system

Abstract

We provide the system. [Solution] A means of collecting users' biometric data and storing it in a database, A means of analyzing stored data to predict the user's health status, A means of providing lifestyle guidance to users based on prediction results, A means of issuing a warning when an abnormal value is detected, 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 persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Diabetic patients need to manage their daily blood glucose levels, adjust their diet and exercise, and it is very burdensome to do these manually. Also, in many cases, it is difficult to quickly detect sudden fluctuations or abnormalities in blood glucose levels and take appropriate measures. As a result, the risk of diabetes complications increases, and there is a risk of deterioration in the quality of life. Therefore, there is a need to provide a system for diabetic patients to more easily and effectively manage their blood glucose levels and maintain their health.

Means for Solving the Problems

[0005] This invention solves the aforementioned problems by providing a system that automatically collects users' biometric data, stores it in a database, and then analyzes it using AI. This system analyzes the stored data to predict the user's health status and automatically provides appropriate lifestyle guidance based on the results. It also has a function to immediately issue a warning if abnormal values ​​are detected, supporting the user's quick response. Furthermore, by collecting user feedback and reflecting that data in the analysis model, it realizes health management that is more tailored to each individual user. The biometric data includes not only blood glucose levels but also dietary content and exercise information, enabling comprehensive health management.

[0006] "Users" refer to individuals who utilize this system, primarily those with diabetes.

[0007] "Biometric data" refers to information about a user's physical condition, including blood glucose levels, dietary information, and exercise information.

[0008] A "database" is an electronic recording medium used to store and manage collected biometric data.

[0009] "Analysis" is the process of using AI algorithms to interpret stored data and extract meaningful information.

[0010] "Health status prediction" refers to estimating future blood sugar levels and other health indicators of users based on analyzed data.

[0011] "Lifestyle guidance" refers to specific advice regarding diet and exercise given to users based on their predicted health status.

[0012] An "abnormal value" refers to a numerical value in a user's health status that deviates from the normal range.

[0013] A "warning" is a notification issued to the user to alert them when an abnormal value is detected.

[0014] "Feedback" refers to providing information regarding the actions taken by the user on the system and the results thereof.

[0015] "Analysis model" is the structure of an AI algorithm constructed based on user data and is used for predicting the health status.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [[ID=]15] [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

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 terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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 numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[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] This invention is a system for supporting blood glucose management in diabetic patients, aiming to collect and analyze users' biometric data and provide appropriate lifestyle guidance. This system is primarily implemented using two components: a "terminal" and a "server."

[0038] First, the device works in conjunction with an insulin pump and blood glucose sensor to periodically collect the user's biometric data. This includes real-time blood glucose measurement, recording of meals, and acquisition of exercise information. This data is transmitted to a server using secure communication methods.

[0039] The server centrally stores the received biometric data in a database and then applies an AI algorithm to analyze the data. Based on the analysis results, it predicts the user's health status and generates specific advice regarding diet and exercise. For example, it might advise reducing carbohydrate intake or suggest appropriate exercise timings in response to a predicted rise in blood sugar levels.

[0040] The device receives advice from the server and notifies the user. The user then adjusts their lifestyle based on this advice and reflects it in their daily life. In addition, the user inputs feedback about their own behavior into the device, which is then sent to the server, allowing the system to continuously improve its analysis model.

[0041] For example, if a user has high blood sugar levels in the morning, the server analyzes past data and current conditions to determine the appropriate insulin dosage. The terminal then communicates this information to the user, allowing them to administer the correct amount of insulin before breakfast. Furthermore, the system advises the user to adjust their carbohydrate intake at breakfast to help stabilize their blood sugar levels.

[0042] Furthermore, this system is equipped with an anomaly detection function that can immediately issue a warning if the user's blood glucose level fluctuates rapidly or exceeds a certain threshold. Users are expected to respond quickly upon receiving this notification. Combined with an early consultation function for medical professionals, it is expected to prevent serious situations.

[0043] Thus, the system of the present invention aims to provide personalized health support that is useful in the daily lives of diabetic patients and to contribute to improving their quality of life.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The device periodically collects the user's biometric data from insulin pumps and blood glucose sensors. Specifically, this includes real-time blood glucose levels, meal records, and exercise information. This data is temporarily stored in a local database on the device.

[0047] Step 2:

[0048] The device transmits the collected biometric data to the server using a secure protocol. The data is encrypted and transferred in a way that protects it from unauthorized access.

[0049] Step 3:

[0050] The server stores the received data in a cloud-based database and organizes it appropriately for each user. The stored data is then used for subsequent analysis.

[0051] Step 4:

[0052] The server applies AI algorithms to analyze the data. This analysis combines past trends with current data to predict the user's health status.

[0053] Step 5:

[0054] Based on the analysis results, the server generates personalized lifestyle advice for each user. This advice includes recommendations for appropriate insulin dosages, dietary adjustments, and exercise.

[0055] Step 6:

[0056] The device notifies the user of the generated advice. Based on the notification, the user adjusts their diet, exercise, and medication.

[0057] Step 7:

[0058] The user acts according to the advice provided by the system and enters feedback on the results into the terminal. This feedback is used for the next analysis.

[0059] Step 8:

[0060] The server analyzes user feedback and improves the AI ​​model to enhance the accuracy of future predictions. This enables the provision of more personalized assistance.

[0061] (Example 1)

[0062] 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."

[0063] Conventional health management systems have faced challenges in effectively collecting and analyzing users' biometric information and providing appropriate health guidance. In particular, there is a need to monitor biometric information such as blood glucose levels in real time and provide immediate health guidance based on that data. Furthermore, it is crucial to immediately recognize abnormal biometric information and take appropriate action. By solving these challenges, it is possible to improve the accuracy of predicting users' health status and enhance their quality of life.

[0064] 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.

[0065] In this invention, the server includes means for collecting the user's biometric information using a measuring device and transmitting it to a central processing unit; means for storing the received biometric information in a data storage device, performing information analysis using a generated artificial intelligence model, and predicting the user's health status; and means for providing guidance on diet and exercise to the user based on the prediction results. This makes it possible to grasp biometric information in real time and immediately predict the user's health status and provide lifestyle guidance.

[0066] "User" refers to an individual who uses this system and provides biometric information.

[0067] "Biometric information" refers to data related to the user's physiological state and lifestyle, such as blood glucose levels, dietary information, and exercise information.

[0068] A "measuring device" refers to hardware used to collect a user's biometric information in real time.

[0069] A "central processing unit" refers to the central device that receives and analyzes biological information transmitted from measuring devices.

[0070] A "data storage device" refers to a storage medium used to safely and efficiently store received biometric information.

[0071] A "generated artificial intelligence model" is a system equipped with algorithms specifically designed for analyzing biometric information and is used to predict the user's health status.

[0072] "Predicting health status" refers to the process of estimating future physiological changes based on collected biometric information.

[0073] "Health guidance" refers to activities that provide specific advice to encourage users to improve their lifestyle habits based on their predicted health condition.

[0074] "Issuing a warning" refers to sending a notification to alert the user to an abnormality when their biometric data exceeds a threshold.

[0075] This invention is a system for managing user health and is implemented primarily using two main components: a "terminal" and a "server."

[0076] The terminal is a device designed to collect biometric information, specifically equipped with a sensor for measuring blood glucose levels, an interface for inputting dietary information, and means for acquiring information about exercise. The terminal collects this data in real time as needed and transmits it to the server using a secure communication protocol.

[0077] The server functions as a central processing unit, storing biometric information transmitted from terminals in a data storage device. The stored data is analyzed using a generated artificial intelligence model. This model is used to predict the user's future health status based on past and present data.

[0078] For example, if a particular user's morning blood glucose level tends to rise sharply compared to past levels, the server analyzes this and suggests appropriate dietary restrictions and exercise guidance. This specific guidance is then communicated to the user via their device.

[0079] Furthermore, the system includes a function that immediately issues a warning when an abnormal value is detected. This allows users to respond quickly, which is expected to prevent serious deterioration of health conditions. By collaborating with medical professionals, further actions can be taken as needed.

[0080] (Example of prompt text to input to the generated AI model)

[0081] "If a user shows signs of a rapid spike in blood sugar, provide advice on effective carbohydrate management methods. Also, suggest appropriate exercise timings."

[0082] This system aims to improve users' quality of life by providing them with personalized health management information.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The terminal collects the user's biometric information through a measuring device. This obtains data such as blood glucose levels, dietary information, and exercise information. Since this information cannot be used directly, it undergoes data formatting processing and is converted into a format that can be sent to the server in the next step.

[0086] Step 2:

[0087] The terminal transmits formatted biometric information to the server using a secure communication protocol. The output here is organized biometric data, which the server receives. The received data is stored directly in the server's data storage device. This storage process also includes data backup.

[0088] Step 3:

[0089] The server inputs accumulated biometric information into a generating AI model. Based on the input data, the AI ​​model uses machine learning algorithms to analyze the data and predict the user's health status. This prediction indicates future blood glucose fluctuations and the level of health risk. As an output of the analysis, recommended lifestyle guidance is generated.

[0090] Step 4:

[0091] The server sends the generated lifestyle guidance to the terminal via a notification function. The terminal notifies the user of this visually or audibly. Specifically, this involves using screen displays, voice guidance, or vibration alerts to draw the user's attention.

[0092] Step 5:

[0093] Users adjust their daily activities based on notifications from their devices. Based on the lifestyle guidance they receive as input, they review their diet and exercise at recommended times. Users input feedback on their actions into their devices, and this feedback is sent to the server for the next cycle. This process contributes to the continuous improvement of the AI ​​model.

[0094] (Application Example 1)

[0095] 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."

[0096] In modern society, diabetic patients are required to properly manage their health and take immediate action to prevent rapid fluctuations in blood glucose levels. However, manual data recording and analysis have limitations, and coordination in situations requiring rapid medical response is difficult. In particular, when self-management is difficult, information sharing with professionals and appropriate guidance may be delayed.

[0097] 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.

[0098] In this invention, the server includes means for collecting user behavior data and storing it in a remote information processing device, means for analyzing the stored information and predicting the user's health index, and means for transmitting the digitized information to a medical institution via a communication medium. As a result, the user's health status is monitored in real time, and when an abnormality is detected, the information is immediately shared with professionals, enabling a rapid response.

[0099] "User behavior data" refers to biological and behavioral information such as blood glucose levels, dietary information, and exercise information necessary to evaluate an individual's health status.

[0100] A "remote information processing device" refers to a computer system that receives, stores, and analyzes collected data.

[0101] A "health index" refers to a numerical or indicator representation of a user's health status, based on collected biometric data.

[0102] "Communication medium" refers to technical means such as internet lines and wireless communication used to transmit data safely and quickly.

[0103] "Digitized information" refers to information obtained by converting biometric data into a format that can be processed electronically.

[0104] "Medical institution" refers to organizations such as hospitals and clinics that employ professionals responsible for monitoring and treating users' health conditions.

[0105] This invention provides an innovative system to support blood glucose management for diabetic patients. The system consists of a terminal and a server, and these two elements complement each other to enable real-time health support for the user.

[0106] First, the device plays a central role in integrating user behavior data. Smartphones and smartwatches work in conjunction with blood glucose sensors, meal logging apps, and exercise tracking devices. This allows for the aggregation of various biometric information related to the user's health, which is then securely transmitted to a server via Bluetooth or Wi-Fi.

[0107] The server stores the received trend data in an integrated database and uses software such as a generating AI model to perform analysis using machine learning algorithms (e.g., TENSORFLOW®). This analysis calculates the user's health index and provides predictions of potential blood glucose fluctuations and health status. The results are used to generate prompt messages, which are then appropriately fed back to the user. The prompt messages are output in the form of instructions to the AI ​​model, such as, "Based on the patient's blood glucose history and meal content this morning, please generate the cause of the rapid rise in blood glucose after lunch and provide advice on diet and exercise for prevention next time."

[0108] The feedback, presented to the user in a compact, visual format, may appear as a smartphone notification, for example, prompting specific actions. As users input health data and provide feedback, the system continuously updates its analytical models, providing more accurate health predictions and advice.

[0109] In addition, the server has the ability to instantly transmit information to medical professionals in the event of a sudden change in health status. This makes it possible to provide users with rapid medical assistance in emergencies. Ultimately, the goal is to improve the quality of life for people with diabetes through these processes.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The device collects user behavior data. Specifically, smartphones and smartwatches work in conjunction with blood glucose sensors and exercise tracking apps to acquire data in real time. This data includes the user's meals, exercise levels, and, most importantly, blood glucose levels. The input is this biometric data, and the output is a set of data.

[0113] Step 2:

[0114] The device collects behavioral data and sends it to the server. Bluetooth or Wi-Fi is used to upload the data securely and quickly. In this process, the input is unsent data on the device, and the output is data stored on the server. Upon receiving the data, the server securely stores it in its storage.

