system

Abstract

We provide the system. [Solution] A means of integrating biometric and environmental information collected from users, A means for predicting the occurrence of headaches using an artificial intelligence model based on the aforementioned integrated information, A means for generating a self-care method customized for the user based on the aforementioned prediction results, A system including means for notifying a user device of the aforementioned self-care method.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Many people who are regularly troubled by headaches have problems in that they cannot identify the causes and occurrence patterns, and cannot implement appropriate preventive measures and self-care methods. As a result, the quality of life deteriorates every time a headache occurs, which has an adverse impact on daily life and work. An object of the present invention is to improve the quality of life of users and to prevent and relieve headaches by providing headache preventive measures and self-care methods optimized for individual users.

Means for Solving the Problems

[0005] This invention provides a system that includes means for integrating biometric and environmental information collected from a user, and means for predicting the occurrence of headaches using an artificial intelligence model based on this integrated information. Furthermore, it proposes a system that includes means for generating a customized self-care method for the user based on the prediction results and notifying the user device of this self-care method. As a result, the user can perform specific self-care tailored to their individual health condition and environmental circumstances, thereby preventing the occurrence of headaches.

[0006] "Biometric information collected from users" refers to data related to the user's health status, including information such as heart rate, sleep patterns, and activity levels.

[0007] "Environmental information" refers to information that includes data on external factors such as weather, atmospheric pressure, and humidity around the user.

[0008] "Means of integration" refers to methods that provide functionality for combining information obtained from different data sources and converting it into a format that can be analyzed and processed.

[0009] An "artificial intelligence model" is an algorithm that uses machine learning and data analysis techniques to learn patterns from past data and make predictions and judgments.

[0010] A "means for predicting headache occurrence" refers to a process that has the function of calculating and predicting the occurrence of headaches in advance based on collected data.

[0011] "Customized self-care methods" refer to instructions and suggestions aimed at preventing or alleviating headaches, tailored to each user's individual health data and lifestyle.

[0012] "Means of notifying user devices" refers to communication technologies and processes for transmitting and displaying generated self-care methods and alert information on electronic devices used by the user. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] 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.

[0018] 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.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system aimed at preventing and self-care for headaches, and specifically provides care methods tailored to individual conditions using the user's health data and environmental information.

[0035] First, the device collects the user's biometric information from wearable devices and smartphones. This information includes heart rate, sleep duration, steps taken, and location data. This allows the device to understand the user's daily health indicators in detail. The device can also receive information manually entered by the user (such as the frequency and severity of headaches).

[0036] Next, the server receives biometric information transmitted from the terminal and obtains current weather information from the weather information provision platform. This allows the server to form a dataset integrating biometric and environmental information. Based on this integrated data, the server uses an artificial intelligence model to predict the occurrence of headaches for each user. The prediction utilizes analysis of past data and pattern recognition.

[0037] Based on the prediction results, the server generates a personalized self-care method for the user. This method is tailored to the user's lifestyle and health condition and designed to help prevent or alleviate headaches. This includes optimizing fluid intake, specific stretching exercises, and suggestions for improving lifestyle habits.

[0038] The generated self-care methods are sent from the server to the device. The device notifies the user of the received self-care methods and predictive alerts. These notifications are displayed on the user's device screen and are provided in a visually easy-to-understand format. For example, when the atmospheric pressure drops rapidly, specific advice such as "Drink plenty of water and take deep breaths regularly" is displayed.

[0039] Ultimately, the user performs the suggested self-care methods based on notifications from their device and provides feedback on the results back to the device. This feedback is sent to a server and used to further improve the accuracy of the artificial intelligence model. For example, if a user increases their fluid intake as suggested and as a result their headache frequency decreases, this data is used to adjust the model.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device collects the user's biometric information from wearable devices and smartphone sensors. This information includes heart rate, sleep duration, steps taken, and location data. This data is transmitted to the server in real time.

[0043] Step 2:

[0044] The server receives biometric information transmitted from the terminal. Simultaneously, it acquires environmental information such as temperature, atmospheric pressure, and humidity from an external weather information infrastructure. This allows the server to create a dataset integrating biometric and environmental information.

[0045] Step 3:

[0046] The server uses an artificial intelligence model based on integrated data to predict the occurrence of headaches for each user. By analyzing past data and identifying specific patterns, it calculates the likelihood of the next headache occurring.

[0047] Step 4:

[0048] The server generates a customized self-care plan for the user based on the prediction results. This plan is designed as a specific action plan tailored to the user's lifestyle and past symptoms.

[0049] Step 5:

[0050] The server notifies the user's device of alerts based on the generated self-care methods and predictions. The notifications are displayed on the user's device and provided in an intuitive and easy-to-understand interface.

[0051] Step 6:

[0052] Users check self-care notifications from their devices and perform the suggested actions. The results of these actions and the effects they experienced are then fed back to the device.

[0053] Step 7:

[0054] The terminal collects user feedback and sends it to the server. The server then uses this data to retrain its artificial intelligence model and improve prediction accuracy.

[0055] (Example 1)

[0056] 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."

[0057] Conventional headache prevention systems have not adequately customized their systems to suit individual users' lifestyles and environmental conditions, making it difficult to provide effective prevention and self-care methods. Furthermore, these systems lacked sufficient mechanisms to effectively utilize user feedback to improve the accuracy of their AI models.

[0058] 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.

[0059] In this invention, the server includes data processing means for integrating biometric and environmental information acquired from the user; analysis means for predicting the occurrence of headaches using a generated AI model based on the integrated data; customization means for generating self-care methods suitable for the user based on the prediction results; and learning means for collecting feedback from the user and improving the accuracy of the generated AI model. This makes it possible to provide the most suitable self-care method for each individual user while improving the accuracy of the model.

[0060] A "user" refers to a person who uses the system to prevent headaches or perform self-care.

[0061] "Biometric information" refers to data that indicates the user's health status, including heart rate, sleep duration, steps taken, and location information.

[0062] "Environmental information" refers to data about the user's external environment, such as weather and surrounding conditions.

[0063] "Data processing means" refers to a system that integrates biological information and environmental information and converts it into a format suitable for analysis.

[0064] A "generative AI model" refers to an artificial intelligence model used to predict the occurrence of headaches based on integrated data.

[0065] "Analysis method" refers to the process of analyzing data using a generative AI model to predict the occurrence of headaches.

[0066] "Customization methods" refer to a system that creates self-care methods tailored to individual users based on prediction results.

[0067] "Notification methods" refer to systems that inform users of self-care methods and predictive alerts via their user devices.

[0068] "Learning method" refers to a mechanism that collects user feedback and uses it to improve the accuracy of the generated AI model.

[0069] One embodiment of the present invention is to construct a system that provides users with individualized and appropriate headache prevention measures by utilizing various digital devices and cloud services.

[0070] First, the terminal will be a portable information terminal such as a smartphone or wearable device. This will allow the terminal to collect biometric information such as the user's heart rate, sleep duration, steps taken, and location information in real time. This collection will require devices such as smartwatches or dedicated mobile applications.

[0071] Next, this biometric information is transmitted to the server via a secure network protocol (e.g., HTTPS). Simultaneously, the server utilizes external information services to obtain weather data and other environmental information.

[0072] The server integrates collected biometric and environmental information and uses a generative AI model to predict headache occurrence. Common analytical tools and algorithmic platforms (e.g., Python and Tensorflow®) are used in this process. The AI ​​model utilizes historical data analysis and pattern recognition techniques to make specific predictions for each user.

[0073] The results based on the predictions are further analyzed on the server, and the most suitable self-care method is customized for each user. This self-care method includes specific advice tailored to your lifestyle (e.g., improving hydration or specific stretching exercises).

[0074] Ultimately, the device presents the user with self-care methods notified by the server. Through the displayed information, the user can then practice specific self-care actions. Furthermore, the user inputs feedback on the results and experience into the device, and this data is sent back to the server. This allows the AI ​​model to continuously learn and improve its accuracy.

[0075] For example, if the AI ​​model predicts a rapid drop in atmospheric pressure, the user will be given a self-care suggestion such as, "Drink an additional liter of water and take deep breaths every hour." An example of a prompt to the generating AI model would be, "Based on past data, what self-care methods should be offered to the user?" This prompt serves as the starting point for the AI ​​model to generate the most suitable suggestions for the user.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The device collects the user's biometric information via wearable devices and smartphones. The input consists of real-time data such as heart rate, sleep duration, steps taken, and location information. The device temporarily stores this data in its internal memory. A data synthesis algorithm is used to generate a formatted dataset, which is then converted into a format that can be sent to the server.

[0079] Step 2:

[0080] The device transmits the collected biometric information to the server using a secure communication protocol. The input is the dataset formatted in step 1. The output is a binary data stream that reaches the server. This transmission allows the server to obtain real-time data and information necessary for immediate analysis.

[0081] Step 3:

[0082] The server stores biometric information received from the terminal in a database and simultaneously acquires weather data from an external information provision system. The input consists of biometric information from the terminal and environmental data obtained from a weather service API. The server integrates this data to create a single dataset suitable for analysis. The output is an integrated dataset formatted for use by AI models.

[0083] Step 4:

[0084] The server uses a generative AI model to predict the occurrence of headaches. The input is the integrated dataset created in step 3. The AI ​​model uses machine learning algorithms to analyze the data and generate prediction results. These prediction results show the probability of headache occurrence and the specific conditions for its occurrence. The output is prediction result data that can be interpreted by experts.

[0085] Step 5:

[0086] The server creates optimal self-care suggestions for the user based on the prediction results. The input is the prediction results obtained from the AI ​​model. The server uses a customized algorithm to generate advice tailored to each user's individual lifestyle. The output is a self-care method expressed in an actionable format.

[0087] Step 6:

[0088] The terminal notifies the user of self-care methods sent from the server. The input is self-care suggestion data from the server. The terminal utilizes a notification system and displays it on the user interface via a smartphone or wearable device. The output is a self-care notification in a format that the user can visually confirm.

[0089] Step 7:

[0090] The user acts according to the self-care notification received. The input is the self-care advice received in step 6. The user performs actions such as hydration and stretching and observes the effects. Feedback is entered into the device, describing the experience and effects, which will be used for the next analysis. The output is the user's actions for the next data accumulation.

[0091] (Application Example 1)

[0092] 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."

[0093] With the proliferation of smart devices, health management using personal biometric data is attracting attention. However, existing systems have the challenge of not being able to provide health management methods optimized for individual users. In particular, there is a problem in that predictions of health conditions, such as headaches, and the provision of specific countermeasures based on those predictions are not adequately addressed.

[0094] 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.

[0095] In this invention, the server includes means for integrating biometric data collected from the user with external environmental data, means for predicting the user's health status using a machine learning model based on the integrated information, and means for generating a self-management method tailored to the user based on the prediction results. This makes it possible to present a self-management method that is appropriate for the user's individual health status.

[0096] "Biometric data" refers to a collection of information that indicates a user's physical condition, such as their heart rate and activity level.

[0097] "External environment data" refers to a collection of information about the user's surrounding environment, such as temperature and atmospheric pressure.

[0098] A "machine learning model" is a computational model that learns trends from past data and makes predictions and judgments about new data.

[0099] "Health status prediction" is the act of using machine learning models to estimate the health problems a user may face in the future.

[0100] "Self-management methods" refer to suggestions for specific actions and measures that users should take to maintain or improve their own health.

