system

Abstract

We provide the system. [Solution] A device for inputting and collecting health-related data, A communication module means for transmitting collected health-related data to the cloud, A database method for storing health-related data on the cloud and managing it as a history, A generation system means for analyzing stored health-related data and evaluating health status and potential health risks, An advice generation unit means that generates personalized health advice based on the analysis results, A system that includes a device to notify the user of the generated advice.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method 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 as a 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] In the conventional annual health check, it is difficult to quickly grasp changes in daily living habits and potential health risks. In addition, especially for the elderly and visually impaired, there is a problem that it is difficult to obtain appropriate feedback for health promotion because there are few means to easily input and manage health information.

Means for Solving the Problems

[0005] This invention provides a system that automatically generates personalized health advice on a continuous basis by combining a device for inputting and collecting health-related data with an analysis system utilizing generation AI. This system can be easily used by the elderly and visually impaired by using an input device equipped with voice recognition functionality. Furthermore, it supports the user's health maintenance by notifying the user's device of the health advice generated based on the analysis results and providing a reminder function to help improve their lifestyle.

[0006] "Health-related data" refers to information used to evaluate an individual's health status, such as diet, exercise levels, sleep duration, weight, and blood pressure.

[0007] An "input device" refers to a device that allows users to input health-related data using voice or text.

[0008] A "communication module" refers to a device that has the function of transmitting data from an input device to a cloud server.

[0009] A "database" refers to a system built on the cloud for storing and managing users' health-related data as a historical record.

[0010] A "generation system" refers to a system that utilizes AI technology to analyze data stored in a cloud-based database and assess the user's health status and potential risks.

[0011] The "advice generation unit" refers to a function that creates personalized health advice for users based on the analysis results from the generation system.

[0012] A "device" refers to a device used to notify the user of the advice generated as a result of the analysis.

[0013] "Voice recognition function" refers to the technology that converts information entered via voice into text.

[0014] A "reminder function" refers to a feature that notifies users at the appropriate time to help them achieve their daily health goals. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0016] 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.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is a system that monitors an individual's health status on a daily basis and provides real-time health advice using generated AI.

[0037] System Configuration

[0038] 1. User device configuration:

[0039] Users register their daily health-related data on the device through voice or text input. The device features voice recognition capabilities, making it accessible to the elderly and visually impaired.

[0040] For example, if a user says aloud, "I had toast and coffee this morning and went for a one-hour jog," that information is converted into text and recorded on the device.

[0041] 2. Data transmission and management:

[0042] The terminal has a built-in communication module, and the collected data is periodically sent to a cloud server. The transmitted data is stored in a cloud database and recorded as historical data.

[0043] This will enable continuous monitoring of the user's health status.

[0044] 3. Analysis using Generative AI:

[0045] The server retrieves information from a cloud database and analyzes the data using generative AI. This allows for an assessment of the user's health status and potential health risks.

[0046] For example, if a user's sleep time falls below 6 hours for a week straight, it may be determined that they have a weakened immune system.

[0047] 4. Generating personalized health advice:

[0048] Based on the analysis results, the server generates personalized health advice for the user. This advice includes suggestions for dietary improvements and exercise plans.

[0049] As a concrete example, specific action guidelines such as "adding fruit containing vitamin C to your breakfast" are proposed.

[0050] 5. Notification of advice and support for implementation:

[0051] The generated health advice is sent to the user's device and notified via voice and text. The device also has a reminder function to prompt action based on the advice.

[0052] For example, an alarm is set to remind you to go to bed at a time that improves sleep quality.

[0053] This system allows users to easily manage their health information in their daily lives and receive support to continue personalized health promotion activities.

[0054] The following describes the processing flow.

[0055] Step 1:

[0056] Users input health-related data via voice or text. The device accepts this input and, in the case of voice data, uses speech recognition to convert it to text. The converted data is formatted and temporarily stored on the device.

[0057] Step 2:

[0058] The device transmits formatted health-related data to a cloud server based on predetermined conditions (e.g., set time intervals). The data is securely transmitted via a communication module.

[0059] Step 3:

[0060] The server stores the received data in a cloud-based database. The data is linked to past records and managed as consistent historical information.

[0061] Step 4:

[0062] The server activates a generating AI to analyze the data stored in the database. The AI ​​assesses the user's health status and identifies health risks based on past trends.

[0063] Step 5:

[0064] The server generates personalized health advice based on the analysis results from the generated AI. This advice includes an action plan tailored to the user and offers suggestions for improving their lifestyle.

[0065] Step 6:

[0066] The device notifies the user of health advice received from the server. Notifications are delivered via both voice and text, and reminders can be set according to the user's situation. Users can adjust their daily activities based on these notifications.

[0067] (Example 1)

[0068] 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."

[0069] In modern society, it is crucial to continuously monitor individual health conditions and implement appropriate health management. However, conventional systems struggle to provide personalized health advice and adequate support for users' daily lives. Therefore, there is a need for a means to effectively monitor individual health conditions and provide real-time advice.

[0070] 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.

[0071] In this invention, the server includes terminal means for inputting and collecting health-related information, communication means for transmitting the collected health-related information to a remote location, and information management device means for storing the health-related information at the remote location and managing it as a history. This makes it possible to provide health advice tailored to each individual user in real time and support its implementation in daily life.

[0072] A "terminal for inputting and collecting health-related information" is a device that can receive voice or text information from a user, convert it into an appropriate data format, and store it.

[0073] "Communication means" refers to the technology or device used to transmit collected information to a remote server, thereby enabling real-time information transmission.

[0074] An "information management device" is a system that stores received health-related information and manages it as a historical record, making it easier to search and analyze the data.

[0075] A "generation system" is a device or software that analyzes stored health-related information to assess an individual's health status and potential health risks.

[0076] A "health guidance generation device" is a device or program that creates optimal health advice for individual users based on the analysis results from a generation system.

[0077] A "terminal" is a device equipped with functions to notify users of generated instructions and prompt them to take action as needed.

[0078] In an embodiment of this invention, a system is specifically described that monitors an individual's health status on a daily basis and provides real-time health advice using a generative AI model.

[0079] User device configuration

[0080] Users input health-related data using a dedicated terminal. The terminal is equipped with voice recognition capabilities, allowing users to provide data via voice or text. For example, if a user voice-inputs, "I ate a salad with lots of vegetables today," the terminal converts that information into text and records it. The terminal is designed to be user-friendly for a wide range of users.

[0081] Data transmission and management

[0082] Data is transmitted from the device to the cloud. A wireless communication module is used as the means of communication. The transmitted data is stored in an information management device on the cloud and managed as each user's past health information. This process enables continuous tracking of the user's health information.

[0083] Analysis by Generative AI

[0084] The server retrieves data from a cloud-based information management system and analyzes it in real time using a generative AI model. The analysis evaluates sleep patterns, eating habits, exercise levels, and other factors to reveal the user's health status and potential risks. For example, a prolonged lack of exercise might indicate a risk of weight gain.

[0085] Generating personalized health advice

[0086] Based on the analysis results of the generated AI model, the server generates personalized health advice for each user. For example, it might provide advice such as, "Since exposure to sunlight promotes the production of vitamin D, we recommend taking a 10-minute walk every day."

[0087] Notification of advice and support for implementation

[0088] The generated health advice is sent to the user's device, and the user receives notifications via voice and text. The device also has a built-in reminder function to encourage the user to follow the suggested health plan on a daily basis. For example, a notification might say, "Make sure to set aside time for relaxation at 8 PM."

[0089] Example of a prompt

[0090] "Based on the user's health data, generate appropriate lifestyle improvement suggestions. For example, provide specific advice on how the user can increase their daily activity level."

[0091] In this way, this system allows users to receive personalized health suggestions tailored to their daily activities, enabling them to engage in individualized health maintenance activities on a daily basis.

[0092] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0093] Step 1:

[0094] Users input daily health-related information into the device via voice or text. The device uses speech recognition technology to convert the voice data into text and saves it in a standardized format. This input data includes information such as diet and exercise time.

[0095] Step 2:

[0096] The device transmits converted and standardized health-related information to a cloud server. During this process, the data is encrypted using communication methods and transmitted securely. As output, the data is notified to the cloud and managed for subsequent analysis.

[0097] Step 3:

[0098] The server retrieves data transmitted from the cloud-based information management device. It then uses a generative AI model to analyze the retrieved data. The analysis evaluates the user's health status based on the input data, identifying potential risks and areas for improvement.

[0099] Step 4:

[0100] The server generates personalized health advice for the user based on the analysis results of the generated AI model. Specifically, it generates plans such as diets that increase certain nutrients and regular exercise routines. The generated advice is provided as output.

[0101] Step 5:

[0102] The device transmits health advice received from the server to the user. Notifications are delivered in both voice and text formats, providing specific action guidelines. A reminder function sets alarms to help the user act on the advice.

[0103] This processing flow allows users to receive personalized health advice on a daily basis and use it to improve their lifestyle habits.

[0104] (Application Example 1)

[0105] 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."

[0106] In personal health management, there is a need for methods to easily collect and analyze daily health data, and then efficiently provide personalized health advice and recommend the purchase of related products. However, conventional technologies lack the means to consistently provide these services, and the connection between purchasing activities such as health foods and health management is particularly insufficient.

[0107] 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.

[0108] In this invention, the server includes a device means for inputting and collecting health-related data, an information communication system means for transmitting the collected health-related data to the cloud, and means for suggesting health-related products based on the user's health information and for purchasing those products through electronic payment. This enables efficient personal health management and the purchase of related products.

[0109] "Health-related data" refers to information related to an individual's health, such as their diet, exercise, and sleep.

[0110] "Device" refers to equipment composed of mechanical parts and software, used to achieve a specific purpose.

[0111] An "information and communication system" refers to the technical means for collecting, processing, and transmitting data, and for communicating with other devices and networks.

[0112] An "information management system" refers to a database or software that stores, organizes, and easily accesses and analyzes collected data.

[0113] A "generative system" refers to a technological mechanism that analyzes and evaluates collected data to produce new information and results.

[0114] A "guidance creation unit" refers to a device or software that has the function of creating personalized guidance and advice based on analysis results.

[0115] A "notification function" refers to a system of alerts and reminders that inform users of important information and advice.

[0116] The system that implements this application provides advanced functions for health management. When a user registers daily health-related data using a voice input device, that information is transmitted to a cloud-based database via a communication system. Because the voice recognition function uses Google® Speech-to-Text API, it can be used intuitively by visually impaired people and the elderly.

[0117] The cloud-based generation system analyzes input data using generative AI models such as OpenAI® GPT-4® to assess the user's health status. Based on the assessment, the guidance creation unit creates personalized advice regarding diet and exercise and sends it to the user's device via a notification function.

[0118] For example, if a user provides information about their breakfast, the AI ​​will generate specific advice such as, "Considering your nutritional balance today, we recommend purchasing a vitamin supplement." This advice is immediately displayed on the smartphone screen, and the recommended product can be easily purchased through an electronic payment system (e.g., Stripe).

[0119] An example of a prompt message is, "The user ate oatmeal and a banana this morning. Please suggest a fitness plan tailored to today's health condition." This allows users to easily manage their health information and improve their quality of health in their daily lives.

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] Users input health-related data into the device via a voice input device. The input voice is converted to text using the Google Speech-to-Text API. This input data is temporarily stored on the device.

[0123] Step 2:

[0124] The terminal sends the converted text data to a cloud database via the communication system. During this process, the data is properly formatted, encrypted according to security protocols, and then transmitted.

[0125] Step 3:

[0126] The server retrieves user health data from a cloud database and performs data analysis using a generative AI model. OpenAI GPT-4 is used for the analysis, and the input data is processed to evaluate the user's health status and trends.

