system

Abstract

We provide the system. [Solution] A means of collecting data to monitor an individual's health status, A means for analyzing the aforementioned data and applying an artificial intelligence model for performing individual health assessments, A means for autonomously setting health goals appropriate to the individual based on the aforementioned health assessment, A means for generating advice related to nutrition, exercise, and rest based on the aforementioned health goals, A system including means for sending and displaying the aforementioned advice on an individual's device.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, with the aging of the population, the cost of medical care and nursing care is increasing, imposing an excessive burden on the younger generation. In addition, among the working-age generation, the risk of lifestyle diseases is increasing, and there is a concern that lack of exercise and irregular lifestyles may have an adverse impact on health. To solve these problems, it is important to continuously monitor individual health conditions and provide individually suitable health promotion methods.

Means for Solving the Problems

[0005] This invention provides a means for collecting data to monitor an individual's health status and applies an artificial intelligence model to analyze that data to perform individually optimized health assessments. Furthermore, it provides a means for autonomously setting individual health goals based on the results of the health assessment and generating advice related to nutrition, exercise, and rest. The generated advice is sent to the individual's terminal and displayed in real time to support the user in improving their lifestyle. In addition, the data can be shared with medical institutions and health facilities as needed to facilitate collaboration with experts. Furthermore, feedback data from the terminal is sent to a server and reflected in the generation of advice for the next time, thereby realizing continuous health management.

[0006] "Individual health status" refers to the totality of information relating to an individual's physical and mental health experiences.

[0007] "Means of data collection" refers to the equipment and technology used to obtain information related to an individual's health status.

[0008] "Analysis" refers to the process of processing and analyzing collected data and drawing meaningful conclusions based on that analysis.

[0009] An "artificial intelligence model" refers to a computer program that identifies patterns and insights from data to support decision-making and prediction.

[0010] "Health assessment" refers to evaluating an individual's health status and determining their current health level and potential risks.

[0011] "Health goals" refer to specific, measurable health-related objectives that an individual should strive to achieve.

[0012] "Means of generating advice" refers to a system that suggests beneficial behaviors and lifestyle habits for an individual based on a health assessment.

[0013] A "terminal" refers to an electronic device used by a user to receive information and interact with it.

[0014] "Medical institutions" refer to organizations such as hospitals and clinics that provide medical services.

[0015] A "health facility" refers to a place that provides equipment and services to promote health.

[0016] The term "expert" refers to a person who possesses advanced knowledge and skills in a specific field.

[0017] "Feedback data" refers to data on reactions and opinions obtained from users.

[0018] A "server" refers to a computer system used for storing and processing data. [Brief explanation of the drawing]

[0019] [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one 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.

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] 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).

[0026] 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."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] This invention is an autonomous system that continuously manages an individual's health status and provides personalized health advice. This system primarily consists of a server, a terminal, and a user, which work together to achieve efficient health management.

[0041] The server first collects health data from individuals. This mainly includes information from wearable devices and smartphones, such as heart rate, steps taken, and sleep patterns. The server then analyzes the collected data and uses an artificial intelligence model to evaluate the individual's health status. Based on this evaluation, it autonomously sets health goals that are appropriate for the user. For example, if the data indicates a lack of exercise, the server might suggest "achieving 7,500 steps per day" as a goal.

[0042] Next, the server generates advice on nutrition, exercise, and rest. This advice is customized based on the user's current condition and goals. For example, if dietary improvements are needed, the server might recommend "consuming 350 grams of vegetables per day." This advice is sent to the device and notified to the user in real time.

[0043] The terminal is responsible for displaying goals and advice received from the server to the user. Through a smartphone app or dedicated device, users can check their daily status and monitor their progress toward goals through a user interface.

[0044] Based on the presented goals and advice, users take actions to increase their awareness of health management in their daily lives. For example, they are encouraged to incorporate walking or running into their routines or to review their eating habits. Data manually entered by the user (e.g., meal details, weight) is sent to the server via the terminal and used for subsequent data analysis and advice generation. Users also provide feedback on changes in their physical condition, which contributes to improving the accuracy of the system.

[0045] Furthermore, the server can share data with medical institutions and healthcare facilities as needed, incorporating expert opinions to achieve even more accurate health management. This approach can support individualized health promotion and contribute to controlling medical costs and reducing the burden on younger generations.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The server collects health data from personal wearable devices and smartphones. This includes information such as heart rate, steps taken, and sleep patterns.

[0049] Step 2:

[0050] The server inputs the collected data into an artificial intelligence model to analyze each individual's health status. Through this analysis, it evaluates the user's past performance and trends, and determines their health risks.

[0051] Step 3:

[0052] Based on the analysis results, the server autonomously sets health goals for each user. For example, if it detects a lack of exercise, it will suggest a specific goal such as "walk 7,500 steps every day."

[0053] Step 4:

[0054] The server generates personalized advice for users regarding nutrition, exercise, and rest. This includes specific suggestions such as "eat foods rich in iron" or "aim for exercise three times a week."

[0055] Step 5:

[0056] The device receives instructions from the server and displays goals and advice to the user. This allows the user to monitor their health status in real time.

[0057] Step 6:

[0058] Users take action to improve their daily lifestyle habits, using the goals and advice displayed on their devices as a guide. For example, they might incorporate running into their daily routine or pay more attention to their diet.

[0059] Step 7:

[0060] Users manually input changes in their physical condition and their progress into their device and send the data to the server. This feedback information is used for future data analysis and advice generation.

[0061] Step 8:

[0062] The server will share data with medical institutions and health facilities as needed, and obtain opinions and advice from external experts to further improve the accuracy of health management.

[0063] (Example 1)

[0064] 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."

[0065] Managing one's own health remains challenging for some due to a lack of education and information. In particular, continuously monitoring one's health status and obtaining appropriate advice based on that monitoring is difficult. Furthermore, individual circumstances are required to determine whether such advice is feasible. Against this backdrop, there is a need for personalized health management systems.

[0066] 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.

[0067] In this invention, the server includes means for collecting information to monitor an individual's health status, means for analyzing the information and applying a machine learning model to perform an individual health assessment, and means for autonomously setting health goals appropriate for the individual based on the health assessment. This makes it possible to provide accurate and practical advice tailored to an individual's health status and to monitor its implementation in real time.

[0068] An "individual" refers to a single person with a specific health condition or lifestyle, and is the subject of health management.

[0069] "Health status" is a comprehensive indicator of an individual's physical and mental health, and is evaluated using data such as heart rate, steps taken, and sleep patterns.

[0070] "Monitoring" refers to the act of continuously observing and recording an individual's health status and tracking any changes.

[0071] "Data" refers to information about an individual's health status, including specific measurements such as heart rate, steps taken, and sleep patterns.

[0072] A "machine learning model" is a collection of algorithms and methods used to analyze collected information and assess an individual's health status.

[0073] "Health goals" refer to specific and achievable objectives set to improve an individual's health status.

[0074] "Autonomous setting" refers to the process of automatically determining individualized goals according to predefined rules or algorithms.

[0075] "Advice" refers to instructions or suggestions regarding nutrition, exercise, and rest necessary to achieve an individual's health goals.

[0076] A "device" is an electronic device that allows an individual to receive information and interact with it, and includes smartphones and dedicated devices.

[0077] "Feedback" refers to information that individuals provide to a system regarding changes in their physical condition and their level of achievement.

[0078] "Institution" refers to an organization with specialized knowledge regarding medical facilities and health management.

[0079] A "facility" refers to an organization or place that has a physical space for providing health management services, such as a clinic or fitness club.

[0080] This invention is a system that continuously manages an individual's health status and provides personalized health advice. This system consists of three components: a server, a terminal, and a user, each playing a specific role to achieve efficient health management.

[0081] The server first collects personal health data from wearable devices and smartphones. This includes information on heart rate, steps taken, and sleep patterns. The server processes this data using programming languages ​​such as Python and R, and performs analysis using generative AI models. These generative AI models utilize machine learning libraries such as TENSORFLOW® and PyTorch. This allows the server to assess individual health status and set optimal health goals.

[0082] Based on these set health goals, the server generates customized advice. This advice covers nutrition, exercise, and rest. For example, if a lack of exercise is detected, it might suggest setting a goal of "achieving 7,500 steps daily." It might also recommend "consuming 350 grams of vegetables per day" in terms of diet. This advice is designed to be actionable for the user in their daily life.

[0083] The device serves to notify the user of health goals and advice sent from the server. This information is displayed to the user in real time via a smartphone app or dedicated device. Users can check an overview of their health status and progress through a dashboard on the device.

[0084] Users begin taking action according to the presented goals and advice. For example, by entering prompts into the system such as "Suggest activities to combat a sedentary lifestyle" or "I want to know my goals for improving my diet this week," they can create a concrete action plan. In addition, users can input information such as their diet and weight into their device and send it to the server, which can then be used for future data analysis.

[0085] When necessary, the server shares data with medical institutions and health facilities, incorporating expert opinions to achieve even more accurate health management. This collaboration not only promotes individual health but also contributes to reducing future medical costs and improving the health management of society as a whole.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The server collects health data from wearable devices and smartphones. This data includes heart rate, step count, and sleep patterns. The input is raw data obtained from the devices, which is then transferred to the server via Bluetooth or Wi-Fi. The output is a structured dataset that is stored on the server. Specifically, the server periodically synchronizes with the devices to retrieve the latest data.

[0089] Step 2:

[0090] The server performs preprocessing on the collected raw data. The input is the raw data collected in step 1, and this preprocessing includes handling missing data and denoising. The output is clean data that can be analyzed. Specifically, it removes outliers and converts the data format.

[0091] Step 3:

[0092] The server analyzes clean data using machine learning algorithms to assess individual health status. This analysis utilizes a generative AI model. The input is pre-processed data, and the output is an evaluation index that indicates individual health status. Specifically, the AI ​​model generates a health score based on heart rate and step count.

[0093] Step 4:

[0094] The server sets health goals based on the analysis results. The input is the health assessment indicator from step 3, and the output is the personalized health goal. Specifically, if a lack of exercise is observed, the goal of "7,500 steps per day" is set.

[0095] Step 5:

[0096] The server generates customized advice based on health goals. The input is the set health goal, and the output is specific advice for the user. For example, it may include a nutritional suggestion such as "consume 350 grams of vegetables per day."

[0097] Step 6:

[0098] The device notifies and displays health goals and advice received from the server to the user. The input is data sent from the server, and the output is visualized information on the device. Specifically, it sends alerts using notification sounds and vibrations and displays information on the app screen.

[0099] Step 7:

[0100] Users adjust their daily behaviors according to the presented health goals and advice. The input is the information displayed on the device, and the output is actions taken to improve health. Specifically, this might include incorporating a walking routine into their daily schedule.

[0101] Step 8:

[0102] The user inputs additional data such as meal details and weight into their device and sends it to the server. The input is user-provided feedback data, and the output is data used for future data analysis and advice. Specifically, the user uses the app, enters meal details, and presses the submit button.

[0103] Step 9:

[0104] The server shares data with healthcare institutions and facilities as needed, and incorporates expert opinions into the system. Input consists of health data collected with user consent, and output is new advice including expert feedback. Specifically, it periodically exchanges information with healthcare institutions via data sharing protocols.

[0105] (Application Example 1)

[0106] 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."

[0107] There is a need for a system that can efficiently analyze various health information and provide personalized health maintenance guidance. However, conventional health management systems struggle to provide intuitive and continuous health guidance tailored to individual living environments. Furthermore, there is a lack of systems that can immediately provide feedback on individual behavior and reflect it in the guidance provided. Therefore, a system is needed that provides appropriate advice based on each individual's health condition through in-home devices, and improves guidance using behavioral data.

[0108] 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.

[0109] In this invention, the server includes means for collecting information to monitor the health status of an individual, means for applying a machine learning model to analyze the information and perform an individual health assessment, and means for autonomously setting health goals appropriate for the individual based on the health assessment. This makes it possible to intuitively provide health guidance tailored to each individual through a home-installed device and to improve the quality of guidance using behavioral data.

[0110] "Individual" refers to a person whose health status is monitored and who is the target of guidance provided by the system.

[0111] "Health status" is a general term for an individual's physical and physiological information, including heart rate, activity level, and sleep quality.

[0112] "Information" refers to data collected to understand an individual's health status, specifically obtained through wearable devices and home-installed equipment.

[0113] A "machine learning model" refers to a statistical method or algorithm used to analyze collected information and perform health assessments tailored to each individual.

[0114] "Health assessment" refers to a method of representing an individual's health status using numerical values ​​and indicators based on collected information, and is intended to help individuals set health goals.

[0115] A "health goal" is a specific target that outlines the health state and behaviors that an individual should achieve, and it is tailored to the individual's circumstances.

[0116] "In-home devices" are electronic devices installed in an individual's living space that provide health guidance through audio or visual means.

[0117] "Guidance" refers to advice and suggestions related to nutrition, exercise, and rest, generated based on health goals, and provided with the aim of promoting individual health.

[0118] This invention is a system that provides personalized health guidance using devices installed in the home. The server collects information such as heart rate, activity level, and sleep patterns from wearable devices and home-installed equipment for the purpose of monitoring the individual's health status. This information is transmitted to the server via wireless communication (e.g., Wi-Fi).

[0119] The server analyzes the collected information using a machine learning model (e.g., TensorFlow). Based on the health assessment obtained as a result of the analysis, it autonomously sets health goals tailored to the individual. These health goals include specific behavioral targets and nutritional guidelines, such as "walk 7,500 steps a day" or "consume 350 grams of vegetables."

[0120] Based on the set health goals, the in-home device uses a speech synthesis system (e.g., Google® Text-to-Speech) to provide specific guidance for achieving those goals. For example, it might say to the user, "The weather is nice today, so why not take a walk to the nearby park?"