[0115] Step 3:

[0116] The server analyzes the received data using a machine learning algorithm. Specifically, a generative AI model is used to predict health indices. The input is user behavior data stored on the server, and the output is a health prediction index and points to watch out for generated by the analysis. The data is cleansed and converted into a format suitable for the algorithm.

[0117] Step 4:

[0118] The server generates prompt messages based on the analysis results and sends them to the terminal. A generation AI model is used to create personalized lifestyle advice tailored to each user. The input is the health prediction result, and the output is the generated prompt message. This prompt message is presented in language that is easy for the user to understand.

[0119] Step 5:

[0120] The device receives a prompt message from the server and notifies the user. The generated advice is displayed visually and clearly using the smartphone's notification function. The input is the generated prompt message, and the output is the advice the user receives on the screen.

[0121] Step 6:

[0122] Users input feedback into their devices and send it to the server. This feedback includes the actions taken and the perceived effects. The input is the user's feedback, and the output is the feedback data stored on the server for use in subsequent analyses. This allows the analysis model to improve over time.

[0123] Step 7:

[0124] When the server detects sudden fluctuations or abnormal values, it immediately sends a notification to healthcare institutions. Trend data is continuously monitored, and if a specific threshold is exceeded, information is automatically sent to healthcare professionals. The input is analyzed data stored on the server, and the output is alert information sent to healthcare institutions. This step enables rapid medical response.

[0125] 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.

[0126] This invention is a system that aims to improve the health and quality of life of diabetic patients by collecting and analyzing both the user's biometric data and emotional state, and providing individually optimized health management advice. This system consists of both "terminal" and "server" components, and is further equipped with an emotion engine that recognizes the user's emotions.

[0127] The device periodically collects blood glucose data from insulin pumps and blood glucose sensors, meal information entered by the user, and exercise information. In parallel, the emotion engine analyzes the user's voice and facial expression data to recognize the user's emotional state. The recognized emotional data, along with other biometric data, is temporarily stored on the device and transmitted to the server via a secure communication method.

[0128] The server stores all received data in a cloud database and analyzes it. The analysis uses AI algorithms to comprehensively evaluate the user's past health data and current emotional data to predict their health status. Based on this prediction, specific lifestyle guidance is created for each individual user, including appropriate insulin adjustments, dietary choices, and exercise timing.

[0129] This guidance is customized to take into account the user's emotions as identified by the emotion engine. For example, if the user is feeling stressed, it may recommend rest to reduce stress. In this way, lifestyle guidance takes into account not only data-based health status but also psychological well-being.

[0130] For example, if the system's emotion engine determines that a user is feeling "stressed" when measuring their blood glucose levels in the morning, the server will notify the user via their device with suggestions for relaxing meal options or recommendations for light exercise. This allows users to become more aware of their own health and take data-driven actions without feeling overwhelmed.

[0131] This invention enables continuous system improvement through user feedback and allows for flexible responses to changes in the user's condition. In this way, the present invention aims to significantly reduce anxiety and burden in daily life by providing advanced support for self-care for diabetic patients.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The device collects the user's blood glucose levels in real time via an insulin pump or blood glucose sensor and stores the data in a local database. It also simultaneously collects meal and exercise information manually entered by the user.

[0135] Step 2:

[0136] An emotion engine runs on the device and analyzes the user's emotional state from their voice and facial expressions. This provides emotional information, such as whether the user is stressed or relaxed. This information is also stored in a local database.

[0137] Step 3:

[0138] The device encrypts all collected data and sends it to the server using a secure communication protocol. This transmission is performed periodically and updated whenever new data becomes available.

[0139] Step 4:

[0140] The server stores the received data in a cloud database and analyzes it using an AI algorithm. The analysis combines past blood glucose information with the user's current emotional state to predict their health.

[0141] Step 5:

[0142] Based on the analysis results, the server generates personalized lifestyle guidance for the user. This guidance includes not only blood sugar management but also mental health advice tailored to the user's emotional state. For example, if the user is feeling stressed, relaxation activities and exercises will be suggested.

[0143] Step 6:

[0144] The device notifies the user of lifestyle guidance received from the server. Based on this information, the user makes choices regarding meals, insulin administration timing, and appropriate exercise.

[0145] Step 7:

[0146] Users input feedback into their device, describing their feelings and changes in emotions after following the advice they received. This feedback is used to improve the system in the future.

[0147] Step 8:

[0148] The server uses user feedback to improve new analysis models and adjust the AI ​​algorithms. This improves the accuracy of subsequent analyses and provides users with more personalized assistance.

[0149] (Example 2)

[0150] 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".

[0151] Traditional health management systems for diabetic patients focus on collecting and analyzing users' biometric data, but they have the drawback of not considering the user's psychological or emotional state, thus failing to provide sufficient individualization. Furthermore, existing systems have limited predictive accuracy, which can prevent them from providing appropriate lifestyle guidance to users. As a result, there are shortcomings in improving the quality of life for users.

[0152] 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.

[0153] In this invention, the server includes means for collecting and storing the user's biometric and emotional data in a storage device, means for analyzing the stored biometric and emotional data using an artificial intelligence algorithm to predict the user's health status, and means for providing personalized lifestyle guidance to the user based on the prediction results and the user's emotional status. This enables advanced individualized health management that takes into account the user's psychological health.

[0154] A "user" refers to a person who uses the system to manage their own health.

[0155] "Biometric data" refers to data that numerically represents the user's physical condition, such as blood glucose levels, dietary information, and exercise information.

[0156] "Emotional data" refers to data that represents the psychological state of a user, analyzed from their voice and facial expressions.

[0157] "Storage device" refers to hardware or software used to temporarily or continuously store collected data.

[0158] "Artificial intelligence algorithms" refer to computational methods that automate data analysis and predict user states.

[0159] "Means of predicting health status" refers to functions that include a process of predicting a user's future health status based on collected data.

[0160] "Personalized lifestyle guidance" refers to health management advice tailored individually based on the user's biometric and emotional data.

[0161] "Warning mechanisms" refer to features that provide alerts when abnormal data or emotional states are detected based on established criteria.

[0162] This invention is a system that provides individually optimized health management advice by collecting and analyzing both the user's biometric and emotional data. The system consists of two main components: a terminal and a server, and also incorporates an emotion engine for recognizing the user's emotions.

[0163] The device uses an insulin pump and blood glucose sensors to measure the user's blood glucose levels. This hardware periodically collects the user's physical information and transfers that data to the device. It also has a software interface for the user to input diet and exercise information, allowing for manual data entry.

[0164] On the other hand, the emotion engine recognizes the user's emotional state by analyzing the user's voice and video data from the camera. This emotional data is used to assess the psychological stress the user may be facing.

[0165] The device temporarily stores collected biometric and emotional data in its memory. This data is then transmitted to a server using secure communication methods. The server stores the received data in a cloud database and analyzes it using a generative AI model. This model comprehensively evaluates the user's past health data and current emotional data to predict the user's health status.

[0166] Based on the analysis results, the server creates personalized lifestyle guidance for the user. This guidance takes into account things like how to adjust insulin, food choices, and the timing of exercise. Furthermore, the guidance is further customized according to the user's emotions as recognized by the emotion engine. For example, if the user is feeling stressed, the guidance may include suggestions for relaxation activities and recommendations for light exercise.

[0167] User feedback is crucial in the implementation of this system. This feedback will be used to continuously improve the system and enable it to respond to changes in user needs.

[0168] As a concrete example, the prompt text to be input to the generative AI model would be as follows:

[0169] "What kind of relaxation routine would you recommend to a user experiencing stress in the morning?"

[0170] Thus, the present invention aims to provide advanced support for self-care in diabetic patients and improve their quality of life.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The device periodically receives the user's blood glucose data from an insulin pump or blood glucose sensor. Furthermore, the user inputs dietary and exercise information via a dedicated application. This information is stored in the device's temporary memory.

[0174] The inputs are sensor outputs and user inputs, and the output is biometric data stored in temporary memory. This prepares an accurate dataset for subsequent analysis.

[0175] Step 2:

[0176] Simultaneously, the device uses its built-in emotion engine to collect emotional data using the user's voice and camera. This data is identified through voice-to-text conversion and facial expression analysis. Emotional information is temporarily stored along with biometric data.

[0177] The input is the user's voice and video data, and the output is analyzed emotion data. This process records the user's psychological state as numerical data.

[0178] Step 3:

[0179] The device securely transmits biometric and emotional data to the server. This communication is conducted using encryption protocols to protect privacy.

[0180] The input is a dataset stored on the device, and the output is encrypted data sent to the server. This ensures data integrity and secure data sharing.

[0181] Step 4:

[0182] The server stores the received data in a cloud database and performs analysis using a generated AI model. It integrates the user's past and current data to predict their health status.

[0183] The input is a dataset stored on the server, and the output is an assessment of the predicted health status. This process calculates a health prediction for each individual user.

[0184] Step 5:

[0185] The server generates personalized lifestyle guidance based on the analysis results. This guidance takes into account the user's emotional state and includes appropriate insulin administration and dietary advice.

[0186] The input is the analysis results from an AI model, and the output is customized health guidance. This allows for the proposal of health management optimized for the user.

[0187] Step 6:

[0188] The device receives lifestyle guidance from the server and notifies the user. The guidance is conveyed to the user through voice and visual interfaces.

[0189] The input is guidance data from the server, and the output is a notification to the user. This process allows users to efficiently manage their daily health.

[0190] (Application Example 2)

[0191] 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".

[0192] In managing the health of diabetic patients, challenges include a lack of personalized health support that takes into account daily fluctuating biometric data and emotional states, as well as a lack of appropriate guidance for engaging in consumption activities without compromising health.

[0193] 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.

[0194] In this invention, the server includes means for collecting and storing the user's biometric and emotional state data in a database, means for analyzing the stored data and predicting the user's health status, and means for providing the user with a health-related plan that takes the emotional state into consideration based on the prediction results. This enables the individual optimization of health management and consumption activities, improving the quality of life and reducing the burden on diabetic patients.

[0195] "User biometric data" refers to information used to assess a user's health status, such as blood glucose levels, dietary information, and exercise information.

[0196] "Emotional state data" refers to information indicating the user's psychological state, obtained from their voice and facial expressions.

[0197] "Methods for saving data to a database" refers to a method of securely and efficiently storing collected data for later analysis.

[0198] "Methods for analysis and prediction" refers to methods that use AI algorithms and other tools based on collected data to evaluate the user's health risks and predict their future condition.

[0199] "Means of providing health-related plans" refers to methods of providing specific advice for improving lifestyle habits and maintaining health, based on the user's health status and emotions.

[0200] "A method of recommending products using electronic payment methods" refers to a method of suggesting healthy products at the appropriate time, taking into account the user's health data.

[0201] "Means of issuing warnings when abnormal values ​​are detected" refers to a method of quickly notifying a user when their biometric data exceeds a set range.

[0202] The system for realizing this invention mainly consists of a terminal and a server. The terminal is responsible for collecting the user's biometric data and emotional state data and securely transmitting this data to the server. The user's biometric data includes blood glucose levels, dietary information, and exercise information, while emotional state data is obtained through voice and facial expression analysis. This data is collected by hardware such as insulin pumps, blood glucose sensors, cameras, and microphones.

[0203] The server stores data in a database and performs analysis using AI algorithms. Cloud services (e.g., AWS® Lambda, Amazon RDS) and AI libraries (e.g., TensorFlow) are used for analysis, predicting the user's health status based on their past and present data. Furthermore, using electronic payment methods, the system recommends healthy products that take the user's emotions into consideration, based on the data analysis results.

[0204] For example, if a user is experiencing stress while shopping, the system will recommend products that can alleviate that stress, such as herbal tea. This recommendation is presented in a way that the user can see in real time via their smartphone.

[0205] When using the generative AI model, prompts such as "Suggest products that can alleviate stress based on the user's current blood glucose level and emotional data" are used. This allows the system to respond quickly to the user's situation.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The device collects the user's blood glucose levels from an insulin pump or blood glucose sensor. Furthermore, it uses a camera and microphone to capture the user's facial expressions and voice data. This data is collected as input to the device. The device processes this biometric data into an appropriate format and stores it temporarily. As output, integrated biometric data and emotional state data are generated.

[0209] Step 2:

[0210] The device transmits the collected data to the server using a secure communication method. During this process, the data is protected using encryption technology, ensuring its integrity and confidentiality in transmission. Inputs include processed biometric data and emotional state data, while the output, encrypted data, reaches the server.

[0211] Step 3:

[0212] The server saves the received data to the database. This saving process includes data integrity checks and ensures the data is stored in a format compatible with the database structure. The input is the data sent from the terminal, and the output is the data saved in a format accessible by the database.