[0127] Step 4:

[0128] The server's guidance generation unit receives the analysis results and generates personalized health advice. In this process, the AI ​​uses prompts to generate appropriate health advice, such as "Please suggest supplements tailored to the user's nutritional balance."

[0129] Step 5:

[0130] The generated health advice is sent from the server to the user's device and displayed as a notification on the screen. The notification also includes a link for the user to make a purchase. The purchase process is conducted through an electronic payment system to ensure user convenience.

[0131] Step 6:

[0132] When a user purchases a recommended product, the terminal securely completes the transaction via an electronic payment system. The input in this step represents the user's intention to purchase, and the output is a notification that the purchase is complete.

[0133] 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.

[0134] This invention is a system that provides personalized health advice that takes into account not only the user's health-related data but also their emotional state. Specifically, it integrates a series of components including an input device, a communication module, a generative AI, and an emotion engine.

[0135] System Configuration

[0136] 1. User device configuration:

[0137] Users input health-related data via voice or text. The device incorporates voice recognition and an emotion engine to analyze the user's emotions from the input voice and text.

[0138] For example, if a user inputs "I'm busy today and feeling stressed" in a voice that expresses fatigue, the device will convert the speech to text and simultaneously analyze the user's emotions based on that content.

[0139] 2. Data transmission and management:

[0140] The device sends analyzed health-related and emotional data to a cloud server. A communication module is responsible for the secure transfer of this data.

[0141] The transmitted data is stored in a cloud-based database and continuously managed as information, including the user's emotional history.

[0142] 3. Comprehensive analysis using generative AI:

[0143] The server uses both health-related and emotional data to perform analysis with generative AI. By evaluating health status, emotional state, and their correlation, more accurate risk assessment becomes possible.

[0144] For example, if someone is experiencing prolonged stress, it may be determined that they need advice on exercise or relaxation techniques that can help reduce stress.

[0145] 4. Generating health advice:

[0146] Based on the analysis results, the server generates personalized health advice for each user. This advice is based on both health-related data and emotional state, resulting in more practical and effective suggestions.

[0147] As a concrete example, they offer advice such as, "An hour-long walk is effective for reducing stress. Try it to refresh yourself."

[0148] 5. Notification of advice and support for implementation:

[0149] The generated health advice is delivered to the user's device, and notifications are sent via voice and text. A reminder function that takes emotional state into account is also provided to support user motivation.

[0150] For example, you can set an alert that says, "I recommend you go to bed a little earlier today. I've set some relaxing music for you to listen to."

[0151] This system enables users to manage their health comprehensively, addressing not only their physical condition but also changes in their emotions. This allows for health promotion through more personalized support.

[0152] The following describes the processing flow.

[0153] Step 1:

[0154] Users provide daily feedback to the device through health-related data and voice input. The device activates its voice recognition function and converts the voice data into text. Additionally, an emotion engine is activated to analyze the user's emotions based on their voice tone and content.

[0155] Step 2:

[0156] The device collects analyzed health and emotional data, formats it, and sends it to a cloud server according to a communication protocol. The communication module ensures secure data transmission.

[0157] Step 3:

[0158] The server receives data sent to the cloud database and stores it in the database. It updates and manages each user's health and emotional history.

[0159] Step 4:

[0160] The server uses generative AI to comprehensively analyze users' health-related and emotional data. This process evaluates health trends, emotional changes, and their correlations.

[0161] Step 5:

[0162] The server generates personalized health advice based on the analysis results. This specific advice takes into account the user's health and emotional data.

[0163] Step 6:

[0164] The generated advice is notified to the device. The device provides the advice to the user in both voice and text, and supports the user's daily health management by setting reminders tailored to their emotional state.

[0165] (Example 2)

[0166] 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".

[0167] In managing users' health, there is a need to provide comprehensive advice that takes into account not only their individual health status but also their emotional state. However, existing technologies are insufficient in generating advice that considers the user's emotional state, and have not been able to effectively provide the information necessary for promoting health.

[0168] 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.

[0169] In this invention, the server includes means for using a generative AI system that analyzes health-related data and emotional data and evaluates health status and emotional state; means for utilizing an advice generation function that generates personalized health advice based on the analysis results; and means for providing a display device that notifies the user device of the generated advice at a timing that takes into account the emotional state. This enables more effective health management and emotional stabilization by providing health advice optimized for each individual user, taking into account both health status and emotional state.

[0170] "Health-related data" refers to information about a user's physical condition and lifestyle, and is used for health assessment and management.

[0171] "Emotional data" refers to information about a user's emotional and psychological state, and is used for health management and personalized advice.

[0172] An "input device" is a device used by users to input health-related and emotional data, and has the function of collecting voice and text data.

[0173] A "communication module" is a mechanism for transmitting collected data to a cloud structure, and is responsible for the secure transfer of data.

[0174] A "cloud structure" is a data storage and processing platform provided via the internet, which enables long-term data storage and historical management.

[0175] A "generative AI system" is a system that uses artificial intelligence models to analyze stored data and evaluate health and emotional states.

[0176] The "advice generation function" is a unit that generates personalized health advice for each user based on the analysis results.

[0177] A "display device" is a device that notifies the user of generated health advice, and presents information visually and audibly.

[0178] This invention is a system that comprehensively manages a user's health and emotional state and provides personalized health advice. The system includes several key components to appropriately collect and analyze health-related and emotional data and provide information tailored to the user's needs.

[0179] First, the user uses the device to input health-related data and emotional states. This device is equipped with a voice recognition function, which converts the user's voice input into text data. It also uses an emotion engine to analyze the user's emotional state from the input data. For example, if the user inputs "I'm very tired today," the device converts this information from voice to text and determines through emotion analysis that the user is fatigued.

[0180] The analyzed data is sent to a cloud structure via a communication module. In the cloud, the data is securely stored and managed along with its historical data. The server receives the transmitted data and uses a generative AI system to comprehensively analyze the user's health and emotional state. This generative AI initiates the analysis using prompts and generates personalized advice. For example, a prompt might be "Please suggest exercises to reduce stress."

[0181] The generated health advice is created by the advice generation function and sent from the server to the terminal. The terminal notifies the user of the advice via voice and text, and provides support for implementation through a reminder function that takes into account the user's emotional state. For example, it makes specific suggestions to support the user, such as, "I recommend you go to bed early today. I'll play some relaxing music."

[0182] This system enables users to manage their health more effectively and personally, taking into account both their physical and emotional state.

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] The user uses the device to input health-related data and emotional information in voice or text format. The device uses speech recognition to convert the voice data into text data. During this process, it analyzes the input data and uses an emotion engine to determine the user's emotional state. For example, if the user says "I'm tired today," the device converts the voice into text and identifies the emotional state, such as "fatigue." The input for this process is voice data, and the output is the analyzed text data and the result of the emotional state evaluation.

[0186] Step 2:

[0187] The device transmits analyzed health-related and sentiment data to a cloud structure. This communication uses a communication module, and the data is transferred while protected by security protocols. Specifically, when data is input, the communication module becomes active and sends data packets to the cloud server. The input for this process is text data and sentiment assessment results, and the output is data stored in the cloud.

[0188] Step 3:

[0189] The server receives data arriving in the cloud and activates a generative AI model. The server uses prompts to begin analyzing the data and comprehensively assesses health and emotional states. For example, it might use a prompt such as, "What activities are recommended when stress levels are high?" The input for this step is data received from the cloud, and the output is personalized health advice generated through the analysis.

[0190] Step 4:

[0191] The server uses its advice generation function to create personalized health advice based on the analysis results. The server then prepares to send the generated advice to the terminal. In this step, the input is advice based on prompt results, and the output is suggestions tailored to the user's needs.

[0192] Step 5:

[0193] The device notifies the user of received health advice and provides a reminder function that takes into account their emotional state. It provides notifications via voice and text, and displays additional alerts and information as needed. The input for this step is advice data from the server, and the output is specific advice received and acted upon by the user.

[0194] (Application Example 2)

[0195] 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".

[0196] In modern society, personal health management is crucial, and there is a demand for information and advice to support it. However, conventional health-related services often provide uniform advice without considering the user's emotional state or stress level, resulting in a lack of personalized support. Therefore, there is a need for a system that provides more accurate health advice, taking into account the user's daily emotional changes.

[0197] 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.

[0198] In this invention, the server includes communication means for transmitting health-related data and emotion-related data to a remote server, data analysis system means for analyzing the health-related data and emotion-related data and evaluating health status, emotional state, and potential health risks, and advice generation device means for generating personalized health advice based on the analysis results. This makes it possible to provide personalized health advice that also takes emotional state into consideration.

[0199] "Health-related data" refers to data that indicates an individual's physical condition and health status, such as information on body temperature, blood pressure, heart rate, and exercise level.

[0200] "Emotion-related data" refers to data that indicates an individual's emotional state, such as information regarding stress levels, happiness, and anxiety.

[0201] An "input device" is a device that receives data in formats such as voice or text, and is a means for users to input health-related data and emotion-related data.

[0202] "Communication means" refers to technical means for transmitting collected data to a remote server, and includes devices and protocols that ensure the secure and efficient transfer of data.

[0203] A "data storage device" is a device for storing received data and managing it as a history; it is a database system that operates on the cloud or a server.

[0204] A "data analysis system" is a system that analyzes received health-related and emotion-related data to evaluate health status and potential health risks.

[0205] A "health advice generation device" is a device that generates personalized health advice for users based on analysis results obtained from a data analysis system.

[0206] A "display device or audio output device" is a device that notifies the user of generated health advice and has the role of providing advice through visual or auditory means.

[0207] To implement this invention, first, an input device is required to collect health-related data and emotion-related data. This device can receive data from the user in voice or text format. For speech recognition, for example, the Google Speech-to-Text API is used. The voice data is converted to text, and then emotion-related data is extracted using an emotion analysis tool such as Microsoft® Azure® Text Analytics.

[0208] Next, the data collected via communication is sent to the cloud. This communication method ensures secure and efficient data transfer. The transmitted data is stored in a data storage device, where database systems provided by AWS® or other cloud platforms are utilized.

[0209] The server uses this data to run a data analysis system. The analysis engine employs a generative AI model, such as OpenAI's GPT model. This allows for a comprehensive analysis of the user's health and emotional state, and enables the assessment of potential health risks.

[0210] The acquired analysis results are converted into personalized health advice by an advice generation device. This advice is provided to the user visually or audibly through a display device or voice output device that notifies them of the generated content. In a physical store example, customers can use voice input at a kiosk terminal to receive real-time health advice tailored to their mood that day.

[0211] For example, if a user enters "I've been extremely tired lately, do you have any advice?", the data analysis system will use this information along with the user's past emotional state to generate personalized advice. An example of a prompt would be, "If the user enters 'I've been extremely tired lately, do you have any advice?', please provide advice that takes into account their emotions and health status."

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] Users input health-related and emotion-related data via voice or text. For example, they might say a phrase like, "I've been feeling tired lately." The input device uses the Google Speech-to-Text API to convert the voice data into text. As a result, text data is output.

[0215] Step 2:

[0216] The device sends the converted text data to a sentiment analysis tool to analyze the user's emotional state. Services such as Microsoft Azure Text Analytics are used to extract sentiment-related data from the text. Output includes indicators such as stress levels and happiness levels.

[0217] Step 3:

[0218] The analysis results and original health-related data are transmitted to a server in the cloud via a communication method. The terminal uses highly secure data transfer protocols such as AWS to protect the data. At this stage, the data is stored in a data storage device on the server.

[0219] Step 4:

[0220] The server integrates accumulated health-related and emotion-related data and inputs it into the data analysis system. Using the OpenAI GPT model, the correlation of the data is evaluated, and the impact of potential health risks and emotional states is analyzed. From this analysis, evaluation results are obtained in the form of specific numerical values ​​or strings.

[0221] Step 5:

[0222] Based on the analysis results, the server uses an advice generation device to create personalized health advice. Here, a generative AI model is used to generate advice that includes suggestions for appropriate exercise and relaxation.

