system

Abstract

We provide the system. [Solution] Means for acquiring lifestyle information and health data, A means of generating an individualized health management plan based on acquired information, A means of sending the generated health management plan to the user's terminal, A means for providing a daily report generated based on the aforementioned lifestyle information and health data to the user's terminal, A means of receiving feedback from users and adjusting health management plans, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, many individuals utilize information and tools related to health management, but those methods are not always optimal for individuals and often remain at a standard offering. Also, since an individual's lifestyle, physical build, genetics, etc. affect health management, individualized responses are required. However, there is a lack of appropriate solutions for this, and there is a problem that effective health management cannot be achieved.

Means for Solving the Problems

[0005] This invention proposes a system that provides a health management plan optimized for each individual user. Specifically, it provides means for acquiring lifestyle information and health data, and means for generating a personalized health management plan using a generation AI based on the acquired information. Furthermore, it provides means for transmitting the generated health management plan to the user's terminal, and means for automatically generating and providing daily reports to the user based on lifestyle information and health data, thereby improving the accuracy of health management. In addition, by including means for adjusting the health management plan based on feedback from the user, continuous and flexible health management is realized.

[0006] "Lifestyle information" refers to data on an individual's daily behavioral patterns, such as diet, exercise, sleep, and activity levels.

[0007] "Health data" refers to data measured to indicate an individual's physical condition and health status, such as heart rate, steps taken, calories burned, and body temperature.

[0008] A "health management plan" is a comprehensive guideline, including diet and exercise plans, designed to improve or maintain an individual's health.

[0009] A "user terminal" refers to an electronic device, such as a smartphone, tablet, or computer, that a user uses to receive or input information.

[0010] A "daily report" is a report that summarizes the user's daily health management status and presents progress and areas for improvement.

[0011] "Feedback" refers to the opinions and requests that users provide regarding the system and their health management plans. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

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

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

[0015] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system that provides personalized health management plans for individual users, supporting the maintenance and improvement of their health. This system mainly consists of a backend that operates on the server side and a frontend that operates on a terminal for user interaction.

[0034] The server has a database for collecting lifestyle and health data, and it has the ability to analyze this information using generating AI. Based on the acquired data, the server generates a health management plan optimized for each user. This plan may include, for example, a meal plan to supplement specific nutrients that are lacking, or an exercise plan to address a lack of exercise. The generated health management plan is sent from the server to the terminal so that the user can implement it in their daily life.

[0035] The device provides an interface for users to input their lifestyle information and health data. Users input information such as their diet, exercise levels, sleep duration, and health data obtained from health devices. This information is transmitted to and stored on a server in real time. Furthermore, the device displays a health management plan transmitted from the server to support the user's understanding and implementation.

[0036] Every day, the server analyzes users' lifestyle information and health data, generating a daily report that includes their achievements for the day and areas for improvement for the following day. This report is delivered to the user's device, allowing them to objectively assess their health status and take necessary corrective actions.

[0037] As a concrete example, let's assume a male user B in his 20s uses this system. User B uses his smartphone to input his meal details (e.g., toast and eggs for breakfast, a sandwich for lunch, and curry for dinner), and the system automatically links in his daily step count and heart rate measured by a wearable device. The server evaluates User B's lifestyle information and detects that he is slightly deficient in calcium. In response, the server suggests a breakfast menu that includes milk and generates a health management plan recommending strength training twice a week as exercise. The next morning, the server automatically generates a daily report based on the previous day's data and achievement level, showing whether specific numerical targets have been met. User B can check this report on his smartphone, reflect on his actions, and use it to improve his daily health.

[0038] In this way, by linking the server and terminal, it is possible to provide users with personalized health management plans and support the improvement of their health.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The terminal provides users with an interface for inputting lifestyle information and health data. Users input their daily diet, exercise levels, and sleep duration, and also link biometric data from wearable devices (e.g., heart rate and steps) to the system.

[0042] Step 2:

[0043] The terminal transmits the entered lifestyle information and health data to the server. The transmitted information is stored in a database temporarily maintained by the server and prepared for analysis.

[0044] Step 3:

[0045] The server uses AI to analyze the received data. The analysis evaluates the user's nutrient intake and exercise patterns, identifying any nutrient deficiencies and suggesting exercise habits that need improvement.

[0046] Step 4:

[0047] The server generates a personalized health management plan based on the analysis results. This plan may include a meal plan incorporating specific foods, or an exercise plan with specific exercise instructions.

[0048] Step 5:

[0049] The generated health management plan is sent from the server to the terminal. The terminal presents the plan to the user in a viewable format and serves as an interface to support daily implementation.

[0050] Step 6:

[0051] The server automatically generates daily reports based on daily data. These reports include the degree to which goals set the previous day were achieved and recommended actions for the following day. The reports are provided to users via their terminals.

[0052] Step 7:

[0053] Users use their devices to send feedback on their health management plans and daily reports to the server. The server uses this feedback to improve and adjust the plans, and then provides them back to the user via the device.

[0054] (Example 1)

[0055] 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."

[0056] In modern society, systematically collecting people's lifestyle and health data and providing health management plans tailored to individual characteristics is extremely important. However, many people are unable to properly assess their health status, which prevents them from developing concrete action plans for self-improvement. This invention aims to solve these problems of insufficient individualization and inadequate feedback.

[0057] 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.

[0058] In this invention, the server includes a device for acquiring lifestyle information and health data, a device for generating a personalized health management plan using a generative AI model that analyzes the information in real time, and a device for transmitting the generated health management plan to an information device. This enables the provision of a personalized health management plan and feedback for specific improvements in daily life.

[0059] "Lifestyle information" refers to information about an individual's daily activities and conditions, including data such as diet, exercise levels, and sleep duration.

[0060] "Health data" refers to measurement data related to an individual's health status, including information such as weight, blood pressure, and heart rate.

[0061] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and generate personalized health management plans.

[0062] A "device" is a set of hardware and software components used to perform a specific function.

[0063] A "plan" is a specific action plan proposed for maintaining or improving an individual's health, and includes things like meal plans and exercise plans.

[0064] "Information devices" refer to electronic devices that individuals use in their daily lives, such as smartphones and tablet devices.

[0065] This invention is a system for providing personalized health management plans for individual users. The system includes a backend that primarily operates on the server side and a frontend that operates on a terminal for user interaction.

[0066] The server houses a database that collects lifestyle information and health data. This includes data on diet, exercise levels, sleep duration, and data from health devices. The server uses a generative AI model to analyze this data obtained from users. Data analysis by the generative AI model generates a health management plan optimized for each user. This plan includes meal plans to supplement specific nutrients and exercise plans to address lack of exercise.

[0067] The generated health management plan is sent from the server to the terminal and presented to the user in a format that allows for daily implementation. The terminal displays this plan clearly to the user, supporting their understanding and execution.

[0068] As a concrete example, consider a male user in his 20s using the system. The user inputs details of his meals using his smartphone, and automatically shares data such as steps taken and heart rate measured by a wearable device. The server analyzes this data and suggests a breakfast incorporating milk to increase calcium intake. It also generates a health management plan recommending strength training twice a week as an exercise plan.

[0069] Furthermore, the server generates a daily report based on the user's lifestyle information and health data. This report includes the user's achievements for the day and areas for improvement for the following day, enabling the user to assess their own health status and take subsequent actions.

[0070] The generative AI model performs analysis using prompts such as, "Generate an optimal health management plan based on the user's lifestyle and health data."

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The device provides users with an interface for inputting lifestyle information and health data. Users input their diet, exercise activities, and sleep duration using their smartphones. The device also automatically receives health data such as heart rate and steps from wearable devices. This entered data is transmitted to the server in real time.

[0074] Step 2:

[0075] The server stores user lifestyle information and health data received from the terminal in a database. The database organizes and stores data for each user. This is performed as a preparatory process for analyzing this organized and stored data.

[0076] Step 3:

[0077] The server analyzes the accumulated data using a generative AI model. Specifically, it evaluates the user's diet and exercise patterns to detect deficiencies in specific nutrients or issues with exercise habits. The prompt used here is "Generate an optimal health management plan based on the user's lifestyle and health data." The resulting output is used as an intermediate result of the personalized health management plan.

[0078] Step 4:

[0079] Based on the analysis results, the server generates a personalized health management plan for each user. Based on the output of the generated AI model, plans such as meal plans to supplement specific nutrients or exercise plans are created. This output becomes prepared information to be provided to the user.

[0080] Step 5:

[0081] The server sends the generated health management plan to the device. The device receives this information and displays it in an easy-to-understand format for the user. The user reviews the plan and gains guidance on how to implement it in their daily life.

[0082] Step 6:

[0083] Every day, the server generates a daily report based on accumulated data and the implementation status of the generated health management plan. This report includes the degree to which the day's goals were achieved and specific areas for improvement for the following day.

[0084] Step 7:

[0085] The server delivers the generated daily report to the terminal. The terminal displays the report, and the user reviews it to assess their health status and obtain information to take necessary actions.

[0086] (Application Example 1)

[0087] 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."

[0088] Current systems designed to support health management tend to offer only general suggestions, making it difficult to provide optimal meal recommendations based on each user's specific health condition and dietary preferences. Furthermore, there is a lack of mechanisms to support healthy choices when dining out or using delivery services. As a result, users are unable to make appropriate choices based on their health condition, hindering their progress in improving their health.

[0089] 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.

[0090] In this invention, the server includes means for acquiring lifestyle information and health data, means for making optimal food suggestions based on health data and food preferences, and means for transmitting the generated health management plan to an information processing device. This enables users to make optimal food choices according to their health condition, and personalized health management is supported more effectively.

[0091] "Lifestyle information" refers to data about an individual's daily life, including activity patterns such as diet, exercise, and sleep.

[0092] "Health data" refers to measurement information that indicates an individual's health status, and includes physiological data such as weight, heart rate, and blood pressure.

[0093] A "personalized health management plan" is a plan for maintaining and improving health that is optimized based on an individual's lifestyle information and health data.

[0094] An "information processing device" is a terminal device operated by a user, and is a device that has the function of inputting and displaying data.

[0095] A "generative artificial intelligence model" is a model that uses AI technology to make suggestions and predictions that meet specific purposes and conditions based on a large amount of data.

[0096] This invention relates to a system and method for implementing health management optimized for individual users. The system mainly consists of a server and terminals.

[0097] The server has a database for acquiring and storing users' lifestyle information and health data. It also utilizes a generative artificial intelligence model to analyze the acquired data and generate personalized health management plans. These plans may include dietary suggestions to supplement specific nutrients and exercise plans to address lack of exercise. The generated plans are transmitted to the user's information processing device via the internet.

[0098] The terminal (information processing device) provides a user interface for users to input their lifestyle information and health data. Users input information such as their dietary history, exercise levels, and health device data, and this information is transmitted to the server in real time. The terminal also displays a health management plan to the user and plays a role in supporting its implementation.

[0099] Specifically, if a user has a health problem such as calcium deficiency, the server can analyze the data and suggest meals that include milk. Furthermore, by using a generative AI model, it's possible to suggest healthy menus tailored to the user's preferences based on their tastes and past selection history.

[0100] The server generates a daily report based on information received from users each day. This report includes the degree of achievement of the health management plan and areas for improvement for the following day, and is designed to make it easy for users to check their own progress.

[0101] A concrete example of a prompt message would be: "Based on the user's health data, please suggest a menu that includes foods rich in calcium and low in calories."

[0102] As described above, the server and terminal work together to provide personalized and effective health management for the user.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] Users input information such as their diet, exercise levels, sleep duration, and health data obtained from health devices using a terminal. This information is transmitted from the terminal to the server in real time. The server stores the acquired raw data in a database and standardizes the format as an initial data processing step.

[0106] Step 2:

[0107] The server inputs acquired lifestyle and health data into a generating AI model for data analysis. Specifically, it calculates one or more health indicators and compares them with historical data and general health guidelines. As a result of this analysis, the user's health status, necessary nutrients, and activity imbalances are identified.

[0108] Step 3:

[0109] The server uses a generative AI model to generate a personalized health management plan based on the analysis results. Based on the input health data and analysis results, it automatically generates a plan that includes suggestions for supplementing specific nutrients, types of exercise, and frequency. At this stage, a prompt message is issued, allowing the AI ​​model to specifically determine the content of the dietary suggestions.

[0110] Step 4:

[0111] The generated health management plan is sent from the server to the terminal and provided to the user. The terminal displays the health plan in an easy-to-understand format and provides an interface to help the user achieve their goals. Here, the user can consult on how to actually implement the plan and adjust the options.

[0112] Step 5:

[0113] The server generates a daily report based on the data collected each day. The report includes feedback on achievement, areas for improvement in health indicators, and activity guidelines for the following day. It is sent to the user's device as a document, allowing them to track their progress in improving their health.

[0114] 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.

[0115] This invention is a system aimed at supporting users' lifestyles and health conditions through personalized health management plans, and further enhancing their effectiveness by taking into account the user's emotional state. This system provides a combination of means for collecting user lifestyle information and health data and generating health management plans based on this information, and an emotion engine that recognizes the user's emotions.

