system

Abstract

We provide the system. [Solution] A means of collecting health indicators from users, An analytical means for analyzing the aforementioned health indicators and predicting risk, A generation means for generating an individualized health improvement plan based on the prediction results obtained by the analysis means, A means of providing the aforementioned health improvement plan to users, Incentive measures that provide incentives to maintain motivation based on the user's behavioral history, Means of promoting participation in local social activities and events, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society with the aging population, maintaining the health of the senior population and improving the quality of life have become important issues. In particular, the barriers to behavioral changes, lack of exercise, increased health risks due to inappropriate eating habits, and the decline in the quality of life due to feelings of isolation and decreased hobbies are prominent. To solve these problems, individualized health management and life support are required, but the conventional approaches often remain at uniform measures, and there is a current situation where it is difficult to provide support suitable for individual needs.

Means for Solving the Problems

[0005] This invention provides a system that collects users' health indicators and analyzes that data using a generating AI to perform personalized risk predictions. It then generates and provides users with an optimal health improvement plan, offering a means to mitigate health risks early on. Furthermore, by including features such as providing incentives based on users' behavioral history and encouraging participation in community activities, it supports continuous health improvement and quality of life enhancement. Thus, this invention is designed to strengthen personalized health management and connections with the local community.

[0006] "Collection methods" refer to devices consisting of equipment and software used to obtain health indicators from users.

[0007] "Analysis tools" refer to functions equipped with algorithms used to interpret collected health indicator data and predict health risks.

[0008] "Generation method" refers to a system for developing personalized health improvement plans for users based on predicted risks.

[0009] "Delivery method" refers to a module that notifies users of the generated health improvement plan and enables them to understand and implement it.

[0010] "Incentive mechanisms" refer to features that take into account the user's behavioral history and provide rewards or benefits associated with achievement to increase motivation.

[0011] "Means of promoting participation" refers to devices or programs that provide information and support to increase users' motivation to participate in community events and activities.

[0012] A "feedback mechanism" refers to a function that has a system in place to collect user experiences and opinions and incorporate them into the next improvement plan.

[0013] "Life log analysis" refers to a method of analyzing long-term health data to detect potential risks and changes in health status early on. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that supports health management for seniors and includes means for collecting, analyzing, generating, providing, providing incentives, and promoting participation. This makes it possible to efficiently manage users' health indicators and promote behavioral change.

[0036] Specifically, this system collects health data through smart devices and terminals that users use on a daily basis. For example, a smartwatch can be used as a terminal to monitor heart rate, steps taken, and sleep patterns. By wearing these devices daily and inputting data about diet and activity through the app, more detailed health indicators are collected.

[0037] The server analyzes the collected data and predicts health risks. For example, it can identify trends in heart rate variability and step count decline, and use this to numerically assess future health risks. Subsequently, based on this information, it uses a generation tool to develop a personalized health improvement plan. This plan includes recommended exercises, nutritional guidance, and sleep improvement measures.

[0038] Users receive this health improvement plan via their device, along with regular behavioral reminders. Incentives make progress visible; for example, digital badges or invitations to local events are displayed upon achieving certain goals. This increases user motivation.

[0039] Furthermore, through participation promotion mechanisms, the server suggests participation in local hobby clubs and health events based on the user's interests and past participation history. Through this, users can deepen their connections with their local community. In this way, this system makes daily health management an enjoyable and sustainable activity for users.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device continuously records health indicators such as heart rate, steps, and sleep data through the smart device worn by the user and the application used. This includes automatic measurements by sensors and data on diet and activity entered manually by the user.

[0043] Step 2:

[0044] The device sends the collected data to the server at regular intervals. The transmission takes place over the internet, and the data is stored in a secure database on the server.

[0045] Step 3:

[0046] The server applies a generative AI algorithm to analyze the received health data. This analysis compares patterns in each indicator and quantifies the health risk for each user. For example, it identifies abnormal heart rate patterns or tendencies toward lack of exercise.

[0047] Step 4:

[0048] The server generates a personalized plan to improve health based on the analysis results. The generated plan includes exercise programs, dietary suggestions, and sleep guidelines tailored to the user.

[0049] Step 5:

[0050] The server sends the generated health improvement plan to the device, which then notifies the user. The user can then view the detailed plan and instructions through the app.

[0051] Step 6:

[0052] Users implement a daily health plan based on notifications received from their device. The progress of the plan is displayed within the app, and users can input their achievements regarding exercise and diet.

[0053] Step 7:

[0054] The server provides incentives to boost user motivation (e.g., badges, invitations to local events) based on the user's behavior history. This encourages active user participation.

[0055] Step 8:

[0056] The server analyzes the user's interests and, taking into account their past activity history, makes suggestions to encourage participation in local events. The terminal then notifies the user of upcoming events through these suggestions.

[0057] (Example 1)

[0058] 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."

[0059] The goal is to provide a system that supports older adults in efficiently managing their health information and taking appropriate health-maintaining behaviors in their daily lives. This requires a mechanism that can detect potential health problems early and encourage appropriate intervention. By collecting health data from older adults and providing personalized improvement plans, it is necessary to maintain their motivation for maintaining their health and deepen their connections with the community.

[0060] 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.

[0061] In this invention, the server includes a collection means equipped with a terminal for collecting biometric information from users, an analysis means including a server for analyzing the biometric information and predicting health risks, and a generation means using a generation AI model that generates an individualized health management plan based on the prediction results obtained from the analysis means. This makes it possible for elderly people to effectively manage their health information and promote appropriate health maintenance behaviors.

[0062] "Collection means" refers to functions that include terminals for collecting biometric information from users, and involves collecting necessary data using smart devices and applications.

[0063] The "analysis method" refers to a control function that utilizes a server to analyze collected biological information and predict health risks from it.

[0064] "Generative means" refers to the function of using a generative AI model to create personalized health management plans based on analysis results.

[0065] "Means of delivery" refers to functions including terminals for appropriately presenting the generated health management plan to the user.

[0066] An "incentive mechanism" is a function that provides performance-based rewards to increase user motivation based on the user's behavioral history.

[0067] "Methods for promoting participation" refer to functions that encourage users to participate in local activities and events.

[0068] A "generative AI model" is a system that includes artificial intelligence to automatically generate personalized plans based on the user's health data.

[0069] This invention is a system that supports the health management of the elderly. This system collects and analyzes health-related information through smart devices and terminals that users use on a daily basis, and provides appropriate feedback to users, thereby promoting improvements in their health behaviors.

[0070] Specifically, the device functions as a smartwatch and mobile application, collecting the user's biometric information. This includes heart rate, steps taken, sleep patterns, diet, and exercise data. The device periodically transmits the collected data to a server using Bluetooth or Wi-Fi. Users can also manually input diet and exercise data through the mobile app.

[0071] The server uses specialized software to analyze the collected data, particularly utilizing generative AI models to assess health risks. Specifically, the AI ​​model analyzes trends in heart rate and step count, and uses predictive algorithms to quantify health risks. For example, if abnormal heart rate or decreased exercise levels are detected, it can provide early warnings about potential future health risks.

[0072] As a means of generation, the server creates a personalized health management plan for each user. This plan includes recommended exercise levels, meal plans, sleep improvement measures, and other suggestions tailored to the user's lifestyle.

[0073] The device provides users with this plan and uses it as an action reminder to help them with their daily activities. Furthermore, incentives make progress visible, and digital badges and rewards are provided based on performance. This helps maintain user motivation.

[0074] Furthermore, using participation promotion tools, the server suggests to users participation in local events and club activities. This allows users to deepen their connections with the local community.

[0075] As a concrete example, there is a case where a user, after utilizing this system, began walking regularly and improved their physical fitness. In this case, the generative AI model provided a plan that adjusted the balance between diet and exercise, leading to an improvement in the user's health awareness.

[0076] Example of a prompt:

[0077] "Could you please describe the specific functions and user experience of the health management support system available to seniors?"

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The device collects the user's daily health data. Specifically, it senses and records biometric information such as heart rate, steps taken, and sleep patterns via a smartwatch or mobile app. This data collection is performed in real time or at set intervals. The input is the user's biometric data, and the output is health information as digital data compiled from that data.

[0081] Step 2:

[0082] The device transmits collected health information to a server. The data is securely transferred to the server using Bluetooth or Wi-Fi. The input is biometric data recorded on the device, and the output is the dataset sent to the server. This process centralizes the data, making it available for subsequent analysis.

[0083] Step 3:

[0084] The server analyzes the received biometric data. It runs a generative AI model to analyze changes in heart rate and exercise, and assess health risks. This analysis uses algorithms to detect anomalies and identify changes in patterns. The input is a dataset stored on the server, and the output is a health assessment and risk prediction as a result of the analysis.

[0085] Step 4:

[0086] The server generates an optimal health management plan for the user based on the analysis results. Utilizing a generation AI model, it creates suggestions for improving exercise, diet, and sleep that match the user's lifestyle. The input is the analysis results, and the output is a personalized health plan. This plan presents areas requiring action in a way that is easy for the user to understand.

[0087] Step 5:

[0088] The device provides the user with a generated health plan. Through commonly used digital tools, it visually displays each part of the health plan and may also allow for the setting of reminders and notifications. The input is the health plan provided by the server, and the output is specific action guidelines available to the user. The reminder function facilitates the execution of these actions.

[0089] Step 6:

[0090] The server provides incentives to users based on their behavioral history and achievements. It offers digital badges and rewards for achieving goals, creating a system to boost user motivation. Inputs are user behavioral data and new health status, while output is incentive information. This continuous process encourages ongoing health management.

[0091] Step 7:

[0092] The server suggests that users participate in local events and activities. Based on past data and interests, it aims to provide appropriate event information and strengthen relationships with the local community. Input is the user's profile information and activity history, and output is event information that users are recommended to participate in. This allows users to enjoy improving their health while feeling a sense of social connection.

[0093] (Application Example 1)

[0094] 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."

[0095] In modern society, health management is a crucial issue, especially for the elderly. However, it is difficult to obtain optimal health improvement measures based on individual health conditions and behaviors, and there is a lack of motivation to sustainably implement them. Furthermore, elderly people who live in environments where it is difficult to connect with their local community often experience social isolation. There is a need to address these challenges and support the elderly so that they can live healthy and fulfilling lives.

[0096] 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.

[0097] In this invention, the server includes data collection means for collecting biometric information from users, data analysis means for analyzing the biometric information and estimating health risks, strategy generation means for constructing individually tailored health improvement strategies, reward provision means for providing rewards, activity participation means, and achievement certificate visualization means. This makes it possible to promote health management and social participation optimized for each individual elderly person.

[0098] "Data collection means" refers to functions for acquiring biometric information such as heart rate, steps taken, and sleep data from users.

[0099] "Data analysis tools" refer to functions that estimate health risks based on collected biometric information and perform necessary analyses.

[0100] A "strategy generation tool" is a function that creates personalized health improvement strategies tailored to the user based on analyzed data.

[0101] "Communication means" refers to the function of transmitting generated health improvement strategies and related information to users.

[0102] A "reward provision mechanism" is a function that provides incentives based on the user's behavioral history and achievement level.

[0103] "Means of participation in activities" refers to functions that promote users' participation in local group activities and events.

[0104] The "Method for Visualizing Achievement Certificates" is a function that displays a visual certificate of achievement when a user achieves their goal.

[0105] The system implementing this invention consists of multiple components and can comprehensively manage the user's health. First, the user collects daily health data using a wearable device such as a smartwatch. These devices record biometric information such as heart rate, steps, and sleep patterns in real time and communicate with a smartphone via Bluetooth. The data is then sent to the cloud and stored in a database.

[0106] Next, the server analyzes biometric information using data analysis platforms such as Python or R. Here, machine learning models (e.g., TENSORFLOW®, PyTorch) are used to predict potential health risks for individual users. Based on these analysis results, a generative AI model is used to generate personalized health improvement strategies.

[0107] Next, the generated health improvement strategy is delivered to the user via communication. The user reviews it on their smartphone and implements an action plan based on their daily health status. Furthermore, a reward system is in place to display a visual achievement certificate, such as a digital badge, on the smartphone interface when goals are achieved. In addition, participation mechanisms encourage participation in local health events and community activities, deepening social connections.

[0108] For example, if a user achieves their goal of 6,000 steps per day, this is visualized within the app as a "Healthy Walking Master" badge, providing the user with a tangible sense of accomplishment. Furthermore, by providing the AI ​​model with instructions such as, "Generate a weekly plan to improve health based on the following data: heart rate trends, recent steps, and sleep patterns," an appropriate health improvement strategy can be constructed.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The device collects biometric information such as the user's heart rate, steps taken, and sleep patterns. This data is recorded in real time using a wearable device and transmitted to a smartphone app via Bluetooth communication. The input is data from the biosensor, and the output is initial data stored in the smartphone app.