[0223] Step 6:

[0224] Finally, the generated health advice is sent to the device and notified to the user via a display or audio output device. This allows the user to receive a concrete action plan that can help improve their lifestyle.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention is a system that monitors an individual's health status on a daily basis and provides real-time health advice using generated AI.

[0242] System Configuration

[0243] 1. User device configuration:

[0244] Users register their daily health-related data on the device through voice or text input. The device features voice recognition capabilities, making it accessible to the elderly and visually impaired.

[0245] For example, if a user says aloud, "I had toast and coffee this morning and went for a one-hour jog," that information is converted into text and recorded on the device.

[0246] 2. Data transmission and management:

[0247] The terminal has a built-in communication module, and the collected data is periodically sent to a cloud server. The transmitted data is stored in a cloud database and recorded as historical data.

[0248] This will enable continuous monitoring of the user's health status.

[0249] 3. Analysis using Generative AI:

[0250] The server retrieves information from a cloud database and analyzes the data using generative AI. This allows for an assessment of the user's health status and potential health risks.

[0251] For example, if a user's sleep time falls below 6 hours for a week straight, it may be determined that they have a weakened immune system.

[0252] 4. Generating personalized health advice:

[0253] Based on the analysis results, the server generates personalized health advice for the user. This advice includes suggestions for dietary improvements and exercise plans.

[0254] As a concrete example, specific action guidelines such as "adding fruit containing vitamin C to your breakfast" are proposed.

[0255] 5. Notification of advice and support for implementation:

[0256] The generated health advice is sent to the user's device and notified via voice and text. The device also has a reminder function to prompt action based on the advice.

[0257] For example, an alarm is set to remind you to go to bed at a time that improves sleep quality.

[0258] This system allows users to easily manage their health information in their daily lives and receive support to continue personalized health promotion activities.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] Users input health-related data via voice or text. The device accepts this input and, in the case of voice data, uses speech recognition to convert it to text. The converted data is formatted and temporarily stored on the device.

[0262] Step 2:

[0263] The device transmits formatted health-related data to a cloud server based on predetermined conditions (e.g., set time intervals). The data is securely transmitted via a communication module.

[0264] Step 3:

[0265] The server stores the received data in a cloud-based database. The data is linked to past records and managed as consistent historical information.

[0266] Step 4:

[0267] The server activates a generating AI to analyze the data stored in the database. The AI ​​assesses the user's health status and identifies health risks based on past trends.

[0268] Step 5:

[0269] The server generates personalized health advice based on the analysis results from the generated AI. This advice includes an action plan tailored to the user and offers suggestions for improving their lifestyle.

[0270] Step 6:

[0271] The device notifies the user of health advice received from the server. Notifications are delivered via both voice and text, and reminders can be set according to the user's situation. Users can adjust their daily activities based on these notifications.

[0272] (Example 1)

[0273] 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."

[0274] In modern society, it is crucial to continuously monitor individual health conditions and implement appropriate health management. However, conventional systems struggle to provide personalized health advice and adequate support for users' daily lives. Therefore, there is a need for a means to effectively monitor individual health conditions and provide real-time advice.

[0275] 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.

[0276] In this invention, the server includes terminal means for inputting and collecting health-related information, communication means for transmitting the collected health-related information to a remote location, and information management device means for storing the health-related information at the remote location and managing it as a history. This makes it possible to provide health advice tailored to each individual user in real time and support its implementation in daily life.

[0277] The "terminal for inputting and collecting health-related information" is a device that can receive voice or text information from a user, convert it into an appropriate data format, and save it.

[0278] The "communication means" is a technology or device for transmitting the collected information to a server in a remote location, thereby realizing real-time transmission of information.

[0279] The "information management device" is a system that stores the received health-related information and manages it as past history, facilitating data search and analysis.

[0280] The "generation system" is a device or software that analyzes based on the stored health-related information and evaluates an individual's health status and potential health risks.

[0281] The "guidance generation device" is a device or program for creating optimal health advice for individual users based on the analysis results by the generation system.

[0282] The "terminal" is a device equipped with a function to notify the user of the generated guidance and prompt actions as necessary.

[0283] In the mode for implementing this invention, a system for daily monitoring an individual's health status and providing real-time health advice by utilizing a generated AI model will be specifically described.

[0284] Configuration of User Device

[0285] The user uses a dedicated terminal to input health-related data. The terminal is equipped with a voice recognition function, and the user can provide data in voice or text. For example, when the user inputs "I ate a salad with a lot of vegetables today" in voice, the terminal converts the information into text format and records it. The terminal has a design that takes into consideration the ease of use by more users.

[0286] Data transmission and management

[0287] Data is transmitted from the terminal to the cloud. As the communication means, a wireless communication module is used. The transmitted data is stored in the information management device on the cloud and managed as the past health information of each user. Through this process, continuous tracking of the user's health information becomes possible.

[0288] Analysis by generative AI

[0289] The server acquires data from the information management device in the cloud and analyzes the data in real time using a generative AI model. In the analysis, sleep patterns, eating habits, exercise status, etc. are evaluated, and the user's health status and potential risks are revealed. For example, if lack of exercise continues, there may be an indication of the risk of weight gain.

[0290] Generation of personalized health advice

[0291] Based on the analysis results of the generative AI model, the server generates optimized health advice for each user. As a specific example, advice such as "Sunlight promotes the production of vitamin D, so it is recommended to take a 10-minute walk every day" is provided.

[0292] Notification of advice and implementation support

[0293] The generated health advice is transmitted to the user's terminal, and the user receives the notification in voice and text. Also, the terminal has a built-in reminder function to encourage the user to routinely execute the proposed health plan. For example, a notification such as "Please ensure relaxation time at 20:00" is issued.

[0294] Examples of prompt sentences

[0295] "Based on the user's health data, generate appropriate lifestyle improvement suggestions. For example, provide specific advice on how the user can increase their daily activity level."

[0296] In this way, this system allows users to receive personalized health suggestions tailored to their daily activities, enabling them to engage in individualized health maintenance activities on a daily basis.

[0297] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0298] Step 1:

[0299] Users input daily health-related information into the device via voice or text. The device uses speech recognition technology to convert the voice data into text and saves it in a standardized format. This input data includes information such as diet and exercise time.

[0300] Step 2:

[0301] The device transmits converted and standardized health-related information to a cloud server. During this process, the data is encrypted using communication methods and transmitted securely. As output, the data is notified to the cloud and managed for subsequent analysis.

[0302] Step 3:

[0303] The server retrieves data transmitted from the cloud-based information management device. It then uses a generative AI model to analyze the retrieved data. The analysis evaluates the user's health status based on the input data, identifying potential risks and areas for improvement.

[0304] Step 4:

[0305] Based on the analysis results of the generative AI model, the server generates personalized health advice for the user. Specifically, it generates things like diets that increase specific nutrients, or plans for regular exercise. As output, the generated advice is obtained.

[0306] Step 5:

[0307] The terminal transmits the health advice notified by the server to the user. The notification is done in both voice and text, and specific action guidelines are presented. An alarm is set through the reminder function to support the user in executing the advice.

[0308] Through this processing flow, the user can receive individualized health advice daily and improve their lifestyle based on it.

[0309] (Application Example 1)

[0310] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In personal health management, there is a need for a method to easily collect daily health data, analyze it, and efficiently provide individualized health advice and purchase related products. However, in the prior art, there is a lack of means to consistently provide these, and in particular, the connection between purchasing activities such as health foods and health management is insufficient.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0313] In this invention, the server includes a device means for inputting and collecting health-related data, an information communication system means for transmitting the collected health-related data to the cloud, and means for suggesting health-related products based on the user's health information and for purchasing those products through electronic payment. This enables efficient personal health management and the purchase of related products.

[0314] "Health-related data" refers to information related to an individual's health, such as their diet, exercise, and sleep.

[0315] "Device" refers to equipment composed of mechanical parts and software, used to achieve a specific purpose.

[0316] An "information and communication system" refers to the technical means for collecting, processing, and transmitting data, and for communicating with other devices and networks.

[0317] An "information management system" refers to a database or software that stores, organizes, and easily accesses and analyzes collected data.

[0318] A "generative system" refers to a technological mechanism that analyzes and evaluates collected data to produce new information and results.

[0319] A "guidance creation unit" refers to a device or software that has the function of creating personalized guidance and advice based on analysis results.

[0320] A "notification function" refers to a system of alerts and reminders that inform users of important information and advice.

[0321] The system that implements this application provides advanced functions for health management. When a user registers daily health-related data using a voice input device, that information is transmitted to a cloud-based database via a communication system. Because the voice recognition function uses the Google Speech-to-Text API, it can be used intuitively by visually impaired people and the elderly.

[0322] The cloud-based generation system analyzes input data using generative AI models such as OpenAI GPT-4 to assess the user's health status. Based on the assessment, the guidance creation unit creates personalized advice regarding diet and exercise and sends it to the user's device via a notification function.

[0323] For example, if a user provides information about their breakfast, the AI ​​will generate specific advice such as, "Considering your nutritional balance today, we recommend purchasing a vitamin supplement." This advice is immediately displayed on the smartphone screen, and the recommended product can be easily purchased through an electronic payment system (e.g., Stripe).

[0324] An example of a prompt message is, "The user ate oatmeal and a banana this morning. Please suggest a fitness plan tailored to today's health condition." This allows users to easily manage their health information and improve their quality of health in their daily lives.

[0325] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0326] Step 1:

[0327] Users input health-related data into the device via a voice input device. The input voice is converted to text using the Google Speech-to-Text API. This input data is temporarily stored on the device.

[0328] Step 2:

[0329] The terminal sends the converted text data to a cloud database via the communication system. During this process, the data is properly formatted, encrypted according to security protocols, and then transmitted.

[0330] Step 3:

[0331] The server retrieves user health data from a cloud database and performs data analysis using a generative AI model. OpenAI GPT-4 is used for the analysis, and the input data is processed to evaluate the user's health status and trends.

[0332] Step 4:

[0333] The server's guidance generation unit receives the analysis results and generates personalized health advice. In this process, the AI ​​uses prompts to generate appropriate health advice, such as "Please suggest supplements tailored to the user's nutritional balance."

[0334] Step 5:

[0335] The generated health advice is sent from the server to the user's device and displayed as a notification on the screen. The notification also includes a link for the user to make a purchase. The purchase process is conducted through an electronic payment system to ensure user convenience.

[0336] Step 6:

[0337] When a user purchases a recommended product, the terminal securely completes the transaction via an electronic payment system. The input in this step represents the user's intention to purchase, and the output is a notification that the purchase is complete.

[0338] 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.

[0339] This invention is a system that provides personalized health advice that takes into account not only the user's health-related data but also their emotional state. Specifically, it integrates a series of components including an input device, a communication module, a generative AI, and an emotion engine.

[0340] System Configuration

[0341] 1. User device configuration:

[0342] Users input health-related data via voice or text. The device incorporates voice recognition and an emotion engine to analyze the user's emotions from the input voice and text.

[0343] For example, if a user inputs "I'm busy today and feeling stressed" in a voice that expresses fatigue, the device will convert the speech to text and simultaneously analyze the user's emotions based on that content.

[0344] 2. Data transmission and management:

[0345] The device sends analyzed health-related and emotional data to a cloud server. A communication module is responsible for the secure transfer of this data.

[0346] The transmitted data is stored in a cloud-based database and continuously managed as information, including the user's emotional history.

[0347] 3. Comprehensive analysis using generative AI:

[0348] The server uses both health-related and emotional data to perform analysis with generative AI. By evaluating health status, emotional state, and their correlation, more accurate risk assessment becomes possible.

[0349] For example, if someone is experiencing prolonged stress, it may be determined that they need advice on exercise or relaxation techniques that can help reduce stress.