[0213] Step 4:

[0214] The server analyzes the stored data using an AI algorithm. This is a process that utilizes a generative AI model to predict the user's health status. The input data consists of stored biometric and emotional state data, and the output is the predicted health status. Based on these results, the server generates health-related plans.

[0215] Step 5:

[0216] The server recommends the most suitable health products to the user based on the analysis results. This is made possible through electronic payment methods. The inputs are insights from the predicted health state and the user's emotional state, and the output is product recommendation information tailored to the user's health state. Specifically, for example, if the user is stressed, it will suggest products with relaxing effects.

[0217] Step 6:

[0218] The user receives recommended information from the server via their device. Based on the information received, the user can engage in healthy consumption activities. The input for this step is data from the server, and the output is manifested as improvements in the user's purchasing behavior and quality of life.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention is a system for supporting blood glucose management in diabetic patients, aiming to collect and analyze users' biometric data and provide appropriate lifestyle guidance. This system is primarily implemented using two components: a "terminal" and a "server."

[0236] First, the device works in conjunction with an insulin pump and blood glucose sensor to periodically collect the user's biometric data. This includes real-time blood glucose measurement, recording of meals, and acquisition of exercise information. This data is transmitted to a server using secure communication methods.

[0237] The server centrally stores the received biometric data in a database and then applies an AI algorithm to analyze the data. Based on the analysis results, it predicts the user's health status and generates specific advice regarding diet and exercise. For example, it might advise reducing carbohydrate intake or suggest appropriate exercise timings in response to a predicted rise in blood sugar levels.

[0238] The device receives advice from the server and notifies the user. The user then adjusts their lifestyle based on this advice and reflects it in their daily life. In addition, the user inputs feedback about their own behavior into the device, which is then sent to the server, allowing the system to continuously improve its analysis model.

[0239] For example, if a user has high blood sugar levels in the morning, the server analyzes past data and current conditions to determine the appropriate insulin dosage. The terminal then communicates this information to the user, allowing them to administer the correct amount of insulin before breakfast. Furthermore, the system advises the user to adjust their carbohydrate intake at breakfast to help stabilize their blood sugar levels.

[0240] Furthermore, this system is equipped with an anomaly detection function that can immediately issue a warning if the user's blood glucose level fluctuates rapidly or exceeds a certain threshold. Users are expected to respond quickly upon receiving this notification. Combined with an early consultation function for medical professionals, it is expected to prevent serious situations.

[0241] Thus, the system of the present invention aims to provide personalized health support that is useful in the daily lives of diabetic patients and to contribute to improving their quality of life.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The device periodically collects the user's biometric data from insulin pumps and blood glucose sensors. Specifically, this includes real-time blood glucose levels, meal records, and exercise information. This data is temporarily stored in a local database on the device.

[0245] Step 2:

[0246] The device transmits the collected biometric data to the server using a secure protocol. The data is encrypted and transferred in a way that protects it from unauthorized access.

[0247] Step 3:

[0248] The server stores the received data in a cloud-based database and organizes it appropriately for each user. The stored data is then used for subsequent analysis.

[0249] Step 4:

[0250] The server applies AI algorithms to analyze the data. This analysis combines past trends with current data to predict the user's health status.

[0251] Step 5:

[0252] Based on the analysis results, the server generates personalized lifestyle advice for each user. This advice includes recommendations for appropriate insulin dosages, dietary adjustments, and exercise.

[0253] Step 6:

[0254] The device notifies the user of the generated advice. Based on the notification, the user adjusts their diet, exercise, and medication.

[0255] Step 7:

[0256] The user acts according to the advice provided by the system and enters feedback on the results into the terminal. This feedback is used for the next analysis.

[0257] Step 8:

[0258] The server analyzes user feedback and improves the AI ​​model to enhance the accuracy of future predictions. This enables the provision of more personalized assistance.

[0259] (Example 1)

[0260] 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."

[0261] Conventional health management systems have faced challenges in effectively collecting and analyzing users' biometric information and providing appropriate health guidance. In particular, there is a need to monitor biometric information such as blood glucose levels in real time and provide immediate health guidance based on that data. Furthermore, it is crucial to immediately recognize abnormal biometric information and take appropriate action. By solving these challenges, it is possible to improve the accuracy of predicting users' health status and enhance their quality of life.

[0262] 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.

[0263] In this invention, the server includes means for collecting the user's biometric information using a measuring device and transmitting it to a central processing unit; means for storing the received biometric information in a data storage device, performing information analysis using a generated artificial intelligence model, and predicting the user's health status; and means for providing guidance on diet and exercise to the user based on the prediction results. This makes it possible to grasp biometric information in real time and immediately predict the user's health status and provide lifestyle guidance.

[0264] "User" refers to an individual who uses this system and provides biometric information.

[0265] "Biometric information" refers to data related to the user's physiological state and lifestyle, such as blood glucose levels, dietary information, and exercise information.

[0266] A "measuring device" refers to hardware used to collect a user's biometric information in real time.

[0267] A "central processing unit" refers to the central device that receives and analyzes biological information transmitted from measuring devices.

[0268] A "data storage device" refers to a storage medium used to safely and efficiently store received biometric information.

[0269] A "generated artificial intelligence model" is a system equipped with algorithms specifically designed for analyzing biometric information and is used to predict the user's health status.

[0270] "Predicting health status" refers to the process of estimating future physiological changes based on collected biometric information.

[0271] "Health guidance" refers to activities that provide specific advice to encourage users to improve their lifestyle habits based on their predicted health condition.

[0272] "Issuing a warning" refers to sending a notification to alert the user to an abnormality when their biometric data exceeds a threshold.

[0273] This invention is a system for managing user health and is implemented primarily using two main components: a "terminal" and a "server."

[0274] The terminal is a device designed to collect biometric information, specifically equipped with a sensor for measuring blood glucose levels, an interface for inputting dietary information, and means for acquiring information about exercise. The terminal collects this data in real time as needed and transmits it to the server using a secure communication protocol.

[0275] The server functions as a central processing unit, storing biometric information transmitted from terminals in a data storage device. The stored data is analyzed using a generated artificial intelligence model. This model is used to predict the user's future health status based on past and present data.

[0276] For example, if a particular user's morning blood glucose level tends to rise sharply compared to past levels, the server analyzes this and suggests appropriate dietary restrictions and exercise guidance. This specific guidance is then communicated to the user via their device.

[0277] Furthermore, the system includes a function that immediately issues a warning when an abnormal value is detected. This allows users to respond quickly, which is expected to prevent serious deterioration of health conditions. By collaborating with medical professionals, further actions can be taken as needed.

[0278] (Example of prompt text to input to the generated AI model)

[0279] "If a user shows signs of a rapid spike in blood sugar, provide advice on effective carbohydrate management methods. Also, suggest appropriate timing for exercise."

[0280] This system aims to improve users' quality of life by providing them with personalized health management information.

[0281] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0282] Step 1:

[0283] The terminal collects the user's biometric information through a measuring device. Thereby, data such as blood glucose level, dietary information, and exercise information are obtained. Since this information cannot be used as it is, format processing is performed to convert it into a form that can be transmitted to the server in the next step.

[0284] Step 2:

[0285] The terminal transmits the formatted biometric information to the server using a secure communication protocol. The output here is the organized biometric information data, and the server receives it. The received data is directly stored in the data storage device within the server. This storage process also includes data backup.

[0286] Step 3:

[0287] The server inputs the accumulated biometric information into a generated AI model. Based on the input data, the AI model performs data analysis using machine learning algorithms to predict the user's health status. This prediction indicates future fluctuations in blood glucose levels and the level of health risks. As the output of the analysis, recommended lifestyle guidance is generated.

[0288] Step 4:

[0289] The server transmits the generated lifestyle guidance to the terminal via a notification function. The terminal notifies the user visually or audibly. Here, as specific operations, screen displays, voice guides, and vibration alerts are used to prompt the user's attention.

[0290] Step 5:

[0291] Users adjust their daily activities based on notifications from their devices. Based on the lifestyle guidance they receive as input, they review their diet and exercise at recommended times. Users input feedback on their actions into their devices, and this feedback is sent to the server for the next cycle. This process contributes to the continuous improvement of the AI ​​model.

[0292] (Application Example 1)

[0293] 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."

[0294] In modern society, diabetic patients are required to properly manage their health and take immediate action to prevent rapid fluctuations in blood glucose levels. However, manual data recording and analysis have limitations, and coordination in situations requiring rapid medical response is difficult. In particular, when self-management is difficult, information sharing with professionals and appropriate guidance may be delayed.

[0295] 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.

[0296] In this invention, the server includes means for collecting user behavior data and storing it in a remote information processing device, means for analyzing the stored information and predicting the user's health index, and means for transmitting the digitized information to a medical institution via a communication medium. As a result, the user's health status is monitored in real time, and when an abnormality is detected, the information is immediately shared with professionals, enabling a rapid response.

[0297] "User behavior data" refers to biological and behavioral information such as blood glucose levels, dietary information, and exercise information necessary to evaluate an individual's health status.

[0298] A "remote information processing device" refers to a computer system that receives, stores, and analyzes collected data.

[0299] A "health index" refers to a numerical or indicator representation of a user's health status, based on collected biometric data.

[0300] "Communication medium" refers to technical means such as internet lines and wireless communication used to transmit data safely and quickly.

[0301] "Digitized information" refers to information obtained by converting biometric data into a format that can be processed electronically.

[0302] "Medical institution" refers to organizations such as hospitals and clinics that employ professionals responsible for monitoring and treating users' health conditions.

[0303] This invention provides an innovative system to support blood glucose management for diabetic patients. The system consists of a terminal and a server, and these two elements complement each other to enable real-time health support for the user.

[0304] First, the device plays a central role in integrating user behavior data. Smartphones and smartwatches work in conjunction with blood glucose sensors, meal logging apps, and exercise tracking devices. This allows for the aggregation of various biometric information related to the user's health, which is then securely transmitted to a server via Bluetooth or Wi-Fi.

[0305] The server stores the received trend data in an integrated database and uses software such as a generative AI model to perform analysis using machine learning algorithms (e.g., TensorFlow). This analysis calculates the user's health index and provides predictions of potential blood glucose fluctuations and health status. The results are then used to generate prompt messages, which are appropriately fed back to the user. The prompt messages are output in the form of instructions to the AI ​​model, such as, "Based on the patient's blood glucose history and meal content this morning, please generate the cause of the rapid rise in blood glucose after lunch and provide advice on diet and exercise for prevention next time."

[0306] Feedback presented to the user in a compact and visual form, such as being displayed as a smartphone notification, prompts specific actions. When the user inputs health data or provides feedback, the system continuously updates the analysis model to provide more accurate health predictions and advice.

[0307] In addition, when there is a sudden change in the health condition, the server has the function of immediately transmitting information to medical professionals. This enables rapid medical support to be provided to the user in case of an emergency. Ultimately, these processes aim to improve the quality of life of diabetic patients.

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The terminal collects the user's trend data. Specifically, a smartphone or smartwatch collaborates with a blood glucose sensor and a motion recording app to obtain data in real time. These data include the user's diet, exercise amount, and very important blood glucose levels. The input is these biological data, and the output is a set of data.

[0311] Step 2:

[0312] The terminal transmits the trend data collected to the server. Using Bluetooth or Wi-Fi, the data is uploaded safely and quickly. At this time, the input is the unsent data on the device, and the output is the data stored on the server. When the server receives the data, it securely stores it in the storage.

[0313] Step 3:

[0314] The server analyzes the received data using a machine learning algorithm. Specifically, a generative AI model is used to predict health indices. The input is user behavior data stored on the server, and the output is a health prediction index and points to watch out for generated by the analysis. The data is cleansed and converted into a format suitable for the algorithm.

[0315] Step 4:

[0316] The server generates prompt messages based on the analysis results and sends them to the terminal. A generation AI model is used to create personalized lifestyle advice tailored to each user. The input is the health prediction result, and the output is the generated prompt message. This prompt message is presented in language that is easy for the user to understand.

[0317] Step 5:

[0318] The device receives a prompt message from the server and notifies the user. The generated advice is displayed visually and clearly using the smartphone's notification function. The input is the generated prompt message, and the output is the advice the user receives on the screen.

[0319] Step 6:

[0320] Users input feedback into their devices and send it to the server. This feedback includes the actions taken and the perceived effects. The input is the user's feedback, and the output is the feedback data stored on the server for use in subsequent analyses. This allows the analysis model to improve over time.

[0321] Step 7:

[0322] When the server detects sudden fluctuations or abnormal values, it immediately sends a notification to healthcare institutions. Trend data is continuously monitored, and if a specific threshold is exceeded, information is automatically sent to healthcare professionals. The input is analyzed data stored on the server, and the output is alert information sent to healthcare institutions. This step enables rapid medical response.