[0101] A "display device" is hardware used to provide information to users visually.

[0102] An "intuitively understandable format" refers to a clear and easy-to-understand display method that allows users to easily grasp the suggested information and advice and take action.

[0103] This invention is implemented as a health management system using smart devices. This system predicts the individual health status based on the user's biometric data and external environmental data, and provides appropriate self-management methods.

[0104] The server integrates biometric data and external environmental data, and uses machine learning models to predict health status based on this data. Specifically, users can use smart wearable devices such as Google Glass® or Vuzix Blade. Data measured by the built-in sensors of these devices is transmitted to the server in real time. On the software side, it utilizes AI models trained on historical data using TensorFlow. Furthermore, cloud infrastructure such as AWS® Lambda is used for data transmission and reception.

[0105] The device notifies the user in an intuitively understandable format based on prediction results sent from the server. This includes displaying alerts and advice visually through the display. For example, if it detects signs of a sudden change in atmospheric pressure, it will display specific advice such as, "Drink plenty of fluids within the next hour."

[0106] The user follows notifications from their device, performs the suggested self-management methods, and provides feedback on the results to the device. This feedback is then sent back to the server to help improve the accuracy of the AI ​​model.

[0107] An example of a prompt message is: "Based on the weather forecast for the user's current location, predict the likelihood of a headache occurring and recommend appropriate self-care methods. If a headache is predicted, provide specific instructions such as hydration and stretching."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The device collects the user's biometric data using sensors from a smart wearable device. This data includes heart rate, sleep duration, and activity level. The collected data is filtered for data processing and sent to a server via a communication module. The input is biometric data, and the output is the data sent to the server.

[0111] Step 2:

[0112] The server receives biometric data from the terminal and integrates it with environmental data obtained from an external data provision platform. Weather forecasts and meteorological conditions are examples of this. This integrated dataset is generated and prepared as input data for the AI ​​model. The input is biometric data and environmental data, and the output is the integrated dataset.

[0113] Step 3:

[0114] The server inputs the integrated dataset into a machine learning model using TensorFlow and predicts the user's health status. This determines the user's future risk of developing headaches. The input is the integrated dataset, and the output is the health status prediction result.

[0115] Step 4:

[0116] The server generates a customized self-management plan for the user based on the prediction results. This includes recommendations for hydration and suggestions for specific exercises. The generated self-management plan is then transferred from the server to the terminal. The input is the health status prediction result, and the output is the self-management plan.

[0117] Step 5:

[0118] The terminal visually displays received self-management instructions to the user. Alerts and suggestions are presented via the display in a way that the user can easily understand and act upon. Input is self-management instructions from the server, and output is a visual display.

[0119] Step 6:

[0120] The user takes action based on notifications from their device and inputs the results as feedback into the device. The feedback data is sent back to the server and used to adjust the accuracy of the AI ​​model. The input is the user's feedback, and the output is the data sent to the server.

[0121] 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.

[0122] This invention provides a system for headache prediction and self-care methods that takes into account the user's emotional state in addition to their health and environmental information. By incorporating an emotional engine, it becomes possible to provide highly accurate predictions and care that take into account the user's emotional stressors.

[0123] First, the device acquires the user's biometric information (heart rate, sleep information, etc.) from the wearable device and smartphone sensors, and then the emotion engine analyzes the user's emotional state from the camera and voice input. The emotion engine identifies the user's emotions using facial recognition and voice tone analysis.

[0124] Next, the device acquires biometric information, emotional data, and location and weather data from environmental sensors installed on the device, and transmits all of this information to the server in real time. The server integrates this data and comprehensively monitors the user's state.

[0125] The server uses integrated data to run an artificial intelligence model that predicts the probability of headache occurrence for each user. This model uses algorithms learned from historical data to analyze headache occurrence patterns under specific conditions, as well as taking into account the user's emotional stress and its impact.

[0126] Based on the prediction results, the server generates customized self-care methods for the user. This process takes into account the user's current emotional state, and can suggest relaxation techniques if emotional stress levels are high.

[0127] The generated self-care methods and prediction results are notified from the server to the device. The device presents the received information to the user and explicitly instructs them on the necessary actions. For example, a notification such as "Your current stress level is high, so we recommend 10 minutes of meditation" might be displayed to the user on their smartphone.

[0128] Ultimately, users perform these self-care suggestions and provide feedback on the results and effects to their device. This allows the system to further improve its accuracy and provide more appropriate care methods. For example, if a user follows the suggestions and performs stretches or meditation, resulting in reduced stress and decreased headache frequency, this data will be used to predict improvements for the next time.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The device collects the user's biometric information from wearable devices and smartphones. This includes heart rate, steps taken, and sleep data. The device also captures the user's facial expressions and voice via cameras and microphones, and an emotion engine analyzes this data to identify the user's emotional state.

[0132] Step 2:

[0133] The device transmits biometric information, emotional data, and location and weather data acquired from environmental sensors to the server in real time. Through this transmission, a comprehensive dataset of the user's state is constructed.

[0134] Step 3:

[0135] The server uses the integrated data received from the terminal to activate an artificial intelligence model. The model has learned from past data and predicts the occurrence of headaches. In doing so, the model considers not only biometric and environmental information but also emotional states to make a multifaceted assessment of factors that cause headaches.

[0136] Step 4:

[0137] Based on the predicted results, the server generates self-care methods optimized for the user. This generation also takes into account the output of the emotion engine, and includes care methods that take into account the user's mental state and mood. For example, if the stress level is high, meditation or relaxation may be suggested.

[0138] Step 5:

[0139] The server notifies the device of the self-care method it has generated. The device then displays this information to the user and suggests specific actions to take. For example, the user's device might display instructions such as "Perform deep breathing exercises for 10 minutes."

[0140] Step 6:

[0141] Users check notifications from their devices and perform the suggested self-care methods. After performing the methods, they provide feedback through their devices about the effects they experienced and the details of what they did.

[0142] Step 7:

[0143] The device collects feedback from the user and sends it to the server. The server uses this data to update the artificial intelligence model, improving the accuracy of future predictions and the quality of self-care suggestions.

[0144] (Example 2)

[0145] 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 will be referred to as the "terminal."

[0146] Conventional health management systems primarily rely on predictions based on biometric and environmental information, failing to adequately reflect the user's emotional state. As a result, the prediction accuracy of health impacts from stress and emotional factors is often insufficient. Furthermore, the lack of personalized self-care suggestions means that these systems may not lead to improvements in the user's lifestyle. Therefore, there is a need for improved prediction accuracy using multifaceted data, including emotional states, and the provision of self-care methods optimized for each user.

[0147] 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.

[0148] In this invention, the server includes means for integrating biometric information, emotional state information, and environmental information collected from the user; means for predicting the probability of developing a head disease using a generative model based on the integrated information; and means for generating a customized self-management method for the user that takes emotional state into consideration based on the prediction results. This makes it possible to provide highly accurate health predictions that take emotional state into consideration and self-care methods optimized for individual users.

[0149] "Biometric information" refers to data about the user's physical condition, such as their heart rate and sleep patterns.

[0150] "Emotional state information" refers to data that indicates the emotional state of a user, analyzed from their facial expressions and tone of voice.

[0151] "Environmental information" refers to data about external conditions, such as the user's location and weather conditions.

[0152] "Integration" refers to the process of combining various types of collected data into a single dataset.

[0153] A "generative model" refers to an artificial intelligence algorithm that learns from past data and is used to predict current and future situations.

[0154] "Probability of head-related illness" refers to a numerical representation of the likelihood that a user will experience symptoms such as headaches.

[0155] "Self-care methods" refer to specific actions and activities that users take on their own to maintain or improve their own health.

[0156] "Self-management methods" refer to suggestions of actions that users should take to control their own health and maintain optimal lifestyle habits.

[0157] This system aims to predict headaches and suggest self-care methods, providing technology for acquiring and processing the user's biometric information, emotional state information, and environmental information. The system implementation utilizes the following hardware and software:

[0158] The system collects the user's biometric information using a wearable device and a smartphone. The wearable device is equipped with a heart rate sensor and an accelerometer, which are used to detect heart rate, sleep patterns, and other data. The smartphone is equipped with a camera and microphone, which capture the user's facial expressions and voice, and analyze their emotional state. Facial recognition software and voice analysis software assist in this process.

[0159] The device can also acquire location information using GPS functionality and obtain weather condition data from external information provision systems via an internet connection.

[0160] All collected data is sent to the server in real time. The server integrates this information and performs data processing and analysis. A generative AI model is used to predict the probability of each user developing a head disorder. This model learns from historical data using machine learning algorithms, achieving highly accurate predictions.

[0161] Taking the user's emotional state into account, the server generates customized self-care methods based on the prediction results. For example, if a high stress level is detected, it can suggest a program to encourage deep breathing or a short meditation session.

[0162] The generated self-care method is notified from the server to the terminal and presented to the user along with specific instructions. The user can then perform the self-care based on this and provide feedback on the results to the terminal. This feedback information is sent back to the server and contributes to further improving the accuracy of the generated AI model.

[0163] A specific example of a prompt message would be, "Propose a headache prediction and self-care method that takes into account the user's biometric and emotional data."

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The device uses sensors from wearable devices and smartphones to acquire the user's heart rate and sleep data. This includes capturing real-time biometric information using heart rate sensors and accelerometers. The input is sensor data, and the output is biometric information formatted as heart rate and sleep patterns.

[0167] Step 2:

[0168] The device analyzes the user's emotional state using the smartphone's camera and voice input functions. Specifically, it analyzes facial expression data captured by facial recognition software and voice tone collected by voice analysis software to determine the user's emotions. The input consists of image data and audio data, and the output is information indicating the user's emotional state.

[0169] Step 3:

[0170] The device obtains location and weather data from external sources via GPS and internet connectivity. This allows for the collection of the user's geographical location and weather information. Inputs are GPS coordinates and data from weather services, while outputs are the user's location information and weather conditions.

[0171] Step 4:

[0172] The device transmits biometric information, emotional state information, and environmental information acquired in steps 1-3 to the server. The server stores this data in an integrated database. The input here is all the data before integration, and the output is the integrated dataset.

[0173] Step 5:

[0174] The server inputs the integrated dataset into a generating AI model. This model learns from historical data and predicts the probability of a user developing a head disorder. The input is the integrated dataset, and the output is probability information about the occurrence of head disorders.

[0175] Step 6:

[0176] The server generates customized self-care methods for the user based on predicted probabilities. This process suggests relaxation and exercise methods that take emotional states into account, based on the output from the generating AI model. The input is the prediction result, and the output is the suggested self-care method.

[0177] Step 7:

[0178] The server notifies the terminal of the generated self-care methods and predicted results. The terminal presents this to the user visually or audibly, prompting specific actions. The input is the notification content from the server, and the output is the presentation of information as instructions to the user.

[0179] Step 8:

[0180] The user performs the suggested self-care method and provides feedback on the results to the device. The device sends this feedback information to a server, which is used to improve the accuracy of predictions by the AI ​​model for future use. The input is the user's performance results, and the output is the feedback data.

[0181] (Application Example 2)

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

[0183] In modern society, health problems caused by lifestyle stress and irregular habits are increasing. Headaches, in particular, are common, and there is a need for more personalized self-care suggestions to prevent their occurrence. However, conventional methods do not provide precise predictions and suggestions that take emotional states into account, making it difficult for users to find appropriate self-care methods. This invention aims to solve these problems and provide users with a more effective and personalized means of health management.