[0116] First, users input their lifestyle information through their device. This includes daily meals, exercise intensity and frequency, and sleep duration. Furthermore, they use their smart devices to link health data, such as heart rate and physical activity levels, with the system. This data is sent from the device to a server, where it is managed comprehensively.

[0117] The server uses collected data and AI to generate personalized health management plans for each user. These plans include meal suggestions to address any nutritional deficiencies and exercise plans to compensate for any lack of daily physical activity. The server also analyzes the user's emotions through an emotion engine and incorporates this information into the health management plan. The emotion engine analyzes voice data entered by the user and facial expression data acquired through the camera to assess the user's stress level and mood.

[0118] As a concrete example, consider the case of user C, a woman in her 30s, using the system. User C enters her meals from the previous day (breakfast: oatmeal, lunch: chicken salad, dinner: sukiyaki) into the terminal and records a 30-minute walk as exercise. At the same time, heart rate data acquired through a wearable device is also sent to the server. In addition, the system records changes in daily stress levels through an emotion engine, and recently, a high stress level has been detected.

[0119] Based on this, the server analyzes user C's data and suggests a possible vitamin D deficiency. Therefore, the server suggests fish dishes for the following week and creates an exercise plan incorporating yoga to reduce stress. These suggestions are sent to the user's device, allowing them to manage their daily health. In addition, the daily report reflects the user's emotional state along with their achievement level, and provides advice for the following day's activities.

[0120] This system goes beyond simple health data analysis, enabling comprehensive health management that addresses the user's emotional well-being. This allows for balanced physical and mental health support.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The terminal provides users with an interface for inputting lifestyle information and health data. Users input their daily diet, exercise levels, and sleep duration, and link health data such as heart rate and steps obtained from smart devices to the system.

[0124] Step 2:

[0125] The terminal transmits the entered lifestyle information and health data to the server. The transmitted data is stored in a database on the server and prepared for analysis.

[0126] Step 3:

[0127] The server uses AI to analyze lifestyle and health data stored in the database. From the analysis results, it understands the user's nutritional intake and exercise patterns, and identifies any deficient nutrients or exercise habits that need improvement.

[0128] Step 4:

[0129] The server generates a personalized health management plan based on the analysis results. This plan includes dietary suggestions to supplement specific nutrients and specific activities to encourage exercise.

[0130] Step 5:

[0131] The generated health management plan is sent from the server to the device. The device displays the received plan to the user and functions as a guideline to help them implement it in their daily life.

[0132] Step 6:

[0133] The server uses an emotion engine to analyze the user's emotional state. The user provides voice and facial expression data via their device, and based on this, the server determines stress levels and emotional changes.

[0134] Step 7:

[0135] Based on the user's emotional state, the server adjusts the health management plan. For example, if the stress level is high, it will suggest additional plans that incorporate relaxation and meditation.

[0136] Step 8:

[0137] The server automatically generates daily reports based on the user's lifestyle information, health data, and emotional state. These reports reflect the progress towards achieving the plan and changes in emotions, providing guidance for the following day's activities.

[0138] Step 9:

[0139] The device delivers the generated daily report to the user. The user can review the report, evaluate their own health and emotional state, and use that information to inform their activities for the following day.

[0140] Step 10:

[0141] Users send their opinions and feedback on their health management plans and daily reports to the server via their devices. The server reviews the plans based on this feedback and, if necessary, provides updated plans back to the devices.

[0142] (Example 2)

[0143] 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".

[0144] In modern society, achieving effective health management tailored to the diverse lifestyles and health conditions of individuals is not easy. In particular, comprehensive planning that takes into account not only physical data but also emotional states is required, but achieving this is difficult. Furthermore, a system is needed to provide personalized health management plans suited to the user in a timely manner and to adjust them based on feedback.

[0145] 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.

[0146] In this invention, the server includes means for acquiring lifestyle information and biometric information, means for integrating the acquired information and generating a personalized health management plan using a generative AI model, and means for analyzing the user's emotional state based on voice data and facial expression data and reflecting this in the health management plan. This enables personalized health management based on each user's lifestyle and emotional state.

[0147] "Lifestyle information" refers to information about a user's daily activities, such as eating, exercise, and sleep.

[0148] "Biometric information" refers to physiological data that indicates the user's physical condition, such as heart rate, steps taken, and physical activity level.

[0149] A "generative AI model" is a model that uses artificial intelligence technology to analyze acquired data and generate personalized health management plans for users.

[0150] A "health management plan" is a personalized management plan that includes suggestions for nutrition, exercise, stress management, and more, based on the user's lifestyle and biometric information.

[0151] "Emotional state" refers to information that indicates the user's psychological state, such as their stress level and mood.

[0152] "Voice data" refers to audio information that records a user's speech and is used to analyze their emotional state.

[0153] "Facial expression data" refers to information about a user's facial expressions and is used to analyze their emotional state.

[0154] This invention provides a system for implementing personalized health management for users. Specifically, it is a system that collects users' lifestyle and biometric information, generates a personalized health management plan based on this information, and provides it to the user. Embodiments of this system are shown below.

[0155] Hardware and software configuration:

[0156] Users operate a dedicated application using devices such as smartphones and tablets. This application provides an interface for inputting lifestyle information such as daily meals, exercise, and sleep duration. Users also collect biometric information such as heart rate using wearable devices such as smartwatches, and this information is automatically transmitted to a server.

[0157] The server integrates the collected information and generates a personalized health management plan using a generative AI model. This AI model can analyze large amounts of data and suggest the optimal balance of nutrition and exercise for the user. In addition, an emotion engine analyzes the user's voice and facial expression data to assess the user's emotional state and reflect it in the health management plan.

[0158] Specific example:

[0159] For example, consider a female user in her 30s using this system. The user records her meals from the previous day (e.g., oatmeal for breakfast, salad for lunch, fish for dinner) through the app and records her heart rate and steps for the day using a wearable device. The server analyzes this data and provides fish-based recipes as meal suggestions for the following week to address any vitamin D deficiency. Additionally, if the emotional engine detects a high-stress state, it suggests an exercise plan incorporating yoga to reduce stress.

[0160] Example of a prompt:

[0161] An example of a prompt used as input to a generative AI model is: "What health plan should be suggested to a female user in her 30s who is experiencing high stress and is deficient in vitamin D?"

[0162] In this way, it becomes possible to provide a system that comprehensively manages users' physical and emotional health, and to support the maintenance of healthy lifestyle habits.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] Users open a dedicated application on their smartphone or tablet and input lifestyle information. This information includes daily meals (e.g., oatmeal for breakfast, chicken salad for lunch), exercise (e.g., 30 minutes of walking), and sleep duration. The entered data is transmitted from the device to the server. The role of this input is to provide basic data for understanding the user's lifestyle.

[0166] Step 2:

[0167] Users collect biometric information such as heart rate and steps using wearable devices like smartwatches and fitness trackers. This information is also transmitted to a server in real time via the device. As output, the server aggregates the data. This data is used to evaluate the user's physical condition and provide physiological data necessary for health management.

[0168] Step 3:

[0169] The server integrates lifestyle and biometric information transmitted from the terminal. All data is organized into a single user profile and stored in a database. Data processing includes data cleaning and formatting standardization. This optimizes the data in preparation for analysis.

[0170] Step 4:

[0171] The server utilizes an AI model based on integrated data to generate personalized health management plans. It analyzes input lifestyle and biometric information to create nutrition and exercise plans tailored to the user. The AI ​​model performs pattern recognition and predictive analysis as part of the data processing, resulting in a personalized health management plan as output.

[0172] Step 5:

[0173] The server uses an emotion engine to recognize the user's emotional state from their voice and facial expression data. Based on the emotion analysis, it evaluates the user's stress level and mood, and incorporates this information into the health management plan, providing a management plan that takes mental health into consideration as output.

[0174] Step 6:

[0175] The server sends the generated health management plan to the terminal. The user checks the suggested meal recipes and exercise plan on the terminal and uses them to manage their daily health. The output is a concrete plan that the user can implement, and individual adjustments can be made through feedback.

[0176] Step 7:

[0177] The user inputs the results of their health management activities, based on the proposed plan, into the terminal. The feedback provided is sent back to the server. The server analyzes the feedback and incorporates and adjusts it into the next health management plan. The output is a more optimized next management plan.

[0178] By progressing through these processing steps, it becomes possible to provide optimal support tailored to the user's health condition.

[0179] (Application Example 2)

[0180] 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".

[0181] In modern society, comprehensive health management tailored to individual lifestyles and emotional states is crucial. Furthermore, it is necessary to guide consumers towards appropriate consumption behavior based on their health status and emotional state. However, conventional systems have struggled to effectively integrate health management and optimized consumption behavior, making it difficult to provide appropriate suggestions to individual users.

[0182] 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.

[0183] In this invention, the server includes means for acquiring lifestyle information and health data, means for generating a personalized health management plan based on the acquired information, and means for transmitting the generated health management plan to the user terminal. This enables the optimization of health management and emotion-based consumption behavior tailored to each individual user.

[0184] "Lifestyle information" refers to information that shows the daily behavioral patterns of individual users, such as their diet, exercise frequency, and sleep duration.

[0185] "Health data" refers to numerical data that directly reflects a user's health status, such as their heart rate and physical activity level.

[0186] A "health management plan" is a personalized plan aimed at maintaining and improving health, created based on acquired lifestyle information and health data.

[0187] "Emotional state" refers to evaluation data that indicates the user's mental health status, such as their stress level and mood.

[0188] An "electronic payment plan" is a payment suggestion program designed to encourage optimal consumer behavior based on the user's emotional state and health condition.

[0189] A "user terminal" refers to a device, such as a smartphone or tablet, that a user directly operates to send and receive information.

[0190] "Feedback" refers to responses to user comments and suggestions, and is information that can be used to adjust health management plans.

[0191] The system for realizing this invention acquires lifestyle information and health data, and generates and provides personalized health management plans. This system mainly consists of a server, user terminals, and various sensors.

[0192] The server centrally manages lifestyle information and health data acquired from users. This includes data transmitted from user devices such as smartphones and wearable devices. Specific data includes heart rate, exercise levels, and dietary information. This data is managed on cloud services (for example, AWS® or Google® Cloud Platform).

[0193] Using a generative AI model, the server generates personalized health management plans. These plans are designed to suggest nutritional balance and appropriate exercise plans. Furthermore, to analyze emotional states, it utilizes emotion engines such as Microsoft Azure's Emotion API to analyze voice and image data. The analysis results evaluate the user's stress level and mood, and these are reflected in the health plan.

[0194] Furthermore, the server also generates an optimal electronic payment plan based on the user's emotional state. This plan offers stress-reducing promotions and purchase suggestions based on the user's emotions and health status. Integration with electronic payment APIs (such as Stripe or PayPal) is envisioned as the payment system.

[0195] For example, the server could suggest a discount on a yoga class to a user in their 30s based on their recent high stress levels. The user terminal would notify the user of this suggestion and help them choose an activity based on it. Daily reports would also reflect their achievements and emotional state, and include advice for the following day's actions.

[0196] An example of a prompt message would be: "Provide daily health management advice based on the user's emotional state and health data. User A works at a desk for about 8 hours a day. He has recently been experiencing stress due to lack of exercise. What activities and dietary recommendations would you suggest?"

[0197] This system allows users to enjoy conveniences tailored to their individual health conditions, enabling them to achieve a holistic lifestyle where physical and mental health are in harmony.

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

[0199] Step 1:

[0200] Users input lifestyle and health data into the terminal. This data includes everyday information such as the user's diet, exercise frequency, and sleep duration, and is transmitted to the server in real time via an application on the terminal. It is crucial to use security protocols (e.g., SSL / TLS) for the transmission and storage of this input data.

[0201] Step 2:

[0202] The server receives the transmitted lifestyle and health data and stores it in a database on the cloud. The input to this process is the raw data sent by the user, and the output is a well-formed dataset. The stored data undergoes preprocessing, including data cleansing, for use in subsequent analysis.

[0203] Step 3:

[0204] The server uses a generative AI model to generate personalized health management plans from the received data. At this stage, the stored data is used as input, the AI ​​model analyzes and infers from the data, and outputs a health plan tailored to the user. The generated plan includes nutritional improvement suggestions and exercise plans.

[0205] Step 4:

[0206] The server uses an emotion analysis engine to analyze the user's emotional state. It receives audio data and image data transmitted from the user's terminal as input and processes the emotion analysis. The output of this step is a quantified representation of the user's emotional state, indicating stress levels and mood.

[0207] Step 5:

[0208] The server generates an optimal electronic payment plan for the user based on the analyzed emotional state. The input consists of the emotional data obtained in step 4 and existing health data, which are used to output promotional and purchase-enhancing suggestions. Specifically, this includes discount information on products and services that help reduce stress.

[0209] Step 6:

[0210] The user terminal receives the health management plan and electronic payment plan sent from the server and notifies the user. The user reviews this and provides feedback to the system with their selected action. This feedback is sent to the server and used to adjust the plan in the future.