[0323] 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.

[0324] This invention is a system that aims to improve the health and quality of life of diabetic patients by collecting and analyzing both the user's biometric data and emotional state, and providing individually optimized health management advice. This system consists of both "terminal" and "server" components, and is further equipped with an emotion engine that recognizes the user's emotions.

[0325] The device periodically collects blood glucose data from insulin pumps and blood glucose sensors, meal information entered by the user, and exercise information. In parallel, the emotion engine analyzes the user's voice and facial expression data to recognize the user's emotional state. The recognized emotional data, along with other biometric data, is temporarily stored on the device and transmitted to the server via a secure communication method.

[0326] The server stores all received data in a cloud database and analyzes it. The analysis uses AI algorithms to comprehensively evaluate the user's past health data and current emotional data to predict their health status. Based on this prediction, specific lifestyle guidance is created for each individual user, including appropriate insulin adjustments, dietary choices, and exercise timing.

[0327] This guidance is customized to take into account the user's emotions as identified by the emotion engine. For example, if the user is feeling stressed, it may recommend rest to reduce stress. In this way, lifestyle guidance takes into account not only data-based health status but also psychological well-being.

[0328] For example, if the system's emotion engine determines that a user is feeling "stressed" when measuring their blood glucose levels in the morning, the server will notify the user via their device with suggestions for relaxing meal options or recommendations for light exercise. This allows users to become more aware of their own health and take data-driven actions without feeling overwhelmed.

[0329] This invention enables continuous system improvement through user feedback and allows for flexible responses to changes in the user's condition. In this way, the present invention aims to significantly reduce anxiety and burden in daily life by providing advanced support for self-care for diabetic patients.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The device collects the user's blood glucose levels in real time via an insulin pump or blood glucose sensor and stores the data in a local database. It also simultaneously collects meal and exercise information manually entered by the user.

[0333] Step 2:

[0334] An emotion engine runs on the device and analyzes the user's emotional state from their voice and facial expressions. This provides emotional information, such as whether the user is stressed or relaxed. This information is also stored in a local database.

[0335] Step 3:

[0336] The device encrypts all collected data and sends it to the server using a secure communication protocol. This transmission is performed periodically and updated whenever new data becomes available.

[0337] Step 4:

[0338] The server stores the received data in a cloud database and analyzes it using an AI algorithm. The analysis combines past blood glucose information with the user's current emotional state to predict their health.

[0339] Step 5:

[0340] Based on the analysis results, the server generates personalized lifestyle guidance for the user. This guidance includes not only blood sugar management but also mental health advice tailored to the user's emotional state. For example, if the user is feeling stressed, relaxation activities and exercises will be suggested.

[0341] Step 6:

[0342] The device notifies the user of lifestyle guidance received from the server. Based on this information, the user makes choices regarding meals, insulin administration timing, and appropriate exercise.

[0343] Step 7:

[0344] Users input feedback into their device, describing their feelings and changes in emotions after following the advice they received. This feedback is used to improve the system in the future.

[0345] Step 8:

[0346] The server uses user feedback to improve new analysis models and adjust the AI ​​algorithms. This improves the accuracy of subsequent analyses and provides users with more personalized assistance.

[0347] (Example 2)

[0348] 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".

[0349] Traditional health management systems for diabetic patients focus on collecting and analyzing users' biometric data, but they have the drawback of not considering the user's psychological or emotional state, thus failing to provide sufficient individualization. Furthermore, existing systems have limited predictive accuracy, which can prevent them from providing appropriate lifestyle guidance to users. As a result, there are shortcomings in improving the quality of life for users.

[0350] 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.

[0351] In this invention, the server includes means for collecting and storing the user's biometric and emotional data in a storage device, means for analyzing the stored biometric and emotional data using an artificial intelligence algorithm to predict the user's health status, and means for providing personalized lifestyle guidance to the user based on the prediction results and the user's emotional status. This enables advanced individualized health management that takes into account the user's psychological health.

[0352] A "user" refers to a person who uses the system to manage their own health.

[0353] "Biometric data" refers to data that numerically represents the user's physical condition, such as blood glucose levels, dietary information, and exercise information.

[0354] "Emotional data" refers to data that represents the psychological state of a user, analyzed from their voice and facial expressions.

[0355] "Storage device" refers to hardware or software used to temporarily or continuously store collected data.

[0356] "Artificial intelligence algorithms" refer to computational methods that automate data analysis and predict user states.

[0357] "Means of predicting health status" refers to functions that include a process of predicting a user's future health status based on collected data.

[0358] "Personalized lifestyle guidance" refers to health management advice tailored individually based on the user's biometric and emotional data.

[0359] "Warning mechanisms" refer to features that provide alerts when abnormal data or emotional states are detected based on established criteria.

[0360] This invention is a system that provides individually optimized health management advice by collecting and analyzing both the user's biometric and emotional data. The system consists of two main components: a terminal and a server, and also incorporates an emotion engine for recognizing the user's emotions.

[0361] The device uses an insulin pump and blood glucose sensors to measure the user's blood glucose levels. This hardware periodically collects the user's physical information and transfers that data to the device. It also has a software interface for the user to input diet and exercise information, allowing for manual data entry.

[0362] On the other hand, the emotion engine recognizes the user's emotional state by analyzing the user's voice and video data from the camera. This emotional data is used to assess the psychological stress the user may be facing.

[0363] The device temporarily stores collected biometric and emotional data in its memory. This data is then transmitted to a server using secure communication methods. The server stores the received data in a cloud database and analyzes it using a generative AI model. This model comprehensively evaluates the user's past health data and current emotional data to predict the user's health status.

[0364] Based on the analysis results, the server creates personalized lifestyle guidance for the user. This guidance takes into account things like how to adjust insulin, food choices, and the timing of exercise. Furthermore, the guidance is further customized according to the user's emotions as recognized by the emotion engine. For example, if the user is feeling stressed, the guidance may include suggestions for relaxation activities and recommendations for light exercise.

[0365] User feedback is crucial in the implementation of this system. This feedback will be used to continuously improve the system and enable it to respond to changes in user needs.

[0366] As a concrete example, the prompt text to be input to the generative AI model would be as follows:

[0367] "What kind of relaxation routine would you recommend to a user experiencing stress in the morning?"

[0368] Thus, the present invention aims to provide advanced support for self-care in diabetic patients and improve their quality of life.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The device periodically receives the user's blood glucose data from an insulin pump or blood glucose sensor. Furthermore, the user inputs dietary and exercise information via a dedicated application. This information is stored in the device's temporary memory.

[0372] The inputs are sensor outputs and user inputs, and the output is biometric data stored in temporary memory. This prepares an accurate dataset for subsequent analysis.

[0373] Step 2:

[0374] Simultaneously, the device uses its built-in emotion engine to collect emotional data using the user's voice and camera. This data is identified through voice-to-text conversion and facial expression analysis. Emotional information is temporarily stored along with biometric data.

[0375] The input is the user's voice and video data, and the output is analyzed emotion data. This process records the user's psychological state as numerical data.

[0376] Step 3:

[0377] The device securely transmits biometric and emotional data to the server. This communication is conducted using encryption protocols to protect privacy.

[0378] The input is a dataset stored on the device, and the output is encrypted data sent to the server. This ensures data integrity and secure data sharing.

[0379] Step 4:

[0380] The server stores the received data in a cloud database and performs analysis using a generated AI model. It integrates the user's past and current data to predict their health status.

[0381] The input is a dataset stored on the server, and the output is an assessment of the predicted health status. This process calculates a health prediction for each individual user.

[0382] Step 5:

[0383] The server generates personalized lifestyle guidance based on the analysis results. This guidance takes into account the user's emotional state and includes appropriate insulin administration and dietary advice.

[0384] The input is the analysis results from an AI model, and the output is customized health guidance. This allows for the proposal of health management optimized for the user.

[0385] Step 6:

[0386] The device receives lifestyle guidance from the server and notifies the user. The guidance is conveyed to the user through voice and visual interfaces.

[0387] The input is guidance data from the server, and the output is a notification to the user. This process allows users to efficiently manage their daily health.

[0388] (Application Example 2)

[0389] 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."

[0390] In managing the health of diabetic patients, challenges include a lack of personalized health support that takes into account daily fluctuating biometric data and emotional states, as well as a lack of appropriate guidance for engaging in consumption activities without compromising health.

[0391] 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.

[0392] In this invention, the server includes means for collecting and storing the user's biometric and emotional state data in a database, means for analyzing the stored data and predicting the user's health status, and means for providing the user with a health-related plan that takes the emotional state into consideration based on the prediction results. This enables the individual optimization of health management and consumption activities, improving the quality of life and reducing the burden on diabetic patients.

[0393] "User biometric data" refers to information used to assess a user's health status, such as blood glucose levels, dietary information, and exercise information.

[0394] "Emotional state data" refers to information indicating the user's psychological state, obtained from their voice and facial expressions.

[0395] "Methods for saving data to a database" refers to a method of securely and efficiently storing collected data for later analysis.

[0396] "Methods for analysis and prediction" refers to methods that use AI algorithms and other tools based on collected data to evaluate the user's health risks and predict their future condition.

[0397] "Means of providing health-related plans" refers to methods of providing specific advice for improving lifestyle habits and maintaining health, based on the user's health status and emotions.

[0398] "A method of recommending products using electronic payment methods" refers to a method of suggesting healthy products at the appropriate time, taking into account the user's health data.

[0399] "Means of issuing warnings when abnormal values ​​are detected" refers to a method of quickly notifying a user when their biometric data exceeds a set range.

[0400] The system for realizing this invention mainly consists of a terminal and a server. The terminal is responsible for collecting the user's biometric data and emotional state data and securely transmitting this data to the server. The user's biometric data includes blood glucose levels, dietary information, and exercise information, while emotional state data is obtained through voice and facial expression analysis. This data is collected by hardware such as insulin pumps, blood glucose sensors, cameras, and microphones.

[0401] The server stores data in a database and performs analysis using AI algorithms. Cloud services (e.g., AWS Lambda, Amazon RDS) and AI libraries (e.g., TensorFlow) are used for the analysis, predicting the user's health status based on their past and present data. Furthermore, using electronic payment methods, the system recommends healthy products that take the user's emotions into consideration, based on the data analysis results.

[0402] For example, if a user is experiencing stress while shopping, the system will recommend products that can alleviate that stress, such as herbal tea. This recommendation is presented in a way that the user can see in real time via their smartphone.

[0403] When using the generative AI model, prompts such as "Suggest products that can alleviate stress based on the user's current blood glucose level and emotional data" are used. This allows the system to respond quickly to the user's situation.

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The device collects the user's blood glucose levels from an insulin pump or blood glucose sensor. Furthermore, it uses a camera and microphone to capture the user's facial expressions and voice data. This data is collected as input to the device. The device processes this biometric data into an appropriate format and stores it temporarily. As output, integrated biometric data and emotional state data are generated.

[0407] Step 2:

[0408] The device transmits the collected data to the server using a secure communication method. During this process, the data is protected using encryption technology, ensuring its integrity and confidentiality in transmission. Inputs include processed biometric data and emotional state data, while the output, encrypted data, reaches the server.

[0409] Step 3:

[0410] The server saves the received data to the database. This saving process includes data integrity checks and ensures the data is stored in a format compatible with the database structure. The input is the data sent from the terminal, and the output is the data saved in a format accessible by the database.

[0411] Step 4:

[0412] The server analyzes the stored data using an AI algorithm. This is a process that utilizes a generative AI model to predict the user's health status. The input data consists of stored biometric and emotional state data, and the output is the predicted health status. Based on these results, the server generates health-related plans.

[0413] Step 5:

[0414] The server recommends the most suitable health products to the user based on the analysis results. This is made possible through electronic payment methods. The inputs are insights from the predicted health state and the user's emotional state, and the output is product recommendation information tailored to the user's health state. Specifically, for example, if the user is stressed, it will suggest products with relaxing effects.

[0415] Step 6:

[0416] The user receives recommended information from the server via their device. Based on the information received, the user can engage in healthy consumption activities. The input for this step is data from the server, and the output is manifested as improvements in the user's purchasing behavior and quality of life.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention is a system for supporting blood glucose management in diabetic patients, aiming to collect and analyze users' biometric data and provide appropriate lifestyle guidance. This system is primarily implemented using two components: a "terminal" and a "server."

[0434] First, the device works in conjunction with an insulin pump and blood glucose sensor to periodically collect the user's biometric data. This includes real-time blood glucose measurement, recording of meals, and acquisition of exercise information. This data is transmitted to a server using secure communication methods.

[0435] The server centrally stores the received biometric data in a database and then applies an AI algorithm to analyze the data. Based on the analysis results, it predicts the user's health status and generates specific advice regarding diet and exercise. For example, it might advise reducing carbohydrate intake or suggest appropriate exercise timings in response to a predicted rise in blood sugar levels.