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

[0185] In this invention, the server includes means for integrating biometric information, environmental information, and emotional information collected from the user; means for predicting the occurrence of headaches using an artificial intelligence model generated based on the integrated information; and means for generating a customized self-care method based on the prediction results and the user's emotional state. This makes it possible to predict headaches and propose personalized self-care that takes into account multiple factors, including emotional state.

[0186] "Biometric information" refers to data related to the user's physical condition, including heart rate and sleep information.

[0187] "Environmental information" refers to information about the user's surroundings, including location information and weather data.

[0188] "Emotional information" refers to information about the user's emotional state, and is data obtained through facial recognition and voice tone analysis.

[0189] An "artificial intelligence model" is a model equipped with a learning algorithm that predicts the occurrence of headaches based on integrated information from users.

[0190] "Self-care methods" refer to actions and activities that users take to prevent or alleviate headaches, and include relaxation techniques.

[0191] A "user interface" is an interface that includes display devices and input means for a user to interact with a system.

[0192] A "user device" is an electronic device used by a user, and includes terminals such as smartphones and smart glasses.

[0193] A "notification" is a message or alert that the system uses to communicate self-care methods or product information to the user.

[0194] "Means of promoting purchase" refer to functions and processes that suggest users purchase goods or services and facilitate that purchase action.

[0195] To implement this invention, a system consisting of smart glasses, a wearable device, a smartphone, and a communication network is utilized. The user wears the smart glasses and, while going about their daily life, periodically acquires biometric information such as heart rate and sleep information from the wearable device. At the same time, the built-in camera and microphone of the smart glasses analyze the user's facial expressions and voice tone to collect emotional information. Furthermore, environmental information, including location information and weather data, is acquired using environmental sensors built into the smartphone.

[0196] The device transmits this information to the server in real time. The server uses digital processing technology to integrate this information and build a detailed user profile, including emotional information. Based on this data, an artificial intelligence model designed with machine learning frameworks such as TensorFlow and PyTorch predicts the occurrence of headaches, and based on that prediction, suggests user-specific self-care methods and health-related products.

[0197] The generated self-care suggestions are displayed on the user's device through the user interface. Specifically, a notification such as "Your current stress level is high, so we recommend 10 minutes of meditation" will appear on the smart glasses' display, along with product information that can help with relaxation.

[0198] In this system, users perform self-care methods tailored to their own health and emotional state, and input the effects as feedback into a terminal. This feedback data is used to improve the accuracy of future predictions. For example, if a user performs a suggested meditation guide and reports that their stress has been reduced, this will be reflected in the next suggestion.

[0199] An example of a prompt message to input to a generative AI model is: "The user's current stress level is high. Please suggest the most suitable self-care products."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] Users wear smart glasses and wearable devices to collect data on a daily basis. Facial and voice data are captured through the smart glasses' camera and microphone, and heart rate and sleep information are collected from the wearable device; this data is then transferred to the terminal. At this stage, the input is the user's real-time biometric and emotional information, which forms the basis for subsequent data analysis.

[0203] Step 2:

[0204] The device utilizes the environmental sensors in the user's smartphone to collect location and weather data. This allows it to obtain data about the external environment in which the user is located. The input for this step is environmental information, which the device uses to complete a dataset that helps understand the user's overall condition.

[0205] Step 3:

[0206] The device transmits collected biometric, emotional, and environmental information to the server in real time. The server integrates this data and comprehensively analyzes the user's current health and emotional state. The output of this process is stored on the server as integrated data and input into the next predictive process.

[0207] Step 4:

[0208] The server runs an artificial intelligence model using integrated data to predict the likelihood of a user experiencing a headache. This step uses integrated data as input and performs calculations through a generative AI model based on historical datasets. The output is the headache prediction result.

[0209] Step 5:

[0210] The server generates customized self-care methods that take into account the user's emotional state based on the prediction results. Here, the AI ​​uses the prediction results (input) to suggest relaxation activities and products suitable for the user. These suggestions are output as self-care suggestions and product information.

[0211] Step 6:

[0212] The server sends self-care methods and product information to the terminal and displays it on the user interface of the user device. The terminal receives this information and displays notifications such as "We recommend meditation" or "Please try our relaxing tea" on the smart glasses' display. This step is a direct output from the system to the user.

[0213] Step 7:

[0214] The user performs the suggested self-care method and inputs feedback on its effectiveness into the device. This feedback is transmitted from the device to the server and used as new data to train the next predictive model. This step is completed by inputting the user's experience and results as feedback into the server.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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".

[0231] This invention is a system aimed at preventing and self-care for headaches, and specifically provides care methods tailored to individual conditions using the user's health data and environmental information.

[0232] First, the device collects the user's biometric information from wearable devices and smartphones. This information includes heart rate, sleep duration, steps taken, and location data. This allows the device to understand the user's daily health indicators in detail. The device can also receive information manually entered by the user (such as the frequency and severity of headaches).

[0233] Next, the server receives biometric information transmitted from the terminal and obtains current weather information from the weather information provision platform. This allows the server to form a dataset integrating biometric and environmental information. Based on this integrated data, the server uses an artificial intelligence model to predict the occurrence of headaches for each user. The prediction utilizes analysis of past data and pattern recognition.

[0234] Based on the prediction results, the server generates a personalized self-care method for the user. This method is tailored to the user's lifestyle and health condition and designed to help prevent or alleviate headaches. This includes optimizing fluid intake, specific stretching exercises, and suggestions for improving lifestyle habits.

[0235] The generated self-care methods are sent from the server to the device. The device notifies the user of the received self-care methods and predictive alerts. These notifications are displayed on the user's device screen and are provided in a visually easy-to-understand format. For example, when the atmospheric pressure drops rapidly, specific advice such as "Drink plenty of water and take deep breaths regularly" is displayed.

[0236] Ultimately, the user performs the suggested self-care methods based on notifications from their device and provides feedback on the results back to the device. This feedback is sent to a server and used to further improve the accuracy of the artificial intelligence model. For example, if a user increases their fluid intake as suggested and as a result their headache frequency decreases, this data is used to adjust the model.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The device collects the user's biometric information from wearable devices and smartphone sensors. This information includes heart rate, sleep duration, steps taken, and location data. This data is transmitted to the server in real time.

[0240] Step 2:

[0241] The server receives biometric information transmitted from the terminal. Simultaneously, it acquires environmental information such as temperature, atmospheric pressure, and humidity from an external weather information infrastructure. This allows the server to create a dataset integrating biometric and environmental information.

[0242] Step 3:

[0243] The server uses an artificial intelligence model based on integrated data to predict the occurrence of headaches for each user. By analyzing past data and identifying specific patterns, it calculates the likelihood of the next headache occurring.

[0244] Step 4:

[0245] The server generates a customized self-care plan for the user based on the prediction results. This plan is designed as a specific action plan tailored to the user's lifestyle and past symptoms.

[0246] Step 5:

[0247] The server notifies the user's device of alerts based on the generated self-care methods and predictions. The notifications are displayed on the user's device and provided in an intuitive and easy-to-understand interface.

[0248] Step 6:

[0249] Users check self-care notifications from their devices and perform the suggested actions. The results of these actions and the effects they experienced are then fed back to the device.

[0250] Step 7:

[0251] The terminal collects user feedback and sends it to the server. The server then uses this data to retrain its artificial intelligence model and improve prediction accuracy.

[0252] (Example 1)

[0253] 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."

[0254] Conventional headache prevention systems have not adequately customized their systems to suit individual users' lifestyles and environmental conditions, making it difficult to provide effective prevention and self-care methods. Furthermore, these systems lacked sufficient mechanisms to effectively utilize user feedback to improve the accuracy of their AI models.

[0255] 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.

[0256] In this invention, the server includes data processing means for integrating biometric and environmental information acquired from the user; analysis means for predicting the occurrence of headaches using a generated AI model based on the integrated data; customization means for generating self-care methods suitable for the user based on the prediction results; and learning means for collecting feedback from the user and improving the accuracy of the generated AI model. This makes it possible to provide the most suitable self-care method for each individual user while improving the accuracy of the model.

[0257] A "user" refers to a person who uses the system to prevent headaches or perform self-care.

[0258] "Biometric information" refers to data that indicates the user's health status, including heart rate, sleep duration, steps taken, and location information.

[0259] "Environmental information" refers to data about the user's external environment, such as weather and surrounding conditions.

[0260] "Data processing means" refers to a system that integrates biological information and environmental information and converts it into a format suitable for analysis.

[0261] A "generative AI model" refers to an artificial intelligence model used to predict the occurrence of headaches based on integrated data.

[0262] "Analysis method" refers to the process of analyzing data using a generative AI model to predict the occurrence of headaches.

[0263] "Customization methods" refer to a system that creates self-care methods tailored to individual users based on prediction results.

[0264] "Notification methods" refer to systems that inform users of self-care methods and predictive alerts via their user devices.

[0265] "Learning method" refers to a mechanism that collects user feedback and uses it to improve the accuracy of the generated AI model.

[0266] One embodiment of the present invention is to construct a system that provides users with individualized and appropriate headache prevention measures by utilizing various digital devices and cloud services.

[0267] First, the terminal will be a portable information terminal such as a smartphone or wearable device. This will allow the terminal to collect biometric information such as the user's heart rate, sleep duration, steps taken, and location information in real time. This collection will require devices such as smartwatches or dedicated mobile applications.

[0268] Next, this biometric information is transmitted to the server via a secure network protocol (e.g., HTTPS). Simultaneously, the server utilizes external information services to obtain weather data and other environmental information.

[0269] The server integrates collected biometric and environmental information and uses a generative AI model to predict headache occurrence. Common analytical tools and algorithmic platforms (e.g., Python and TensorFlow) are used in this process. The AI ​​model utilizes historical data analysis and pattern recognition techniques to make specific predictions for each user.

[0270] The results based on the predictions are further analyzed on the server, and the most suitable self-care method is customized for each user. This self-care method includes specific advice tailored to your lifestyle (e.g., improving hydration or specific stretching exercises).

[0271] Ultimately, the device presents the user with self-care methods notified by the server. Through the displayed information, the user can then practice specific self-care actions. Furthermore, the user inputs feedback on the results and experience into the device, and this data is sent back to the server. This allows the AI ​​model to continuously learn and improve its accuracy.

[0272] For example, if the AI ​​model predicts a rapid drop in atmospheric pressure, the user will be given a self-care suggestion such as, "Drink an additional liter of water and take deep breaths every hour." An example of a prompt to the generating AI model would be, "Based on past data, what self-care methods should be offered to the user?" This prompt serves as the starting point for the AI ​​model to generate the most suitable suggestions for the user.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The device collects the user's biometric information via wearable devices and smartphones. The input consists of real-time data such as heart rate, sleep duration, steps taken, and location information. The device temporarily stores this data in its internal memory. A data synthesis algorithm is used to generate a formatted dataset, which is then converted into a format that can be sent to the server.

[0276] Step 2:

[0277] The device transmits the collected biometric information to the server using a secure communication protocol. The input is the dataset formatted in step 1. The output is a binary data stream that reaches the server. This transmission allows the server to obtain real-time data and information necessary for immediate analysis.

[0278] Step 3:

[0279] The server stores biometric information received from the terminal in a database and simultaneously acquires weather data from an external information provision system. The input consists of biometric information from the terminal and environmental data obtained from a weather service API. The server integrates this data to create a single dataset suitable for analysis. The output is an integrated dataset formatted for use by AI models.

