to normal values ​​and performing long-term trend analysis. Furthermore, it extracts nutritional information based on photos of meals to understand the user's nutrient intake trends.

[0421] Based on the analysis results, the server generates personalized health management advice tailored to the user's needs. For example, if the user is deficient in certain nutrients, it will suggest a meal plan to compensate for that deficiency. If the server determines that the user is not getting enough exercise, it can provide an exercise plan that is easy to incorporate into daily life. Furthermore, if necessary, it may recommend online medical consultations or encourage the user to consider specific insurance products.

[0422] The generated suggestions are notified to the user through a dedicated application on their smartphone. By referring to these suggestions, users can put the information they receive into practice in their daily lives and use it to help them lead a healthier lifestyle.

[0423] The implementation of this invention enables the utilization of a wide range of health data, facilitating users to easily understand their own health status and take appropriate actions. In this way, it is possible to improve the overall efficiency of health management.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The device collects vital data and photos of meals through the user's wearable device or smartphone. This data includes heart rate, steps taken, calories burned, body temperature, and details of meals eaten.

[0427] Step 2:

[0428] The device encrypts the collected health data and transmits it to the server via the internet. Secure communication protocols are used to protect data privacy during this process.

[0429] Step 3:

[0430] The server receives health data sent from the terminal and stores it in a database. The stored data is then prepared for use in subsequent analysis processes.

[0431] Step 4:

[0432] The server analyzes stored health data using machine learning algorithms and image recognition technology. This allows it to evaluate the user's health patterns, diet, and nutrient intake.

[0433] Step 5:

[0434] The server evaluates the user's health status based on the analysis results and generates personalized health management advice. This advice includes suggestions for dietary improvements, exercise plans, and, if necessary, suggestions for online medical consultations.

[0435] Step 6:

[0436] The server sends the generated health management advice to the user's device.

[0437] Step 7:

[0438] The device notifies the user of advice received from the server. The user can then review the suggestions through the application and incorporate them into their daily life.

[0439] (Example 1)

[0440] 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."

[0441] In modern society, it is crucial for individuals to accurately understand their own health status and manage their health appropriately based on that understanding. However, there is a lack of means to efficiently collect diverse biometric information and provide personalized health recommendations based on that information. Conventional systems are limited to analyzing only a portion of the data, making it difficult to achieve comprehensive health promotion.

[0442] 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.

[0443] In this invention, the server includes a device means for acquiring biometric information from a user, an information processing device means for processing the biometric information acquired from the device means, and a proposal generation means for providing personalized health management suggestions to the user based on the processing results. This makes it possible to comprehensively analyze a wide range of the user's health data and quickly provide specific health suggestions that meet the individual needs of the user.

[0444] A "user" is an individual or organization that uses the system to record health information and receives suggestions based on the analysis results.

[0445] "Biometric information" refers to all data that represents the user's health status, including heart rate, steps taken, body temperature, and records of meals eaten.

[0446] "Device means" refers to equipment or devices used to acquire biometric information from a user, and includes wearable devices and smartphones.

[0447] "Information processing device means" refers to a central processing unit or server system used to analyze acquired biological information and evaluate health status.

[0448] "Proposal generation means" refers to a function or process that creates specific proposals for health management based on analysis results generated by an information processing device.

[0449] "Notification means" refers to methods or system functions for communicating proposals created by the proposal generation means to the user and prompting them to take necessary actions.

[0450] A "generative AI model" is an artificial intelligence system that utilizes machine learning technology to automatically analyze input data and generate suggestions.

[0451] This invention is a system that provides personalized health management suggestions using a user's biometric information. This system primarily utilizes device means, information processing means, suggestion generation means, and notification means.

[0452] First, users collect daily biometric information using a device. This device includes wearable devices and smartphones, and acquires data such as heart rate, steps taken, body temperature, and photos of meals. Wearable devices can record data in real time using built-in sensors and store it in a dedicated application.

[0453] The collected data is transmitted from the terminal to an information processing device. This information processing device is located on a server system and performs data analysis. The analysis uses a generative AI model to compare the current data with past data, detect outliers, and evaluate long-term health trends. This analysis provides a comprehensive understanding of the user's health status.

[0454] Based on the analysis results, the suggestion generation mechanism is activated. The server creates personalized health management suggestions according to the user's current health status. This includes suggestions for dietary improvements and exercise plans. The generating AI model provides specific and practical advice by considering the user's preferences and lifestyle.

[0455] Ultimately, suggestions are communicated to the user through a notification system. Users can review the suggestions via a dedicated application and use them to improve their daily health management. These suggestions are delivered instantly via push notifications, allowing users to take action to improve their health based on the information.

[0456] For example, for a user diagnosed with a lack of exercise, the server can suggest an exercise plan that can be easily done indoors. Furthermore, if a user is lacking in nutrients, the server can provide a list of specific foods or recipes.

[0457] An example of a prompt for the generating AI model is: "Analyze user C's recent health data and suggest any particularly noteworthy abnormal patterns and health management advice based on them."

[0458] By implementing this system, users can effectively utilize their health data and achieve personalized health management.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] Users collect biometric information using wearable devices and smartphones. Inputs include data such as heart rate, steps taken, body temperature, and photos of meals. Specifically, users wear these devices during their daily lives and acquire data using a dedicated application. The output is biometric information stored within the device.

[0462] Step 2:

[0463] The terminal transmits the collected biometric information to the server. The input is the various biometric information recorded in step 1. Specifically, the terminal sends the data via an internet connection. The data is encrypted using a security protocol. The output is the biometric information received by the server.

[0464] Step 3:

[0465] The server analyzes the received biometric information. The input is the biometric information sent in step 2. Specifically, the server uses a generative AI model to detect anomalies and analyze health trends. Each data point is compared to the normal range using a machine learning algorithm. The output is the analysis results regarding the user's health status.

[0466] Step 4:

[0467] The server generates health management suggestions based on the analysis results. The input is the analysis results from step 3. Specifically, the server utilizes the suggestion generation mechanism to create personalized health management advice tailored to the user's lifecycle and preferences. The output is specific health suggestions for the user.