[0121] Furthermore, in this invention, the in-home device records the individual's behavior based on the provided guidance and feeds this data back to a server. This feedback information is used to generate the next guidance, improving its quality. This system makes it possible to achieve individualized health management and improve the quality of life.

[0122] A concrete example of a prompt used in a generative AI model is, "Based on yesterday's data, the AI ​​has determined that you are not getting enough exercise. Please generate gentle words to encourage the user to exercise." This enables the automatic generation of health guidance, allowing for personalized advice.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The server collects health data such as heart rate, activity level, and sleep patterns from wearable devices and home-installed equipment. This input data is sent to the server as sensor data, which stores it in a database. The accuracy of the data is guaranteed by pre-calibrated devices.

[0126] Step 2:

[0127] The server analyzes the collected data using a machine learning model. The input is the health data collected and stored in Step 1. During the data analysis, the model quantifies individual health conditions and generates evaluation indicators. These analysis results are output as evaluation data necessary for setting subsequent health goals.

[0128] Step 3:

[0129] The server sets individual health goals based on the analysis results. In this step, based on the evaluation data from step 2, it generates health goals such as "walk 7,500 steps a day." The generated health goals are structured as specific action plans and output as the next set of instructions.

[0130] Step 4:

[0131] The terminal (a device installed in the home) notifies the user of received health goals and guidance instructions via voice or display. The input here is the guidance instructions sent from the server in step 3. The notifications are made in an intuitive and easy-to-understand format using a speech synthesis system.

[0132] Step 5:

[0133] When a user acts based on instructions from the device, the device automatically records that action. This input data includes video and time information related to the actual action. The collected feedback information serves as input for the next step, serving as improvement suggestions.

[0134] Step 6:

[0135] The server uses the feedback information obtained in Step 5 to perform data analysis to improve the instruction content. Based on this evidence data, it adjusts subsequent instruction to be more appropriate. The output is feedback data that contributes to improving the quality of future health instruction models.

[0136] 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.

[0137] This invention combines a system that autonomously manages an individual's health with an emotion engine that recognizes the user's emotions. The system consists of a server, terminals, and users, with each device working in cooperation with the others.

[0138] The server first collects data representing the user's health status from wearable devices and smartphones. This data includes heart rate, steps taken, and sleep patterns. This data is analyzed by an artificial intelligence model on the server to assess the user's health status. Based on this assessment, the server autonomously sets health goals tailored to each individual user. Furthermore, the server generates specific advice regarding nutrition, exercise, and rest, and sends this to the device.

[0139] A key feature of this system is its built-in emotion engine. This engine recognizes the user's emotional state through user data and feedback. For example, if it determines that the user is experiencing high stress levels, the server will provide relaxation-focused advice. Furthermore, health goals can be dynamically adjusted according to the user's emotional state. For instance, if the system recognizes the user as being in a very fulfilling mental state, it might consider setting slightly higher exercise goals to challenge them.

[0140] The device receives data sent from the server and displays it in an easy-to-understand format for the user. Through the device, users can check their daily health status and emotional advice, and use this information to manage their lifestyle and health. Information and feedback manually entered by the user are sent to the server via the device and used for subsequent data analysis and advice generation.

[0141] Users take the advice to heart and engage in activities to improve their lifestyle. This includes everyday health-promoting activities such as maintaining a walking routine and eating a balanced diet. In addition, they can receive follow-up support from professionals from an emotional care perspective. The server can share data, including emotional data, with medical institutions and health facilities, and incorporate expert opinions to further improve health management.

[0142] In this way, the system, which combines an emotion engine, helps improve the user's overall health through deep health and emotional insights.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The server collects user health data from wearable devices and smartphones. This includes heart rate, steps taken, sleep patterns, and even voice and facial expression data.

[0146] Step 2:

[0147] The server inputs the collected health data into an artificial intelligence model to analyze the user's current health status. This analysis assesses the user's health level and potential risks.

[0148] Step 3:

[0149] The server autonomously sets health goals for the user based on the analysis results. These goals provide specific indicators necessary for improving the user's health, such as exercise frequency and dietary balance.

[0150] Step 4:

[0151] The server uses an emotion engine to analyze the user's voice and facial expression data to recognize their emotional state. If the emotion indicates stress or fatigue, the advice provided will be adjusted accordingly.

[0152] Step 5:

[0153] The server generates personalized advice on nutrition, exercise, and rest based on your health and emotional state. For example, if you are experiencing high stress levels, it may include advice recommending relaxation techniques.

[0154] Step 6:

[0155] The server sends the generated advice to the user's device, allowing them to view it. The advice may also include emotionally sensitive reminders to help improve progress.

[0156] Step 7:

[0157] The terminal is designed to visually display advice and goals received from the server, making them easy for the user to understand.

[0158] Step 8:

[0159] Users review the advice displayed on their devices and put it into practice in their daily lives. For example, they might be encouraged to make morning walks a habit or to take time for relaxation before going to bed at night.

[0160] Step 9:

[0161] Users input their progress, changes in physical condition, and emotional feedback into their device, which is then sent to the server. This feedback contributes to improving the accuracy of subsequent analyses.

[0162] Step 10:

[0163] The server shares new data, including emotions, with healthcare institutions and facilities, enabling further follow-up and advice from experts. These experts can then collaborate to optimize the user's health management plan.

[0164] (Example 2)

[0165] 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".

[0166] In modern society, managing one's health and understanding one's emotional state are crucial, but there is a lack of effective means to integrate and utilize these aspects on a daily basis. Furthermore, a lack of mechanisms to dynamically adjust individual health goals and activity suggestions based on the user's emotional state is a significant challenge.

[0167] 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.

[0168] In this invention, the server includes a device for collecting individual physiological data, a device for analyzing the physiological data and applying a machine learning model for performing individual health assessments, and a device for recognizing the individual's emotional state based on the health assessment. This makes it possible to set appropriate health goals and suggest activities based on the individual's health and emotional state.

[0169] "Physiological data" refers to information related to an individual's physical condition, including data such as heart rate, steps taken, and sleep patterns.

[0170] A "machine learning model" refers to an algorithm or method used to analyze patterns and trends based on collected data and perform individual health assessments.

[0171] "Emotional state" refers to information that evaluates an individual's psychological state and stress level, and is estimated from feedback data, audio data, and other sources.

[0172] "Health goals" refer to specific objectives set to improve an individual's health status, and include indicators related to exercise, nutrition, and rest.

[0173] "Activity suggestions" refer to advice on personalized daily activities and habits, generated based on health goals and emotional states.

[0174] A "specialized institution" refers to a professional organization, such as a medical institution or health facility, that evaluates and follows up on health data and emotional data.

[0175] An "information terminal" is an electronic device used by a user, and includes smartphones, tablets, personal computers, and other similar devices.

[0176] A "central processing unit" refers to a computer system that collects, analyzes, and manages data generated.

[0177] This system integrates individual health management and emotional state assessment, and functions through the collaborative efforts of the server, terminal, and user. A specific implementation is described below.

[0178] The server first collects physiological data from the user's wearable device or smartphone. This includes heart rate, steps taken, and sleep patterns, and the data is securely transferred to the server via an API. The server then runs machine learning models using TensorFlow or PyTorch to analyze the collected data. This model evaluates the user's health status and outputs the results as specific numerical values ​​and graphs.

[0179] Furthermore, the server uses an emotion engine to recognize the user's emotional state. Here, natural language processing is used to analyze user feedback and voice data to estimate emotional changes and stress levels. Based on this recognition, the server autonomously sets health goals appropriate for the user. These health goals may include, for example, daily walking distance and recommended nutritional intake.

[0180] The server sends the generated health goals and activity suggestions to the device. The device receives these and displays a screen to visualize them in an easy-to-understand format for the user. For example, it may show the current progress in a dashboard format or provide important advice to the user using push notifications.

[0181] As a concrete example, consider a scenario where a user is seeking specific advice to help maintain their daily exercise habits and manage stress. The prompt might read something like, "Consider your daily exercise habits and generate specific advice for stress management."

[0182] Users can use the advice provided via the device to improve their lifestyle. Furthermore, users can input feedback into the device, which is used for subsequent data analysis and activity suggestion generation. The server can share the obtained data with specialized organizations as needed, incorporating expert opinions to provide more accurate health management.

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

[0184] Step 1:

[0185] The server acquires physiological data from wearable devices and smartphones. This data includes heart rate, steps taken, and sleep patterns, and is sent from the user's device to the server via an API. The input physiological data is stored on the server and prepared for the next analysis step. The output is the organized physiological data.

[0186] Step 2:

[0187] The server inputs the collected physiological data into a machine learning model. The model used is based on TensorFlow and PyTorch, and analyzes the data to assess the user's health status. This assessment calculates activity levels and stress indicators. The output generates the assessment results, which are organized for each user.

[0188] Step 3:

[0189] The server analyzes the user's emotional state using an emotion engine. Inputs include user-provided feedback and audio data. Natural language processing techniques are used to estimate the type and intensity of the emotion. The output is an evaluation of the current emotional state.

[0190] Step 4:

[0191] The server sets appropriate health goals based on the results of the health assessment and emotional state. The generating AI model receives the prompt "Suggest appropriate health goals for this user" as input. Health goals include exercise levels and dietary guidelines. This results in the output of personalized health goals.

[0192] Step 5:

[0193] The server sends the generated health goals and activity suggestions to the device. The transmitted data is visualized on the device and displayed in an easy-to-understand manner for the user. It receives instructions from the server as input and outputs them on the screen as dashboards and notifications.

[0194] Step 6:

[0195] Users manage their health based on the information displayed on their device. They adjust their daily habits based on the advice they receive and submit feedback via their device. This feedback is used for the next data analysis.

[0196] Step 7:

[0197] The server shares user health and emotional data with specialized organizations as needed. The input is data obtained with the user's consent, and the output facilitates feedback to experts. This allows users to receive detailed health management based on expert opinions.

[0198] (Application Example 2)

[0199] 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".

[0200] In modern society, personal health management is a crucial issue, but conventional systems often limit themselves to monitoring and evaluating health status, lacking integration with emotional states. Because health advice is not provided that takes an individual's emotional state into account, more effective health improvement is delayed. Furthermore, given the lack of direct support within the home environment, there is a need for systems that provide advice tailored to individual lifestyles.

[0201] 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.

[0202] In this invention, the server includes means for collecting data to monitor an individual's health status, means for analyzing the data and applying an artificial intelligence algorithm for performing an individualized health assessment, and means for recognizing emotional states and dynamically adjusting health goals and advice. This enables the provision of real-time health advice based on an individual's emotional state, making health management in the home environment more personalized and effectively supported.

[0203] "Means of data collection" refers to methods and technologies for acquiring various information about an individual's health status using sensors and electronic devices.

[0204] "Means of applying artificial intelligence algorithms" refers to techniques that use machine learning and statistical methods to perform computational processing in order to analyze and evaluate acquired health data.

[0205] "Means for autonomously setting health goals" refer to methods and technologies for automatically determining health improvement goals tailored to each individual based on analysis results.

[0206] "Means of generating advice related to nutrition, exercise, and rest" refers to technologies that provide individuals with specific actionable guidelines in accordance with established health goals.

[0207] "Means of recognizing emotional states" refer to methods and technologies for detecting and interpreting an individual's feelings and emotional state from data such as voice, text, and facial expressions.

[0208] "Means of providing health advice based on emotion recognition through home devices" refers to technology that transmits health guidelines optimized according to an individual's emotional state through devices used in a home environment.

[0209] An "information processing device" is an electronic device used for receiving, analyzing, storing, and processing data, and usually refers to servers, personal computers, and similar devices.

[0210] This system aims to effectively monitor the health and emotional state of individual users and provide optimal health goals and advice. The server collects personal health data according to pre-programmed procedures and analyzes it using artificial intelligence algorithms. The collected data includes heart rate, steps taken, and sleep patterns.

[0211] Based on the analysis results, the server autonomously sets individual health goals and generates advice related to nutrition, exercise, and rest. The user's emotional state is read from the collected data and determined using emotion recognition technology. The determined emotional state is used to adjust the health goals and advice.

[0212] The device visually and audibly notifies the user of health goals and advice transmitted from the server. Smart display technology can be used in the device. As a home appliance, the robot provides this information within an emotional context and offers appropriate follow-up to the user.

[0213] For example, if the server determines that the user is in a relaxed state, it will recommend light stretching or rest to maintain relaxation rather than exercise advice for stress relief. The AI ​​generates optimal advice using prompts such as, "Analyze the user's health data and generate health advice that takes into account their current emotional state. If the user is relaxed, suggest actions to maintain that relaxation."

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] Users record health data such as heart rate, steps, and sleep patterns using wearable devices and smartphones. This data is collected from sensors in each device and transmitted to a server via wireless communication. New health data is received as input and used as control information.

[0217] Step 2:

[0218] The server inputs received health data into an artificial intelligence algorithm to perform individual health assessments. TensorFlow and PyTorch are used to model the user's current health status. During this process, past and current data are compared to analyze health trends, and the assessment results are obtained as output.

[0219] Step 3:

[0220] The server autonomously sets health goals tailored to the user based on the health assessment results. Health indicators based on the assessment results, such as daily step goals and calorie intake, are used to set these goals. The set health goals are then input into the next advice generation step.

[0221] Step 4:

[0222] The server executes an emotion recognition algorithm to estimate the user's emotional state. Using natural language processing techniques, it analyzes user feedback comments and voice data to quantify emotions. This numerical information is used to refine health advice and influence the generation of new advice.

[0223] Step 5:

[0224] The server generates advice on nutrition, exercise, and rest based on health goals and emotion recognition results. Using a generative AI model, it constructs optimal advice corresponding to prompt statements, creating meaningful content for the user. The output consists of specific health action guidelines.