[0280] Step 4:

[0281] The server uses a generative AI model to predict the occurrence of headaches. The input is the integrated dataset created in step 3. The AI ​​model uses machine learning algorithms to analyze the data and generate prediction results. These prediction results show the probability of headache occurrence and the specific conditions for its occurrence. The output is prediction result data that can be interpreted by experts.

[0282] Step 5:

[0283] The server creates an optimal self-care proposal for the user based on the prediction results. The input is the prediction results obtained from the AI model. The server uses a customization algorithm to generate advice according to the individual lifestyle of the user. The output is a self-care method expressed in an executable form.

[0284] Step 6:

[0285] The terminal notifies the user of the self-care method sent from the server. The input is the self-care proposal data from the server. The terminal utilizes the notification system and displays it on the user interface via a smartphone or a wearable device. The output is a self-care notification in a form that the user can visually confirm.

[0286] Step 7:

[0287] The user acts according to the received self-care notification. The input is the self-care advice received in Step 6. The user performs water intake and stretches and observes the effects. The feedback is to input the experience and effects into the terminal and utilized for the next analysis. The output is the user's execution results for the next data accumulation.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] With the spread of smart devices, health management using personal biometric data has attracted attention. However, existing systems have the problem that it is difficult to provide a health management method optimized for individual users. In particular, there is a problem that the prediction of health conditions such as headaches and the presentation of specific countermeasures based on the results are not sufficiently carried out.

[0291] 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.

[0292] In this invention, the server includes means for integrating biometric data collected from the user with external environmental data, means for predicting the user's health status using a machine learning model based on the integrated information, and means for generating a self-management method tailored to the user based on the prediction results. This makes it possible to present a self-management method that is appropriate for the user's individual health status.

[0293] "Biometric data" refers to a collection of information that indicates a user's physical condition, such as their heart rate and activity level.

[0294] "External environment data" refers to a collection of information about the user's surrounding environment, such as temperature and atmospheric pressure.

[0295] A "machine learning model" is a computational model that learns trends from past data and makes predictions and judgments about new data.

[0296] "Health status prediction" is the act of using machine learning models to estimate the health problems a user may face in the future.

[0297] "Self-management methods" refer to suggestions for specific actions and measures that users should take to maintain or improve their own health.

[0298] A "display device" is hardware used to provide information to users visually.

[0299] An "intuitively understandable format" refers to a clear and easy-to-understand display method that allows users to easily grasp the suggested information and advice and take action.

[0300] This invention is implemented as a health management system using smart devices. This system predicts the individual health status based on the user's biometric data and external environmental data, and provides appropriate self-management methods.

[0301] The server integrates biometric data and external environmental data, and uses machine learning models to predict health status based on this data. Specifically, users can use smart wearable devices such as Google Glass or Vuzix Blade. Data measured by the built-in sensors of these devices is transmitted to the server in real time. On the software side, it utilizes AI models trained on historical data using TensorFlow. Furthermore, cloud infrastructure such as AWS Lambda is used for data transmission and reception.

[0302] The device notifies the user in an intuitively understandable format based on prediction results sent from the server. This includes displaying alerts and advice visually through the display. For example, if it detects signs of a sudden change in atmospheric pressure, it will display specific advice such as, "Drink plenty of fluids within the next hour."

[0303] The user follows notifications from their device, performs the suggested self-management methods, and provides feedback on the results to the device. This feedback is then sent back to the server to help improve the accuracy of the AI ​​model.

[0304] An example of a prompt message is: "Based on the weather forecast for the user's current location, predict the likelihood of a headache occurring and recommend appropriate self-care methods. If a headache is predicted, provide specific instructions such as hydration and stretching."

[0305] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0306] Step 1:

[0307] The terminal collects the user's biometric data using sensors of smart wearable devices. This data includes heart rate, sleep duration, and activity level. The collected data is filtered for data processing and transmitted to the server via a communication module. The input is biometric data, and the output is the data transmitted to the server.

[0308] Step 2:

[0309] The server receives the biometric data received from the terminal and integrates it with the environmental data obtained from an external data providing infrastructure. Here, weather forecasts and meteorological conditions are cited as examples. This integrated data set is generated and prepared as input data for the AI model. The input is biometric data and environmental data, and the output is the integrated data set.

[0310] Step 3:

[0311] The server inputs the integrated data set into a machine learning model using TensorFlow to predict the user's health status. Thereby, the future risk of headache occurrence for the user is determined. The input is the integrated data set, and the output is the health status prediction result.

[0312] Step 4:

[0313] Based on the prediction result, the server generates a customized self-management method for the user. This includes recommendations for water intake and suggestions for specific exercises. The generated self-management method is transferred from the server to the terminal. The input is the health status prediction result, and the output is the self-management method.

[0314] Step 5:

[0315] The terminal visually displays the received self-management method to the user. Alerts and suggestions are presented in a form that the user can easily understand and execute via a display. The input is the self-management method from the server, and the output is the visual display.

[0316] Step 6:

[0317] The user takes action based on notifications from their device and inputs the results as feedback into the device. The feedback data is sent back to the server and used to adjust the accuracy of the AI ​​model. The input is the user's feedback, and the output is the data sent to the server.

[0318] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0319] This invention provides a system for headache prediction and self-care methods that takes into account the user's emotional state in addition to their health and environmental information. By incorporating an emotional engine, it becomes possible to provide highly accurate predictions and care that take into account the user's emotional stressors.

[0320] First, the device acquires the user's biometric information (heart rate, sleep information, etc.) from the wearable device and smartphone sensors, and then the emotion engine analyzes the user's emotional state from the camera and voice input. The emotion engine identifies the user's emotions using facial recognition and voice tone analysis.

[0321] Next, the device acquires biometric information, emotional data, and location and weather data from environmental sensors installed on the device, and transmits all of this information to the server in real time. The server integrates this data and comprehensively monitors the user's state.

[0322] The server uses integrated data to run an artificial intelligence model that predicts the probability of headache occurrence for each user. This model uses algorithms learned from historical data to analyze headache occurrence patterns under specific conditions, as well as taking into account the user's emotional stress and its impact.

[0323] Based on the prediction results, the server generates customized self-care methods for the user. This process takes into account the user's current emotional state, and can suggest relaxation techniques if emotional stress levels are high.

[0324] The generated self-care methods and prediction results are notified from the server to the device. The device presents the received information to the user and explicitly instructs them on the necessary actions. For example, a notification such as "Your current stress level is high, so we recommend 10 minutes of meditation" might be displayed to the user on their smartphone.

[0325] Ultimately, users perform these self-care suggestions and provide feedback on the results and effects to their device. This allows the system to further improve its accuracy and provide more appropriate care methods. For example, if a user follows the suggestions and performs stretches or meditation, resulting in reduced stress and decreased headache frequency, this data will be used to predict improvements for the next time.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The device collects the user's biometric information from wearable devices and smartphones. This includes heart rate, steps taken, and sleep data. The device also captures the user's facial expressions and voice via cameras and microphones, and an emotion engine analyzes this data to identify the user's emotional state.

[0329] Step 2:

[0330] The device transmits biometric information, emotional data, and location and weather data acquired from environmental sensors to the server in real time. Through this transmission, a comprehensive dataset of the user's state is constructed.

[0331] Step 3:

[0332] The server uses the integrated data received from the terminal to activate an artificial intelligence model. The model has learned from past data and predicts the occurrence of headaches. In doing so, the model considers not only biometric and environmental information but also emotional states to make a multifaceted assessment of factors that cause headaches.

[0333] Step 4:

[0334] Based on the predicted results, the server generates self-care methods optimized for the user. This generation also takes into account the output of the emotion engine, and includes care methods that take into account the user's mental state and mood. For example, if the stress level is high, meditation or relaxation may be suggested.

[0335] Step 5:

[0336] The server notifies the device of the self-care method it has generated. The device then displays this information to the user and suggests specific actions to take. For example, the user's device might display instructions such as "Perform deep breathing exercises for 10 minutes."

[0337] Step 6:

[0338] Users check notifications from their devices and perform the suggested self-care methods. After performing the methods, they provide feedback through their devices about the effects they experienced and the details of what they did.

[0339] Step 7:

[0340] The device collects feedback from the user and sends it to the server. The server uses this data to update the artificial intelligence model, improving the accuracy of future predictions and the quality of self-care suggestions.

[0341] (Example 2)

[0342] 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".

[0343] Conventional health management systems primarily rely on predictions based on biometric and environmental information, failing to adequately reflect the user's emotional state. As a result, the prediction accuracy of health impacts from stress and emotional factors is often insufficient. Furthermore, the lack of personalized self-care suggestions means that these systems may not lead to improvements in the user's lifestyle. Therefore, there is a need for improved prediction accuracy using multifaceted data, including emotional states, and the provision of self-care methods optimized for each user.

[0344] 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.

[0345] In this invention, the server includes means for integrating biometric information, emotional state information, and environmental information collected from the user; means for predicting the probability of developing a head disease using a generative model based on the integrated information; and means for generating a customized self-management method for the user that takes emotional state into consideration based on the prediction results. This makes it possible to provide highly accurate health predictions that take emotional state into consideration and self-care methods optimized for individual users.

[0346] "Biometric information" refers to data about the user's physical condition, such as their heart rate and sleep patterns.

[0347] "Emotional state information" refers to data that indicates the emotional state of a user, analyzed from their facial expressions and tone of voice.

[0348] "Environmental information" refers to data about external conditions, such as the user's location and weather conditions.

[0349] "Integration" refers to the process of combining various types of collected data into a single dataset.

[0350] A "generative model" refers to an artificial intelligence algorithm that learns from past data and is used to predict current and future situations.

[0351] "Probability of head-related illness" refers to a numerical representation of the likelihood that a user will experience symptoms such as headaches.

[0352] "Self-care methods" refer to specific actions and activities that users take on their own to maintain or improve their own health.

[0353] "Self-management methods" refer to suggestions of actions that users should take to control their own health and maintain optimal lifestyle habits.

[0354] This system aims to predict headaches and suggest self-care methods, providing technology for acquiring and processing the user's biometric information, emotional state information, and environmental information. The system implementation utilizes the following hardware and software:

[0355] The system collects the user's biometric information using a wearable device and a smartphone. The wearable device is equipped with a heart rate sensor and an accelerometer, which are used to detect heart rate, sleep patterns, and other data. The smartphone is equipped with a camera and microphone, which capture the user's facial expressions and voice, and analyze their emotional state. Facial recognition software and voice analysis software assist in this process.

[0356] The device can also acquire location information using GPS functionality and obtain weather condition data from external information provision systems via an internet connection.

[0357] All collected data is sent to the server in real time. The server integrates this information and performs data processing and analysis. A generative AI model is used to predict the probability of each user developing a head disorder. This model learns from historical data using machine learning algorithms, achieving highly accurate predictions.

[0358] Taking the user's emotional state into account, the server generates customized self-care methods based on the prediction results. For example, if a high stress level is detected, it can suggest a program to encourage deep breathing or a short meditation session.

[0359] The generated self-care method is notified from the server to the terminal and presented to the user along with specific instructions. The user can then perform the self-care based on this and provide feedback on the results to the terminal. This feedback information is sent back to the server and contributes to further improving the accuracy of the generated AI model.

