system

Abstract

We provide the system. [Solution] A means of collecting health-related information from a device, including location information, physical activity data, heart rate data, and sleep data. A means of sending collected health-related information to a cloud server for integration and organization, A means of analyzing integrated health-related information in real time using AI technology, A means for creating a health management program that includes individualized exercise programs, nutritional guidance, and relaxation guidance for each user, based on the analysis results. A means of notifying the user of the created health management program on the device and providing it to the user, A means of collecting user feedback and adapting and improving the health management program based on that feedback, 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 conventional health management systems, it has been difficult to effectively integrate data collected from multiple devices and provide personalized feedback and advice based on the individual health status of users in real time. There have also been problems with the secure management of the collected data and the adaptability of the provided health management programs.

Means for Solving the Problems

[0005] This invention collects health-related information such as location information, physical activity data, heart rate data, and sleep data from a device, and securely transmits, integrates, and organizes that data on a cloud server. The collected data is analyzed in real time using generation AI technology, and based on the analysis results, a health management program is created that includes a user-specific exercise program, nutritional guidance, and relaxation guidance. This invention effectively supports the user's health by notifying them of the created program on the device and providing it to them. Furthermore, it aims to promote sustainable health by collecting user feedback and adapting and improving the health management program based on that feedback.

[0006] "Location information" refers to data about the user's physical location, collected through a device.

[0007] "Physical activity data" refers to data that provides information about a user's activity level, such as the amount of exercise they do, the distance they travel, and the calories they burn.

[0008] "Heart rate data" refers to information about the rhythm and speed of a user's heart rate, and is data that serves as an indicator of their health status.

[0009] "Sleep data" refers to information that shows a user's sleep patterns and sleep quality, and is used to evaluate their state of rest.

[0010] "Device" refers to smartphones and wearable devices used in a user's daily life, which are devices that collect and notify users of health-related information.

[0011] A "cloud server" is a remote server that stores, manages, and processes data via the internet, and plays a role in integrating data from multiple devices.

[0012] "Generative AI technology" is an artificial intelligence technology that uses collected data to generate individualized analyses and appropriate programs for each user.

[0013] "Analysis results" refer to information that represents conclusions and insights obtained after analysis using AI technology generated by the cloud server.

[0014] A "health management program" is a support plan that includes exercise, nutrition, and relaxation, individually designed for each user based on analysis results.

[0015] "Feedback" refers to the opinions and data that users submit regarding their actions and results based on the health management program they are provided with. [Brief explanation of the drawing]

[0016] [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] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, the numbered 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.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The AI ​​agent for integrating health data of this invention begins by utilizing smartphones and wearable devices used in the user's daily life to collect various health-related data. The device automatically records the user's physical activity, heart rate, location information, sleep patterns, etc., encrypts this data, and transmits it to a cloud server in a secure manner.

[0038] The server integrates and organizes received data in real time and performs detailed analysis using generative AI technology. The analysis process identifies user health patterns and trends and evaluates their individual health status based on these. Based on the evaluation results, the server generates a health management program that includes detailed guidance on exercise, diet, and relaxation.

[0039] The generated program is notified to the device, and the user can review its contents and incorporate them into their daily activities. For example, if a user is determined to be inactive, the device will send a notification suggesting 30 minutes of exercise daily. At the same time, the device will monitor the calories burned and achievements made during the user's travel and activities to track their progress.

[0040] Furthermore, users can provide feedback on their engagement with and experiences with the provided health management program. For example, if they find a particular meal plan difficult to follow, they can report this to the system. This feedback is then sent back to the cloud server, where the AI ​​processes it to improve the program. This ensures that users receive more appropriate health management in the long term.

[0041] Thus, the present invention provides continuous support for users' health status through real-time analysis of collected health-related data and the provision of personalized programs.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device continuously collects health-related data such as physical activity, heart rate, location information, and sleep patterns through the user's smartphone or wearable device. This includes acquiring data from sensors and GPS location information.

[0045] Step 2:

[0046] The device encrypts the collected data and sends it to the cloud server in a secure state. This transmission is performed periodically to protect user privacy.

[0047] Step 3:

[0048] The server receives the transmitted data, classifies it by user, and integrates it. It standardizes the data format and stores it in a centralized database.

[0049] Step 4:

[0050] The server uses generative AI technology to analyze integrated data in real time and evaluate users' health status and behavioral patterns. Here, anomaly detection and health trend extraction are performed through statistical analysis and machine learning.

[0051] Step 5:

[0052] Based on the analysis results, the server automatically generates a health management program optimized for the user. This program includes recommended exercise routines, meal plans, relaxation methods, and more.

[0053] Step 6:

[0054] The device notifies the user of the generated health management program. The notification is sent via push or in-app message, allowing the user to review the specific content and incorporate it into their daily activities.

[0055] Step 7:

[0056] Users follow the provided health management program and send feedback to the server via their device, inputting their progress and impressions as feedback.

[0057] Step 8:

[0058] The server collects and analyzes user feedback and adjusts and improves the health management program as needed. This adaptive process optimizes support for users.

[0059] (Example 1)

[0060] 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."

[0061] Modern lifestyles are diversifying, and there is a growing demand for appropriate healthcare tailored to individual health conditions and lifestyles. However, traditional methods have made personalized health management difficult due to the fragmentation of collected data and the lack of appropriate analytical techniques. Therefore, there is a need for technology that can efficiently and safely collect and analyze health-related data using information devices used daily, and provide users with health management programs optimized for their needs.

[0062] 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.

[0063] In this invention, the server includes means for collecting location data, activity data, biometric information, and rest data from multiple information terminals; means for transmitting the collected information to a data processing device via a communication network for integration and organization; and means for sequentially analyzing the integrated information based on digital technology. This enables the provision of an optimal health management program for each individual user, and facilitates adaptive and sustainable improvement of their health status.

[0064] An "information terminal" is an electronic device that an individual can wear or carry with them, enabling the collection and transmission of data.

[0065] "Location data" refers to data that indicates the geographical location of a user.

[0066] "Activity data" refers to data that shows the amount of physical activity and type of exercise a user engages in.

[0067] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate, blood pressure, and body temperature.

[0068] "Rest data" refers to data that shows the user's sleep patterns and rest times.

[0069] A "communication network" is an information transmission infrastructure for sending and receiving data.

[0070] A "data processing device" is a computer system used to integrate and analyze received information.

[0071] "Digital technology" refers to the technical means for electronically processing, displaying, or storing information.

[0072] A "health management program" is a proposed structure that includes specialized guidance and plans aimed at improving the health status of users.

[0073] An "encouragement message" is a word of encouragement or notification intended to motivate users.

[0074] This invention is a health management system in which an information terminal and a data processing device work in conjunction. The terminal uses electronic devices such as smartphones and wearable devices to collect the user's location data, activity data, biometric information, and rest data. This collected data is encrypted and securely transmitted to the data processing device via a communication network.

[0075] The server functions as a data processing unit, integrating received data and performing real-time analysis using a generative AI model. Specifically, it uses TENSORFLOW® or similar machine learning frameworks to analyze users' health patterns and generate personalized health management programs. These programs include exercise instructions, dietary guidance, and relaxation suggestions, tailored to each user's individual health condition.

[0076] For example, if the user enters a prompt message such as, "I'm a 30-year-old male, I do desk work and go to the gym once a week, but I'm not losing weight. I'd like some dietary advice," the system will use this information to suggest the most suitable meal plan for the user.

[0077] The generated health management program is notified to the device and provided so that users can easily refer to and implement it in their daily lives. Users can also send the results and feedback of this program to the server. Based on the collected feedback, the server uses a generated AI model to improve the program's accuracy and refine it to better suit the user.

[0078] In this way, the system constantly optimizes the user's health status and supports sustainable health management.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The device uses smartphones and wearable devices to collect user location data, activity data, biometric information, and rest data. This input data is acquired in real time using sensor functions. For example, heart rate is measured by the pulse sensor of the wearable device, and activity data is measured by an accelerometer. The acquired data is temporarily stored on the device.

[0082] Step 2:

[0083] The device encrypts the collected data and sends it to a server in the cloud using a secure communication protocol. In this step, an encryption algorithm is applied to ensure data security. The output is encrypted health-related data.

[0084] Step 3:

[0085] The server decrypts the received encrypted data, stores it in a database, and then performs data integration processing. This process aggregates data in different formats and reconstructs it into a consistent format. The input is decrypted health-related data, and the output is integrated health data.

[0086] Step 4:

[0087] The server sequentially analyzes the integrated data using a generation AI model. Here, machine learning algorithms are applied to predict user health status and trends, and identify health patterns. The input is integrated health data, and the output is the analysis results.

[0088] Step 5:

[0089] The server generates a personalized health management program based on the analysis results. The generating AI model designs exercise instructions and dietary guidance optimized for each user's individual health condition. The input is the analysis results, and the output is the health management program.

[0090] Step 6:

[0091] The device receives the generated health management program and sends a notification to the user. Upon receiving this notification, the user can review the program's contents and decide whether or not to implement it. The input is the health management program, and the output is the notification to the user.

[0092] Step 7:

[0093] Users send feedback on the program's execution status and implementation through their terminals. This feedback includes evaluations and suggestions for improvement regarding meal plans and exercise recommendations. Input is the user's feedback, and output is the feedback sent to the server.

[0094] Step 8:

[0095] The server receives user feedback and uses a generative AI model to adapt and improve the health management program. By incorporating feedback, subsequent suggestions become more personalized. The input is user feedback, and the output is the updated health management program.

[0096] (Application Example 1)

[0097] 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."

[0098] Traditional health management technologies have faced challenges in centrally utilizing diverse health data from individual users to provide personalized health guidance. Furthermore, they lacked sufficient activity recommendations that considered various urban public facilities and environmental factors, making it difficult for users to select optimal health activities. Additionally, they lacked adequate motivation to maintain users' commitment to health management.

[0099] 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.

[0100] In this invention, the server includes means for collecting location information, physical activity data, heart rate data, and sleep data from a recording device; means for recommending optimal activity locations and times to the user, taking into account the usage status of public facilities and environmental information; and means for generating motivational messages based on the user's behavior and achievement level. This enables the provision of individually optimized health management programs utilizing the user's health data, the recommendation of optimal activities based on the urban environment, and the improvement of the user's motivation for continuous health management.

[0101] "Location information" refers to data obtained by mobile devices and recording devices that indicates the user's current geographical location.

[0102] "Physical activity data" refers to data that describes a user's daily physical movements, such as the amount of exercise they do, the distance they travel, and the number of steps they take.

[0103] "Heart rate data" refers to data that shows the speed and rhythm of a user's heart rate, and is primarily used to assess their health status.

[0104] "Sleep data" refers to data collected on the user's sleep duration, quality, and patterns.

[0105] A "recording device" refers to equipment such as smartphones and wearable devices that measure and record health-related information.

[0106] A "remote server" refers to a computer server accessible via a network that stores and processes collected data.

[0107] "Data analysis technology" refers to technical methods for processing large amounts of collected data and extracting meaning and patterns.

[0108] A "health guidance program" is a program that includes guidance on exercise, nutrition management, and relaxation provided to users based on their individual health data.

[0109] "Public facility usage status" refers to information that shows the extent to which facilities such as gyms and parks within a city are being used.

[0110] "Environmental information" refers to data that shows external conditions that affect users' activities, such as weather, temperature, and road congestion.

[0111] A "motivational message" is a message of encouragement or guidance provided to users to promote continuous health management.

[0112] The system for implementing this invention mainly consists of a recording device, a remote server, and a communication network.

[0113] The recording device automatically collects the user's location information, physical activity data, heart rate data, and sleep data. This data is acquired using smartphones or wearable devices, encrypted in real time, and transmitted to a remote server.

[0114] The remote server runs a generative AI model using Python or PyTorch to analyze the health-related information sent to it. The server further integrates public facility usage data and environmental information to generate an optimal health guidance program for the user. This program includes exercise plans, nutritional guidance, and relaxation guidance.

[0115] The health guidance program generated by the server is notified to the recording device. Users can receive the program via their smartphone or smart glasses and incorporate it into their daily activities. In particular, optimal activities and visit times at places like gyms and parks in the city are recommended, allowing users to efficiently maintain a healthy lifestyle.

[0116] As an example, consider a case where a user wants to go for a run. The system takes into account the weather, road congestion, and the crowding situation at nearby parks, and makes a specific suggestion such as, "Taking advantage of today's sunny weather, we recommend a 15-minute run in the main park at 8:00 AM." In this way, users can choose the most suitable health activity based on the information provided.

[0117] An example of a prompt to the generating AI model might be, "Based on user A's activity history today and current health status, please suggest the most suitable relaxation activity." This would enable automated suggestions to be made effectively.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The device collects location information, physical activity data, heart rate data, and sleep data from the user. This raw data is obtained from sensors and GPS and secured using encryption technology. At this point, the input is raw data, and the output is encrypted data.

[0121] Step 2:

[0122] The device transmits encrypted health-related information to a remote server via a communication network. The input is encrypted data, and the output is the transmitted data packet.

[0123] Step 3:

[0124] The server decrypts the received health-related information and stores it in a database. The input is encrypted data, and the output is analyzable health-related information.

[0125] Step 4:

[0126] The server uses a generative AI model to analyze the accumulated information. It extracts users' health patterns and links them to public facility usage data and environmental information. The input is analyzable data, and the output is a proposed health guidance program optimized for the user.