[0112] Step 2:

[0113] The device transmits biometric information collected via a smartphone to the cloud. The data transmitted to the cloud is stored in a database and ready for analysis. The input is data accumulated in the smartphone app, and the output is data stored in the database on the cloud.

[0114] Step 3:

[0115] The server retrieves biometric information from a cloud-based database and uses Python or R data analysis tools to predict health risks. Machine learning models (e.g., TensorFlow, PyTorch) are used to analyze the input data and numerically evaluate the user's health risk. The output is the predicted health risk.

[0116] Step 4:

[0117] The server uses an AI model to generate personalized health improvement strategies based on the analysis results. The input is the predicted health risk, and the output is an improvement strategy optimized for the user. Specifically, the prompt message "Generate a weekly plan to improve health based on the following data" is provided to the AI ​​model.

[0118] Step 5:

[0119] The server generates a health improvement strategy and transmits it to the terminal using a communication method. The terminal notifies the user of the strategy, making it available for viewing through a smartphone app. The input is the generated health improvement strategy, and the output is the improvement plan information displayed to the user.

[0120] Step 6:

[0121] Users perform daily health actions using a smartphone app. The user's activity history is sent back to the cloud via the device and used as feedback for future improvement strategies. The input is data on the actions performed by the user, and the output is the feedback data.

[0122] Step 7:

[0123] The server uses a reward system to visualize achievement certificates on the user's device based on their goal attainment. These certificates include digital badges and announcements of local events, aiming to boost user motivation. Input is the user's goal attainment status, and output is the displayed achievement certificates and incentive information.

[0124] 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.

[0125] This invention is a health support system for seniors that, in addition to providing individualized improvement plans based on the user's health indicators, incorporates an emotion engine that recognizes the user's emotional state and optimizes health management accordingly.

[0126] This system first uses smart devices and mobile terminals as collection tools to continuously record the user's physiological data. In addition to the user's heart rate, body temperature, and exercise data, the terminals utilize boundary sensors to acquire information related to emotions, such as voice and facial expressions.

[0127] Upon receiving this data, the server uses analytical tools to predict health risks. Generative AI analyzes patterns and identifies potential risks to the user's health. Based on these results, the generator creates a personalized lifestyle improvement plan for each user.

[0128] A key feature of this invention is the inclusion of an emotion engine. The server uses this emotion engine to analyze the user's current emotional state. For example, if it determines that the user is experiencing high stress levels, it adds activities to promote relaxation to the suggestions. This enables the creation of a plan that also considers the user's psychological well-being.

[0129] The emotional engine also influences the provision of incentives. It flexibly adjusts incentives to stimulate achievement motivation based on the user's emotional state, providing badges and rewards accordingly. In this way, emotionally sensitive support activities can improve the user's quality of life.

[0130] Users can receive these personalized plans through their devices. The devices guide daily activities through applications and support user behavior with emotionally resonant content. Furthermore, participation encouragement features provide information on local events and support participation in community activities.

[0131] This invention adopts a form that comprehensively supports the user's physical and psychological health, and allows for flexible responses to individual needs.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The device uses a smartwatch or smartphone app to record heart rate, body temperature, steps, voice, and facial expression data to collect the user's biometric information. Voice and facial expression data, in particular, play a crucial role for the emotion engine.

[0135] Step 2:

[0136] The device transmits collected biometric and emotion-related data to a server. Encryption protocols are used to ensure the secure transmission of this data.

[0137] Step 3:

[0138] The server uses generative AI to analyze received biometric and emotion-related data. It identifies health risks based on health indicators and analyzes potential risks to the user. In particular, emotion analysis detects changes in voice tone and facial expressions to assess stress levels.

[0139] Step 4:

[0140] Based on the analysis results, the server generates a personalized health improvement plan for the user. This plan includes exercise and dietary advice to improve physical health, as well as relaxation activities to adapt to emotional states and measures to improve mental health.

[0141] Step 5:

[0142] Through the emotion engine, the server adjusts incentives based on the user's emotional state and generates digital badges and rewards to enhance a sense of accomplishment. It also makes motivational adjustments as needed, such as easing overly challenging goals.

[0143] Step 6:

[0144] The server sends the generated health improvement plan and incentive information to the device. The device then notifies the user of this information through the app and provides detailed instructions.

[0145] Step 7:

[0146] Users perform their daily activities according to improvement plans provided on their devices and record their progress and experiences in the app. They are also encouraged to actively participate by receiving the provided incentives and being able to check their progress.

[0147] Step 8:

[0148] The server utilizes a feedback mechanism to collect user feedback and incorporate it into the generation of the next health improvement plan. Based on user behavior data and feedback, it provides more personalized measures.

[0149] This process makes it possible to support the sustained improvement of users' physical and mental health.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] The present invention aims to provide a system that can comprehensively support not only physical health but also psychological health in the health management of senior citizens. Furthermore, it aims to improve the quality of life for users by providing flexible incentives that take into account the user's emotional state and functions that strengthen connections with the local community.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes information acquisition means for collecting health indicators and emotional data from users; analysis means for analyzing the health indicators and emotional data to predict health and psychological states; and plan creation means for generating personalized health and psychological support plans based on the prediction results obtained by the analysis means. This enables comprehensive support aimed at improving physical and psychological health.

[0155] "Information acquisition means" refers to the execution environment of a device or software for collecting health indicators and emotional data from users.

[0156] "Analysis means" refers to technical methods and devices used to analyze collected health indicators and emotional data to predict the user's health and psychological state.

[0157] "Plan creation means" refers to a process or system that generates individualized health and psychological support plans based on prediction results obtained through analysis means.

[0158] "Guidance means" refers to a means of communication or interface for providing users with the generated health and psychological support plan.

[0159] A "motivational tool" is a method or device for adjusting and providing incentives according to the user's emotional state.

[0160] "Activity promotion means" refers to functions that provide information and support to encourage participation in community activities and events.

[0161] "Data analysis means" refers to analytical techniques or devices that use long-term health data and emotional data to detect potential health and psychological risks at an early stage.

[0162] This invention is a system that comprehensively supports the physical and psychological health of primarily senior citizens. Users collect health indicators and emotional data using smart devices or mobile terminals. This data includes heart rate, body temperature, exercise information, and emotion-related information such as voice and facial expressions.

[0163] The device continuously samples this physiological and emotional data and sends it to the server in a fixed format. Data transmission is encrypted using the SSL / TLS protocol and is secure.

[0164] The server uses a generative AI model to analyze incoming data. The AI ​​model uses patterns learned from historical data to predict the user's health and psychological state. This allows for the creation of personalized health improvement and psychological support plans for each user.

[0165] For example, the server evaluates the user's stress level from their heart rate and facial expression data and generates instructions in the form of a prompt such as, "Evaluate the user's current stress level from their heart rate and facial expression data, and suggest recommended relaxation activities." This information is tailored based on the user's specific health goals.

[0166] After a plan is created, the server guides the user to provide an appropriate support plan. The terminal supports the user's daily activities through the application and provides real-time feedback. The user receives this plan and can use it in their daily life.

[0167] Furthermore, based on the results of psychological state analysis using the emotion engine, incentives and rewards are provided as motivation to maintain user enthusiasm. In this way, support for optimizing the user's health and psychological state can be achieved.

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

[0169] Step 1:

[0170] The device collects physiological and emotional data from the user. Inputs include exercise data from a heart rate sensor, body temperature sensor, and accelerometer, as well as voice and facial expression data from a microphone and camera. This data is converted to a digital format and standardized within the device. The output is a unified dataset.

[0171] Step 2:

[0172] The device periodically sends collected data to the server. It uses a processed dataset as input. Transmission is secure using the SSL / TLS protocol. The output is encrypted data delivered to the server.

[0173] Step 3:

[0174] The server analyzes the received data. The input is data sent from the terminal. The server passes this data through a generative AI model to evaluate the user's health and psychological state. Statistical methods and machine learning algorithms are used in this process. The output is the analysis results regarding the user's health and psychological state.

[0175] Step 4:

[0176] The server generates personalized support plans based on the analysis results. The analysis results obtained in step 3 are used as input. Using the generated AI model and predefined prompts, it creates health maintenance activities and psychological state improvement plans. The output is a support plan customized for each user.

[0177] Step 5:

[0178] The server sends the generated support plan to the terminal. The input is the plan generated in step 4. The output is the individual support plan transmitted to the terminal.

[0179] Step 6:

[0180] The terminal presents the user with a support plan. The input is the support plan received from the server. Through the application, it suggests the most suitable activities and relaxation activities for the user in real time. The output is guidance information displayed on the user's screen.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] For elderly individuals and other users to comprehensively manage their health and improve their quality of life, individualized care that considers emotional states in addition to physiological health indicators is necessary. However, conventional health management systems lack mechanisms to adequately analyze emotional states and utilize them for health improvement. As a result, incentives and promotion of social participation tailored to users' emotions are insufficient, making it difficult to achieve sustainable health improvement.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0185] In this invention, the server includes an acquisition means for collecting health-related information from users, an analysis means for analyzing the health-related information and emotional information to predict risks, and a creation means for creating an individualized health improvement plan based on the prediction results and emotional state obtained by the analysis means. This enables integrated management of the user's physiological and psychological health, and allows for sustainable health improvement tailored to each individual's lifestyle.

[0186] "Means of acquisition" refers to means that have the function of collecting health-related information from users.

[0187] "Analysis means" refers to means that have the function of analyzing collected health-related information and emotional information to predict health risks.

[0188] "Creation means" refers to means that have the function of generating an individualized health improvement plan based on the results of the analysis means.

[0189] A "presentation means" is a means that has the function of displaying and proposing the generated health improvement plan to the user.

[0190] A "stimulant" is a means that provides appropriate incentives according to the user's emotional state and has the function of stimulating their desire to achieve.

[0191] "Promotional measures" refer to means that provide information that encourages participation in local social activities and events, and have the function of promoting users' social participation.

[0192] An "emotion analysis tool" is a tool that has the function of evaluating a user's emotional state in real time.

[0193] This invention is a system for comprehensively managing the health status of users, including the elderly. This system acquires health-related and emotional information from users through a specific device, and uses this information to perform analysis and create improvement plans.

[0194] The server first has a mechanism to continuously acquire physiological data such as the user's heart rate, body temperature, and activity level. In addition, it also collects emotion-related data such as voice and facial expressions. The acquired data is sent to the server and analyzed by analytical means. In particular, generative AI models are used to identify health risks tailored to each individual user.

[0195] Based on the analysis results, the server creates a personalized health improvement plan for each user. This plan includes suggestions for relaxation activities and physical exercises that take into account the user's emotional state. Furthermore, if the emotional analysis determines that the user is experiencing stress, countermeasures are immediately incorporated into the plan.

[0196] Users can view this plan on their own devices. The device presents the generated plan in a visually appealing and user-friendly format, supporting users in managing their daily health. It also offers emotion-based incentives to encourage motivation. Furthermore, the device provides information on local social activities and events, encouraging users to actively participate in society.

[0197] For example, if a user records in their diary that they "haven't been feeling well all morning," the system will determine that they are experiencing stress based on this text and collected physiological data. At this point, the user will be offered suggestions such as relaxation music or a suitable walking route. To achieve this, a prompt message such as "Based on the user's heart rate, body temperature, exercise data, and the text 'I'm feeling a little unwell,' consider assessing their stress level and developing countermeasures" is provided to the generating AI model.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The device uses sensors to acquire data such as the user's heart rate, body temperature, activity level, voice, and facial expressions in real time. This data is collected from the user and transmitted to the next step.

[0201] Step 2:

[0202] The server receives physiological and emotional data transmitted from the terminal and stores it in a database. The input is data from sensors, and the output is information converted into an easy-to-use data format.

[0203] Step 3:

[0204] The server analyzes the stored data using analytical tools. It utilizes a generative AI model to predict health risks from this data. The input to this analysis is the collected health data, and the output is the predicted health risk for the user.

[0205] Step 4:

[0206] The server generates a lifestyle improvement plan based on the analysis results. This plan includes suggestions for stress reduction activities and recommendations for healthy exercise. The input is the predicted results and the current emotional state, and the output is the personalized improvement plan.

[0207] Step 5:

[0208] The terminal presents the user with a health improvement plan sent from the server. The plan is displayed in a visualized format to support its implementation. The input is the generated improvement plan, and the output is user-readable guide information.

[0209] Step 6:

[0210] The user performs activities and exercises presented via the device. The device receives feedback on these activities and sends it to the server. The input is the user's activity record, and the output is feedback data.