[0211] Step 7:

[0212] The server receives user feedback and updates and optimizes the health management plan. The feedback data is evaluated as input and used to improve the next health management plan. This creates a more effective health promotion cycle 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 those described above. 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 shown 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 provides personalized health management plans for individual users, supporting the maintenance and improvement of their health. This system mainly consists of a backend that operates on the server side and a frontend that operates on a terminal for user interaction.

[0230] The server has a database for collecting lifestyle and health data, and it has the ability to analyze this information using generating AI. Based on the acquired data, the server generates a health management plan optimized for each user. This plan may include, for example, a meal plan to supplement specific nutrients that are lacking, or an exercise plan to address a lack of exercise. The generated health management plan is sent from the server to the terminal so that the user can implement it in their daily life.

[0231] The device provides an interface for users to input their lifestyle information and health data. Users input information such as their diet, exercise levels, sleep duration, and health data obtained from health devices. This information is transmitted to and stored on a server in real time. Furthermore, the device displays a health management plan transmitted from the server to support the user's understanding and implementation.

[0232] Every day, the server analyzes users' lifestyle information and health data, generating a daily report that includes their achievements for the day and areas for improvement for the following day. This report is delivered to the user's device, allowing them to objectively assess their health status and take necessary corrective actions.

[0233] As a concrete example, let's assume a male user B in his 20s uses this system. User B uses his smartphone to input his meal details (e.g., toast and eggs for breakfast, a sandwich for lunch, and curry for dinner), and the system automatically links in his daily step count and heart rate measured by a wearable device. The server evaluates User B's lifestyle information and detects that he is slightly deficient in calcium. In response, the server suggests a breakfast menu that includes milk and generates a health management plan recommending strength training twice a week as exercise. The next morning, the server automatically generates a daily report based on the previous day's data and achievement level, showing whether specific numerical targets have been met. User B can check this report on his smartphone, reflect on his actions, and use it to improve his daily health.

[0234] In this way, by linking the server and terminal, it is possible to provide users with personalized health management plans and support the improvement of their health.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The terminal provides users with an interface for inputting lifestyle information and health data. Users input their daily diet, exercise levels, and sleep duration, and also link biometric data from wearable devices (e.g., heart rate and steps) to the system.

[0238] Step 2:

[0239] The terminal transmits the entered lifestyle information and health data to the server. The transmitted information is stored in a database temporarily maintained by the server and prepared for analysis.

[0240] Step 3:

[0241] The server uses AI to analyze the received data. The analysis evaluates the user's nutrient intake and exercise patterns, identifying any nutrient deficiencies and suggesting exercise habits that need improvement.

[0242] Step 4:

[0243] The server generates a personalized health management plan based on the analysis results. This plan may include a meal plan incorporating specific foods, or an exercise plan with specific exercise instructions.

[0244] Step 5:

[0245] The generated health management plan is sent from the server to the terminal. The terminal presents the plan to the user in a viewable format and serves as an interface to support daily implementation.

[0246] Step 6:

[0247] The server automatically generates daily reports based on daily data. These reports include the degree to which goals set the previous day were achieved and recommended actions for the following day. The reports are provided to users via their terminals.

[0248] Step 7:

[0249] Users use their devices to send feedback on their health management plans and daily reports to the server. The server uses this feedback to improve and adjust the plans, and then provides them back to the user via the device.

[0250] (Example 1)

[0251] 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."

[0252] In modern society, systematically collecting people's lifestyle and health data and providing health management plans tailored to individual characteristics is extremely important. However, many people are unable to properly assess their health status, which prevents them from developing concrete action plans for self-improvement. This invention aims to solve these problems of insufficient individualization and inadequate feedback.

[0253] 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.

[0254] In this invention, the server includes a device for acquiring lifestyle information and health data, a device for generating a personalized health management plan using a generative AI model that analyzes the information in real time, and a device for transmitting the generated health management plan to an information device. This enables the provision of a personalized health management plan and feedback for specific improvements in daily life.

[0255] "Lifestyle information" refers to information about an individual's daily activities and conditions, including data such as diet, exercise levels, and sleep duration.

[0256] "Health data" refers to measurement data related to an individual's health status, including information such as weight, blood pressure, and heart rate.

[0257] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and generate personalized health management plans.

[0258] A "device" is a set of hardware and software components used to perform a specific function.

[0259] A "plan" is a specific action plan proposed for maintaining or improving an individual's health, and includes things like meal plans and exercise plans.

[0260] "Information devices" refer to electronic devices that individuals use in their daily lives, such as smartphones and tablet devices.

[0261] This invention is a system for providing personalized health management plans for individual users. The system includes a backend that primarily operates on the server side and a frontend that operates on a terminal for user interaction.

[0262] The server houses a database that collects lifestyle information and health data. This includes data on diet, exercise levels, sleep duration, and data from health devices. The server uses a generative AI model to analyze this data obtained from users. Data analysis by the generative AI model generates a health management plan optimized for each user. This plan includes meal plans to supplement specific nutrients and exercise plans to address lack of exercise.

[0263] The generated health management plan is sent from the server to the terminal and presented to the user in a format that allows for daily implementation. The terminal displays this plan clearly to the user, supporting their understanding and execution.

[0264] As a concrete example, consider a male user in his 20s using the system. The user inputs details of his meals using his smartphone, and automatically shares data such as steps taken and heart rate measured by a wearable device. The server analyzes this data and suggests a breakfast incorporating milk to increase calcium intake. It also generates a health management plan recommending strength training twice a week as an exercise plan.

[0265] Furthermore, the server generates a daily report based on the user's lifestyle information and health data. This report includes the user's achievements for the day and areas for improvement for the following day, enabling the user to assess their own health status and take subsequent actions.

[0266] The generative AI model performs analysis using prompts such as, "Generate an optimal health management plan based on the user's lifestyle and health data."

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

[0268] Step 1:

[0269] The device provides users with an interface for inputting lifestyle information and health data. Users input their diet, exercise activities, and sleep duration using their smartphones. The device also automatically receives health data such as heart rate and steps from wearable devices. This entered data is transmitted to the server in real time.

[0270] Step 2:

[0271] The server stores user lifestyle information and health data received from the terminal in a database. The database organizes and stores data for each user. This is performed as a preparatory process for analyzing this organized and stored data.

[0272] Step 3:

[0273] The server analyzes the accumulated data using a generative AI model. Specifically, it evaluates the user's diet and exercise patterns to detect deficiencies in specific nutrients or issues with exercise habits. The prompt used here is "Generate an optimal health management plan based on the user's lifestyle and health data." The resulting output is used as an intermediate result of the personalized health management plan.

[0274] Step 4:

[0275] Based on the analysis results, the server generates a personalized health management plan for each user. Based on the output of the generated AI model, plans such as meal plans to supplement specific nutrients or exercise plans are created. This output becomes prepared information to be provided to the user.

[0276] Step 5:

[0277] The server sends the generated health management plan to the device. The device receives this information and displays it in an easy-to-understand format for the user. The user reviews the plan and gains guidance on how to implement it in their daily life.

[0278] Step 6:

[0279] Every day, the server generates a daily report based on accumulated data and the implementation status of the generated health management plan. This report includes the degree to which the day's goals were achieved and specific areas for improvement for the following day.

[0280] Step 7:

[0281] The server distributes the generated daily report to the terminal. The terminal displays the report, and the user reviews it to check their health status and obtain information for taking necessary measures.

[0282] (Application Example 1)

[0283] 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".

[0284] The current system for supporting health management mainly focuses on general suggestions and has difficulty making optimal diet suggestions based on the specific health conditions and food preferences of individual users. In addition, there is a lack of a mechanism to support healthy choices when using eating out or delivery services. As a result, users are unable to make appropriate choices according to their health status, and there is a problem that health improvement does not progress.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for acquiring lifestyle information and health data, means for making optimal food suggestions based on health data and food preferences, and means for transmitting the generated health management plan to the information processing device. Thereby, the user can make optimal food choices according to their health status, and individualized health management is more effectively supported.

[0287] "Lifestyle information" is data related to an individual's daily life and includes activity patterns such as diet, exercise, and sleep.

[0288] "Health data" is measurement information indicating an individual's health status and includes physiological data such as weight, heart rate, and blood pressure.

[0289] A "personalized health management plan" is a plan for maintaining and improving health that is optimized based on an individual's lifestyle information and health data.

[0290] An "information processing device" is a terminal device operated by a user, and is a device that has the function of inputting and displaying data.

[0291] A "generative artificial intelligence model" is a model that uses AI technology to make suggestions and predictions that meet specific purposes and conditions based on a large amount of data.

[0292] This invention relates to a system and method for implementing health management optimized for individual users. The system mainly consists of a server and terminals.

[0293] The server has a database for acquiring and storing users' lifestyle information and health data. It also utilizes a generative artificial intelligence model to analyze the acquired data and generate personalized health management plans. These plans may include dietary suggestions to supplement specific nutrients and exercise plans to address lack of exercise. The generated plans are transmitted to the user's information processing device via the internet.

[0294] The terminal (information processing device) provides a user interface for users to input their lifestyle information and health data. Users input information such as their dietary history, exercise levels, and health device data, and this information is transmitted to the server in real time. The terminal also displays a health management plan to the user and plays a role in supporting its implementation.

[0295] Specifically, if a user has a health problem such as calcium deficiency, the server can analyze the data and suggest meals that include milk. Furthermore, by using a generative AI model, it's possible to suggest healthy menus tailored to the user's preferences based on their tastes and past selection history.

[0296] The server generates a daily report based on information received from users each day. This report includes the degree of achievement of the health management plan and areas for improvement for the following day, and is designed to make it easy for users to check their own progress.

[0297] A concrete example of a prompt message would be: "Based on the user's health data, please suggest a menu that includes foods rich in calcium and low in calories."

[0298] As described above, the server and terminal work together to provide personalized and effective health management for the user.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] Users input information such as their diet, exercise levels, sleep duration, and health data obtained from health devices using a terminal. This information is transmitted from the terminal to the server in real time. The server stores the acquired raw data in a database and standardizes the format as an initial data processing step.

[0302] Step 2:

[0303] The server inputs acquired lifestyle and health data into a generating AI model for data analysis. Specifically, it calculates one or more health indicators and compares them with historical data and general health guidelines. As a result of this analysis, the user's health status, necessary nutrients, and activity imbalances are identified.

[0304] Step 3:

[0305] The server uses a generative AI model to generate an individualized health management plan based on the analysis results. Based on the input health data and analysis results, it automatically generates a plan that includes suggestions for supplementing specific nutrients, types of exercise, frequency, etc. At this stage, a prompt sentence is issued to specifically determine the content of the diet suggestions for the AI model.

[0306] Step 4:

[0307] The generated health management plan is transmitted from the server to the terminal and provided to the user. The terminal displays the health plan in a user-friendly format and provides an interface to help achieve the goals. Here, the user can consult on how to actually incorporate the plan or adjust the options.

[0308] Step 5: [[ID=1,3]]

[0309] The server generates a daily report based on the data obtained daily. The report includes feedback on the degree of achievement, areas for improvement of health indicators, and activity guidelines for the next day, and is transmitted to the terminal as a document so that the user can check the progress of their health improvement.

[0310] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0311] The present invention is a system aimed at supporting the user's lifestyle and health status with an individualized health management plan and further enhancing the effect by taking into account the user's emotional state. This system combines means for integrating the user's lifestyle information and health data and generating a health management plan based on this, and an emotion engine for recognizing the user's emotions.

[0312] First, users input their lifestyle information through their device. This includes daily meals, exercise intensity and frequency, and sleep duration. Furthermore, they use their smart devices to link health data, such as heart rate and physical activity levels, with the system. This data is sent from the device to a server, where it is managed comprehensively.

[0313] The server uses collected data and AI to generate personalized health management plans for each user. These plans include meal suggestions to address any nutritional deficiencies and exercise plans to compensate for any lack of daily physical activity. The server also analyzes the user's emotions through an emotion engine and incorporates this information into the health management plan. The emotion engine analyzes voice data entered by the user and facial expression data acquired through the camera to assess the user's stress level and mood.

[0314] As a concrete example, consider the case of user C, a woman in her 30s, using the system. User C enters her meals from the previous day (breakfast: oatmeal, lunch: chicken salad, dinner: sukiyaki) into the terminal and records a 30-minute walk as exercise. At the same time, heart rate data acquired through a wearable device is also sent to the server. In addition, the system records changes in daily stress levels through an emotion engine, and recently, a high stress level has been detected.

[0315] Based on this, the server analyzes user C's data and suggests a possible vitamin D deficiency. Therefore, the server suggests fish dishes for the following week and creates an exercise plan incorporating yoga to reduce stress. These suggestions are sent to the user's device, allowing them to manage their daily health. In addition, the daily report reflects the user's emotional state along with their achievement level, and provides advice for the following day's activities.