[0468] Step 5:

[0469] The device notifies the user of the generated health recommendations. The input is the health recommendations generated in step 4. Specifically, the device sends a push notification to the user through a dedicated application. The output is the user who received the notified health recommendations.

[0470] This process allows users to efficiently manage their own health.

[0471] (Application Example 1)

[0472] 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."

[0473] In modern life, it is crucial to provide health guidance tailored to individual users and health-promoting activities in their daily lives. However, many health management systems offer only standardized suggestions and struggle to address individual needs. Furthermore, users lack efficient means of monitoring and maintaining their health status within their homes. To address these challenges, there is a need for a system that operates autonomously and provides advanced analysis and suggestions based on individual health data.

[0474] 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.

[0475] In this invention, the server includes an information processing device means for acquiring biometric information from a user, a computing device means for analyzing the biometric information acquired from the information processing device means, and a suggestion generation means for providing health guidance suggestions to the user based on the analysis results. This enables the provision of customized health suggestions to each user, allowing for autonomous health management within the home.

[0476] "Information processing device means" refers to a device and its functions that acquire biometric information from a user.

[0477] "Computation device means" refers to a device and its functions that perform calculations for analyzing acquired biological information.

[0478] "Suggestion generation method" refers to a function that generates health guidance and suggestions for the user based on the analysis results.

[0479] "Output device means" refers to a device and its function for presenting the generated proposal to the user in audio or visual form.

[0480] "Autonomous mobility means" refers to a function that operates autonomously, moves around the user's surroundings, and collects information supplementarily.

[0481] "Plan generation means" refers to a function that proposes health promotion activities in daily life based on the analysis results.

[0482] This invention utilizes an information processing device to acquire the user's biometric information. This information processing device is incorporated into a wearable device worn by the user or into a consumer robot installed in the home. This device collects vital data such as heart rate, body temperature, and activity level in real time.

[0483] The server receives this data via the internet and analyzes it using computing devices. Machine learning models (e.g., TensorFlow) are used for the analysis to evaluate the user's health status and long-term trends.

[0484] The suggestion generation means generates suggestions based on the analysis results that are useful for the user's health guidance and lifestyle improvement. These suggestions are presented to the user via an output device means, either by voice or display. The autonomous mobility means allows the robot to patrol the user's home, collecting additional environmental information to aid in health management.

[0485] For example, while a user is getting ready in the morning, the robot might suggest, "Why not add a little more protein to your breakfast?" and even provide specific food examples such as eggs and tofu. Furthermore, the generative AI model uses the user's daily activity records to offer motivational advice such as, "Let's continue with light jogging twice a week."

[0486] Examples of prompts when using the generative AI model include: "Based on the user's recent health data, please suggest an exercise plan for the weekend," and "Based on the user's nutritional intake data, please suggest a dinner menu for this week."

[0487] This system allows users to easily monitor their health status in their daily lives and achieve a healthier lifestyle through appropriate health-promoting activities.

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] The device collects the user's biometric information.

[0491] The input is sensor signals from the wearable device worn by the user, which are recorded as vital data by the acquisition device. The output provides biometric information such as heart rate, body temperature, and activity level. This data is stored in the terminal in real time.

[0492] Step 2:

[0493] The device sends the collected biometric information to the server.

[0494] The input is a dataset of biometric data obtained in Step 1, and this data is sent to the server via Wi-Fi. The output here is the comprehensive biometric data received by the server. This process ensures that data is collected on the server in a timely manner.

[0495] Step 3:

[0496] The server analyzes the biometric information it receives.

[0497] The input consists of all biometric data sent to the server in Step 2. A generative AI model (e.g., TensorFlow) is used to detect anomalies and perform trend analysis on the data. The output is a health status assessment derived from the analyzed data. This assessment provides analysis results tailored to the user's health condition.

[0498] Step 4:

[0499] The server generates health guidance suggestions based on the analysis results.

[0500] The input is the health status assessment analyzed in step 3. Based on this, the suggestion generation system plans individual health guidance for the user. The output includes specific dietary advice and exercise plans. The generated suggestions are tailored to the user's lifestyle.

[0501] Step 5:

[0502] The server generates suggestions which are then presented to the user via the terminal.

[0503] The input is the health guidance suggestion generated in step 4. The terminal notifies the user of this through voice guidance and display. The output is a presentation of the health suggestion in a format that the user can easily understand. This step allows the user to respond appropriately to the suggested actions.

[0504] Step 6:

[0505] Robots, as autonomous means of transportation, supplementarily collect environmental information.

[0506] The input consists of information from devices and existing home sensors. Based on this information, the robot continues to collect data while moving around the home. As output, additional information about the environment is sent to the server. This step ensures that environmental factors necessary for health management are taken into consideration.

[0507] 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.

[0508] This invention is a system that provides personalized health management suggestions by utilizing a user's health data and emotional information. This system uses an emotion engine installed in the user's terminal to collect and analyze daily emotional data, integrate it with health data, and comprehensively evaluate the user's health status.

[0509] Users utilize smartphones and wearable devices in their daily lives, and these devices collect vital data. Furthermore, these devices acquire user voice and text data through microphones and text input. This allows an emotion engine to analyze the user's emotional state and evaluate, for example, stress levels and happiness levels.

[0510] The device encrypts the collected health and emotional data and sends it to the server. The server stores the received data in a database and performs detailed analysis using machine learning algorithms. This analysis generates health management advice that takes into account the user's emotional state while maintaining healthy patterns. Specifically, if a high-stress state is detected, the advice can focus on relaxation exercises and dietary recommendations.

[0511] The generated health management suggestions are notified to the user's device. Users can review these suggestions through the application and use them as a reference when adjusting their daily activities. The system also regularly collects user feedback and uses it to improve the quality of the suggestions.