[0436] The device receives advice from the server and notifies the user. The user then adjusts their lifestyle based on this advice and reflects it in their daily life. In addition, the user inputs feedback about their own behavior into the device, which is then sent to the server, allowing the system to continuously improve its analysis model.

[0437] For example, if a user has high blood sugar levels in the morning, the server analyzes past data and current conditions to determine the appropriate insulin dosage. The terminal then communicates this information to the user, allowing them to administer the correct amount of insulin before breakfast. Furthermore, the system advises the user to adjust their carbohydrate intake at breakfast to help stabilize their blood sugar levels.

[0438] Furthermore, this system is equipped with an anomaly detection function that can immediately issue a warning if the user's blood glucose level fluctuates rapidly or exceeds a certain threshold. Users are expected to respond quickly upon receiving this notification. Combined with an early consultation function for medical professionals, it is expected to prevent serious situations.

[0439] Thus, the system of the present invention aims to provide personalized health support that is useful in the daily lives of diabetic patients and to contribute to improving their quality of life.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The device periodically collects the user's biometric data from insulin pumps and blood glucose sensors. Specifically, this includes real-time blood glucose levels, meal records, and exercise information. This data is temporarily stored in a local database on the device.

[0443] Step 2:

[0444] The device transmits the collected biometric data to the server using a secure protocol. The data is encrypted and transferred in a way that protects it from unauthorized access.

[0445] Step 3:

[0446] The server stores the received data in a cloud-based database and organizes it appropriately for each user. The stored data is then used for subsequent analysis.

[0447] Step 4:

[0448] The server applies AI algorithms to analyze the data. This analysis combines past trends with current data to predict the user's health status.

[0449] Step 5:

[0450] Based on the analysis results, the server generates personalized lifestyle advice for each user. This advice includes recommendations for appropriate insulin dosages, dietary adjustments, and exercise.

[0451] Step 6:

[0452] The device notifies the user of the generated advice. Based on the notification, the user adjusts their diet, exercise, and medication.

[0453] Step 7:

[0454] The user acts according to the advice provided by the system and enters feedback on the results into the terminal. This feedback is used for the next analysis.

[0455] Step 8:

[0456] The server analyzes user feedback and improves the AI ​​model to enhance the accuracy of future predictions. This enables the provision of more personalized assistance.

[0457] (Example 1)

[0458] 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."

[0459] Conventional health management systems have faced challenges in effectively collecting and analyzing users' biometric information and providing appropriate health guidance. In particular, there is a need to monitor biometric information such as blood glucose levels in real time and provide immediate health guidance based on that data. Furthermore, it is crucial to immediately recognize abnormal biometric information and take appropriate action. By solving these challenges, it is possible to improve the accuracy of predicting users' health status and enhance their quality of life.

[0460] 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.

[0461] In this invention, the server includes means for collecting the user's biometric information using a measuring device and transmitting it to a central processing unit; means for storing the received biometric information in a data storage device, performing information analysis using a generated artificial intelligence model, and predicting the user's health status; and means for providing guidance on diet and exercise to the user based on the prediction results. This makes it possible to grasp biometric information in real time and immediately predict the user's health status and provide lifestyle guidance.

[0462] "User" refers to an individual who uses this system and provides biometric information.

[0463] "Biometric information" refers to data related to the user's physiological state and lifestyle, such as blood glucose levels, dietary information, and exercise information.

[0464] A "measuring device" refers to hardware used to collect a user's biometric information in real time.

[0465] A "central processing unit" refers to the central device that receives and analyzes biological information transmitted from measuring devices.

[0466] A "data storage device" refers to a storage medium used to safely and efficiently store received biometric information.

[0467] A "generated artificial intelligence model" is a system equipped with algorithms specifically designed for analyzing biometric information and is used to predict the user's health status.

[0468] "Predicting health status" refers to the process of estimating future physiological changes based on collected biometric information.

[0469] "Health guidance" refers to activities that provide specific advice to encourage users to improve their lifestyle habits based on their predicted health condition.

[0470] "Issuing a warning" refers to sending a notification to alert the user to an abnormality when their biometric data exceeds a threshold.

[0471] This invention is a system for managing user health and is implemented primarily using two main components: a "terminal" and a "server."

[0472] The terminal is a device designed to collect biometric information, specifically equipped with a sensor for measuring blood glucose levels, an interface for inputting dietary information, and means for acquiring information about exercise. The terminal collects this data in real time as needed and transmits it to the server using a secure communication protocol.

[0473] The server functions as a central processing unit, storing biometric information transmitted from terminals in a data storage device. The stored data is analyzed using a generated artificial intelligence model. This model is used to predict the user's future health status based on past and present data.

[0474] For example, if a particular user's morning blood glucose level tends to rise sharply compared to past levels, the server analyzes this and suggests appropriate dietary restrictions and exercise guidance. This specific guidance is then communicated to the user via their device.

[0475] Furthermore, the system includes a function that immediately issues a warning when an abnormal value is detected. This allows users to respond quickly, which is expected to prevent serious deterioration of health conditions. By collaborating with medical professionals, further actions can be taken as needed.

[0476] (Example of prompt text to input to the generated AI model)

[0477] "If a user shows signs of a rapid spike in blood sugar, provide advice on effective carbohydrate management methods. Also, suggest appropriate timing for exercise."

[0478] This system aims to improve users' quality of life by providing them with personalized health management information.

[0479] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0480] Step 1:

[0481] The terminal collects the user's biometric information through a measuring device. This obtains data such as blood glucose levels, dietary information, and exercise information. Since this information cannot be used directly, it undergoes data formatting processing and is converted into a format that can be sent to the server in the next step.

[0482] Step 2:

[0483] The terminal transmits formatted biometric information to the server using a secure communication protocol. The output here is organized biometric data, which the server receives. The received data is stored directly in the server's data storage device. This storage process also includes data backup.

[0484] Step 3:

[0485] The server inputs accumulated biometric information into a generating AI model. Based on the input data, the AI ​​model uses machine learning algorithms to analyze the data and predict the user's health status. This prediction indicates future blood glucose fluctuations and the level of health risk. As an output of the analysis, recommended lifestyle guidance is generated.

[0486] Step 4:

[0487] The server sends the generated lifestyle guidance to the terminal via a notification function. The terminal notifies the user of this visually or audibly. Specifically, this involves using screen displays, voice guidance, or vibration alerts to draw the user's attention.

[0488] Step 5:

[0489] Users adjust their daily activities based on notifications from their devices. Based on the lifestyle guidance they receive as input, they review their diet and exercise at recommended times. Users input feedback on their actions into their devices, and this feedback is sent to the server for the next cycle. This process contributes to the continuous improvement of the AI ​​model.

[0490] (Application Example 1)

[0491] 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."

[0492] In modern society, diabetic patients are required to properly manage their health and take immediate action to prevent rapid fluctuations in blood glucose levels. However, manual data recording and analysis have limitations, and coordination in situations requiring rapid medical response is difficult. In particular, when self-management is difficult, information sharing with professionals and appropriate guidance may be delayed.

[0493] 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.

[0494] In this invention, the server includes means for collecting user behavior data and storing it in a remote information processing device, means for analyzing the stored information and predicting the user's health index, and means for transmitting the digitized information to a medical institution via a communication medium. As a result, the user's health status is monitored in real time, and when an abnormality is detected, the information is immediately shared with professionals, enabling a rapid response.

[0495] "User behavior data" refers to biological and behavioral information such as blood glucose levels, dietary information, and exercise information necessary to evaluate an individual's health status.

[0496] A "remote information processing device" refers to a computer system that receives, stores, and analyzes collected data.

[0497] A "health index" refers to a numerical or indicator representation of a user's health status, based on collected biometric data.

[0498] "Communication medium" refers to technical means such as internet lines and wireless communication used to transmit data safely and quickly.

[0499] "Digitized information" refers to information obtained by converting biometric data into a format that can be processed electronically.

[0500] "Medical institution" refers to organizations such as hospitals and clinics that employ professionals responsible for monitoring and treating users' health conditions.

[0501] This invention provides an innovative system to support blood glucose management for diabetic patients. The system consists of a terminal and a server, and these two elements complement each other to enable real-time health support for the user.

[0502] First, the device plays a central role in integrating user behavior data. Smartphones and smartwatches work in conjunction with blood glucose sensors, meal logging apps, and exercise tracking devices. This allows for the aggregation of various biometric information related to the user's health, which is then securely transmitted to a server via Bluetooth or Wi-Fi.

[0503] The server stores the received trend data in an integrated database and uses software such as a generative AI model to perform analysis using machine learning algorithms (e.g., TensorFlow). This analysis calculates the user's health index and provides predictions of potential blood glucose fluctuations and health status. The results are then used to generate prompt messages, which are appropriately fed back to the user. The prompt messages are output in the form of instructions to the AI ​​model, such as, "Based on the patient's blood glucose history and meal content this morning, please generate the cause of the rapid rise in blood glucose after lunch and provide advice on diet and exercise for prevention next time."

[0504] The feedback, presented to the user in a compact, visual format, may appear as a smartphone notification, for example, prompting specific actions. As users input health data and provide feedback, the system continuously updates its analytical models, providing more accurate health predictions and advice.

[0505] In addition, the server has the ability to instantly transmit information to medical professionals in the event of a sudden change in health status. This makes it possible to provide users with rapid medical assistance in emergencies. Ultimately, the goal is to improve the quality of life for people with diabetes through these processes.

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] The device collects user behavior data. Specifically, smartphones and smartwatches work in conjunction with blood glucose sensors and exercise tracking apps to acquire data in real time. This data includes the user's meals, exercise levels, and, most importantly, blood glucose levels. The input is this biometric data, and the output is a set of data.

[0509] Step 2:

[0510] The device collects behavioral data and sends it to the server. Bluetooth or Wi-Fi is used to upload the data securely and quickly. In this process, the input is unsent data on the device, and the output is data stored on the server. Upon receiving the data, the server securely stores it in its storage.

[0511] Step 3:

[0512] The server analyzes the received data using a machine learning algorithm. Specifically, a generative AI model is used to predict health indices. The input is user behavior data stored on the server, and the output is a health prediction index and points to watch out for generated by the analysis. The data is cleansed and converted into a format suitable for the algorithm.

[0513] Step 4:

[0514] The server generates prompt messages based on the analysis results and sends them to the terminal. A generation AI model is used to create personalized lifestyle advice tailored to each user. The input is the health prediction result, and the output is the generated prompt message. This prompt message is presented in language that is easy for the user to understand.

[0515] Step 5:

[0516] The device receives a prompt message from the server and notifies the user. The generated advice is displayed visually and clearly using the smartphone's notification function. The input is the generated prompt message, and the output is the advice the user receives on the screen.

[0517] Step 6:

[0518] Users input feedback into their devices and send it to the server. This feedback includes the actions taken and the perceived effects. The input is the user's feedback, and the output is the feedback data stored on the server for use in subsequent analyses. This allows the analysis model to improve over time.

[0519] Step 7:

[0520] When the server detects sudden fluctuations or abnormal values, it immediately sends a notification to healthcare institutions. Trend data is continuously monitored, and if a specific threshold is exceeded, information is automatically sent to healthcare professionals. The input is analyzed data stored on the server, and the output is alert information sent to healthcare institutions. This step enables rapid medical response.

[0521] 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.

[0522] This invention is a system that aims to improve the health and quality of life of diabetic patients by collecting and analyzing both the user's biometric data and emotional state, and providing individually optimized health management advice. This system consists of both "terminal" and "server" components, and is further equipped with an emotion engine that recognizes the user's emotions.

[0523] The device periodically collects blood glucose data from insulin pumps and blood glucose sensors, meal information entered by the user, and exercise information. In parallel, the emotion engine analyzes the user's voice and facial expression data to recognize the user's emotional state. The recognized emotional data, along with other biometric data, is temporarily stored on the device and transmitted to the server via a secure communication method.

[0524] The server stores all received data in a cloud database and analyzes it. The analysis uses AI algorithms to comprehensively evaluate the user's past health data and current emotional data to predict their health status. Based on this prediction, specific lifestyle guidance is created for each individual user, including appropriate insulin adjustments, dietary choices, and exercise timing.

[0525] This guidance is customized to take into account the user's emotions as identified by the emotion engine. For example, if the user is feeling stressed, it may recommend rest to reduce stress. In this way, lifestyle guidance takes into account not only data-based health status but also psychological well-being.

[0526] For example, if the system's emotion engine determines that a user is feeling "stressed" when measuring their blood glucose levels in the morning, the server will notify the user via their device with suggestions for relaxing meal options or recommendations for light exercise. This allows users to become more aware of their own health and take data-driven actions without feeling overwhelmed.