[0316] This system goes beyond simple health data analysis, enabling comprehensive health management that addresses the user's emotional well-being. This allows for balanced physical and mental health support.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The terminal provides users with an interface for inputting lifestyle information and health data. Users input their daily diet, exercise levels, and sleep duration, and link health data such as heart rate and steps obtained from smart devices to the system.

[0320] Step 2:

[0321] The terminal transmits the entered lifestyle information and health data to the server. The transmitted data is stored in a database on the server and prepared for analysis.

[0322] Step 3:

[0323] The server uses AI to analyze lifestyle and health data stored in the database. From the analysis results, it understands the user's nutritional intake and exercise patterns, and identifies any deficient nutrients or exercise habits that need improvement.

[0324] Step 4:

[0325] The server generates a personalized health management plan based on the analysis results. This plan includes dietary suggestions to supplement specific nutrients and specific activities to encourage exercise.

[0326] Step 5:

[0327] The generated health management plan is sent from the server to the device. The device displays the received plan to the user and functions as a guideline to help them implement it in their daily life.

[0328] Step 6:

[0329] The server uses an emotion engine to analyze the user's emotional state. The user provides voice and facial expression data via their device, and based on this, the server determines stress levels and emotional changes.

[0330] Step 7:

[0331] Based on the user's emotional state, the server adjusts the health management plan. For example, if the stress level is high, it will suggest additional plans that incorporate relaxation and meditation.

[0332] Step 8:

[0333] The server automatically generates daily reports based on the user's lifestyle information, health data, and emotional state. These reports reflect the progress towards achieving the plan and changes in emotions, providing guidance for the following day's activities.

[0334] Step 9:

[0335] The device delivers the generated daily report to the user. The user can review the report, evaluate their own health and emotional state, and use that information to inform their activities for the following day.

[0336] Step 10:

[0337] Users send their opinions and feedback on their health management plans and daily reports to the server via their devices. The server reviews the plans based on this feedback and, if necessary, provides updated plans back to the devices.

[0338] (Example 2)

[0339] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0340] In modern society, achieving effective health management tailored to the diverse lifestyles and health conditions of individuals is not easy. In particular, comprehensive planning that takes into account not only physical data but also emotional states is required, but achieving this is difficult. Furthermore, a system is needed to provide personalized health management plans suited to the user in a timely manner and to adjust them based on feedback.

[0341] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0342] In this invention, the server includes means for acquiring lifestyle information and biometric information, means for integrating the acquired information and generating a personalized health management plan using a generative AI model, and means for analyzing the user's emotional state based on voice data and facial expression data and reflecting this in the health management plan. This enables personalized health management based on each user's lifestyle and emotional state.

[0343] "Lifestyle information" refers to information about a user's daily activities, such as eating, exercise, and sleep.

[0344] "Biometric information" refers to physiological data that indicates the user's physical condition, such as heart rate, steps taken, and physical activity level.

[0345] A "generative AI model" is a model that uses artificial intelligence technology to analyze acquired data and generate personalized health management plans for users.

[0346] A "health management plan" is a personalized management plan that includes suggestions for nutrition, exercise, stress management, and more, based on the user's lifestyle and biometric information.

[0347] "Emotional state" refers to information that indicates the user's psychological state, such as their stress level and mood.

[0348] "Voice data" refers to audio information that records a user's speech and is used to analyze their emotional state.

[0349] "Facial expression data" refers to information about a user's facial expressions and is used to analyze their emotional state.

[0350] This invention provides a system for implementing personalized health management for users. Specifically, it is a system that collects users' lifestyle and biometric information, generates a personalized health management plan based on this information, and provides it to the user. Embodiments of this system are shown below.

[0351] Hardware and software configuration:

[0352] Users operate a dedicated application using devices such as smartphones and tablets. This application provides an interface for inputting lifestyle information such as daily meals, exercise, and sleep duration. Users also collect biometric information such as heart rate using wearable devices such as smartwatches, and this information is automatically transmitted to a server.

[0353] The server integrates the collected information and generates a personalized health management plan using a generative AI model. This AI model can analyze large amounts of data and suggest the optimal balance of nutrition and exercise for the user. In addition, an emotion engine analyzes the user's voice and facial expression data to assess the user's emotional state and reflect it in the health management plan.

[0354] Specific example:

[0355] For example, consider a female user in her 30s using this system. The user records her meals from the previous day (e.g., oatmeal for breakfast, salad for lunch, fish for dinner) through the app and records her heart rate and steps for the day using a wearable device. The server analyzes this data and provides fish-based recipes as meal suggestions for the following week to address any vitamin D deficiency. Additionally, if the emotional engine detects a high-stress state, it suggests an exercise plan incorporating yoga to reduce stress.

[0356] Example of a prompt:

[0357] An example of a prompt used as input to a generative AI model is: "What health plan should be suggested to a female user in her 30s who is experiencing high stress and is deficient in vitamin D?"

[0358] In this way, it becomes possible to provide a system that comprehensively manages users' physical and emotional health, and to support the maintenance of healthy lifestyle habits.

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1:

[0361] Users open a dedicated application on their smartphone or tablet and input lifestyle information. This information includes daily meals (e.g., oatmeal for breakfast, chicken salad for lunch), exercise (e.g., 30 minutes of walking), and sleep duration. The entered data is transmitted from the device to the server. The role of this input is to provide basic data for understanding the user's lifestyle.

[0362] Step 2:

[0363] Users collect biometric information such as heart rate and steps using wearable devices like smartwatches and fitness trackers. This information is also transmitted to a server in real time via the device. As output, the server aggregates the data. This data is used to evaluate the user's physical condition and provide physiological data necessary for health management.

[0364] Step 3:

[0365] The server integrates lifestyle and biometric information transmitted from the terminal. All data is organized into a single user profile and stored in a database. Data processing includes data cleaning and formatting standardization. This optimizes the data in preparation for analysis.

[0366] Step 4:

[0367] The server utilizes an AI model based on integrated data to generate personalized health management plans. It analyzes input lifestyle and biometric information to create nutrition and exercise plans tailored to the user. The AI ​​model performs pattern recognition and predictive analysis as part of the data processing, resulting in a personalized health management plan as output.

[0368] Step 5:

[0369] The server uses an emotion engine to recognize the user's emotional state from their voice and facial expression data. Based on the emotion analysis, it evaluates the user's stress level and mood, and incorporates this information into the health management plan, providing a management plan that takes mental health into consideration as output.

[0370] Step 6:

[0371] The server sends the generated health management plan to the terminal. The user checks the suggested meal recipes and exercise plan on the terminal and uses them to manage their daily health. The output is a concrete plan that the user can implement, and individual adjustments can be made through feedback.

[0372] Step 7:

[0373] The user inputs the results of their health management activities, based on the proposed plan, into the terminal. The feedback provided is sent back to the server. The server analyzes the feedback and incorporates and adjusts it into the next health management plan. The output is a more optimized next management plan.

[0374] By progressing through these processing steps, it becomes possible to provide optimal support tailored to the user's health condition.

[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] In modern society, comprehensive health management tailored to individual lifestyles and emotional states is crucial. Furthermore, it is necessary to guide consumers towards appropriate consumption behavior based on their health status and emotional state. However, conventional systems have struggled to effectively integrate health management and optimized consumption behavior, making it difficult to provide appropriate suggestions to individual users.

[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 means for acquiring lifestyle information and health data, means for generating a personalized health management plan based on the acquired information, and means for transmitting the generated health management plan to the user terminal. This enables the optimization of health management and emotion-based consumption behavior tailored to each individual user.

[0380] "Lifestyle information" refers to information that shows the daily behavioral patterns of individual users, such as their diet, exercise frequency, and sleep duration.

[0381] "Health data" refers to numerical data that directly reflects a user's health status, such as their heart rate and physical activity level.

[0382] A "health management plan" is a personalized plan aimed at maintaining and improving health, created based on acquired lifestyle information and health data.

[0383] "Emotional state" refers to evaluation data that indicates the user's mental health status, such as their stress level and mood.

[0384] An "electronic payment plan" is a payment suggestion program designed to encourage optimal consumer behavior based on the user's emotional state and health condition.

[0385] A "user terminal" refers to a device, such as a smartphone or tablet, that a user directly operates to send and receive information.

[0386] "Feedback" refers to responses to user comments and suggestions, and is information that can be used to adjust health management plans.

[0387] The system for realizing this invention acquires lifestyle information and health data, and generates and provides personalized health management plans. This system mainly consists of a server, user terminals, and various sensors.

[0388] The server centrally manages lifestyle and health data acquired from users. This includes data transmitted from user devices such as smartphones and wearable devices. Specific data examples include heart rate, exercise levels, and dietary information. This data is managed on cloud services (such as AWS or Google Cloud Platform).

[0389] Using a generative AI model, the server generates personalized health management plans. These plans are designed to suggest nutritional balance and appropriate exercise schedules. Furthermore, to analyze emotional states, it utilizes emotion engines such as Microsoft Azure's Emotion API to analyze voice and image data. The analysis results evaluate the user's stress level and mood, and these are reflected in the health plan.

[0390] Furthermore, the server also generates an optimal electronic payment plan based on the user's emotional state. This plan offers stress-reducing promotions and purchase suggestions based on the user's emotions and health status. Integration with electronic payment APIs (such as Stripe or PayPal) is envisioned as the payment system.

[0391] For example, the server could suggest a discount on a yoga class to a user in their 30s based on their recent high stress levels. The user terminal would notify the user of this suggestion and help them choose an activity based on it. Daily reports would also reflect their achievements and emotional state, and include advice for the following day's actions.

[0392] An example of a prompt message would be: "Provide daily health management advice based on the user's emotional state and health data. User A works at a desk for about 8 hours a day. He has recently been experiencing stress due to lack of exercise. What activities and dietary recommendations would you suggest?"

[0393] This system allows users to enjoy conveniences tailored to their individual health conditions, enabling them to achieve a holistic lifestyle where physical and mental health are in harmony.

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] Users input lifestyle and health data into the terminal. This data includes everyday information such as the user's diet, exercise frequency, and sleep duration, and is transmitted to the server in real time via an application on the terminal. It is crucial to use security protocols (e.g., SSL / TLS) for the transmission and storage of this input data.

[0397] Step 2:

[0398] The server receives the transmitted lifestyle and health data and stores it in a database on the cloud. The input to this process is the raw data sent by the user, and the output is a well-formed dataset. The stored data undergoes preprocessing, including data cleansing, for use in subsequent analysis.

[0399] Step 3:

[0400] The server uses a generative AI model to generate personalized health management plans from the received data. At this stage, the stored data is used as input, the AI ​​model analyzes and infers from the data, and outputs a health plan tailored to the user. The generated plan includes nutritional improvement suggestions and exercise plans.

[0401] Step 4:

[0402] The server uses an emotion analysis engine to analyze the user's emotional state. It receives audio data and image data transmitted from the user's terminal as input and processes the emotion analysis. The output of this step is a quantified representation of the user's emotional state, indicating stress levels and mood.

[0403] Step 5:

[0404] The server generates an optimal electronic payment plan for the user based on the analyzed emotional state. The input consists of the emotional data obtained in step 4 and existing health data, which are used to output promotional and purchase-enhancing suggestions. Specifically, this includes discount information on products and services that help reduce stress.

[0405] Step 6:

[0406] The user terminal receives the health management plan and electronic payment plan sent from the server and notifies the user. The user reviews this and provides feedback to the system with their selected action. This feedback is sent to the server and used to adjust the plan in the future.

[0407] Step 7:

[0408] The server receives user feedback and updates and optimizes the health management plan. The feedback data is evaluated as input and used to improve the next health management plan. This creates a more effective health promotion cycle for the user.

[0409] 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.

[0410] 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 those described above. 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 shown 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.

[0411] 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.

[0412] [Third Embodiment]

[0413] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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".

[0425] This invention is a system that provides personalized health management plans for individual users, supporting the maintenance and improvement of their health. This system mainly consists of a backend that operates on the server side and a frontend that operates on a terminal for user interaction.

[0426] The server has a database for collecting lifestyle and health data, and it has the ability to analyze this information using generating AI. Based on the acquired data, the server generates a health management plan optimized for each user. This plan may include, for example, a meal plan to supplement specific nutrients that are lacking, or an exercise plan to address a lack of exercise. The generated health management plan is sent from the server to the terminal so that the user can implement it in their daily life.

[0427] The device provides an interface for users to input their lifestyle information and health data. Users input information such as their diet, exercise levels, sleep duration, and health data obtained from health devices. This information is transmitted to and stored on a server in real time. Furthermore, the device displays a health management plan transmitted from the server to support the user's understanding and implementation.

[0428] Every day, the server analyzes users' lifestyle information and health data, generating a daily report that includes their achievements for the day and areas for improvement for the following day. This report is delivered to the user's device, allowing them to objectively assess their health status and take necessary corrective actions.