[0225] Step 6:

[0226] The server sends the generated advice to the user's device. The device receives this information and notifies the user using a smart display or voice output. Hardware operations here include speech synthesis technology and display capabilities.

[0227] Step 7:

[0228] Users receive notifications from their devices and incorporate them into their daily lives to align with their health goals. Users also send feedback to the server via their devices, which uses this feedback for subsequent data analysis. This feedback loop allows the system to continuously improve its personalization.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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".

[0245] This invention is an autonomous system that continuously manages an individual's health status and provides personalized health advice. This system primarily consists of a server, a terminal, and a user, which work together to achieve efficient health management.

[0246] The server first collects health data from individuals. This mainly includes information from wearable devices and smartphones, such as heart rate, steps taken, and sleep patterns. The server then analyzes the collected data and uses an artificial intelligence model to evaluate the individual's health status. Based on this evaluation, it autonomously sets health goals that are appropriate for the user. For example, if the data indicates a lack of exercise, the server might suggest "achieving 7,500 steps per day" as a goal.

[0247] Next, the server generates advice on nutrition, exercise, and rest. This advice is customized based on the user's current condition and goals. For example, if dietary improvements are needed, the server might recommend "consuming 350 grams of vegetables per day." This advice is sent to the device and notified to the user in real time.

[0248] The terminal is responsible for displaying goals and advice received from the server to the user. Through a smartphone app or dedicated device, users can check their daily status and monitor their progress toward goals through a user interface.

[0249] Based on the presented goals and advice, users take actions to increase their awareness of health management in their daily lives. For example, they are encouraged to incorporate walking or running into their routines or to review their eating habits. Data manually entered by the user (e.g., meal details, weight) is sent to the server via the terminal and used for subsequent data analysis and advice generation. Users also provide feedback on changes in their physical condition, which contributes to improving the accuracy of the system.

[0250] Furthermore, the server can share data with medical institutions and healthcare facilities as needed, incorporating expert opinions to achieve even more accurate health management. This approach can support individualized health promotion and contribute to controlling medical costs and reducing the burden on younger generations.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The server collects health data from personal wearable devices and smartphones. This includes information such as heart rate, steps taken, and sleep patterns.

[0254] Step 2:

[0255] The server inputs the collected data into an artificial intelligence model to analyze each individual's health status. Through this analysis, it evaluates the user's past performance and trends, and determines their health risks.

[0256] Step 3:

[0257] Based on the analysis results, the server autonomously sets health goals for each user. For example, if it detects a lack of exercise, it will suggest a specific goal such as "walk 7,500 steps every day."

[0258] Step 4:

[0259] The server generates personalized advice for users regarding nutrition, exercise, and rest. This includes specific suggestions such as "eat foods rich in iron" or "aim for exercise three times a week."

[0260] Step 5:

[0261] The device receives instructions from the server and displays goals and advice to the user. This allows the user to monitor their health status in real time.

[0262] Step 6:

[0263] Users take action to improve their daily lifestyle habits, using the goals and advice displayed on their devices as a guide. For example, they might incorporate running into their daily routine or pay more attention to their diet.

[0264] Step 7:

[0265] Users manually input changes in their physical condition and their progress into their device and send the data to the server. This feedback information is used for future data analysis and advice generation.

[0266] Step 8:

[0267] The server will share data with medical institutions and health facilities as needed, and obtain opinions and advice from external experts to further improve the accuracy of health management.

[0268] (Example 1)

[0269] 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."

[0270] Managing one's own health remains challenging for some due to a lack of education and information. In particular, continuously monitoring one's health status and obtaining appropriate advice based on that monitoring is difficult. Furthermore, individual circumstances are required to determine whether such advice is feasible. Against this backdrop, there is a need for personalized health management systems.

[0271] 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.

[0272] In this invention, the server includes means for collecting information to monitor an individual's health status, means for analyzing the information and applying a machine learning model to perform an individual health assessment, and means for autonomously setting health goals appropriate for the individual based on the health assessment. This makes it possible to provide accurate and practical advice tailored to an individual's health status and to monitor its implementation in real time.

[0273] An "individual" refers to a single person with a specific health condition or lifestyle, and is the subject of health management.

[0274] "Health status" is a comprehensive indicator of an individual's physical and mental health, and is evaluated using data such as heart rate, steps taken, and sleep patterns.

[0275] "Monitoring" refers to the act of continuously observing and recording an individual's health status and tracking any changes.

[0276] "Data" refers to information about an individual's health status, including specific measurements such as heart rate, steps taken, and sleep patterns.

[0277] A "machine learning model" is a collection of algorithms and methods used to analyze collected information and assess an individual's health status.

[0278] "Health goals" refer to specific and achievable objectives set to improve an individual's health status.

[0279] "Autonomous setting" refers to the process of automatically determining individualized goals according to predefined rules or algorithms.

[0280] "Advice" refers to instructions or suggestions regarding nutrition, exercise, and rest necessary to achieve an individual's health goals.

[0281] A "device" is an electronic device that allows an individual to receive information and interact with it, and includes smartphones and dedicated devices.

[0282] "Feedback" refers to information that individuals provide to a system regarding changes in their physical condition and their level of achievement.

[0283] "Institution" refers to an organization with specialized knowledge regarding medical facilities and health management.

[0284] "Facility" refers to an organization or place with a physical location that provides health management, including, for example, clinics and fitness clubs.

[0285] This invention is a system that continuously manages an individual's health status and provides customized health advice. This system is composed of three parties: a server, a terminal, and a user, and realizes efficient health management by each playing a specific role.

[0286] The server first collects an individual's health data from wearable devices and smartphones. This includes information on heart rate, number of steps, and sleep patterns. The server processes these data using programming languages such as Python and R, and performs analysis using a generated AI model. This generated AI model utilizes machine learning libraries such as TensorFlow and PyTorch. Thereby, the server evaluates an individual's health status and sets optimal health goals.

[0287] Based on the set health goals, the server generates customized advice. The content of the advice relates to nutrition, exercise, and rest. For example, in the case where lack of exercise is detected, a specific example such as setting the goal of "achieving 7,500 steps per day" can be considered. Also, in terms of diet, it may be recommended to "consume 350 grams of vegetables per day". These pieces of advice are feasible for the user to execute in daily life.

[0288] The terminal plays the role of notifying the user of the health goals and advice sent from the server. This information is displayed to the user in real time through a smartphone app or a dedicated device. The user can check the overview and progress of their health status via the dashboard on the terminal.

[0289] Users begin taking action according to the presented goals and advice. For example, by entering prompts into the system such as "Suggest activities to combat a sedentary lifestyle" or "I want to know my goals for improving my diet this week," they can create a concrete action plan. In addition, users can input information such as their diet and weight into their device and send it to the server, which can then be used for future data analysis.

[0290] When necessary, the server shares data with medical institutions and health facilities, incorporating expert opinions to achieve even more accurate health management. This collaboration not only promotes individual health but also contributes to reducing future medical costs and improving the health management of society as a whole.

[0291] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0292] Step 1:

[0293] The server collects health data from wearable devices and smartphones. This data includes heart rate, step count, and sleep patterns. The input is raw data obtained from the devices, which is then transferred to the server via Bluetooth or Wi-Fi. The output is a structured dataset that is stored on the server. Specifically, the server periodically synchronizes with the devices to retrieve the latest data.

[0294] Step 2:

[0295] The server performs preprocessing on the collected raw data. The input is the raw data collected in step 1, and this preprocessing includes handling missing data and denoising. The output is clean data that can be analyzed. Specifically, it removes outliers and converts the data format.

[0296] Step 3:

[0297] The server analyzes clean data using machine learning algorithms to assess individual health status. This analysis utilizes a generative AI model. The input is pre-processed data, and the output is an evaluation index that indicates individual health status. Specifically, the AI ​​model generates a health score based on heart rate and step count.

[0298] Step 4:

[0299] The server sets health goals based on the analysis results. The input is the health assessment indicator from step 3, and the output is the personalized health goal. Specifically, if a lack of exercise is observed, the goal of "7,500 steps per day" is set.

[0300] Step 5:

[0301] The server generates customized advice based on health goals. The input is the set health goal, and the output is specific advice for the user. For example, it may include a nutritional suggestion such as "consume 350 grams of vegetables per day."

[0302] Step 6:

[0303] The device notifies and displays health goals and advice received from the server to the user. The input is data sent from the server, and the output is visualized information on the device. Specifically, it sends alerts using notification sounds and vibrations and displays information on the app screen.

[0304] Step 7:

[0305] Users adjust their daily behaviors according to the presented health goals and advice. The input is the information displayed on the device, and the output is actions taken to improve health. Specifically, this might include incorporating a walking routine into their daily schedule.

[0306] Step 8:

[0307] The user inputs additional data such as meal content and weight into the terminal and sends it to the server. The input is the feedback data entered by the user, and the output is the data used for the next data analysis and advice. As a specific operation, the user uses the application to enter the details of the meal and presses the send button.

[0308] Step 9:

[0309] The server shares the data with medical institutions and health facilities as needed, and further reflects the opinions of experts in the system. The input is the health data collected with the user's consent, and the output is new advice including expert feedback. As a specific operation, it exchanges information with medical institutions through a data sharing protocol on a regular basis.

[0310] (Application Example 1)

[0311] 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".

[0312] There is a demand for a system that can efficiently analyze various health information and provide guidance for maintaining personal health. However, in conventional health management systems, it is difficult to provide intuitive and continuous health guidance according to individual living environments. In addition, there is also a lack of a system that can immediately provide feedback on an individual's behavior and reflect it in the guidance content. Therefore, there is a need for a system that provides appropriate advice through home-installed devices based on the health status of each individual and improves the guidance using behavior data.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0314] In this invention, the server includes means for collecting information to monitor the health status of an individual, means for applying a machine learning model to analyze the information and perform an individual health assessment, and means for autonomously setting health goals appropriate for the individual based on the health assessment. This makes it possible to intuitively provide health guidance tailored to each individual through a home-installed device and to improve the quality of guidance using behavioral data.

[0315] "Individual" refers to a person whose health status is monitored and who is the target of guidance provided by the system.

[0316] "Health status" is a general term for an individual's physical and physiological information, including heart rate, activity level, and sleep quality.

[0317] "Information" refers to data collected to understand an individual's health status, specifically obtained through wearable devices and home-installed equipment.

[0318] A "machine learning model" refers to a statistical method or algorithm used to analyze collected information and perform health assessments tailored to each individual.

[0319] "Health assessment" refers to a method of representing an individual's health status using numerical values ​​and indicators based on collected information, and is intended to help individuals set health goals.

[0320] A "health goal" is a specific target that outlines the health state and behaviors that an individual should achieve, and it is tailored to the individual's circumstances.

[0321] "In-home devices" are electronic devices installed in an individual's living space that provide health guidance through audio or visual means.

[0322] "Guidance" refers to advice and suggestions related to nutrition, exercise, and rest, generated based on health goals, and provided with the aim of promoting individual health.

[0323] This invention is a system that provides personalized health guidance using devices installed in the home. The server collects information such as heart rate, activity level, and sleep patterns from wearable devices and home-installed equipment for the purpose of monitoring the individual's health status. This information is transmitted to the server via wireless communication (e.g., Wi-Fi).

[0324] The server analyzes the collected information using a machine learning model (e.g., TensorFlow). Based on the health assessment obtained as a result of the analysis, it autonomously sets health goals tailored to the individual. These health goals include specific behavioral targets and nutritional guidelines, such as "walk 7,500 steps a day" or "consume 350 grams of vegetables."

[0325] Based on the set health goals, the in-home device uses a speech synthesis system (e.g., Google Text-to-Speech) to provide specific guidance for achieving those goals. For example, it might say to the user, "The weather is nice today, so why not take a walk to the nearby park?"

[0326] Furthermore, in this invention, the in-home device records the individual's behavior based on the provided guidance and feeds this data back to a server. This feedback information is used to generate the next guidance, improving its quality. This system makes it possible to achieve individualized health management and improve the quality of life.

[0327] A concrete example of a prompt used in a generative AI model is, "Based on yesterday's data, the AI ​​has determined that you are not getting enough exercise. Please generate gentle words to encourage the user to exercise." This enables the automatic generation of health guidance, allowing for personalized advice.

[0328] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0329] Step 1:

[0330] The server collects health data such as heart rate, activity level, and sleep patterns from wearable devices and home-installed equipment. This input data is sent to the server as sensor data, which stores it in a database. The accuracy of the data is guaranteed by pre-calibrated devices.

[0331] Step 2:

[0332] The server analyzes the collected data using a machine learning model. The input is the health data collected and stored in Step 1. During the data analysis, the model quantifies individual health conditions and generates evaluation indicators. These analysis results are output as evaluation data necessary for setting subsequent health goals.

[0333] Step 3:

[0334] The server sets individual health goals based on the analysis results. In this step, based on the evaluation data from step 2, it generates health goals such as "walk 7,500 steps a day." The generated health goals are structured as specific action plans and output as the next set of instructions.

[0335] Step 4:

[0336] The terminal (a device installed in the home) notifies the user of received health goals and guidance instructions via voice or display. The input here is the guidance instructions sent from the server in step 3. The notifications are made in an intuitive and easy-to-understand format using a speech synthesis system.

[0337] Step 5:

[0338] When a user acts based on instructions from the device, the device automatically records that action. This input data includes video and time information related to the actual action. The collected feedback information serves as input for the next step, serving as improvement suggestions.

[0339] Step 6:

[0340] The server uses the feedback information obtained in Step 5 to perform data analysis to improve the instruction content. Based on this evidence data, it adjusts subsequent instruction to be more appropriate. The output is feedback data that contributes to improving the quality of future health instruction models.

[0341] 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.