[0360] A specific example of a prompt message would be, "Propose a headache prediction and self-care method that takes into account the user's biometric and emotional data."

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] The device uses sensors from wearable devices and smartphones to acquire the user's heart rate and sleep data. This includes capturing real-time biometric information using heart rate sensors and accelerometers. The input is sensor data, and the output is biometric information formatted as heart rate and sleep patterns.

[0364] Step 2:

[0365] The device analyzes the user's emotional state using the smartphone's camera and voice input functions. Specifically, it analyzes facial expression data captured by facial recognition software and voice tone collected by voice analysis software to determine the user's emotions. The input consists of image data and audio data, and the output is information indicating the user's emotional state.

[0366] Step 3:

[0367] The device obtains location and weather data from external sources via GPS and internet connectivity. This allows for the collection of the user's geographical location and weather information. Inputs are GPS coordinates and data from weather services, while outputs are the user's location information and weather conditions.

[0368] Step 4:

[0369] The device transmits biometric information, emotional state information, and environmental information acquired in steps 1-3 to the server. The server stores this data in an integrated database. The input here is all the data before integration, and the output is the integrated dataset.

[0370] Step 5:

[0371] The server inputs the integrated dataset into a generating AI model. This model learns from historical data and predicts the probability of a user developing a head disorder. The input is the integrated dataset, and the output is probability information about the occurrence of head disorders.

[0372] Step 6:

[0373] The server generates customized self-care methods for the user based on predicted probabilities. This process suggests relaxation and exercise methods that take emotional states into account, based on the output from the generating AI model. The input is the prediction result, and the output is the suggested self-care method.

[0374] Step 7:

[0375] The server notifies the terminal of the generated self-care methods and predicted results. The terminal presents this to the user visually or audibly, prompting specific actions. The input is the notification content from the server, and the output is the presentation of information as instructions to the user.

[0376] Step 8:

[0377] The user performs the suggested self-care method and provides feedback on the results to the device. The device sends this feedback information to a server, which is used to improve the accuracy of predictions by the AI ​​model for future use. The input is the user's performance results, and the output is the feedback data.

[0378] (Application Example 2)

[0379] 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."

[0380] In modern society, health problems caused by lifestyle stress and irregular habits are increasing. Headaches, in particular, are common, and there is a need for more personalized self-care suggestions to prevent their occurrence. However, conventional methods do not provide precise predictions and suggestions that take emotional states into account, making it difficult for users to find appropriate self-care methods. This invention aims to solve these problems and provide users with a more effective and personalized means of health management.

[0381] 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.

[0382] In this invention, the server includes means for integrating biometric information, environmental information, and emotional information collected from the user; means for predicting the occurrence of headaches using an artificial intelligence model generated based on the integrated information; and means for generating a customized self-care method based on the prediction results and the user's emotional state. This makes it possible to predict headaches and propose personalized self-care that takes into account multiple factors, including emotional state.

[0383] "Biometric information" refers to data related to the user's physical condition, including heart rate and sleep information.

[0384] "Environmental information" refers to information about the user's surroundings, including location information and weather data.

[0385] "Emotional information" refers to information about the user's emotional state, and is data obtained through facial recognition and voice tone analysis.

[0386] An "artificial intelligence model" is a model equipped with a learning algorithm that predicts the occurrence of headaches based on integrated information from users.

[0387] "Self-care methods" refer to actions and activities that users take to prevent or alleviate headaches, and include relaxation techniques.

[0388] A "user interface" is an interface that includes display devices and input means for a user to interact with a system.

[0389] A "user device" is an electronic device used by a user, and includes terminals such as smartphones and smart glasses.

[0390] A "notification" is a message or alert that the system uses to communicate self-care methods or product information to the user.

[0391] "Means of promoting purchase" refer to functions and processes that suggest users purchase goods or services and facilitate that purchase action.

[0392] To implement this invention, a system consisting of smart glasses, a wearable device, a smartphone, and a communication network is utilized. The user wears the smart glasses and, while going about their daily life, periodically acquires biometric information such as heart rate and sleep information from the wearable device. At the same time, the built-in camera and microphone of the smart glasses analyze the user's facial expressions and voice tone to collect emotional information. Furthermore, environmental information, including location information and weather data, is acquired using environmental sensors built into the smartphone.

[0393] The device transmits this information to the server in real time. The server uses digital processing technology to integrate this information and build a detailed user profile, including emotional information. Based on this data, an artificial intelligence model designed with machine learning frameworks such as TensorFlow and PyTorch predicts the occurrence of headaches, and based on that prediction, suggests user-specific self-care methods and health-related products.

[0394] The generated self-care suggestions are displayed on the user's device through the user interface. Specifically, a notification such as "Your current stress level is high, so we recommend 10 minutes of meditation" will appear on the smart glasses' display, along with product information that can help with relaxation.

[0395] In this system, users perform self-care methods tailored to their own health and emotional state, and input the effects as feedback into a terminal. This feedback data is used to improve the accuracy of future predictions. For example, if a user performs a suggested meditation guide and reports that their stress has been reduced, this will be reflected in the next suggestion.

[0396] An example of a prompt message to input to a generative AI model is: "The user's current stress level is high. Please suggest the most suitable self-care products."

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] Users wear smart glasses and wearable devices to collect data on a daily basis. Facial and voice data are captured through the smart glasses' camera and microphone, and heart rate and sleep information are collected from the wearable device; this data is then transferred to the terminal. At this stage, the input is the user's real-time biometric and emotional information, which forms the basis for subsequent data analysis.

[0400] Step 2:

[0401] The device utilizes the environmental sensors in the user's smartphone to collect location and weather data. This allows it to obtain data about the external environment in which the user is located. The input for this step is environmental information, which the device uses to complete a dataset that helps understand the user's overall condition.

[0402] Step 3:

[0403] The device transmits collected biometric, emotional, and environmental information to the server in real time. The server integrates this data and comprehensively analyzes the user's current health and emotional state. The output of this process is stored on the server as integrated data and input into the next predictive process.

[0404] Step 4:

[0405] The server runs an artificial intelligence model using integrated data to predict the likelihood of a user experiencing a headache. This step uses integrated data as input and performs calculations through a generative AI model based on historical datasets. The output is the headache prediction result.

[0406] Step 5:

[0407] The server generates customized self-care methods that take into account the user's emotional state based on the prediction results. Here, the AI ​​uses the prediction results (input) to suggest relaxation activities and products suitable for the user. These suggestions are output as self-care suggestions and product information.

[0408] Step 6:

[0409] The server sends self-care methods and product information to the terminal and displays it on the user interface of the user device. The terminal receives this information and displays notifications such as "We recommend meditation" or "Please try our relaxing tea" on the smart glasses' display. This step is a direct output from the system to the user.

[0410] Step 7:

[0411] The user performs the suggested self-care method and inputs feedback on its effectiveness into the device. This feedback is transmitted from the device to the server and used as new data to train the next predictive model. This step is completed by inputting the user's experience and results as feedback into the server.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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".

[0428] This invention is a system aimed at preventing and self-care for headaches, and specifically provides care methods tailored to individual conditions using the user's health data and environmental information.

[0429] First, the device collects the user's biometric information from wearable devices and smartphones. This information includes heart rate, sleep duration, steps taken, and location data. This allows the device to understand the user's daily health indicators in detail. The device can also receive information manually entered by the user (such as the frequency and severity of headaches).

[0430] Next, the server receives biometric information transmitted from the terminal and obtains current weather information from the weather information provision platform. This allows the server to form a dataset integrating biometric and environmental information. Based on this integrated data, the server uses an artificial intelligence model to predict the occurrence of headaches for each user. The prediction utilizes analysis of past data and pattern recognition.

[0431] Based on the prediction results, the server generates a personalized self-care method for the user. This method is tailored to the user's lifestyle and health condition and designed to help prevent or alleviate headaches. This includes optimizing fluid intake, specific stretching exercises, and suggestions for improving lifestyle habits.

[0432] The generated self-care methods are sent from the server to the device. The device notifies the user of the received self-care methods and predictive alerts. These notifications are displayed on the user's device screen and are provided in a visually easy-to-understand format. For example, when the atmospheric pressure drops rapidly, specific advice such as "Drink plenty of water and take deep breaths regularly" is displayed.

[0433] Ultimately, the user performs the suggested self-care methods based on notifications from their device and provides feedback on the results back to the device. This feedback is sent to a server and used to further improve the accuracy of the artificial intelligence model. For example, if a user increases their fluid intake as suggested and as a result their headache frequency decreases, this data is used to adjust the model.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] The device collects the user's biometric information from wearable devices and smartphone sensors. This information includes heart rate, sleep duration, steps taken, and location data. This data is transmitted to the server in real time.

[0437] Step 2:

[0438] The server receives biometric information transmitted from the terminal. Simultaneously, it acquires environmental information such as temperature, atmospheric pressure, and humidity from an external weather information infrastructure. This allows the server to create a dataset integrating biometric and environmental information.

[0439] Step 3:

[0440] The server uses an artificial intelligence model based on integrated data to predict the occurrence of headaches for each user. By analyzing past data and identifying specific patterns, it calculates the likelihood of the next headache occurring.

[0441] Step 4:

[0442] The server generates a customized self-care plan for the user based on the prediction results. This plan is designed as a specific action plan tailored to the user's lifestyle and past symptoms.

[0443] Step 5:

[0444] The server notifies the user's device of alerts based on the generated self-care methods and predictions. The notifications are displayed on the user's device and provided in an intuitive and easy-to-understand interface.

[0445] Step 6:

[0446] Users check self-care notifications from their devices and perform the suggested actions. The results of these actions and the effects they experienced are then fed back to the device.

[0447] Step 7:

[0448] The terminal collects user feedback and sends it to the server. The server then uses this data to retrain its artificial intelligence model and improve prediction accuracy.

[0449] (Example 1)

[0450] 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."

[0451] Conventional headache prevention systems have not adequately customized their systems to suit individual users' lifestyles and environmental conditions, making it difficult to provide effective prevention and self-care methods. Furthermore, these systems lacked sufficient mechanisms to effectively utilize user feedback to improve the accuracy of their AI models.

[0452] 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.

[0453] In this invention, the server includes data processing means for integrating biometric and environmental information acquired from the user; analysis means for predicting the occurrence of headaches using a generated AI model based on the integrated data; customization means for generating self-care methods suitable for the user based on the prediction results; and learning means for collecting feedback from the user and improving the accuracy of the generated AI model. This makes it possible to provide the most suitable self-care method for each individual user while improving the accuracy of the model.

[0454] A "user" refers to a person who uses the system to prevent headaches or perform self-care.

[0455] "Biometric information" refers to data that indicates the user's health status, including heart rate, sleep duration, steps taken, and location information.

[0456] "Environmental information" refers to data about the user's external environment, such as weather and surrounding conditions.

[0457] "Data processing means" refers to a system that integrates biological information and environmental information and converts it into a format suitable for analysis.

[0458] A "generative AI model" refers to an artificial intelligence model used to predict the occurrence of headaches based on integrated data.

[0459] "Analysis method" refers to the process of analyzing data using a generative AI model to predict the occurrence of headaches.

[0460] "Customization methods" refer to a system that creates self-care methods tailored to individual users based on prediction results.

[0461] "Notification methods" refer to systems that inform users of self-care methods and predictive alerts via their user devices.