[0429] As a concrete example, let's assume a male user B in his 20s uses this system. User B uses his smartphone to input his meal details (e.g., toast and eggs for breakfast, a sandwich for lunch, and curry for dinner), and the system automatically links in his daily step count and heart rate measured by a wearable device. The server evaluates User B's lifestyle information and detects that he is slightly deficient in calcium. In response, the server suggests a breakfast menu that includes milk and generates a health management plan recommending strength training twice a week as exercise. The next morning, the server automatically generates a daily report based on the previous day's data and achievement level, showing whether specific numerical targets have been met. User B can check this report on his smartphone, reflect on his actions, and use it to improve his daily health.

[0430] In this way, by linking the server and terminal, it is possible to provide users with personalized health management plans and support the improvement of their health.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] The terminal provides users with an interface for inputting lifestyle information and health data. Users input their daily diet, exercise levels, and sleep duration, and also link biometric data from wearable devices (e.g., heart rate and steps) to the system.

[0434] Step 2:

[0435] The terminal transmits the entered lifestyle information and health data to the server. The transmitted information is stored in a database temporarily maintained by the server and prepared for analysis.

[0436] Step 3:

[0437] The server uses AI to analyze the received data. The analysis evaluates the user's nutrient intake and exercise patterns, identifying any nutrient deficiencies and suggesting exercise habits that need improvement.

[0438] Step 4:

[0439] The server generates a personalized health management plan based on the analysis results. This plan may include a meal plan incorporating specific foods, or an exercise plan with specific exercise instructions.

[0440] Step 5:

[0441] The generated health management plan is sent from the server to the terminal. The terminal presents the plan to the user in a viewable format and serves as an interface to support daily implementation.

[0442] Step 6:

[0443] The server automatically generates daily reports based on daily data. These reports include the degree to which goals set the previous day were achieved and recommended actions for the following day. The reports are provided to users via their terminals.

[0444] Step 7:

[0445] Users use their devices to send feedback on their health management plans and daily reports to the server. The server uses this feedback to improve and adjust the plans, and then provides them back to the user via the device.

[0446] (Example 1)

[0447] 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."

[0448] In modern society, systematically collecting people's lifestyle and health data and providing health management plans tailored to individual characteristics is extremely important. However, many people are unable to properly assess their health status, which prevents them from developing concrete action plans for self-improvement. This invention aims to solve these problems of insufficient individualization and inadequate feedback.

[0449] 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.

[0450] In this invention, the server includes a device for acquiring lifestyle information and health data, a device for generating a personalized health management plan using a generative AI model that analyzes the information in real time, and a device for transmitting the generated health management plan to an information device. This enables the provision of a personalized health management plan and feedback for specific improvements in daily life.

[0451] "Lifestyle information" refers to information about an individual's daily activities and conditions, including data such as diet, exercise levels, and sleep duration.

[0452] "Health data" refers to measurement data related to an individual's health status, including information such as weight, blood pressure, and heart rate.

[0453] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and generate personalized health management plans.

[0454] A "device" is a set of hardware and software components used to perform a specific function.

[0455] A "plan" is a specific action plan proposed for maintaining or improving an individual's health, and includes things like meal plans and exercise plans.

[0456] "Information devices" refer to electronic devices that individuals use in their daily lives, such as smartphones and tablet devices.

[0457] This invention is a system for providing personalized health management plans for individual users. The system includes a backend that primarily operates on the server side and a frontend that operates on a terminal for user interaction.

[0458] The server houses a database that collects lifestyle information and health data. This includes data on diet, exercise levels, sleep duration, and data from health devices. The server uses a generative AI model to analyze this data obtained from users. Data analysis by the generative AI model generates a health management plan optimized for each user. This plan includes meal plans to supplement specific nutrients and exercise plans to address lack of exercise.

[0459] The generated health management plan is sent from the server to the terminal and presented to the user in a format that allows for daily implementation. The terminal displays this plan clearly to the user, supporting their understanding and execution.

[0460] As a concrete example, consider a male user in his 20s using the system. The user inputs details of his meals using his smartphone, and automatically shares data such as steps taken and heart rate measured by a wearable device. The server analyzes this data and suggests a breakfast incorporating milk to increase calcium intake. It also generates a health management plan recommending strength training twice a week as an exercise plan.

[0461] Furthermore, the server generates a daily report based on the user's lifestyle information and health data. This report includes the user's achievements for the day and areas for improvement for the following day, enabling the user to assess their own health status and take subsequent actions.

[0462] The generative AI model performs analysis using prompts such as, "Generate an optimal health management plan based on the user's lifestyle and health data."

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The device provides users with an interface for inputting lifestyle information and health data. Users input their diet, exercise activities, and sleep duration using their smartphones. The device also automatically receives health data such as heart rate and steps from wearable devices. This entered data is transmitted to the server in real time.

[0466] Step 2:

[0467] The server stores user lifestyle information and health data received from the terminal in a database. The database organizes and stores data for each user. This is performed as a preparatory process for analyzing this organized and stored data.

[0468] Step 3:

[0469] The server analyzes the accumulated data using a generative AI model. Specifically, it evaluates the user's diet and exercise patterns to detect deficiencies in specific nutrients or issues with exercise habits. The prompt used here is "Generate an optimal health management plan based on the user's lifestyle and health data." The resulting output is used as an intermediate result of the personalized health management plan.

[0470] Step 4:

[0471] Based on the analysis results, the server generates a personalized health management plan for each user. Based on the output of the generated AI model, plans such as meal plans to supplement specific nutrients or exercise plans are created. This output becomes prepared information to be provided to the user.

[0472] Step 5:

[0473] The server sends the generated health management plan to the device. The device receives this information and displays it in an easy-to-understand format for the user. The user reviews the plan and gains guidance on how to implement it in their daily life.

[0474] Step 6:

[0475] Every day, the server generates a daily report based on accumulated data and the implementation status of the generated health management plan. This report includes the degree to which the day's goals were achieved and specific areas for improvement for the following day.

[0476] Step 7:

[0477] The server delivers the generated daily report to the terminal. The terminal displays the report, and the user reviews it to assess their health status and obtain information to take necessary actions.

[0478] (Application Example 1)

[0479] 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."

[0480] Current systems designed to support health management tend to offer only general suggestions, making it difficult to provide optimal meal recommendations based on each user's specific health condition and dietary preferences. Furthermore, there is a lack of mechanisms to support healthy choices when dining out or using delivery services. As a result, users are unable to make appropriate choices based on their health condition, hindering their progress in improving their health.

[0481] 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.

[0482] In this invention, the server includes means for acquiring lifestyle information and health data, means for making optimal food suggestions based on health data and food preferences, and means for transmitting the generated health management plan to an information processing device. This enables users to make optimal food choices according to their health condition, and personalized health management is supported more effectively.

[0483] "Lifestyle information" refers to data about an individual's daily life, including activity patterns such as diet, exercise, and sleep.

[0484] "Health data" refers to measurement information that indicates an individual's health status, and includes physiological data such as weight, heart rate, and blood pressure.

[0485] A "personalized health management plan" is a plan for maintaining and improving health that is optimized based on an individual's lifestyle information and health data.

[0486] An "information processing device" is a terminal device operated by a user, and is a device that has the function of inputting and displaying data.

[0487] A "generative artificial intelligence model" is a model that uses AI technology to make suggestions and predictions that meet specific purposes and conditions based on a large amount of data.

[0488] This invention relates to a system and method for implementing health management optimized for individual users. The system mainly consists of a server and terminals.

[0489] The server has a database for acquiring and storing users' lifestyle information and health data. It also utilizes a generative artificial intelligence model to analyze the acquired data and generate personalized health management plans. These plans may include dietary suggestions to supplement specific nutrients and exercise plans to address lack of exercise. The generated plans are transmitted to the user's information processing device via the internet.

[0490] The terminal (information processing device) provides a user interface for users to input their lifestyle information and health data. Users input information such as their dietary history, exercise levels, and health device data, and this information is transmitted to the server in real time. The terminal also displays a health management plan to the user and plays a role in supporting its implementation.

[0491] Specifically, if a user has a health problem such as calcium deficiency, the server can analyze the data and suggest meals that include milk. Furthermore, by using a generative AI model, it's possible to suggest healthy menus tailored to the user's preferences based on their tastes and past selection history.

[0492] The server generates a daily report based on information received from users each day. This report includes the degree of achievement of the health management plan and areas for improvement for the following day, and is designed to make it easy for users to check their own progress.

[0493] A concrete example of a prompt message would be: "Based on the user's health data, please suggest a menu that includes foods rich in calcium and low in calories."

[0494] As described above, the server and terminal work together to provide personalized and effective health management for the user.

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] Users input information such as their diet, exercise levels, sleep duration, and health data obtained from health devices using a terminal. This information is transmitted from the terminal to the server in real time. The server stores the acquired raw data in a database and standardizes the format as an initial data processing step.

[0498] Step 2:

[0499] The server inputs acquired lifestyle and health data into a generating AI model for data analysis. Specifically, it calculates one or more health indicators and compares them with historical data and general health guidelines. As a result of this analysis, the user's health status, necessary nutrients, and activity imbalances are identified.

[0500] Step 3:

[0501] The server uses a generative AI model to generate a personalized health management plan based on the analysis results. Based on the input health data and analysis results, it automatically generates a plan that includes suggestions for supplementing specific nutrients, types of exercise, and frequency. At this stage, a prompt message is issued, allowing the AI ​​model to specifically determine the content of the dietary suggestions.

[0502] Step 4:

[0503] The generated health management plan is sent from the server to the terminal and provided to the user. The terminal displays the health plan in an easy-to-understand format and provides an interface to help the user achieve their goals. Here, the user can consult on how to actually implement the plan and adjust the options.

[0504] Step 5:

[0505] The server generates a daily report based on the data collected each day. The report includes feedback on achievement, areas for improvement in health indicators, and activity guidelines for the following day. It is sent to the user's device as a document, allowing them to track their progress in improving their health.

[0506] 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.

[0507] This invention is a system aimed at supporting users' lifestyles and health conditions through personalized health management plans, and further enhancing their effectiveness by taking into account the user's emotional state. This system provides a combination of means for collecting user lifestyle information and health data and generating health management plans based on this information, and an emotion engine that recognizes the user's emotions.

[0508] First, users input their lifestyle information through their device. This includes daily meals, exercise intensity and frequency, and sleep duration. Furthermore, they use their smart devices to link health data, such as heart rate and physical activity levels, with the system. This data is sent from the device to a server, where it is managed comprehensively.

[0509] The server uses collected data and AI to generate personalized health management plans for each user. These plans include meal suggestions to address any nutritional deficiencies and exercise plans to compensate for any lack of daily physical activity. The server also analyzes the user's emotions through an emotion engine and incorporates this information into the health management plan. The emotion engine analyzes voice data entered by the user and facial expression data acquired through the camera to assess the user's stress level and mood.

[0510] As a concrete example, consider the case of user C, a woman in her 30s, using the system. User C enters her meals from the previous day (breakfast: oatmeal, lunch: chicken salad, dinner: sukiyaki) into the terminal and records a 30-minute walk as exercise. At the same time, heart rate data acquired through a wearable device is also sent to the server. In addition, the system records changes in daily stress levels through an emotion engine, and recently, a high stress level has been detected.

[0511] Based on this, the server analyzes user C's data and suggests a possible vitamin D deficiency. Therefore, the server suggests fish dishes for the following week and creates an exercise plan incorporating yoga to reduce stress. These suggestions are sent to the user's device, allowing them to manage their daily health. In addition, the daily report reflects the user's emotional state along with their achievement level, and provides advice for the following day's activities.

[0512] This system goes beyond simple health data analysis, enabling comprehensive health management that addresses the user's emotional well-being. This allows for balanced physical and mental health support.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] The terminal provides users with an interface for inputting lifestyle information and health data. Users input their daily diet, exercise levels, and sleep duration, and link health data such as heart rate and steps obtained from smart devices to the system.

[0516] Step 2:

[0517] The terminal transmits the entered lifestyle information and health data to the server. The transmitted data is stored in a database on the server and prepared for analysis.

[0518] Step 3:

[0519] The server uses AI to analyze lifestyle and health data stored in the database. From the analysis results, it understands the user's nutritional intake and exercise patterns, and identifies any deficient nutrients or exercise habits that need improvement.

[0520] Step 4:

[0521] The server generates a personalized health management plan based on the analysis results. This plan includes dietary suggestions to supplement specific nutrients and specific activities to encourage exercise.

[0522] Step 5:

[0523] The generated health management plan is sent from the server to the device. The device displays the received plan to the user and functions as a guideline to help them implement it in their daily life.

[0524] Step 6:

[0525] The server uses an emotion engine to analyze the user's emotional state. The user provides voice and facial expression data via their device, and based on this, the server determines stress levels and emotional changes.

[0526] Step 7:

[0527] Based on the user's emotional state, the server adjusts the health management plan. For example, if the stress level is high, it will suggest additional plans that incorporate relaxation and meditation.

[0528] Step 8:

[0529] The server automatically generates daily reports based on the user's lifestyle information, health data, and emotional state. These reports reflect the progress towards achieving the plan and changes in emotions, providing guidance for the following day's activities.

[0530] Step 9:

[0531] The device delivers the generated daily report to the user. The user can review the report, evaluate their own health and emotional state, and use that information to inform their activities for the following day.