[0512] This invention combines sentiment analysis with conventional health data to enable more detailed and personalized health management. This allows users to take more appropriate actions, contributing to maintaining health and improving their quality of life.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] The device collects vital data such as heart rate, steps taken, and body temperature in real time through the user's smartphone or wearable device.

[0516] Step 2:

[0517] The device acquires emotional data through the user's voice or text input. The emotion engine analyzes this data to identify the user's emotional state.

[0518] Step 3:

[0519] The device integrates collected health and emotional data, encrypts it, and then sends it to the server. Secure communication protocols are used to protect data privacy.

[0520] Step 4:

[0521] The server stores the received data in a database. Then, machine learning algorithms are used to analyze this data and evaluate the user's health status and emotional tendencies.

[0522] Step 5:

[0523] Based on the analysis results, the server generates personalized health management suggestions for each user. The suggestions can be adjusted according to the user's current emotional state, potentially including stress reduction techniques or dietary improvements.

[0524] Step 6:

[0525] The server sends the generated suggestions to the user's terminal.

[0526] Step 7:

[0527] The device notifies the user of received suggestions and displays details using a dedicated application.

[0528] Step 8:

[0529] Users improve their emotional and physical state by reviewing the suggested health management advice and putting it into practice in their daily lives.

[0530] (Example 2)

[0531] 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."

[0532] Conventional health management systems simply collect and present users' health information, making it difficult to provide personalized health management advice that takes into account mental states such as emotions and stress levels. This creates a challenge in obtaining specific and effective advice to improve users' quality of life.

[0533] 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.

[0534] In this invention, the server includes an information terminal means for acquiring health information from the user, an analysis means for analyzing the acquired voice and text information to evaluate the emotional state, and a computation means for analyzing health and emotional information using machine learning techniques. This makes it possible to provide personalized health management suggestions that take into account not only the user's physical state but also their mental state.

[0535] An "information terminal device" is a device that has the function of collecting health information, voice, and text information from users, and analyzing or transmitting that information.

[0536] "Analysis means" refers to a device or program used to analyze collected audio and text information and evaluate the user's emotional state.

[0537] "Transmission means" refers to a function used to securely transmit health information and emotional information acquired by an information terminal to a server.

[0538] "Computation means" refers to a device or function that stores health information and emotional information received on a server and analyzes it in detail using machine learning techniques.

[0539] The "proposal generation means" is a function that generates personalized health management suggestions for the user based on the analysis results.

[0540] "Communication means" refers to a device or function used to notify the user of the generated health management suggestions.

[0541] This invention is a system that integrates a user's health and emotional information to provide personalized health management suggestions. First, the user collects biometric information such as heart rate, steps taken, and sleep duration using a smartphone or wearable device. These information terminals then acquire voice and text data via microphones and text input, and evaluate the user's emotional state using built-in emotion analysis tools. This analysis uses a combination of natural language processing tools and speech processing algorithms to identify the user's stress level and well-being.

[0542] The device encrypts the analyzed emotional and health information using the SSL / TLS protocol and sends it to the server. The server stores the received data in a database and performs detailed data analysis using Python-based machine learning frameworks (e.g., TensorFlow, PyTorch). Specifically, it generates personalized health management suggestions based on the user's recent physical condition and mood through data pattern recognition and modeling of the relationship between emotions and health status.

[0543] The generated suggestions are notified to the user's device via communication. Upon receiving this notification, the user can use the application to review the suggestions and utilize them to improve their daily behavior. For example, if the analysis results indicate that the user is experiencing excessive stress, the suggestion generation system might recommend "meditating for 10 minutes every morning." An example of a prompt to the generating AI model could be a question such as, "What actions would you suggest to reduce the user's stress?"

[0544] This invention enables users to receive more detailed and reliable advice regarding their physical and mental health, thereby contributing to an improved quality of life.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] Users collect biometric information using smartphones and wearable devices. Specifically, information such as heart rate, steps taken, and sleep duration are acquired as input data. This data is collected by information terminals within the device and treated as basic biometric data to indicate health status.

[0548] Step 2:

[0549] The device acquires voice and text data through the microphone and text input. Input data includes voice messages such as "How are you feeling today?" and text entered into a memo app. Based on this, the device's emotion analysis system uses natural language processing tools to evaluate the emotional state and outputs stress levels and happiness levels. This output data is recorded as the user's emotional information.

[0550] Step 3:

[0551] The device encrypts the collected health and emotional information using the SSL / TLS protocol and sends it to the server. In this process, encrypted data is used as input and securely transferred to the server. The output is the encrypted data received on the server side.

[0552] Step 4:

[0553] The server stores the received data in a database. In this step, the raw data, which has been decrypted, becomes the input. The output is the stored data. After storage, the server analyzes the data using machine learning tools. Specifically, data pattern recognition is performed using TensorFlow. As a result of the analysis, feature data related to the user's health and emotional state is output.

[0554] Step 5:

[0555] The server generates personalized health management suggestions based on the analysis results. The input is the analyzed feature data, and the generating AI model inputs prompt sentences. For example, these might include questions such as, "What is the best relaxation method for a user experiencing high stress?" The output is specific health management suggestions.

[0556] Step 6:

[0557] The server sends the generated suggestions to the terminal via a communication method. The input is the generated health management suggestions, and the output is notification information displayed on the user's terminal. The user can review this information and incorporate the suggested actions into their daily life.

[0558] (Application Example 2)

[0559] 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."

[0560] In user health management, conventional systems are based on generalized health data and are unable to provide suggestions that adequately consider each individual's emotional state. Therefore, there is a need for a system that can provide more detailed and personalized health advice and improve quality of life.

[0561] 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.

[0562] In this invention, the server includes a communication device means for acquiring health information and emotional information from the user, an information processing means for analyzing the health information and emotional information acquired from the communication device means, and a proposal generation means for providing personalized health management proposals based on the analysis results. This makes it possible to provide personalized health management proposals that comprehensively consider the user's health and emotional state.

[0563] A "user" refers to an individual who uses a system.

[0564] "Health information" refers to data that includes physiological indicators such as heart rate and activity level, and represents the user's physical condition.