[0462] "Learning method" refers to a mechanism that collects user feedback and uses it to improve the accuracy of the generated AI model.

[0463] One embodiment of the present invention is to construct a system that provides users with individualized and appropriate headache prevention measures by utilizing various digital devices and cloud services.

[0464] First, the terminal will be a portable information terminal such as a smartphone or wearable device. This will allow the terminal to collect biometric information such as the user's heart rate, sleep duration, steps taken, and location information in real time. This collection will require devices such as smartwatches or dedicated mobile applications.

[0465] Next, this biometric information is transmitted to the server via a secure network protocol (e.g., HTTPS). Simultaneously, the server utilizes external information services to obtain weather data and other environmental information.

[0466] The server integrates collected biometric and environmental information and uses a generative AI model to predict headache occurrence. Common analytical tools and algorithmic platforms (e.g., Python and TensorFlow) are used in this process. The AI ​​model utilizes historical data analysis and pattern recognition techniques to make specific predictions for each user.

[0467] The results based on the predictions are further analyzed on the server, and the most suitable self-care method is customized for each user. This self-care method includes specific advice tailored to your lifestyle (e.g., improving hydration or specific stretching exercises).

[0468] Ultimately, the device presents the user with self-care methods notified by the server. Through the displayed information, the user can then practice specific self-care actions. Furthermore, the user inputs feedback on the results and experience into the device, and this data is sent back to the server. This allows the AI ​​model to continuously learn and improve its accuracy.

[0469] For example, if the AI ​​model predicts a rapid drop in atmospheric pressure, the user will be given a self-care suggestion such as, "Drink an additional liter of water and take deep breaths every hour." An example of a prompt to the generating AI model would be, "Based on past data, what self-care methods should be offered to the user?" This prompt serves as the starting point for the AI ​​model to generate the most suitable suggestions for the user.

[0470] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0471] Step 1:

[0472] The device collects the user's biometric information via wearable devices and smartphones. The input consists of real-time data such as heart rate, sleep duration, steps taken, and location information. The device temporarily stores this data in its internal memory. A data synthesis algorithm is used to generate a formatted dataset, which is then converted into a format that can be sent to the server.

[0473] Step 2:

[0474] The device transmits the collected biometric information to the server using a secure communication protocol. The input is the dataset formatted in step 1. The output is a binary data stream that reaches the server. This transmission allows the server to obtain real-time data and information necessary for immediate analysis.

[0475] Step 3:

[0476] The server stores biometric information received from the terminal in a database and simultaneously acquires weather data from an external information provision system. The input consists of biometric information from the terminal and environmental data obtained from a weather service API. The server integrates this data to create a single dataset suitable for analysis. The output is an integrated dataset formatted for use by AI models.

[0477] Step 4:

[0478] The server uses a generative AI model to predict the occurrence of headaches. The input is the integrated dataset created in step 3. The AI ​​model uses machine learning algorithms to analyze the data and generate prediction results. These prediction results show the probability of headache occurrence and the specific conditions for its occurrence. The output is prediction result data that can be interpreted by experts.

[0479] Step 5:

[0480] The server creates optimal self-care suggestions for the user based on the prediction results. The input is the prediction results obtained from the AI ​​model. The server uses a customized algorithm to generate advice tailored to each user's individual lifestyle. The output is a self-care method expressed in an actionable format.

[0481] Step 6:

[0482] The terminal notifies the user of self-care methods sent from the server. The input is self-care suggestion data from the server. The terminal utilizes a notification system and displays it on the user interface via a smartphone or wearable device. The output is a self-care notification in a format that the user can visually confirm.

[0483] Step 7:

[0484] The user acts according to the self-care notification received. The input is the self-care advice received in step 6. The user performs actions such as hydration and stretching and observes the effects. Feedback is entered into the device, describing the experience and effects, which will be used for the next analysis. The output is the user's actions for the next data accumulation.

[0485] (Application Example 1)

[0486] 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."

[0487] With the proliferation of smart devices, health management using personal biometric data is attracting attention. However, existing systems have the challenge of not being able to provide health management methods optimized for individual users. In particular, there is a problem in that predictions of health conditions, such as headaches, and the provision of specific countermeasures based on those predictions are not adequately addressed.

[0488] 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.

[0489] In this invention, the server includes means for integrating biometric data collected from the user with external environmental data, means for predicting the user's health status using a machine learning model based on the integrated information, and means for generating a self-management method tailored to the user based on the prediction results. This makes it possible to present a self-management method that is appropriate for the user's individual health status.

[0490] "Biometric data" refers to a collection of information that indicates a user's physical condition, such as their heart rate and activity level.

[0491] "External environment data" refers to a collection of information about the user's surrounding environment, such as temperature and atmospheric pressure.

[0492] A "machine learning model" is a computational model that learns trends from past data and makes predictions and judgments about new data.

[0493] "Health status prediction" is the act of using machine learning models to estimate the health problems a user may face in the future.

[0494] "Self-management methods" refer to suggestions for specific actions and measures that users should take to maintain or improve their own health.

[0495] A "display device" is hardware used to provide information to users visually.

[0496] An "intuitively understandable format" refers to a clear and easy-to-understand display method that allows users to easily grasp the suggested information and advice and take action.

[0497] This invention is implemented as a health management system using smart devices. This system predicts the individual health status based on the user's biometric data and external environmental data, and provides appropriate self-management methods.

[0498] The server integrates biometric data and external environmental data, and uses machine learning models to predict health status based on this data. Specifically, users can use smart wearable devices such as Google Glass or Vuzix Blade. Data measured by the built-in sensors of these devices is transmitted to the server in real time. On the software side, it utilizes AI models trained on historical data using TensorFlow. Furthermore, cloud infrastructure such as AWS Lambda is used for data transmission and reception.

[0499] The device notifies the user in an intuitively understandable format based on prediction results sent from the server. This includes displaying alerts and advice visually through the display. For example, if it detects signs of a sudden change in atmospheric pressure, it will display specific advice such as, "Drink plenty of fluids within the next hour."

[0500] The user follows notifications from their device, performs the suggested self-management methods, and provides feedback on the results to the device. This feedback is then sent back to the server to help improve the accuracy of the AI ​​model.

[0501] An example of a prompt message is: "Based on the weather forecast for the user's current location, predict the likelihood of a headache occurring and recommend appropriate self-care methods. If a headache is predicted, provide specific instructions such as hydration and stretching."

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] The device collects the user's biometric data using sensors from a smart wearable device. This data includes heart rate, sleep duration, and activity level. The collected data is filtered for data processing and sent to a server via a communication module. The input is biometric data, and the output is the data sent to the server.

[0505] Step 2:

[0506] The server receives biometric data from the terminal and integrates it with environmental data obtained from an external data provision platform. Weather forecasts and meteorological conditions are examples of this. This integrated dataset is generated and prepared as input data for the AI ​​model. The input is biometric data and environmental data, and the output is the integrated dataset.

[0507] Step 3:

[0508] The server inputs the integrated dataset into a machine learning model using TensorFlow and predicts the user's health status. This determines the user's future risk of developing headaches. The input is the integrated dataset, and the output is the health status prediction result.

[0509] Step 4:

[0510] The server generates a customized self-management plan for the user based on the prediction results. This includes recommendations for hydration and suggestions for specific exercises. The generated self-management plan is then transferred from the server to the terminal. The input is the health status prediction result, and the output is the self-management plan.

[0511] Step 5:

[0512] The terminal visually displays received self-management instructions to the user. Alerts and suggestions are presented via the display in a way that the user can easily understand and act upon. Input is self-management instructions from the server, and output is a visual display.

[0513] Step 6:

[0514] The user takes action based on notifications from their device and inputs the results as feedback into the device. The feedback data is sent back to the server and used to adjust the accuracy of the AI ​​model. The input is the user's feedback, and the output is the data sent to the server.

[0515] 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.

[0516] This invention provides a system for headache prediction and self-care methods that takes into account the user's emotional state in addition to their health and environmental information. By incorporating an emotional engine, it becomes possible to provide highly accurate predictions and care that take into account the user's emotional stressors.

[0517] First, the device acquires the user's biometric information (heart rate, sleep information, etc.) from the wearable device and smartphone sensors, and then the emotion engine analyzes the user's emotional state from the camera and voice input. The emotion engine identifies the user's emotions using facial recognition and voice tone analysis.

[0518] Next, the device acquires biometric information, emotional data, and location and weather data from environmental sensors installed on the device, and transmits all of this information to the server in real time. The server integrates this data and comprehensively monitors the user's state.

[0519] The server uses integrated data to run an artificial intelligence model that predicts the probability of headache occurrence for each user. This model uses algorithms learned from historical data to analyze headache occurrence patterns under specific conditions, as well as taking into account the user's emotional stress and its impact.

[0520] Based on the prediction results, the server generates customized self-care methods for the user. This process takes into account the user's current emotional state, and can suggest relaxation techniques if emotional stress levels are high.

[0521] The generated self-care methods and prediction results are notified from the server to the device. The device presents the received information to the user and explicitly instructs them on the necessary actions. For example, a notification such as "Your current stress level is high, so we recommend 10 minutes of meditation" might be displayed to the user on their smartphone.

[0522] Ultimately, users perform these self-care suggestions and provide feedback on the results and effects to their device. This allows the system to further improve its accuracy and provide more appropriate care methods. For example, if a user follows the suggestions and performs stretches or meditation, resulting in reduced stress and decreased headache frequency, this data will be used to predict improvements for the next time.

[0523] The following describes the processing flow.

[0524] Step 1:

[0525] The device collects the user's biometric information from wearable devices and smartphones. This includes heart rate, steps taken, and sleep data. The device also captures the user's facial expressions and voice via cameras and microphones, and an emotion engine analyzes this data to identify the user's emotional state.

[0526] Step 2:

[0527] The device transmits biometric information, emotional data, and location and weather data acquired from environmental sensors to the server in real time. Through this transmission, a comprehensive dataset of the user's state is constructed.

[0528] Step 3:

[0529] The server uses the integrated data received from the terminal to activate an artificial intelligence model. The model has learned from past data and predicts the occurrence of headaches. In doing so, the model considers not only biometric and environmental information but also emotional states to make a multifaceted assessment of factors that cause headaches.

[0530] Step 4:

[0531] Based on the predicted results, the server generates self-care methods optimized for the user. This generation also takes into account the output of the emotion engine, and includes care methods that take into account the user's mental state and mood. For example, if the stress level is high, meditation or relaxation may be suggested.

[0532] Step 5:

[0533] The server notifies the device of the self-care method it has generated. The device then displays this information to the user and suggests specific actions to take. For example, the user's device might display instructions such as "Perform deep breathing exercises for 10 minutes."

[0534] Step 6:

[0535] Users check notifications from their devices and perform the suggested self-care methods. After performing the methods, they provide feedback through their devices about the effects they experienced and the details of what they did.

[0536] Step 7:

[0537] The device collects feedback from the user and sends it to the server. The server uses this data to update the artificial intelligence model, improving the accuracy of future predictions and the quality of self-care suggestions.

[0538] (Example 2)

[0539] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0540] Conventional health management systems primarily rely on predictions based on biometric and environmental information, failing to adequately reflect the user's emotional state. As a result, the prediction accuracy of health impacts from stress and emotional factors is often insufficient. Furthermore, the lack of personalized self-care suggestions means that these systems may not lead to improvements in the user's lifestyle. Therefore, there is a need for improved prediction accuracy using multifaceted data, including emotional states, and the provision of self-care methods optimized for each user.