[0527] This invention enables continuous system improvement through user feedback and allows for flexible responses to changes in the user's condition. In this way, the present invention aims to significantly reduce anxiety and burden in daily life by providing advanced support for self-care for diabetic patients.

[0528] The following describes the processing flow.

[0529] Step 1:

[0530] The device collects the user's blood glucose levels in real time via an insulin pump or blood glucose sensor and stores the data in a local database. It also simultaneously collects meal and exercise information manually entered by the user.

[0531] Step 2:

[0532] An emotion engine runs on the device and analyzes the user's emotional state from their voice and facial expressions. This provides emotional information, such as whether the user is stressed or relaxed. This information is also stored in a local database.

[0533] Step 3:

[0534] The device encrypts all collected data and sends it to the server using a secure communication protocol. This transmission is performed periodically and updated whenever new data becomes available.

[0535] Step 4:

[0536] The server stores the received data in a cloud database and analyzes it using an AI algorithm. The analysis combines past blood glucose information with the user's current emotional state to predict their health.

[0537] Step 5:

[0538] Based on the analysis results, the server generates personalized lifestyle guidance for the user. This guidance includes not only blood sugar management but also mental health advice tailored to the user's emotional state. For example, if the user is feeling stressed, relaxation activities and exercises will be suggested.

[0539] Step 6:

[0540] The device notifies the user of lifestyle guidance received from the server. Based on this information, the user makes choices regarding meals, insulin administration timing, and appropriate exercise.

[0541] Step 7:

[0542] Users input feedback into their device, describing their feelings and changes in emotions after following the advice they received. This feedback is used to improve the system in the future.

[0543] Step 8:

[0544] The server uses user feedback to improve new analysis models and adjust the AI ​​algorithms. This improves the accuracy of subsequent analyses and provides users with more personalized assistance.

[0545] (Example 2)

[0546] 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."

[0547] Traditional health management systems for diabetic patients focus on collecting and analyzing users' biometric data, but they have the drawback of not considering the user's psychological or emotional state, thus failing to provide sufficient individualization. Furthermore, existing systems have limited predictive accuracy, which can prevent them from providing appropriate lifestyle guidance to users. As a result, there are shortcomings in improving the quality of life for users.

[0548] 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.

[0549] In this invention, the server includes means for collecting and storing the user's biometric and emotional data in a storage device, means for analyzing the stored biometric and emotional data using an artificial intelligence algorithm to predict the user's health status, and means for providing personalized lifestyle guidance to the user based on the prediction results and the user's emotional status. This enables advanced individualized health management that takes into account the user's psychological health.

[0550] A "user" refers to a person who uses the system to manage their own health.

[0551] "Biometric data" refers to data that numerically represents the user's physical condition, such as blood glucose levels, dietary information, and exercise information.

[0552] "Emotional data" refers to data that represents the psychological state of a user, analyzed from their voice and facial expressions.

[0553] "Storage device" refers to hardware or software used to temporarily or continuously store collected data.

[0554] "Artificial intelligence algorithms" refer to computational methods that automate data analysis and predict user states.

[0555] "Means of predicting health status" refers to functions that include a process of predicting a user's future health status based on collected data.

[0556] "Personalized lifestyle guidance" refers to health management advice tailored individually based on the user's biometric and emotional data.

[0557] "Warning mechanisms" refer to features that provide alerts when abnormal data or emotional states are detected based on established criteria.

[0558] This invention is a system that provides individually optimized health management advice by collecting and analyzing both the user's biometric and emotional data. The system consists of two main components: a terminal and a server, and also incorporates an emotion engine for recognizing the user's emotions.

[0559] The device uses an insulin pump and blood glucose sensors to measure the user's blood glucose levels. This hardware periodically collects the user's physical information and transfers that data to the device. It also has a software interface for the user to input diet and exercise information, allowing for manual data entry.

[0560] On the other hand, the emotion engine recognizes the user's emotional state by analyzing the user's voice and video data from the camera. This emotional data is used to assess the psychological stress the user may be facing.

[0561] The device temporarily stores collected biometric and emotional data in its memory. This data is then transmitted to a server using secure communication methods. The server stores the received data in a cloud database and analyzes it using a generative AI model. This model comprehensively evaluates the user's past health data and current emotional data to predict the user's health status.

[0562] Based on the analysis results, the server creates personalized lifestyle guidance for the user. This guidance takes into account things like how to adjust insulin, food choices, and the timing of exercise. Furthermore, the guidance is further customized according to the user's emotions as recognized by the emotion engine. For example, if the user is feeling stressed, the guidance may include suggestions for relaxation activities and recommendations for light exercise.

[0563] User feedback is crucial in the implementation of this system. This feedback will be used to continuously improve the system and enable it to respond to changes in user needs.

[0564] As a concrete example, the prompt text to be input to the generative AI model would be as follows:

[0565] "What kind of relaxation routine would you recommend to a user experiencing stress in the morning?"

[0566] Thus, the present invention aims to provide advanced support for self-care in diabetic patients and improve their quality of life.

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] The device periodically receives the user's blood glucose data from an insulin pump or blood glucose sensor. Furthermore, the user inputs dietary and exercise information via a dedicated application. This information is stored in the device's temporary memory.

[0570] The inputs are sensor outputs and user inputs, and the output is biometric data stored in temporary memory. This prepares an accurate dataset for subsequent analysis.

[0571] Step 2:

[0572] Simultaneously, the device uses its built-in emotion engine to collect emotional data using the user's voice and camera. This data is identified through voice-to-text conversion and facial expression analysis. Emotional information is temporarily stored along with biometric data.

[0573] The input is the user's voice and video data, and the output is analyzed emotion data. This process records the user's psychological state as numerical data.

[0574] Step 3:

[0575] The device securely transmits biometric and emotional data to the server. This communication is conducted using encryption protocols to protect privacy.

[0576] The input is a dataset stored on the device, and the output is encrypted data sent to the server. This ensures data integrity and secure data sharing.

[0577] Step 4:

[0578] The server stores the received data in a cloud database and performs analysis using a generated AI model. It integrates the user's past and current data to predict their health status.

[0579] The input is a dataset stored on the server, and the output is an assessment of the predicted health status. This process calculates a health prediction for each individual user.

[0580] Step 5:

[0581] The server generates personalized lifestyle guidance based on the analysis results. This guidance takes into account the user's emotional state and includes appropriate insulin administration and dietary advice.

[0582] The input is the analysis results from an AI model, and the output is customized health guidance. This allows for the proposal of health management optimized for the user.

[0583] Step 6:

[0584] The device receives lifestyle guidance from the server and notifies the user. The guidance is conveyed to the user through voice and visual interfaces.

[0585] The input is guidance data from the server, and the output is a notification to the user. This process allows users to efficiently manage their daily health.

[0586] (Application Example 2)

[0587] 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."

[0588] In managing the health of diabetic patients, challenges include a lack of personalized health support that takes into account daily fluctuating biometric data and emotional states, as well as a lack of appropriate guidance for engaging in consumption activities without compromising health.

[0589] 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.

[0590] In this invention, the server includes means for collecting and storing the user's biometric and emotional state data in a database, means for analyzing the stored data and predicting the user's health status, and means for providing the user with a health-related plan that takes the emotional state into consideration based on the prediction results. This enables the individual optimization of health management and consumption activities, improving the quality of life and reducing the burden on diabetic patients.

[0591] "User biometric data" refers to information used to assess a user's health status, such as blood glucose levels, dietary information, and exercise information.

[0592] "Emotional state data" refers to information indicating the user's psychological state, obtained from their voice and facial expressions.

[0593] "Methods for saving data to a database" refers to a method of securely and efficiently storing collected data for later analysis.

[0594] "Methods for analysis and prediction" refers to methods that use AI algorithms and other tools based on collected data to evaluate the user's health risks and predict their future condition.

[0595] "Means of providing health-related plans" refers to methods of providing specific advice for improving lifestyle habits and maintaining health, based on the user's health status and emotions.

[0596] "A method of recommending products using electronic payment methods" refers to a method of suggesting healthy products at the appropriate time, taking into account the user's health data.

[0597] "Means of issuing warnings when abnormal values ​​are detected" refers to a method of quickly notifying a user when their biometric data exceeds a set range.

[0598] The system for realizing this invention mainly consists of a terminal and a server. The terminal is responsible for collecting the user's biometric data and emotional state data and securely transmitting this data to the server. The user's biometric data includes blood glucose levels, dietary information, and exercise information, while emotional state data is obtained through voice and facial expression analysis. This data is collected by hardware such as insulin pumps, blood glucose sensors, cameras, and microphones.

[0599] The server stores data in a database and performs analysis using AI algorithms. Cloud services (e.g., AWS Lambda, Amazon RDS) and AI libraries (e.g., TensorFlow) are used for the analysis, predicting the user's health status based on their past and present data. Furthermore, using electronic payment methods, the system recommends healthy products that take the user's emotions into consideration, based on the data analysis results.

[0600] For example, if a user is experiencing stress while shopping, the system will recommend products that can alleviate that stress, such as herbal tea. This recommendation is presented in a way that the user can see in real time via their smartphone.

[0601] When using the generative AI model, prompts such as "Suggest products that can alleviate stress based on the user's current blood glucose level and emotional data" are used. This allows the system to respond quickly to the user's situation.

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The device collects the user's blood glucose levels from an insulin pump or blood glucose sensor. Furthermore, it uses a camera and microphone to capture the user's facial expressions and voice data. This data is collected as input to the device. The device processes this biometric data into an appropriate format and stores it temporarily. As output, integrated biometric data and emotional state data are generated.

[0605] Step 2:

[0606] The device transmits the collected data to the server using a secure communication method. During this process, the data is protected using encryption technology, ensuring its integrity and confidentiality in transmission. Inputs include processed biometric data and emotional state data, while the output, encrypted data, reaches the server.

[0607] Step 3:

[0608] The server saves the received data to the database. This saving process includes data integrity checks and ensures the data is stored in a format compatible with the database structure. The input is the data sent from the terminal, and the output is the data saved in a format accessible by the database.

[0609] Step 4:

[0610] The server analyzes the stored data using an AI algorithm. This is a process that utilizes a generative AI model to predict the user's health status. The input data consists of stored biometric and emotional state data, and the output is the predicted health status. Based on these results, the server generates health-related plans.

[0611] Step 5:

[0612] The server recommends the most suitable health products to the user based on the analysis results. This is made possible through electronic payment methods. The inputs are insights from the predicted health state and the user's emotional state, and the output is product recommendation information tailored to the user's health state. Specifically, for example, if the user is stressed, it will suggest products with relaxing effects.

[0613] Step 6:

[0614] The user receives recommended information from the server via their device. Based on the information received, the user can engage in healthy consumption activities. The input for this step is data from the server, and the output is manifested as improvements in the user's purchasing behavior and quality of life.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] This invention is a system for supporting blood glucose management in diabetic patients, aiming to collect and analyze users' biometric data and provide appropriate lifestyle guidance. This system is primarily implemented using two components: a "terminal" and a "server."

[0633] First, the device works in conjunction with an insulin pump and blood glucose sensor to periodically collect the user's biometric data. This includes real-time blood glucose measurement, recording of meals, and acquisition of exercise information. This data is transmitted to a server using secure communication methods.

[0634] The server centrally stores the received biometric data in a database and then applies an AI algorithm to analyze the data. Based on the analysis results, it predicts the user's health status and generates specific advice regarding diet and exercise. For example, it might advise reducing carbohydrate intake or suggest appropriate exercise timings in response to a predicted rise in blood sugar levels.

[0635] The device receives advice from the server and notifies the user. The user then adjusts their lifestyle based on this advice and reflects it in their daily life. In addition, the user inputs feedback about their own behavior into the device, which is then sent to the server, allowing the system to continuously improve its analysis model.

[0636] For example, if a user has high blood sugar levels in the morning, the server analyzes past data and current conditions to determine the appropriate insulin dosage. The terminal then communicates this information to the user, allowing them to administer the correct amount of insulin before breakfast. Furthermore, the system advises the user to adjust their carbohydrate intake at breakfast to help stabilize their blood sugar levels.

[0637] Furthermore, this system is equipped with an anomaly detection function that can immediately issue a warning if the user's blood glucose level fluctuates rapidly or exceeds a certain threshold. Users are expected to respond quickly upon receiving this notification. Combined with an early consultation function for medical professionals, it is expected to prevent serious situations.

[0638] Thus, the system of the present invention aims to provide personalized health support that is useful in the daily lives of diabetic patients and to contribute to improving their quality of life.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] The device periodically collects the user's biometric data from insulin pumps and blood glucose sensors. Specifically, this includes real-time blood glucose levels, meal records, and exercise information. This data is temporarily stored in a local database on the device.