[0211] Step 7:

[0212] The terminal provides information on local social activities and events, promoting user community participation. Input is information from an event database, and output is event announcements for the user.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention is a system that supports health management for seniors and includes means for collecting, analyzing, generating, providing, providing incentives, and promoting participation. This makes it possible to efficiently manage users' health indicators and promote behavioral change.

[0230] Specifically, this system collects health data through smart devices and terminals that users use on a daily basis. For example, a smartwatch can be used as a terminal to monitor heart rate, steps taken, and sleep patterns. By wearing these devices daily and inputting data about diet and activity through the app, more detailed health indicators are collected.

[0231] The server analyzes the collected data and predicts health risks. For example, it can identify trends in heart rate variability and step count decline, and use this to numerically assess future health risks. Subsequently, based on this information, it uses a generation tool to develop a personalized health improvement plan. This plan includes recommended exercises, nutritional guidance, and sleep improvement measures.

[0232] Users receive this health improvement plan via their device, along with regular behavioral reminders. Incentives make progress visible; for example, digital badges or invitations to local events are displayed upon achieving certain goals. This increases user motivation.

[0233] Furthermore, through participation promotion mechanisms, the server suggests participation in local hobby clubs and health events based on the user's interests and past participation history. Through this, users can deepen their connections with their local community. In this way, this system makes daily health management an enjoyable and sustainable activity for users.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The device continuously records health indicators such as heart rate, steps, and sleep data through the smart device worn by the user and the application used. This includes automatic measurements by sensors and data on diet and activity entered manually by the user.

[0237] Step 2:

[0238] The device sends the collected data to the server at regular intervals. The transmission takes place over the internet, and the data is stored in a secure database on the server.

[0239] Step 3:

[0240] The server applies a generative AI algorithm to analyze the received health data. This analysis compares patterns in each indicator and quantifies the health risk for each user. For example, it identifies abnormal heart rate patterns or tendencies toward lack of exercise.

[0241] Step 4:

[0242] The server generates a personalized plan to improve health based on the analysis results. The generated plan includes exercise programs, dietary suggestions, and sleep guidelines tailored to the user.

[0243] Step 5:

[0244] The server sends the generated health improvement plan to the device, which then notifies the user. The user can then view the detailed plan and instructions through the app.

[0245] Step 6:

[0246] Users implement a daily health plan based on notifications received from their device. The progress of the plan is displayed within the app, and users can input their achievements regarding exercise and diet.

[0247] Step 7:

[0248] The server provides incentives to boost user motivation (e.g., badges, invitations to local events) based on the user's behavior history. This encourages active user participation.

[0249] Step 8:

[0250] The server analyzes the user's interests and, taking into account their past activity history, makes suggestions to encourage participation in local events. The terminal then notifies the user of upcoming events through these suggestions.

[0251] (Example 1)

[0252] 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."

[0253] The goal is to provide a system that supports older adults in efficiently managing their health information and taking appropriate health-maintaining behaviors in their daily lives. This requires a mechanism that can detect potential health problems early and encourage appropriate intervention. By collecting health data from older adults and providing personalized improvement plans, it is necessary to maintain their motivation for maintaining their health and deepen their connections with the community.

[0254] 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.

[0255] In this invention, the server includes a collection means equipped with a terminal for collecting biometric information from users, an analysis means including a server for analyzing the biometric information and predicting health risks, and a generation means using a generation AI model that generates an individualized health management plan based on the prediction results obtained from the analysis means. This makes it possible for elderly people to effectively manage their health information and promote appropriate health maintenance behaviors.

[0256] "Collection means" refers to functions that include terminals for collecting biometric information from users, and involves collecting necessary data using smart devices and applications.

[0257] The "analysis method" refers to a control function that utilizes a server to analyze collected biological information and predict health risks from it.

[0258] "Generative means" refers to the function of using a generative AI model to create personalized health management plans based on analysis results.

[0259] "Means of delivery" refers to functions including terminals for appropriately presenting the generated health management plan to the user.

[0260] An "incentive mechanism" is a function that provides performance-based rewards to increase user motivation based on the user's behavioral history.

[0261] "Methods for promoting participation" refer to functions that encourage users to participate in local activities and events.

[0262] A "generative AI model" is a system that includes artificial intelligence to automatically generate personalized plans based on the user's health data.

[0263] This invention is a system that supports the health management of the elderly. This system collects and analyzes health-related information through smart devices and terminals that users use on a daily basis, and provides appropriate feedback to users, thereby promoting improvements in their health behaviors.

[0264] Specifically, the device functions as a smartwatch and mobile application, collecting the user's biometric information. This includes heart rate, steps taken, sleep patterns, diet, and exercise data. The device periodically transmits the collected data to a server using Bluetooth or Wi-Fi. Users can also manually input diet and exercise data through the mobile app.

[0265] The server uses specialized software to analyze the collected data, particularly utilizing generative AI models to assess health risks. Specifically, the AI ​​model analyzes trends in heart rate and step count, and uses predictive algorithms to quantify health risks. For example, if abnormal heart rate or decreased exercise levels are detected, it can provide early warnings about potential future health risks.

[0266] As a means of generation, the server creates a personalized health management plan for each user. This plan includes recommended exercise levels, meal plans, sleep improvement measures, and other suggestions tailored to the user's lifestyle.

[0267] The device provides users with this plan and uses it as an action reminder to help them with their daily activities. Furthermore, incentives make progress visible, and digital badges and rewards are provided based on performance. This helps maintain user motivation.

[0268] Furthermore, using participation promotion tools, the server suggests to users participation in local events and club activities. This allows users to deepen their connections with the local community.

[0269] As a concrete example, there is a case where a user, after utilizing this system, began walking regularly and improved their physical fitness. In this case, the generative AI model provided a plan that adjusted the balance between diet and exercise, leading to an improvement in the user's health awareness.

[0270] Example of a prompt:

[0271] "Could you please describe the specific functions and user experience of the health management support system available to seniors?"

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The device collects the user's daily health data. Specifically, it senses and records biometric information such as heart rate, steps taken, and sleep patterns via a smartwatch or mobile app. This data collection is performed in real time or at set intervals. The input is the user's biometric data, and the output is health information as digital data compiled from that data.

[0275] Step 2:

[0276] The device transmits collected health information to a server. The data is securely transferred to the server using Bluetooth or Wi-Fi. The input is biometric data recorded on the device, and the output is the dataset sent to the server. This process centralizes the data, making it available for subsequent analysis.

[0277] Step 3:

[0278] The server analyzes the received biometric data. It runs a generative AI model to analyze changes in heart rate and exercise, and assess health risks. This analysis uses algorithms to detect anomalies and identify changes in patterns. The input is a dataset stored on the server, and the output is a health assessment and risk prediction as a result of the analysis.

[0279] Step 4:

[0280] The server generates an optimal health management plan for the user based on the analysis results. Utilizing a generation AI model, it creates suggestions for improving exercise, diet, and sleep that match the user's lifestyle. The input is the analysis results, and the output is a personalized health plan. This plan presents areas requiring action in a way that is easy for the user to understand.

[0281] Step 5:

[0282] The device provides the user with a generated health plan. Through commonly used digital tools, it visually displays each part of the health plan and may also allow for the setting of reminders and notifications. The input is the health plan provided by the server, and the output is specific action guidelines available to the user. The reminder function facilitates the execution of these actions.

[0283] Step 6:

[0284] The server provides incentives to users based on their behavior history and achievements. It prepares digital badges and benefits as rewards for achieved goals, and arranges a mechanism to enhance users' motivation. The input is the user's behavior data and new health status, and the output is incentive information. By continuing this, continuous health management is promoted.

[0285] Step 7:

[0286] The server makes proposals to encourage users to participate in local events and activities. Based on past data and interests, it provides appropriate event information, aiming to strengthen the relationship with the local community. The input is the user's profile information and behavior history, and the output is event information recommending participation. Thereby, users can enjoy health improvement while feeling a social connection.

[0287] (Application Example 1)

[0288] 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".

[0289] In modern society, health management is an important issue, especially for the elderly. However, it is difficult to obtain optimal health improvement measures based on individual health status and behavior, and there is also a lack of motivation to continuously implement them. Furthermore, the elderly in an environment where it is difficult to have a connection with the local community often feel a sense of social isolation. There is a need to solve these problems and support the elderly to live a healthy and rich life.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0291] In this invention, the server includes data collection means for collecting biometric information from users, data analysis means for analyzing the biometric information and estimating health risks, strategy generation means for constructing individually tailored health improvement strategies, reward provision means for providing rewards, activity participation means, and achievement certificate visualization means. This makes it possible to promote health management and social participation optimized for each individual elderly person.

[0292] "Data collection means" refers to functions for acquiring biometric information such as heart rate, steps taken, and sleep data from users.

[0293] "Data analysis tools" refer to functions that estimate health risks based on collected biometric information and perform necessary analyses.

[0294] A "strategy generation tool" is a function that creates personalized health improvement strategies tailored to the user based on analyzed data.

[0295] "Communication means" refers to the function of transmitting generated health improvement strategies and related information to users.

[0296] A "reward provision mechanism" is a function that provides incentives based on the user's behavioral history and achievement level.

[0297] "Means of participation in activities" refers to functions that promote users' participation in local group activities and events.

[0298] The "Method for Visualizing Achievement Certificates" is a function that displays a visual certificate of achievement when a user achieves their goal.

[0299] The system implementing this invention consists of multiple components and can comprehensively manage the user's health. First, the user collects daily health data using a wearable device such as a smartwatch. These devices record biometric information such as heart rate, steps, and sleep patterns in real time and communicate with a smartphone via Bluetooth. The data is then sent to the cloud and stored in a database.

[0300] Next, the server analyzes biometric information using data analysis platforms such as Python or R. Here, machine learning models (e.g., TensorFlow, PyTorch) are used to predict potential health risks for individual users. Based on these analysis results, a generative AI model is used to generate personalized health improvement strategies.

[0301] Next, the generated health improvement strategy is delivered to the user via communication. The user reviews it on their smartphone and implements an action plan based on their daily health status. Furthermore, a reward system is in place to display a visual achievement certificate, such as a digital badge, on the smartphone interface when goals are achieved. In addition, participation mechanisms encourage participation in local health events and community activities, deepening social connections.

[0302] For example, if a user achieves their goal of 6,000 steps per day, this is visualized within the app as a "Healthy Walking Master" badge, providing the user with a tangible sense of accomplishment. Furthermore, by providing the AI ​​model with instructions such as, "Generate a weekly plan to improve health based on the following data: heart rate trends, recent steps, and sleep patterns," an appropriate health improvement strategy can be constructed.

[0303] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0304] Step 1:

[0305] The terminal collects biometric information such as the user's heart rate, step count, sleep pattern, etc. Using a wearable device, this data is recorded in real time and transmitted to a smartphone app via Bluetooth communication. The input is the data from the biometric sensor, and the output is the initial data stored in the smartphone app.

[0306] Step 2:

[0307] The terminal transmits the biometric information collected via the smartphone to the cloud. The data transmitted to the cloud is stored in a database and is ready for analysis. The input is the data stored in the smartphone app, and the output is the data stored in the database on the cloud.

[0308] Step 3:

[0309] The server obtains biometric information from the database on the cloud and uses data analysis tools such as Python or R to predict health risks. By using a machine learning model (e.g., TensorFlow, PyTorch), the input data is analyzed and the user's health risk is evaluated numerically. The output is the prediction result of the health risk.

[0310] Step 4:

[0311] The server uses a generative AI model to generate an individualized health improvement strategy based on the analysis results. The input is the prediction result of the health risk, and the output is the improvement strategy optimized for the user. As a specific operation, the prompt sentence "Please generate a weekly plan to improve the health status based on the following data." is provided to the AI model.

[0312] Step 5:

[0313] The server generates a health improvement strategy and transmits it to the terminal using a communication method. The terminal notifies the user of the strategy, making it available for viewing through a smartphone app. The input is the generated health improvement strategy, and the output is the improvement plan information displayed to the user.

[0314] Step 6:

[0315] Users perform daily health actions using a smartphone app. The user's activity history is sent back to the cloud via the device and used as feedback for future improvement strategies. The input is data on the actions performed by the user, and the output is the feedback data.

[0316] Step 7:

[0317] The server uses a reward system to visualize achievement certificates on the user's device based on their goal attainment. These certificates include digital badges and announcements of local events, aiming to boost user motivation. Input is the user's goal attainment status, and output is the displayed achievement certificates and incentive information.

[0318] 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.

[0319] This invention is a health support system for seniors that, in addition to providing individualized improvement plans based on the user's health indicators, incorporates an emotion engine that recognizes the user's emotional state and optimizes health management accordingly.

[0320] This system first uses smart devices and mobile terminals as collection tools to continuously record the user's physiological data. In addition to the user's heart rate, body temperature, and exercise data, the terminals utilize boundary sensors to acquire information related to emotions, such as voice and facial expressions.