[0342] This invention combines a system that autonomously manages an individual's health with an emotion engine that recognizes the user's emotions. The system consists of a server, terminals, and users, with each device working in cooperation with the others.

[0343] The server first collects data representing the user's health status from wearable devices and smartphones. This data includes heart rate, steps taken, and sleep patterns. This data is analyzed by an artificial intelligence model on the server to assess the user's health status. Based on this assessment, the server autonomously sets health goals tailored to each individual user. Furthermore, the server generates specific advice regarding nutrition, exercise, and rest, and sends this to the device.

[0344] A key feature of this system is its built-in emotion engine. This engine recognizes the user's emotional state through user data and feedback. For example, if it determines that the user is experiencing high stress levels, the server will provide relaxation-focused advice. Furthermore, health goals can be dynamically adjusted according to the user's emotional state. For instance, if the system recognizes the user as being in a very fulfilling mental state, it might consider setting slightly higher exercise goals to challenge them.

[0345] The device receives data sent from the server and displays it in an easy-to-understand format for the user. Through the device, users can check their daily health status and emotional advice, and use this information to manage their lifestyle and health. Information and feedback manually entered by the user are sent to the server via the device and used for subsequent data analysis and advice generation.

[0346] Users take the advice to heart and engage in activities to improve their lifestyle. This includes everyday health-promoting activities such as maintaining a walking routine and eating a balanced diet. In addition, they can receive follow-up support from professionals from an emotional care perspective. The server can share data, including emotional data, with medical institutions and health facilities, and incorporate expert opinions to further improve health management.

[0347] In this way, the system, which combines an emotion engine, helps improve the user's overall health through deep health and emotional insights.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] The server collects user health data from wearable devices and smartphones. This includes heart rate, steps taken, sleep patterns, and even voice and facial expression data.

[0351] Step 2:

[0352] The server inputs the collected health data into an artificial intelligence model to analyze the user's current health status. This analysis assesses the user's health level and potential risks.

[0353] Step 3:

[0354] The server autonomously sets health goals for the user based on the analysis results. These goals provide specific indicators necessary for improving the user's health, such as exercise frequency and dietary balance.

[0355] Step 4:

[0356] The server uses an emotion engine to analyze the user's voice and facial expression data to recognize their emotional state. If the emotion indicates stress or fatigue, the advice provided will be adjusted accordingly.

[0357] Step 5:

[0358] The server generates personalized advice on nutrition, exercise, and rest based on your health and emotional state. For example, if you are experiencing high stress levels, it may include advice recommending relaxation techniques.

[0359] Step 6:

[0360] The server sends the generated advice to the user's device, allowing them to view it. The advice may also include emotionally sensitive reminders to help improve progress.

[0361] Step 7:

[0362] The terminal is designed to visually display advice and goals received from the server, making them easy for the user to understand.

[0363] Step 8:

[0364] Users review the advice displayed on their devices and put it into practice in their daily lives. For example, they might be encouraged to make morning walks a habit or to take time for relaxation before going to bed at night.

[0365] Step 9:

[0366] Users input their progress, changes in physical condition, and emotional feedback into their device, which is then sent to the server. This feedback contributes to improving the accuracy of subsequent analyses.

[0367] Step 10:

[0368] The server shares new data, including emotions, with healthcare institutions and facilities, enabling further follow-up and advice from experts. These experts can then collaborate to optimize the user's health management plan.

[0369] (Example 2)

[0370] 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".

[0371] In modern society, managing one's health and understanding one's emotional state are crucial, but there is a lack of effective means to integrate and utilize these aspects on a daily basis. Furthermore, a lack of mechanisms to dynamically adjust individual health goals and activity suggestions based on the user's emotional state is a significant challenge.

[0372] 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.

[0373] In this invention, the server includes a device for collecting individual physiological data, a device for analyzing the physiological data and applying a machine learning model for performing individual health assessments, and a device for recognizing the individual's emotional state based on the health assessment. This makes it possible to set appropriate health goals and suggest activities based on the individual's health and emotional state.

[0374] "Physiological data" refers to information related to an individual's physical condition, including data such as heart rate, steps taken, and sleep patterns.

[0375] A "machine learning model" refers to an algorithm or method used to analyze patterns and trends based on collected data and perform individual health assessments.

[0376] "Emotional state" refers to information that evaluates an individual's psychological state and stress level, and is estimated from feedback data, audio data, and other sources.

[0377] "Health goals" refer to specific objectives set to improve an individual's health status, and include indicators related to exercise, nutrition, and rest.

[0378] "Activity suggestions" refer to advice on personalized daily activities and habits, generated based on health goals and emotional states.

[0379] A "specialized institution" refers to a professional organization, such as a medical institution or health facility, that evaluates and follows up on health data and emotional data.

[0380] An "information terminal" is an electronic device used by a user, and includes smartphones, tablets, personal computers, and other similar devices.

[0381] A "central processing unit" refers to a computer system that collects, analyzes, and manages data generated.

[0382] This system integrates individual health management and emotional state assessment, and functions through the collaborative efforts of the server, terminal, and user. A specific implementation is described below.

[0383] The server first collects physiological data from the user's wearable device or smartphone. This includes heart rate, steps taken, and sleep patterns, and the data is securely transferred to the server via an API. The server then runs machine learning models using TensorFlow or PyTorch to analyze the collected data. This model evaluates the user's health status and outputs the results as specific numerical values ​​and graphs.

[0384] Furthermore, the server uses an emotion engine to recognize the user's emotional state. Here, natural language processing is used to analyze user feedback and voice data to estimate emotional changes and stress levels. Based on this recognition, the server autonomously sets health goals appropriate for the user. These health goals may include, for example, daily walking distance and recommended nutritional intake.

[0385] The server sends the generated health goals and activity suggestions to the device. The device receives these and displays a screen to visualize them in an easy-to-understand format for the user. For example, it may show the current progress in a dashboard format or provide important advice to the user using push notifications.

[0386] As a concrete example, consider a scenario where a user is seeking specific advice to help maintain their daily exercise habits and manage stress. The prompt might read something like, "Consider your daily exercise habits and generate specific advice for stress management."

[0387] Users can use the advice provided via the device to improve their lifestyle. Furthermore, users can input feedback into the device, which is used for subsequent data analysis and activity suggestion generation. The server can share the obtained data with specialized organizations as needed, incorporating expert opinions to provide more accurate health management.

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

[0389] Step 1:

[0390] The server acquires physiological data from wearable devices and smartphones. This data includes heart rate, steps taken, and sleep patterns, and is sent from the user's device to the server via an API. The input physiological data is stored on the server and prepared for the next analysis step. The output is the organized physiological data.

[0391] Step 2:

[0392] The server inputs the collected physiological data into a machine learning model. The model used is based on TensorFlow and PyTorch, and analyzes the data to assess the user's health status. This assessment calculates activity levels and stress indicators. The output generates the assessment results, which are organized for each user.

[0393] Step 3:

[0394] The server analyzes the user's emotional state using an emotion engine. Inputs include user-provided feedback and audio data. Natural language processing techniques are used to estimate the type and intensity of the emotion. The output is an evaluation of the current emotional state.

[0395] Step 4:

[0396] The server sets appropriate health goals based on the results of the health assessment and emotional state. The generating AI model receives the prompt "Suggest appropriate health goals for this user" as input. Health goals include exercise levels and dietary guidelines. This results in the output of personalized health goals.

[0397] Step 5:

[0398] The server sends the generated health goals and activity suggestions to the device. The transmitted data is visualized on the device and displayed in an easy-to-understand manner for the user. It receives instructions from the server as input and outputs them on the screen as dashboards and notifications.

[0399] Step 6:

[0400] Users manage their health based on the information displayed on their device. They adjust their daily habits based on the advice they receive and submit feedback via their device. This feedback is used for the next data analysis.

[0401] Step 7:

[0402] The server shares user health and emotional data with specialized organizations as needed. The input is data obtained with the user's consent, and the output facilitates feedback to experts. This allows users to receive detailed health management based on expert opinions.

[0403] (Application Example 2)

[0404] 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 as the "terminal".

[0405] In modern society, personal health management is a crucial issue, but conventional systems often limit themselves to monitoring and evaluating health status, lacking integration with emotional states. Because health advice is not provided that takes an individual's emotional state into account, more effective health improvement is delayed. Furthermore, given the lack of direct support within the home environment, there is a need for systems that provide advice tailored to individual lifestyles.

[0406] 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.

[0407] In this invention, the server includes means for collecting data to monitor an individual's health status, means for analyzing the data and applying an artificial intelligence algorithm for performing an individualized health assessment, and means for recognizing emotional states and dynamically adjusting health goals and advice. This enables the provision of real-time health advice based on an individual's emotional state, making health management in the home environment more personalized and effectively supported.

[0408] "Means of data collection" refers to methods and technologies for acquiring various information about an individual's health status using sensors and electronic devices.

[0409] "Means of applying artificial intelligence algorithms" refers to techniques that use machine learning and statistical methods to perform computational processing in order to analyze and evaluate acquired health data.

[0410] "Means for autonomously setting health goals" refer to methods and technologies for automatically determining health improvement goals tailored to each individual based on analysis results.

[0411] "Means of generating advice related to nutrition, exercise, and rest" refers to technologies that provide individuals with specific actionable guidelines in accordance with established health goals.

[0412] "Means of recognizing emotional states" refer to methods and technologies for detecting and interpreting an individual's feelings and emotional state from data such as voice, text, and facial expressions.

[0413] "Means of providing health advice based on emotion recognition through home devices" refers to technology that transmits health guidelines optimized according to an individual's emotional state through devices used in a home environment.

[0414] An "information processing device" is an electronic device used for receiving, analyzing, storing, and processing data, and usually refers to servers, personal computers, and similar devices.

[0415] This system aims to effectively monitor the health and emotional state of individual users and provide optimal health goals and advice. The server collects personal health data according to pre-programmed procedures and analyzes it using artificial intelligence algorithms. The collected data includes heart rate, steps taken, and sleep patterns.

[0416] Based on the analysis results, the server autonomously sets individual health goals and generates advice related to nutrition, exercise, and rest. The user's emotional state is read from the collected data and determined using emotion recognition technology. The determined emotional state is used to adjust the health goals and advice.

[0417] The device visually and audibly notifies the user of health goals and advice transmitted from the server. Smart display technology can be used in the device. As a home appliance, the robot provides this information within an emotional context and offers appropriate follow-up to the user.

[0418] For example, if the server determines that the user is in a relaxed state, it will recommend light stretching or rest to maintain relaxation rather than exercise advice for stress relief. The AI ​​generates optimal advice using prompts such as, "Analyze the user's health data and generate health advice that takes into account their current emotional state. If the user is relaxed, suggest actions to maintain that relaxation."

[0419] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0420] Step 1:

[0421] Users record health data such as heart rate, steps, and sleep patterns using wearable devices and smartphones. This data is collected from sensors in each device and transmitted to a server via wireless communication. New health data is received as input and used as control information.

[0422] Step 2:

[0423] The server inputs received health data into an artificial intelligence algorithm to perform individual health assessments. TensorFlow and PyTorch are used to model the user's current health status. During this process, past and current data are compared to analyze health trends, and the assessment results are obtained as output.

[0424] Step 3:

[0425] The server autonomously sets health goals tailored to the user based on the health assessment results. Health indicators based on the assessment results, such as daily step goals and calorie intake, are used to set these goals. The set health goals are then input into the next advice generation step.

[0426] Step 4:

[0427] The server executes an emotion recognition algorithm to estimate the user's emotional state. Using natural language processing techniques, it analyzes user feedback comments and voice data to quantify emotions. This numerical information is used to refine health advice and influence the generation of new advice.

[0428] Step 5:

[0429] The server generates advice on nutrition, exercise, and rest based on health goals and emotion recognition results. Using a generative AI model, it constructs optimal advice corresponding to prompt statements, creating meaningful content for the user. The output consists of specific health action guidelines.

[0430] Step 6:

[0431] The server sends the generated advice to the user's device. The device receives this information and notifies the user using a smart display or voice output. Hardware operations here include speech synthesis technology and display capabilities.

[0432] Step 7:

[0433] Users receive notifications from their devices and incorporate them into their daily lives to align with their health goals. Users also send feedback to the server via their devices, which uses this feedback for subsequent data analysis. This feedback loop allows the system to continuously improve its personalization.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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".

[0450] This invention is an autonomous system that continuously manages an individual's health status and provides personalized health advice. This system primarily consists of a server, a terminal, and a user, which work together to achieve efficient health management.

[0451] The server first collects health data from individuals. This mainly includes information from wearable devices and smartphones, such as heart rate, steps taken, and sleep patterns. The server then analyzes the collected data and uses an artificial intelligence model to evaluate the individual's health status. Based on this evaluation, it autonomously sets health goals that are appropriate for the user. For example, if the data indicates a lack of exercise, the server might suggest "achieving 7,500 steps per day" as a goal.

[0452] Next, the server generates advice on nutrition, exercise, and rest. This advice is customized based on the user's current condition and goals. For example, if dietary improvements are needed, the server might recommend "consuming 350 grams of vegetables per day." This advice is sent to the device and notified to the user in real time.

[0453] The terminal is responsible for displaying goals and advice received from the server to the user. Through a smartphone app or dedicated device, users can check their daily status and monitor their progress toward goals through a user interface.

[0454] Based on the presented goals and advice, users take actions to increase their awareness of health management in their daily lives. For example, they are encouraged to incorporate walking or running into their routines or to review their eating habits. Data manually entered by the user (e.g., meal details, weight) is sent to the server via the terminal and used for subsequent data analysis and advice generation. Users also provide feedback on changes in their physical condition, which contributes to improving the accuracy of the system.