[0127] Step 5:

[0128] The server generates individualized health guidance programs for each user and creates motivational messages based on the program's content. The input is a draft health guidance program, and the output is the specific health guidance program and messages.

[0129] Step 6:

[0130] The server notifies the terminal of the generated health guidance program. The input is the guidance program and message, and the output is the notification content.

[0131] Step 7:

[0132] Users receive health guidance programs through their devices and incorporate them into their daily lives. The input is the notified program, and the output is the user's healthy activities.

[0133] Step 8:

[0134] The device collects user feedback and sends it back to the server. The input is the user's feedback, and the output is the feedback data passed to the server.

[0135] Step 9:

[0136] The server analyzes the feedback and revises the health guidance program as needed. The input is the feedback data, and the output is the revised health guidance program.

[0137] 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.

[0138] This invention is a system that combines a health data integration AI agent with an emotion engine that recognizes the user's emotions. This system makes it possible to understand the user's physical and emotional state and provide more personalized health management.

[0139] In this system, terminals collect user location information, physical activity, heart rate, and sleep data through smartphones and wearable devices, as well as voice data and usage patterns. Voice data includes everyday conversations and voice input from the user. This data is encrypted and transmitted securely to a cloud server.

[0140] The server integrates the transmitted data for each user and uses generative AI technology to analyze their health status. This analysis includes an emotion engine to understand the user's emotional state. The emotion engine analyzes voice data and usage patterns to recognize the user's emotions and evaluates the emotional patterns along with their health status.

[0141] Based on the analysis results, the server generates a health management program that includes a personalized exercise program, nutritional guidance, and relaxation guidance for each user. Furthermore, it has the ability to adjust the suggestions according to the recognized emotional state. For example, it can suggest a program with enhanced relaxation elements to a user experiencing stress.

[0142] The device notifies the user of the generated health management program. This notification allows the user to receive specific activity guidelines tailored to their health and emotional state, which they can then incorporate into their daily activities. The user then executes the suggested program and inputs their progress and feedback into the system.

[0143] The server collects and analyzes user feedback and uses an emotion engine to provide encouraging and comforting messages tailored to the user's emotional state. This can boost user motivation and promote sustainable health management.

[0144] Thus, the system of the present invention integrates health-related data and emotional data to realize an improved healthcare platform that provides user-centric support.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] The device collects health-related information such as location data, physical activity data, heart rate, and sleep data through the user's smartphone or wearable device, and also records the user's voice tone and usage patterns. Voice data is obtained from normal conversations and voice interactions with the device.

[0148] Step 2:

[0149] The device encrypts the collected health-related information and voice data and securely transmits it to a cloud server. This protects data confidentiality and user privacy.

[0150] Step 3:

[0151] The server integrates and organizes the received data for each user. It standardizes the data format and stores it in a database to prepare it for health status analysis and emotion recognition.

[0152] Step 4:

[0153] The server uses generative AI technology and an emotion engine to analyze integrated data in real time. It identifies health patterns while simultaneously analyzing voice data to recognize the user's emotional state and evaluates the relationship between the two.

[0154] Step 5:

[0155] The server generates a health management program based on the user's health status and perceived emotions. For example, if stress is detected, it will suggest a program that includes specific stress relief measures, such as relaxation techniques.

[0156] Step 6:

[0157] The device notifies the user of the generated health management program and provides details. The notification arrives as a push notification or in-app message, and the user can select and incorporate the recommended actions into their daily life.

[0158] Step 7:

[0159] Users practice the actions provided in the program on a daily basis and send feedback from their device regarding their progress and perceived results. This feedback includes comments on changes in their emotions and the usefulness of the suggestions.

[0160] Step 8:

[0161] The server collects and analyzes user feedback and provides encouraging and comforting messages tailored to the user's emotional state. It also adapts and improves the health management program as needed to support the user's sustainable health management.

[0162] (Example 2)

[0163] 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".

[0164] In health management, integrating and analyzing an individual's physical and emotional data to provide a personalized and optimized health management plan has been a challenging task. Conventional systems lacked the ability to integrate diverse data and simultaneously evaluate a user's health and emotional state, making it difficult to achieve sustainable health management while maintaining user motivation.

[0165] 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.

[0166] This invention includes a server that analyzes voice data and usage patterns to recognize the user's emotions and evaluate emotional patterns along with their health status; a server that instantly analyzes health-related information using generative AI technology; and a server that collects user responses and adapts and improves health management information based on those responses. This makes it possible to provide an individually optimized health management plan that takes into account both the user's physical and emotional state.

[0167] "Location data" refers to information indicating the user's current location, and is obtained using GPS or other location-determining technologies.

[0168] "Activity data" refers to information about a user's physical movement and daily activities, and is acquired through accelerometers and activity trackers.

[0169] "Medical data" refers to physiological indicators of a user's health status, such as heart rate, blood pressure, and body temperature, and is collected from wearable devices and other sources.

[0170] "Rest data" refers to information about a user's sleep patterns and rest status, and is obtained from sleep sensors and tracking applications.

[0171] An "information processing device" is a device that collects data and provides or receives information through an interface with the user, and includes smartphones and wearable devices.

[0172] A "remote computer" is a server that provides cloud services and is connected via a network for the purpose of storing, integrating, and analyzing data.

[0173] "Machine learning technology" is an artificial intelligence technology that uses large amounts of data to detect specific patterns and anomalies and support decision-making, and includes generative AI models.

[0174] "Voice data" refers to information that records the user's voice and conversation content, and can be analyzed using speech recognition technology.

[0175] "Usage trends" refer to data that shows the habits and patterns of how users use devices and applications, and are used to analyze usage patterns.

[0176] "Health management information" refers to personalized guidance and programs on exercise, nutrition, and relaxation, tailored to each user's health condition.

[0177] "Responses" refer to feedback and evaluations of the results of actions taken by users regarding their health management information, and these are entered into the system.

[0178] This invention is a data-integrated system for individually optimizing health management. This system collects and analyzes users' physical and emotional data to provide personalized health management information. The main devices used are smartphones and wearable devices as information processing devices.

[0179] The device collects location data, activity data, medical data, and rest data from the user and periodically transmits this data to a remote computer. This uses secure communication methods employing encryption technology. This makes it possible to send data to the cloud while preventing information leaks.

[0180] The server integrates the received data and analyzes it immediately using generative AI technology. Specifically, it utilizes machine learning techniques to comprehensively evaluate the user's health status. It also uses an emotion engine that analyzes voice data and usage trends to recognize the user's emotional state and reflect it in the health information. Based on the analysis results, health management information is generated that includes individually optimized exercise programs, nutritional guidance, and relaxation guidance.

[0181] The generated health management information is then sent back to the terminal and provided to the user. The user uses this information to perform health maintenance activities and provides feedback on the results. Based on this feedback, the server continuously adapts and improves the health management information.

[0182] For example, if the emotion engine determines that a user is in a certain state of stress, the server will recommend relaxation-focused activities. An example of a prompt message might be, "Measure the user's stress level from recent voice recordings and activity data, and generate a relaxation program based on the results."

[0183] Thus, this invention enables users to receive personalized healthcare support, which can help them manage their health effectively and sustainably.

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] The device collects the user's location data, activity data, medical data, and rest data. This includes using various sensors such as GPS, accelerometers, and heart rate sensors to acquire data in real time. Input is raw data from each sensor, and output is formatted health-related information.

[0187] Step 2:

[0188] The device encrypts the collected health-related information and securely transmits it to a remote computer. The use of the SSL / TLS protocol encrypts the communication, reducing the risk of information leakage. Input is formatted health-related information, and output is encrypted data.

[0189] Step 3:

[0190] The server integrates the transmitted data and stores it in a database. Here, data organization and integration take place, generating user-specific datasets. The input is encrypted data, and the output is integrated data organized by user.

[0191] Step 4:

[0192] The server analyzes integrated data using a generation AI model. Here, it assesses the user's health status and recognizes their emotional state from voice data and usage patterns using an emotion engine. The input is integrated user data, and the output is the analysis results indicating health and emotional state.

[0193] Step 5:

[0194] The server generates personalized health management information for the user based on the analysis results. This information includes exercise programs, nutritional guidance, and relaxation instructions, tailored to the user's current condition. The input is the analysis results, and the output is the generated health management information.

[0195] Step 6:

[0196] The device notifies the user of health management information sent from the server. This information is provided in a format that the user can immediately access, such as push notifications or email. The input is the generated health management information, and the output is a notification converted into a user-accessible format.

[0197] Step 7:

[0198] Users perform daily activities based on the provided health management information and send the results and their impressions as feedback to the system. This feedback includes activity completion status, areas for improvement, and personal comments. The input is the results of activities based on the health management information, and the output is the user's feedback data.

[0199] Step 8:

[0200] The server collects and analyzes user feedback and uses an emotion engine to generate encouraging and comforting messages based on that feedback. This helps to improve user motivation and support sustainable health management. The input is user feedback data, and the output is the generated encouraging and comforting messages.

[0201] (Application Example 2)

[0202] 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".

[0203] In modern society, there is a need to precisely understand individual health conditions and provide appropriate health management at the right time. While conventional systems collect and analyze health data, they do not adequately provide personalized health management that takes into account an individual's emotional state. Furthermore, because health management that includes emotional support such as encouragement and comfort is not provided, it is difficult to maintain users' continuous motivation.

[0204] 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.

[0205] In this invention, the server includes means for collecting health-related information, including location information, physical activity information, heart rate information, and sleep information, means for transmitting the collected health-related information to a remote server, integrating and organizing it, and means for analyzing the user's emotional state from voice and behavioral patterns and adjusting the health management plan. This makes it possible to provide personalized health management and emotion-based encouragement that is tailored to the individual emotional state of the user.

[0206] "Location information" refers to coordinates and regional information that indicate the user's current location.

[0207] "Physical activity information" refers to numerical data that quantifies a user's exercise status and movement history.

[0208] "Heart rate information" refers to data that measures the user's heart rate over time.

[0209] "Sleep information" refers to data that records the user's sleep duration and sleep quality.

[0210] The term "device" refers to any equipment that collects information and enables communication.

[0211] A "remote server" is a remote computer that sends and receives information via the internet.

[0212] "Integration and organization" refers to the process of gathering multiple pieces of information and organizing them systematically.

[0213] "Generative AI technology" is a technology that uses artificial intelligence algorithms to perform data analysis.

[0214] A "health management plan" refers to specific guidelines for maintaining and improving the health of individual users.

[0215] "Notification" refers to the act of informing a user of specific information.

[0216] "Voice and behavioral patterns" refer to data that shows the user's speech and movement tendencies.

[0217] An "encouraging message" is a positive message sent with the aim of boosting the user's motivation.

[0218] To implement this invention, a smartphone or wearable device is used as the apparatus. These devices collect location information, physical activity information, heart rate information, and sleep information from the user. This information is encrypted and securely transmitted to a remote server. This server integrates the collected information using a Python®-based AI analysis engine and an emotion recognition API, and performs real-time analysis using generative AI technology.

[0219] The server generates a personalized health management plan for each user, including exercise plans, nutritional guidance, and relaxation guidance, based on analyzed health-related information and emotional data. This plan is then adjusted by a generating AI model according to the user's emotional state. Specifically, emotional states derived from voice data and behavioral patterns are considered, and enhanced relaxation is suggested for users experiencing stress.

[0220] The device notifies the user of the generated health management plan and encourages them to act as suggested. It also sends user feedback back to the server, which is used to adapt and improve the health management plan. Furthermore, it monitors the user's behavior and emotional state and provides encouraging messages based on their progress and emotions.

[0221] As a concrete example, a scenario could be imagined where an elderly person living alone receives a suggestion from the device saying, "Your heart rate seems a little fast. Shall we take some deep breaths to relax?" followed by a robot playing calming music. An example of a prompt to the generative AI model would be, "The user's heart rate has increased. Please suggest something to help them refresh."

[0222] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0223] Step 1:

[0224] The device collects location information, physical activity information, heart rate information, and sleep information from the user. This information is digital data obtained from wearable devices and smartphones, and is collected in real time through the device's sensors. The collected data is securely encrypted using encryption technology.

[0225] Step 2:

[0226] The device securely transmits encrypted health-related information to a remote server. The transmitted data is decrypted and integrated within the server. In the data integration process, each piece of data is organized chronologically and treated as a single integrated dataset.

[0227] Step 3:

[0228] The server analyzes integrated health-related information using AI technology. Here, a Python-based AI analysis engine is used to model the user's health status. The input is integrated data, and the output is a health status assessment index. Based on each index, the AI ​​model determines future health risks and necessary interventions.

[0229] Step 4:

[0230] The server analyzes the user's emotional state using an emotion recognition API based on voice data and behavioral patterns. Based on the analysis results, it makes necessary adjustments to the health management plan. In this process, the input is emotion-related data, and the output is the emotion evaluation result. A generative AI model generates an optimal health management plan based on the emotional state.

[0231] Step 5:

[0232] The server notifies the terminal of the analysis results and the adjusted health management plan. The terminal sends a notification to the user, providing specific exercise plans and relaxation guidance. The user, upon receiving the notification, adjusts their daily life based on the provided plan.

[0233] Step 6:

[0234] Users follow the provided health management plan and input feedback into their device. This feedback includes their opinions and changes in feelings regarding the plan. This feedback is then sent back to the server and used to improve future plans.