[0565] "Emotional information" refers to data such as stress levels and happiness levels, which are analyzed from the user's voice and text data.

[0566] "Communication device means" refers to a device or interface for collecting health information and emotional information from users.

[0567] "Information processing means" refers to technologies or software functions for analyzing collected health and emotional information and evaluating the user's health and emotional state.

[0568] The "proposal generation means" is a function that creates personalized health management proposals based on the analysis results of the information processing means.

[0569] "Display device means" refers to a screen or interface for notifying the user of the generated health management suggestions.

[0570] A "feedback collection method" is a function that obtains responses and opinions from users regarding suggestions and uses them to improve the quality of those suggestions.

[0571] "Physiological indicators" are data that constitute a part of health information, indicating the user's physical condition, such as heart rate and body temperature.

[0572] "Machine learning techniques" are algorithms and models used to effectively analyze health and emotional information, identifying patterns and trends from data.

[0573] The system implementing this invention comprehensively analyzes the user's health and emotional information and generates personalized health management suggestions. The system is configured as follows:

[0574] First, the user utilizes a communication device, specifically a wearable device such as a smartphone or smart glasses. This device collects health information representing physiological indicators such as heart rate and activity level. Furthermore, it collects the user's voice and text data using the device's built-in microphone and text input function, and an emotion engine analyzes this emotional information.

[0575] This data is encrypted by a communication device and then transmitted to a server. The server uses information processing to analyze health and emotional information. This process utilizes machine learning technology to quantify the user's emotional state and evaluate it in relation to their health state. Then, a suggestion generation device generates personalized health management suggestions based on these analysis results.

[0576] The generated health management suggestions are communicated to the user via a display device. The user can then incorporate the suggestions into their daily life. Furthermore, a feedback collection device allows the user to provide feedback to the system regarding their reactions to the suggestions. This feedback helps the system evolve and contributes to the generation of better suggestions in the future.

[0577] For example, the system can notify a user experiencing high stress levels that they need relaxation and suggest yoga stretches or relaxing music. An example of a prompt message used by the generative AI model in this case would be: "The user is experiencing high stress levels and needs relaxation. Please suggest appropriate exercises or relaxation methods."

[0578] In this way, users can receive suggestions that comprehensively consider their health and emotional state, thereby improving their quality of life.

[0579] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0580] Step 1:

[0581] The device collects health information from the user. At this stage, sensors in smartphones or wearable devices acquire physiological indicators such as heart rate and activity level, and input this data as health information. The output is the collected health information data.

[0582] Step 2:

[0583] The device collects user voice and text data as emotional information. It acquires voice and text data using a microphone and text input function, and an emotion engine analyzes this data to output the user's emotional information. The output is the analyzed emotional information.

[0584] Step 3:

[0585] The device encrypts the health information obtained in Step 1 and the emotional information obtained in Step 2 and sends them to the server. The output is the encrypted health information and emotional information.

[0586] Step 4:

[0587] The server decrypts the received encrypted data and analyzes it using information processing tools. This analysis utilizes machine learning techniques to assess the user's overall health and emotional state based on the input data. The output is evaluation data, including the analysis results.

[0588] Step 5:

[0589] The server generates personalized health management suggestions based on the analysis results. The suggestion generation mechanism takes evaluation data as input and outputs health suggestions tailored to the user's needs. The output is the generated health management suggestion.

[0590] Step 6:

[0591] The server sends the generated health management suggestions to the terminal, which then notifies the user using a display device. The user can then view these suggestions on the displayed screen. The output is the health management suggestions displayed to the user.

[0592] Step 7:

[0593] Users provide feedback to the system, including their opinions and reactions to the system's health management suggestions. The terminal collects this feedback as input and sends it to the server as data to improve the quality of the system's suggestions. The output is the feedback data.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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".

[0611] This invention is a system that provides individually customized health management suggestions using a user's health data. This system works by collecting health data using wearable devices or smartphones worn by the user and transmitting the data to a server via the internet.

[0612] Users first record their daily activity data through their smartphones or wearable devices. This includes heart rate, steps taken, body temperature, and photos of meals. This data is collected within the system by the device and sent to the server in real time or at specified intervals.

[0613] The server analyzes the received data to comprehensively assess the user's health status. This includes identifying abnormal patterns by comparing the data to normal values ​​and performing long-term trend analysis. Furthermore, it extracts nutritional information based on photos of meals to understand the user's nutrient intake trends.

[0614] Based on the analysis results, the server generates personalized health management advice tailored to the user's needs. For example, if the user is deficient in certain nutrients, it will suggest a meal plan to compensate for that deficiency. If the server determines that the user is not getting enough exercise, it can provide an exercise plan that is easy to incorporate into daily life. Furthermore, if necessary, it may recommend online medical consultations or encourage the user to consider specific insurance products.

[0615] The generated suggestions are notified to the user through a dedicated application on their smartphone. By referring to these suggestions, users can put the information they receive into practice in their daily lives and use it to help them lead a healthier lifestyle.

[0616] The implementation of this invention enables the utilization of a wide range of health data, facilitating users to easily understand their own health status and take appropriate actions. In this way, it is possible to improve the overall efficiency of health management.

[0617] The following describes the processing flow.

[0618] Step 1:

[0619] The device collects vital data and photos of meals through the user's wearable device or smartphone. This data includes heart rate, steps taken, calories burned, body temperature, and details of meals eaten.

[0620] Step 2:

[0621] The device encrypts the collected health data and transmits it to the server via the internet. Secure communication protocols are used to protect data privacy during this process.

[0622] Step 3:

[0623] The server receives health data sent from the terminal and stores it in a database. The stored data is then prepared for use in subsequent analysis processes.

[0624] Step 4:

[0625] The server analyzes stored health data using machine learning algorithms and image recognition technology. This allows it to evaluate the user's health patterns, diet, and nutrient intake.