[0455] Furthermore, the server can share data with medical institutions and healthcare facilities as needed, incorporating expert opinions to achieve even more accurate health management. This approach can support individualized health promotion and contribute to controlling medical costs and reducing the burden on younger generations.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] The server collects health data from personal wearable devices and smartphones. This includes information such as heart rate, steps taken, and sleep patterns.

[0459] Step 2:

[0460] The server inputs the collected data into an artificial intelligence model to analyze each individual's health status. Through this analysis, it evaluates the user's past performance and trends, and determines their health risks.

[0461] Step 3:

[0462] Based on the analysis results, the server autonomously sets health goals for each user. For example, if it detects a lack of exercise, it will suggest a specific goal such as "walk 7,500 steps every day."

[0463] Step 4:

[0464] The server generates personalized advice for users regarding nutrition, exercise, and rest. This includes specific suggestions such as "eat foods rich in iron" or "aim for exercise three times a week."

[0465] Step 5:

[0466] The device receives instructions from the server and displays goals and advice to the user. This allows the user to monitor their health status in real time.

[0467] Step 6:

[0468] Users take action to improve their daily lifestyle habits, using the goals and advice displayed on their devices as a guide. For example, they might incorporate running into their daily routine or pay more attention to their diet.

[0469] Step 7:

[0470] Users manually input changes in their physical condition and their progress into their device and send the data to the server. This feedback information is used for future data analysis and advice generation.

[0471] Step 8:

[0472] The server will share data with medical institutions and health facilities as needed, and obtain opinions and advice from external experts to further improve the accuracy of health management.

[0473] (Example 1)

[0474] 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."

[0475] Managing one's own health remains challenging for some due to a lack of education and information. In particular, continuously monitoring one's health status and obtaining appropriate advice based on that monitoring is difficult. Furthermore, individual circumstances are required to determine whether such advice is feasible. Against this backdrop, there is a need for personalized health management systems.

[0476] 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.

[0477] In this invention, the server includes means for collecting information to monitor an individual's health status, means for analyzing the information and applying a machine learning model to perform an individual health assessment, and means for autonomously setting health goals appropriate for the individual based on the health assessment. This makes it possible to provide accurate and practical advice tailored to an individual's health status and to monitor its implementation in real time.

[0478] An "individual" refers to a single person with a specific health condition or lifestyle, and is the subject of health management.

[0479] "Health status" is a comprehensive indicator of an individual's physical and mental health, and is evaluated using data such as heart rate, steps taken, and sleep patterns.

[0480] "Monitoring" refers to the act of continuously observing and recording an individual's health status and tracking any changes.

[0481] "Data" refers to information about an individual's health status, including specific measurements such as heart rate, steps taken, and sleep patterns.

[0482] A "machine learning model" is a collection of algorithms and methods used to analyze collected information and assess an individual's health status.

[0483] "Health goals" refer to specific and achievable objectives set to improve an individual's health status.

[0484] "Autonomous setting" refers to the process of automatically determining individualized goals according to predefined rules or algorithms.

[0485] "Advice" refers to instructions or suggestions regarding nutrition, exercise, and rest necessary to achieve an individual's health goals.

[0486] A "device" is an electronic device that allows an individual to receive information and interact with it, and includes smartphones and dedicated devices.

[0487] "Feedback" refers to information that individuals provide to a system regarding changes in their physical condition and their level of achievement.

[0488] "Institution" refers to an organization with specialized knowledge regarding medical facilities and health management.

[0489] A "facility" refers to an organization or place that has a physical space for providing health management services, such as a clinic or fitness club.

[0490] This invention is a system that continuously manages an individual's health status and provides personalized health advice. This system consists of three components: a server, a terminal, and a user, each playing a specific role to achieve efficient health management.

[0491] The server first collects personal health data from wearable devices and smartphones. This includes information on heart rate, steps taken, and sleep patterns. The server processes this data using programming languages ​​such as Python and R, and performs analysis using generative AI models. These generative AI models utilize machine learning libraries such as TensorFlow and PyTorch. This allows the server to assess individual health status and set optimal health goals.

[0492] Based on these set health goals, the server generates customized advice. This advice covers nutrition, exercise, and rest. For example, if a lack of exercise is detected, it might suggest setting a goal of "achieving 7,500 steps daily." It might also recommend "consuming 350 grams of vegetables per day" in terms of diet. This advice is designed to be actionable for the user in their daily life.

[0493] The device serves to notify the user of health goals and advice sent from the server. This information is displayed to the user in real time via a smartphone app or dedicated device. Users can check an overview of their health status and progress through a dashboard on the device.

[0494] Users begin taking action according to the presented goals and advice. For example, by entering prompts into the system such as "Suggest activities to combat a sedentary lifestyle" or "I want to know my goals for improving my diet this week," they can create a concrete action plan. In addition, users can input information such as their diet and weight into their device and send it to the server, which can then be used for future data analysis.

[0495] When necessary, the server shares data with medical institutions and health facilities, incorporating expert opinions to achieve even more accurate health management. This collaboration not only promotes individual health but also contributes to reducing future medical costs and improving the health management of society as a whole.

[0496] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0497] Step 1:

[0498] The server collects health data from wearable devices and smartphones. This data includes heart rate, step count, and sleep patterns. The input is raw data obtained from the devices, which is then transferred to the server via Bluetooth or Wi-Fi. The output is a structured dataset that is stored on the server. Specifically, the server periodically synchronizes with the devices to retrieve the latest data.

[0499] Step 2:

[0500] The server performs preprocessing on the collected raw data. The input is the raw data collected in step 1, and this preprocessing includes handling missing data and denoising. The output is clean data that can be analyzed. Specifically, it removes outliers and converts the data format.

[0501] Step 3:

[0502] The server analyzes clean data using machine learning algorithms to assess individual health status. This analysis utilizes a generative AI model. The input is pre-processed data, and the output is an evaluation index that indicates individual health status. Specifically, the AI ​​model generates a health score based on heart rate and step count.

[0503] Step 4:

[0504] The server sets health goals based on the analysis results. The input is the health assessment indicator from step 3, and the output is the personalized health goal. Specifically, if a lack of exercise is observed, the goal of "7,500 steps per day" is set.

[0505] Step 5:

[0506] The server generates customized advice based on health goals. The input is the set health goal, and the output is specific advice for the user. For example, it may include a nutritional suggestion such as "consume 350 grams of vegetables per day."

[0507] Step 6:

[0508] The device notifies and displays health goals and advice received from the server to the user. The input is data sent from the server, and the output is visualized information on the device. Specifically, it sends alerts using notification sounds and vibrations and displays information on the app screen.

[0509] Step 7:

[0510] Users adjust their daily behaviors according to the presented health goals and advice. The input is the information displayed on the device, and the output is actions taken to improve health. Specifically, this might include incorporating a walking routine into their daily schedule.

[0511] Step 8:

[0512] The user inputs additional data such as meal details and weight into their device and sends it to the server. The input is user-provided feedback data, and the output is data used for future data analysis and advice. Specifically, the user uses the app, enters meal details, and presses the submit button.

[0513] Step 9:

[0514] The server shares data with healthcare institutions and facilities as needed, and incorporates expert opinions into the system. Input consists of health data collected with user consent, and output is new advice including expert feedback. Specifically, it periodically exchanges information with healthcare institutions via data sharing protocols.

[0515] (Application Example 1)

[0516] 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."

[0517] There is a need for a system that can efficiently analyze various health information and provide personalized health maintenance guidance. However, conventional health management systems struggle to provide intuitive and continuous health guidance tailored to individual living environments. Furthermore, there is a lack of systems that can immediately provide feedback on individual behavior and reflect it in the guidance provided. Therefore, a system is needed that provides appropriate advice based on each individual's health condition through in-home devices, and improves guidance using behavioral data.

[0518] 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.

[0519] In this invention, the server includes means for collecting information to monitor the health status of an individual, means for applying a machine learning model to analyze the information and perform an individual health assessment, and means for autonomously setting health goals appropriate for the individual based on the health assessment. This makes it possible to intuitively provide health guidance tailored to each individual through a home-installed device and to improve the quality of guidance using behavioral data.

[0520] "Individual" refers to a person whose health status is monitored and who is the target of guidance provided by the system.

[0521] "Health status" is a general term for an individual's physical and physiological information, including heart rate, activity level, and sleep quality.

[0522] "Information" refers to data collected to understand an individual's health status, specifically obtained through wearable devices and home-installed equipment.

[0523] A "machine learning model" refers to a statistical method or algorithm used to analyze collected information and perform health assessments tailored to each individual.

[0524] "Health assessment" refers to a method of representing an individual's health status using numerical values ​​and indicators based on collected information, and is intended to help individuals set health goals.

[0525] A "health goal" is a specific target that outlines the health state and behaviors that an individual should achieve, and it is tailored to the individual's circumstances.

[0526] "In-home devices" are electronic devices installed in an individual's living space that provide health guidance through audio or visual means.

[0527] "Guidance" refers to advice and suggestions related to nutrition, exercise, and rest, generated based on health goals, and provided with the aim of promoting individual health.

[0528] This invention is a system that provides personalized health guidance using devices installed in the home. The server collects information such as heart rate, activity level, and sleep patterns from wearable devices and home-installed equipment for the purpose of monitoring the individual's health status. This information is transmitted to the server via wireless communication (e.g., Wi-Fi).

[0529] The server analyzes the collected information using a machine learning model (e.g., TensorFlow). Based on the health assessment obtained as a result of the analysis, it autonomously sets health goals tailored to the individual. These health goals include specific behavioral targets and nutritional guidelines, such as "walk 7,500 steps a day" or "consume 350 grams of vegetables."

[0530] Based on the set health goals, the in-home device uses a speech synthesis system (e.g., Google Text-to-Speech) to provide specific guidance for achieving those goals. For example, it might say to the user, "The weather is nice today, so why not take a walk to the nearby park?"

[0531] Furthermore, in this invention, the in-home device records the individual's behavior based on the provided guidance and feeds this data back to a server. This feedback information is used to generate the next guidance, improving its quality. This system makes it possible to achieve individualized health management and improve the quality of life.

[0532] A concrete example of a prompt used in a generative AI model is, "Based on yesterday's data, the AI ​​has determined that you are not getting enough exercise. Please generate gentle words to encourage the user to exercise." This enables the automatic generation of health guidance, allowing for personalized advice.

[0533] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0534] Step 1:

[0535] The server collects health data such as heart rate, activity level, and sleep patterns from wearable devices and home-installed equipment. This input data is sent to the server as sensor data, which stores it in a database. The accuracy of the data is guaranteed by pre-calibrated devices.

[0536] Step 2:

[0537] The server analyzes the collected data using a machine learning model. The input is the health data collected and stored in Step 1. During the data analysis, the model quantifies individual health conditions and generates evaluation indicators. These analysis results are output as evaluation data necessary for setting subsequent health goals.

[0538] Step 3:

[0539] The server sets individual health goals based on the analysis results. In this step, based on the evaluation data from step 2, it generates health goals such as "walk 7,500 steps a day." The generated health goals are structured as specific action plans and output as the next set of instructions.

[0540] Step 4:

[0541] The terminal (a device installed in the home) notifies the user of received health goals and guidance instructions via voice or display. The input here is the guidance instructions sent from the server in step 3. The notifications are made in an intuitive and easy-to-understand format using a speech synthesis system.

[0542] Step 5:

[0543] When a user acts based on instructions from the device, the device automatically records that action. This input data includes video and time information related to the actual action. The collected feedback information serves as input for the next step, serving as improvement suggestions.

[0544] Step 6:

[0545] The server uses the feedback information obtained in Step 5 to perform data analysis to improve the instruction content. Based on this evidence data, it adjusts subsequent instruction to be more appropriate. The output is feedback data that contributes to improving the quality of future health instruction models.

[0546] 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.

[0547] This invention combines a system that autonomously manages an individual's health with an emotion engine that recognizes the user's emotions. The system consists of a server, terminals, and users, with each device working in cooperation with the others.

[0548] The server first collects data representing the user's health status from wearable devices and smartphones. This data includes heart rate, steps taken, and sleep patterns. This data is analyzed by an artificial intelligence model on the server to assess the user's health status. Based on this assessment, the server autonomously sets health goals tailored to each individual user. Furthermore, the server generates specific advice regarding nutrition, exercise, and rest, and sends this to the device.

[0549] A key feature of this system is its built-in emotion engine. This engine recognizes the user's emotional state through user data and feedback. For example, if it determines that the user is experiencing high stress levels, the server will provide relaxation-focused advice. Furthermore, health goals can be dynamically adjusted according to the user's emotional state. For instance, if the system recognizes the user as being in a very fulfilling mental state, it might consider setting slightly higher exercise goals to challenge them.

[0550] The device receives data sent from the server and displays it in an easy-to-understand format for the user. Through the device, users can check their daily health status and emotional advice, and use this information to manage their lifestyle and health. Information and feedback manually entered by the user are sent to the server via the device and used for subsequent data analysis and advice generation.

[0551] Users take the advice to heart and engage in activities to improve their lifestyle. This includes everyday health-promoting activities such as maintaining a walking routine and eating a balanced diet. In addition, they can receive follow-up support from professionals from an emotional care perspective. The server can share data, including emotional data, with medical institutions and health facilities, and incorporate expert opinions to further improve health management.

[0552] In this way, the system, which combines an emotion engine, helps improve the user's overall health through deep health and emotional insights.

[0553] The following describes the processing flow.

[0554] Step 1:

[0555] The server collects user health data from wearable devices and smartphones. This includes heart rate, steps taken, sleep patterns, and even voice and facial expression data.

[0556] Step 2:

[0557] The server inputs the collected health data into an artificial intelligence model to analyze the user's current health status. This analysis assesses the user's health level and potential risks.

[0558] Step 3:

[0559] The server autonomously sets health goals for the user based on the analysis results. These goals provide specific indicators necessary for improving the user's health, such as exercise frequency and dietary balance.