[0321] Upon receiving this data, the server uses analytical tools to predict health risks. Generative AI analyzes patterns and identifies potential risks to the user's health. Based on these results, the generator creates a personalized lifestyle improvement plan for each user.

[0322] A key feature of this invention is the inclusion of an emotion engine. The server uses this emotion engine to analyze the user's current emotional state. For example, if it determines that the user is experiencing high stress levels, it adds activities to promote relaxation to the suggestions. This enables the creation of a plan that also considers the user's psychological well-being.

[0323] The emotional engine also influences the provision of incentives. It flexibly adjusts incentives to stimulate achievement motivation based on the user's emotional state, providing badges and rewards accordingly. In this way, emotionally sensitive support activities can improve the user's quality of life.

[0324] Users can receive these personalized plans through their devices. The devices guide daily activities through applications and support user behavior with emotionally resonant content. Furthermore, participation encouragement features provide information on local events and support participation in community activities.

[0325] This invention adopts a form that comprehensively supports the user's physical and psychological health, and allows for flexible responses to individual needs.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The device uses a smartwatch or smartphone app to record heart rate, body temperature, steps, voice, and facial expression data to collect the user's biometric information. Voice and facial expression data, in particular, play a crucial role for the emotion engine.

[0329] Step 2:

[0330] The device transmits collected biometric and emotion-related data to a server. Encryption protocols are used to ensure the secure transmission of this data.

[0331] Step 3:

[0332] The server uses generative AI to analyze received biometric and emotion-related data. It identifies health risks based on health indicators and analyzes potential risks to the user. In particular, emotion analysis detects changes in voice tone and facial expressions to assess stress levels.

[0333] Step 4:

[0334] Based on the analysis results, the server generates a personalized health improvement plan for the user. This plan includes exercise and dietary advice to improve physical health, as well as relaxation activities to adapt to emotional states and measures to improve mental health.

[0335] Step 5:

[0336] Through the emotion engine, the server adjusts incentives based on the user's emotional state and generates digital badges and rewards to enhance a sense of accomplishment. It also makes motivational adjustments as needed, such as easing overly challenging goals.

[0337] Step 6:

[0338] The server sends the generated health improvement plan and incentive information to the device. The device then notifies the user of this information through the app and provides detailed instructions.

[0339] Step 7:

[0340] Users perform their daily activities according to improvement plans provided on their devices and record their progress and experiences in the app. They are also encouraged to actively participate by receiving the provided incentives and being able to check their progress.

[0341] Step 8:

[0342] The server utilizes a feedback mechanism to collect user feedback and incorporate it into the generation of the next health improvement plan. Based on user behavior data and feedback, it provides more personalized measures.

[0343] This process makes it possible to support the sustained improvement of users' physical and mental health.

[0344] (Example 2)

[0345] 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".

[0346] The present invention aims to provide a system that can comprehensively support not only physical health but also psychological health in the health management of senior citizens. Furthermore, it aims to improve the quality of life for users by providing flexible incentives that take into account the user's emotional state and functions that strengthen connections with the local community.

[0347] 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.

[0348] In this invention, the server includes information acquisition means for collecting health indicators and emotional data from users; analysis means for analyzing the health indicators and emotional data to predict health and psychological states; and plan creation means for generating personalized health and psychological support plans based on the prediction results obtained by the analysis means. This enables comprehensive support aimed at improving physical and psychological health.

[0349] "Information acquisition means" refers to the execution environment of a device or software for collecting health indicators and emotional data from users.

[0350] "Analysis means" refers to technical methods and devices used to analyze collected health indicators and emotional data to predict the user's health and psychological state.

[0351] "Plan creation means" refers to a process or system that generates individualized health and psychological support plans based on prediction results obtained through analysis means.

[0352] "Guidance means" refers to a means of communication or interface for providing users with the generated health and psychological support plan.

[0353] A "motivational tool" is a method or device for adjusting and providing incentives according to the user's emotional state.

[0354] "Activity promotion means" refers to functions that provide information and support to encourage participation in community activities and events.

[0355] "Data analysis means" refers to analytical techniques or devices that use long-term health data and emotional data to detect potential health and psychological risks at an early stage.

[0356] This invention is a system that comprehensively supports the physical and psychological health of primarily senior citizens. Users collect health indicators and emotional data using smart devices or mobile terminals. This data includes heart rate, body temperature, exercise information, and emotion-related information such as voice and facial expressions.

[0357] The device continuously samples this physiological and emotional data and sends it to the server in a fixed format. Data transmission is encrypted using the SSL / TLS protocol and is secure.

[0358] The server uses a generative AI model to analyze incoming data. The AI ​​model uses patterns learned from historical data to predict the user's health and psychological state. This allows for the creation of personalized health improvement and psychological support plans for each user.

[0359] For example, the server evaluates the user's stress level from their heart rate and facial expression data and generates instructions in the form of a prompt such as, "Evaluate the user's current stress level from their heart rate and facial expression data, and suggest recommended relaxation activities." This information is tailored based on the user's specific health goals.

[0360] After a plan is created, the server guides the user to provide an appropriate support plan. The terminal supports the user's daily activities through the application and provides real-time feedback. The user receives this plan and can use it in their daily life.

[0361] Furthermore, based on the results of psychological state analysis using the emotion engine, incentives and rewards are provided as motivation to maintain user enthusiasm. In this way, support for optimizing the user's health and psychological state can be achieved.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The device collects physiological and emotional data from the user. Inputs include exercise data from a heart rate sensor, body temperature sensor, and accelerometer, as well as voice and facial expression data from a microphone and camera. This data is converted to a digital format and standardized within the device. The output is a unified dataset.

[0365] Step 2:

[0366] The device periodically sends collected data to the server. It uses a processed dataset as input. Transmission is secure using the SSL / TLS protocol. The output is encrypted data delivered to the server.

[0367] Step 3:

[0368] The server analyzes the received data. The input is data sent from the terminal. The server passes this data through a generative AI model to evaluate the user's health and psychological state. Statistical methods and machine learning algorithms are used in this process. The output is the analysis results regarding the user's health and psychological state.

[0369] Step 4:

[0370] The server generates personalized support plans based on the analysis results. The analysis results obtained in step 3 are used as input. Using the generated AI model and predefined prompts, it creates health maintenance activities and psychological state improvement plans. The output is a support plan customized for each user.

[0371] Step 5:

[0372] The server sends the generated support plan to the terminal. The input is the plan generated in step 4. The output is the individual support plan transmitted to the terminal.

[0373] Step 6:

[0374] The terminal presents the user with a support plan. The input is the support plan received from the server. Through the application, it suggests the most suitable activities and relaxation activities for the user in real time. The output is guidance information displayed on the user's screen.

[0375] (Application Example 2)

[0376] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0377] For elderly individuals and other users to comprehensively manage their health and improve their quality of life, individualized care that considers emotional states in addition to physiological health indicators is necessary. However, conventional health management systems lack mechanisms to adequately analyze emotional states and utilize them for health improvement. As a result, incentives and promotion of social participation tailored to users' emotions are insufficient, making it difficult to achieve sustainable health improvement.

[0378] 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.

[0379] In this invention, the server includes an acquisition means for collecting health-related information from users, an analysis means for analyzing the health-related information and emotional information to predict risks, and a creation means for creating an individualized health improvement plan based on the prediction results and emotional state obtained by the analysis means. This enables integrated management of the user's physiological and psychological health, and allows for sustainable health improvement tailored to each individual's lifestyle.

[0380] "Means of acquisition" refers to means that have the function of collecting health-related information from users.

[0381] "Analysis means" refers to means that have the function of analyzing collected health-related information and emotional information to predict health risks.

[0382] "Creation means" refers to means that have the function of generating an individualized health improvement plan based on the results of the analysis means.

[0383] A "presentation means" is a means that has the function of displaying and proposing the generated health improvement plan to the user.

[0384] A "stimulant" is a means that provides appropriate incentives according to the user's emotional state and has the function of stimulating their desire to achieve.

[0385] "Promotional measures" refer to means that provide information that encourages participation in local social activities and events, and have the function of promoting users' social participation.

[0386] An "emotion analysis tool" is a tool that has the function of evaluating a user's emotional state in real time.

[0387] This invention is a system for comprehensively managing the health status of users, including the elderly. This system acquires health-related and emotional information from users through a specific device, and uses this information to perform analysis and create improvement plans.

[0388] The server first has a mechanism to continuously acquire physiological data such as the user's heart rate, body temperature, and activity level. In addition, it also collects emotion-related data such as voice and facial expressions. The acquired data is sent to the server and analyzed by analytical means. In particular, generative AI models are used to identify health risks tailored to each individual user.

[0389] Based on the analysis results, the server creates a personalized health improvement plan for each user. This plan includes suggestions for relaxation activities and physical exercises that take into account the user's emotional state. Furthermore, if the emotional analysis determines that the user is experiencing stress, countermeasures are immediately incorporated into the plan.

[0390] Users can view this plan on their own devices. The device presents the generated plan in a visually appealing and user-friendly format, supporting users in managing their daily health. It also offers emotion-based incentives to encourage motivation. Furthermore, the device provides information on local social activities and events, encouraging users to actively participate in society.

[0391] For example, if a user records in their diary that they "haven't been feeling well all morning," the system will determine that they are experiencing stress based on this text and collected physiological data. At this point, the user will be offered suggestions such as relaxation music or a suitable walking route. To achieve this, a prompt message such as "Based on the user's heart rate, body temperature, exercise data, and the text 'I'm feeling a little unwell,' consider assessing their stress level and developing countermeasures" is provided to the generating AI model.

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The device uses sensors to acquire data such as the user's heart rate, body temperature, activity level, voice, and facial expressions in real time. This data is collected from the user and transmitted to the next step.

[0395] Step 2:

[0396] The server receives physiological and emotional data transmitted from the terminal and stores it in a database. The input is data from sensors, and the output is information converted into an easy-to-use data format.

[0397] Step 3:

[0398] The server analyzes the stored data using analytical tools. It utilizes a generative AI model to predict health risks from this data. The input to this analysis is the collected health data, and the output is the predicted health risk for the user.

[0399] Step 4:

[0400] The server generates a lifestyle improvement plan based on the analysis results. This plan includes suggestions for stress reduction activities and recommendations for healthy exercise. The input is the predicted results and the current emotional state, and the output is the personalized improvement plan.

[0401] Step 5:

[0402] The terminal presents the user with a health improvement plan sent from the server. The plan is displayed in a visualized format to support its implementation. The input is the generated improvement plan, and the output is user-readable guide information.

[0403] Step 6:

[0404] The user performs activities and exercises presented via the device. The device receives feedback on these activities and sends it to the server. The input is the user's activity record, and the output is feedback data.

[0405] Step 7:

[0406] The terminal provides information on local social activities and events, promoting user community participation. Input is information from an event database, and output is event announcements for the user.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] This invention is a system that supports health management for seniors and includes means for collecting, analyzing, generating, providing, providing incentives, and promoting participation. This makes it possible to efficiently manage users' health indicators and promote behavioral change.

[0424] Specifically, this system collects health data through smart devices and terminals that users use on a daily basis. For example, a smartwatch can be used as a terminal to monitor heart rate, steps taken, and sleep patterns. By wearing these devices daily and inputting data about diet and activity through the app, more detailed health indicators are collected.

[0425] The server analyzes the collected data and predicts health risks. For example, it can identify trends in heart rate variability and step count decline, and use this to numerically assess future health risks. Subsequently, based on this information, it uses a generation tool to develop a personalized health improvement plan. This plan includes recommended exercises, nutritional guidance, and sleep improvement measures.

[0426] Users receive this health improvement plan via their device, along with regular behavioral reminders. Incentives make progress visible; for example, digital badges or invitations to local events are displayed upon achieving certain goals. This increases user motivation.

[0427] Furthermore, through participation promotion mechanisms, the server suggests participation in local hobby clubs and health events based on the user's interests and past participation history. Through this, users can deepen their connections with their local community. In this way, this system makes daily health management an enjoyable and sustainable activity for users.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The device continuously records health indicators such as heart rate, steps, and sleep data through the smart device worn by the user and the application used. This includes automatic measurements by sensors and data on diet and activity entered manually by the user.

[0431] Step 2:

[0432] The device sends the collected data to the server at regular intervals. The transmission takes place over the internet, and the data is stored in a secure database on the server.

[0433] Step 3:

[0434] The server applies a generative AI algorithm to analyze the received health data. This analysis compares patterns in each indicator and quantifies the health risk for each user. For example, it identifies abnormal heart rate patterns or tendencies toward lack of exercise.