[0350] 4. Generating health advice:

[0351] Based on the analysis results, the server generates personalized health advice for each user. This advice is based on both health-related data and emotional state, resulting in more practical and effective suggestions.

[0352] As a concrete example, they offer advice such as, "An hour-long walk is effective for reducing stress. Try it to refresh yourself."

[0353] 5. Notification of advice and support for implementation:

[0354] The generated health advice is delivered to the user's device, and notifications are sent via voice and text. A reminder function that takes emotional state into account is also provided to support user motivation.

[0355] For example, you can set an alert that says, "I recommend you go to bed a little earlier today. I've set some relaxing music for you to listen to."

[0356] This system enables users to manage their health comprehensively, addressing not only their physical condition but also changes in their emotions. This allows for health promotion through more personalized support.

[0357] The following describes the processing flow.

[0358] Step 1:

[0359] Users provide daily feedback to the device through health-related data and voice input. The device activates its voice recognition function and converts the voice data into text. Additionally, an emotion engine is activated to analyze the user's emotions based on their voice tone and content.

[0360] Step 2:

[0361] The device collects analyzed health and emotional data, formats it, and sends it to a cloud server according to a communication protocol. The communication module ensures secure data transmission.

[0362] Step 3:

[0363] The server receives data sent to the cloud database and stores it in the database. It updates and manages each user's health and emotional history.

[0364] Step 4:

[0365] The server uses generative AI to comprehensively analyze users' health-related and emotional data. This process evaluates health trends, emotional changes, and their correlations.

[0366] Step 5:

[0367] The server generates personalized health advice based on the analysis results. This specific advice takes into account the user's health and emotional data.

[0368] Step 6:

[0369] The generated advice is notified to the device. The device provides the advice to the user in both voice and text, and supports the user's daily health management by setting reminders tailored to their emotional state.

[0370] (Example 2)

[0371] 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".

[0372] In managing users' health, there is a need to provide comprehensive advice that takes into account not only their individual health status but also their emotional state. However, existing technologies are insufficient in generating advice that considers the user's emotional state, and have not been able to effectively provide the information necessary for promoting health.

[0373] 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.

[0374] In this invention, the server includes means for using a generative AI system that analyzes health-related data and emotional data and evaluates health status and emotional state; means for utilizing an advice generation function that generates personalized health advice based on the analysis results; and means for providing a display device that notifies the user device of the generated advice at a timing that takes into account the emotional state. This enables more effective health management and emotional stabilization by providing health advice optimized for each individual user, taking into account both health status and emotional state.

[0375] "Health-related data" refers to information about a user's physical condition and lifestyle, and is used for health assessment and management.

[0376] "Emotional data" refers to information about a user's emotional and psychological state, and is used for health management and personalized advice.

[0377] An "input device" is a device used by users to input health-related and emotional data, and has the function of collecting voice and text data.

[0378] A "communication module" is a mechanism for transmitting collected data to a cloud structure, and is responsible for the secure transfer of data.

[0379] A "cloud structure" is a data storage and processing platform provided via the internet, which enables long-term data storage and historical management.

[0380] A "generative AI system" is a system that uses artificial intelligence models to analyze stored data and evaluate health and emotional states.

[0381] The "advice generation function" is a unit that generates personalized health advice for each user based on the analysis results.

[0382] A "display device" is a device that notifies the user of generated health advice, and presents information visually and audibly.

[0383] This invention is a system that comprehensively manages a user's health and emotional state and provides personalized health advice. The system includes several key components to appropriately collect and analyze health-related and emotional data and provide information tailored to the user's needs.

[0384] First, the user uses the device to input health-related data and emotional states. This device is equipped with a voice recognition function, which converts the user's voice input into text data. It also uses an emotion engine to analyze the user's emotional state from the input data. For example, if the user inputs "I'm very tired today," the device converts this information from voice to text and determines through emotion analysis that the user is fatigued.

[0385] The analyzed data is sent to a cloud structure via a communication module. In the cloud, the data is securely stored and managed along with its historical data. The server receives the transmitted data and uses a generative AI system to comprehensively analyze the user's health and emotional state. This generative AI initiates the analysis using prompts and generates personalized advice. For example, a prompt might be "Please suggest exercises to reduce stress."

[0386] The generated health advice is created by the advice generation function and sent from the server to the terminal. The terminal notifies the user of the advice via voice and text, and provides support for implementation through a reminder function that takes into account the user's emotional state. For example, it makes specific suggestions to support the user, such as, "I recommend you go to bed early today. I'll play some relaxing music."

[0387] This system enables users to manage their health more effectively and personally, taking into account both their physical and emotional state.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] The user uses the device to input health-related data and emotional information in voice or text format. The device uses speech recognition to convert the voice data into text data. During this process, it analyzes the input data and uses an emotion engine to determine the user's emotional state. For example, if the user says "I'm tired today," the device converts the voice into text and identifies the emotional state, such as "fatigue." The input for this process is voice data, and the output is the analyzed text data and the result of the emotional state evaluation.

[0391] Step 2:

[0392] The device transmits analyzed health-related and sentiment data to a cloud structure. This communication uses a communication module, and the data is transferred while protected by security protocols. Specifically, when data is input, the communication module becomes active and sends data packets to the cloud server. The input for this process is text data and sentiment assessment results, and the output is data stored in the cloud.

[0393] Step 3:

[0394] The server receives data arriving in the cloud and activates a generative AI model. The server uses prompts to begin analyzing the data and comprehensively assesses health and emotional states. For example, it might use a prompt such as, "What activities are recommended when stress levels are high?" The input for this step is data received from the cloud, and the output is personalized health advice generated through the analysis.

[0395] Step 4:

[0396] The server uses its advice generation function to create personalized health advice based on the analysis results. The server then prepares to send the generated advice to the terminal. In this step, the input is advice based on prompt results, and the output is suggestions tailored to the user's needs.

[0397] Step 5:

[0398] The device notifies the user of received health advice and provides a reminder function that takes into account their emotional state. It provides notifications via voice and text, and displays additional alerts and information as needed. The input for this step is advice data from the server, and the output is specific advice received and acted upon by the user.

[0399] (Application Example 2)

[0400] 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."

[0401] In modern society, personal health management is crucial, and there is a demand for information and advice to support it. However, conventional health-related services often provide uniform advice without considering the user's emotional state or stress level, resulting in a lack of personalized support. Therefore, there is a need for a system that provides more accurate health advice, taking into account the user's daily emotional changes.

[0402] 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.

[0403] In this invention, the server includes communication means for transmitting health-related data and emotion-related data to a remote server, data analysis system means for analyzing the health-related data and emotion-related data and evaluating health status, emotional state, and potential health risks, and advice generation device means for generating personalized health advice based on the analysis results. This makes it possible to provide personalized health advice that also takes emotional state into consideration.

[0404] "Health-related data" refers to data that indicates an individual's physical condition and health status, such as information on body temperature, blood pressure, heart rate, and exercise level.

[0405] "Emotion-related data" refers to data that indicates an individual's emotional state, such as information regarding stress levels, happiness, and anxiety.

[0406] An "input device" is a device that receives data in formats such as voice or text, and is a means for users to input health-related data and emotion-related data.

[0407] "Communication means" refers to technical means for transmitting collected data to a remote server, and includes devices and protocols that ensure the secure and efficient transfer of data.

[0408] A "data storage device" is a device for storing received data and managing it as a history; it is a database system that operates on the cloud or a server.

[0409] A "data analysis system" is a system that analyzes received health-related and emotion-related data to evaluate health status and potential health risks.

[0410] A "health advice generation device" is a device that generates personalized health advice for users based on analysis results obtained from a data analysis system.

[0411] A "display device or audio output device" is a device that notifies the user of generated health advice and has the role of providing advice through visual or auditory means.

[0412] To implement this invention, first, an input device is required to collect health-related data and emotion-related data. This device can receive data from the user in voice or text format. For speech recognition, for example, the Google Speech-to-Text API can be used. The voice data is converted to text, and then emotion-related data is extracted using an emotion analysis tool such as Microsoft Azure Text Analytics.

[0413] Next, the data collected via communication is sent to the cloud. This communication method ensures secure and efficient data transfer. The transmitted data is stored in a data storage device, where database systems provided by AWS or other cloud platforms are utilized.

[0414] The server uses this data to run a data analysis system. The analysis engine employs a generative AI model, such as OpenAI's GPT model. This allows for a comprehensive analysis of the user's health and emotional state, and enables the assessment of potential health risks.

[0415] The acquired analysis results are converted into personalized health advice by an advice generation device. This advice is provided to the user visually or audibly through a display device or voice output device that notifies them of the generated content. In a physical store example, customers can use voice input at a kiosk terminal to receive real-time health advice tailored to their mood that day.

[0416] For example, if a user enters "I've been extremely tired lately, do you have any advice?", the data analysis system will use this information along with the user's past emotional state to generate personalized advice. An example of a prompt would be, "If the user enters 'I've been extremely tired lately, do you have any advice?', please provide advice that takes into account their emotions and health status."

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] Users input health-related and emotion-related data via voice or text. For example, they might say a phrase like, "I've been feeling tired lately." The input device uses the Google Speech-to-Text API to convert the voice data into text. As a result, text data is output.

[0420] Step 2:

[0421] The device sends the converted text data to a sentiment analysis tool to analyze the user's emotional state. Services such as Microsoft Azure Text Analytics are used to extract sentiment-related data from the text. Output includes indicators such as stress levels and happiness levels.

[0422] Step 3:

[0423] The analysis results and original health-related data are transmitted to a server in the cloud via a communication method. The terminal uses highly secure data transfer protocols such as AWS to protect the data. At this stage, the data is stored in a data storage device on the server.

[0424] Step 4:

[0425] The server integrates accumulated health-related and emotion-related data and inputs it into the data analysis system. Using the OpenAI GPT model, the correlation of the data is evaluated, and the impact of potential health risks and emotional states is analyzed. From this analysis, evaluation results are obtained in the form of specific numerical values ​​or strings.

[0426] Step 5:

[0427] Based on the analysis results, the server uses an advice generation device to create personalized health advice. Here, a generative AI model is used to generate advice that includes suggestions for appropriate exercise and relaxation.

[0428] Step 6:

[0429] Finally, the generated health advice is sent to the device and notified to the user via a display or audio output device. This allows the user to receive a concrete action plan that can help improve their lifestyle.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] [Third Embodiment]

[0434] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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".

[0446] This invention is a system that monitors an individual's health status on a daily basis and provides real-time health advice using generated AI.

[0447] System Configuration

[0448] 1. User device configuration:

[0449] Users register their daily health-related data on the device through voice or text input. The device features voice recognition capabilities, making it accessible to the elderly and visually impaired.

[0450] For example, if a user says aloud, "I had toast and coffee this morning and went for a one-hour jog," that information is converted into text and recorded on the device.

[0451] 2. Data transmission and management:

[0452] The terminal has a built-in communication module, and the collected data is periodically sent to a cloud server. The transmitted data is stored in a cloud database and recorded as historical data.

[0453] This will enable continuous monitoring of the user's health status.

[0454] 3. Analysis using Generative AI:

[0455] The server retrieves information from a cloud database and analyzes the data using generative AI. This allows for an assessment of the user's health status and potential health risks.

[0456] For example, if a user's sleep time falls below 6 hours for a week straight, it may be determined that they have a weakened immune system.

[0457] 4. Generating personalized health advice:

[0458] Based on the analysis results, the server generates personalized health advice for the user. This advice includes suggestions for dietary improvements and exercise plans.

[0459] As a concrete example, specific action guidelines such as "adding fruit containing vitamin C to your breakfast" are proposed.

[0460] 5. Notification of advice and support for implementation:

[0461] The generated health advice is sent to the user's device and notified via voice and text. The device also has a reminder function to prompt action based on the advice.