[0541] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0542] In this invention, the server includes means for integrating biometric information, emotional state information, and environmental information collected from the user; means for predicting the probability of developing a head disease using a generative model based on the integrated information; and means for generating a customized self-management method for the user that takes emotional state into consideration based on the prediction results. This makes it possible to provide highly accurate health predictions that take emotional state into consideration and self-care methods optimized for individual users.

[0543] "Biometric information" refers to data about the user's physical condition, such as their heart rate and sleep patterns.

[0544] "Emotional state information" refers to data that indicates the emotional state of a user, analyzed from their facial expressions and tone of voice.

[0545] "Environmental information" refers to data about external conditions, such as the user's location and weather conditions.

[0546] "Integration" refers to the process of combining various types of collected data into a single dataset.

[0547] A "generative model" refers to an artificial intelligence algorithm that learns from past data and is used to predict current and future situations.

[0548] "Probability of head-related illness" refers to a numerical representation of the likelihood that a user will experience symptoms such as headaches.

[0549] "Self-care methods" refer to specific actions and activities that users take on their own to maintain or improve their own health.

[0550] "Self-management methods" refer to suggestions of actions that users should take to control their own health and maintain optimal lifestyle habits.

[0551] This system aims to predict headaches and suggest self-care methods, providing technology for acquiring and processing the user's biometric information, emotional state information, and environmental information. The system implementation utilizes the following hardware and software:

[0552] The system collects the user's biometric information using a wearable device and a smartphone. The wearable device is equipped with a heart rate sensor and an accelerometer, which are used to detect heart rate, sleep patterns, and other data. The smartphone is equipped with a camera and microphone, which capture the user's facial expressions and voice, and analyze their emotional state. Facial recognition software and voice analysis software assist in this process.

[0553] The device can also acquire location information using GPS functionality and obtain weather condition data from external information provision systems via an internet connection.

[0554] All collected data is sent to the server in real time. The server integrates this information and performs data processing and analysis. A generative AI model is used to predict the probability of each user developing a head disorder. This model learns from historical data using machine learning algorithms, achieving highly accurate predictions.

[0555] Taking the user's emotional state into account, the server generates customized self-care methods based on the prediction results. For example, if a high stress level is detected, it can suggest a program to encourage deep breathing or a short meditation session.

[0556] The generated self-care method is notified from the server to the terminal and presented to the user along with specific instructions. The user can then perform the self-care based on this and provide feedback on the results to the terminal. This feedback information is sent back to the server and contributes to further improving the accuracy of the generated AI model.

[0557] A specific example of a prompt message would be, "Propose a headache prediction and self-care method that takes into account the user's biometric and emotional data."

[0558] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0559] Step 1:

[0560] The device uses sensors from wearable devices and smartphones to acquire the user's heart rate and sleep data. This includes capturing real-time biometric information using heart rate sensors and accelerometers. The input is sensor data, and the output is biometric information formatted as heart rate and sleep patterns.

[0561] Step 2:

[0562] The device analyzes the user's emotional state using the smartphone's camera and voice input functions. Specifically, it analyzes facial expression data captured by facial recognition software and voice tone collected by voice analysis software to determine the user's emotions. The input consists of image data and audio data, and the output is information indicating the user's emotional state.

[0563] Step 3:

[0564] The device obtains location and weather data from external sources via GPS and internet connectivity. This allows for the collection of the user's geographical location and weather information. Inputs are GPS coordinates and data from weather services, while outputs are the user's location information and weather conditions.

[0565] Step 4:

[0566] The device transmits biometric information, emotional state information, and environmental information acquired in steps 1-3 to the server. The server stores this data in an integrated database. The input here is all the data before integration, and the output is the integrated dataset.

[0567] Step 5:

[0568] The server inputs the integrated dataset into a generating AI model. This model learns from historical data and predicts the probability of a user developing a head disorder. The input is the integrated dataset, and the output is probability information about the occurrence of head disorders.

[0569] Step 6:

[0570] The server generates customized self-care methods for the user based on predicted probabilities. This process suggests relaxation and exercise methods that take emotional states into account, based on the output from the generating AI model. The input is the prediction result, and the output is the suggested self-care method.

[0571] Step 7:

[0572] The server notifies the terminal of the generated self-care methods and predicted results. The terminal presents this to the user visually or audibly, prompting specific actions. The input is the notification content from the server, and the output is the presentation of information as instructions to the user.

[0573] Step 8:

[0574] The user performs the suggested self-care method and provides feedback on the results to the device. The device sends this feedback information to a server, which is used to improve the accuracy of predictions by the AI ​​model for future use. The input is the user's performance results, and the output is the feedback data.

[0575] (Application Example 2)

[0576] 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."

[0577] In modern society, health problems caused by lifestyle stress and irregular habits are increasing. Headaches, in particular, are common, and there is a need for more personalized self-care suggestions to prevent their occurrence. However, conventional methods do not provide precise predictions and suggestions that take emotional states into account, making it difficult for users to find appropriate self-care methods. This invention aims to solve these problems and provide users with a more effective and personalized means of health management.

[0578] 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.

[0579] In this invention, the server includes means for integrating biometric information, environmental information, and emotional information collected from the user; means for predicting the occurrence of headaches using an artificial intelligence model generated based on the integrated information; and means for generating a customized self-care method based on the prediction results and the user's emotional state. This makes it possible to predict headaches and propose personalized self-care that takes into account multiple factors, including emotional state.

[0580] "Biometric information" refers to data related to the user's physical condition, including heart rate and sleep information.

[0581] "Environmental information" refers to information about the user's surroundings, including location information and weather data.

[0582] "Emotional information" refers to information about the user's emotional state, and is data obtained through facial recognition and voice tone analysis.

[0583] An "artificial intelligence model" is a model equipped with a learning algorithm that predicts the occurrence of headaches based on integrated information from users.

[0584] "Self-care methods" refer to actions and activities that users take to prevent or alleviate headaches, and include relaxation techniques.

[0585] A "user interface" is an interface that includes display devices and input means for a user to interact with a system.

[0586] A "user device" is an electronic device used by a user, and includes terminals such as smartphones and smart glasses.

[0587] A "notification" is a message or alert that the system uses to communicate self-care methods or product information to the user.

[0588] "Means of promoting purchase" refer to functions and processes that suggest users purchase goods or services and facilitate that purchase action.

[0589] To implement this invention, a system consisting of smart glasses, a wearable device, a smartphone, and a communication network is utilized. The user wears the smart glasses and, while going about their daily life, periodically acquires biometric information such as heart rate and sleep information from the wearable device. At the same time, the built-in camera and microphone of the smart glasses analyze the user's facial expressions and voice tone to collect emotional information. Furthermore, environmental information, including location information and weather data, is acquired using environmental sensors built into the smartphone.

[0590] The device transmits this information to the server in real time. The server uses digital processing technology to integrate this information and build a detailed user profile, including emotional information. Based on this data, an artificial intelligence model designed with machine learning frameworks such as TensorFlow and PyTorch predicts the occurrence of headaches, and based on that prediction, suggests user-specific self-care methods and health-related products.

[0591] The generated self-care suggestions are displayed on the user's device through the user interface. Specifically, a notification such as "Your current stress level is high, so we recommend 10 minutes of meditation" will appear on the smart glasses' display, along with product information that can help with relaxation.

[0592] In this system, users perform self-care methods tailored to their own health and emotional state, and input the effects as feedback into a terminal. This feedback data is used to improve the accuracy of future predictions. For example, if a user performs a suggested meditation guide and reports that their stress has been reduced, this will be reflected in the next suggestion.

[0593] An example of a prompt message to input to a generative AI model is: "The user's current stress level is high. Please suggest the most suitable self-care products."

[0594] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0595] Step 1:

[0596] Users wear smart glasses and wearable devices to collect data on a daily basis. Facial and voice data are captured through the smart glasses' camera and microphone, and heart rate and sleep information are collected from the wearable device; this data is then transferred to the terminal. At this stage, the input is the user's real-time biometric and emotional information, which forms the basis for subsequent data analysis.

[0597] Step 2:

[0598] The device utilizes the environmental sensors in the user's smartphone to collect location and weather data. This allows it to obtain data about the external environment in which the user is located. The input for this step is environmental information, which the device uses to complete a dataset that helps understand the user's overall condition.

[0599] Step 3:

[0600] The device transmits collected biometric, emotional, and environmental information to the server in real time. The server integrates this data and comprehensively analyzes the user's current health and emotional state. The output of this process is stored on the server as integrated data and input into the next predictive process.

[0601] Step 4:

[0602] The server runs an artificial intelligence model using integrated data to predict the likelihood of a user experiencing a headache. This step uses integrated data as input and performs calculations through a generative AI model based on historical datasets. The output is the headache prediction result.

[0603] Step 5:

[0604] The server generates customized self-care methods that take into account the user's emotional state based on the prediction results. Here, the AI ​​uses the prediction results (input) to suggest relaxation activities and products suitable for the user. These suggestions are output as self-care suggestions and product information.

[0605] Step 6:

[0606] The server sends self-care methods and product information to the terminal and displays it on the user interface of the user device. The terminal receives this information and displays notifications such as "We recommend meditation" or "Please try our relaxing tea" on the smart glasses' display. This step is a direct output from the system to the user.

[0607] Step 7:

[0608] The user performs the suggested self-care method and inputs feedback on its effectiveness into the device. This feedback is transmitted from the device to the server and used as new data to train the next predictive model. This step is completed by inputting the user's experience and results as feedback into the server.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] [Fourth Embodiment]

[0613] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0614] 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.

[0615] 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).

[0616] 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.

[0617] 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.

[0618] 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).

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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".

[0626] This invention is a system aimed at preventing and self-care for headaches, and specifically provides care methods tailored to individual conditions using the user's health data and environmental information.

[0627] First, the device collects the user's biometric information from wearable devices and smartphones. This information includes heart rate, sleep duration, steps taken, and location data. This allows the device to understand the user's daily health indicators in detail. The device can also receive information manually entered by the user (such as the frequency and severity of headaches).

[0628] Next, the server receives biometric information transmitted from the terminal and obtains current weather information from the weather information provision platform. This allows the server to form a dataset integrating biometric and environmental information. Based on this integrated data, the server uses an artificial intelligence model to predict the occurrence of headaches for each user. The prediction utilizes analysis of past data and pattern recognition.

[0629] Based on the prediction results, the server generates a personalized self-care method for the user. This method is tailored to the user's lifestyle and health condition and designed to help prevent or alleviate headaches. This includes optimizing fluid intake, specific stretching exercises, and suggestions for improving lifestyle habits.

[0630] The generated self-care methods are sent from the server to the device. The device notifies the user of the received self-care methods and predictive alerts. These notifications are displayed on the user's device screen and are provided in a visually easy-to-understand format. For example, when the atmospheric pressure drops rapidly, specific advice such as "Drink plenty of water and take deep breaths regularly" is displayed.

[0631] Ultimately, the user performs the suggested self-care methods based on notifications from their device and provides feedback on the results back to the device. This feedback is sent to a server and used to further improve the accuracy of the artificial intelligence model. For example, if a user increases their fluid intake as suggested and as a result their headache frequency decreases, this data is used to adjust the model.

[0632] The following describes the processing flow.