[0626] Step 5:

[0627] The server evaluates the user's health status based on the analysis results and generates personalized health management advice. This advice includes suggestions for dietary improvements, exercise plans, and, if necessary, suggestions for online medical consultations.

[0628] Step 6:

[0629] The server sends the generated health management advice to the user's device.

[0630] Step 7:

[0631] The device notifies the user of advice received from the server. The user can then review the suggestions through the application and incorporate them into their daily life.

[0632] (Example 1)

[0633] 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".

[0634] In modern society, it is crucial for individuals to accurately understand their own health status and manage their health appropriately based on that understanding. However, there is a lack of means to efficiently collect diverse biometric information and provide personalized health recommendations based on that information. Conventional systems are limited to analyzing only a portion of the data, making it difficult to achieve comprehensive health promotion.

[0635] 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.

[0636] In this invention, the server includes a device means for acquiring biometric information from a user, an information processing device means for processing the biometric information acquired from the device means, and a proposal generation means for providing personalized health management suggestions to the user based on the processing results. This makes it possible to comprehensively analyze a wide range of the user's health data and quickly provide specific health suggestions that meet the individual needs of the user.

[0637] A "user" is an individual or organization that uses the system to record health information and receives suggestions based on the analysis results.

[0638] "Biometric information" refers to all data that represents the user's health status, including heart rate, steps taken, body temperature, and records of meals eaten.

[0639] "Device means" refers to equipment or devices used to acquire biometric information from a user, and includes wearable devices and smartphones.

[0640] "Information processing device means" refers to a central processing unit or server system used to analyze acquired biological information and evaluate health status.

[0641] "Proposal generation means" refers to a function or process that creates specific proposals for health management based on analysis results generated by an information processing device.

[0642] "Notification means" refers to methods or system functions for communicating proposals created by the proposal generation means to the user and prompting them to take necessary actions.

[0643] A "generative AI model" is an artificial intelligence system that utilizes machine learning technology to automatically analyze input data and generate suggestions.

[0644] This invention is a system that provides personalized health management suggestions using a user's biometric information. This system primarily utilizes device means, information processing means, suggestion generation means, and notification means.

[0645] First, users collect daily biometric information using a device. This device includes wearable devices and smartphones, and acquires data such as heart rate, steps taken, body temperature, and photos of meals. Wearable devices can record data in real time using built-in sensors and store it in a dedicated application.

[0646] The collected data is transmitted from the terminal to an information processing device. This information processing device is located on a server system and performs data analysis. The analysis uses a generative AI model to compare the current data with past data, detect outliers, and evaluate long-term health trends. This analysis provides a comprehensive understanding of the user's health status.

[0647] Based on the analysis results, the suggestion generation mechanism is activated. The server creates personalized health management suggestions according to the user's current health status. This includes suggestions for dietary improvements and exercise plans. The generating AI model provides specific and practical advice by considering the user's preferences and lifestyle.

[0648] Ultimately, suggestions are communicated to the user through a notification system. Users can review the suggestions via a dedicated application and use them to improve their daily health management. These suggestions are delivered instantly via push notifications, allowing users to take action to improve their health based on the information.

[0649] For example, for a user diagnosed with a lack of exercise, the server can suggest an exercise plan that can be easily done indoors. Furthermore, if a user is lacking in nutrients, the server can provide a list of specific foods or recipes.

[0650] An example of a prompt for the generating AI model is: "Analyze user C's recent health data and suggest any particularly noteworthy abnormal patterns and health management advice based on them."

[0651] By implementing this system, users can effectively utilize their health data and achieve personalized health management.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] Users collect biometric information using wearable devices and smartphones. Inputs include data such as heart rate, steps taken, body temperature, and photos of meals. Specifically, users wear these devices during their daily lives and acquire data using a dedicated application. The output is biometric information stored within the device.

[0655] Step 2:

[0656] The terminal transmits the collected biometric information to the server. The input is the various biometric information recorded in step 1. Specifically, the terminal sends the data via an internet connection. The data is encrypted using a security protocol. The output is the biometric information received by the server.

[0657] Step 3:

[0658] The server analyzes the received biometric information. The input is the biometric information sent in step 2. Specifically, the server uses a generative AI model to detect anomalies and analyze health trends. Each data point is compared to the normal range using a machine learning algorithm. The output is the analysis results regarding the user's health status.

[0659] Step 4:

[0660] The server generates health management suggestions based on the analysis results. The input is the analysis results from step 3. Specifically, the server utilizes the suggestion generation mechanism to create personalized health management advice tailored to the user's lifecycle and preferences. The output is specific health suggestions for the user.

[0661] Step 5:

[0662] The device notifies the user of the generated health recommendations. The input is the health recommendations generated in step 4. Specifically, the device sends a push notification to the user through a dedicated application. The output is the user who received the notified health recommendations.

[0663] This process allows users to efficiently manage their own health.

[0664] (Application Example 1)

[0665] 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".

[0666] In modern life, it is crucial to provide health guidance tailored to individual users and health-promoting activities in their daily lives. However, many health management systems offer only standardized suggestions and struggle to address individual needs. Furthermore, users lack efficient means of monitoring and maintaining their health status within their homes. To address these challenges, there is a need for a system that operates autonomously and provides advanced analysis and suggestions based on individual health data.

[0667] 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.

[0668] In this invention, the server includes an information processing device means for acquiring biometric information from a user, a computing device means for analyzing the biometric information acquired from the information processing device means, and a suggestion generation means for providing health guidance suggestions to the user based on the analysis results. This enables the provision of customized health suggestions to each user, allowing for autonomous health management within the home.

[0669] "Information processing device means" refers to a device and its functions that acquire biometric information from a user.

[0670] "Computation device means" refers to a device and its functions that perform calculations for analyzing acquired biological information.

[0671] "Suggestion generation method" refers to a function that generates health guidance and suggestions for the user based on the analysis results.

[0672] "Output device means" refers to a device and its function for presenting the generated proposal to the user in audio or visual form.

[0673] "Autonomous mobility means" refers to a function that operates autonomously, moves around the user's surroundings, and collects information supplementarily.