[0642] Step 2:

[0643] The device transmits the collected biometric data to the server using a secure protocol. The data is encrypted and transferred in a way that protects it from unauthorized access.

[0644] Step 3:

[0645] The server stores the received data in a cloud-based database and organizes it appropriately for each user. The stored data is then used for subsequent analysis.

[0646] Step 4:

[0647] The server applies AI algorithms to analyze the data. This analysis combines past trends with current data to predict the user's health status.

[0648] Step 5:

[0649] Based on the analysis results, the server generates personalized lifestyle advice for each user. This advice includes recommendations for appropriate insulin dosages, dietary adjustments, and exercise.

[0650] Step 6:

[0651] The device notifies the user of the generated advice. Based on the notification, the user adjusts their diet, exercise, and medication.

[0652] Step 7:

[0653] The user acts according to the advice provided by the system and enters feedback on the results into the terminal. This feedback is used for the next analysis.

[0654] Step 8:

[0655] The server analyzes user feedback and improves the AI ​​model to enhance the accuracy of future predictions. This enables the provision of more personalized assistance.

[0656] (Example 1)

[0657] 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".

[0658] Conventional health management systems have faced challenges in effectively collecting and analyzing users' biometric information and providing appropriate health guidance. In particular, there is a need to monitor biometric information such as blood glucose levels in real time and provide immediate health guidance based on that data. Furthermore, it is crucial to immediately recognize abnormal biometric information and take appropriate action. By solving these challenges, it is possible to improve the accuracy of predicting users' health status and enhance their quality of life.

[0659] 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.

[0660] In this invention, the server includes means for collecting the user's biometric information using a measuring device and transmitting it to a central processing unit; means for storing the received biometric information in a data storage device, performing information analysis using a generated artificial intelligence model, and predicting the user's health status; and means for providing guidance on diet and exercise to the user based on the prediction results. This makes it possible to grasp biometric information in real time and immediately predict the user's health status and provide lifestyle guidance.

[0661] "User" refers to an individual who uses this system and provides biometric information.

[0662] "Biometric information" refers to data related to the user's physiological state and lifestyle, such as blood glucose levels, dietary information, and exercise information.

[0663] A "measuring device" refers to hardware used to collect a user's biometric information in real time.

[0664] A "central processing unit" refers to the central device that receives and analyzes biological information transmitted from measuring devices.

[0665] A "data storage device" refers to a storage medium used to safely and efficiently store received biometric information.

[0666] A "generated artificial intelligence model" is a system equipped with algorithms specifically designed for analyzing biometric information and is used to predict the user's health status.

[0667] "Predicting health status" refers to the process of estimating future physiological changes based on collected biometric information.

[0668] "Health guidance" refers to activities that provide specific advice to encourage users to improve their lifestyle habits based on their predicted health condition.

[0669] "Issuing a warning" refers to sending a notification to alert the user to an abnormality when their biometric data exceeds a threshold.

[0670] This invention is a system for managing user health and is implemented primarily using two main components: a "terminal" and a "server."

[0671] The terminal is a device designed to collect biometric information, specifically equipped with a sensor for measuring blood glucose levels, an interface for inputting dietary information, and means for acquiring information about exercise. The terminal collects this data in real time as needed and transmits it to the server using a secure communication protocol.

[0672] The server functions as a central processing unit, storing biometric information transmitted from terminals in a data storage device. The stored data is analyzed using a generated artificial intelligence model. This model is used to predict the user's future health status based on past and present data.

[0673] For example, if a particular user's morning blood glucose level tends to rise sharply compared to past levels, the server analyzes this and suggests appropriate dietary restrictions and exercise guidance. This specific guidance is then communicated to the user via their device.

[0674] Furthermore, the system includes a function that immediately issues a warning when an abnormal value is detected. This allows users to respond quickly, which is expected to prevent serious deterioration of health conditions. By collaborating with medical professionals, further actions can be taken as needed.

[0675] (Example of prompt text to input to the generated AI model)

[0676] "If a user shows signs of a rapid spike in blood sugar, provide advice on effective carbohydrate management methods. Also, suggest appropriate timing for exercise."

[0677] This system aims to improve users' quality of life by providing them with personalized health management information.

[0678] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0679] Step 1:

[0680] The terminal collects the user's biometric information through a measuring device. This obtains data such as blood glucose levels, dietary information, and exercise information. Since this information cannot be used directly, it undergoes data formatting processing and is converted into a format that can be sent to the server in the next step.

[0681] Step 2:

[0682] The terminal transmits formatted biometric information to the server using a secure communication protocol. The output here is organized biometric data, which the server receives. The received data is stored directly in the server's data storage device. This storage process also includes data backup.

[0683] Step 3:

[0684] The server inputs accumulated biometric information into a generating AI model. Based on the input data, the AI ​​model uses machine learning algorithms to analyze the data and predict the user's health status. This prediction indicates future blood glucose fluctuations and the level of health risk. As an output of the analysis, recommended lifestyle guidance is generated.

[0685] Step 4:

[0686] The server sends the generated lifestyle guidance to the terminal via a notification function. The terminal notifies the user of this visually or audibly. Specifically, this involves using screen displays, voice guidance, or vibration alerts to draw the user's attention.

[0687] Step 5:

[0688] Users adjust their daily activities based on notifications from their devices. Based on the lifestyle guidance they receive as input, they review their diet and exercise at recommended times. Users input feedback on their actions into their devices, and this feedback is sent to the server for the next cycle. This process contributes to the continuous improvement of the AI ​​model.

[0689] (Application Example 1)

[0690] 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".

[0691] In modern society, diabetic patients are required to properly manage their health and take immediate action to prevent rapid fluctuations in blood glucose levels. However, manual data recording and analysis have limitations, and coordination in situations requiring rapid medical response is difficult. In particular, when self-management is difficult, information sharing with professionals and appropriate guidance may be delayed.

[0692] 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.

[0693] In this invention, the server includes means for collecting user behavior data and storing it in a remote information processing device, means for analyzing the stored information and predicting the user's health index, and means for transmitting the digitized information to a medical institution via a communication medium. As a result, the user's health status is monitored in real time, and when an abnormality is detected, the information is immediately shared with professionals, enabling a rapid response.

[0694] "User behavior data" refers to biological and behavioral information such as blood glucose levels, dietary information, and exercise information necessary to evaluate an individual's health status.

[0695] A "remote information processing device" refers to a computer system that receives, stores, and analyzes collected data.

[0696] A "health index" refers to a numerical or indicator representation of a user's health status, based on collected biometric data.

[0697] "Communication medium" refers to technical means such as internet lines and wireless communication used to transmit data safely and quickly.

[0698] "Digitized information" refers to information obtained by converting biometric data into a format that can be processed electronically.

[0699] "Medical institution" refers to organizations such as hospitals and clinics that employ professionals responsible for monitoring and treating users' health conditions.

[0700] This invention provides an innovative system to support blood glucose management for diabetic patients. The system consists of a terminal and a server, and these two elements complement each other to enable real-time health support for the user.

[0701] First, the device plays a central role in integrating user behavior data. Smartphones and smartwatches work in conjunction with blood glucose sensors, meal logging apps, and exercise tracking devices. This allows for the aggregation of various biometric information related to the user's health, which is then securely transmitted to a server via Bluetooth or Wi-Fi.

[0702] The server stores the received trend data in an integrated database and uses software such as a generative AI model to perform analysis using machine learning algorithms (e.g., TensorFlow). This analysis calculates the user's health index and provides predictions of potential blood glucose fluctuations and health status. The results are then used to generate prompt messages, which are appropriately fed back to the user. The prompt messages are output in the form of instructions to the AI ​​model, such as, "Based on the patient's blood glucose history and meal content this morning, please generate the cause of the rapid rise in blood glucose after lunch and provide advice on diet and exercise for prevention next time."

[0703] The feedback, presented to the user in a compact, visual format, may appear as a smartphone notification, for example, prompting specific actions. As users input health data and provide feedback, the system continuously updates its analytical models, providing more accurate health predictions and advice.

[0704] In addition, the server has the ability to instantly transmit information to medical professionals in the event of a sudden change in health status. This makes it possible to provide users with rapid medical assistance in emergencies. Ultimately, the goal is to improve the quality of life for people with diabetes through these processes.

[0705] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0706] Step 1:

[0707] The device collects user behavior data. Specifically, smartphones and smartwatches work in conjunction with blood glucose sensors and exercise tracking apps to acquire data in real time. This data includes the user's meals, exercise levels, and, most importantly, blood glucose levels. The input is this biometric data, and the output is a set of data.

[0708] Step 2:

[0709] The device collects behavioral data and sends it to the server. Bluetooth or Wi-Fi is used to upload the data securely and quickly. In this process, the input is unsent data on the device, and the output is data stored on the server. Upon receiving the data, the server securely stores it in its storage.

[0710] Step 3:

[0711] The server analyzes the received data using a machine learning algorithm. Specifically, a generative AI model is used to predict health indices. The input is user behavior data stored on the server, and the output is a health prediction index and points to watch out for generated by the analysis. The data is cleansed and converted into a format suitable for the algorithm.

[0712] Step 4:

[0713] The server generates prompt messages based on the analysis results and sends them to the terminal. A generation AI model is used to create personalized lifestyle advice tailored to each user. The input is the health prediction result, and the output is the generated prompt message. This prompt message is presented in language that is easy for the user to understand.

[0714] Step 5:

[0715] The device receives a prompt message from the server and notifies the user. The generated advice is displayed visually and clearly using the smartphone's notification function. The input is the generated prompt message, and the output is the advice the user receives on the screen.

[0716] Step 6:

[0717] Users input feedback into their devices and send it to the server. This feedback includes the actions taken and the perceived effects. The input is the user's feedback, and the output is the feedback data stored on the server for use in subsequent analyses. This allows the analysis model to improve over time.

[0718] Step 7:

[0719] When the server detects sudden fluctuations or abnormal values, it immediately sends a notification to healthcare institutions. Trend data is continuously monitored, and if a specific threshold is exceeded, information is automatically sent to healthcare professionals. The input is analyzed data stored on the server, and the output is alert information sent to healthcare institutions. This step enables rapid medical response.

[0720] 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.

[0721] This invention is a system that aims to improve the health and quality of life of diabetic patients by collecting and analyzing both the user's biometric data and emotional state, and providing individually optimized health management advice. This system consists of both "terminal" and "server" components, and is further equipped with an emotion engine that recognizes the user's emotions.

[0722] The device periodically collects blood glucose data from insulin pumps and blood glucose sensors, meal information entered by the user, and exercise information. In parallel, the emotion engine analyzes the user's voice and facial expression data to recognize the user's emotional state. The recognized emotional data, along with other biometric data, is temporarily stored on the device and transmitted to the server via a secure communication method.

[0723] The server stores all received data in a cloud database and analyzes it. The analysis uses AI algorithms to comprehensively evaluate the user's past health data and current emotional data to predict their health status. Based on this prediction, specific lifestyle guidance is created for each individual user, including appropriate insulin adjustments, dietary choices, and exercise timing.

[0724] This guidance is customized to take into account the user's emotions as identified by the emotion engine. For example, if the user is feeling stressed, it may recommend rest to reduce stress. In this way, lifestyle guidance takes into account not only data-based health status but also psychological well-being.

[0725] For example, if the system's emotion engine determines that a user is feeling "stressed" when measuring their blood glucose levels in the morning, the server will notify the user via their device with suggestions for relaxing meal options or recommendations for light exercise. This allows users to become more aware of their own health and take data-driven actions without feeling overwhelmed.

[0726] This invention enables continuous system improvement through user feedback and allows for flexible responses to changes in the user's condition. In this way, the present invention aims to significantly reduce anxiety and burden in daily life by providing advanced support for self-care for diabetic patients.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] The device collects the user's blood glucose levels in real time via an insulin pump or blood glucose sensor and stores the data in a local database. It also simultaneously collects meal and exercise information manually entered by the user.

[0730] Step 2:

[0731] An emotion engine runs on the device and analyzes the user's emotional state from their voice and facial expressions. This provides emotional information, such as whether the user is stressed or relaxed. This information is also stored in a local database.

[0732] Step 3:

[0733] The device encrypts all collected data and sends it to the server using a secure communication protocol. This transmission is performed periodically and updated whenever new data becomes available.

[0734] Step 4:

[0735] The server stores the received data in a cloud database and analyzes it using an AI algorithm. The analysis combines past blood glucose information with the user's current emotional state to predict their health.

[0736] Step 5:

[0737] Based on the analysis results, the server generates personalized lifestyle guidance for the user. This guidance includes not only blood sugar management but also mental health advice tailored to the user's emotional state. For example, if the user is feeling stressed, relaxation activities and exercises will be suggested.

[0738] Step 6:

[0739] The device notifies the user of lifestyle guidance received from the server. Based on this information, the user makes choices regarding meals, insulin administration timing, and appropriate exercise.