[0532] Step 10:

[0533] Users send their opinions and feedback on their health management plans and daily reports to the server via their devices. The server reviews the plans based on this feedback and, if necessary, provides updated plans back to the devices.

[0534] (Example 2)

[0535] 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."

[0536] In modern society, achieving effective health management tailored to the diverse lifestyles and health conditions of individuals is not easy. In particular, comprehensive planning that takes into account not only physical data but also emotional states is required, but achieving this is difficult. Furthermore, a system is needed to provide personalized health management plans suited to the user in a timely manner and to adjust them based on feedback.

[0537] 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.

[0538] In this invention, the server includes means for acquiring lifestyle information and biometric information, means for integrating the acquired information and generating a personalized health management plan using a generative AI model, and means for analyzing the user's emotional state based on voice data and facial expression data and reflecting this in the health management plan. This enables personalized health management based on each user's lifestyle and emotional state.

[0539] "Lifestyle information" refers to information about a user's daily activities, such as eating, exercise, and sleep.

[0540] "Biometric information" refers to physiological data that indicates the user's physical condition, such as heart rate, steps taken, and physical activity level.

[0541] A "generative AI model" is a model that uses artificial intelligence technology to analyze acquired data and generate personalized health management plans for users.

[0542] A "health management plan" is a personalized management plan that includes suggestions for nutrition, exercise, stress management, and more, based on the user's lifestyle and biometric information.

[0543] "Emotional state" refers to information that indicates the user's psychological state, such as their stress level and mood.

[0544] "Voice data" refers to audio information that records a user's speech and is used to analyze their emotional state.

[0545] "Facial expression data" refers to information about a user's facial expressions and is used to analyze their emotional state.

[0546] This invention provides a system for implementing personalized health management for users. Specifically, it is a system that collects users' lifestyle and biometric information, generates a personalized health management plan based on this information, and provides it to the user. Embodiments of this system are shown below.

[0547] Hardware and software configuration:

[0548] Users operate a dedicated application using devices such as smartphones and tablets. This application provides an interface for inputting lifestyle information such as daily meals, exercise, and sleep duration. Users also collect biometric information such as heart rate using wearable devices such as smartwatches, and this information is automatically transmitted to a server.

[0549] The server integrates the collected information and generates a personalized health management plan using a generative AI model. This AI model can analyze large amounts of data and suggest the optimal balance of nutrition and exercise for the user. In addition, an emotion engine analyzes the user's voice and facial expression data to assess the user's emotional state and reflect it in the health management plan.

[0550] Specific example:

[0551] For example, consider a female user in her 30s using this system. The user records her meals from the previous day (e.g., oatmeal for breakfast, salad for lunch, fish for dinner) through the app and records her heart rate and steps for the day using a wearable device. The server analyzes this data and provides fish-based recipes as meal suggestions for the following week to address any vitamin D deficiency. Additionally, if the emotional engine detects a high-stress state, it suggests an exercise plan incorporating yoga to reduce stress.

[0552] Example of a prompt:

[0553] An example of a prompt used as input to a generative AI model is: "What health plan should be suggested to a female user in her 30s who is experiencing high stress and is deficient in vitamin D?"

[0554] In this way, it becomes possible to provide a system that comprehensively manages users' physical and emotional health, and to support the maintenance of healthy lifestyle habits.

[0555] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0556] Step 1:

[0557] Users open a dedicated application on their smartphone or tablet and input lifestyle information. This information includes daily meals (e.g., oatmeal for breakfast, chicken salad for lunch), exercise (e.g., 30 minutes of walking), and sleep duration. The entered data is transmitted from the device to the server. The role of this input is to provide basic data for understanding the user's lifestyle.

[0558] Step 2:

[0559] Users collect biometric information such as heart rate and steps using wearable devices like smartwatches and fitness trackers. This information is also transmitted to a server in real time via the device. As output, the server aggregates the data. This data is used to evaluate the user's physical condition and provide physiological data necessary for health management.

[0560] Step 3:

[0561] The server integrates lifestyle and biometric information transmitted from the terminal. All data is organized into a single user profile and stored in a database. Data processing includes data cleaning and formatting standardization. This optimizes the data in preparation for analysis.

[0562] Step 4:

[0563] The server utilizes an AI model based on integrated data to generate personalized health management plans. It analyzes input lifestyle and biometric information to create nutrition and exercise plans tailored to the user. The AI ​​model performs pattern recognition and predictive analysis as part of the data processing, resulting in a personalized health management plan as output.

[0564] Step 5:

[0565] The server uses an emotion engine to recognize the user's emotional state from their voice and facial expression data. Based on the emotion analysis, it evaluates the user's stress level and mood, and incorporates this information into the health management plan, providing a management plan that takes mental health into consideration as output.

[0566] Step 6:

[0567] The server sends the generated health management plan to the terminal. The user checks the suggested meal recipes and exercise plan on the terminal and uses them to manage their daily health. The output is a concrete plan that the user can implement, and individual adjustments can be made through feedback.

[0568] Step 7:

[0569] The user inputs the results of their health management activities, based on the proposed plan, into the terminal. The feedback provided is sent back to the server. The server analyzes the feedback and incorporates and adjusts it into the next health management plan. The output is a more optimized next management plan.

[0570] By progressing through these processing steps, it becomes possible to provide optimal support tailored to the user's health condition.

[0571] (Application Example 2)

[0572] 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."

[0573] In modern society, comprehensive health management tailored to individual lifestyles and emotional states is crucial. Furthermore, it is necessary to guide consumers towards appropriate consumption behavior based on their health status and emotional state. However, conventional systems have struggled to effectively integrate health management and optimized consumption behavior, making it difficult to provide appropriate suggestions to individual users.

[0574] 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.

[0575] In this invention, the server includes means for acquiring lifestyle information and health data, means for generating a personalized health management plan based on the acquired information, and means for transmitting the generated health management plan to the user terminal. This enables the optimization of health management and emotion-based consumption behavior tailored to each individual user.

[0576] "Lifestyle information" refers to information that shows the daily behavioral patterns of individual users, such as their diet, exercise frequency, and sleep duration.

[0577] "Health data" refers to numerical data that directly reflects a user's health status, such as their heart rate and physical activity level.

[0578] A "health management plan" is a personalized plan aimed at maintaining and improving health, created based on acquired lifestyle information and health data.

[0579] "Emotional state" refers to evaluation data that indicates the user's mental health status, such as their stress level and mood.

[0580] An "electronic payment plan" is a payment suggestion program designed to encourage optimal consumer behavior based on the user's emotional state and health condition.

[0581] A "user terminal" refers to a device, such as a smartphone or tablet, that a user directly operates to send and receive information.

[0582] "Feedback" refers to responses to user comments and suggestions, and is information that can be used to adjust health management plans.

[0583] The system for realizing this invention acquires lifestyle information and health data, and generates and provides personalized health management plans. This system mainly consists of a server, user terminals, and various sensors.

[0584] The server centrally manages lifestyle and health data acquired from users. This includes data transmitted from user devices such as smartphones and wearable devices. Specific data examples include heart rate, exercise levels, and dietary information. This data is managed on cloud services (such as AWS or Google Cloud Platform).

[0585] Using a generative AI model, the server generates personalized health management plans. These plans are designed to suggest nutritional balance and appropriate exercise schedules. Furthermore, to analyze emotional states, it utilizes emotion engines such as Microsoft Azure's Emotion API to analyze voice and image data. The analysis results evaluate the user's stress level and mood, and these are reflected in the health plan.

[0586] Furthermore, the server also generates an optimal electronic payment plan based on the user's emotional state. This plan offers stress-reducing promotions and purchase suggestions based on the user's emotions and health status. Integration with electronic payment APIs (such as Stripe or PayPal) is envisioned as the payment system.

[0587] For example, the server could suggest a discount on a yoga class to a user in their 30s based on their recent high stress levels. The user terminal would notify the user of this suggestion and help them choose an activity based on it. Daily reports would also reflect their achievements and emotional state, and include advice for the following day's actions.

[0588] An example of a prompt message would be: "Provide daily health management advice based on the user's emotional state and health data. User A works at a desk for about 8 hours a day. He has recently been experiencing stress due to lack of exercise. What activities and dietary recommendations would you suggest?"

[0589] This system allows users to enjoy conveniences tailored to their individual health conditions, enabling them to achieve a holistic lifestyle where physical and mental health are in harmony.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] Users input lifestyle and health data into the terminal. This data includes everyday information such as the user's diet, exercise frequency, and sleep duration, and is transmitted to the server in real time via an application on the terminal. It is crucial to use security protocols (e.g., SSL / TLS) for the transmission and storage of this input data.

[0593] Step 2:

[0594] The server receives the transmitted lifestyle and health data and stores it in a database on the cloud. The input to this process is the raw data sent by the user, and the output is a well-formed dataset. The stored data undergoes preprocessing, including data cleansing, for use in subsequent analysis.

[0595] Step 3:

[0596] The server uses a generative AI model to generate personalized health management plans from the received data. At this stage, the stored data is used as input, the AI ​​model analyzes and infers from the data, and outputs a health plan tailored to the user. The generated plan includes nutritional improvement suggestions and exercise plans.

[0597] Step 4:

[0598] The server uses an emotion analysis engine to analyze the user's emotional state. It receives audio data and image data transmitted from the user's terminal as input and processes the emotion analysis. The output of this step is a quantified representation of the user's emotional state, indicating stress levels and mood.

[0599] Step 5:

[0600] The server generates an optimal electronic payment plan for the user based on the analyzed emotional state. The input consists of the emotional data obtained in step 4 and existing health data, which are used to output promotional and purchase-enhancing suggestions. Specifically, this includes discount information on products and services that help reduce stress.

[0601] Step 6:

[0602] The user terminal receives the health management plan and electronic payment plan sent from the server and notifies the user. The user reviews this and provides feedback to the system with their selected action. This feedback is sent to the server and used to adjust the plan in the future.

[0603] Step 7:

[0604] The server receives user feedback and updates and optimizes the health management plan. The feedback data is evaluated as input and used to improve the next health management plan. This creates a more effective health promotion cycle for the user.

[0605] 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.

[0606] 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 those described above. 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 shown 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.

[0607] 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.

[0608] [Fourth Embodiment]

[0609] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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".

[0622] This invention is a system that provides personalized health management plans for individual users, supporting the maintenance and improvement of their health. This system mainly consists of a backend that operates on the server side and a frontend that operates on a terminal for user interaction.

[0623] The server has a database for collecting lifestyle and health data, and it has the ability to analyze this information using generating AI. Based on the acquired data, the server generates a health management plan optimized for each user. This plan may include, for example, a meal plan to supplement specific nutrients that are lacking, or an exercise plan to address a lack of exercise. The generated health management plan is sent from the server to the terminal so that the user can implement it in their daily life.

[0624] The device provides an interface for users to input their lifestyle information and health data. Users input information such as their diet, exercise levels, sleep duration, and health data obtained from health devices. This information is transmitted to and stored on a server in real time. Furthermore, the device displays a health management plan transmitted from the server to support the user's understanding and implementation.

[0625] Every day, the server analyzes users' lifestyle information and health data, generating a daily report that includes their achievements for the day and areas for improvement for the following day. This report is delivered to the user's device, allowing them to objectively assess their health status and take necessary corrective actions.

[0626] As a concrete example, let's assume a male user B in his 20s uses this system. User B uses his smartphone to input his meal details (e.g., toast and eggs for breakfast, a sandwich for lunch, and curry for dinner), and the system automatically links in his daily step count and heart rate measured by a wearable device. The server evaluates User B's lifestyle information and detects that he is slightly deficient in calcium. In response, the server suggests a breakfast menu that includes milk and generates a health management plan recommending strength training twice a week as exercise. The next morning, the server automatically generates a daily report based on the previous day's data and achievement level, showing whether specific numerical targets have been met. User B can check this report on his smartphone, reflect on his actions, and use it to improve his daily health.

[0627] In this way, by linking the server and terminal, it is possible to provide users with personalized health management plans and support the improvement of their health.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The terminal provides users with an interface for inputting lifestyle information and health data. Users input their daily diet, exercise levels, and sleep duration, and also link biometric data from wearable devices (e.g., heart rate and steps) to the system.

[0631] Step 2:

[0632] The terminal transmits the entered lifestyle information and health data to the server. The transmitted information is stored in a database temporarily maintained by the server and prepared for analysis.

[0633] Step 3:

[0634] The server uses AI to analyze the received data. The analysis evaluates the user's nutrient intake and exercise patterns, identifying any nutrient deficiencies and suggesting exercise habits that need improvement.

[0635] Step 4:

[0636] The server generates a personalized health management plan based on the analysis results. This plan may include a meal plan incorporating specific foods, or an exercise plan with specific exercise instructions.

[0637] Step 5:

[0638] The generated health management plan is sent from the server to the terminal. The terminal presents the plan to the user in a viewable format and serves as an interface to support daily implementation.