[0235] Step 7:

[0236] The server analyzes user feedback and behavioral history to generate encouraging messages tailored to the user's emotional state and health condition. Using a generative AI model, it designs messages to maintain user motivation and sends them to the user via their device.

[0237] 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.

[0238] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0239] 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.

[0240] [Second Embodiment]

[0241] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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).

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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".

[0253] The AI ​​agent for integrating health data of this invention begins by utilizing smartphones and wearable devices used in the user's daily life to collect various health-related data. The device automatically records the user's physical activity, heart rate, location information, sleep patterns, etc., encrypts this data, and transmits it to a cloud server in a secure manner.

[0254] The server integrates and organizes received data in real time and performs detailed analysis using generative AI technology. The analysis process identifies user health patterns and trends and evaluates their individual health status based on these. Based on the evaluation results, the server generates a health management program that includes detailed guidance on exercise, diet, and relaxation.

[0255] The generated program is notified to the device, and the user can review its contents and incorporate them into their daily activities. For example, if a user is determined to be inactive, the device will send a notification suggesting 30 minutes of exercise daily. At the same time, the device will monitor the calories burned and achievements made during the user's travel and activities to track their progress.

[0256] Furthermore, users can provide feedback on their engagement with and experiences with the provided health management program. For example, if they find a particular meal plan difficult to follow, they can report this to the system. This feedback is then sent back to the cloud server, where the AI ​​processes it to improve the program. This ensures that users receive more appropriate health management in the long term.

[0257] Thus, the present invention provides continuous support for users' health status through real-time analysis of collected health-related data and the provision of personalized programs.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] The device continuously collects health-related data such as physical activity, heart rate, location information, and sleep patterns through the user's smartphone or wearable device. This includes acquiring data from sensors and GPS location information.

[0261] Step 2:

[0262] The device encrypts the collected data and sends it to the cloud server in a secure state. This transmission is performed periodically to protect user privacy.

[0263] Step 3:

[0264] The server receives the transmitted data, classifies it by user, and integrates it. It standardizes the data format and stores it in a centralized database.

[0265] Step 4:

[0266] The server uses generative AI technology to analyze integrated data in real time and evaluate users' health status and behavioral patterns. Here, anomaly detection and health trend extraction are performed through statistical analysis and machine learning.

[0267] Step 5:

[0268] Based on the analysis results, the server automatically generates a health management program optimized for the user. This program includes recommended exercise routines, meal plans, relaxation methods, and more.

[0269] Step 6:

[0270] The device notifies the user of the generated health management program. The notification is sent via push or in-app message, allowing the user to review the specific content and incorporate it into their daily activities.

[0271] Step 7:

[0272] Users follow the provided health management program and send feedback to the server via their device, inputting their progress and impressions as feedback.

[0273] Step 8:

[0274] The server collects and analyzes user feedback and adjusts and improves the health management program as needed. This adaptive process optimizes support for users.

[0275] (Example 1)

[0276] 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."

[0277] Modern lifestyles are diversifying, and there is a growing demand for appropriate healthcare tailored to individual health conditions and lifestyles. However, traditional methods have made personalized health management difficult due to the fragmentation of collected data and the lack of appropriate analytical techniques. Therefore, there is a need for technology that can efficiently and safely collect and analyze health-related data using information devices used daily, and provide users with health management programs optimized for their needs.

[0278] 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.

[0279] In this invention, the server includes means for collecting location data, activity data, biometric information, and rest data from multiple information terminals; means for transmitting the collected information to a data processing device via a communication network for integration and organization; and means for sequentially analyzing the integrated information based on digital technology. This enables the provision of an optimal health management program for each individual user, and facilitates adaptive and sustainable improvement of their health status.

[0280] An "information terminal" is an electronic device that an individual can wear or carry with them, enabling the collection and transmission of data.

[0281] "Location data" refers to data that indicates the geographical location of a user.

[0282] "Activity data" refers to data indicating the amount of physical activity and types of exercise of a user.

[0283] "Biological information" refers to data indicating the physical state of a user, such as heart rate, blood pressure, body temperature, etc.

[0284] "Rest data" refers to data indicating the sleep pattern and rest time of a user.

[0285] "Communication network" refers to an information transmission infrastructure for sending and receiving data.

[0286] "Data processing device" refers to a computer system for integrating and analyzing received information.

[0287] "Digital technology" refers to technical means for electronically processing, displaying, or storing information.

[0288] "Health management program" refers to a configuration plan including guidance and plans specialized for improving the health state of a user.

[0289] "Incentive message" refers to words of encouragement or notifications aimed at motivating a user.

[0290] This invention is a health management system in which an information terminal and a data processing device cooperate to function. The terminal uses electronic devices such as smartphones and wearable devices to collect the location data, activity data, biological information, and rest data of a user. The collected data is encrypted and securely transmitted to the data processing device via a communication network.

[0291] The server functions as a data processing unit, integrating received data and performing real-time analysis using generative AI models. Specifically, it uses TensorFlow or similar machine learning frameworks to analyze users' health patterns and generate personalized health management programs. These programs include exercise instructions, dietary guidance, and relaxation suggestions, tailored to each user's individual health condition.

[0292] For example, if the user enters a prompt message such as, "I'm a 30-year-old male, I do desk work and go to the gym once a week, but I'm not losing weight. I'd like some dietary advice," the system will use this information to suggest the most suitable meal plan for the user.

[0293] The generated health management program is notified to the device and provided so that users can easily refer to and implement it in their daily lives. Users can also send the results and feedback of this program to the server. Based on the collected feedback, the server uses a generated AI model to improve the program's accuracy and refine it to better suit the user.

[0294] In this way, the system constantly optimizes the user's health status and supports sustainable health management.

[0295] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0296] Step 1:

[0297] The device uses smartphones and wearable devices to collect user location data, activity data, biometric information, and rest data. This input data is acquired in real time using sensor functions. For example, heart rate is measured by the pulse sensor of the wearable device, and activity data is measured by an accelerometer. The acquired data is temporarily stored on the device.

[0298] Step 2:

[0299] The terminal encrypts the collected data and transmits it to the server on the cloud using a secure communication protocol. In this step, an encryption algorithm is applied to ensure data security. The output is encrypted health-related data.

[0300] Step 3:

[0301] The server decrypts the received encrypted data, stores it in the database, and then performs data integration processing. Here, different forms of data are aggregated and reconfigured into a consistent form. The input is the decrypted health-related data, and the output is the integrated health data.

[0302] Step 4:

[0303] The server sequentially analyzes the integrated data using a generative AI model. Here, machine learning algorithms are applied to predict the user's health status and trends and identify health patterns. The input is the integrated health data, and the output is the analysis result.

[0304] Step 5:

[0305] The server generates a personalized health management program based on the analysis result. The generative AI model designs exercise instructions and diet guidance optimized for the user's individual health status. The input is the analysis result, and the output is the health management program.

[0306] Step 6:

[0307] The terminal receives the generated health management program and sends a notification to the user. When the user receives this notification, they can check the content of the program and decide whether to implement it. The input is the health management program, and the output is the notification to the user.

[0308] Step 7:

[0309] Users send feedback on the program's execution status and implementation through their terminals. This feedback includes evaluations and suggestions for improvement regarding meal plans and exercise recommendations. Input is the user's feedback, and output is the feedback sent to the server.

[0310] Step 8:

[0311] The server receives user feedback and uses a generative AI model to adapt and improve the health management program. By incorporating feedback, subsequent suggestions become more personalized. The input is user feedback, and the output is the updated health management program.

[0312] (Application Example 1)

[0313] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0314] Traditional health management technologies have faced challenges in centrally utilizing diverse health data from individual users to provide personalized health guidance. Furthermore, they lacked sufficient activity recommendations that considered various urban public facilities and environmental factors, making it difficult for users to select optimal health activities. Additionally, they lacked adequate motivation to maintain users' commitment to health management.

[0315] 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.

[0316] In this invention, the server includes means for collecting location information, physical activity data, heart rate data, and sleep data from a recording device; means for recommending optimal activity locations and times to the user, taking into account the usage status of public facilities and environmental information; and means for generating motivational messages based on the user's behavior and achievement level. This enables the provision of individually optimized health management programs utilizing the user's health data, the recommendation of optimal activities based on the urban environment, and the improvement of the user's motivation for continuous health management.

[0317] "Location information" refers to data obtained by mobile devices and recording devices that indicates the user's current geographical location.

[0318] "Physical activity data" refers to data that describes a user's daily physical movements, such as the amount of exercise they do, the distance they travel, and the number of steps they take.

[0319] "Heart rate data" refers to data that shows the speed and rhythm of a user's heart rate, and is primarily used to assess their health status.

[0320] "Sleep data" refers to data collected on the user's sleep duration, quality, and patterns.

[0321] A "recording device" refers to equipment such as smartphones and wearable devices that measure and record health-related information.

[0322] A "remote server" refers to a computer server accessible via a network that stores and processes collected data.

[0323] "Data analysis technology" refers to technical methods for processing large amounts of collected data and extracting meaning and patterns.

[0324] A "health guidance program" is a program that includes guidance on exercise, nutrition management, and relaxation provided to users based on their individual health data.

[0325] "Public facility usage status" refers to information that shows the extent to which facilities such as gyms and parks within a city are being used.

[0326] "Environmental information" refers to data that shows external conditions that affect users' activities, such as weather, temperature, and road congestion.

[0327] A "motivational message" is a message of encouragement or guidance provided to users to promote continuous health management.

[0328] The system for implementing this invention mainly consists of a recording device, a remote server, and a communication network.

[0329] The recording device automatically collects the user's location information, physical activity data, heart rate data, and sleep data. This data is acquired using smartphones or wearable devices, encrypted in real time, and transmitted to a remote server.

[0330] The remote server runs a generative AI model using Python or PyTorch to analyze the health-related information sent to it. The server further integrates public facility usage data and environmental information to generate an optimal health guidance program for the user. This program includes exercise plans, nutritional guidance, and relaxation guidance.

[0331] The health guidance program generated by the server is notified to the recording device. Users can receive the program via their smartphone or smart glasses and incorporate it into their daily activities. In particular, optimal activities and visit times at places like gyms and parks in the city are recommended, allowing users to efficiently maintain a healthy lifestyle.

[0332] As an example, consider a case where a user wants to go for a run. The system takes into account the weather, road congestion, and the crowding situation at nearby parks, and makes a specific suggestion such as, "Taking advantage of today's sunny weather, we recommend a 15-minute run in the main park at 8:00 AM." In this way, users can choose the most suitable health activity based on the information provided.

[0333] An example of a prompt to the generating AI model might be, "Based on user A's activity history today and current health status, please suggest the most suitable relaxation activity." This would enable automated suggestions to be made effectively.

[0334] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0335] Step 1:

[0336] The device collects location information, physical activity data, heart rate data, and sleep data from the user. This raw data is obtained from sensors and GPS and secured using encryption technology. At this point, the input is raw data, and the output is encrypted data.

[0337] Step 2:

[0338] The device transmits encrypted health-related information to a remote server via a communication network. The input is encrypted data, and the output is the transmitted data packet.

[0339] Step 3:

[0340] The server decrypts the received health-related information and stores it in a database. The input is encrypted data, and the output is analyzable health-related information.

[0341] Step 4:

[0342] The server uses a generative AI model to analyze the accumulated information. It extracts users' health patterns and links them to public facility usage data and environmental information. The input is analyzable data, and the output is a proposed health guidance program optimized for the user.

[0343] Step 5:

[0344] The server generates individualized health guidance programs for each user and creates motivational messages based on the program's content. The input is a draft health guidance program, and the output is the specific health guidance program and messages.

[0345] Step 6:

[0346] The server notifies the terminal of the generated health guidance program. The input is the guidance program and message, and the output is the notification content.

[0347] Step 7:

[0348] Users receive health guidance programs through their devices and incorporate them into their daily lives. The input is the notified program, and the output is the user's healthy activities.

[0349] Step 8:

[0350] The device collects user feedback and sends it back to the server. The input is the user's feedback, and the output is the feedback data passed to the server.

[0351] Step 9:

[0352] The server analyzes the feedback and revises the health guidance program as needed. The input is the feedback data, and the output is the revised health guidance program.

[0353] 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.

[0354] This invention is a system that combines a health data integration AI agent with an emotion engine that recognizes the user's emotions. This system makes it possible to understand the user's physical and emotional state and provide more personalized health management.

[0355] In this system, terminals collect user location information, physical activity, heart rate, and sleep data through smartphones and wearable devices, as well as voice data and usage patterns. Voice data includes everyday conversations and voice input from the user. This data is encrypted and transmitted securely to a cloud server.

[0356] The server integrates the transmitted data for each user and uses generative AI technology to analyze their health status. This analysis includes an emotion engine to understand the user's emotional state. The emotion engine analyzes voice data and usage patterns to recognize the user's emotions and evaluates the emotional patterns along with their health status.

[0357] Based on the analysis results, the server generates a health management program that includes a personalized exercise program, nutritional guidance, and relaxation guidance for each user. Furthermore, it has the ability to adjust the suggestions according to the recognized emotional state. For example, it can suggest a program with enhanced relaxation elements to a user experiencing stress.

[0358] The device notifies the user of the generated health management program. This notification allows the user to receive specific activity guidelines tailored to their health and emotional state, which they can then incorporate into their daily activities. The user then executes the suggested program and inputs their progress and feedback into the system.