[0560] Step 4:

[0561] The server uses an emotion engine to analyze the user's voice and facial expression data to recognize their emotional state. If the emotion indicates stress or fatigue, the advice provided will be adjusted accordingly.

[0562] Step 5:

[0563] The server generates personalized advice on nutrition, exercise, and rest based on your health and emotional state. For example, if you are experiencing high stress levels, it may include advice recommending relaxation techniques.

[0564] Step 6:

[0565] The server sends the generated advice to the user's device, allowing them to view it. The advice may also include emotionally sensitive reminders to help improve progress.

[0566] Step 7:

[0567] The terminal is designed to visually display advice and goals received from the server, making them easy for the user to understand.

[0568] Step 8:

[0569] Users review the advice displayed on their devices and put it into practice in their daily lives. For example, they might be encouraged to make morning walks a habit or to take time for relaxation before going to bed at night.

[0570] Step 9:

[0571] Users input their progress, changes in physical condition, and emotional feedback into their device, which is then sent to the server. This feedback contributes to improving the accuracy of subsequent analyses.

[0572] Step 10:

[0573] The server shares new data, including emotions, with healthcare institutions and facilities, enabling further follow-up and advice from experts. These experts can then collaborate to optimize the user's health management plan.

[0574] (Example 2)

[0575] 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."

[0576] In modern society, managing one's health and understanding one's emotional state are crucial, but there is a lack of effective means to integrate and utilize these aspects on a daily basis. Furthermore, a lack of mechanisms to dynamically adjust individual health goals and activity suggestions based on the user's emotional state is a significant challenge.

[0577] 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.

[0578] In this invention, the server includes a device for collecting individual physiological data, a device for analyzing the physiological data and applying a machine learning model for performing individual health assessments, and a device for recognizing the individual's emotional state based on the health assessment. This makes it possible to set appropriate health goals and suggest activities based on the individual's health and emotional state.

[0579] "Physiological data" refers to information related to an individual's physical condition, including data such as heart rate, steps taken, and sleep patterns.

[0580] A "machine learning model" refers to an algorithm or method used to analyze patterns and trends based on collected data and perform individual health assessments.

[0581] "Emotional state" refers to information that evaluates an individual's psychological state and stress level, and is estimated from feedback data, audio data, and other sources.

[0582] "Health goals" refer to specific objectives set to improve an individual's health status, and include indicators related to exercise, nutrition, and rest.

[0583] "Activity suggestions" refer to advice on personalized daily activities and habits, generated based on health goals and emotional states.

[0584] A "specialized institution" refers to a professional organization, such as a medical institution or health facility, that evaluates and follows up on health data and emotional data.

[0585] An "information terminal" is an electronic device used by a user, and includes smartphones, tablets, personal computers, and other similar devices.

[0586] A "central processing unit" refers to a computer system that collects, analyzes, and manages data generated.

[0587] This system integrates individual health management and emotional state assessment, and functions through the collaborative efforts of the server, terminal, and user. A specific implementation is described below.

[0588] The server first collects physiological data from the user's wearable device or smartphone. This includes heart rate, steps taken, and sleep patterns, and the data is securely transferred to the server via an API. The server then runs machine learning models using TensorFlow or PyTorch to analyze the collected data. This model evaluates the user's health status and outputs the results as specific numerical values ​​and graphs.

[0589] Furthermore, the server uses an emotion engine to recognize the user's emotional state. Here, natural language processing is used to analyze user feedback and voice data to estimate emotional changes and stress levels. Based on this recognition, the server autonomously sets health goals appropriate for the user. These health goals may include, for example, daily walking distance and recommended nutritional intake.

[0590] The server sends the generated health goals and activity suggestions to the device. The device receives these and displays a screen to visualize them in an easy-to-understand format for the user. For example, it may show the current progress in a dashboard format or provide important advice to the user using push notifications.

[0591] As a concrete example, consider a scenario where a user is seeking specific advice to help maintain their daily exercise habits and manage stress. The prompt might read something like, "Consider your daily exercise habits and generate specific advice for stress management."

[0592] Users can use the advice provided via the device to improve their lifestyle. Furthermore, users can input feedback into the device, which is used for subsequent data analysis and activity suggestion generation. The server can share the obtained data with specialized organizations as needed, incorporating expert opinions to provide more accurate health management.

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

[0594] Step 1:

[0595] The server acquires physiological data from wearable devices and smartphones. This data includes heart rate, steps taken, and sleep patterns, and is sent from the user's device to the server via an API. The input physiological data is stored on the server and prepared for the next analysis step. The output is the organized physiological data.

[0596] Step 2:

[0597] The server inputs the collected physiological data into a machine learning model. The model used is based on TensorFlow and PyTorch, and analyzes the data to assess the user's health status. This assessment calculates activity levels and stress indicators. The output generates the assessment results, which are organized for each user.

[0598] Step 3:

[0599] The server analyzes the user's emotional state using an emotion engine. Inputs include user-provided feedback and audio data. Natural language processing techniques are used to estimate the type and intensity of the emotion. The output is an evaluation of the current emotional state.

[0600] Step 4:

[0601] The server sets appropriate health goals based on the results of the health assessment and emotional state. The generating AI model receives the prompt "Suggest appropriate health goals for this user" as input. Health goals include exercise levels and dietary guidelines. This results in the output of personalized health goals.

[0602] Step 5:

[0603] The server sends the generated health goals and activity suggestions to the device. The transmitted data is visualized on the device and displayed in an easy-to-understand manner for the user. It receives instructions from the server as input and outputs them on the screen as dashboards and notifications.

[0604] Step 6:

[0605] Users manage their health based on the information displayed on their device. They adjust their daily habits based on the advice they receive and submit feedback via their device. This feedback is used for the next data analysis.

[0606] Step 7:

[0607] The server shares user health and emotional data with specialized organizations as needed. The input is data obtained with the user's consent, and the output facilitates feedback to experts. This allows users to receive detailed health management based on expert opinions.

[0608] (Application Example 2)

[0609] 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."

[0610] In modern society, personal health management is a crucial issue, but conventional systems often limit themselves to monitoring and evaluating health status, lacking integration with emotional states. Because health advice is not provided that takes an individual's emotional state into account, more effective health improvement is delayed. Furthermore, given the lack of direct support within the home environment, there is a need for systems that provide advice tailored to individual lifestyles.

[0611] 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.

[0612] In this invention, the server includes means for collecting data to monitor an individual's health status, means for analyzing the data and applying an artificial intelligence algorithm for performing an individualized health assessment, and means for recognizing emotional states and dynamically adjusting health goals and advice. This enables the provision of real-time health advice based on an individual's emotional state, making health management in the home environment more personalized and effectively supported.

[0613] "Means of data collection" refers to methods and technologies for acquiring various information about an individual's health status using sensors and electronic devices.

[0614] "Means of applying artificial intelligence algorithms" refers to techniques that use machine learning and statistical methods to perform computational processing in order to analyze and evaluate acquired health data.

[0615] "Means for autonomously setting health goals" refer to methods and technologies for automatically determining health improvement goals tailored to each individual based on analysis results.

[0616] "Means of generating advice related to nutrition, exercise, and rest" refers to technologies that provide individuals with specific actionable guidelines in accordance with established health goals.

[0617] "Means of recognizing emotional states" refer to methods and technologies for detecting and interpreting an individual's feelings and emotional state from data such as voice, text, and facial expressions.

[0618] "Means of providing health advice based on emotion recognition through home devices" refers to technology that transmits health guidelines optimized according to an individual's emotional state through devices used in a home environment.

[0619] An "information processing device" is an electronic device used for receiving, analyzing, storing, and processing data, and usually refers to servers, personal computers, and similar devices.

[0620] This system aims to effectively monitor the health and emotional state of individual users and provide optimal health goals and advice. The server collects personal health data according to pre-programmed procedures and analyzes it using artificial intelligence algorithms. The collected data includes heart rate, steps taken, and sleep patterns.

[0621] Based on the analysis results, the server autonomously sets individual health goals and generates advice related to nutrition, exercise, and rest. The user's emotional state is read from the collected data and determined using emotion recognition technology. The determined emotional state is used to adjust the health goals and advice.

[0622] The device visually and audibly notifies the user of health goals and advice transmitted from the server. Smart display technology can be used in the device. As a home appliance, the robot provides this information within an emotional context and offers appropriate follow-up to the user.

[0623] For example, if the server determines that the user is in a relaxed state, it will recommend light stretching or rest to maintain relaxation rather than exercise advice for stress relief. The AI ​​generates optimal advice using prompts such as, "Analyze the user's health data and generate health advice that takes into account their current emotional state. If the user is relaxed, suggest actions to maintain that relaxation."

[0624] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0625] Step 1:

[0626] Users record health data such as heart rate, steps, and sleep patterns using wearable devices and smartphones. This data is collected from sensors in each device and transmitted to a server via wireless communication. New health data is received as input and used as control information.

[0627] Step 2:

[0628] The server inputs received health data into an artificial intelligence algorithm to perform individual health assessments. TensorFlow and PyTorch are used to model the user's current health status. During this process, past and current data are compared to analyze health trends, and the assessment results are obtained as output.

[0629] Step 3:

[0630] The server autonomously sets health goals tailored to the user based on the health assessment results. Health indicators based on the assessment results, such as daily step goals and calorie intake, are used to set these goals. The set health goals are then input into the next advice generation step.

[0631] Step 4:

[0632] The server executes an emotion recognition algorithm to estimate the user's emotional state. Using natural language processing techniques, it analyzes user feedback comments and voice data to quantify emotions. This numerical information is used to refine health advice and influence the generation of new advice.

[0633] Step 5:

[0634] The server generates advice on nutrition, exercise, and rest based on health goals and emotion recognition results. Using a generative AI model, it constructs optimal advice corresponding to prompt statements, creating meaningful content for the user. The output consists of specific health action guidelines.

[0635] Step 6:

[0636] The server sends the generated advice to the user's device. The device receives this information and notifies the user using a smart display or voice output. Hardware operations here include speech synthesis technology and display capabilities.

[0637] Step 7:

[0638] Users receive notifications from their devices and incorporate them into their daily lives to align with their health goals. Users also send feedback to the server via their devices, which uses this feedback for subsequent data analysis. This feedback loop allows the system to continuously improve its personalization.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] [Fourth Embodiment]

[0643] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0644] 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.

[0645] 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).

[0646] 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.

[0647] 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.

[0648] 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).

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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".

[0656] This invention is an autonomous system that continuously manages an individual's health status and provides personalized health advice. This system primarily consists of a server, a terminal, and a user, which work together to achieve efficient health management.

[0657] The server first collects health data from individuals. This mainly includes information from wearable devices and smartphones, such as heart rate, steps taken, and sleep patterns. The server then analyzes the collected data and uses an artificial intelligence model to evaluate the individual's health status. Based on this evaluation, it autonomously sets health goals that are appropriate for the user. For example, if the data indicates a lack of exercise, the server might suggest "achieving 7,500 steps per day" as a goal.

[0658] Next, the server generates advice on nutrition, exercise, and rest. This advice is customized based on the user's current condition and goals. For example, if dietary improvements are needed, the server might recommend "consuming 350 grams of vegetables per day." This advice is sent to the device and notified to the user in real time.

[0659] The terminal is responsible for displaying goals and advice received from the server to the user. Through a smartphone app or dedicated device, users can check their daily status and monitor their progress toward goals through a user interface.

[0660] Based on the presented goals and advice, users take actions to increase their awareness of health management in their daily lives. For example, they are encouraged to incorporate walking or running into their routines or to review their eating habits. Data manually entered by the user (e.g., meal details, weight) is sent to the server via the terminal and used for subsequent data analysis and advice generation. Users also provide feedback on changes in their physical condition, which contributes to improving the accuracy of the system.

[0661] Furthermore, the server can share data with medical institutions and healthcare facilities as needed, incorporating expert opinions to achieve even more accurate health management. This approach can support individualized health promotion and contribute to controlling medical costs and reducing the burden on younger generations.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The server collects health data from personal wearable devices and smartphones. This includes information such as heart rate, steps taken, and sleep patterns.

[0665] Step 2:

[0666] The server inputs the collected data into an artificial intelligence model to analyze each individual's health status. Through this analysis, it evaluates the user's past performance and trends, and determines their health risks.

[0667] Step 3:

[0668] Based on the analysis results, the server autonomously sets health goals for each user. For example, if it detects a lack of exercise, it will suggest a specific goal such as "walk 7,500 steps every day."

[0669] Step 4:

[0670] The server generates personalized advice for users regarding nutrition, exercise, and rest. This includes specific suggestions such as "eat foods rich in iron" or "aim for exercise three times a week."

[0671] Step 5:

[0672] The device receives instructions from the server and displays goals and advice to the user. This allows the user to monitor their health status in real time.

[0673] Step 6:

[0674] Users take action to improve their daily lifestyle habits, using the goals and advice displayed on their devices as a guide. For example, they might incorporate running into their daily routine or pay more attention to their diet.

[0675] Step 7:

[0676] Users manually input changes in their physical condition and their progress into their device and send the data to the server. This feedback information is used for future data analysis and advice generation.

[0677] Step 8:

[0678] The server will share data with medical institutions and health facilities as needed, and obtain opinions and advice from external experts to further improve the accuracy of health management.

[0679] (Example 1)

[0680] 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".

[0681] Managing one's own health remains challenging for some due to a lack of education and information. In particular, continuously monitoring one's health status and obtaining appropriate advice based on that monitoring is difficult. Furthermore, individual circumstances are required to determine whether such advice is feasible. Against this backdrop, there is a need for personalized health management systems.

[0682] 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.