[0674] "Plan generation means" refers to a function that proposes health promotion activities in daily life based on the analysis results.

[0675] This invention utilizes an information processing device to acquire the user's biometric information. This information processing device is incorporated into a wearable device worn by the user or into a consumer robot installed in the home. This device collects vital data such as heart rate, body temperature, and activity level in real time.

[0676] The server receives this data via the internet and analyzes it using computing devices. Machine learning models (e.g., TensorFlow) are used for the analysis to evaluate the user's health status and long-term trends.

[0677] The suggestion generation means generates suggestions based on the analysis results that are useful for the user's health guidance and lifestyle improvement. These suggestions are presented to the user via an output device means, either by voice or display. The autonomous mobility means allows the robot to patrol the user's home, collecting additional environmental information to aid in health management.

[0678] For example, while a user is getting ready in the morning, the robot might suggest, "Why not add a little more protein to your breakfast?" and even provide specific food examples such as eggs and tofu. Furthermore, the generative AI model uses the user's daily activity records to offer motivational advice such as, "Let's continue with light jogging twice a week."

[0679] Examples of prompts when using the generative AI model include: "Based on the user's recent health data, please suggest an exercise plan for the weekend," and "Based on the user's nutritional intake data, please suggest a dinner menu for this week."

[0680] This system allows users to easily monitor their health status in their daily lives and achieve a healthier lifestyle through appropriate health-promoting activities.

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] The device collects the user's biometric information.

[0684] The input is sensor signals from the wearable device worn by the user, which are recorded as vital data by the acquisition device. The output provides biometric information such as heart rate, body temperature, and activity level. This data is stored in the terminal in real time.

[0685] Step 2:

[0686] The device sends the collected biometric information to the server.

[0687] The input is a dataset of biometric data obtained in Step 1, and this data is sent to the server via Wi-Fi. The output here is the comprehensive biometric data received by the server. This process ensures that data is collected on the server in a timely manner.

[0688] Step 3:

[0689] The server analyzes the biometric information it receives.

[0690] The input consists of all biometric data sent to the server in Step 2. A generative AI model (e.g., TensorFlow) is used to detect anomalies and perform trend analysis on the data. The output is a health status assessment derived from the analyzed data. This assessment provides analysis results tailored to the user's health condition.

[0691] Step 4:

[0692] The server generates health guidance suggestions based on the analysis results.

[0693] The input is the health status assessment analyzed in step 3. Based on this, the suggestion generation system plans individual health guidance for the user. The output includes specific dietary advice and exercise plans. The generated suggestions are tailored to the user's lifestyle.

[0694] Step 5:

[0695] The server generates suggestions which are then presented to the user via the terminal.

[0696] The input is the health guidance suggestion generated in step 4. The terminal notifies the user of this through voice guidance and display. The output is a presentation of the health suggestion in a format that the user can easily understand. This step allows the user to respond appropriately to the suggested actions.

[0697] Step 6:

[0698] Robots, as autonomous means of transportation, supplementarily collect environmental information.

[0699] The input consists of information from devices and existing home sensors. Based on this information, the robot continues to collect data while moving around the home. As output, additional information about the environment is sent to the server. This step ensures that environmental factors necessary for health management are taken into consideration.

[0700] 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.

[0701] This invention is a system that provides personalized health management suggestions by utilizing a user's health data and emotional information. This system uses an emotion engine installed in the user's terminal to collect and analyze daily emotional data, integrate it with health data, and comprehensively evaluate the user's health status.

[0702] Users utilize smartphones and wearable devices in their daily lives, and these devices collect vital data. Furthermore, these devices acquire user voice and text data through microphones and text input. This allows an emotion engine to analyze the user's emotional state and evaluate, for example, stress levels and happiness levels.

[0703] The device encrypts the collected health and emotional data and sends it to the server. The server stores the received data in a database and performs detailed analysis using machine learning algorithms. This analysis generates health management advice that takes into account the user's emotional state while maintaining healthy patterns. Specifically, if a high-stress state is detected, the advice can focus on relaxation exercises and dietary recommendations.

[0704] The generated health management suggestions are notified to the user's device. Users can review these suggestions through the application and use them as a reference when adjusting their daily activities. The system also regularly collects user feedback and uses it to improve the quality of the suggestions.

[0705] This invention combines sentiment analysis with conventional health data to enable more detailed and personalized health management. This allows users to take more appropriate actions, contributing to maintaining health and improving their quality of life.

[0706] The following describes the processing flow.

[0707] Step 1:

[0708] The device collects vital data such as heart rate, steps taken, and body temperature in real time through the user's smartphone or wearable device.

[0709] Step 2:

[0710] The device acquires emotional data through the user's voice or text input. The emotion engine analyzes this data to identify the user's emotional state.

[0711] Step 3:

[0712] The device integrates collected health and emotional data, encrypts it, and then sends it to the server. Secure communication protocols are used to protect data privacy.

[0713] Step 4:

[0714] The server stores the received data in a database. Then, machine learning algorithms are used to analyze this data and evaluate the user's health status and emotional tendencies.

[0715] Step 5:

[0716] Based on the analysis results, the server generates personalized health management suggestions for each user. The suggestions can be adjusted according to the user's current emotional state, potentially including stress reduction techniques or dietary improvements.

[0717] Step 6:

[0718] The server sends the generated suggestions to the user's terminal.

[0719] Step 7:

[0720] The device notifies the user of received suggestions and displays details using a dedicated application.

[0721] Step 8:

[0722] Users improve their emotional and physical state by reviewing the suggested health management advice and putting it into practice in their daily lives.

[0723] (Example 2)

[0724] 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".

[0725] Conventional health management systems simply collect and present users' health information, making it difficult to provide personalized health management advice that takes into account mental states such as emotions and stress levels. This creates a challenge in obtaining specific and effective advice to improve users' quality of life.

[0726] 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.