[0359] The server collects and analyzes user feedback and uses an emotion engine to provide encouraging and comforting messages tailored to the user's emotional state. This can boost user motivation and promote sustainable health management.

[0360] Thus, the system of the present invention integrates health-related data and emotional data to realize an improved healthcare platform that provides user-centric support.

[0361] The following describes the processing flow.

[0362] Step 1:

[0363] The device collects health-related information such as location data, physical activity data, heart rate, and sleep data through the user's smartphone or wearable device, and also records the user's voice tone and usage patterns. Voice data is obtained from normal conversations and voice interactions with the device.

[0364] Step 2:

[0365] The device encrypts the collected health-related information and voice data and securely transmits it to a cloud server. This protects data confidentiality and user privacy.

[0366] Step 3:

[0367] The server integrates and organizes the received data for each user. It standardizes the data format and stores it in a database to prepare it for health status analysis and emotion recognition.

[0368] Step 4:

[0369] The server uses generative AI technology and an emotion engine to analyze integrated data in real time. It identifies health patterns while simultaneously analyzing voice data to recognize the user's emotional state and evaluates the relationship between the two.

[0370] Step 5:

[0371] The server generates a health management program based on the user's health status and perceived emotions. For example, if stress is detected, it will suggest a program that includes specific stress relief measures, such as relaxation techniques.

[0372] Step 6:

[0373] The device notifies the user of the generated health management program and provides details. The notification arrives as a push notification or in-app message, and the user can select and incorporate the recommended actions into their daily life.

[0374] Step 7:

[0375] Users practice the actions provided in the program on a daily basis and send feedback from their device regarding their progress and perceived results. This feedback includes comments on changes in their emotions and the usefulness of the suggestions.

[0376] Step 8:

[0377] The server collects and analyzes user feedback and provides encouraging and comforting messages tailored to the user's emotional state. It also adapts and improves the health management program as needed to support the user's sustainable health management.

[0378] (Example 2)

[0379] 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".

[0380] In health management, integrating and analyzing an individual's physical and emotional data to provide a personalized and optimized health management plan has been a challenging task. Conventional systems lacked the ability to integrate diverse data and simultaneously evaluate a user's health and emotional state, making it difficult to achieve sustainable health management while maintaining user motivation.

[0381] 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.

[0382] This invention includes a server that analyzes voice data and usage patterns to recognize the user's emotions and evaluate emotional patterns along with their health status; a server that instantly analyzes health-related information using generative AI technology; and a server that collects user responses and adapts and improves health management information based on those responses. This makes it possible to provide an individually optimized health management plan that takes into account both the user's physical and emotional state.

[0383] "Location data" refers to information indicating the user's current location, and is obtained using GPS or other location-determining technologies.

[0384] "Activity data" refers to information about a user's physical movement and daily activities, and is acquired through accelerometers and activity trackers.

[0385] "Medical data" refers to physiological indicators of a user's health status, such as heart rate, blood pressure, and body temperature, and is collected from wearable devices and other sources.

[0386] "Rest data" refers to information about a user's sleep patterns and rest status, and is obtained from sleep sensors and tracking applications.

[0387] An "information processing device" is a device that collects data and provides or receives information through an interface with the user, and includes smartphones and wearable devices.

[0388] A "remote computer" is a server that provides cloud services and is connected via a network for the purpose of storing, integrating, and analyzing data.

[0389] "Machine learning technology" is an artificial intelligence technology that uses large amounts of data to detect specific patterns and anomalies and support decision-making, and includes generative AI models.

[0390] "Voice data" refers to information that records the user's voice and conversation content, and can be analyzed using speech recognition technology.

[0391] "Usage trends" refer to data that shows the habits and patterns of how users use devices and applications, and are used to analyze usage patterns.

[0392] "Health management information" refers to personalized guidance and programs on exercise, nutrition, and relaxation, tailored to each user's health condition.

[0393] "Responses" refer to feedback and evaluations of the results of actions taken by users regarding their health management information, and these are entered into the system.

[0394] This invention is a data-integrated system for individually optimizing health management. This system collects and analyzes users' physical and emotional data to provide personalized health management information. The main devices used are smartphones and wearable devices as information processing devices.

[0395] The device collects location data, activity data, medical data, and rest data from the user and periodically transmits this data to a remote computer. This uses secure communication methods employing encryption technology. This makes it possible to send data to the cloud while preventing information leaks.

[0396] The server integrates the received data and analyzes it immediately using generative AI technology. Specifically, it utilizes machine learning techniques to comprehensively evaluate the user's health status. It also uses an emotion engine that analyzes voice data and usage trends to recognize the user's emotional state and reflect it in the health information. Based on the analysis results, health management information is generated that includes individually optimized exercise programs, nutritional guidance, and relaxation guidance.

[0397] The generated health management information is then sent back to the terminal and provided to the user. The user uses this information to perform health maintenance activities and provides feedback on the results. Based on this feedback, the server continuously adapts and improves the health management information.

[0398] For example, if the emotion engine determines that a user is in a certain state of stress, the server will recommend relaxation-focused activities. An example of a prompt message might be, "Measure the user's stress level from recent voice recordings and activity data, and generate a relaxation program based on the results."

[0399] Thus, this invention enables users to receive personalized healthcare support, which can help them manage their health effectively and sustainably.

[0400] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0401] Step 1:

[0402] The device collects the user's location data, activity data, medical data, and rest data. This includes using various sensors such as GPS, accelerometers, and heart rate sensors to acquire data in real time. Input is raw data from each sensor, and output is formatted health-related information.

[0403] Step 2:

[0404] The device encrypts the collected health-related information and securely transmits it to a remote computer. The use of the SSL / TLS protocol encrypts the communication, reducing the risk of information leakage. Input is formatted health-related information, and output is encrypted data.

[0405] Step 3:

[0406] The server integrates the transmitted data and stores it in a database. Here, data organization and integration take place, generating user-specific datasets. The input is encrypted data, and the output is integrated data organized by user.

[0407] Step 4:

[0408] The server analyzes integrated data using a generation AI model. Here, it assesses the user's health status and recognizes their emotional state from voice data and usage patterns using an emotion engine. The input is integrated user data, and the output is the analysis results indicating health and emotional state.

[0409] Step 5:

[0410] The server generates personalized health management information for the user based on the analysis results. This information includes exercise programs, nutritional guidance, and relaxation instructions, tailored to the user's current condition. The input is the analysis results, and the output is the generated health management information.

[0411] Step 6:

[0412] The device notifies the user of health management information sent from the server. This information is provided in a format that the user can immediately access, such as push notifications or email. The input is the generated health management information, and the output is a notification converted into a user-accessible format.

[0413] Step 7:

[0414] Users perform daily activities based on the provided health management information and send the results and their impressions as feedback to the system. This feedback includes activity completion status, areas for improvement, and personal comments. The input is the results of activities based on the health management information, and the output is the user's feedback data.

[0415] Step 8:

[0416] The server collects and analyzes user feedback and uses an emotion engine to generate encouraging and comforting messages based on that feedback. This helps to improve user motivation and support sustainable health management. The input is user feedback data, and the output is the generated encouraging and comforting messages.

[0417] (Application Example 2)

[0418] 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."

[0419] In modern society, there is a need to precisely understand individual health conditions and provide appropriate health management at the right time. While conventional systems collect and analyze health data, they do not adequately provide personalized health management that takes into account an individual's emotional state. Furthermore, because health management that includes emotional support such as encouragement and comfort is not provided, it is difficult to maintain users' continuous motivation.

[0420] 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.

[0421] In this invention, the server includes means for collecting health-related information, including location information, physical activity information, heart rate information, and sleep information, means for transmitting the collected health-related information to a remote server, integrating and organizing it, and means for analyzing the user's emotional state from voice and behavioral patterns and adjusting the health management plan. This makes it possible to provide personalized health management and emotion-based encouragement that is tailored to the individual emotional state of the user.

[0422] "Location information" refers to coordinates and regional information that indicate the user's current location.

[0423] "Physical activity information" refers to numerical data that quantifies a user's exercise status and movement history.

[0424] "Heart rate information" refers to data that measures the user's heart rate over time.

[0425] "Sleep information" refers to data that records the user's sleep duration and sleep quality.

[0426] The term "device" refers to any equipment that collects information and enables communication.

[0427] A "remote server" is a remote computer that sends and receives information via the internet.

[0428] "Integration and organization" refers to the process of gathering multiple pieces of information and organizing them systematically.

[0429] "Generative AI technology" is a technology that uses artificial intelligence algorithms to perform data analysis.

[0430] A "health management plan" refers to specific guidelines for maintaining and improving the health of individual users.

[0431] "Notification" refers to the act of informing a user of specific information.

[0432] "Voice and behavioral patterns" refer to data that shows the user's speech and movement tendencies.

[0433] An "encouraging message" is a positive message sent with the aim of boosting the user's motivation.

[0434] To implement this invention, a smartphone or wearable device is used as the apparatus. These devices collect location information, physical activity information, heart rate information, and sleep information from the user. This information is encrypted and securely transmitted to a remote server. This server integrates the collected information using a Python-based AI analysis engine and an emotion recognition API, and performs real-time analysis using generative AI technology.

[0435] The server generates a personalized health management plan for each user, including exercise plans, nutritional guidance, and relaxation guidance, based on analyzed health-related information and emotional data. This plan is then adjusted by a generating AI model according to the user's emotional state. Specifically, emotional states derived from voice data and behavioral patterns are considered, and enhanced relaxation is suggested for users experiencing stress.

[0436] The device notifies the user of the generated health management plan and encourages them to act as suggested. It also sends user feedback back to the server, which is used to adapt and improve the health management plan. Furthermore, it monitors the user's behavior and emotional state and provides encouraging messages based on their progress and emotions.

[0437] As a concrete example, a scenario could be imagined where an elderly person living alone receives a suggestion from the device saying, "Your heart rate seems a little fast. Shall we take some deep breaths to relax?" followed by a robot playing calming music. An example of a prompt to the generative AI model would be, "The user's heart rate has increased. Please suggest something to help them refresh."

[0438] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0439] Step 1:

[0440] The device collects location information, physical activity information, heart rate information, and sleep information from the user. This information is digital data obtained from wearable devices and smartphones, and is collected in real time through the device's sensors. The collected data is securely encrypted using encryption technology.

[0441] Step 2:

[0442] The device securely transmits encrypted health-related information to a remote server. The transmitted data is decrypted and integrated within the server. In the data integration process, each piece of data is organized chronologically and treated as a single integrated dataset.

[0443] Step 3:

[0444] The server analyzes integrated health-related information using AI technology. Here, a Python-based AI analysis engine is used to model the user's health status. The input is integrated data, and the output is a health status assessment index. Based on each index, the AI ​​model determines future health risks and necessary interventions.

[0445] Step 4:

[0446] The server analyzes the user's emotional state using an emotion recognition API based on voice data and behavioral patterns. Based on the analysis results, it makes necessary adjustments to the health management plan. In this process, the input is emotion-related data, and the output is the emotion evaluation result. A generative AI model generates an optimal health management plan based on the emotional state.

[0447] Step 5:

[0448] The server notifies the terminal of the analysis results and the adjusted health management plan. The terminal sends a notification to the user, providing specific exercise plans and relaxation guidance. The user, upon receiving the notification, adjusts their daily life based on the provided plan.

[0449] Step 6:

[0450] Users follow the provided health management plan and input feedback into their device. This feedback includes their opinions and changes in feelings regarding the plan. This feedback is then sent back to the server and used to improve future plans.

[0451] Step 7:

[0452] The server analyzes user feedback and behavioral history to generate encouraging messages tailored to the user's emotional state and health condition. Using a generative AI model, it designs messages to maintain user motivation and sends them to the user via their device.

[0453] 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.

[0454] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0455] 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.

[0456] [Third Embodiment]

[0457] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0458] 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.

[0459] 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).

[0460] 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.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] 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".

[0469] The AI ​​agent for integrating health data of this invention begins by utilizing smartphones and wearable devices used in the user's daily life to collect various health-related data. The device automatically records the user's physical activity, heart rate, location information, sleep patterns, etc., encrypts this data, and transmits it to a cloud server in a secure manner.

[0470] The server integrates and organizes received data in real time and performs detailed analysis using generative AI technology. The analysis process identifies user health patterns and trends and evaluates their individual health status based on these. Based on the evaluation results, the server generates a health management program that includes detailed guidance on exercise, diet, and relaxation.

[0471] The generated program is notified to the device, and the user can review its contents and incorporate them into their daily activities. For example, if a user is determined to be inactive, the device will send a notification suggesting 30 minutes of exercise daily. At the same time, the device will monitor the calories burned and achievements made during the user's travel and activities to track their progress.

[0472] Furthermore, users can provide feedback on their engagement with and experiences with the provided health management program. For example, if they find a particular meal plan difficult to follow, they can report this to the system. This feedback is then sent back to the cloud server, where the AI ​​processes it to improve the program. This ensures that users receive more appropriate health management in the long term.

[0473] Thus, the present invention provides continuous support for users' health status through real-time analysis of collected health-related data and the provision of personalized programs.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] The device continuously collects health-related data such as physical activity, heart rate, location information, and sleep patterns through the user's smartphone or wearable device. This includes acquiring data from sensors and GPS location information.

[0477] Step 2:

[0478] The device encrypts the collected data and sends it to the cloud server in a secure state. This transmission is performed periodically to protect user privacy.

[0479] Step 3:

[0480] The server receives the transmitted data, classifies it by user, and integrates it. It standardizes the data format and stores it in a centralized database.