[0639] Step 6:

[0640] The server automatically generates daily reports based on daily data. These reports include the degree to which goals set the previous day were achieved and recommended actions for the following day. The reports are provided to users via their terminals.

[0641] Step 7:

[0642] Users use their devices to send feedback on their health management plans and daily reports to the server. The server uses this feedback to improve and adjust the plans, and then provides them back to the user via the device.

[0643] (Example 1)

[0644] 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".

[0645] In modern society, systematically collecting people's lifestyle and health data and providing health management plans tailored to individual characteristics is extremely important. However, many people are unable to properly assess their health status, which prevents them from developing concrete action plans for self-improvement. This invention aims to solve these problems of insufficient individualization and inadequate feedback.

[0646] 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.

[0647] In this invention, the server includes a device for acquiring lifestyle information and health data, a device for generating a personalized health management plan using a generative AI model that analyzes the information in real time, and a device for transmitting the generated health management plan to an information device. This enables the provision of a personalized health management plan and feedback for specific improvements in daily life.

[0648] "Lifestyle information" refers to information about an individual's daily activities and conditions, including data such as diet, exercise levels, and sleep duration.

[0649] "Health data" refers to measurement data related to an individual's health status, including information such as weight, blood pressure, and heart rate.

[0650] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and generate personalized health management plans.

[0651] A "device" is a set of hardware and software components used to perform a specific function.

[0652] A "plan" is a specific action plan proposed for maintaining or improving an individual's health, and includes things like meal plans and exercise plans.

[0653] "Information devices" refer to electronic devices that individuals use in their daily lives, such as smartphones and tablet devices.

[0654] This invention is a system for providing personalized health management plans for individual users. The system includes a backend that primarily operates on the server side and a frontend that operates on a terminal for user interaction.

[0655] The server houses a database that collects lifestyle information and health data. This includes data on diet, exercise levels, sleep duration, and data from health devices. The server uses a generative AI model to analyze this data obtained from users. Data analysis by the generative AI model generates a health management plan optimized for each user. This plan includes meal plans to supplement specific nutrients and exercise plans to address lack of exercise.

[0656] The generated health management plan is sent from the server to the terminal and presented to the user in a format that allows for daily implementation. The terminal displays this plan clearly to the user, supporting their understanding and execution.

[0657] As a concrete example, consider a male user in his 20s using the system. The user inputs details of his meals using his smartphone, and automatically shares data such as steps taken and heart rate measured by a wearable device. The server analyzes this data and suggests a breakfast incorporating milk to increase calcium intake. It also generates a health management plan recommending strength training twice a week as an exercise plan.

[0658] Furthermore, the server generates a daily report based on the user's lifestyle information and health data. This report includes the user's achievements for the day and areas for improvement for the following day, enabling the user to assess their own health status and take subsequent actions.

[0659] The generative AI model performs analysis using prompts such as, "Generate an optimal health management plan based on the user's lifestyle and health data."

[0660] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0661] Step 1:

[0662] The device provides users with an interface for inputting lifestyle information and health data. Users input their diet, exercise activities, and sleep duration using their smartphones. The device also automatically receives health data such as heart rate and steps from wearable devices. This entered data is transmitted to the server in real time.

[0663] Step 2:

[0664] The server stores user lifestyle information and health data received from the terminal in a database. The database organizes and stores data for each user. This is performed as a preparatory process for analyzing this organized and stored data.

[0665] Step 3:

[0666] The server analyzes the accumulated data using a generative AI model. Specifically, it evaluates the user's diet and exercise patterns to detect deficiencies in specific nutrients or issues with exercise habits. The prompt used here is "Generate an optimal health management plan based on the user's lifestyle and health data." The resulting output is used as an intermediate result of the personalized health management plan.

[0667] Step 4:

[0668] Based on the analysis results, the server generates a personalized health management plan for each user. Based on the output of the generated AI model, plans such as meal plans to supplement specific nutrients or exercise plans are created. This output becomes prepared information to be provided to the user.

[0669] Step 5:

[0670] The server sends the generated health management plan to the device. The device receives this information and displays it in an easy-to-understand format for the user. The user reviews the plan and gains guidance on how to implement it in their daily life.

[0671] Step 6:

[0672] Every day, the server generates a daily report based on accumulated data and the implementation status of the generated health management plan. This report includes the degree to which the day's goals were achieved and specific areas for improvement for the following day.

[0673] Step 7:

[0674] The server delivers the generated daily report to the terminal. The terminal displays the report, and the user reviews it to assess their health status and obtain information to take necessary actions.

[0675] (Application Example 1)

[0676] 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".

[0677] Current systems designed to support health management tend to offer only general suggestions, making it difficult to provide optimal meal recommendations based on each user's specific health condition and dietary preferences. Furthermore, there is a lack of mechanisms to support healthy choices when dining out or using delivery services. As a result, users are unable to make appropriate choices based on their health condition, hindering their progress in improving their health.

[0678] 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.

[0679] In this invention, the server includes means for acquiring lifestyle information and health data, means for making optimal food suggestions based on health data and food preferences, and means for transmitting the generated health management plan to an information processing device. This enables users to make optimal food choices according to their health condition, and personalized health management is supported more effectively.

[0680] "Lifestyle information" refers to data about an individual's daily life, including activity patterns such as diet, exercise, and sleep.

[0681] "Health data" refers to measurement information that indicates an individual's health status, and includes physiological data such as weight, heart rate, and blood pressure.

[0682] A "personalized health management plan" is a plan for maintaining and improving health that is optimized based on an individual's lifestyle information and health data.

[0683] An "information processing device" is a terminal device operated by a user, and is a device that has the function of inputting and displaying data.

[0684] A "generative artificial intelligence model" is a model that uses AI technology to make suggestions and predictions that meet specific purposes and conditions based on a large amount of data.

[0685] This invention relates to a system and method for implementing health management optimized for individual users. The system mainly consists of a server and terminals.

[0686] The server has a database for acquiring and storing users' lifestyle information and health data. It also utilizes a generative artificial intelligence model to analyze the acquired data and generate personalized health management plans. These plans may include dietary suggestions to supplement specific nutrients and exercise plans to address lack of exercise. The generated plans are transmitted to the user's information processing device via the internet.

[0687] The terminal (information processing device) provides a user interface for users to input their lifestyle information and health data. Users input information such as their dietary history, exercise levels, and health device data, and this information is transmitted to the server in real time. The terminal also displays a health management plan to the user and plays a role in supporting its implementation.

[0688] Specifically, if a user has a health problem such as calcium deficiency, the server can analyze the data and suggest meals that include milk. Furthermore, by using a generative AI model, it's possible to suggest healthy menus tailored to the user's preferences based on their tastes and past selection history.

[0689] The server generates a daily report based on information received from users each day. This report includes the degree of achievement of the health management plan and areas for improvement for the following day, and is designed to make it easy for users to check their own progress.

[0690] A concrete example of a prompt message would be: "Based on the user's health data, please suggest a menu that includes foods rich in calcium and low in calories."

[0691] As described above, the server and terminal work together to provide personalized and effective health management for the user.

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

[0693] Step 1:

[0694] Users input information such as their diet, exercise levels, sleep duration, and health data obtained from health devices using a terminal. This information is transmitted from the terminal to the server in real time. The server stores the acquired raw data in a database and standardizes the format as an initial data processing step.

[0695] Step 2:

[0696] The server inputs acquired lifestyle and health data into a generating AI model for data analysis. Specifically, it calculates one or more health indicators and compares them with historical data and general health guidelines. As a result of this analysis, the user's health status, necessary nutrients, and activity imbalances are identified.

[0697] Step 3:

[0698] The server uses a generative AI model to generate a personalized health management plan based on the analysis results. Based on the input health data and analysis results, it automatically generates a plan that includes suggestions for supplementing specific nutrients, types of exercise, and frequency. At this stage, a prompt message is issued, allowing the AI ​​model to specifically determine the content of the dietary suggestions.

[0699] Step 4:

[0700] The generated health management plan is sent from the server to the terminal and provided to the user. The terminal displays the health plan in an easy-to-understand format and provides an interface to help the user achieve their goals. Here, the user can consult on how to actually implement the plan and adjust the options.

[0701] Step 5:

[0702] The server generates a daily report based on the data collected each day. The report includes feedback on achievement, areas for improvement in health indicators, and activity guidelines for the following day. It is sent to the user's device as a document, allowing them to track their progress in improving their health.

[0703] 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.

[0704] This invention is a system aimed at supporting users' lifestyles and health conditions through personalized health management plans, and further enhancing their effectiveness by taking into account the user's emotional state. This system provides a combination of means for collecting user lifestyle information and health data and generating health management plans based on this information, and an emotion engine that recognizes the user's emotions.

[0705] First, users input their lifestyle information through their device. This includes daily meals, exercise intensity and frequency, and sleep duration. Furthermore, they use their smart devices to link health data, such as heart rate and physical activity levels, with the system. This data is sent from the device to a server, where it is managed comprehensively.

[0706] The server uses collected data and AI to generate personalized health management plans for each user. These plans include meal suggestions to address any nutritional deficiencies and exercise plans to compensate for any lack of daily physical activity. The server also analyzes the user's emotions through an emotion engine and incorporates this information into the health management plan. The emotion engine analyzes voice data entered by the user and facial expression data acquired through the camera to assess the user's stress level and mood.

[0707] As a concrete example, consider the case of user C, a woman in her 30s, using the system. User C enters her meals from the previous day (breakfast: oatmeal, lunch: chicken salad, dinner: sukiyaki) into the terminal and records a 30-minute walk as exercise. At the same time, heart rate data acquired through a wearable device is also sent to the server. In addition, the system records changes in daily stress levels through an emotion engine, and recently, a high stress level has been detected.

[0708] Based on this, the server analyzes user C's data and suggests a possible vitamin D deficiency. Therefore, the server suggests fish dishes for the following week and creates an exercise plan incorporating yoga to reduce stress. These suggestions are sent to the user's device, allowing them to manage their daily health. In addition, the daily report reflects the user's emotional state along with their achievement level, and provides advice for the following day's activities.

[0709] This system goes beyond simple health data analysis, enabling comprehensive health management that addresses the user's emotional well-being. This allows for balanced physical and mental health support.

[0710] The following describes the processing flow.

[0711] Step 1:

[0712] The terminal provides users with an interface for inputting lifestyle information and health data. Users input their daily diet, exercise levels, and sleep duration, and link health data such as heart rate and steps obtained from smart devices to the system.

[0713] Step 2:

[0714] The terminal transmits the entered lifestyle information and health data to the server. The transmitted data is stored in a database on the server and prepared for analysis.

[0715] Step 3:

[0716] The server uses AI to analyze lifestyle and health data stored in the database. From the analysis results, it understands the user's nutritional intake and exercise patterns, and identifies any deficient nutrients or exercise habits that need improvement.

[0717] Step 4:

[0718] The server generates a personalized health management plan based on the analysis results. This plan includes dietary suggestions to supplement specific nutrients and specific activities to encourage exercise.

[0719] Step 5:

[0720] The generated health management plan is sent from the server to the device. The device displays the received plan to the user and functions as a guideline to help them implement it in their daily life.

[0721] Step 6:

[0722] The server uses an emotion engine to analyze the user's emotional state. The user provides voice and facial expression data via their device, and based on this, the server determines stress levels and emotional changes.

[0723] Step 7:

[0724] Based on the user's emotional state, the server adjusts the health management plan. For example, if the stress level is high, it will suggest additional plans that incorporate relaxation and meditation.

[0725] Step 8:

[0726] The server automatically generates daily reports based on the user's lifestyle information, health data, and emotional state. These reports reflect the progress towards achieving the plan and changes in emotions, providing guidance for the following day's activities.

[0727] Step 9:

[0728] The device delivers the generated daily report to the user. The user can review the report, evaluate their own health and emotional state, and use that information to inform their activities for the following day.

[0729] Step 10:

[0730] Users send their opinions and feedback on their health management plans and daily reports to the server via their devices. The server reviews the plans based on this feedback and, if necessary, provides updated plans back to the devices.

[0731] (Example 2)

[0732] 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".

[0733] In modern society, achieving effective health management tailored to the diverse lifestyles and health conditions of individuals is not easy. In particular, comprehensive planning that takes into account not only physical data but also emotional states is required, but achieving this is difficult. Furthermore, a system is needed to provide personalized health management plans suited to the user in a timely manner and to adjust them based on feedback.

[0734] 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.

[0735] In this invention, the server includes means for acquiring lifestyle information and biometric information, means for integrating the acquired information and generating a personalized health management plan using a generative AI model, and means for analyzing the user's emotional state based on voice data and facial expression data and reflecting this in the health management plan. This enables personalized health management based on each user's lifestyle and emotional state.

[0736] "Lifestyle information" refers to information about a user's daily activities, such as eating, exercise, and sleep.

[0737] "Biometric information" refers to physiological data that indicates the user's physical condition, such as heart rate, steps taken, and physical activity level.

[0738] A "generative AI model" is a model that uses artificial intelligence technology to analyze acquired data and generate personalized health management plans for users.