[0683] In this invention, the server includes means for collecting information to monitor an individual's health status, means for analyzing the information and applying a machine learning model to perform an individual health assessment, and means for autonomously setting health goals appropriate for the individual based on the health assessment. This makes it possible to provide accurate and practical advice tailored to an individual's health status and to monitor its implementation in real time.

[0684] An "individual" refers to a single person with a specific health condition or lifestyle, and is the subject of health management.

[0685] "Health status" is a comprehensive indicator of an individual's physical and mental health, and is evaluated using data such as heart rate, steps taken, and sleep patterns.

[0686] "Monitoring" refers to the act of continuously observing and recording an individual's health status and tracking any changes.

[0687] "Data" refers to information about an individual's health status, including specific measurements such as heart rate, steps taken, and sleep patterns.

[0688] A "machine learning model" is a collection of algorithms and methods used to analyze collected information and assess an individual's health status.

[0689] "Health goals" refer to specific and achievable objectives set to improve an individual's health status.

[0690] "Autonomous setting" refers to the process of automatically determining individualized goals according to predefined rules or algorithms.

[0691] "Advice" refers to instructions or suggestions regarding nutrition, exercise, and rest necessary to achieve an individual's health goals.

[0692] A "device" is an electronic device that allows an individual to receive information and interact with it, and includes smartphones and dedicated devices.

[0693] "Feedback" refers to information that individuals provide to a system regarding changes in their physical condition and their level of achievement.

[0694] "Institution" refers to an organization with specialized knowledge regarding medical facilities and health management.

[0695] A "facility" refers to an organization or place that has a physical space for providing health management services, such as a clinic or fitness club.

[0696] This invention is a system that continuously manages an individual's health status and provides personalized health advice. This system consists of three components: a server, a terminal, and a user, each playing a specific role to achieve efficient health management.

[0697] The server first collects personal health data from wearable devices and smartphones. This includes information on heart rate, steps taken, and sleep patterns. The server processes this data using programming languages ​​such as Python and R, and performs analysis using generative AI models. These generative AI models utilize machine learning libraries such as TensorFlow and PyTorch. This allows the server to assess individual health status and set optimal health goals.

[0698] Based on these set health goals, the server generates customized advice. This advice covers nutrition, exercise, and rest. For example, if a lack of exercise is detected, it might suggest setting a goal of "achieving 7,500 steps daily." It might also recommend "consuming 350 grams of vegetables per day" in terms of diet. This advice is designed to be actionable for the user in their daily life.

[0699] The device serves to notify the user of health goals and advice sent from the server. This information is displayed to the user in real time via a smartphone app or dedicated device. Users can check an overview of their health status and progress through a dashboard on the device.

[0700] Users begin taking action according to the presented goals and advice. For example, by entering prompts into the system such as "Suggest activities to combat a sedentary lifestyle" or "I want to know my goals for improving my diet this week," they can create a concrete action plan. In addition, users can input information such as their diet and weight into their device and send it to the server, which can then be used for future data analysis.

[0701] When necessary, the server shares data with medical institutions and health facilities, incorporating expert opinions to achieve even more accurate health management. This collaboration not only promotes individual health but also contributes to reducing future medical costs and improving the health management of society as a whole.

[0702] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0703] Step 1:

[0704] The server collects health data from wearable devices and smartphones. This data includes heart rate, step count, and sleep patterns. The input is raw data obtained from the devices, which is then transferred to the server via Bluetooth or Wi-Fi. The output is a structured dataset that is stored on the server. Specifically, the server periodically synchronizes with the devices to retrieve the latest data.

[0705] Step 2:

[0706] The server performs preprocessing on the collected raw data. The input is the raw data collected in step 1, and this preprocessing includes handling missing data and denoising. The output is clean data that can be analyzed. Specifically, it removes outliers and converts the data format.

[0707] Step 3:

[0708] The server analyzes clean data using machine learning algorithms to assess individual health status. This analysis utilizes a generative AI model. The input is pre-processed data, and the output is an evaluation index that indicates individual health status. Specifically, the AI ​​model generates a health score based on heart rate and step count.

[0709] Step 4:

[0710] The server sets health goals based on the analysis results. The input is the health assessment indicator from step 3, and the output is the personalized health goal. Specifically, if a lack of exercise is observed, the goal of "7,500 steps per day" is set.

[0711] Step 5:

[0712] The server generates customized advice based on health goals. The input is the set health goal, and the output is specific advice for the user. For example, it may include a nutritional suggestion such as "consume 350 grams of vegetables per day."

[0713] Step 6:

[0714] The device notifies and displays health goals and advice received from the server to the user. The input is data sent from the server, and the output is visualized information on the device. Specifically, it sends alerts using notification sounds and vibrations and displays information on the app screen.

[0715] Step 7:

[0716] Users adjust their daily behaviors according to the presented health goals and advice. The input is the information displayed on the device, and the output is actions taken to improve health. Specifically, this might include incorporating a walking routine into their daily schedule.

[0717] Step 8:

[0718] The user inputs additional data such as meal details and weight into their device and sends it to the server. The input is user-provided feedback data, and the output is data used for future data analysis and advice. Specifically, the user uses the app, enters meal details, and presses the submit button.

[0719] Step 9:

[0720] The server shares data with healthcare institutions and facilities as needed, and incorporates expert opinions into the system. Input consists of health data collected with user consent, and output is new advice including expert feedback. Specifically, it periodically exchanges information with healthcare institutions via data sharing protocols.

[0721] (Application Example 1)

[0722] 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".

[0723] There is a need for a system that can efficiently analyze various health information and provide personalized health maintenance guidance. However, conventional health management systems struggle to provide intuitive and continuous health guidance tailored to individual living environments. Furthermore, there is a lack of systems that can immediately provide feedback on individual behavior and reflect it in the guidance provided. Therefore, a system is needed that provides appropriate advice based on each individual's health condition through in-home devices, and improves guidance using behavioral data.

[0724] 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.

[0725] In this invention, the server includes means for collecting information to monitor the health status of an individual, means for applying a machine learning model to analyze the information and perform an individual health assessment, and means for autonomously setting health goals appropriate for the individual based on the health assessment. This makes it possible to intuitively provide health guidance tailored to each individual through a home-installed device and to improve the quality of guidance using behavioral data.

[0726] "Individual" refers to a person whose health status is monitored and who is the target of guidance provided by the system.

[0727] "Health status" is a general term for an individual's physical and physiological information, including heart rate, activity level, and sleep quality.

[0728] "Information" refers to data collected to understand an individual's health status, specifically obtained through wearable devices and home-installed equipment.

[0729] A "machine learning model" refers to a statistical method or algorithm used to analyze collected information and perform health assessments tailored to each individual.

[0730] "Health assessment" refers to a method of representing an individual's health status using numerical values ​​and indicators based on collected information, and is intended to help individuals set health goals.

[0731] A "health goal" is a specific target that outlines the health state and behaviors that an individual should achieve, and it is tailored to the individual's circumstances.

[0732] "In-home devices" are electronic devices installed in an individual's living space that provide health guidance through audio or visual means.

[0733] "Guidance" refers to advice and suggestions related to nutrition, exercise, and rest, generated based on health goals, and provided with the aim of promoting individual health.

[0734] This invention is a system that provides personalized health guidance using devices installed in the home. The server collects information such as heart rate, activity level, and sleep patterns from wearable devices and home-installed equipment for the purpose of monitoring the individual's health status. This information is transmitted to the server via wireless communication (e.g., Wi-Fi).

[0735] The server analyzes the collected information using a machine learning model (e.g., TensorFlow). Based on the health assessment obtained as a result of the analysis, it autonomously sets health goals tailored to the individual. These health goals include specific behavioral targets and nutritional guidelines, such as "walk 7,500 steps a day" or "consume 350 grams of vegetables."

[0736] Based on the set health goals, the in-home device uses a speech synthesis system (e.g., Google Text-to-Speech) to provide specific guidance for achieving those goals. For example, it might say to the user, "The weather is nice today, so why not take a walk to the nearby park?"

[0737] Furthermore, in this invention, the in-home device records the individual's behavior based on the provided guidance and feeds this data back to a server. This feedback information is used to generate the next guidance, improving its quality. This system makes it possible to achieve individualized health management and improve the quality of life.

[0738] A concrete example of a prompt used in a generative AI model is, "Based on yesterday's data, the AI ​​has determined that you are not getting enough exercise. Please generate gentle words to encourage the user to exercise." This enables the automatic generation of health guidance, allowing for personalized advice.

[0739] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0740] Step 1:

[0741] The server collects health data such as heart rate, activity level, and sleep patterns from wearable devices and home-installed equipment. This input data is sent to the server as sensor data, which stores it in a database. The accuracy of the data is guaranteed by pre-calibrated devices.

[0742] Step 2:

[0743] The server analyzes the collected data using a machine learning model. The input is the health data collected and stored in Step 1. During the data analysis, the model quantifies individual health conditions and generates evaluation indicators. These analysis results are output as evaluation data necessary for setting subsequent health goals.

[0744] Step 3:

[0745] The server sets individual health goals based on the analysis results. In this step, based on the evaluation data from step 2, it generates health goals such as "walk 7,500 steps a day." The generated health goals are structured as specific action plans and output as the next set of instructions.

[0746] Step 4:

[0747] The terminal (a device installed in the home) notifies the user of received health goals and guidance instructions via voice or display. The input here is the guidance instructions sent from the server in step 3. The notifications are made in an intuitive and easy-to-understand format using a speech synthesis system.

[0748] Step 5:

[0749] When a user acts based on instructions from the device, the device automatically records that action. This input data includes video and time information related to the actual action. The collected feedback information serves as input for the next step, serving as improvement suggestions.

[0750] Step 6:

[0751] The server uses the feedback information obtained in Step 5 to perform data analysis to improve the instruction content. Based on this evidence data, it adjusts subsequent instruction to be more appropriate. The output is feedback data that contributes to improving the quality of future health instruction models.

[0752] 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.

[0753] This invention combines a system that autonomously manages an individual's health with an emotion engine that recognizes the user's emotions. The system consists of a server, terminals, and users, with each device working in cooperation with the others.

[0754] The server first collects data representing the user's health status from wearable devices and smartphones. This data includes heart rate, steps taken, and sleep patterns. This data is analyzed by an artificial intelligence model on the server to assess the user's health status. Based on this assessment, the server autonomously sets health goals tailored to each individual user. Furthermore, the server generates specific advice regarding nutrition, exercise, and rest, and sends this to the device.

[0755] A key feature of this system is its built-in emotion engine. This engine recognizes the user's emotional state through user data and feedback. For example, if it determines that the user is experiencing high stress levels, the server will provide relaxation-focused advice. Furthermore, health goals can be dynamically adjusted according to the user's emotional state. For instance, if the system recognizes the user as being in a very fulfilling mental state, it might consider setting slightly higher exercise goals to challenge them.

[0756] The device receives data sent from the server and displays it in an easy-to-understand format for the user. Through the device, users can check their daily health status and emotional advice, and use this information to manage their lifestyle and health. Information and feedback manually entered by the user are sent to the server via the device and used for subsequent data analysis and advice generation.

[0757] Users take the advice to heart and engage in activities to improve their lifestyle. This includes everyday health-promoting activities such as maintaining a walking routine and eating a balanced diet. In addition, they can receive follow-up support from professionals from an emotional care perspective. The server can share data, including emotional data, with medical institutions and health facilities, and incorporate expert opinions to further improve health management.

[0758] In this way, the system, which combines an emotion engine, helps improve the user's overall health through deep health and emotional insights.

[0759] The following describes the processing flow.

[0760] Step 1:

[0761] The server collects user health data from wearable devices and smartphones. This includes heart rate, steps taken, sleep patterns, and even voice and facial expression data.

[0762] Step 2:

[0763] The server inputs the collected health data into an artificial intelligence model to analyze the user's current health status. This analysis assesses the user's health level and potential risks.

[0764] Step 3:

[0765] The server autonomously sets health goals for the user based on the analysis results. These goals provide specific indicators necessary for improving the user's health, such as exercise frequency and dietary balance.

[0766] Step 4:

[0767] The server uses an emotion engine to analyze the user's voice and facial expression data to recognize their emotional state. If the emotion indicates stress or fatigue, the advice provided will be adjusted accordingly.

[0768] Step 5:

[0769] The server generates personalized advice on nutrition, exercise, and rest based on your health and emotional state. For example, if you are experiencing high stress levels, it may include advice recommending relaxation techniques.

[0770] Step 6:

[0771] The server sends the generated advice to the user's device, allowing them to view it. The advice may also include emotionally sensitive reminders to help improve progress.

[0772] Step 7:

[0773] The terminal is designed to visually display advice and goals received from the server, making them easy for the user to understand.

[0774] Step 8:

[0775] Users review the advice displayed on their devices and put it into practice in their daily lives. For example, they might be encouraged to make morning walks a habit or to take time for relaxation before going to bed at night.

[0776] Step 9:

[0777] Users input their progress, changes in physical condition, and emotional feedback into their device, which is then sent to the server. This feedback contributes to improving the accuracy of subsequent analyses.

[0778] Step 10:

[0779] The server shares new data, including emotions, with healthcare institutions and facilities, enabling further follow-up and advice from experts. These experts can then collaborate to optimize the user's health management plan.

[0780] (Example 2)

[0781] 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".

[0782] In modern society, managing one's health and understanding one's emotional state are crucial, but there is a lack of effective means to integrate and utilize these aspects on a daily basis. Furthermore, a lack of mechanisms to dynamically adjust individual health goals and activity suggestions based on the user's emotional state is a significant challenge.

[0783] 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.

[0784] In this invention, the server includes a device for collecting individual physiological data, a device for analyzing the physiological data and applying a machine learning model for performing individual health assessments, and a device for recognizing the individual's emotional state based on the health assessment. This makes it possible to set appropriate health goals and suggest activities based on the individual's health and emotional state.