[0481] Step 4:

[0482] The server uses generative AI technology to analyze integrated data in real time and evaluate users' health status and behavioral patterns. Here, anomaly detection and health trend extraction are performed through statistical analysis and machine learning.

[0483] Step 5:

[0484] Based on the analysis results, the server automatically generates a health management program optimized for the user. This program includes recommended exercise routines, meal plans, relaxation methods, and more.

[0485] Step 6:

[0486] The device notifies the user of the generated health management program. The notification is sent via push or in-app message, allowing the user to review the specific content and incorporate it into their daily activities.

[0487] Step 7:

[0488] Users follow the provided health management program and send feedback to the server via their device, inputting their progress and impressions as feedback.

[0489] Step 8:

[0490] The server collects and analyzes user feedback and adjusts and improves the health management program as needed. This adaptive process optimizes support for users.

[0491] (Example 1)

[0492] 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."

[0493] Modern lifestyles are diversifying, and there is a growing demand for appropriate healthcare tailored to individual health conditions and lifestyles. However, traditional methods have made personalized health management difficult due to the fragmentation of collected data and the lack of appropriate analytical techniques. Therefore, there is a need for technology that can efficiently and safely collect and analyze health-related data using information devices used daily, and provide users with health management programs optimized for their needs.

[0494] 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.

[0495] In this invention, the server includes means for collecting location data, activity data, biometric information, and rest data from multiple information terminals; means for transmitting the collected information to a data processing device via a communication network for integration and organization; and means for sequentially analyzing the integrated information based on digital technology. This enables the provision of an optimal health management program for each individual user, and facilitates adaptive and sustainable improvement of their health status.

[0496] An "information terminal" is an electronic device that an individual can wear or carry with them, enabling the collection and transmission of data.

[0497] "Location data" refers to data that indicates the geographical location of a user.

[0498] "Activity data" refers to data that shows the amount of physical activity and type of exercise a user engages in.

[0499] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate, blood pressure, and body temperature.

[0500] "Rest data" refers to data that shows the user's sleep patterns and rest times.

[0501] A "communication network" is an information transmission infrastructure for sending and receiving data.

[0502] A "data processing device" is a computer system used to integrate and analyze received information.

[0503] "Digital technology" refers to the technical means for electronically processing, displaying, or storing information.

[0504] A "health management program" is a proposed structure that includes specialized guidance and plans aimed at improving the health status of users.

[0505] An "encouragement message" is a word of encouragement or notification intended to motivate users.

[0506] This invention is a health management system in which an information terminal and a data processing device work in conjunction. The terminal uses electronic devices such as smartphones and wearable devices to collect the user's location data, activity data, biometric information, and rest data. This collected data is encrypted and securely transmitted to the data processing device via a communication network.

[0507] The server functions as a data processing unit, integrating received data and performing real-time analysis using generative AI models. Specifically, it uses TensorFlow or similar machine learning frameworks to analyze users' health patterns and generate personalized health management programs. These programs include exercise instructions, dietary guidance, and relaxation suggestions, tailored to each user's individual health condition.

[0508] For example, if the user enters a prompt message such as, "I'm a 30-year-old male, I do desk work and go to the gym once a week, but I'm not losing weight. I'd like some dietary advice," the system will use this information to suggest the most suitable meal plan for the user.

[0509] The generated health management program is notified to the device and provided so that users can easily refer to and implement it in their daily lives. Users can also send the results and feedback of this program to the server. Based on the collected feedback, the server uses a generated AI model to improve the program's accuracy and refine it to better suit the user.

[0510] In this way, the system constantly optimizes the user's health status and supports sustainable health management.

[0511] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0512] Step 1:

[0513] The device uses smartphones and wearable devices to collect user location data, activity data, biometric information, and rest data. This input data is acquired in real time using sensor functions. For example, heart rate is measured by the pulse sensor of the wearable device, and activity data is measured by an accelerometer. The acquired data is temporarily stored on the device.

[0514] Step 2:

[0515] The device encrypts the collected data and sends it to a server in the cloud using a secure communication protocol. In this step, an encryption algorithm is applied to ensure data security. The output is encrypted health-related data.

[0516] Step 3:

[0517] The server decrypts the received encrypted data, stores it in a database, and then performs data integration processing. This process aggregates data in different formats and reconstructs it into a consistent format. The input is decrypted health-related data, and the output is integrated health data.

[0518] Step 4:

[0519] The server sequentially analyzes the integrated data using a generation AI model. Here, machine learning algorithms are applied to predict user health status and trends, and identify health patterns. The input is integrated health data, and the output is the analysis results.

[0520] Step 5:

[0521] The server generates a personalized health management program based on the analysis results. The generating AI model designs exercise instructions and dietary guidance optimized for each user's individual health condition. The input is the analysis results, and the output is the health management program.

[0522] Step 6:

[0523] The device receives the generated health management program and sends a notification to the user. Upon receiving this notification, the user can review the program's contents and decide whether or not to implement it. The input is the health management program, and the output is the notification to the user.

[0524] Step 7:

[0525] Users send feedback on the program's execution status and implementation through their terminals. This feedback includes evaluations and suggestions for improvement regarding meal plans and exercise recommendations. Input is the user's feedback, and output is the feedback sent to the server.

[0526] Step 8:

[0527] The server receives user feedback and uses a generative AI model to adapt and improve the health management program. By incorporating feedback, subsequent suggestions become more personalized. The input is user feedback, and the output is the updated health management program.

[0528] (Application Example 1)

[0529] 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."

[0530] Traditional health management technologies have faced challenges in centrally utilizing diverse health data from individual users to provide personalized health guidance. Furthermore, they lacked sufficient activity recommendations that considered various urban public facilities and environmental factors, making it difficult for users to select optimal health activities. Additionally, they lacked adequate motivation to maintain users' commitment to health management.

[0531] 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.

[0532] In this invention, the server includes means for collecting location information, physical activity data, heart rate data, and sleep data from a recording device; means for recommending optimal activity locations and times to the user, taking into account the usage status of public facilities and environmental information; and means for generating motivational messages based on the user's behavior and achievement level. This enables the provision of individually optimized health management programs utilizing the user's health data, the recommendation of optimal activities based on the urban environment, and the improvement of the user's motivation for continuous health management.

[0533] "Location information" refers to data obtained by mobile devices and recording devices that indicates the user's current geographical location.

[0534] "Physical activity data" refers to data that describes a user's daily physical movements, such as the amount of exercise they do, the distance they travel, and the number of steps they take.

[0535] "Heart rate data" refers to data that shows the speed and rhythm of a user's heart rate, and is primarily used to assess their health status.

[0536] "Sleep data" refers to data collected on the user's sleep duration, quality, and patterns.

[0537] A "recording device" refers to equipment such as smartphones and wearable devices that measure and record health-related information.

[0538] A "remote server" refers to a computer server accessible via a network that stores and processes collected data.

[0539] "Data analysis technology" refers to technical methods for processing large amounts of collected data and extracting meaning and patterns.

[0540] A "health guidance program" is a program that includes guidance on exercise, nutrition management, and relaxation provided to users based on their individual health data.

[0541] "Public facility usage status" refers to information that shows the extent to which facilities such as gyms and parks within a city are being used.

[0542] "Environmental information" refers to data that shows external conditions that affect users' activities, such as weather, temperature, and road congestion.

[0543] A "motivational message" is a message of encouragement or guidance provided to users to promote continuous health management.

[0544] The system for implementing this invention mainly consists of a recording device, a remote server, and a communication network.

[0545] The recording device automatically collects the user's location information, physical activity data, heart rate data, and sleep data. This data is acquired using smartphones or wearable devices, encrypted in real time, and transmitted to a remote server.

[0546] The remote server runs a generative AI model using Python or PyTorch to analyze the health-related information sent to it. The server further integrates public facility usage data and environmental information to generate an optimal health guidance program for the user. This program includes exercise plans, nutritional guidance, and relaxation guidance.

[0547] The health guidance program generated by the server is notified to the recording device. Users can receive the program via their smartphone or smart glasses and incorporate it into their daily activities. In particular, optimal activities and visit times at places like gyms and parks in the city are recommended, allowing users to efficiently maintain a healthy lifestyle.

[0548] As an example, consider a case where a user wants to go for a run. The system takes into account the weather, road congestion, and the crowding situation at nearby parks, and makes a specific suggestion such as, "Taking advantage of today's sunny weather, we recommend a 15-minute run in the main park at 8:00 AM." In this way, users can choose the most suitable health activity based on the information provided.

[0549] An example of a prompt to the generating AI model might be, "Based on user A's activity history today and current health status, please suggest the most suitable relaxation activity." This would enable automated suggestions to be made effectively.

[0550] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0551] Step 1:

[0552] The device collects location information, physical activity data, heart rate data, and sleep data from the user. This raw data is obtained from sensors and GPS and secured using encryption technology. At this point, the input is raw data, and the output is encrypted data.

[0553] Step 2:

[0554] The device transmits encrypted health-related information to a remote server via a communication network. The input is encrypted data, and the output is the transmitted data packet.

[0555] Step 3:

[0556] The server decrypts the received health-related information and stores it in a database. The input is encrypted data, and the output is analyzable health-related information.

[0557] Step 4:

[0558] The server uses a generative AI model to analyze the accumulated information. It extracts users' health patterns and links them to public facility usage data and environmental information. The input is analyzable data, and the output is a proposed health guidance program optimized for the user.

[0559] Step 5:

[0560] The server generates individualized health guidance programs for each user and creates motivational messages based on the program's content. The input is a draft health guidance program, and the output is the specific health guidance program and messages.

[0561] Step 6:

[0562] The server notifies the terminal of the generated health guidance program. The input is the guidance program and message, and the output is the notification content.

[0563] Step 7:

[0564] Users receive health guidance programs through their devices and incorporate them into their daily lives. The input is the notified program, and the output is the user's healthy activities.

[0565] Step 8:

[0566] The device collects user feedback and sends it back to the server. The input is the user's feedback, and the output is the feedback data passed to the server.

[0567] Step 9:

[0568] The server analyzes the feedback and revises the health guidance program as needed. The input is the feedback data, and the output is the revised health guidance program.

[0569] 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.

[0570] This invention is a system that combines a health data integration AI agent with an emotion engine that recognizes the user's emotions. This system makes it possible to understand the user's physical and emotional state and provide more personalized health management.

[0571] In this system, terminals collect user location information, physical activity, heart rate, and sleep data through smartphones and wearable devices, as well as voice data and usage patterns. Voice data includes everyday conversations and voice input from the user. This data is encrypted and transmitted securely to a cloud server.

[0572] The server integrates the transmitted data for each user and uses generative AI technology to analyze their health status. This analysis includes an emotion engine to understand the user's emotional state. The emotion engine analyzes voice data and usage patterns to recognize the user's emotions and evaluates the emotional patterns along with their health status.

[0573] Based on the analysis results, the server generates a health management program that includes a personalized exercise program, nutritional guidance, and relaxation guidance for each user. Furthermore, it has the ability to adjust the suggestions according to the recognized emotional state. For example, it can suggest a program with enhanced relaxation elements to a user experiencing stress.

[0574] The device notifies the user of the generated health management program. This notification allows the user to receive specific activity guidelines tailored to their health and emotional state, which they can then incorporate into their daily activities. The user then executes the suggested program and inputs their progress and feedback into the system.

[0575] The server collects and analyzes user feedback and uses an emotion engine to provide encouraging and comforting messages tailored to the user's emotional state. This can boost user motivation and promote sustainable health management.

[0576] Thus, the system of the present invention integrates health-related data and emotional data to realize an improved healthcare platform that provides user-centric support.

[0577] The following describes the processing flow.

[0578] Step 1:

[0579] The device collects health-related information such as location data, physical activity data, heart rate, and sleep data through the user's smartphone or wearable device, and also records the user's voice tone and usage patterns. Voice data is obtained from normal conversations and voice interactions with the device.

[0580] Step 2:

[0581] The device encrypts the collected health-related information and voice data and securely transmits it to a cloud server. This protects data confidentiality and user privacy.

[0582] Step 3:

[0583] The server integrates and organizes the received data for each user. It standardizes the data format and stores it in a database to prepare it for health status analysis and emotion recognition.

[0584] Step 4:

[0585] The server uses generative AI technology and an emotion engine to analyze integrated data in real time. It identifies health patterns while simultaneously analyzing voice data to recognize the user's emotional state and evaluates the relationship between the two.

[0586] Step 5:

[0587] The server generates a health management program based on the user's health status and perceived emotions. For example, if stress is detected, it will suggest a program that includes specific stress relief measures, such as relaxation techniques.

[0588] Step 6:

[0589] The device notifies the user of the generated health management program and provides details. The notification arrives as a push notification or in-app message, and the user can select and incorporate the recommended actions into their daily life.

[0590] Step 7:

[0591] Users practice the actions provided in the program on a daily basis and send feedback from their device regarding their progress and perceived results. This feedback includes comments on changes in their emotions and the usefulness of the suggestions.

[0592] Step 8:

[0593] The server collects and analyzes user feedback and provides encouraging and comforting messages tailored to the user's emotional state. It also adapts and improves the health management program as needed to support the user's sustainable health management.

[0594] (Example 2)

[0595] 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."