[0740] Step 7:

[0741] Users input feedback into their device, describing their feelings and changes in emotions after following the advice they received. This feedback is used to improve the system in the future.

[0742] Step 8:

[0743] The server uses user feedback to improve new analysis models and adjust the AI ​​algorithms. This improves the accuracy of subsequent analyses and provides users with more personalized assistance.

[0744] (Example 2)

[0745] 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".

[0746] Traditional health management systems for diabetic patients focus on collecting and analyzing users' biometric data, but they have the drawback of not considering the user's psychological or emotional state, thus failing to provide sufficient individualization. Furthermore, existing systems have limited predictive accuracy, which can prevent them from providing appropriate lifestyle guidance to users. As a result, there are shortcomings in improving the quality of life for users.

[0747] 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.

[0748] In this invention, the server includes means for collecting and storing the user's biometric and emotional data in a storage device, means for analyzing the stored biometric and emotional data using an artificial intelligence algorithm to predict the user's health status, and means for providing personalized lifestyle guidance to the user based on the prediction results and the user's emotional status. This enables advanced individualized health management that takes into account the user's psychological health.

[0749] A "user" refers to a person who uses the system to manage their own health.

[0750] "Biometric data" refers to data that numerically represents the user's physical condition, such as blood glucose levels, dietary information, and exercise information.

[0751] "Emotional data" refers to data that represents the psychological state of a user, analyzed from their voice and facial expressions.

[0752] "Storage device" refers to hardware or software used to temporarily or continuously store collected data.

[0753] "Artificial intelligence algorithms" refer to computational methods that automate data analysis and predict user states.

[0754] "Means of predicting health status" refers to functions that include a process of predicting a user's future health status based on collected data.

[0755] "Personalized lifestyle guidance" refers to health management advice tailored individually based on the user's biometric and emotional data.

[0756] "Warning mechanisms" refer to features that provide alerts when abnormal data or emotional states are detected based on established criteria.

[0757] This invention is a system that provides individually optimized health management advice by collecting and analyzing both the user's biometric and emotional data. The system consists of two main components: a terminal and a server, and also incorporates an emotion engine for recognizing the user's emotions.

[0758] The device uses an insulin pump and blood glucose sensors to measure the user's blood glucose levels. This hardware periodically collects the user's physical information and transfers that data to the device. It also has a software interface for the user to input diet and exercise information, allowing for manual data entry.

[0759] On the other hand, the emotion engine recognizes the user's emotional state by analyzing the user's voice and video data from the camera. This emotional data is used to assess the psychological stress the user may be facing.

[0760] The device temporarily stores collected biometric and emotional data in its memory. This data is then transmitted to a server using secure communication methods. The server stores the received data in a cloud database and analyzes it using a generative AI model. This model comprehensively evaluates the user's past health data and current emotional data to predict the user's health status.

[0761] Based on the analysis results, the server creates personalized lifestyle guidance for the user. This guidance takes into account things like how to adjust insulin, food choices, and the timing of exercise. Furthermore, the guidance is further customized according to the user's emotions as recognized by the emotion engine. For example, if the user is feeling stressed, the guidance may include suggestions for relaxation activities and recommendations for light exercise.

[0762] User feedback is crucial in the implementation of this system. This feedback will be used to continuously improve the system and enable it to respond to changes in user needs.

[0763] As a concrete example, the prompt text to be input to the generative AI model would be as follows:

[0764] "What kind of relaxation routine would you recommend to a user experiencing stress in the morning?"

[0765] Thus, the present invention aims to provide advanced support for self-care in diabetic patients and improve their quality of life.

[0766] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0767] Step 1:

[0768] The device periodically receives the user's blood glucose data from an insulin pump or blood glucose sensor. Furthermore, the user inputs dietary and exercise information via a dedicated application. This information is stored in the device's temporary memory.

[0769] The inputs are sensor outputs and user inputs, and the output is biometric data stored in temporary memory. This prepares an accurate dataset for subsequent analysis.

[0770] Step 2:

[0771] Simultaneously, the device uses its built-in emotion engine to collect emotional data using the user's voice and camera. This data is identified through voice-to-text conversion and facial expression analysis. Emotional information is temporarily stored along with biometric data.

[0772] The input is the user's voice and video data, and the output is analyzed emotion data. This process records the user's psychological state as numerical data.

[0773] Step 3:

[0774] The device securely transmits biometric and emotional data to the server. This communication is conducted using encryption protocols to protect privacy.

[0775] The input is a dataset stored on the device, and the output is encrypted data sent to the server. This ensures data integrity and secure data sharing.

[0776] Step 4:

[0777] The server stores the received data in a cloud database and performs analysis using a generated AI model. It integrates the user's past and current data to predict their health status.

[0778] The input is a dataset stored on the server, and the output is an assessment of the predicted health status. This process calculates a health prediction for each individual user.

[0779] Step 5:

[0780] The server generates personalized lifestyle guidance based on the analysis results. This guidance takes into account the user's emotional state and includes appropriate insulin administration and dietary advice.

[0781] The input is the analysis results from an AI model, and the output is customized health guidance. This allows for the proposal of health management optimized for the user.

[0782] Step 6:

[0783] The device receives lifestyle guidance from the server and notifies the user. The guidance is conveyed to the user through voice and visual interfaces.

[0784] The input is guidance data from the server, and the output is a notification to the user. This process allows users to efficiently manage their daily health.

[0785] (Application Example 2)

[0786] 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".

[0787] In managing the health of diabetic patients, challenges include a lack of personalized health support that takes into account daily fluctuating biometric data and emotional states, as well as a lack of appropriate guidance for engaging in consumption activities without compromising health.

[0788] 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.

[0789] In this invention, the server includes means for collecting and storing the user's biometric and emotional state data in a database, means for analyzing the stored data and predicting the user's health status, and means for providing the user with a health-related plan that takes the emotional state into consideration based on the prediction results. This enables the individual optimization of health management and consumption activities, improving the quality of life and reducing the burden on diabetic patients.

[0790] "User biometric data" refers to information used to assess a user's health status, such as blood glucose levels, dietary information, and exercise information.

[0791] "Emotional state data" refers to information indicating the user's psychological state, obtained from their voice and facial expressions.

[0792] "Methods for saving data to a database" refers to a method of securely and efficiently storing collected data for later analysis.

[0793] "Methods for analysis and prediction" refers to methods that use AI algorithms and other tools based on collected data to evaluate the user's health risks and predict their future condition.

[0794] "Means of providing health-related plans" refers to methods of providing specific advice for improving lifestyle habits and maintaining health, based on the user's health status and emotions.

[0795] "A method of recommending products using electronic payment methods" refers to a method of suggesting healthy products at the appropriate time, taking into account the user's health data.

[0796] "Means of issuing warnings when abnormal values ​​are detected" refers to a method of quickly notifying a user when their biometric data exceeds a set range.

[0797] The system for realizing this invention mainly consists of a terminal and a server. The terminal is responsible for collecting the user's biometric data and emotional state data and securely transmitting this data to the server. The user's biometric data includes blood glucose levels, dietary information, and exercise information, while emotional state data is obtained through voice and facial expression analysis. This data is collected by hardware such as insulin pumps, blood glucose sensors, cameras, and microphones.

[0798] The server stores data in a database and performs analysis using AI algorithms. Cloud services (e.g., AWS Lambda, Amazon RDS) and AI libraries (e.g., TensorFlow) are used for the analysis, predicting the user's health status based on their past and present data. Furthermore, using electronic payment methods, the system recommends healthy products that take the user's emotions into consideration, based on the data analysis results.

[0799] For example, if a user is experiencing stress while shopping, the system will recommend products that can alleviate that stress, such as herbal tea. This recommendation is presented in a way that the user can see in real time via their smartphone.

[0800] When using the generative AI model, prompts such as "Suggest products that can alleviate stress based on the user's current blood glucose level and emotional data" are used. This allows the system to respond quickly to the user's situation.

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The device collects the user's blood glucose levels from an insulin pump or blood glucose sensor. Furthermore, it uses a camera and microphone to capture the user's facial expressions and voice data. This data is collected as input to the device. The device processes this biometric data into an appropriate format and stores it temporarily. As output, integrated biometric data and emotional state data are generated.

[0804] Step 2:

[0805] The device transmits the collected data to the server using a secure communication method. During this process, the data is protected using encryption technology, ensuring its integrity and confidentiality in transmission. Inputs include processed biometric data and emotional state data, while the output, encrypted data, reaches the server.

[0806] Step 3:

[0807] The server saves the received data to the database. This saving process includes data integrity checks and ensures the data is stored in a format compatible with the database structure. The input is the data sent from the terminal, and the output is the data saved in a format accessible by the database.

[0808] Step 4:

[0809] The server analyzes the stored data using an AI algorithm. This is a process that utilizes a generative AI model to predict the user's health status. The input data consists of stored biometric and emotional state data, and the output is the predicted health status. Based on these results, the server generates health-related plans.

[0810] Step 5:

[0811] The server recommends the most suitable health products to the user based on the analysis results. This is made possible through electronic payment methods. The inputs are insights from the predicted health state and the user's emotional state, and the output is product recommendation information tailored to the user's health state. Specifically, for example, if the user is stressed, it will suggest products with relaxing effects.

[0812] Step 6:

[0813] The user receives recommended information from the server via their device. Based on the information received, the user can engage in healthy consumption activities. The input for this step is data from the server, and the output is manifested as improvements in the user's purchasing behavior and quality of life.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] A means of collecting users' biometric data and storing it in a database,

[0838] A means of analyzing stored data to predict the user's health status,

[0839] A means of providing lifestyle guidance to users based on prediction results,

[0840] A means of issuing a warning when an abnormal value is detected,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, which collects user feedback and uses it for analysis.

[0844] (Claim 3)

[0845] The system according to claim 1, which includes blood glucose levels, dietary information, and exercise information as biometric data.

[0846] "Example 1"

[0847] (Claim 1)

[0848] A means for collecting the user's biometric information using a measuring device and transmitting it to a central processing unit,

[0849] A means of predicting health status by storing received biometric information in a data storage device and performing information analysis using a generated artificial intelligence model,

[0850] A means of providing guidance on diet and exercise to users based on the prediction results,

[0851] A means of outputting a warning when biometric information falls outside a pre-set threshold,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, which collects information about user behavior and uses this information for analysis.

[0855] (Claim 3)

[0856] The system according to claim 1, which includes blood glucose levels, dietary information, and exercise information as biological information.

[0857] "Application Example 1"

[0858] (Claim 1)

[0859] A means for collecting user behavior data and storing it in a remote information processing device,

[0860] A means of analyzing accumulated information and predicting the user's health index,

[0861] A means of providing users with lifestyle improvement suggestions based on predicted results,

[0862] A means of sending a notification when a warning value is detected,

[0863] A means of transmitting digitized information to medical institutions via communication media,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, which collects user behavioral feedback and uses it for analysis.

[0867] (Claim 3)

[0868] The system according to claim 1, which includes blood glucose levels, dietary information, and exercise information as trend data.

[0869] "Example 2 of combining an emotion engine"

[0870] (Claim 1)

[0871] A means of collecting and storing users' biometric and emotional data in a storage device,

[0872] A means of predicting a user's health status by analyzing stored biometric and emotional data using artificial intelligence algorithms,

[0873] A means of providing personalized lifestyle guidance to users based on prediction results and the user's emotional state,

[0874] A means of issuing a warning when an abnormal value or change in emotion is detected,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, which collects user feedback and uses it for analysis and improvement of lifestyle guidance.

[0878] (Claim 3)

[0879] The system according to claim 1, which includes blood glucose levels, dietary information, exercise information, and user emotional data as biometric data.

[0880] "Application example 2 when combining with an emotional engine"

[0881] (Claim 1)

[0882] A means of collecting and storing users' biometric data and emotional state data in a database,

[0883] A means of analyzing stored data to predict the user's health status,

[0884] A means of providing users with health-related plans that take their emotional state into consideration, based on prediction results.

[0885] A means of recommending healthy products using electronic payment methods,

[0886] A means of issuing a warning when an abnormal value is detected,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, which collects user feedback, uses it for analysis, and recommends products according to the user's emotional state.

[0890] (Claim 3)

[0891] The system according to claim 1, which includes blood glucose levels, dietary information, exercise information, and emotional data as biometric data. [Explanation of Symbols]

[0892] 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 users' biometric data and storing it in a database, A means of analyzing stored data to predict the user's health status, A means of providing lifestyle guidance to users based on prediction results, A means of issuing a warning when an abnormal value is detected, A system that includes this.

2. The system according to claim 1, which collects user feedback and uses it for analysis.

3. The system according to claim 1, which includes blood glucose levels, dietary information, and exercise information as biometric data.

Images