[0435] Step 4:

[0436] The server generates a personalized plan to improve health based on the analysis results. The generated plan includes exercise programs, dietary suggestions, and sleep guidelines tailored to the user.

[0437] Step 5:

[0438] The server sends the generated health improvement plan to the device, which then notifies the user. The user can then view the detailed plan and instructions through the app.

[0439] Step 6:

[0440] Users implement a daily health plan based on notifications received from their device. The progress of the plan is displayed within the app, and users can input their achievements regarding exercise and diet.

[0441] Step 7:

[0442] The server provides incentives to boost user motivation (e.g., badges, invitations to local events) based on the user's behavior history. This encourages active user participation.

[0443] Step 8:

[0444] The server analyzes the user's interests and, taking into account their past activity history, makes suggestions to encourage participation in local events. The terminal then notifies the user of upcoming events through these suggestions.

[0445] (Example 1)

[0446] 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."

[0447] The goal is to provide a system that supports older adults in efficiently managing their health information and taking appropriate health-maintaining behaviors in their daily lives. This requires a mechanism that can detect potential health problems early and encourage appropriate intervention. By collecting health data from older adults and providing personalized improvement plans, it is necessary to maintain their motivation for maintaining their health and deepen their connections with the community.

[0448] 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.

[0449] In this invention, the server includes a collection means equipped with a terminal for collecting biometric information from users, an analysis means including a server for analyzing the biometric information and predicting health risks, and a generation means using a generation AI model that generates an individualized health management plan based on the prediction results obtained from the analysis means. This makes it possible for elderly people to effectively manage their health information and promote appropriate health maintenance behaviors.

[0450] "Collection means" refers to functions that include terminals for collecting biometric information from users, and involves collecting necessary data using smart devices and applications.

[0451] The "analysis method" refers to a control function that utilizes a server to analyze collected biological information and predict health risks from it.

[0452] "Generative means" refers to the function of using a generative AI model to create personalized health management plans based on analysis results.

[0453] "Means of delivery" refers to functions including terminals for appropriately presenting the generated health management plan to the user.

[0454] An "incentive mechanism" is a function that provides performance-based rewards to increase user motivation based on the user's behavioral history.

[0455] "Methods for promoting participation" refer to functions that encourage users to participate in local activities and events.

[0456] A "generative AI model" is a system that includes artificial intelligence to automatically generate personalized plans based on the user's health data.

[0457] This invention is a system that supports the health management of the elderly. This system collects and analyzes health-related information through smart devices and terminals that users use on a daily basis, and provides appropriate feedback to users, thereby promoting improvements in their health behaviors.

[0458] Specifically, the device functions as a smartwatch and mobile application, collecting the user's biometric information. This includes heart rate, steps taken, sleep patterns, diet, and exercise data. The device periodically transmits the collected data to a server using Bluetooth or Wi-Fi. Users can also manually input diet and exercise data through the mobile app.

[0459] The server uses specialized software to analyze the collected data, particularly utilizing generative AI models to assess health risks. Specifically, the AI ​​model analyzes trends in heart rate and step count, and uses predictive algorithms to quantify health risks. For example, if abnormal heart rate or decreased exercise levels are detected, it can provide early warnings about potential future health risks.

[0460] As a means of generation, the server creates a personalized health management plan for each user. This plan includes recommended exercise levels, meal plans, sleep improvement measures, and other suggestions tailored to the user's lifestyle.

[0461] The device provides users with this plan and uses it as an action reminder to help them with their daily activities. Furthermore, incentives make progress visible, and digital badges and rewards are provided based on performance. This helps maintain user motivation.

[0462] Furthermore, using participation promotion tools, the server suggests to users participation in local events and club activities. This allows users to deepen their connections with the local community.

[0463] As a concrete example, there is a case where a user, after utilizing this system, began walking regularly and improved their physical fitness. In this case, the generative AI model provided a plan that adjusted the balance between diet and exercise, leading to an improvement in the user's health awareness.

[0464] Example of a prompt:

[0465] "Could you please describe the specific functions and user experience of the health management support system available to seniors?"

[0466] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0467] Step 1:

[0468] The device collects the user's daily health data. Specifically, it senses and records biometric information such as heart rate, steps taken, and sleep patterns via a smartwatch or mobile app. This data collection is performed in real time or at set intervals. The input is the user's biometric data, and the output is health information as digital data compiled from that data.

[0469] Step 2:

[0470] The device transmits collected health information to a server. The data is securely transferred to the server using Bluetooth or Wi-Fi. The input is biometric data recorded on the device, and the output is the dataset sent to the server. This process centralizes the data, making it available for subsequent analysis.

[0471] Step 3:

[0472] The server analyzes the received biometric data. It runs a generative AI model to analyze changes in heart rate and exercise, and assess health risks. This analysis uses algorithms to detect anomalies and identify changes in patterns. The input is a dataset stored on the server, and the output is a health assessment and risk prediction as a result of the analysis.

[0473] Step 4:

[0474] The server generates an optimal health management plan for the user based on the analysis results. Utilizing a generation AI model, it creates suggestions for improving exercise, diet, and sleep that match the user's lifestyle. The input is the analysis results, and the output is a personalized health plan. This plan presents areas requiring action in a way that is easy for the user to understand.

[0475] Step 5:

[0476] The device provides the user with a generated health plan. Through commonly used digital tools, it visually displays each part of the health plan and may also allow for the setting of reminders and notifications. The input is the health plan provided by the server, and the output is specific action guidelines available to the user. The reminder function facilitates the execution of these actions.

[0477] Step 6:

[0478] The server provides incentives to users based on their behavioral history and achievements. It offers digital badges and rewards for achieving goals, creating a system to boost user motivation. Inputs are user behavioral data and new health status, while output is incentive information. This continuous process encourages ongoing health management.

[0479] Step 7:

[0480] The server suggests that users participate in local events and activities. Based on past data and interests, it aims to provide appropriate event information and strengthen relationships with the local community. Input is the user's profile information and activity history, and output is event information that users are recommended to participate in. This allows users to enjoy improving their health while feeling a sense of social connection.

[0481] (Application Example 1)

[0482] 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."

[0483] In modern society, health management is a crucial issue, especially for the elderly. However, it is difficult to obtain optimal health improvement measures based on individual health conditions and behaviors, and there is a lack of motivation to sustainably implement them. Furthermore, elderly people who live in environments where it is difficult to connect with their local community often experience social isolation. There is a need to address these challenges and support the elderly so that they can live healthy and fulfilling lives.

[0484] 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.

[0485] In this invention, the server includes data collection means for collecting biometric information from users, data analysis means for analyzing the biometric information and estimating health risks, strategy generation means for constructing individually tailored health improvement strategies, reward provision means for providing rewards, activity participation means, and achievement certificate visualization means. This makes it possible to promote health management and social participation optimized for each individual elderly person.

[0486] "Data collection means" refers to functions for acquiring biometric information such as heart rate, steps taken, and sleep data from users.

[0487] "Data analysis tools" refer to functions that estimate health risks based on collected biometric information and perform necessary analyses.

[0488] A "strategy generation tool" is a function that creates personalized health improvement strategies tailored to the user based on analyzed data.

[0489] "Communication means" refers to the function of transmitting generated health improvement strategies and related information to users.

[0490] A "reward provision mechanism" is a function that provides incentives based on the user's behavioral history and achievement level.

[0491] "Means of participation in activities" refers to functions that promote users' participation in local group activities and events.

[0492] The "Method for Visualizing Achievement Certificates" is a function that displays a visual certificate of achievement when a user achieves their goal.

[0493] The system implementing this invention consists of multiple components and can comprehensively manage the user's health. First, the user collects daily health data using a wearable device such as a smartwatch. These devices record biometric information such as heart rate, steps, and sleep patterns in real time and communicate with a smartphone via Bluetooth. The data is then sent to the cloud and stored in a database.

[0494] Next, the server analyzes biometric information using data analysis platforms such as Python or R. Here, machine learning models (e.g., TensorFlow, PyTorch) are used to predict potential health risks for individual users. Based on these analysis results, a generative AI model is used to generate personalized health improvement strategies.

[0495] Next, the generated health improvement strategy is delivered to the user via communication. The user reviews it on their smartphone and implements an action plan based on their daily health status. Furthermore, a reward system is in place to display a visual achievement certificate, such as a digital badge, on the smartphone interface when goals are achieved. In addition, participation mechanisms encourage participation in local health events and community activities, deepening social connections.

[0496] For example, if a user achieves their goal of 6,000 steps per day, this is visualized within the app as a "Healthy Walking Master" badge, providing the user with a tangible sense of accomplishment. Furthermore, by providing the AI ​​model with instructions such as, "Generate a weekly plan to improve health based on the following data: heart rate trends, recent steps, and sleep patterns," an appropriate health improvement strategy can be constructed.

[0497] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0498] Step 1:

[0499] The device collects biometric information such as the user's heart rate, steps taken, and sleep patterns. This data is recorded in real time using a wearable device and transmitted to a smartphone app via Bluetooth communication. The input is data from the biosensor, and the output is initial data stored in the smartphone app.

[0500] Step 2:

[0501] The device transmits biometric information collected via a smartphone to the cloud. The data transmitted to the cloud is stored in a database and ready for analysis. The input is data accumulated in the smartphone app, and the output is data stored in the database on the cloud.

[0502] Step 3:

[0503] The server retrieves biometric information from a cloud-based database and uses Python or R data analysis tools to predict health risks. Machine learning models (e.g., TensorFlow, PyTorch) are used to analyze the input data and numerically evaluate the user's health risk. The output is the predicted health risk.

[0504] Step 4:

[0505] The server uses an AI model to generate personalized health improvement strategies based on the analysis results. The input is the predicted health risk, and the output is an improvement strategy optimized for the user. Specifically, the prompt message "Generate a weekly plan to improve health based on the following data" is provided to the AI ​​model.

[0506] Step 5:

[0507] The server generates a health improvement strategy and transmits it to the terminal using a communication method. The terminal notifies the user of the strategy, making it available for viewing through a smartphone app. The input is the generated health improvement strategy, and the output is the improvement plan information displayed to the user.

[0508] Step 6:

[0509] Users perform daily health actions using a smartphone app. The user's activity history is sent back to the cloud via the device and used as feedback for future improvement strategies. The input is data on the actions performed by the user, and the output is the feedback data.

[0510] Step 7:

[0511] The server uses a reward system to visualize achievement certificates on the user's device based on their goal attainment. These certificates include digital badges and announcements of local events, aiming to boost user motivation. Input is the user's goal attainment status, and output is the displayed achievement certificates and incentive information.

[0512] 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.

[0513] This invention is a health support system for seniors that, in addition to providing individualized improvement plans based on the user's health indicators, incorporates an emotion engine that recognizes the user's emotional state and optimizes health management accordingly.

[0514] This system first uses smart devices and mobile terminals as collection tools to continuously record the user's physiological data. In addition to the user's heart rate, body temperature, and exercise data, the terminals utilize boundary sensors to acquire information related to emotions, such as voice and facial expressions.

[0515] Upon receiving this data, the server uses analytical tools to predict health risks. Generative AI analyzes patterns and identifies potential risks to the user's health. Based on these results, the generator creates a personalized lifestyle improvement plan for each user.

[0516] A key feature of this invention is the inclusion of an emotion engine. The server uses this emotion engine to analyze the user's current emotional state. For example, if it determines that the user is experiencing high stress levels, it adds activities to promote relaxation to the suggestions. This enables the creation of a plan that also considers the user's psychological well-being.

[0517] The emotional engine also influences the provision of incentives. It flexibly adjusts incentives to stimulate achievement motivation based on the user's emotional state, providing badges and rewards accordingly. In this way, emotionally sensitive support activities can improve the user's quality of life.

[0518] Users can receive these personalized plans through their devices. The devices guide daily activities through applications and support user behavior with emotionally resonant content. Furthermore, participation encouragement features provide information on local events and support participation in community activities.

[0519] This invention adopts a form that comprehensively supports the user's physical and psychological health, and allows for flexible responses to individual needs.

[0520] The following describes the processing flow.

[0521] Step 1:

[0522] The device uses a smartwatch or smartphone app to record heart rate, body temperature, steps, voice, and facial expression data to collect the user's biometric information. Voice and facial expression data, in particular, play a crucial role for the emotion engine.

[0523] Step 2:

[0524] The device transmits collected biometric and emotion-related data to a server. Encryption protocols are used to ensure the secure transmission of this data.

[0525] Step 3:

[0526] The server uses generative AI to analyze received biometric and emotion-related data. It identifies health risks based on health indicators and analyzes potential risks to the user. In particular, emotion analysis detects changes in voice tone and facial expressions to assess stress levels.