[0596] In health management, integrating and analyzing an individual's physical and emotional data to provide a personalized and optimized health management plan has been a challenging task. Conventional systems lacked the ability to integrate diverse data and simultaneously evaluate a user's health and emotional state, making it difficult to achieve sustainable health management while maintaining user motivation.

[0597] 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.

[0598] This invention includes a server that analyzes voice data and usage patterns to recognize the user's emotions and evaluate emotional patterns along with their health status; a server that instantly analyzes health-related information using generative AI technology; and a server that collects user responses and adapts and improves health management information based on those responses. This makes it possible to provide an individually optimized health management plan that takes into account both the user's physical and emotional state.

[0599] "Location data" refers to information indicating the user's current location, and is obtained using GPS or other location-determining technologies.

[0600] "Activity data" refers to information about a user's physical movement and daily activities, and is acquired through accelerometers and activity trackers.

[0601] "Medical data" refers to physiological indicators of a user's health status, such as heart rate, blood pressure, and body temperature, and is collected from wearable devices and other sources.

[0602] "Rest data" refers to information about a user's sleep patterns and rest status, and is obtained from sleep sensors and tracking applications.

[0603] An "information processing device" is a device that collects data and provides or receives information through an interface with the user, and includes smartphones and wearable devices.

[0604] A "remote computer" is a server that provides cloud services and is connected via a network for the purpose of storing, integrating, and analyzing data.

[0605] "Machine learning technology" is an artificial intelligence technology that uses large amounts of data to detect specific patterns and anomalies and support decision-making, and includes generative AI models.

[0606] "Voice data" refers to information that records the user's voice and conversation content, and can be analyzed using speech recognition technology.

[0607] "Usage trends" refer to data that shows the habits and patterns of how users use devices and applications, and are used to analyze usage patterns.

[0608] "Health management information" refers to personalized guidance and programs on exercise, nutrition, and relaxation, tailored to each user's health condition.

[0609] "Responses" refer to feedback and evaluations of the results of actions taken by users regarding their health management information, and these are entered into the system.

[0610] This invention is a data-integrated system for individually optimizing health management. This system collects and analyzes users' physical and emotional data to provide personalized health management information. The main devices used are smartphones and wearable devices as information processing devices.

[0611] The device collects location data, activity data, medical data, and rest data from the user and periodically transmits this data to a remote computer. This uses secure communication methods employing encryption technology. This makes it possible to send data to the cloud while preventing information leaks.

[0612] The server integrates the received data and analyzes it immediately using generative AI technology. Specifically, it utilizes machine learning techniques to comprehensively evaluate the user's health status. It also uses an emotion engine that analyzes voice data and usage trends to recognize the user's emotional state and reflect it in the health information. Based on the analysis results, health management information is generated that includes individually optimized exercise programs, nutritional guidance, and relaxation guidance.

[0613] The generated health management information is then sent back to the terminal and provided to the user. The user uses this information to perform health maintenance activities and provides feedback on the results. Based on this feedback, the server continuously adapts and improves the health management information.

[0614] For example, if the emotion engine determines that a user is in a certain state of stress, the server will recommend relaxation-focused activities. An example of a prompt message might be, "Measure the user's stress level from recent voice recordings and activity data, and generate a relaxation program based on the results."

[0615] Thus, this invention enables users to receive personalized healthcare support, which can help them manage their health effectively and sustainably.

[0616] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0617] Step 1:

[0618] The device collects the user's location data, activity data, medical data, and rest data. This includes using various sensors such as GPS, accelerometers, and heart rate sensors to acquire data in real time. Input is raw data from each sensor, and output is formatted health-related information.

[0619] Step 2:

[0620] The device encrypts the collected health-related information and securely transmits it to a remote computer. The use of the SSL / TLS protocol encrypts the communication, reducing the risk of information leakage. Input is formatted health-related information, and output is encrypted data.

[0621] Step 3:

[0622] The server integrates the transmitted data and stores it in a database. Here, data organization and integration take place, generating user-specific datasets. The input is encrypted data, and the output is integrated data organized by user.

[0623] Step 4:

[0624] The server analyzes integrated data using a generation AI model. Here, it assesses the user's health status and recognizes their emotional state from voice data and usage patterns using an emotion engine. The input is integrated user data, and the output is the analysis results indicating health and emotional state.

[0625] Step 5:

[0626] The server generates personalized health management information for the user based on the analysis results. This information includes exercise programs, nutritional guidance, and relaxation instructions, tailored to the user's current condition. The input is the analysis results, and the output is the generated health management information.

[0627] Step 6:

[0628] The device notifies the user of health management information sent from the server. This information is provided in a format that the user can immediately access, such as push notifications or email. The input is the generated health management information, and the output is a notification converted into a user-accessible format.

[0629] Step 7:

[0630] Users perform daily activities based on the provided health management information and send the results and their impressions as feedback to the system. This feedback includes activity completion status, areas for improvement, and personal comments. The input is the results of activities based on the health management information, and the output is the user's feedback data.

[0631] Step 8:

[0632] The server collects and analyzes user feedback and uses an emotion engine to generate encouraging and comforting messages based on that feedback. This helps to improve user motivation and support sustainable health management. The input is user feedback data, and the output is the generated encouraging and comforting messages.

[0633] (Application Example 2)

[0634] 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."

[0635] In modern society, there is a need to precisely understand individual health conditions and provide appropriate health management at the right time. While conventional systems collect and analyze health data, they do not adequately provide personalized health management that takes into account an individual's emotional state. Furthermore, because health management that includes emotional support such as encouragement and comfort is not provided, it is difficult to maintain users' continuous motivation.

[0636] 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.

[0637] In this invention, the server includes means for collecting health-related information, including location information, physical activity information, heart rate information, and sleep information, means for transmitting the collected health-related information to a remote server, integrating and organizing it, and means for analyzing the user's emotional state from voice and behavioral patterns and adjusting the health management plan. This makes it possible to provide personalized health management and emotion-based encouragement that is tailored to the individual emotional state of the user.

[0638] "Location information" refers to coordinates and regional information that indicate the user's current location.

[0639] "Physical activity information" refers to numerical data that quantifies a user's exercise status and movement history.

[0640] "Heart rate information" refers to data that measures the user's heart rate over time.

[0641] "Sleep information" refers to data that records the user's sleep duration and sleep quality.

[0642] The term "device" refers to any equipment that collects information and enables communication.

[0643] A "remote server" is a remote computer that sends and receives information via the internet.

[0644] "Integration and organization" refers to the process of gathering multiple pieces of information and organizing them systematically.

[0645] "Generative AI technology" is a technology that uses artificial intelligence algorithms to perform data analysis.

[0646] A "health management plan" refers to specific guidelines for maintaining and improving the health of individual users.

[0647] "Notification" refers to the act of informing a user of specific information.

[0648] "Voice and behavioral patterns" refer to data that shows the user's speech and movement tendencies.

[0649] An "encouraging message" is a positive message sent with the aim of boosting the user's motivation.

[0650] To implement this invention, a smartphone or wearable device is used as the apparatus. These devices collect location information, physical activity information, heart rate information, and sleep information from the user. This information is encrypted and securely transmitted to a remote server. This server integrates the collected information using a Python-based AI analysis engine and an emotion recognition API, and performs real-time analysis using generative AI technology.

[0651] The server generates a personalized health management plan for each user, including exercise plans, nutritional guidance, and relaxation guidance, based on analyzed health-related information and emotional data. This plan is then adjusted by a generating AI model according to the user's emotional state. Specifically, emotional states derived from voice data and behavioral patterns are considered, and enhanced relaxation is suggested for users experiencing stress.

[0652] The device notifies the user of the generated health management plan and encourages them to act as suggested. It also sends user feedback back to the server, which is used to adapt and improve the health management plan. Furthermore, it monitors the user's behavior and emotional state and provides encouraging messages based on their progress and emotions.

[0653] As a concrete example, a scenario could be imagined where an elderly person living alone receives a suggestion from the device saying, "Your heart rate seems a little fast. Shall we take some deep breaths to relax?" followed by a robot playing calming music. An example of a prompt to the generative AI model would be, "The user's heart rate has increased. Please suggest something to help them refresh."

[0654] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0655] Step 1:

[0656] The device collects location information, physical activity information, heart rate information, and sleep information from the user. This information is digital data obtained from wearable devices and smartphones, and is collected in real time through the device's sensors. The collected data is securely encrypted using encryption technology.

[0657] Step 2:

[0658] The device securely transmits encrypted health-related information to a remote server. The transmitted data is decrypted and integrated within the server. In the data integration process, each piece of data is organized chronologically and treated as a single integrated dataset.

[0659] Step 3:

[0660] The server analyzes integrated health-related information using AI technology. Here, a Python-based AI analysis engine is used to model the user's health status. The input is integrated data, and the output is a health status assessment index. Based on each index, the AI ​​model determines future health risks and necessary interventions.

[0661] Step 4:

[0662] The server analyzes the user's emotional state using an emotion recognition API based on voice data and behavioral patterns. Based on the analysis results, it makes necessary adjustments to the health management plan. In this process, the input is emotion-related data, and the output is the emotion evaluation result. A generative AI model generates an optimal health management plan based on the emotional state.

[0663] Step 5:

[0664] The server notifies the terminal of the analysis results and the adjusted health management plan. The terminal sends a notification to the user, providing specific exercise plans and relaxation guidance. The user, upon receiving the notification, adjusts their daily life based on the provided plan.

[0665] Step 6:

[0666] Users follow the provided health management plan and input feedback into their device. This feedback includes their opinions and changes in feelings regarding the plan. This feedback is then sent back to the server and used to improve future plans.

[0667] Step 7:

[0668] The server analyzes user feedback and behavioral history to generate encouraging messages tailored to the user's emotional state and health condition. Using a generative AI model, it designs messages to maintain user motivation and sends them to the user via their device.

[0669] 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.

[0670] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0671] 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.

[0672] [Fourth Embodiment]

[0673] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0674] 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.

[0675] 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).

[0676] 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.

[0677] 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.

[0678] 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).

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] 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.

[0684] 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.

[0685] 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".

[0686] The AI ​​agent for integrating health data of this invention begins by utilizing smartphones and wearable devices used in the user's daily life to collect various health-related data. The device automatically records the user's physical activity, heart rate, location information, sleep patterns, etc., encrypts this data, and transmits it to a cloud server in a secure manner.

[0687] The server integrates and organizes received data in real time and performs detailed analysis using generative AI technology. The analysis process identifies user health patterns and trends and evaluates their individual health status based on these. Based on the evaluation results, the server generates a health management program that includes detailed guidance on exercise, diet, and relaxation.

[0688] The generated program is notified to the device, and the user can review its contents and incorporate them into their daily activities. For example, if a user is determined to be inactive, the device will send a notification suggesting 30 minutes of exercise daily. At the same time, the device will monitor the calories burned and achievements made during the user's travel and activities to track their progress.

[0689] Furthermore, users can provide feedback on their engagement with and experiences with the provided health management program. For example, if they find a particular meal plan difficult to follow, they can report this to the system. This feedback is then sent back to the cloud server, where the AI ​​processes it to improve the program. This ensures that users receive more appropriate health management in the long term.

[0690] Thus, the present invention provides continuous support for users' health status through real-time analysis of collected health-related data and the provision of personalized programs.

[0691] The following describes the processing flow.

[0692] Step 1:

[0693] The device continuously collects health-related data such as physical activity, heart rate, location information, and sleep patterns through the user's smartphone or wearable device. This includes acquiring data from sensors and GPS location information.

[0694] Step 2:

[0695] The device encrypts the collected data and sends it to the cloud server in a secure state. This transmission is performed periodically to protect user privacy.

[0696] Step 3:

[0697] The server receives the transmitted data, classifies it by user, and integrates it. It standardizes the data format and stores it in a centralized database.

[0698] Step 4:

[0699] The server uses generative AI technology to analyze integrated data in real time and evaluate users' health status and behavioral patterns. Here, anomaly detection and health trend extraction are performed through statistical analysis and machine learning.

[0700] Step 5:

[0701] Based on the analysis results, the server automatically generates a health management program optimized for the user. This program includes recommended exercise routines, meal plans, relaxation methods, and more.

[0702] Step 6:

[0703] The device notifies the user of the generated health management program. The notification is sent via push or in-app message, allowing the user to review the specific content and incorporate it into their daily activities.

[0704] Step 7:

[0705] Users follow the provided health management program and send feedback to the server via their device, inputting their progress and impressions as feedback.

[0706] Step 8:

[0707] The server collects and analyzes user feedback and adjusts and improves the health management program as needed. This adaptive process optimizes support for users.

[0708] (Example 1)

[0709] 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".

[0710] Modern lifestyles are diversifying, and there is a growing demand for appropriate healthcare tailored to individual health conditions and lifestyles. However, traditional methods have made personalized health management difficult due to the fragmentation of collected data and the lack of appropriate analytical techniques. Therefore, there is a need for technology that can efficiently and safely collect and analyze health-related data using information devices used daily, and provide users with health management programs optimized for their needs.

[0711] 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.

[0712] In this invention, the server includes means for collecting location data, activity data, biometric information, and rest data from multiple information terminals; means for transmitting the collected information to a data processing device via a communication network for integration and organization; and means for sequentially analyzing the integrated information based on digital technology. This enables the provision of an optimal health management program for each individual user, and facilitates adaptive and sustainable improvement of their health status.

[0713] An "information terminal" is an electronic device that an individual can wear or carry with them, enabling the collection and transmission of data.

[0714] "Location data" refers to data that indicates the geographical location of a user.