[0739] A "health management plan" is a personalized management plan that includes suggestions for nutrition, exercise, stress management, and more, based on the user's lifestyle and biometric information.

[0740] "Emotional state" refers to information that indicates the user's psychological state, such as their stress level and mood.

[0741] "Voice data" refers to audio information that records a user's speech and is used to analyze their emotional state.

[0742] "Facial expression data" refers to information about a user's facial expressions and is used to analyze their emotional state.

[0743] This invention provides a system for implementing personalized health management for users. Specifically, it is a system that collects users' lifestyle and biometric information, generates a personalized health management plan based on this information, and provides it to the user. Embodiments of this system are shown below.

[0744] Hardware and software configuration:

[0745] Users operate a dedicated application using devices such as smartphones and tablets. This application provides an interface for inputting lifestyle information such as daily meals, exercise, and sleep duration. Users also collect biometric information such as heart rate using wearable devices such as smartwatches, and this information is automatically transmitted to a server.

[0746] The server integrates the collected information and generates a personalized health management plan using a generative AI model. This AI model can analyze large amounts of data and suggest the optimal balance of nutrition and exercise for the user. In addition, an emotion engine analyzes the user's voice and facial expression data to assess the user's emotional state and reflect it in the health management plan.

[0747] Specific example:

[0748] For example, consider a female user in her 30s using this system. The user records her meals from the previous day (e.g., oatmeal for breakfast, salad for lunch, fish for dinner) through the app and records her heart rate and steps for the day using a wearable device. The server analyzes this data and provides fish-based recipes as meal suggestions for the following week to address any vitamin D deficiency. Additionally, if the emotional engine detects a high-stress state, it suggests an exercise plan incorporating yoga to reduce stress.

[0749] Example of a prompt:

[0750] An example of a prompt used as input to a generative AI model is: "What health plan should be suggested to a female user in her 30s who is experiencing high stress and is deficient in vitamin D?"

[0751] In this way, it becomes possible to provide a system that comprehensively manages users' physical and emotional health, and to support the maintenance of healthy lifestyle habits.

[0752] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0753] Step 1:

[0754] Users open a dedicated application on their smartphone or tablet and input lifestyle information. This information includes daily meals (e.g., oatmeal for breakfast, chicken salad for lunch), exercise (e.g., 30 minutes of walking), and sleep duration. The entered data is transmitted from the device to the server. The role of this input is to provide basic data for understanding the user's lifestyle.

[0755] Step 2:

[0756] Users collect biometric information such as heart rate and steps using wearable devices like smartwatches and fitness trackers. This information is also transmitted to a server in real time via the device. As output, the server aggregates the data. This data is used to evaluate the user's physical condition and provide physiological data necessary for health management.

[0757] Step 3:

[0758] The server integrates lifestyle and biometric information transmitted from the terminal. All data is organized into a single user profile and stored in a database. Data processing includes data cleaning and formatting standardization. This optimizes the data in preparation for analysis.

[0759] Step 4:

[0760] The server utilizes an AI model based on integrated data to generate personalized health management plans. It analyzes input lifestyle and biometric information to create nutrition and exercise plans tailored to the user. The AI ​​model performs pattern recognition and predictive analysis as part of the data processing, resulting in a personalized health management plan as output.

[0761] Step 5:

[0762] The server uses an emotion engine to recognize the user's emotional state from their voice and facial expression data. Based on the emotion analysis, it evaluates the user's stress level and mood, and incorporates this information into the health management plan, providing a management plan that takes mental health into consideration as output.

[0763] Step 6:

[0764] The server sends the generated health management plan to the terminal. The user checks the suggested meal recipes and exercise plan on the terminal and uses them to manage their daily health. The output is a concrete plan that the user can implement, and individual adjustments can be made through feedback.

[0765] Step 7:

[0766] The user inputs the results of their health management activities, based on the proposed plan, into the terminal. The feedback provided is sent back to the server. The server analyzes the feedback and incorporates and adjusts it into the next health management plan. The output is a more optimized next management plan.

[0767] By progressing through these processing steps, it becomes possible to provide optimal support tailored to the user's health condition.

[0768] (Application Example 2)

[0769] 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".

[0770] In modern society, comprehensive health management tailored to individual lifestyles and emotional states is crucial. Furthermore, it is necessary to guide consumers towards appropriate consumption behavior based on their health status and emotional state. However, conventional systems have struggled to effectively integrate health management and optimized consumption behavior, making it difficult to provide appropriate suggestions to individual users.

[0771] 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.

[0772] In this invention, the server includes means for acquiring lifestyle information and health data, means for generating a personalized health management plan based on the acquired information, and means for transmitting the generated health management plan to the user terminal. This enables the optimization of health management and emotion-based consumption behavior tailored to each individual user.

[0773] "Lifestyle information" refers to information that shows the daily behavioral patterns of individual users, such as their diet, exercise frequency, and sleep duration.

[0774] "Health data" refers to numerical data that directly reflects a user's health status, such as their heart rate and physical activity level.

[0775] A "health management plan" is a personalized plan aimed at maintaining and improving health, created based on acquired lifestyle information and health data.

[0776] "Emotional state" refers to evaluation data that indicates the user's mental health status, such as their stress level and mood.

[0777] An "electronic payment plan" is a payment suggestion program designed to encourage optimal consumer behavior based on the user's emotional state and health condition.

[0778] A "user terminal" refers to a device, such as a smartphone or tablet, that a user directly operates to send and receive information.

[0779] "Feedback" refers to responses to user comments and suggestions, and is information that can be used to adjust health management plans.

[0780] The system for realizing this invention acquires lifestyle information and health data, and generates and provides personalized health management plans. This system mainly consists of a server, user terminals, and various sensors.

[0781] The server centrally manages lifestyle and health data acquired from users. This includes data transmitted from user devices such as smartphones and wearable devices. Specific data examples include heart rate, exercise levels, and dietary information. This data is managed on cloud services (such as AWS or Google Cloud Platform).

[0782] Using a generative AI model, the server generates personalized health management plans. These plans are designed to suggest nutritional balance and appropriate exercise schedules. Furthermore, to analyze emotional states, it utilizes emotion engines such as Microsoft Azure's Emotion API to analyze voice and image data. The analysis results evaluate the user's stress level and mood, and these are reflected in the health plan.

[0783] Furthermore, the server also generates an optimal electronic payment plan based on the user's emotional state. This plan offers stress-reducing promotions and purchase suggestions based on the user's emotions and health status. Integration with electronic payment APIs (such as Stripe or PayPal) is envisioned as the payment system.

[0784] For example, the server could suggest a discount on a yoga class to a user in their 30s based on their recent high stress levels. The user terminal would notify the user of this suggestion and help them choose an activity based on it. Daily reports would also reflect their achievements and emotional state, and include advice for the following day's actions.

[0785] An example of a prompt message would be: "Provide daily health management advice based on the user's emotional state and health data. User A works at a desk for about 8 hours a day. He has recently been experiencing stress due to lack of exercise. What activities and dietary recommendations would you suggest?"

[0786] This system allows users to enjoy conveniences tailored to their individual health conditions, enabling them to achieve a holistic lifestyle where physical and mental health are in harmony.

[0787] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0788] Step 1:

[0789] Users input lifestyle and health data into the terminal. This data includes everyday information such as the user's diet, exercise frequency, and sleep duration, and is transmitted to the server in real time via an application on the terminal. It is crucial to use security protocols (e.g., SSL / TLS) for the transmission and storage of this input data.

[0790] Step 2:

[0791] The server receives the transmitted lifestyle and health data and stores it in a database on the cloud. The input to this process is the raw data sent by the user, and the output is a well-formed dataset. The stored data undergoes preprocessing, including data cleansing, for use in subsequent analysis.

[0792] Step 3:

[0793] The server uses a generative AI model to generate personalized health management plans from the received data. At this stage, the stored data is used as input, the AI ​​model analyzes and infers from the data, and outputs a health plan tailored to the user. The generated plan includes nutritional improvement suggestions and exercise plans.

[0794] Step 4:

[0795] The server uses an emotion analysis engine to analyze the user's emotional state. It receives audio data and image data transmitted from the user's terminal as input and processes the emotion analysis. The output of this step is a quantified representation of the user's emotional state, indicating stress levels and mood.

[0796] Step 5:

[0797] The server generates an optimal electronic payment plan for the user based on the analyzed emotional state. The input consists of the emotional data obtained in step 4 and existing health data, which are used to output promotional and purchase-enhancing suggestions. Specifically, this includes discount information on products and services that help reduce stress.

[0798] Step 6:

[0799] The user terminal receives the health management plan and electronic payment plan sent from the server and notifies the user. The user reviews this and provides feedback to the system with their selected action. This feedback is sent to the server and used to adjust the plan in the future.

[0800] Step 7:

[0801] The server receives user feedback and updates and optimizes the health management plan. The feedback data is evaluated as input and used to improve the next health management plan. This creates a more effective health promotion cycle for the user.

[0802] 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.

[0803] 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 those described above. 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 shown 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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."

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0823] The following is further disclosed regarding the embodiments described above.

[0824] (Claim 1)

[0825] Means for acquiring lifestyle information and health data,

[0826] A means of generating an individualized health management plan based on acquired information,

[0827] A means of sending the generated health management plan to the user's terminal,

[0828] A means for providing a daily report generated based on the aforementioned lifestyle information and health data to the user's terminal,

[0829] A means of receiving feedback from users and adjusting health management plans,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, wherein the health management plan includes a meal plan to address specific nutrient deficiencies.

[0833] (Claim 3)

[0834] The system according to claim 1, wherein the health management plan includes an exercise plan based on the user's exercise patterns.

[0835] "Example 1"

[0836] (Claim 1)

[0837] A device for acquiring lifestyle information and health data,

[0838] A device that generates personalized health management plans using a generative AI model that analyzes information in real time,

[0839] A device that transmits the generated health management plan to an information device,

[0840] A device that generates daily reports and provides them to information devices,

[0841] A device that receives feedback from information devices and adjusts the health management plan,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, wherein the health management plan includes a nutrition plan to address nutrient deficiencies.

[0845] (Claim 3)

[0846] The system according to claim 1, wherein the health management plan includes an exercise plan based on the user's exercise habits.

[0847] "Application Example 1"

[0848] (Claim 1)

[0849] Means for acquiring lifestyle information and health data,

[0850] A means of generating an individualized health management plan based on acquired information,

[0851] A means for transmitting the generated health management plan to an information processing device,

[0852] A means for providing a daily report generated based on the aforementioned lifestyle information and health data to an information processing device,

[0853] A means of receiving feedback from users and adjusting health management plans,

[0854] A means of providing optimal food recommendations based on health data and dietary preferences,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein the health management plan includes dietary suggestions to address specific nutrient deficiencies.

[0858] (Claim 3)

[0859] The system according to claim 1, wherein the health management plan includes food selection that takes nutritional balance and calories into consideration using a generative artificial intelligence model.

[0860] "Example 2 of combining an emotion engine"

[0861] (Claim 1)

[0862] Means for acquiring lifestyle information and biometric information,

[0863] A means for integrating acquired information and generating a personalized health management plan using a generative AI model,

[0864] A means for analyzing the user's emotional state based on voice data and facial expression data and reflecting it in the health management plan,

[0865] A means for transmitting the generated health management plan to the user's terminal,

[0866] A means for providing a daily report generated based on the aforementioned lifestyle information and biometric information to the user's terminal,

[0867] A means of receiving feedback from users and adjusting health management plans,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, wherein the health management plan includes dietary suggestions to address specific nutrient deficiencies.

[0871] (Claim 3)

[0872] The system according to claim 1, wherein the health management plan includes exercise suggestions based on the user's physical activity patterns.

[0873] "Application example 2 when combining with an emotional engine"

[0874] (Claim 1)

[0875] Means for acquiring lifestyle information and health data,

[0876] A means of generating an individualized health management plan based on acquired information,

[0877] A means of sending the generated health management plan to the user's terminal,

[0878] A means for providing a daily report generated based on the aforementioned lifestyle information and health data to the user's terminal,

[0879] A means of receiving feedback from users and adjusting health management plans,

[0880] A means of analyzing the emotional state of users,

[0881] A means for generating an electronic payment plan that optimizes consumer behavior based on analyzed emotional states,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, wherein the health management plan includes a meal plan to address specific nutrient deficiencies.

[0885] (Claim 3)

[0886] The system according to claim 1, wherein the health management plan includes an exercise plan based on the user's exercise patterns. [Explanation of Symbols]

[0887] 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. Means for acquiring lifestyle information and health data, A means of generating an individualized health management plan based on acquired information, A means of sending the generated health management plan to the user's terminal, A means for providing a daily report generated based on the aforementioned lifestyle information and health data to the user's terminal, A means of receiving feedback from users and adjusting health management plans, A system that includes this.

2. The system according to claim 1, wherein the health management plan includes a meal plan to address specific nutrient deficiencies.

3. The system according to claim 1, wherein the health management plan includes an exercise plan based on the user's exercise pattern.

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