[0527] Step 4:

[0528] Based on the analysis results, the server generates a personalized health improvement plan for the user. This plan includes exercise and dietary advice to improve physical health, as well as relaxation activities to adapt to emotional states and measures to improve mental health.

[0529] Step 5:

[0530] Through the emotion engine, the server adjusts incentives based on the user's emotional state and generates digital badges and rewards to enhance a sense of accomplishment. It also makes motivational adjustments as needed, such as easing overly challenging goals.

[0531] Step 6:

[0532] The server sends the generated health improvement plan and incentive information to the device. The device then notifies the user of this information through the app and provides detailed instructions.

[0533] Step 7:

[0534] Users perform their daily activities according to improvement plans provided on their devices and record their progress and experiences in the app. They are also encouraged to actively participate by receiving the provided incentives and being able to check their progress.

[0535] Step 8:

[0536] The server utilizes a feedback mechanism to collect user feedback and incorporate it into the generation of the next health improvement plan. Based on user behavior data and feedback, it provides more personalized measures.

[0537] This process makes it possible to support the sustained improvement of users' physical and mental health.

[0538] (Example 2)

[0539] 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."

[0540] The present invention aims to provide a system that can comprehensively support not only physical health but also psychological health in the health management of senior citizens. Furthermore, it aims to improve the quality of life for users by providing flexible incentives that take into account the user's emotional state and functions that strengthen connections with the local community.

[0541] 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.

[0542] In this invention, the server includes information acquisition means for collecting health indicators and emotional data from users; analysis means for analyzing the health indicators and emotional data to predict health and psychological states; and plan creation means for generating personalized health and psychological support plans based on the prediction results obtained by the analysis means. This enables comprehensive support aimed at improving physical and psychological health.

[0543] "Information acquisition means" refers to the execution environment of a device or software for collecting health indicators and emotional data from users.

[0544] "Analysis means" refers to technical methods and devices used to analyze collected health indicators and emotional data to predict the user's health and psychological state.

[0545] "Plan creation means" refers to a process or system that generates individualized health and psychological support plans based on prediction results obtained through analysis means.

[0546] "Guidance means" refers to a means of communication or interface for providing users with the generated health and psychological support plan.

[0547] A "motivational tool" is a method or device for adjusting and providing incentives according to the user's emotional state.

[0548] "Activity promotion means" refers to functions that provide information and support to encourage participation in community activities and events.

[0549] "Data analysis means" refers to analytical techniques or devices that use long-term health data and emotional data to detect potential health and psychological risks at an early stage.

[0550] This invention is a system that comprehensively supports the physical and psychological health of primarily senior citizens. Users collect health indicators and emotional data using smart devices or mobile terminals. This data includes heart rate, body temperature, exercise information, and emotion-related information such as voice and facial expressions.

[0551] The device continuously samples this physiological and emotional data and sends it to the server in a fixed format. Data transmission is encrypted using the SSL / TLS protocol and is secure.

[0552] The server uses a generative AI model to analyze incoming data. The AI ​​model uses patterns learned from historical data to predict the user's health and psychological state. This allows for the creation of personalized health improvement and psychological support plans for each user.

[0553] For example, the server evaluates the user's stress level from their heart rate and facial expression data and generates instructions in the form of a prompt such as, "Evaluate the user's current stress level from their heart rate and facial expression data, and suggest recommended relaxation activities." This information is tailored based on the user's specific health goals.

[0554] After a plan is created, the server guides the user to provide an appropriate support plan. The terminal supports the user's daily activities through the application and provides real-time feedback. The user receives this plan and can use it in their daily life.

[0555] Furthermore, based on the results of psychological state analysis using the emotion engine, incentives and rewards are provided as motivation to maintain user enthusiasm. In this way, support for optimizing the user's health and psychological state can be achieved.

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] The device collects physiological and emotional data from the user. Inputs include exercise data from a heart rate sensor, body temperature sensor, and accelerometer, as well as voice and facial expression data from a microphone and camera. This data is converted to a digital format and standardized within the device. The output is a unified dataset.

[0559] Step 2:

[0560] The device periodically sends collected data to the server. It uses a processed dataset as input. Transmission is secure using the SSL / TLS protocol. The output is encrypted data delivered to the server.

[0561] Step 3:

[0562] The server analyzes the received data. The input is data sent from the terminal. The server passes this data through a generative AI model to evaluate the user's health and psychological state. Statistical methods and machine learning algorithms are used in this process. The output is the analysis results regarding the user's health and psychological state.

[0563] Step 4:

[0564] The server generates personalized support plans based on the analysis results. The analysis results obtained in step 3 are used as input. Using the generated AI model and predefined prompts, it creates health maintenance activities and psychological state improvement plans. The output is a support plan customized for each user.

[0565] Step 5:

[0566] The server sends the generated support plan to the terminal. The input is the plan generated in step 4. The output is the individual support plan transmitted to the terminal.

[0567] Step 6:

[0568] The terminal presents the user with a support plan. The input is the support plan received from the server. Through the application, it suggests the most suitable activities and relaxation activities for the user in real time. The output is guidance information displayed on the user's screen.

[0569] (Application Example 2)

[0570] 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."

[0571] For elderly individuals and other users to comprehensively manage their health and improve their quality of life, individualized care that considers emotional states in addition to physiological health indicators is necessary. However, conventional health management systems lack mechanisms to adequately analyze emotional states and utilize them for health improvement. As a result, incentives and promotion of social participation tailored to users' emotions are insufficient, making it difficult to achieve sustainable health improvement.

[0572] 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.

[0573] In this invention, the server includes an acquisition means for collecting health-related information from users, an analysis means for analyzing the health-related information and emotional information to predict risks, and a creation means for creating an individualized health improvement plan based on the prediction results and emotional state obtained by the analysis means. This enables integrated management of the user's physiological and psychological health, and allows for sustainable health improvement tailored to each individual's lifestyle.

[0574] "Means of acquisition" refers to means that have the function of collecting health-related information from users.

[0575] "Analysis means" refers to means that have the function of analyzing collected health-related information and emotional information to predict health risks.

[0576] "Creation means" refers to means that have the function of generating an individualized health improvement plan based on the results of the analysis means.

[0577] A "presentation means" is a means that has the function of displaying and proposing the generated health improvement plan to the user.

[0578] A "stimulant" is a means that provides appropriate incentives according to the user's emotional state and has the function of stimulating their desire to achieve.

[0579] "Promotional measures" refer to means that provide information that encourages participation in local social activities and events, and have the function of promoting users' social participation.

[0580] An "emotion analysis tool" is a tool that has the function of evaluating a user's emotional state in real time.

[0581] This invention is a system for comprehensively managing the health status of users, including the elderly. This system acquires health-related and emotional information from users through a specific device, and uses this information to perform analysis and create improvement plans.

[0582] The server first has a mechanism to continuously acquire physiological data such as the user's heart rate, body temperature, and activity level. In addition, it also collects emotion-related data such as voice and facial expressions. The acquired data is sent to the server and analyzed by analytical means. In particular, generative AI models are used to identify health risks tailored to each individual user.

[0583] Based on the analysis results, the server creates a personalized health improvement plan for each user. This plan includes suggestions for relaxation activities and physical exercises that take into account the user's emotional state. Furthermore, if the emotional analysis determines that the user is experiencing stress, countermeasures are immediately incorporated into the plan.

[0584] Users can view this plan on their own devices. The device presents the generated plan in a visually appealing and user-friendly format, supporting users in managing their daily health. It also offers emotion-based incentives to encourage motivation. Furthermore, the device provides information on local social activities and events, encouraging users to actively participate in society.

[0585] For example, if a user records in their diary that they "haven't been feeling well all morning," the system will determine that they are experiencing stress based on this text and collected physiological data. At this point, the user will be offered suggestions such as relaxation music or a suitable walking route. To achieve this, a prompt message such as "Based on the user's heart rate, body temperature, exercise data, and the text 'I'm feeling a little unwell,' consider assessing their stress level and developing countermeasures" is provided to the generating AI model.

[0586] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0587] Step 1:

[0588] The device uses sensors to acquire data such as the user's heart rate, body temperature, activity level, voice, and facial expressions in real time. This data is collected from the user and transmitted to the next step.

[0589] Step 2:

[0590] The server receives physiological and emotional data transmitted from the terminal and stores it in a database. The input is data from sensors, and the output is information converted into an easy-to-use data format.

[0591] Step 3:

[0592] The server analyzes the stored data using analytical tools. It utilizes a generative AI model to predict health risks from this data. The input to this analysis is the collected health data, and the output is the predicted health risk for the user.

[0593] Step 4:

[0594] The server generates a lifestyle improvement plan based on the analysis results. This plan includes suggestions for stress reduction activities and recommendations for healthy exercise. The input is the predicted results and the current emotional state, and the output is the personalized improvement plan.

[0595] Step 5:

[0596] The terminal presents the user with a health improvement plan sent from the server. The plan is displayed in a visualized format to support its implementation. The input is the generated improvement plan, and the output is user-readable guide information.

[0597] Step 6:

[0598] The user performs activities and exercises presented via the device. The device receives feedback on these activities and sends it to the server. The input is the user's activity record, and the output is feedback data.

[0599] Step 7:

[0600] The terminal provides information on local social activities and events, promoting user community participation. Input is information from an event database, and output is event announcements for the user.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] [Fourth Embodiment]

[0605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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).

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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".

[0618] This invention is a system that supports health management for seniors and includes means for collecting, analyzing, generating, providing, providing incentives, and promoting participation. This makes it possible to efficiently manage users' health indicators and promote behavioral change.

[0619] Specifically, this system collects health data through smart devices and terminals that users use on a daily basis. For example, a smartwatch can be used as a terminal to monitor heart rate, steps taken, and sleep patterns. By wearing these devices daily and inputting data about diet and activity through the app, more detailed health indicators are collected.

[0620] The server analyzes the collected data and predicts health risks. For example, it can identify trends in heart rate variability and step count decline, and use this to numerically assess future health risks. Subsequently, based on this information, it uses a generation tool to develop a personalized health improvement plan. This plan includes recommended exercises, nutritional guidance, and sleep improvement measures.

[0621] Users receive this health improvement plan via their device, along with regular behavioral reminders. Incentives make progress visible; for example, digital badges or invitations to local events are displayed upon achieving certain goals. This increases user motivation.

[0622] Furthermore, through participation promotion mechanisms, the server suggests participation in local hobby clubs and health events based on the user's interests and past participation history. Through this, users can deepen their connections with their local community. In this way, this system makes daily health management an enjoyable and sustainable activity for users.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The device continuously records health indicators such as heart rate, steps, and sleep data through the smart device worn by the user and the application used. This includes automatic measurements by sensors and data on diet and activity entered manually by the user.

[0626] Step 2:

[0627] The device sends the collected data to the server at regular intervals. The transmission takes place over the internet, and the data is stored in a secure database on the server.

[0628] Step 3:

[0629] The server applies a generative AI algorithm to analyze the received health data. This analysis compares patterns in each indicator and quantifies the health risk for each user. For example, it identifies abnormal heart rate patterns or tendencies toward lack of exercise.

[0630] Step 4:

[0631] The server generates a personalized plan to improve health based on the analysis results. The generated plan includes exercise programs, dietary suggestions, and sleep guidelines tailored to the user.

[0632] Step 5:

[0633] The server sends the generated health improvement plan to the device, which then notifies the user. The user can then view the detailed plan and instructions through the app.

[0634] Step 6:

[0635] Users implement a daily health plan based on notifications received from their device. The progress of the plan is displayed within the app, and users can input their achievements regarding exercise and diet.

[0636] Step 7:

[0637] The server provides incentives to boost user motivation (e.g., badges, invitations to local events) based on the user's behavior history. This encourages active user participation.

[0638] Step 8:

[0639] The server analyzes the user's interests and, taking into account their past activity history, makes suggestions to encourage participation in local events. The terminal then notifies the user of upcoming events through these suggestions.

[0640] (Example 1)

[0641] 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".

[0642] The goal is to provide a system that supports older adults in efficiently managing their health information and taking appropriate health-maintaining behaviors in their daily lives. This requires a mechanism that can detect potential health problems early and encourage appropriate intervention. By collecting health data from older adults and providing personalized improvement plans, it is necessary to maintain their motivation for maintaining their health and deepen their connections with the community.

[0643] 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.

[0644] In this invention, the server includes a collection means equipped with a terminal for collecting biometric information from users, an analysis means including a server for analyzing the biometric information and predicting health risks, and a generation means using a generation AI model that generates an individualized health management plan based on the prediction results obtained from the analysis means. This makes it possible for elderly people to effectively manage their health information and promote appropriate health maintenance behaviors.

[0645] "Collection means" refers to functions that include terminals for collecting biometric information from users, and involves collecting necessary data using smart devices and applications.