[0715] "Activity data" refers to data that shows the amount of physical activity and type of exercise a user engages in.

[0716] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate, blood pressure, and body temperature.

[0717] "Rest data" refers to data that shows the user's sleep patterns and rest times.

[0718] A "communication network" is an information transmission infrastructure for sending and receiving data.

[0719] A "data processing device" is a computer system used to integrate and analyze received information.

[0720] "Digital technology" refers to the technical means for electronically processing, displaying, or storing information.

[0721] A "health management program" is a proposed structure that includes specialized guidance and plans aimed at improving the health status of users.

[0722] An "encouragement message" is a word of encouragement or notification intended to motivate users.

[0723] This invention is a health management system in which an information terminal and a data processing device work in conjunction. The terminal uses electronic devices such as smartphones and wearable devices to collect the user's location data, activity data, biometric information, and rest data. This collected data is encrypted and securely transmitted to the data processing device via a communication network.

[0724] The server functions as a data processing unit, integrating received data and performing real-time analysis using generative AI models. Specifically, it uses TensorFlow or similar machine learning frameworks to analyze users' health patterns and generate personalized health management programs. These programs include exercise instructions, dietary guidance, and relaxation suggestions, tailored to each user's individual health condition.

[0725] For example, if the user enters a prompt message such as, "I'm a 30-year-old male, I do desk work and go to the gym once a week, but I'm not losing weight. I'd like some dietary advice," the system will use this information to suggest the most suitable meal plan for the user.

[0726] The generated health management program is notified to the device and provided so that users can easily refer to and implement it in their daily lives. Users can also send the results and feedback of this program to the server. Based on the collected feedback, the server uses a generated AI model to improve the program's accuracy and refine it to better suit the user.

[0727] In this way, the system constantly optimizes the user's health status and supports sustainable health management.

[0728] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0729] Step 1:

[0730] The device uses smartphones and wearable devices to collect user location data, activity data, biometric information, and rest data. This input data is acquired in real time using sensor functions. For example, heart rate is measured by the pulse sensor of the wearable device, and activity data is measured by an accelerometer. The acquired data is temporarily stored on the device.

[0731] Step 2:

[0732] The device encrypts the collected data and sends it to a server in the cloud using a secure communication protocol. In this step, an encryption algorithm is applied to ensure data security. The output is encrypted health-related data.

[0733] Step 3:

[0734] The server decrypts the received encrypted data, stores it in a database, and then performs data integration processing. This process aggregates data in different formats and reconstructs it into a consistent format. The input is decrypted health-related data, and the output is integrated health data.

[0735] Step 4:

[0736] The server sequentially analyzes the integrated data using a generation AI model. Here, machine learning algorithms are applied to predict user health status and trends, and identify health patterns. The input is integrated health data, and the output is the analysis results.

[0737] Step 5:

[0738] The server generates a personalized health management program based on the analysis results. The generating AI model designs exercise instructions and dietary guidance optimized for each user's individual health condition. The input is the analysis results, and the output is the health management program.

[0739] Step 6:

[0740] The device receives the generated health management program and sends a notification to the user. Upon receiving this notification, the user can review the program's contents and decide whether or not to implement it. The input is the health management program, and the output is the notification to the user.

[0741] Step 7:

[0742] Users send feedback on the program's execution status and implementation through their terminals. This feedback includes evaluations and suggestions for improvement regarding meal plans and exercise recommendations. Input is the user's feedback, and output is the feedback sent to the server.

[0743] Step 8:

[0744] The server receives user feedback and uses a generative AI model to adapt and improve the health management program. By incorporating feedback, subsequent suggestions become more personalized. The input is user feedback, and the output is the updated health management program.

[0745] (Application Example 1)

[0746] 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".

[0747] Traditional health management technologies have faced challenges in centrally utilizing diverse health data from individual users to provide personalized health guidance. Furthermore, they lacked sufficient activity recommendations that considered various urban public facilities and environmental factors, making it difficult for users to select optimal health activities. Additionally, they lacked adequate motivation to maintain users' commitment to health management.

[0748] 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.

[0749] In this invention, the server includes means for collecting location information, physical activity data, heart rate data, and sleep data from a recording device; means for recommending optimal activity locations and times to the user, taking into account the usage status of public facilities and environmental information; and means for generating motivational messages based on the user's behavior and achievement level. This enables the provision of individually optimized health management programs utilizing the user's health data, the recommendation of optimal activities based on the urban environment, and the improvement of the user's motivation for continuous health management.

[0750] "Location information" refers to data obtained by mobile devices and recording devices that indicates the user's current geographical location.

[0751] "Physical activity data" refers to data that describes a user's daily physical movements, such as the amount of exercise they do, the distance they travel, and the number of steps they take.

[0752] "Heart rate data" refers to data that shows the speed and rhythm of a user's heart rate, and is primarily used to assess their health status.

[0753] "Sleep data" refers to data collected on the user's sleep duration, quality, and patterns.

[0754] A "recording device" refers to equipment such as smartphones and wearable devices that measure and record health-related information.

[0755] A "remote server" refers to a computer server accessible via a network that stores and processes collected data.

[0756] "Data analysis technology" refers to technical methods for processing large amounts of collected data and extracting meaning and patterns.

[0757] A "health guidance program" is a program that includes guidance on exercise, nutrition management, and relaxation provided to users based on their individual health data.

[0758] "Public facility usage status" refers to information that shows the extent to which facilities such as gyms and parks within a city are being used.

[0759] "Environmental information" refers to data that shows external conditions that affect users' activities, such as weather, temperature, and road congestion.

[0760] A "motivational message" is a message of encouragement or guidance provided to users to promote continuous health management.

[0761] The system for implementing this invention mainly consists of a recording device, a remote server, and a communication network.

[0762] The recording device automatically collects the user's location information, physical activity data, heart rate data, and sleep data. This data is acquired using smartphones or wearable devices, encrypted in real time, and transmitted to a remote server.

[0763] The remote server runs a generative AI model using Python or PyTorch to analyze the health-related information sent to it. The server further integrates public facility usage data and environmental information to generate an optimal health guidance program for the user. This program includes exercise plans, nutritional guidance, and relaxation guidance.

[0764] The health guidance program generated by the server is notified to the recording device. Users can receive the program via their smartphone or smart glasses and incorporate it into their daily activities. In particular, optimal activities and visit times at places like gyms and parks in the city are recommended, allowing users to efficiently maintain a healthy lifestyle.

[0765] As an example, consider a case where a user wants to go for a run. The system takes into account the weather, road congestion, and the crowding situation at nearby parks, and makes a specific suggestion such as, "Taking advantage of today's sunny weather, we recommend a 15-minute run in the main park at 8:00 AM." In this way, users can choose the most suitable health activity based on the information provided.

[0766] An example of a prompt to the generating AI model might be, "Based on user A's activity history today and current health status, please suggest the most suitable relaxation activity." This would enable automated suggestions to be made effectively.

[0767] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0768] Step 1:

[0769] The device collects location information, physical activity data, heart rate data, and sleep data from the user. This raw data is obtained from sensors and GPS and secured using encryption technology. At this point, the input is raw data, and the output is encrypted data.

[0770] Step 2:

[0771] The device transmits encrypted health-related information to a remote server via a communication network. The input is encrypted data, and the output is the transmitted data packet.

[0772] Step 3:

[0773] The server decrypts the received health-related information and stores it in a database. The input is encrypted data, and the output is analyzable health-related information.

[0774] Step 4:

[0775] The server uses a generative AI model to analyze the accumulated information. It extracts users' health patterns and links them to public facility usage data and environmental information. The input is analyzable data, and the output is a proposed health guidance program optimized for the user.

[0776] Step 5:

[0777] The server generates individualized health guidance programs for each user and creates motivational messages based on the program's content. The input is a draft health guidance program, and the output is the specific health guidance program and messages.

[0778] Step 6:

[0779] The server notifies the terminal of the generated health guidance program. The input is the guidance program and message, and the output is the notification content.

[0780] Step 7:

[0781] Users receive health guidance programs through their devices and incorporate them into their daily lives. The input is the notified program, and the output is the user's healthy activities.

[0782] Step 8:

[0783] The device collects user feedback and sends it back to the server. The input is the user's feedback, and the output is the feedback data passed to the server.

[0784] Step 9:

[0785] The server analyzes the feedback and revises the health guidance program as needed. The input is the feedback data, and the output is the revised health guidance program.

[0786] 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.

[0787] This invention is a system that combines a health data integration AI agent with an emotion engine that recognizes the user's emotions. This system makes it possible to understand the user's physical and emotional state and provide more personalized health management.

[0788] In this system, terminals collect user location information, physical activity, heart rate, and sleep data through smartphones and wearable devices, as well as voice data and usage patterns. Voice data includes everyday conversations and voice input from the user. This data is encrypted and transmitted securely to a cloud server.

[0789] The server integrates the transmitted data for each user and uses generative AI technology to analyze their health status. This analysis includes an emotion engine to understand the user's emotional state. The emotion engine analyzes voice data and usage patterns to recognize the user's emotions and evaluates the emotional patterns along with their health status.

[0790] Based on the analysis results, the server generates a health management program that includes a personalized exercise program, nutritional guidance, and relaxation guidance for each user. Furthermore, it has the ability to adjust the suggestions according to the recognized emotional state. For example, it can suggest a program with enhanced relaxation elements to a user experiencing stress.

[0791] The device notifies the user of the generated health management program. This notification allows the user to receive specific activity guidelines tailored to their health and emotional state, which they can then incorporate into their daily activities. The user then executes the suggested program and inputs their progress and feedback into the system.

[0792] The server collects and analyzes user feedback and uses an emotion engine to provide encouraging and comforting messages tailored to the user's emotional state. This can boost user motivation and promote sustainable health management.

[0793] Thus, the system of the present invention integrates health-related data and emotional data to realize an improved healthcare platform that provides user-centric support.

[0794] The following describes the processing flow.

[0795] Step 1:

[0796] The device collects health-related information such as location data, physical activity data, heart rate, and sleep data through the user's smartphone or wearable device, and also records the user's voice tone and usage patterns. Voice data is obtained from normal conversations and voice interactions with the device.

[0797] Step 2:

[0798] The device encrypts the collected health-related information and voice data and securely transmits it to a cloud server. This protects data confidentiality and user privacy.

[0799] Step 3:

[0800] The server integrates and organizes the received data for each user. It standardizes the data format and stores it in a database to prepare it for health status analysis and emotion recognition.

[0801] Step 4:

[0802] The server uses generative AI technology and an emotion engine to analyze integrated data in real time. It identifies health patterns while simultaneously analyzing voice data to recognize the user's emotional state and evaluates the relationship between the two.

[0803] Step 5:

[0804] The server generates a health management program based on the user's health status and perceived emotions. For example, if stress is detected, it will suggest a program that includes specific stress relief measures, such as relaxation techniques.

[0805] Step 6:

[0806] The device notifies the user of the generated health management program and provides details. The notification arrives as a push notification or in-app message, and the user can select and incorporate the recommended actions into their daily life.

[0807] Step 7:

[0808] Users practice the actions provided in the program on a daily basis and send feedback from their device regarding their progress and perceived results. This feedback includes comments on changes in their emotions and the usefulness of the suggestions.

[0809] Step 8:

[0810] The server collects and analyzes user feedback and provides encouraging and comforting messages tailored to the user's emotional state. It also adapts and improves the health management program as needed to support the user's sustainable health management.

[0811] (Example 2)

[0812] 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".

[0813] In health management, integrating and analyzing an individual's physical and emotional data to provide a personalized and optimized health management plan has been a challenging task. Conventional systems lacked the ability to integrate diverse data and simultaneously evaluate a user's health and emotional state, making it difficult to achieve sustainable health management while maintaining user motivation.

[0814] 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.

[0815] This invention includes a server that analyzes voice data and usage patterns to recognize the user's emotions and evaluate emotional patterns along with their health status; a server that instantly analyzes health-related information using generative AI technology; and a server that collects user responses and adapts and improves health management information based on those responses. This makes it possible to provide an individually optimized health management plan that takes into account both the user's physical and emotional state.

[0816] "Location data" refers to information indicating the user's current location, and is obtained using GPS or other location-determining technologies.

[0817] "Activity data" refers to information about a user's physical movement and daily activities, and is acquired through accelerometers and activity trackers.

[0818] "Medical data" refers to physiological indicators of a user's health status, such as heart rate, blood pressure, and body temperature, and is collected from wearable devices and other sources.

[0819] "Rest data" refers to information about a user's sleep patterns and rest status, and is obtained from sleep sensors and tracking applications.

[0820] An "information processing device" is a device that collects data and provides or receives information through an interface with the user, and includes smartphones and wearable devices.

[0821] A "remote computer" is a server that provides cloud services and is connected via a network for the purpose of storing, integrating, and analyzing data.

[0822] "Machine learning technology" is an artificial intelligence technology that uses large amounts of data to detect specific patterns and anomalies and support decision-making, and includes generative AI models.

[0823] "Voice data" refers to information that records the user's voice and conversation content, and can be analyzed using speech recognition technology.

[0824] "Usage trends" refer to data that shows the habits and patterns of how users use devices and applications, and are used to analyze usage patterns.

[0825] "Health management information" refers to personalized guidance and programs on exercise, nutrition, and relaxation, tailored to each user's health condition.

[0826] "Responses" refer to feedback and evaluations of the results of actions taken by users regarding their health management information, and these are entered into the system.