[0462] For example, an alarm is set to remind you to go to bed at a time that improves sleep quality.

[0463] This system allows users to easily manage their health information in their daily lives and receive support to continue personalized health promotion activities.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] Users input health-related data via voice or text. The device accepts this input and, in the case of voice data, uses speech recognition to convert it to text. The converted data is formatted and temporarily stored on the device.

[0467] Step 2:

[0468] The device transmits formatted health-related data to a cloud server based on predetermined conditions (e.g., set time intervals). The data is securely transmitted via a communication module.

[0469] Step 3:

[0470] The server stores the received data in a cloud-based database. The data is linked to past records and managed as consistent historical information.

[0471] Step 4:

[0472] The server activates a generating AI to analyze the data stored in the database. The AI ​​assesses the user's health status and identifies health risks based on past trends.

[0473] Step 5:

[0474] The server generates personalized health advice based on the analysis results from the generated AI. This advice includes an action plan tailored to the user and offers suggestions for improving their lifestyle.

[0475] Step 6:

[0476] The device notifies the user of health advice received from the server. Notifications are delivered via both voice and text, and reminders can be set according to the user's situation. Users can adjust their daily activities based on these notifications.

[0477] (Example 1)

[0478] 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."

[0479] In modern society, it is crucial to continuously monitor individual health conditions and implement appropriate health management. However, conventional systems struggle to provide personalized health advice and adequate support for users' daily lives. Therefore, there is a need for a means to effectively monitor individual health conditions and provide real-time advice.

[0480] 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.

[0481] In this invention, the server includes terminal means for inputting and collecting health-related information, communication means for transmitting the collected health-related information to a remote location, and information management device means for storing the health-related information at the remote location and managing it as a history. This makes it possible to provide health advice tailored to each individual user in real time and support its implementation in daily life.

[0482] A "terminal for inputting and collecting health-related information" is a device that can receive voice or text information from a user, convert it into an appropriate data format, and store it.

[0483] "Communication means" refers to the technology or device used to transmit collected information to a remote server, thereby enabling real-time information transmission.

[0484] An "information management device" is a system that stores received health-related information and manages it as a historical record, making it easier to search and analyze the data.

[0485] A "generation system" is a device or software that analyzes stored health-related information to assess an individual's health status and potential health risks.

[0486] A "health guidance generation device" is a device or program that creates optimal health advice for individual users based on the analysis results from a generation system.

[0487] A "terminal" is a device equipped with functions to notify users of generated instructions and prompt them to take action as needed.

[0488] In an embodiment of this invention, a system is specifically described that monitors an individual's health status on a daily basis and provides real-time health advice using a generative AI model.

[0489] User device configuration

[0490] Users input health-related data using a dedicated terminal. The terminal is equipped with voice recognition capabilities, allowing users to provide data via voice or text. For example, if a user voice-inputs, "I ate a salad with lots of vegetables today," the terminal converts that information into text and records it. The terminal is designed to be user-friendly for a wide range of users.

[0491] Data transmission and management

[0492] Data is transmitted from the device to the cloud. A wireless communication module is used as the means of communication. The transmitted data is stored in an information management device on the cloud and managed as each user's past health information. This process enables continuous tracking of the user's health information.

[0493] Analysis by Generative AI

[0494] The server retrieves data from a cloud-based information management system and analyzes it in real time using a generative AI model. The analysis evaluates sleep patterns, eating habits, exercise levels, and other factors to reveal the user's health status and potential risks. For example, a prolonged lack of exercise might indicate a risk of weight gain.

[0495] Generating personalized health advice

[0496] Based on the analysis results of the generated AI model, the server generates personalized health advice for each user. For example, it might provide advice such as, "Since exposure to sunlight promotes the production of vitamin D, we recommend taking a 10-minute walk every day."

[0497] Notification of advice and support for implementation

[0498] The generated health advice is sent to the user's device, and the user receives notifications via voice and text. The device also has a built-in reminder function to encourage the user to follow the suggested health plan on a daily basis. For example, a notification might say, "Make sure to set aside time for relaxation at 8 PM."

[0499] Example of a prompt

[0500] "Based on the user's health data, generate appropriate lifestyle improvement suggestions. For example, provide specific advice on how the user can increase their daily activity level."

[0501] In this way, this system allows users to receive personalized health suggestions tailored to their daily activities, enabling them to engage in individualized health maintenance activities on a daily basis.

[0502] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0503] Step 1:

[0504] Users input daily health-related information into the device via voice or text. The device uses speech recognition technology to convert the voice data into text and saves it in a standardized format. This input data includes information such as diet and exercise time.

[0505] Step 2:

[0506] The device transmits converted and standardized health-related information to a cloud server. During this process, the data is encrypted using communication methods and transmitted securely. As output, the data is notified to the cloud and managed for subsequent analysis.

[0507] Step 3:

[0508] The server retrieves data transmitted from the cloud-based information management device. It then uses a generative AI model to analyze the retrieved data. The analysis evaluates the user's health status based on the input data, identifying potential risks and areas for improvement.

[0509] Step 4:

[0510] The server generates personalized health advice for the user based on the analysis results of the generated AI model. Specifically, it generates plans such as diets that increase certain nutrients and regular exercise routines. The generated advice is provided as output.

[0511] Step 5:

[0512] The device transmits health advice received from the server to the user. Notifications are delivered in both voice and text formats, providing specific action guidelines. A reminder function sets alarms to help the user act on the advice.

[0513] This processing flow allows users to receive personalized health advice on a daily basis and use it to improve their lifestyle habits.

[0514] (Application Example 1)

[0515] 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."

[0516] In personal health management, there is a need for methods to easily collect and analyze daily health data, and then efficiently provide personalized health advice and recommend the purchase of related products. However, conventional technologies lack the means to consistently provide these services, and the connection between purchasing activities such as health foods and health management is particularly insufficient.

[0517] 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.

[0518] In this invention, the server includes a device means for inputting and collecting health-related data, an information communication system means for transmitting the collected health-related data to the cloud, and means for suggesting health-related products based on the user's health information and for purchasing those products through electronic payment. This enables efficient personal health management and the purchase of related products.

[0519] "Health-related data" refers to information related to an individual's health, such as their diet, exercise, and sleep.

[0520] "Device" refers to equipment composed of mechanical parts and software, used to achieve a specific purpose.

[0521] An "information and communication system" refers to the technical means for collecting, processing, and transmitting data, and for communicating with other devices and networks.

[0522] An "information management system" refers to a database or software that stores, organizes, and easily accesses and analyzes collected data.

[0523] A "generative system" refers to a technological mechanism that analyzes and evaluates collected data to produce new information and results.

[0524] A "guidance creation unit" refers to a device or software that has the function of creating personalized guidance and advice based on analysis results.

[0525] A "notification function" refers to a system of alerts and reminders that inform users of important information and advice.

[0526] The system that implements this application provides advanced functions for health management. When a user registers daily health-related data using a voice input device, that information is transmitted to a cloud-based database via a communication system. Because the voice recognition function uses the Google Speech-to-Text API, it can be used intuitively by visually impaired people and the elderly.

[0527] The cloud-based generation system analyzes input data using generative AI models such as OpenAI GPT-4 to assess the user's health status. Based on the assessment, the guidance creation unit creates personalized advice regarding diet and exercise and sends it to the user's device via a notification function.

[0528] For example, if a user provides information about their breakfast, the AI ​​will generate specific advice such as, "Considering your nutritional balance today, we recommend purchasing a vitamin supplement." This advice is immediately displayed on the smartphone screen, and the recommended product can be easily purchased through an electronic payment system (e.g., Stripe).

[0529] An example of a prompt message is, "The user ate oatmeal and a banana this morning. Please suggest a fitness plan tailored to today's health condition." This allows users to easily manage their health information and improve their quality of health in their daily lives.

[0530] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0531] Step 1:

[0532] Users input health-related data into the device via a voice input device. The input voice is converted to text using the Google Speech-to-Text API. This input data is temporarily stored on the device.

[0533] Step 2:

[0534] The terminal sends the converted text data to a cloud database via the communication system. During this process, the data is properly formatted, encrypted according to security protocols, and then transmitted.

[0535] Step 3:

[0536] The server retrieves user health data from a cloud database and performs data analysis using a generative AI model. OpenAI GPT-4 is used for the analysis, and the input data is processed to evaluate the user's health status and trends.

[0537] Step 4:

[0538] The server's guidance generation unit receives the analysis results and generates personalized health advice. In this process, the AI ​​uses prompts to generate appropriate health advice, such as "Please suggest supplements tailored to the user's nutritional balance."

[0539] Step 5:

[0540] The generated health advice is sent from the server to the user's device and displayed as a notification on the screen. The notification also includes a link for the user to make a purchase. The purchase process is conducted through an electronic payment system to ensure user convenience.

[0541] Step 6:

[0542] When a user purchases a recommended product, the terminal securely completes the transaction via an electronic payment system. The input in this step represents the user's intention to purchase, and the output is a notification that the purchase is complete.

[0543] 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.

[0544] This invention is a system that provides personalized health advice that takes into account not only the user's health-related data but also their emotional state. Specifically, it integrates a series of components including an input device, a communication module, a generative AI, and an emotion engine.

[0545] System Configuration

[0546] 1. User device configuration:

[0547] Users input health-related data via voice or text. The device incorporates voice recognition and an emotion engine to analyze the user's emotions from the input voice and text.

[0548] For example, if a user inputs "I'm busy today and feeling stressed" in a voice that expresses fatigue, the device will convert the speech to text and simultaneously analyze the user's emotions based on that content.

[0549] 2. Data transmission and management:

[0550] The device sends analyzed health-related and emotional data to a cloud server. A communication module is responsible for the secure transfer of this data.

[0551] The transmitted data is stored in a cloud-based database and continuously managed as information, including the user's emotional history.

[0552] 3. Comprehensive analysis using generative AI:

[0553] The server uses both health-related and emotional data to perform analysis with generative AI. By evaluating health status, emotional state, and their correlation, more accurate risk assessment becomes possible.

[0554] For example, if someone is experiencing prolonged stress, it may be determined that they need advice on exercise or relaxation techniques that can help reduce stress.

[0555] 4. Generating health advice:

[0556] Based on the analysis results, the server generates personalized health advice for each user. This advice is based on both health-related data and emotional state, resulting in more practical and effective suggestions.

[0557] As a concrete example, they offer advice such as, "An hour-long walk is effective for reducing stress. Try it to refresh yourself."

[0558] 5. Notification of advice and support for implementation:

[0559] The generated health advice is delivered to the user's device, and notifications are sent via voice and text. A reminder function that takes emotional state into account is also provided to support user motivation.

[0560] For example, you can set an alert that says, "I recommend you go to bed a little earlier today. I've set some relaxing music for you to listen to."

[0561] This system enables users to manage their health comprehensively, addressing not only their physical condition but also changes in their emotions. This allows for health promotion through more personalized support.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] Users provide daily feedback to the device through health-related data and voice input. The device activates its voice recognition function and converts the voice data into text. Additionally, an emotion engine is activated to analyze the user's emotions based on their voice tone and content.

[0565] Step 2:

[0566] The device collects analyzed health and emotional data, formats it, and sends it to a cloud server according to a communication protocol. The communication module ensures secure data transmission.

[0567] Step 3:

[0568] The server receives data sent to the cloud database and stores it in the database. It updates and manages each user's health and emotional history.

[0569] Step 4:

[0570] The server uses generative AI to comprehensively analyze users' health-related and emotional data. This process evaluates health trends, emotional changes, and their correlations.

[0571] Step 5:

[0572] The server generates personalized health advice based on the analysis results. This specific advice takes into account the user's health and emotional data.

[0573] Step 6:

[0574] The generated advice is notified to the device. The device provides the advice to the user in both voice and text, and supports the user's daily health management by setting reminders tailored to their emotional state.