[0646] The "analysis method" refers to a control function that utilizes a server to analyze collected biological information and predict health risks from it.

[0647] "Generative means" refers to the function of using a generative AI model to create personalized health management plans based on analysis results.

[0648] "Means of delivery" refers to functions including terminals for appropriately presenting the generated health management plan to the user.

[0649] An "incentive mechanism" is a function that provides performance-based rewards to increase user motivation based on the user's behavioral history.

[0650] "Methods for promoting participation" refer to functions that encourage users to participate in local activities and events.

[0651] A "generative AI model" is a system that includes artificial intelligence to automatically generate personalized plans based on the user's health data.

[0652] This invention is a system that supports the health management of the elderly. This system collects and analyzes health-related information through smart devices and terminals that users use on a daily basis, and provides appropriate feedback to users, thereby promoting improvements in their health behaviors.

[0653] Specifically, the device functions as a smartwatch and mobile application, collecting the user's biometric information. This includes heart rate, steps taken, sleep patterns, diet, and exercise data. The device periodically transmits the collected data to a server using Bluetooth or Wi-Fi. Users can also manually input diet and exercise data through the mobile app.

[0654] The server uses specialized software to analyze the collected data, particularly utilizing generative AI models to assess health risks. Specifically, the AI ​​model analyzes trends in heart rate and step count, and uses predictive algorithms to quantify health risks. For example, if abnormal heart rate or decreased exercise levels are detected, it can provide early warnings about potential future health risks.

[0655] As a means of generation, the server creates a personalized health management plan for each user. This plan includes recommended exercise levels, meal plans, sleep improvement measures, and other suggestions tailored to the user's lifestyle.

[0656] The device provides users with this plan and uses it as an action reminder to help them with their daily activities. Furthermore, incentives make progress visible, and digital badges and rewards are provided based on performance. This helps maintain user motivation.

[0657] Furthermore, using participation promotion tools, the server suggests to users participation in local events and club activities. This allows users to deepen their connections with the local community.

[0658] As a concrete example, there is a case where a user, after utilizing this system, began walking regularly and improved their physical fitness. In this case, the generative AI model provided a plan that adjusted the balance between diet and exercise, leading to an improvement in the user's health awareness.

[0659] Example of a prompt:

[0660] "Could you please describe the specific functions and user experience of the health management support system available to seniors?"

[0661] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0662] Step 1:

[0663] The device collects the user's daily health data. Specifically, it senses and records biometric information such as heart rate, steps taken, and sleep patterns via a smartwatch or mobile app. This data collection is performed in real time or at set intervals. The input is the user's biometric data, and the output is health information as digital data compiled from that data.

[0664] Step 2:

[0665] The device transmits collected health information to a server. The data is securely transferred to the server using Bluetooth or Wi-Fi. The input is biometric data recorded on the device, and the output is the dataset sent to the server. This process centralizes the data, making it available for subsequent analysis.

[0666] Step 3:

[0667] The server analyzes the received biometric data. It runs a generative AI model to analyze changes in heart rate and exercise, and assess health risks. This analysis uses algorithms to detect anomalies and identify changes in patterns. The input is a dataset stored on the server, and the output is a health assessment and risk prediction as a result of the analysis.

[0668] Step 4:

[0669] The server generates an optimal health management plan for the user based on the analysis results. Utilizing a generation AI model, it creates suggestions for improving exercise, diet, and sleep that match the user's lifestyle. The input is the analysis results, and the output is a personalized health plan. This plan presents areas requiring action in a way that is easy for the user to understand.

[0670] Step 5:

[0671] The device provides the user with a generated health plan. Through commonly used digital tools, it visually displays each part of the health plan and may also allow for the setting of reminders and notifications. The input is the health plan provided by the server, and the output is specific action guidelines available to the user. The reminder function facilitates the execution of these actions.

[0672] Step 6:

[0673] The server provides incentives to users based on their behavioral history and achievements. It offers digital badges and rewards for achieving goals, creating a system to boost user motivation. Inputs are user behavioral data and new health status, while output is incentive information. This continuous process encourages ongoing health management.

[0674] Step 7:

[0675] The server suggests that users participate in local events and activities. Based on past data and interests, it aims to provide appropriate event information and strengthen relationships with the local community. Input is the user's profile information and activity history, and output is event information that users are recommended to participate in. This allows users to enjoy improving their health while feeling a sense of social connection.

[0676] (Application Example 1)

[0677] 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".

[0678] In modern society, health management is a crucial issue, especially for the elderly. However, it is difficult to obtain optimal health improvement measures based on individual health conditions and behaviors, and there is a lack of motivation to sustainably implement them. Furthermore, elderly people who live in environments where it is difficult to connect with their local community often experience social isolation. There is a need to address these challenges and support the elderly so that they can live healthy and fulfilling lives.

[0679] 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.

[0680] In this invention, the server includes data collection means for collecting biometric information from users, data analysis means for analyzing the biometric information and estimating health risks, strategy generation means for constructing individually tailored health improvement strategies, reward provision means for providing rewards, activity participation means, and achievement certificate visualization means. This makes it possible to promote health management and social participation optimized for each individual elderly person.

[0681] "Data collection means" refers to functions for acquiring biometric information such as heart rate, steps taken, and sleep data from users.

[0682] "Data analysis tools" refer to functions that estimate health risks based on collected biometric information and perform necessary analyses.

[0683] A "strategy generation tool" is a function that creates personalized health improvement strategies tailored to the user based on analyzed data.

[0684] "Communication means" refers to the function of transmitting generated health improvement strategies and related information to users.

[0685] A "reward provision mechanism" is a function that provides incentives based on the user's behavioral history and achievement level.

[0686] "Means of participation in activities" refers to functions that promote users' participation in local group activities and events.

[0687] The "Method for Visualizing Achievement Certificates" is a function that displays a visual certificate of achievement when a user achieves their goal.

[0688] The system implementing this invention consists of multiple components and can comprehensively manage the user's health. First, the user collects daily health data using a wearable device such as a smartwatch. These devices record biometric information such as heart rate, steps, and sleep patterns in real time and communicate with a smartphone via Bluetooth. The data is then sent to the cloud and stored in a database.

[0689] Next, the server analyzes biometric information using data analysis platforms such as Python or R. Here, machine learning models (e.g., TensorFlow, PyTorch) are used to predict potential health risks for individual users. Based on these analysis results, a generative AI model is used to generate personalized health improvement strategies.

[0690] Next, the generated health improvement strategy is delivered to the user via communication. The user reviews it on their smartphone and implements an action plan based on their daily health status. Furthermore, a reward system is in place to display a visual achievement certificate, such as a digital badge, on the smartphone interface when goals are achieved. In addition, participation mechanisms encourage participation in local health events and community activities, deepening social connections.

[0691] For example, if a user achieves their goal of 6,000 steps per day, this is visualized within the app as a "Healthy Walking Master" badge, providing the user with a tangible sense of accomplishment. Furthermore, by providing the AI ​​model with instructions such as, "Generate a weekly plan to improve health based on the following data: heart rate trends, recent steps, and sleep patterns," an appropriate health improvement strategy can be constructed.

[0692] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0693] Step 1:

[0694] The device collects biometric information such as the user's heart rate, steps taken, and sleep patterns. This data is recorded in real time using a wearable device and transmitted to a smartphone app via Bluetooth communication. The input is data from the biosensor, and the output is initial data stored in the smartphone app.

[0695] Step 2:

[0696] The device transmits biometric information collected via a smartphone to the cloud. The data transmitted to the cloud is stored in a database and ready for analysis. The input is data accumulated in the smartphone app, and the output is data stored in the database on the cloud.

[0697] Step 3:

[0698] The server retrieves biometric information from a cloud-based database and uses Python or R data analysis tools to predict health risks. Machine learning models (e.g., TensorFlow, PyTorch) are used to analyze the input data and numerically evaluate the user's health risk. The output is the predicted health risk.

[0699] Step 4:

[0700] The server uses an AI model to generate personalized health improvement strategies based on the analysis results. The input is the predicted health risk, and the output is an improvement strategy optimized for the user. Specifically, the prompt message "Generate a weekly plan to improve health based on the following data" is provided to the AI ​​model.

[0701] Step 5:

[0702] The server generates a health improvement strategy and transmits it to the terminal using a communication method. The terminal notifies the user of the strategy, making it available for viewing through a smartphone app. The input is the generated health improvement strategy, and the output is the improvement plan information displayed to the user.

[0703] Step 6:

[0704] Users perform daily health actions using a smartphone app. The user's activity history is sent back to the cloud via the device and used as feedback for future improvement strategies. The input is data on the actions performed by the user, and the output is the feedback data.

[0705] Step 7:

[0706] The server uses a reward system to visualize achievement certificates on the user's device based on their goal attainment. These certificates include digital badges and announcements of local events, aiming to boost user motivation. Input is the user's goal attainment status, and output is the displayed achievement certificates and incentive information.

[0707] 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.

[0708] This invention is a health support system for seniors that, in addition to providing individualized improvement plans based on the user's health indicators, incorporates an emotion engine that recognizes the user's emotional state and optimizes health management accordingly.

[0709] This system first uses smart devices and mobile terminals as collection tools to continuously record the user's physiological data. In addition to the user's heart rate, body temperature, and exercise data, the terminals utilize boundary sensors to acquire information related to emotions, such as voice and facial expressions.

[0710] Upon receiving this data, the server uses analytical tools to predict health risks. Generative AI analyzes patterns and identifies potential risks to the user's health. Based on these results, the generator creates a personalized lifestyle improvement plan for each user.

[0711] A key feature of this invention is the inclusion of an emotion engine. The server uses this emotion engine to analyze the user's current emotional state. For example, if it determines that the user is experiencing high stress levels, it adds activities to promote relaxation to the suggestions. This enables the creation of a plan that also considers the user's psychological well-being.

[0712] The emotional engine also influences the provision of incentives. It flexibly adjusts incentives to stimulate achievement motivation based on the user's emotional state, providing badges and rewards accordingly. In this way, emotionally sensitive support activities can improve the user's quality of life.

[0713] Users can receive these personalized plans through their devices. The devices guide daily activities through applications and support user behavior with emotionally resonant content. Furthermore, participation encouragement features provide information on local events and support participation in community activities.

[0714] This invention adopts a form that comprehensively supports the user's physical and psychological health, and allows for flexible responses to individual needs.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The device uses a smartwatch or smartphone app to record heart rate, body temperature, steps, voice, and facial expression data to collect the user's biometric information. Voice and facial expression data, in particular, play a crucial role for the emotion engine.

[0718] Step 2:

[0719] The device transmits collected biometric and emotion-related data to a server. Encryption protocols are used to ensure the secure transmission of this data.

[0720] Step 3:

[0721] The server uses generative AI to analyze received biometric and emotion-related data. It identifies health risks based on health indicators and analyzes potential risks to the user. In particular, emotion analysis detects changes in voice tone and facial expressions to assess stress levels.

[0722] Step 4:

[0723] Based on the analysis results, the server generates a personalized health improvement plan for the user. This plan includes exercise and dietary advice to improve physical health, as well as relaxation activities to adapt to emotional states and measures to improve mental health.

[0724] Step 5:

[0725] Through the emotion engine, the server adjusts incentives based on the user's emotional state and generates digital badges and rewards to enhance a sense of accomplishment. It also makes motivational adjustments as needed, such as easing overly challenging goals.

[0726] Step 6:

[0727] The server sends the generated health improvement plan and incentive information to the device. The device then notifies the user of this information through the app and provides detailed instructions.

[0728] Step 7:

[0729] Users perform their daily activities according to improvement plans provided on their devices and record their progress and experiences in the app. They are also encouraged to actively participate by receiving the provided incentives and being able to check their progress.

[0730] Step 8:

[0731] The server utilizes a feedback mechanism to collect user feedback and incorporate it into the generation of the next health improvement plan. Based on user behavior data and feedback, it provides more personalized measures.

[0732] This process makes it possible to support the sustained improvement of users' physical and mental health.

[0733] (Example 2)

[0734] 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".

[0735] The present invention aims to provide a system that can comprehensively support not only physical health but also psychological health in the health management of senior citizens. Furthermore, it aims to improve the quality of life for users by providing flexible incentives that take into account the user's emotional state and functions that strengthen connections with the local community.

[0736] 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.

[0737] In this invention, the server includes information acquisition means for collecting health indicators and emotional data from users; analysis means for analyzing the health indicators and emotional data to predict health and psychological states; and plan creation means for generating personalized health and psychological support plans based on the prediction results obtained by the analysis means. This enables comprehensive support aimed at improving physical and psychological health.

[0738] "Information acquisition means" refers to the execution environment of a device or software for collecting health indicators and emotional data from users.