[0727] In this invention, the server includes an information terminal means for acquiring health information from the user, an analysis means for analyzing the acquired voice and text information to evaluate the emotional state, and a computation means for analyzing health and emotional information using machine learning techniques. This makes it possible to provide personalized health management suggestions that take into account not only the user's physical state but also their mental state.

[0728] An "information terminal device" is a device that has the function of collecting health information, voice, and text information from users, and analyzing or transmitting that information.

[0729] "Analysis means" refers to a device or program used to analyze collected audio and text information and evaluate the user's emotional state.

[0730] "Transmission means" refers to a function used to securely transmit health information and emotional information acquired by an information terminal to a server.

[0731] "Computation means" refers to a device or function that stores health information and emotional information received on a server and analyzes it in detail using machine learning techniques.

[0732] The "proposal generation means" is a function that generates personalized health management suggestions for the user based on the analysis results.

[0733] "Communication means" refers to a device or function used to notify the user of the generated health management suggestions.

[0734] This invention is a system that integrates a user's health and emotional information to provide personalized health management suggestions. First, the user collects biometric information such as heart rate, steps taken, and sleep duration using a smartphone or wearable device. These information terminals then acquire voice and text data via microphones and text input, and evaluate the user's emotional state using built-in emotion analysis tools. This analysis uses a combination of natural language processing tools and speech processing algorithms to identify the user's stress level and well-being.

[0735] The device encrypts the analyzed emotional and health information using the SSL / TLS protocol and sends it to the server. The server stores the received data in a database and performs detailed data analysis using Python-based machine learning frameworks (e.g., TensorFlow, PyTorch). Specifically, it generates personalized health management suggestions based on the user's recent physical condition and mood through data pattern recognition and modeling of the relationship between emotions and health status.

[0736] The generated suggestions are notified to the user's device via communication. Upon receiving this notification, the user can use the application to review the suggestions and utilize them to improve their daily behavior. For example, if the analysis results indicate that the user is experiencing excessive stress, the suggestion generation system might recommend "meditating for 10 minutes every morning." An example of a prompt to the generating AI model could be a question such as, "What actions would you suggest to reduce the user's stress?"

[0737] This invention enables users to receive more detailed and reliable advice regarding their physical and mental health, thereby contributing to an improved quality of life.

[0738] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0739] Step 1:

[0740] Users collect biometric information using smartphones and wearable devices. Specifically, information such as heart rate, steps taken, and sleep duration are acquired as input data. This data is collected by information terminals within the device and treated as basic biometric data to indicate health status.

[0741] Step 2:

[0742] The device acquires voice and text data through the microphone and text input. Input data includes voice messages such as "How are you feeling today?" and text entered into a memo app. Based on this, the device's emotion analysis system uses natural language processing tools to evaluate the emotional state and outputs stress levels and happiness levels. This output data is recorded as the user's emotional information.

[0743] Step 3:

[0744] The device encrypts the collected health and emotional information using the SSL / TLS protocol and sends it to the server. In this process, encrypted data is used as input and securely transferred to the server. The output is the encrypted data received on the server side.

[0745] Step 4:

[0746] The server stores the received data in a database. In this step, the raw data, which has been decrypted, becomes the input. The output is the stored data. After storage, the server analyzes the data using machine learning tools. Specifically, data pattern recognition is performed using TensorFlow. As a result of the analysis, feature data related to the user's health and emotional state is output.

[0747] Step 5:

[0748] The server generates personalized health management suggestions based on the analysis results. The input is the analyzed feature data, and the generating AI model inputs prompt sentences. For example, these might include questions such as, "What is the best relaxation method for a user experiencing high stress?" The output is specific health management suggestions.

[0749] Step 6:

[0750] The server sends the generated suggestions to the terminal via a communication method. The input is the generated health management suggestions, and the output is notification information displayed on the user's terminal. The user can review this information and incorporate the suggested actions into their daily life.

[0751] (Application Example 2)

[0752] 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".

[0753] In user health management, conventional systems are based on generalized health data and are unable to provide suggestions that adequately consider each individual's emotional state. Therefore, there is a need for a system that can provide more detailed and personalized health advice and improve quality of life.

[0754] 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.

[0755] In this invention, the server includes a communication device means for acquiring health information and emotional information from the user, an information processing means for analyzing the health information and emotional information acquired from the communication device means, and a proposal generation means for providing personalized health management proposals based on the analysis results. This makes it possible to provide personalized health management proposals that comprehensively consider the user's health and emotional state.

[0756] A "user" refers to an individual who uses a system.

[0757] "Health information" refers to data that includes physiological indicators such as heart rate and activity level, and represents the user's physical condition.

[0758] "Emotional information" refers to data such as stress levels and happiness levels, which are analyzed from the user's voice and text data.

[0759] "Communication device means" refers to a device or interface for collecting health information and emotional information from users.

[0760] "Information processing means" refers to technologies or software functions for analyzing collected health and emotional information and evaluating the user's health and emotional state.

[0761] The "proposal generation means" is a function that creates personalized health management proposals based on the analysis results of the information processing means.

[0762] "Display device means" refers to a screen or interface for notifying the user of the generated health management suggestions.

[0763] A "feedback collection method" is a function that obtains responses and opinions from users regarding suggestions and uses them to improve the quality of those suggestions.

[0764] "Physiological indicators" are data that constitute a part of health information, indicating the user's physical condition, such as heart rate and body temperature.

[0765] "Machine learning techniques" are algorithms and models used to effectively analyze health and emotional information, identifying patterns and trends from data.

[0766] The system implementing this invention comprehensively analyzes the user's health and emotional information and generates personalized health management suggestions. The system is configured as follows:

[0767] First, the user utilizes a communication device, specifically a wearable device such as a smartphone or smart glasses. This device collects health information representing physiological indicators such as heart rate and activity level. Furthermore, it collects the user's voice and text data using the device's built-in microphone and text input function, and an emotion engine analyzes this emotional information.

[0768] This data is encrypted by a communication device and then transmitted to a server. The server uses information processing to analyze health and emotional information. This process utilizes machine learning technology to quantify the user's emotional state and evaluate it in relation to their health state. Then, a suggestion generation device generates personalized health management suggestions based on these analysis results.