[0575] (Example 2)

[0576] 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."

[0577] In managing users' health, there is a need to provide comprehensive advice that takes into account not only their individual health status but also their emotional state. However, existing technologies are insufficient in generating advice that considers the user's emotional state, and have not been able to effectively provide the information necessary for promoting health.

[0578] 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.

[0579] In this invention, the server includes means for using a generative AI system that analyzes health-related data and emotional data and evaluates health status and emotional state; means for utilizing an advice generation function that generates personalized health advice based on the analysis results; and means for providing a display device that notifies the user device of the generated advice at a timing that takes into account the emotional state. This enables more effective health management and emotional stabilization by providing health advice optimized for each individual user, taking into account both health status and emotional state.

[0580] "Health-related data" refers to information about a user's physical condition and lifestyle, and is used for health assessment and management.

[0581] "Emotional data" refers to information about a user's emotional and psychological state, and is used for health management and personalized advice.

[0582] An "input device" is a device used by users to input health-related and emotional data, and has the function of collecting voice and text data.

[0583] A "communication module" is a mechanism for transmitting collected data to a cloud structure, and is responsible for the secure transfer of data.

[0584] A "cloud structure" is a data storage and processing platform provided via the internet, which enables long-term data storage and historical management.

[0585] A "generative AI system" is a system that uses artificial intelligence models to analyze stored data and evaluate health and emotional states.

[0586] The "advice generation function" is a unit that generates personalized health advice for each user based on the analysis results.

[0587] A "display device" is a device that notifies the user of generated health advice, and presents information visually and audibly.

[0588] This invention is a system that comprehensively manages a user's health and emotional state and provides personalized health advice. The system includes several key components to appropriately collect and analyze health-related and emotional data and provide information tailored to the user's needs.

[0589] First, the user uses the device to input health-related data and emotional states. This device is equipped with a voice recognition function, which converts the user's voice input into text data. It also uses an emotion engine to analyze the user's emotional state from the input data. For example, if the user inputs "I'm very tired today," the device converts this information from voice to text and determines through emotion analysis that the user is fatigued.

[0590] The analyzed data is sent to a cloud structure via a communication module. In the cloud, the data is securely stored and managed along with its historical data. The server receives the transmitted data and uses a generative AI system to comprehensively analyze the user's health and emotional state. This generative AI initiates the analysis using prompts and generates personalized advice. For example, a prompt might be "Please suggest exercises to reduce stress."

[0591] The generated health advice is created by the advice generation function and sent from the server to the terminal. The terminal notifies the user of the advice via voice and text, and provides support for implementation through a reminder function that takes into account the user's emotional state. For example, it makes specific suggestions to support the user, such as, "I recommend you go to bed early today. I'll play some relaxing music."

[0592] This system enables users to manage their health more effectively and personally, taking into account both their physical and emotional state.

[0593] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0594] Step 1:

[0595] The user uses the device to input health-related data and emotional information in voice or text format. The device uses speech recognition to convert the voice data into text data. During this process, it analyzes the input data and uses an emotion engine to determine the user's emotional state. For example, if the user says "I'm tired today," the device converts the voice into text and identifies the emotional state, such as "fatigue." The input for this process is voice data, and the output is the analyzed text data and the result of the emotional state evaluation.

[0596] Step 2:

[0597] The device transmits analyzed health-related and sentiment data to a cloud structure. This communication uses a communication module, and the data is transferred while protected by security protocols. Specifically, when data is input, the communication module becomes active and sends data packets to the cloud server. The input for this process is text data and sentiment assessment results, and the output is data stored in the cloud.

[0598] Step 3:

[0599] The server receives data arriving in the cloud and activates a generative AI model. The server uses prompts to begin analyzing the data and comprehensively assesses health and emotional states. For example, it might use a prompt such as, "What activities are recommended when stress levels are high?" The input for this step is data received from the cloud, and the output is personalized health advice generated through the analysis.

[0600] Step 4:

[0601] The server uses its advice generation function to create personalized health advice based on the analysis results. The server then prepares to send the generated advice to the terminal. In this step, the input is advice based on prompt results, and the output is suggestions tailored to the user's needs.

[0602] Step 5:

[0603] The device notifies the user of received health advice and provides a reminder function that takes into account their emotional state. It provides notifications via voice and text, and displays additional alerts and information as needed. The input for this step is advice data from the server, and the output is specific advice received and acted upon by the user.

[0604] (Application Example 2)

[0605] 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."

[0606] In modern society, personal health management is crucial, and there is a demand for information and advice to support it. However, conventional health-related services often provide uniform advice without considering the user's emotional state or stress level, resulting in a lack of personalized support. Therefore, there is a need for a system that provides more accurate health advice, taking into account the user's daily emotional changes.

[0607] 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.

[0608] In this invention, the server includes communication means for transmitting health-related data and emotion-related data to a remote server, data analysis system means for analyzing the health-related data and emotion-related data and evaluating health status, emotional state, and potential health risks, and advice generation device means for generating personalized health advice based on the analysis results. This makes it possible to provide personalized health advice that also takes emotional state into consideration.

[0609] "Health-related data" refers to data that indicates an individual's physical condition and health status, such as information on body temperature, blood pressure, heart rate, and exercise level.

[0610] "Emotion-related data" refers to data that indicates an individual's emotional state, such as information regarding stress levels, happiness, and anxiety.

[0611] An "input device" is a device that receives data in formats such as voice or text, and is a means for users to input health-related data and emotion-related data.

[0612] "Communication means" refers to technical means for transmitting collected data to a remote server, and includes devices and protocols that ensure the secure and efficient transfer of data.

[0613] A "data storage device" is a device for storing received data and managing it as a history; it is a database system that operates on the cloud or a server.

[0614] A "data analysis system" is a system that analyzes received health-related and emotion-related data to evaluate health status and potential health risks.

[0615] A "health advice generation device" is a device that generates personalized health advice for users based on analysis results obtained from a data analysis system.

[0616] A "display device or audio output device" is a device that notifies the user of generated health advice and has the role of providing advice through visual or auditory means.

[0617] To implement this invention, first, an input device is required to collect health-related data and emotion-related data. This device can receive data from the user in voice or text format. For speech recognition, for example, the Google Speech-to-Text API can be used. The voice data is converted to text, and then emotion-related data is extracted using an emotion analysis tool such as Microsoft Azure Text Analytics.

[0618] Next, the data collected via communication is sent to the cloud. This communication method ensures secure and efficient data transfer. The transmitted data is stored in a data storage device, where database systems provided by AWS or other cloud platforms are utilized.

[0619] The server uses this data to run a data analysis system. The analysis engine employs a generative AI model, such as OpenAI's GPT model. This allows for a comprehensive analysis of the user's health and emotional state, and enables the assessment of potential health risks.

[0620] The acquired analysis results are converted into personalized health advice by an advice generation device. This advice is provided to the user visually or audibly through a display device or voice output device that notifies them of the generated content. In a physical store example, customers can use voice input at a kiosk terminal to receive real-time health advice tailored to their mood that day.

[0621] For example, if a user enters "I've been extremely tired lately, do you have any advice?", the data analysis system will use this information along with the user's past emotional state to generate personalized advice. An example of a prompt would be, "If the user enters 'I've been extremely tired lately, do you have any advice?', please provide advice that takes into account their emotions and health status."

[0622] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0623] Step 1:

[0624] Users input health-related and emotion-related data via voice or text. For example, they might say a phrase like, "I've been feeling tired lately." The input device uses the Google Speech-to-Text API to convert the voice data into text. As a result, text data is output.

[0625] Step 2:

[0626] The device sends the converted text data to a sentiment analysis tool to analyze the user's emotional state. Services such as Microsoft Azure Text Analytics are used to extract sentiment-related data from the text. Output includes indicators such as stress levels and happiness levels.

[0627] Step 3:

[0628] The analysis results and original health-related data are transmitted to a server in the cloud via a communication method. The terminal uses highly secure data transfer protocols such as AWS to protect the data. At this stage, the data is stored in a data storage device on the server.

[0629] Step 4:

[0630] The server integrates accumulated health-related and emotion-related data and inputs it into the data analysis system. Using the OpenAI GPT model, the correlation of the data is evaluated, and the impact of potential health risks and emotional states is analyzed. From this analysis, evaluation results are obtained in the form of specific numerical values ​​or strings.

[0631] Step 5:

[0632] Based on the analysis results, the server uses an advice generation device to create personalized health advice. Here, a generative AI model is used to generate advice that includes suggestions for appropriate exercise and relaxation.

[0633] Step 6:

[0634] Finally, the generated health advice is sent to the device and notified to the user via a display or audio output device. This allows the user to receive a concrete action plan that can help improve their lifestyle.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] [Fourth Embodiment]

[0639] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0640] 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.

[0641] 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).

[0642] 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.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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".

[0652] This invention is a system that monitors an individual's health status on a daily basis and provides real-time health advice using generated AI.

[0653] System Configuration

[0654] 1. User device configuration:

[0655] Users register their daily health-related data on the device through voice or text input. The device features voice recognition capabilities, making it accessible to the elderly and visually impaired.

[0656] For example, if a user says aloud, "I had toast and coffee this morning and went for a one-hour jog," that information is converted into text and recorded on the device.

[0657] 2. Data transmission and management:

[0658] The terminal has a built-in communication module, and the collected data is periodically sent to a cloud server. The transmitted data is stored in a cloud database and recorded as historical data.

[0659] This will enable continuous monitoring of the user's health status.

[0660] 3. Analysis using Generative AI:

[0661] The server retrieves information from a cloud database and analyzes the data using generative AI. This allows for an assessment of the user's health status and potential health risks.

[0662] For example, if a user's sleep time falls below 6 hours for a week straight, it may be determined that they have a weakened immune system.

[0663] 4. Generating personalized health advice:

[0664] Based on the analysis results, the server generates personalized health advice for the user. This advice includes suggestions for dietary improvements and exercise plans.

[0665] As a concrete example, specific action guidelines such as "adding fruit containing vitamin C to your breakfast" are proposed.

[0666] 5. Notification of advice and support for implementation:

[0667] The generated health advice is sent to the user's device and notified via voice and text. The device also has a reminder function to prompt action based on the advice.

[0668] For example, an alarm is set to remind you to go to bed at a time that improves sleep quality.

[0669] This system allows users to easily manage their health information in their daily lives and receive support to continue personalized health promotion activities.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] Users input health-related data via voice or text. The device accepts this input and, in the case of voice data, uses speech recognition to convert it to text. The converted data is formatted and temporarily stored on the device.

[0673] Step 2:

[0674] The device transmits formatted health-related data to a cloud server based on predetermined conditions (e.g., set time intervals). The data is securely transmitted via a communication module.

[0675] Step 3:

[0676] The server stores the received data in a cloud-based database. The data is linked to past records and managed as consistent historical information.

[0677] Step 4:

[0678] The server activates a generating AI to analyze the data stored in the database. The AI ​​assesses the user's health status and identifies health risks based on past trends.

[0679] Step 5:

[0680] The server generates personalized health advice based on the analysis results from the generated AI. This advice includes an action plan tailored to the user and offers suggestions for improving their lifestyle.

[0681] Step 6:

[0682] The device notifies the user of health advice received from the server. Notifications are delivered via both voice and text, and reminders can be set according to the user's situation. Users can adjust their daily activities based on these notifications.

[0683] (Example 1)

[0684] 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".

[0685] In modern society, it is crucial to continuously monitor individual health conditions and implement appropriate health management. However, conventional systems struggle to provide personalized health advice and adequate support for users' daily lives. Therefore, there is a need for a means to effectively monitor individual health conditions and provide real-time advice.

[0686] 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.