[0739] "Analysis means" refers to technical methods and devices used to analyze collected health indicators and emotional data to predict the user's health and psychological state.

[0740] "Plan creation means" refers to a process or system that generates individualized health and psychological support plans based on prediction results obtained through analysis means.

[0741] "Guidance means" refers to a means of communication or interface for providing users with the generated health and psychological support plan.

[0742] A "motivational tool" is a method or device for adjusting and providing incentives according to the user's emotional state.

[0743] "Activity promotion means" refers to functions that provide information and support to encourage participation in community activities and events.

[0744] "Data analysis means" refers to analytical techniques or devices that use long-term health data and emotional data to detect potential health and psychological risks at an early stage.

[0745] This invention is a system that comprehensively supports the physical and psychological health of primarily senior citizens. Users collect health indicators and emotional data using smart devices or mobile terminals. This data includes heart rate, body temperature, exercise information, and emotion-related information such as voice and facial expressions.

[0746] The device continuously samples this physiological and emotional data and sends it to the server in a fixed format. Data transmission is encrypted using the SSL / TLS protocol and is secure.

[0747] The server uses a generative AI model to analyze incoming data. The AI ​​model uses patterns learned from historical data to predict the user's health and psychological state. This allows for the creation of personalized health improvement and psychological support plans for each user.

[0748] For example, the server evaluates the user's stress level from their heart rate and facial expression data and generates instructions in the form of a prompt such as, "Evaluate the user's current stress level from their heart rate and facial expression data, and suggest recommended relaxation activities." This information is tailored based on the user's specific health goals.

[0749] After a plan is created, the server guides the user to provide an appropriate support plan. The terminal supports the user's daily activities through the application and provides real-time feedback. The user receives this plan and can use it in their daily life.

[0750] Furthermore, based on the results of psychological state analysis using the emotion engine, incentives and rewards are provided as motivation to maintain user enthusiasm. In this way, support for optimizing the user's health and psychological state can be achieved.

[0751] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0752] Step 1:

[0753] The device collects physiological and emotional data from the user. Inputs include exercise data from a heart rate sensor, body temperature sensor, and accelerometer, as well as voice and facial expression data from a microphone and camera. This data is converted to a digital format and standardized within the device. The output is a unified dataset.

[0754] Step 2:

[0755] The device periodically sends collected data to the server. It uses a processed dataset as input. Transmission is secure using the SSL / TLS protocol. The output is encrypted data delivered to the server.

[0756] Step 3:

[0757] The server analyzes the received data. The input is data sent from the terminal. The server passes this data through a generative AI model to evaluate the user's health and psychological state. Statistical methods and machine learning algorithms are used in this process. The output is the analysis results regarding the user's health and psychological state.

[0758] Step 4:

[0759] The server generates personalized support plans based on the analysis results. The analysis results obtained in step 3 are used as input. Using the generated AI model and predefined prompts, it creates health maintenance activities and psychological state improvement plans. The output is a support plan customized for each user.

[0760] Step 5:

[0761] The server sends the generated support plan to the terminal. The input is the plan generated in step 4. The output is the individual support plan transmitted to the terminal.

[0762] Step 6:

[0763] The terminal presents the user with a support plan. The input is the support plan received from the server. Through the application, it suggests the most suitable activities and relaxation activities for the user in real time. The output is guidance information displayed on the user's screen.

[0764] (Application Example 2)

[0765] 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".

[0766] For elderly individuals and other users to comprehensively manage their health and improve their quality of life, individualized care that considers emotional states in addition to physiological health indicators is necessary. However, conventional health management systems lack mechanisms to adequately analyze emotional states and utilize them for health improvement. As a result, incentives and promotion of social participation tailored to users' emotions are insufficient, making it difficult to achieve sustainable health improvement.

[0767] 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.

[0768] In this invention, the server includes an acquisition means for collecting health-related information from users, an analysis means for analyzing the health-related information and emotional information to predict risks, and a creation means for creating an individualized health improvement plan based on the prediction results and emotional state obtained by the analysis means. This enables integrated management of the user's physiological and psychological health, and allows for sustainable health improvement tailored to each individual's lifestyle.

[0769] "Means of acquisition" refers to means that have the function of collecting health-related information from users.

[0770] "Analysis means" refers to means that have the function of analyzing collected health-related information and emotional information to predict health risks.

[0771] "Creation means" refers to means that have the function of generating an individualized health improvement plan based on the results of the analysis means.

[0772] A "presentation means" is a means that has the function of displaying and proposing the generated health improvement plan to the user.

[0773] A "stimulant" is a means that provides appropriate incentives according to the user's emotional state and has the function of stimulating their desire to achieve.

[0774] "Promotional measures" refer to means that provide information that encourages participation in local social activities and events, and have the function of promoting users' social participation.

[0775] An "emotion analysis tool" is a tool that has the function of evaluating a user's emotional state in real time.

[0776] This invention is a system for comprehensively managing the health status of users, including the elderly. This system acquires health-related and emotional information from users through a specific device, and uses this information to perform analysis and create improvement plans.

[0777] The server first has a mechanism to continuously acquire physiological data such as the user's heart rate, body temperature, and activity level. In addition, it also collects emotion-related data such as voice and facial expressions. The acquired data is sent to the server and analyzed by analytical means. In particular, generative AI models are used to identify health risks tailored to each individual user.

[0778] Based on the analysis results, the server creates a personalized health improvement plan for each user. This plan includes suggestions for relaxation activities and physical exercises that take into account the user's emotional state. Furthermore, if the emotional analysis determines that the user is experiencing stress, countermeasures are immediately incorporated into the plan.

[0779] Users can view this plan on their own devices. The device presents the generated plan in a visually appealing and user-friendly format, supporting users in managing their daily health. It also offers emotion-based incentives to encourage motivation. Furthermore, the device provides information on local social activities and events, encouraging users to actively participate in society.

[0780] For example, if a user records in their diary that they "haven't been feeling well all morning," the system will determine that they are experiencing stress based on this text and collected physiological data. At this point, the user will be offered suggestions such as relaxation music or a suitable walking route. To achieve this, a prompt message such as "Based on the user's heart rate, body temperature, exercise data, and the text 'I'm feeling a little unwell,' consider assessing their stress level and developing countermeasures" is provided to the generating AI model.

[0781] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0782] Step 1:

[0783] The device uses sensors to acquire data such as the user's heart rate, body temperature, activity level, voice, and facial expressions in real time. This data is collected from the user and transmitted to the next step.

[0784] Step 2:

[0785] The server receives physiological and emotional data transmitted from the terminal and stores it in a database. The input is data from sensors, and the output is information converted into an easy-to-use data format.

[0786] Step 3:

[0787] The server analyzes the stored data using analytical tools. It utilizes a generative AI model to predict health risks from this data. The input to this analysis is the collected health data, and the output is the predicted health risk for the user.

[0788] Step 4:

[0789] The server generates a lifestyle improvement plan based on the analysis results. This plan includes suggestions for stress reduction activities and recommendations for healthy exercise. The input is the predicted results and the current emotional state, and the output is the personalized improvement plan.

[0790] Step 5:

[0791] The terminal presents the user with a health improvement plan sent from the server. The plan is displayed in a visualized format to support its implementation. The input is the generated improvement plan, and the output is user-readable guide information.

[0792] Step 6:

[0793] The user performs activities and exercises presented via the device. The device receives feedback on these activities and sends it to the server. The input is the user's activity record, and the output is feedback data.

[0794] Step 7:

[0795] The terminal provides information on local social activities and events, promoting user community participation. Input is information from an event database, and output is event announcements for the user.

[0796] 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.

[0797] 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.

[0798] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0799] 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.

[0800] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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."

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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 this memory.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0817] The following is further disclosed regarding the embodiments described above.

[0818] (Claim 1)

[0819] A means of collecting health indicators from users,

[0820] An analytical means for analyzing the aforementioned health indicators and predicting risk,

[0821] A generation means for generating an individualized health improvement plan based on the prediction results obtained by the analysis means,

[0822] A means of providing the aforementioned health improvement plan to users,

[0823] Incentive measures that provide incentives to maintain motivation based on the user's behavioral history,

[0824] Means of promoting participation in local social activities and events,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, further comprising a feedback means for collecting feedback on user health indicators and incorporating it into the following personalized improvement plan using the generation means.

[0828] (Claim 3)

[0829] The system according to claim 1, wherein the analytical means for predicting the aforementioned risks includes means for performing life log analysis to detect potential risks early, using long-term health data for prediction.

[0830] "Example 1"

[0831] (Claim 1)

[0832] A collection means equipped with a terminal for collecting biometric information from users,

[0833] Analysis means including a server for analyzing the aforementioned biological information and predicting health risks,

[0834] A generation means that uses a generation AI model to generate an individualized health management plan based on the prediction results obtained from the analysis means,

[0835] A means of providing the aforementioned health management plan, including a terminal for presenting the plan to the user,

[0836] An incentive mechanism that provides performance-based rewards to promote motivation based on the user's behavioral history,

[0837] Means of promoting participation that encourage participation in local activities and events,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, further comprising means for collecting feedback on the user's biometric information and processing feedback to reflect it in the next personalized health management plan using the generation means.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the analytical means for predicting health risks includes a function to analyze long-term health data and perform life log analysis to detect potential risks early.

[0843] "Application Example 1"

[0844] (Claim 1)

[0845] A data collection method for collecting biometric information from users,

[0846] A data analysis means for analyzing the aforementioned biological information and estimating health risks,

[0847] A strategy generation means for constructing individually adjusted health improvement strategies based on the estimation results obtained by the analysis means,

[0848] A communication means that provides the aforementioned health improvement strategy to users via communication,

[0849] A reward system that provides rewards to maintain psychological motivation based on the user's behavioral history,

[0850] Means of participation in activities to promote participation in local collective activities and events,

[0851] A means of visualizing achievement that displays visual evidence when an action goal is achieved,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, further comprising a response collection means for collecting responses regarding the user's biometric information and reflecting them in the strategy generation means for the next personalized improvement strategy.

[0855] (Claim 3)

[0856] The system according to claim 1, wherein the data analysis means for estimating the health risk includes means for performing estimation using time-dependent health information and for performing lifestyle record analysis to identify potential risks at an early stage.

[0857] "Example 2 of combining an emotion engine"

[0858] (Claim 1)

[0859] A means of acquiring information to collect health indicators and emotional data from users,

[0860] An analytical means for predicting health status and psychological state by analyzing the aforementioned health indicators and emotional data,

[0861] A plan creation means that generates an individualized health and psychological support plan based on the prediction results obtained by the analysis means,

[0862] A means of providing the aforementioned health and psychological support plan to users,

[0863] Motivational means that flexibly adjust and provide incentives according to the user's emotional state,

[0864] Means of promoting activities to encourage participation in local social activities and events,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, further comprising an evaluation means for collecting feedback on the user's health indicators and emotional data and reflecting it in the next individualized support plan using the plan creation means.

[0868] (Claim 3)

[0869] The system according to claim 1, wherein the analytical means for predicting health and psychological state includes data analysis means for making predictions using long-term health data and emotional data, and for detecting potential health risks and psychological risks at an early stage.

[0870] "Application example 2 when combining with an emotional engine"

[0871] (Claim 1)

[0872] A means of collecting health-related information from users,

[0873] An analytical means for analyzing the aforementioned health-related information and emotional information to predict risk,

[0874] A creation means for creating an individualized health improvement plan based on the prediction results and emotional state obtained by the aforementioned analysis means,

[0875] A means for presenting the aforementioned health improvement plan to the user,

[0876] A stimulus that provides incentives to elicit a desire for achievement according to the user's emotional state,

[0877] A means of promoting interest in local social activities and events,

[0878] A means of emotional analysis that evaluates the emotional state of users in real time,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, further comprising a feedback means for collecting feedback based on the user's health indicators and emotional information and for incorporating it into the next personalized improvement plan using the creation means.

[0882] (Claim 3)

[0883] The system according to claim 1, wherein the analytical means for predicting the risks includes means for performing data analysis to detect potential risks and psychological states early, using long-term health data and emotional data to make predictions. [Explanation of Symbols]

[0884] 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 health indicators from users, An analytical means for analyzing the aforementioned health indicators and predicting risk, A generation means for generating an individualized health improvement plan based on the prediction results obtained by the analysis means, A means of providing the aforementioned health improvement plan to users, Incentive measures that provide incentives to maintain motivation based on the user's behavioral history, Means of promoting participation in local social activities and events, A system that includes this.

2. The system according to claim 1, further comprising a feedback means for collecting feedback on users' health indicators and incorporating it into the following personalized improvement plan using the generation means.

3. The system according to claim 1, wherein the analytical means for predicting the aforementioned risks includes means for performing life log analysis to detect potential risks early, using long-term health data for prediction.

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