[0785] "Physiological data" refers to information related to an individual's physical condition, including data such as heart rate, steps taken, and sleep patterns.

[0786] A "machine learning model" refers to an algorithm or method used to analyze patterns and trends based on collected data and perform individual health assessments.

[0787] "Emotional state" refers to information that evaluates an individual's psychological state and stress level, and is estimated from feedback data, audio data, and other sources.

[0788] "Health goals" refer to specific objectives set to improve an individual's health status, and include indicators related to exercise, nutrition, and rest.

[0789] "Activity suggestions" refer to advice on personalized daily activities and habits, generated based on health goals and emotional states.

[0790] A "specialized institution" refers to a professional organization, such as a medical institution or health facility, that evaluates and follows up on health data and emotional data.

[0791] An "information terminal" is an electronic device used by a user, and includes smartphones, tablets, personal computers, and other similar devices.

[0792] A "central processing unit" refers to a computer system that collects, analyzes, and manages data generated.

[0793] This system integrates individual health management and emotional state assessment, and functions through the collaborative efforts of the server, terminal, and user. A specific implementation is described below.

[0794] The server first collects physiological data from the user's wearable device or smartphone. This includes heart rate, steps taken, and sleep patterns, and the data is securely transferred to the server via an API. The server then runs machine learning models using TensorFlow or PyTorch to analyze the collected data. This model evaluates the user's health status and outputs the results as specific numerical values ​​and graphs.

[0795] Furthermore, the server uses an emotion engine to recognize the user's emotional state. Here, natural language processing is used to analyze user feedback and voice data to estimate emotional changes and stress levels. Based on this recognition, the server autonomously sets health goals appropriate for the user. These health goals may include, for example, daily walking distance and recommended nutritional intake.

[0796] The server sends the generated health goals and activity suggestions to the device. The device receives these and displays a screen to visualize them in an easy-to-understand format for the user. For example, it may show the current progress in a dashboard format or provide important advice to the user using push notifications.

[0797] As a concrete example, consider a scenario where a user is seeking specific advice to help maintain their daily exercise habits and manage stress. The prompt might read something like, "Consider your daily exercise habits and generate specific advice for stress management."

[0798] Users can use the advice provided via the device to improve their lifestyle. Furthermore, users can input feedback into the device, which is used for subsequent data analysis and activity suggestion generation. The server can share the obtained data with specialized organizations as needed, incorporating expert opinions to provide more accurate health management.

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

[0800] Step 1:

[0801] The server acquires physiological data from wearable devices and smartphones. This data includes heart rate, steps taken, and sleep patterns, and is sent from the user's device to the server via an API. The input physiological data is stored on the server and prepared for the next analysis step. The output is the organized physiological data.

[0802] Step 2:

[0803] The server inputs the collected physiological data into a machine learning model. The model used is based on TensorFlow and PyTorch, and analyzes the data to assess the user's health status. This assessment calculates activity levels and stress indicators. The output generates the assessment results, which are organized for each user.

[0804] Step 3:

[0805] The server analyzes the user's emotional state using an emotion engine. Inputs include user-provided feedback and audio data. Natural language processing techniques are used to estimate the type and intensity of the emotion. The output is an evaluation of the current emotional state.

[0806] Step 4:

[0807] The server sets appropriate health goals based on the results of the health assessment and emotional state. The generating AI model receives the prompt "Suggest appropriate health goals for this user" as input. Health goals include exercise levels and dietary guidelines. This results in the output of personalized health goals.

[0808] Step 5:

[0809] The server sends the generated health goals and activity suggestions to the device. The transmitted data is visualized on the device and displayed in an easy-to-understand manner for the user. It receives instructions from the server as input and outputs them on the screen as dashboards and notifications.

[0810] Step 6:

[0811] Users manage their health based on the information displayed on their device. They adjust their daily habits based on the advice they receive and submit feedback via their device. This feedback is used for the next data analysis.

[0812] Step 7:

[0813] The server shares user health and emotional data with specialized organizations as needed. The input is data obtained with the user's consent, and the output facilitates feedback to experts. This allows users to receive detailed health management based on expert opinions.

[0814] (Application Example 2)

[0815] 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".

[0816] In modern society, personal health management is a crucial issue, but conventional systems often limit themselves to monitoring and evaluating health status, lacking integration with emotional states. Because health advice is not provided that takes an individual's emotional state into account, more effective health improvement is delayed. Furthermore, given the lack of direct support within the home environment, there is a need for systems that provide advice tailored to individual lifestyles.

[0817] 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.

[0818] In this invention, the server includes means for collecting data to monitor an individual's health status, means for analyzing the data and applying an artificial intelligence algorithm for performing an individualized health assessment, and means for recognizing emotional states and dynamically adjusting health goals and advice. This enables the provision of real-time health advice based on an individual's emotional state, making health management in the home environment more personalized and effectively supported.

[0819] "Means of data collection" refers to methods and technologies for acquiring various information about an individual's health status using sensors and electronic devices.

[0820] "Means of applying artificial intelligence algorithms" refers to techniques that use machine learning and statistical methods to perform computational processing in order to analyze and evaluate acquired health data.

[0821] "Means for autonomously setting health goals" refer to methods and technologies for automatically determining health improvement goals tailored to each individual based on analysis results.

[0822] "Means of generating advice related to nutrition, exercise, and rest" refers to technologies that provide individuals with specific actionable guidelines in accordance with established health goals.

[0823] "Means of recognizing emotional states" refer to methods and technologies for detecting and interpreting an individual's feelings and emotional state from data such as voice, text, and facial expressions.

[0824] "Means of providing health advice based on emotion recognition through home devices" refers to technology that transmits health guidelines optimized according to an individual's emotional state through devices used in a home environment.

[0825] An "information processing device" is an electronic device used for receiving, analyzing, storing, and processing data, and usually refers to servers, personal computers, and similar devices.

[0826] This system aims to effectively monitor the health and emotional state of individual users and provide optimal health goals and advice. The server collects personal health data according to pre-programmed procedures and analyzes it using artificial intelligence algorithms. The collected data includes heart rate, steps taken, and sleep patterns.

[0827] Based on the analysis results, the server autonomously sets individual health goals and generates advice related to nutrition, exercise, and rest. The user's emotional state is read from the collected data and determined using emotion recognition technology. The determined emotional state is used to adjust the health goals and advice.

[0828] The device visually and audibly notifies the user of health goals and advice transmitted from the server. Smart display technology can be used in the device. As a home appliance, the robot provides this information within an emotional context and offers appropriate follow-up to the user.

[0829] For example, if the server determines that the user is in a relaxed state, it will recommend light stretching or rest to maintain relaxation rather than exercise advice for stress relief. The AI ​​generates optimal advice using prompts such as, "Analyze the user's health data and generate health advice that takes into account their current emotional state. If the user is relaxed, suggest actions to maintain that relaxation."

[0830] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0831] Step 1:

[0832] Users record health data such as heart rate, steps, and sleep patterns using wearable devices and smartphones. This data is collected from sensors in each device and transmitted to a server via wireless communication. New health data is received as input and used as control information.

[0833] Step 2:

[0834] The server inputs received health data into an artificial intelligence algorithm to perform individual health assessments. TensorFlow and PyTorch are used to model the user's current health status. During this process, past and current data are compared to analyze health trends, and the assessment results are obtained as output.

[0835] Step 3:

[0836] The server autonomously sets health goals tailored to the user based on the health assessment results. Health indicators based on the assessment results, such as daily step goals and calorie intake, are used to set these goals. The set health goals are then input into the next advice generation step.

[0837] Step 4:

[0838] The server executes an emotion recognition algorithm to estimate the user's emotional state. Using natural language processing techniques, it analyzes user feedback comments and voice data to quantify emotions. This numerical information is used to refine health advice and influence the generation of new advice.

[0839] Step 5:

[0840] The server generates advice on nutrition, exercise, and rest based on health goals and emotion recognition results. Using a generative AI model, it constructs optimal advice corresponding to prompt statements, creating meaningful content for the user. The output consists of specific health action guidelines.

[0841] Step 6:

[0842] The server sends the generated advice to the user's device. The device receives this information and notifies the user using a smart display or voice output. Hardware operations here include speech synthesis technology and display capabilities.

[0843] Step 7:

[0844] Users receive notifications from their devices and incorporate them into their daily lives to align with their health goals. Users also send feedback to the server via their devices, which uses this feedback for subsequent data analysis. This feedback loop allows the system to continuously improve its personalization.

[0845] 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.

[0846] 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.

[0847] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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."

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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 as being incorporated by reference.

[0866] The following is further disclosed regarding the embodiments described above.

[0867] (Claim 1)

[0868] A means of collecting data to monitor an individual's health status,

[0869] A means for analyzing the aforementioned data and applying an artificial intelligence model for performing individual health assessments,

[0870] A means for autonomously setting health goals appropriate to the individual based on the aforementioned health assessment,

[0871] A means for generating advice related to nutrition, exercise, and rest based on the aforementioned health goals,

[0872] A system including means for sending and displaying the aforementioned advice on an individual's device.

[0873] (Claim 2)

[0874] The system according to claim 1, further comprising means for sharing the aforementioned data with medical institutions and health facilities and for collaborating with experts.

[0875] (Claim 3)

[0876] The system according to claim 1, further comprising means for transmitting feedback data from the terminal to a server and reflecting it in the generation of the next advice.

[0877] "Example 1"

[0878] (Claim 1)

[0879] Means for collecting information to monitor an individual's health status,

[0880] A means for analyzing the aforementioned information and applying a machine learning model to perform individual health assessments,

[0881] A means for autonomously setting health goals appropriate to the individual based on the aforementioned health assessment,

[0882] A means for generating advice related to nutrition, exercise, and rest based on the aforementioned health goals,

[0883] A means of sending and displaying the aforementioned advice on an individual's device,

[0884] A means of taking into account the additional information entered by the aforementioned individual and reflecting it in the next information analysis and advice generation,

[0885] A means of sharing the aforementioned information with organizations and facilities and collaborating with experts.

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, which incorporates means for visualizing the aforementioned health goals and advice via a terminal and enabling the individual to check their progress.

[0889] (Claim 3)

[0890] The system according to claim 1, comprising means for collecting feedback generated from an individual's actions based on the aforementioned advice, and contributing to improving the accuracy of the system.

[0891] "Application Example 1"

[0892] (Claim 1)

[0893] Means for collecting information to monitor the health status of individuals,

[0894] A means for analyzing the aforementioned information and applying a machine learning model to perform individual health assessments,

[0895] A means for autonomously setting health goals appropriate to the individual based on the aforementioned health assessment,

[0896] A means for generating guidance related to nutrition, exercise, and rest based on the aforementioned health goals,

[0897] A means of transmitting the aforementioned instructions to a device installed in the home and providing them in audio or visual form,

[0898] A means for collecting video data of actions taken based on the guidance provided by the aforementioned in-home device, and for incorporating this data into the generation of the next guidance,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, further comprising means for sharing the aforementioned information with medical institutions and health facilities and for collaborating with experts.

[0902] (Claim 3)

[0903] The system according to claim 1, further comprising means for transmitting feedback information from the in-home device to a server and reflecting it in the generation of the next instruction.

[0904] "Example 2 of combining an emotion engine"

[0905] (Claim 1)

[0906] A device for collecting individual physiological data,

[0907] A device that analyzes the aforementioned physiological data and applies a machine learning model for performing individual health assessments,

[0908] A device that recognizes an individual's emotional state based on the aforementioned health assessment,

[0909] A device that autonomously sets health goals tailored to the individual based on the aforementioned recognition results,

[0910] A device that generates activity suggestions based on the aforementioned health goals,

[0911] A system including a device that transmits and displays the aforementioned activity proposal on an individual's information terminal.

[0912] (Claim 2)

[0913] The system according to claim 1, further comprising a device for sharing the aforementioned physiological data and emotional state data with specialized institutions and for collaborating with experts.

[0914] (Claim 3)

[0915] The system according to claim 1, further comprising a device that transmits feedback data from the information terminal to a central processing unit and reflects it in the generation of the next activity proposal.

[0916] "Application example 2 when combining with an emotional engine"

[0917] (Claim 1)

[0918] A means of collecting data to monitor an individual's health status,

[0919] A means for analyzing the aforementioned data and applying an artificial intelligence algorithm for performing individual health assessments,

[0920] A means for autonomously setting health goals appropriate to the individual based on the aforementioned health assessment,

[0921] A means for generating advice related to nutrition, exercise, and rest based on the aforementioned health goals,

[0922] A means for transmitting and displaying the aforementioned advice on a personal information device,

[0923] A means of recognizing emotional states and dynamically adjusting health goals and advice,

[0924] A system that includes means for providing health advice based on emotion recognition in home devices.

[0925] (Claim 2)

[0926] The system according to claim 1, further comprising means for sharing the aforementioned data with medical institutions and health facilities and for collaborating with experts.

[0927] (Claim 3)

[0928] The system according to claim 1, further comprising means for transmitting feedback data from the terminal to an information processing device and reflecting it in the generation of the next advice. [Explanation of Symbols]

[0929] 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 means of collecting data to monitor an individual's health status, A means for analyzing the aforementioned data and applying an artificial intelligence model for performing individual health assessments, A means for autonomously setting health goals appropriate to the individual based on the aforementioned health assessment, A means for generating advice related to nutrition, exercise, and rest based on the aforementioned health goals, A system including means for sending and displaying the aforementioned advice on an individual's device.

2. The system according to claim 1, further comprising means for sharing the aforementioned data with medical institutions and health facilities and for collaborating with experts.

3. The system according to claim 1, further comprising means for transmitting feedback data from the terminal to a server and reflecting it in the generation of the next advice.

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