[0687] In this invention, the server includes terminal means for inputting and collecting health-related information, communication means for transmitting the collected health-related information to a remote location, and information management device means for storing the health-related information at the remote location and managing it as a history. This makes it possible to provide health advice tailored to each individual user in real time and support its implementation in daily life.

[0688] A "terminal for inputting and collecting health-related information" is a device that can receive voice or text information from a user, convert it into an appropriate data format, and store it.

[0689] "Communication means" refers to the technology or device used to transmit collected information to a remote server, thereby enabling real-time information transmission.

[0690] An "information management device" is a system that stores received health-related information and manages it as a historical record, making it easier to search and analyze the data.

[0691] A "generation system" is a device or software that analyzes stored health-related information to assess an individual's health status and potential health risks.

[0692] A "health guidance generation device" is a device or program that creates optimal health advice for individual users based on the analysis results from a generation system.

[0693] A "terminal" is a device equipped with functions to notify users of generated instructions and prompt them to take action as needed.

[0694] In an embodiment of this invention, a system is specifically described that monitors an individual's health status on a daily basis and provides real-time health advice using a generative AI model.

[0695] User device configuration

[0696] Users input health-related data using a dedicated terminal. The terminal is equipped with voice recognition capabilities, allowing users to provide data via voice or text. For example, if a user voice-inputs, "I ate a salad with lots of vegetables today," the terminal converts that information into text and records it. The terminal is designed to be user-friendly for a wide range of users.

[0697] Data transmission and management

[0698] Data is transmitted from the device to the cloud. A wireless communication module is used as the means of communication. The transmitted data is stored in an information management device on the cloud and managed as each user's past health information. This process enables continuous tracking of the user's health information.

[0699] Analysis by Generative AI

[0700] The server retrieves data from a cloud-based information management system and analyzes it in real time using a generative AI model. The analysis evaluates sleep patterns, eating habits, exercise levels, and other factors to reveal the user's health status and potential risks. For example, a prolonged lack of exercise might indicate a risk of weight gain.

[0701] Generating personalized health advice

[0702] Based on the analysis results of the generated AI model, the server generates personalized health advice for each user. For example, it might provide advice such as, "Since exposure to sunlight promotes the production of vitamin D, we recommend taking a 10-minute walk every day."

[0703] Notification of advice and support for implementation

[0704] The generated health advice is sent to the user's device, and the user receives notifications via voice and text. The device also has a built-in reminder function to encourage the user to follow the suggested health plan on a daily basis. For example, a notification might say, "Make sure to set aside time for relaxation at 8 PM."

[0705] Example of a prompt

[0706] "Based on the user's health data, generate appropriate lifestyle improvement suggestions. For example, provide specific advice on how the user can increase their daily activity level."

[0707] In this way, this system allows users to receive personalized health suggestions tailored to their daily activities, enabling them to engage in individualized health maintenance activities on a daily basis.

[0708] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0709] Step 1:

[0710] Users input daily health-related information into the device via voice or text. The device uses speech recognition technology to convert the voice data into text and saves it in a standardized format. This input data includes information such as diet and exercise time.

[0711] Step 2:

[0712] The device transmits converted and standardized health-related information to a cloud server. During this process, the data is encrypted using communication methods and transmitted securely. As output, the data is notified to the cloud and managed for subsequent analysis.

[0713] Step 3:

[0714] The server retrieves data transmitted from the cloud-based information management device. It then uses a generative AI model to analyze the retrieved data. The analysis evaluates the user's health status based on the input data, identifying potential risks and areas for improvement.

[0715] Step 4:

[0716] The server generates personalized health advice for the user based on the analysis results of the generated AI model. Specifically, it generates plans such as diets that increase certain nutrients and regular exercise routines. The generated advice is provided as output.

[0717] Step 5:

[0718] The device transmits health advice received from the server to the user. Notifications are delivered in both voice and text formats, providing specific action guidelines. A reminder function sets alarms to help the user act on the advice.

[0719] This processing flow allows users to receive personalized health advice on a daily basis and use it to improve their lifestyle habits.

[0720] (Application Example 1)

[0721] 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".

[0722] In personal health management, there is a need for methods to easily collect and analyze daily health data, and then efficiently provide personalized health advice and recommend the purchase of related products. However, conventional technologies lack the means to consistently provide these services, and the connection between purchasing activities such as health foods and health management is particularly insufficient.

[0723] 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.

[0724] In this invention, the server includes a device means for inputting and collecting health-related data, an information communication system means for transmitting the collected health-related data to the cloud, and means for suggesting health-related products based on the user's health information and for purchasing those products through electronic payment. This enables efficient personal health management and the purchase of related products.

[0725] "Health-related data" refers to information related to an individual's health, such as their diet, exercise, and sleep.

[0726] "Device" refers to equipment composed of mechanical parts and software, used to achieve a specific purpose.

[0727] An "information and communication system" refers to the technical means for collecting, processing, and transmitting data, and for communicating with other devices and networks.

[0728] An "information management system" refers to a database or software that stores, organizes, and easily accesses and analyzes collected data.

[0729] A "generative system" refers to a technological mechanism that analyzes and evaluates collected data to produce new information and results.

[0730] A "guidance creation unit" refers to a device or software that has the function of creating personalized guidance and advice based on analysis results.

[0731] A "notification function" refers to a system of alerts and reminders that inform users of important information and advice.

[0732] The system that implements this application provides advanced functions for health management. When a user registers daily health-related data using a voice input device, that information is transmitted to a cloud-based database via a communication system. Because the voice recognition function uses the Google Speech-to-Text API, it can be used intuitively by visually impaired people and the elderly.

[0733] The cloud-based generation system analyzes input data using generative AI models such as OpenAI GPT-4 to assess the user's health status. Based on the assessment, the guidance creation unit creates personalized advice regarding diet and exercise and sends it to the user's device via a notification function.

[0734] For example, if a user provides information about their breakfast, the AI ​​will generate specific advice such as, "Considering your nutritional balance today, we recommend purchasing a vitamin supplement." This advice is immediately displayed on the smartphone screen, and the recommended product can be easily purchased through an electronic payment system (e.g., Stripe).

[0735] An example of a prompt message is, "The user ate oatmeal and a banana this morning. Please suggest a fitness plan tailored to today's health condition." This allows users to easily manage their health information and improve their quality of health in their daily lives.

[0736] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0737] Step 1:

[0738] Users input health-related data into the device via a voice input device. The input voice is converted to text using the Google Speech-to-Text API. This input data is temporarily stored on the device.

[0739] Step 2:

[0740] The terminal sends the converted text data to a cloud database via the communication system. During this process, the data is properly formatted, encrypted according to security protocols, and then transmitted.

[0741] Step 3:

[0742] The server retrieves user health data from a cloud database and performs data analysis using a generative AI model. OpenAI GPT-4 is used for the analysis, and the input data is processed to evaluate the user's health status and trends.

[0743] Step 4:

[0744] The server's guidance generation unit receives the analysis results and generates personalized health advice. In this process, the AI ​​uses prompts to generate appropriate health advice, such as "Please suggest supplements tailored to the user's nutritional balance."

[0745] Step 5:

[0746] The generated health advice is sent from the server to the user's device and displayed as a notification on the screen. The notification also includes a link for the user to make a purchase. The purchase process is conducted through an electronic payment system to ensure user convenience.

[0747] Step 6:

[0748] When a user purchases a recommended product, the terminal securely completes the transaction via an electronic payment system. The input in this step represents the user's intention to purchase, and the output is a notification that the purchase is complete.

[0749] 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.

[0750] This invention is a system that provides personalized health advice that takes into account not only the user's health-related data but also their emotional state. Specifically, it integrates a series of components including an input device, a communication module, a generative AI, and an emotion engine.

[0751] System Configuration

[0752] 1. User device configuration:

[0753] Users input health-related data via voice or text. The device incorporates voice recognition and an emotion engine to analyze the user's emotions from the input voice and text.

[0754] For example, if a user inputs "I'm busy today and feeling stressed" in a voice that expresses fatigue, the device will convert the speech to text and simultaneously analyze the user's emotions based on that content.

[0755] 2. Data transmission and management:

[0756] The device sends analyzed health-related and emotional data to a cloud server. A communication module is responsible for the secure transfer of this data.

[0757] The transmitted data is stored in a cloud-based database and continuously managed as information, including the user's emotional history.

[0758] 3. Comprehensive analysis using generative AI:

[0759] The server uses both health-related and emotional data to perform analysis with generative AI. By evaluating health status, emotional state, and their correlation, more accurate risk assessment becomes possible.

[0760] For example, if someone is experiencing prolonged stress, it may be determined that they need advice on exercise or relaxation techniques that can help reduce stress.

[0761] 4. Generating health advice:

[0762] Based on the analysis results, the server generates personalized health advice for each user. This advice is based on both health-related data and emotional state, resulting in more practical and effective suggestions.

[0763] As a concrete example, they offer advice such as, "An hour-long walk is effective for reducing stress. Try it to refresh yourself."

[0764] 5. Notification of advice and support for implementation:

[0765] The generated health advice is delivered to the user's device, and notifications are sent via voice and text. A reminder function that takes emotional state into account is also provided to support user motivation.

[0766] For example, you can set an alert that says, "I recommend you go to bed a little earlier today. I've set some relaxing music for you to listen to."

[0767] This system enables users to manage their health comprehensively, addressing not only their physical condition but also changes in their emotions. This allows for health promotion through more personalized support.

[0768] The following describes the processing flow.

[0769] Step 1:

[0770] Users provide daily feedback to the device through health-related data and voice input. The device activates its voice recognition function and converts the voice data into text. Additionally, an emotion engine is activated to analyze the user's emotions based on their voice tone and content.

[0771] Step 2:

[0772] The device collects analyzed health and emotional data, formats it, and sends it to a cloud server according to a communication protocol. The communication module ensures secure data transmission.

[0773] Step 3:

[0774] The server receives data sent to the cloud database and stores it in the database. It updates and manages each user's health and emotional history.

[0775] Step 4:

[0776] The server uses generative AI to comprehensively analyze users' health-related and emotional data. This process evaluates health trends, emotional changes, and their correlations.

[0777] Step 5:

[0778] The server generates personalized health advice based on the analysis results. This specific advice takes into account the user's health and emotional data.

[0779] Step 6:

[0780] The generated advice is notified to the device. The device provides the advice to the user in both voice and text, and supports the user's daily health management by setting reminders tailored to their emotional state.

[0781] (Example 2)

[0782] 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".

[0783] In managing users' health, there is a need to provide comprehensive advice that takes into account not only their individual health status but also their emotional state. However, existing technologies are insufficient in generating advice that considers the user's emotional state, and have not been able to effectively provide the information necessary for promoting health.

[0784] 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.

[0785] In this invention, the server includes means for using a generative AI system that analyzes health-related data and emotional data and evaluates health status and emotional state; means for utilizing an advice generation function that generates personalized health advice based on the analysis results; and means for providing a display device that notifies the user device of the generated advice at a timing that takes into account the emotional state. This enables more effective health management and emotional stabilization by providing health advice optimized for each individual user, taking into account both health status and emotional state.

[0786] "Health-related data" refers to information about a user's physical condition and lifestyle, and is used for health assessment and management.

[0787] "Emotional data" refers to information about a user's emotional and psychological state, and is used for health management and personalized advice.

[0788] An "input device" is a device used by users to input health-related and emotional data, and has the function of collecting voice and text data.

[0789] A "communication module" is a mechanism for transmitting collected data to a cloud structure, and is responsible for the secure transfer of data.

[0790] A "cloud structure" is a data storage and processing platform provided via the internet, which enables long-term data storage and historical management.

[0791] A "generative AI system" is a system that uses artificial intelligence models to analyze stored data and evaluate health and emotional states.

[0792] The "advice generation function" is a unit that generates personalized health advice for each user based on the analysis results.