[0769] The generated health management suggestions are communicated to the user via a display device. The user can then incorporate the suggestions into their daily life. Furthermore, a feedback collection device allows the user to provide feedback to the system regarding their reactions to the suggestions. This feedback helps the system evolve and contributes to the generation of better suggestions in the future.

[0770] For example, the system can notify a user experiencing high stress levels that they need relaxation and suggest yoga stretches or relaxing music. An example of a prompt message used by the generative AI model in this case would be: "The user is experiencing high stress levels and needs relaxation. Please suggest appropriate exercises or relaxation methods."

[0771] In this way, users can receive suggestions that comprehensively consider their health and emotional state, thereby improving their quality of life.

[0772] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0773] Step 1:

[0774] The device collects health information from the user. At this stage, sensors in smartphones or wearable devices acquire physiological indicators such as heart rate and activity level, and input this data as health information. The output is the collected health information data.

[0775] Step 2:

[0776] The device collects user voice and text data as emotional information. It acquires voice and text data using a microphone and text input function, and an emotion engine analyzes this data to output the user's emotional information. The output is the analyzed emotional information.

[0777] Step 3:

[0778] The device encrypts the health information obtained in Step 1 and the emotional information obtained in Step 2 and sends them to the server. The output is the encrypted health information and emotional information.

[0779] Step 4:

[0780] The server decrypts the received encrypted data and analyzes it using information processing tools. This analysis utilizes machine learning techniques to assess the user's overall health and emotional state based on the input data. The output is evaluation data, including the analysis results.

[0781] Step 5:

[0782] The server generates personalized health management suggestions based on the analysis results. The suggestion generation mechanism takes evaluation data as input and outputs health suggestions tailored to the user's needs. The output is the generated health management suggestion.

[0783] Step 6:

[0784] The server sends the generated health management suggestions to the terminal, which then notifies the user using a display device. The user can then view these suggestions on the displayed screen. The output is the health management suggestions displayed to the user.

[0785] Step 7:

[0786] Users provide feedback to the system, including their opinions and reactions to the system's health management suggestions. The terminal collects this feedback as input and sends it to the server as data to improve the quality of the system's suggestions. The output is the feedback data.

[0787] 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.

[0788] 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.

[0789] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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."

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] The following is further disclosed regarding the embodiments described above.

[0809] (Claim 1)

[0810] A terminal means for acquiring health data from users,

[0811] A server means for analyzing health data acquired from the terminal means,

[0812] A proposal generation means that provides health management suggestions to the user based on the aforementioned analysis results,

[0813] A display means for presenting the aforementioned proposal to the user,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, wherein the terminal means collects the user's vital data.

[0817] (Claim 3)

[0818] The system according to claim 1, wherein the server means analyzes the health data using a machine learning algorithm.

[0819] "Example 1"

[0820] (Claim 1)

[0821] A device means for acquiring biometric information from a user,

[0822] Information processing device means for processing biological information acquired from the aforementioned device means,

[0823] A proposal generation means that provides personalized health management suggestions to the user based on the processing results,

[0824] A notification means for notifying the user of the aforementioned proposal,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, wherein the device means collects data on the user's daily activities.

[0828] (Claim 3)

[0829] The system according to claim 1, wherein the information processing device analyzes the biological information using a generated AI model.

[0830] "Application Example 1"

[0831] (Claim 1)

[0832] Information processing device means for acquiring biometric information from a user,

[0833] A computing device means for analyzing biological information acquired from the aforementioned information processing device means,

[0834] A proposal generation means that provides health guidance suggestions to the user based on the aforementioned analysis results,

[0835] Output device means for presenting the aforementioned proposal to the user audibly or visually,

[0836] An autonomous mobility device that moves autonomously around the user and supplementarily collects health information,

[0837] Based on the aforementioned analysis results, a plan generation means for proposing health promotion activities in daily life,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the information processing device means collects the user's biometric data and lifestyle activity information.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the computing device means analyzes the biological information and lifestyle activity information using a machine learning model.

[0843] "Example 2 of combining an emotion engine"

[0844] (Claim 1)

[0845] An information terminal means for acquiring health information from users,

[0846] An analysis means for analyzing voice and text information acquired by the information terminal means to evaluate the emotional state,

[0847] A transmission means that encrypts and transmits health information and emotional information acquired from the aforementioned information terminal means,

[0848] The transmitted information is stored in a database, and a computing means is used to analyze health and emotional information using machine learning techniques.

[0849] Based on the analysis results, a proposal generation means generates personalized health management suggestions for the user,

[0850] A communication means for notifying the user of the aforementioned proposal,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, wherein the information terminal means collects the user's biometric information.

[0854] (Claim 3)

[0855] The system according to claim 1, wherein the calculation means inputs a generative model to generate health management proposals.

[0856] "Application example 2 when combining with an emotional engine"

[0857] (Claim 1)

[0858] A communication device means for acquiring health information and emotional information from a user,

[0859] Information processing means for analyzing health information and emotional information acquired from the aforementioned communication device means,

[0860] A proposal generation means that provides personalized health management suggestions based on the aforementioned analysis results,

[0861] A display device for notifying the user of the aforementioned proposal,

[0862] A feedback collection means for obtaining user feedback and improving the quality of the aforementioned proposal,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, wherein the communication device means collects the user's physiological indicators.

[0866] (Claim 3)

[0867] The system according to claim 1, wherein the information processing means analyzes the health information and emotional information using machine learning technology. [Explanation of Symbols]

[0868] 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. A terminal means for acquiring health data from users, A server means for analyzing health data acquired from the terminal means, A proposal generation means that provides health management suggestions to the user based on the aforementioned analysis results, A display means for presenting the aforementioned proposal to the user, A system that includes this.

2. The system according to claim 1, wherein the terminal means collects the user's vital data.

3. The system according to claim 1, wherein the server means analyzes the health data using a machine learning algorithm.

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