[0827] This invention is a data-integrated system for individually optimizing health management. This system collects and analyzes users' physical and emotional data to provide personalized health management information. The main devices used are smartphones and wearable devices as information processing devices.

[0828] The device collects location data, activity data, medical data, and rest data from the user and periodically transmits this data to a remote computer. This uses secure communication methods employing encryption technology. This makes it possible to send data to the cloud while preventing information leaks.

[0829] The server integrates the received data and analyzes it immediately using generative AI technology. Specifically, it utilizes machine learning techniques to comprehensively evaluate the user's health status. It also uses an emotion engine that analyzes voice data and usage trends to recognize the user's emotional state and reflect it in the health information. Based on the analysis results, health management information is generated that includes individually optimized exercise programs, nutritional guidance, and relaxation guidance.

[0830] The generated health management information is then sent back to the terminal and provided to the user. The user uses this information to perform health maintenance activities and provides feedback on the results. Based on this feedback, the server continuously adapts and improves the health management information.

[0831] For example, if the emotion engine determines that a user is in a certain state of stress, the server will recommend relaxation-focused activities. An example of a prompt message might be, "Measure the user's stress level from recent voice recordings and activity data, and generate a relaxation program based on the results."

[0832] Thus, this invention enables users to receive personalized healthcare support, which can help them manage their health effectively and sustainably.

[0833] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0834] Step 1:

[0835] The device collects the user's location data, activity data, medical data, and rest data. This includes using various sensors such as GPS, accelerometers, and heart rate sensors to acquire data in real time. Input is raw data from each sensor, and output is formatted health-related information.

[0836] Step 2:

[0837] The device encrypts the collected health-related information and securely transmits it to a remote computer. The use of the SSL / TLS protocol encrypts the communication, reducing the risk of information leakage. Input is formatted health-related information, and output is encrypted data.

[0838] Step 3:

[0839] The server integrates the transmitted data and stores it in a database. Here, data organization and integration take place, generating user-specific datasets. The input is encrypted data, and the output is integrated data organized by user.

[0840] Step 4:

[0841] The server analyzes integrated data using a generation AI model. Here, it assesses the user's health status and recognizes their emotional state from voice data and usage patterns using an emotion engine. The input is integrated user data, and the output is the analysis results indicating health and emotional state.

[0842] Step 5:

[0843] The server generates personalized health management information for the user based on the analysis results. This information includes exercise programs, nutritional guidance, and relaxation instructions, tailored to the user's current condition. The input is the analysis results, and the output is the generated health management information.

[0844] Step 6:

[0845] The device notifies the user of health management information sent from the server. This information is provided in a format that the user can immediately access, such as push notifications or email. The input is the generated health management information, and the output is a notification converted into a user-accessible format.

[0846] Step 7:

[0847] Users perform daily activities based on the provided health management information and send the results and their impressions as feedback to the system. This feedback includes activity completion status, areas for improvement, and personal comments. The input is the results of activities based on the health management information, and the output is the user's feedback data.

[0848] Step 8:

[0849] The server collects and analyzes user feedback and uses an emotion engine to generate encouraging and comforting messages based on that feedback. This helps to improve user motivation and support sustainable health management. The input is user feedback data, and the output is the generated encouraging and comforting messages.

[0850] (Application Example 2)

[0851] 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".

[0852] In modern society, there is a need to precisely understand individual health conditions and provide appropriate health management at the right time. While conventional systems collect and analyze health data, they do not adequately provide personalized health management that takes into account an individual's emotional state. Furthermore, because health management that includes emotional support such as encouragement and comfort is not provided, it is difficult to maintain users' continuous motivation.

[0853] 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.

[0854] In this invention, the server includes means for collecting health-related information, including location information, physical activity information, heart rate information, and sleep information, means for transmitting the collected health-related information to a remote server, integrating and organizing it, and means for analyzing the user's emotional state from voice and behavioral patterns and adjusting the health management plan. This makes it possible to provide personalized health management and emotion-based encouragement that is tailored to the individual emotional state of the user.

[0855] "Location information" refers to coordinates and regional information that indicate the user's current location.

[0856] "Physical activity information" refers to numerical data that quantifies a user's exercise status and movement history.

[0857] "Heart rate information" refers to data that measures the user's heart rate over time.

[0858] "Sleep information" refers to data that records the user's sleep duration and sleep quality.

[0859] The term "device" refers to any equipment that collects information and enables communication.

[0860] A "remote server" is a remote computer that sends and receives information via the internet.

[0861] "Integration and organization" refers to the process of gathering multiple pieces of information and organizing them systematically.

[0862] "Generative AI technology" is a technology that uses artificial intelligence algorithms to perform data analysis.

[0863] A "health management plan" refers to specific guidelines for maintaining and improving the health of individual users.

[0864] "Notification" refers to the act of informing a user of specific information.

[0865] "Voice and behavioral patterns" refer to data that shows the user's speech and movement tendencies.

[0866] An "encouraging message" is a positive message sent with the aim of boosting the user's motivation.

[0867] To implement this invention, a smartphone or wearable device is used as the apparatus. These devices collect location information, physical activity information, heart rate information, and sleep information from the user. This information is encrypted and securely transmitted to a remote server. This server integrates the collected information using a Python-based AI analysis engine and an emotion recognition API, and performs real-time analysis using generative AI technology.

[0868] The server generates a personalized health management plan for each user, including exercise plans, nutritional guidance, and relaxation guidance, based on analyzed health-related information and emotional data. This plan is then adjusted by a generating AI model according to the user's emotional state. Specifically, emotional states derived from voice data and behavioral patterns are considered, and enhanced relaxation is suggested for users experiencing stress.

[0869] The device notifies the user of the generated health management plan and encourages them to act as suggested. It also sends user feedback back to the server, which is used to adapt and improve the health management plan. Furthermore, it monitors the user's behavior and emotional state and provides encouraging messages based on their progress and emotions.

[0870] As a concrete example, a scenario could be imagined where an elderly person living alone receives a suggestion from the device saying, "Your heart rate seems a little fast. Shall we take some deep breaths to relax?" followed by a robot playing calming music. An example of a prompt to the generative AI model would be, "The user's heart rate has increased. Please suggest something to help them refresh."

[0871] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0872] Step 1:

[0873] The device collects location information, physical activity information, heart rate information, and sleep information from the user. This information is digital data obtained from wearable devices and smartphones, and is collected in real time through the device's sensors. The collected data is securely encrypted using encryption technology.

[0874] Step 2:

[0875] The device securely transmits encrypted health-related information to a remote server. The transmitted data is decrypted and integrated within the server. In the data integration process, each piece of data is organized chronologically and treated as a single integrated dataset.

[0876] Step 3:

[0877] The server analyzes integrated health-related information using AI technology. Here, a Python-based AI analysis engine is used to model the user's health status. The input is integrated data, and the output is a health status assessment index. Based on each index, the AI ​​model determines future health risks and necessary interventions.

[0878] Step 4:

[0879] The server analyzes the user's emotional state using an emotion recognition API based on voice data and behavioral patterns. Based on the analysis results, it makes necessary adjustments to the health management plan. In this process, the input is emotion-related data, and the output is the emotion evaluation result. A generative AI model generates an optimal health management plan based on the emotional state.

[0880] Step 5:

[0881] The server notifies the terminal of the analysis results and the adjusted health management plan. The terminal sends a notification to the user, providing specific exercise plans and relaxation guidance. The user, upon receiving the notification, adjusts their daily life based on the provided plan.

[0882] Step 6:

[0883] Users follow the provided health management plan and input feedback into their device. This feedback includes their opinions and changes in feelings regarding the plan. This feedback is then sent back to the server and used to improve future plans.

[0884] Step 7:

[0885] The server analyzes user feedback and behavioral history to generate encouraging messages tailored to the user's emotional state and health condition. Using a generative AI model, it designs messages to maintain user motivation and sends them to the user via their device.

[0886] 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.

[0887] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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."

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0907] The following is further disclosed regarding the embodiments described above.

[0908] (Claim 1)

[0909] A means of collecting health-related information from a device, including location information, physical activity data, heart rate data, and sleep data.

[0910] A means of sending collected health-related information to a cloud server for integration and organization,

[0911] A means of analyzing integrated health-related information in real time using AI technology,

[0912] A means for creating a health management program that includes individualized exercise programs, nutritional guidance, and relaxation guidance for each user, based on the analysis results.

[0913] A means of notifying the user of the created health management program on the device and providing it to the user,

[0914] A means of collecting user feedback and adapting and improving the health management program based on that feedback,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, which encrypts the collected health-related information and securely transmits it to a cloud server.

[0918] (Claim 3)

[0919] The system according to claim 1, which monitors the user's behavior based on the generated health management program and provides motivational messages according to the degree of achievement.

[0920] "Example 1"

[0921] (Claim 1)

[0922] A means for collecting location data, activity data, biometric information, and rest data from multiple information terminals,

[0923] A means for transmitting collected information to a data processing device via a communication network, and for integrating and organizing it,

[0924] A means of sequentially analyzing integrated information based on digital technology,

[0925] Based on the analysis results, a means of constructing an individualized plan for each user, including activity instructions, dietary guidance, and relaxation guidance,

[0926] A means of notifying the user of the configured individual plan on an information terminal and providing it to the user,

[0927] A means of collecting user feedback and adjusting and updating individual plans based on the collected results,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, wherein the information to be recorded is encrypted and transmitted securely to a data processing device.

[0931] (Claim 3)

[0932] The system according to claim 1, which monitors the user's behavior based on a configured individual plan and provides encouraging messages according to the achievement status.

[0933] "Application Example 1"

[0934] (Claim 1)

[0935] A means for collecting health-related information, including location information, physical activity data, heart rate data, and sleep data, from a recording device,

[0936] A means of transmitting collected health-related information to a remote server for integration and organization,

[0937] A means of analyzing integrated health-related information in real time using data analysis technology,

[0938] A means for generating a health guidance program that includes individualized exercise programs, nutritional guidance, and relaxation guidance for each user, based on the analysis results.

[0939] A means of notifying a recording device of the generated health guidance program and providing it to the user,

[0940] A means of collecting feedback from users and adapting and improving health guidance programs based on that feedback,

[0941] A means of recommending the optimal location and time for health activities based on public facility usage, environmental information, and user activity history,

[0942] A system that includes this.

[0943] (Claim 2)

[0944] The system according to claim 1, which performs data protection processing on collected health-related information and securely transmits it to a remote server.

[0945] (Claim 3)

[0946] The system according to claim 1, which observes the user's behavior based on the generated health guidance program and generates motivational messages according to the degree of achievement.

[0947] "Example 2 of combining an emotion engine"

[0948] (Claim 1)

[0949] A means for collecting health-related information, including location data, activity data, medical data, and rest data, from an information processing device,

[0950] A means of transmitting collected health-related information to a remote computer, integrating and organizing it,

[0951] A means of instantly analyzing integrated health-related information using machine learning technology,

[0952] A means for creating health management information, including individualized exercise guidance, dietary guidance, and stress relief guidance for each user, based on the analysis results.

[0953] A means of notifying the created health management information to the information processing device and providing it to the user,

[0954] A means of collecting user feedback and adapting and improving health management information based on that feedback,

[0955] A method for recognizing user emotions by analyzing voice data and usage patterns, and for evaluating emotional patterns along with health status,

[0956] A system that includes this.

[0957] (Claim 2)

[0958] The system according to claim 1, which encodes the collected health-related information and securely transmits it to a remote computer.

[0959] (Claim 3)

[0960] The system according to claim 1, which monitors the user's behavior based on generated health management information and provides motivational messages according to the level of achievement.

[0961] "Application example 2 when combining with an emotional engine"

[0962] (Claim 1)

[0963] A means for collecting health-related information from a device, including location information, physical activity information, heart rate information, and sleep information.

[0964] A means of transmitting collected health-related information to a remote server for integration and organization,

[0965] A means of analyzing integrated health-related information in real time using AI technology,

[0966] A means of creating a health management plan that includes individualized exercise plans, nutritional guidance, and relaxation guidance for each user, based on the analysis results.

[0967] A means of notifying the device of the created health management plan and providing it to the user,

[0968] A means of analyzing the user's emotional state from voice and behavioral patterns and adjusting the health management plan accordingly.

[0969] A means of collecting feedback from users and adapting and improving health management plans based on that feedback,

[0970] A system that includes this.

[0971] (Claim 2)

[0972] The system according to claim 1, which encrypts the collected health-related information and securely transmits it to a remote server.

[0973] (Claim 3)

[0974] The system according to claim 1, which monitors the user's behavior based on the generated health management plan and provides encouraging messages according to the degree of achievement and emotional state. [Explanation of Symbols]

[0975] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting health-related information from a device, including location information, physical activity data, heart rate data, and sleep data. A means of sending collected health-related information to a cloud server for integration and organization, A means of analyzing integrated health-related information in real time using AI technology, A means for creating a health management program that includes individualized exercise programs, nutritional guidance, and relaxation guidance for each user, based on the analysis results. A means of notifying the user of the created health management program on the device and providing it to the user, A means of collecting user feedback and adapting and improving the health management program based on that feedback, A system that includes this.

2. The system according to claim 1, which encrypts the collected health-related information and securely transmits it to a cloud server.

3. The system according to claim 1, which monitors the user's behavior based on the generated health management program and provides motivational messages according to the degree of achievement.

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