[0793] A "display device" is a device that notifies the user of generated health advice, and presents information visually and audibly.

[0794] This invention is a system that comprehensively manages a user's health and emotional state and provides personalized health advice. The system includes several key components to appropriately collect and analyze health-related and emotional data and provide information tailored to the user's needs.

[0795] First, the user uses the device to input health-related data and emotional states. This device is equipped with a voice recognition function, which converts the user's voice input into text data. It also uses an emotion engine to analyze the user's emotional state from the input data. For example, if the user inputs "I'm very tired today," the device converts this information from voice to text and determines through emotion analysis that the user is fatigued.

[0796] The analyzed data is sent to a cloud structure via a communication module. In the cloud, the data is securely stored and managed along with its historical data. The server receives the transmitted data and uses a generative AI system to comprehensively analyze the user's health and emotional state. This generative AI initiates the analysis using prompts and generates personalized advice. For example, a prompt might be "Please suggest exercises to reduce stress."

[0797] The generated health advice is created by the advice generation function and sent from the server to the terminal. The terminal notifies the user of the advice via voice and text, and provides support for implementation through a reminder function that takes into account the user's emotional state. For example, it makes specific suggestions to support the user, such as, "I recommend you go to bed early today. I'll play some relaxing music."

[0798] This system enables users to manage their health more effectively and personally, taking into account both their physical and emotional state.

[0799] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0800] Step 1:

[0801] The user uses the device to input health-related data and emotional information in voice or text format. The device uses speech recognition to convert the voice data into text data. During this process, it analyzes the input data and uses an emotion engine to determine the user's emotional state. For example, if the user says "I'm tired today," the device converts the voice into text and identifies the emotional state, such as "fatigue." The input for this process is voice data, and the output is the analyzed text data and the result of the emotional state evaluation.

[0802] Step 2:

[0803] The device transmits analyzed health-related and sentiment data to a cloud structure. This communication uses a communication module, and the data is transferred while protected by security protocols. Specifically, when data is input, the communication module becomes active and sends data packets to the cloud server. The input for this process is text data and sentiment assessment results, and the output is data stored in the cloud.

[0804] Step 3:

[0805] The server receives data arriving in the cloud and activates a generative AI model. The server uses prompts to begin analyzing the data and comprehensively assesses health and emotional states. For example, it might use a prompt such as, "What activities are recommended when stress levels are high?" The input for this step is data received from the cloud, and the output is personalized health advice generated through the analysis.

[0806] Step 4:

[0807] The server uses its advice generation function to create personalized health advice based on the analysis results. The server then prepares to send the generated advice to the terminal. In this step, the input is advice based on prompt results, and the output is suggestions tailored to the user's needs.

[0808] Step 5:

[0809] The device notifies the user of received health advice and provides a reminder function that takes into account their emotional state. It provides notifications via voice and text, and displays additional alerts and information as needed. The input for this step is advice data from the server, and the output is specific advice received and acted upon by the user.

[0810] (Application Example 2)

[0811] 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".

[0812] In modern society, personal health management is crucial, and there is a demand for information and advice to support it. However, conventional health-related services often provide uniform advice without considering the user's emotional state or stress level, resulting in a lack of personalized support. Therefore, there is a need for a system that provides more accurate health advice, taking into account the user's daily emotional changes.

[0813] 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.

[0814] In this invention, the server includes communication means for transmitting health-related data and emotion-related data to a remote server, data analysis system means for analyzing the health-related data and emotion-related data and evaluating health status, emotional state, and potential health risks, and advice generation device means for generating personalized health advice based on the analysis results. This makes it possible to provide personalized health advice that also takes emotional state into consideration.

[0815] "Health-related data" refers to data that indicates an individual's physical condition and health status, such as information on body temperature, blood pressure, heart rate, and exercise level.

[0816] "Emotion-related data" refers to data that indicates an individual's emotional state, such as information regarding stress levels, happiness, and anxiety.

[0817] An "input device" is a device that receives data in formats such as voice or text, and is a means for users to input health-related data and emotion-related data.

[0818] "Communication means" refers to technical means for transmitting collected data to a remote server, and includes devices and protocols that ensure the secure and efficient transfer of data.

[0819] A "data storage device" is a device for storing received data and managing it as a history; it is a database system that operates on the cloud or a server.

[0820] A "data analysis system" is a system that analyzes received health-related and emotion-related data to evaluate health status and potential health risks.

[0821] A "health advice generation device" is a device that generates personalized health advice for users based on analysis results obtained from a data analysis system.

[0822] A "display device or audio output device" is a device that notifies the user of generated health advice and has the role of providing advice through visual or auditory means.

[0823] To implement this invention, first, an input device is required to collect health-related data and emotion-related data. This device can receive data from the user in voice or text format. For speech recognition, for example, the Google Speech-to-Text API can be used. The voice data is converted to text, and then emotion-related data is extracted using an emotion analysis tool such as Microsoft Azure Text Analytics.

[0824] Next, the data collected via communication is sent to the cloud. This communication method ensures secure and efficient data transfer. The transmitted data is stored in a data storage device, where database systems provided by AWS or other cloud platforms are utilized.

[0825] The server uses this data to run a data analysis system. The analysis engine employs a generative AI model, such as OpenAI's GPT model. This allows for a comprehensive analysis of the user's health and emotional state, and enables the assessment of potential health risks.

[0826] The acquired analysis results are converted into personalized health advice by an advice generation device. This advice is provided to the user visually or audibly through a display device or voice output device that notifies them of the generated content. In a physical store example, customers can use voice input at a kiosk terminal to receive real-time health advice tailored to their mood that day.

[0827] For example, if a user enters "I've been extremely tired lately, do you have any advice?", the data analysis system will use this information along with the user's past emotional state to generate personalized advice. An example of a prompt would be, "If the user enters 'I've been extremely tired lately, do you have any advice?', please provide advice that takes into account their emotions and health status."

[0828] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0829] Step 1:

[0830] Users input health-related and emotion-related data via voice or text. For example, they might say a phrase like, "I've been feeling tired lately." The input device uses the Google Speech-to-Text API to convert the voice data into text. As a result, text data is output.

[0831] Step 2:

[0832] The device sends the converted text data to a sentiment analysis tool to analyze the user's emotional state. Services such as Microsoft Azure Text Analytics are used to extract sentiment-related data from the text. Output includes indicators such as stress levels and happiness levels.

[0833] Step 3:

[0834] The analysis results and original health-related data are transmitted to a server in the cloud via a communication method. The terminal uses highly secure data transfer protocols such as AWS to protect the data. At this stage, the data is stored in a data storage device on the server.

[0835] Step 4:

[0836] The server integrates accumulated health-related and emotion-related data and inputs it into the data analysis system. Using the OpenAI GPT model, the correlation of the data is evaluated, and the impact of potential health risks and emotional states is analyzed. From this analysis, evaluation results are obtained in the form of specific numerical values ​​or strings.

[0837] Step 5:

[0838] Based on the analysis results, the server uses an advice generation device to create personalized health advice. Here, a generative AI model is used to generate advice that includes suggestions for appropriate exercise and relaxation.

[0839] Step 6:

[0840] Finally, the generated health advice is sent to the device and notified to the user via a display or audio output device. This allows the user to receive a concrete action plan that can help improve their lifestyle.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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."

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] The following is further disclosed regarding the embodiments described above.

[0863] (Claim 1)

[0864] A device for inputting and collecting health-related data,

[0865] A communication module means for transmitting collected health-related data to the cloud,

[0866] A database method for storing health-related data on the cloud and managing it as a history,

[0867] A generation system means for analyzing stored health-related data and evaluating health status and potential health risks,

[0868] An advice generation unit means that generates personalized health advice based on the analysis results,

[0869] A system that includes a device to notify the user of the generated advice.

[0870] (Claim 2)

[0871] The system according to claim 1, wherein the input device has a speech recognition function and includes a process for converting speech to text.

[0872] (Claim 3)

[0873] The system according to claim 1, comprising a function to schedule health advice and set reminders so that the user can implement it in their daily life.

[0874] "Example 1"

[0875] (Claim 1)

[0876] A terminal device for inputting and collecting health-related information,

[0877] A communication means for transmitting collected health-related information to a remote location,

[0878] An information management device means for storing health-related information remotely and managing it as a history,

[0879] A generation system means for analyzing stored health-related information and evaluating health status and potential health risks,

[0880] A guidance generation device means that generates personalized health guidance based on analysis results,

[0881] A system including a terminal that notifies users of the generated instruction.

[0882] (Claim 2)

[0883] The system according to claim 1, wherein the input terminal is equipped with a speech recognition function and includes a process for converting speech into text information.

[0884] (Claim 3)

[0885] The system according to claim 1, comprising a function to schedule health guidance and set reminders so that users can carry it out in their daily lives.

[0886] "Application Example 1"

[0887] (Claim 1)

[0888] A device and means for inputting and collecting health-related data,

[0889] An information and communication system means for transmitting collected health-related data to the cloud,

[0890] An information management system that stores health-related data on the cloud and manages it as a history,

[0891] A generation system means for analyzing stored health-related data and evaluating health status and potential health risks,

[0892] A guidance creation unit means that generates personalized health advice based on analysis results,

[0893] A device means for notifying the user of the generated advice,

[0894] A system that suggests health-related products based on the user's health information and includes a means for purchasing those products through electronic payment.

[0895] (Claim 2)

[0896] The system according to claim 1, wherein the input device has a speech recognition function and includes a process for converting speech to text.

[0897] (Claim 3)

[0898] The system according to claim 1, which schedules health advice and includes a notification function to enable users to implement it in their daily lives.

[0899] "Example 2 of combining an emotion engine"

[0900] (Claim 1)

[0901] An input device means for inputting and collecting health-related data and emotional data,

[0902] A communication module means for transmitting collected data to a cloud structure,

[0903] A data storage facility means that stores data on a cloud structure and manages its history,

[0904] A generative AI system means that analyzes stored data and evaluates health status and emotional state,

[0905] A means for generating personalized health advice based on analysis results,

[0906] A system including a display device that notifies the user device of the generated advice.

[0907] (Claim 2)

[0908] The system according to claim 1, wherein the input device has a speech recognition function and includes processing to convert speech to text and to analyze emotions.

[0909] (Claim 3)

[0910] The system according to claim 1, comprising a function to schedule health advice and set reminders that take into account the user's emotional state so that the advice can be carried out during daily activities.

[0911] "Application example 2 when combining with an emotional engine"

[0912] (Claim 1)

[0913] An input device means for inputting and collecting health-related data and emotion-related data,

[0914] Communication means for transmitting collected health-related data and emotion-related data to a remote server,

[0915] A data storage device means for storing health-related data and emotion-related data on a remote server and managing them as history,

[0916] A data analysis system means for analyzing stored health-related data and emotion-related data to evaluate health status, emotional state, and potential health risks,

[0917] An advice generation device means that generates personalized health advice based on analysis results,

[0918] A system including a display device or audio output device that notifies the user of the generated advice.

[0919] (Claim 2)

[0920] The system according to claim 1, wherein the input device has a speech recognition function and includes a process for converting speech into text.

[0921] (Claim 3)

[0922] The system according to claim 1, comprising a function to schedule health advice and set up a reminder device so that users can implement it in their daily lives. [Explanation of Symbols]

[0923] 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 device for inputting and collecting health-related data, A communication module means for transmitting collected health-related data to the cloud, A database method for storing health-related data on the cloud and managing it as a history, A generation system means for analyzing stored health-related data and evaluating health status and potential health risks, An advice generation unit means that generates personalized health advice based on the analysis results, A system that includes a device to notify the user of the generated advice.

2. The system according to claim 1, wherein the input device has a speech recognition function and includes a process for converting speech to text.

3. The system according to claim 1, further comprising a function to schedule health advice and set reminders so that the user can implement it in their daily life.

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