[0633] Step 1:

[0634] The device collects the user's biometric information from wearable devices and smartphone sensors. This information includes heart rate, sleep duration, steps taken, and location data. This data is transmitted to the server in real time.

[0635] Step 2:

[0636] The server receives biometric information transmitted from the terminal. Simultaneously, it acquires environmental information such as temperature, atmospheric pressure, and humidity from an external weather information infrastructure. This allows the server to create a dataset integrating biometric and environmental information.

[0637] Step 3:

[0638] The server uses an artificial intelligence model based on integrated data to predict the occurrence of headaches for each user. By analyzing past data and identifying specific patterns, it calculates the likelihood of the next headache occurring.

[0639] Step 4:

[0640] The server generates a customized self-care plan for the user based on the prediction results. This plan is designed as a specific action plan tailored to the user's lifestyle and past symptoms.

[0641] Step 5:

[0642] The server notifies the user's device of alerts based on the generated self-care methods and predictions. The notifications are displayed on the user's device and provided in an intuitive and easy-to-understand interface.

[0643] Step 6:

[0644] Users check self-care notifications from their devices and perform the suggested actions. The results of these actions and the effects they experienced are then fed back to the device.

[0645] Step 7:

[0646] The terminal collects user feedback and sends it to the server. The server then uses this data to retrain its artificial intelligence model and improve prediction accuracy.

[0647] (Example 1)

[0648] 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".

[0649] Conventional headache prevention systems have not adequately customized their systems to suit individual users' lifestyles and environmental conditions, making it difficult to provide effective prevention and self-care methods. Furthermore, these systems lacked sufficient mechanisms to effectively utilize user feedback to improve the accuracy of their AI models.

[0650] 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.

[0651] In this invention, the server includes data processing means for integrating biometric and environmental information acquired from the user; analysis means for predicting the occurrence of headaches using a generated AI model based on the integrated data; customization means for generating self-care methods suitable for the user based on the prediction results; and learning means for collecting feedback from the user and improving the accuracy of the generated AI model. This makes it possible to provide the most suitable self-care method for each individual user while improving the accuracy of the model.

[0652] A "user" refers to a person who uses the system to prevent headaches or perform self-care.

[0653] "Biometric information" refers to data that indicates the user's health status, including heart rate, sleep duration, steps taken, and location information.

[0654] "Environmental information" refers to data about the user's external environment, such as weather and surrounding conditions.

[0655] "Data processing means" refers to a system that integrates biological information and environmental information and converts it into a format suitable for analysis.

[0656] A "generative AI model" refers to an artificial intelligence model used to predict the occurrence of headaches based on integrated data.

[0657] "Analysis method" refers to the process of analyzing data using a generative AI model to predict the occurrence of headaches.

[0658] "Customization methods" refer to a system that creates self-care methods tailored to individual users based on prediction results.

[0659] "Notification methods" refer to systems that inform users of self-care methods and predictive alerts via their user devices.

[0660] "Learning method" refers to a mechanism that collects user feedback and uses it to improve the accuracy of the generated AI model.

[0661] One embodiment of the present invention is to construct a system that provides users with individualized and appropriate headache prevention measures by utilizing various digital devices and cloud services.

[0662] First, the terminal will be a portable information terminal such as a smartphone or wearable device. This will allow the terminal to collect biometric information such as the user's heart rate, sleep duration, steps taken, and location information in real time. This collection will require devices such as smartwatches or dedicated mobile applications.

[0663] Next, this biometric information is transmitted to the server via a secure network protocol (e.g., HTTPS). Simultaneously, the server utilizes external information services to obtain weather data and other environmental information.

[0664] The server integrates collected biometric and environmental information and uses a generative AI model to predict headache occurrence. Common analytical tools and algorithmic platforms (e.g., Python and TensorFlow) are used in this process. The AI ​​model utilizes historical data analysis and pattern recognition techniques to make specific predictions for each user.

[0665] The results based on the predictions are further analyzed on the server, and the most suitable self-care method is customized for each user. This self-care method includes specific advice tailored to your lifestyle (e.g., improving hydration or specific stretching exercises).

[0666] Ultimately, the device presents the user with self-care methods notified by the server. Through the displayed information, the user can then practice specific self-care actions. Furthermore, the user inputs feedback on the results and experience into the device, and this data is sent back to the server. This allows the AI ​​model to continuously learn and improve its accuracy.

[0667] For example, if the AI ​​model predicts a rapid drop in atmospheric pressure, the user will be given a self-care suggestion such as, "Drink an additional liter of water and take deep breaths every hour." An example of a prompt to the generating AI model would be, "Based on past data, what self-care methods should be offered to the user?" This prompt serves as the starting point for the AI ​​model to generate the most suitable suggestions for the user.

[0668] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0669] Step 1:

[0670] The device collects the user's biometric information via wearable devices and smartphones. The input consists of real-time data such as heart rate, sleep duration, steps taken, and location information. The device temporarily stores this data in its internal memory. A data synthesis algorithm is used to generate a formatted dataset, which is then converted into a format that can be sent to the server.

[0671] Step 2:

[0672] The device transmits the collected biometric information to the server using a secure communication protocol. The input is the dataset formatted in step 1. The output is a binary data stream that reaches the server. This transmission allows the server to obtain real-time data and information necessary for immediate analysis.

[0673] Step 3:

[0674] The server stores biometric information received from the terminal in a database and simultaneously acquires weather data from an external information provision system. The input consists of biometric information from the terminal and environmental data obtained from a weather service API. The server integrates this data to create a single dataset suitable for analysis. The output is an integrated dataset formatted for use by AI models.

[0675] Step 4:

[0676] The server uses a generative AI model to predict the occurrence of headaches. The input is the integrated dataset created in step 3. The AI ​​model uses machine learning algorithms to analyze the data and generate prediction results. These prediction results show the probability of headache occurrence and the specific conditions for its occurrence. The output is prediction result data that can be interpreted by experts.

[0677] Step 5:

[0678] The server creates optimal self-care suggestions for the user based on the prediction results. The input is the prediction results obtained from the AI ​​model. The server uses a customized algorithm to generate advice tailored to each user's individual lifestyle. The output is a self-care method expressed in an actionable format.

[0679] Step 6:

[0680] The terminal notifies the user of self-care methods sent from the server. The input is self-care suggestion data from the server. The terminal utilizes a notification system and displays it on the user interface via a smartphone or wearable device. The output is a self-care notification in a format that the user can visually confirm.

[0681] Step 7:

[0682] The user acts according to the self-care notification received. The input is the self-care advice received in step 6. The user performs actions such as hydration and stretching and observes the effects. Feedback is entered into the device, describing the experience and effects, which will be used for the next analysis. The output is the user's actions for the next data accumulation.

[0683] (Application Example 1)

[0684] 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".

[0685] With the proliferation of smart devices, health management using personal biometric data is attracting attention. However, existing systems have the challenge of not being able to provide health management methods optimized for individual users. In particular, there is a problem in that predictions of health conditions, such as headaches, and the provision of specific countermeasures based on those predictions are not adequately addressed.

[0686] 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.

[0687] In this invention, the server includes means for integrating biometric data collected from the user with external environmental data, means for predicting the user's health status using a machine learning model based on the integrated information, and means for generating a self-management method tailored to the user based on the prediction results. This makes it possible to present a self-management method that is appropriate for the user's individual health status.

[0688] "Biometric data" refers to a collection of information that indicates a user's physical condition, such as their heart rate and activity level.

[0689] "External environment data" refers to a collection of information about the user's surrounding environment, such as temperature and atmospheric pressure.

[0690] A "machine learning model" is a computational model that learns trends from past data and makes predictions and judgments about new data.

[0691] "Health status prediction" is the act of using machine learning models to estimate the health problems a user may face in the future.

[0692] "Self-management methods" refer to suggestions for specific actions and measures that users should take to maintain or improve their own health.

[0693] A "display device" is hardware used to provide information to users visually.

[0694] An "intuitively understandable format" refers to a clear and easy-to-understand display method that allows users to easily grasp the suggested information and advice and take action.

[0695] This invention is implemented as a health management system using smart devices. This system predicts the individual health status based on the user's biometric data and external environmental data, and provides appropriate self-management methods.

[0696] The server integrates biometric data and external environmental data, and uses machine learning models to predict health status based on this data. Specifically, users can use smart wearable devices such as Google Glass or Vuzix Blade. Data measured by the built-in sensors of these devices is transmitted to the server in real time. On the software side, it utilizes AI models trained on historical data using TensorFlow. Furthermore, cloud infrastructure such as AWS Lambda is used for data transmission and reception.

[0697] The device notifies the user in an intuitively understandable format based on prediction results sent from the server. This includes displaying alerts and advice visually through the display. For example, if it detects signs of a sudden change in atmospheric pressure, it will display specific advice such as, "Drink plenty of fluids within the next hour."

[0698] The user follows notifications from their device, performs the suggested self-management methods, and provides feedback on the results to the device. This feedback is then sent back to the server to help improve the accuracy of the AI ​​model.

[0699] An example of a prompt message is: "Based on the weather forecast for the user's current location, predict the likelihood of a headache occurring and recommend appropriate self-care methods. If a headache is predicted, provide specific instructions such as hydration and stretching."

[0700] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0701] Step 1:

[0702] The device collects the user's biometric data using sensors from a smart wearable device. This data includes heart rate, sleep duration, and activity level. The collected data is filtered for data processing and sent to a server via a communication module. The input is biometric data, and the output is the data sent to the server.

[0703] Step 2:

[0704] The server receives biometric data from the terminal and integrates it with environmental data obtained from an external data provision platform. Weather forecasts and meteorological conditions are examples of this. This integrated dataset is generated and prepared as input data for the AI ​​model. The input is biometric data and environmental data, and the output is the integrated dataset.

[0705] Step 3:

[0706] The server inputs the integrated dataset into a machine learning model using TensorFlow and predicts the user's health status. This determines the user's future risk of developing headaches. The input is the integrated dataset, and the output is the health status prediction result.

[0707] Step 4:

[0708] The server generates a customized self-management plan for the user based on the prediction results. This includes recommendations for hydration and suggestions for specific exercises. The generated self-management plan is then transferred from the server to the terminal. The input is the health status prediction result, and the output is the self-management plan.

[0709] Step 5:

[0710] The terminal visually displays received self-management instructions to the user. Alerts and suggestions are presented via the display in a way that the user can easily understand and act upon. Input is self-management instructions from the server, and output is a visual display.

[0711] Step 6:

[0712] The user takes action based on notifications from their device and inputs the results as feedback into the device. The feedback data is sent back to the server and used to adjust the accuracy of the AI ​​model. The input is the user's feedback, and the output is the data sent to the server.

[0713] 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.

[0714] This invention provides a system for headache prediction and self-care methods that takes into account the user's emotional state in addition to their health and environmental information. By incorporating an emotional engine, it becomes possible to provide highly accurate predictions and care that take into account the user's emotional stressors.

[0715] First, the device acquires the user's biometric information (heart rate, sleep information, etc.) from the wearable device and smartphone sensors, and then the emotion engine analyzes the user's emotional state from the camera and voice input. The emotion engine identifies the user's emotions using facial recognition and voice tone analysis.

[0716] Next, the device acquires biometric information, emotional data, and location and weather data from environmental sensors installed on the device, and transmits all of this information to the server in real time. The server integrates this data and comprehensively monitors the user's state.

[0717] The server uses integrated data to run an artificial intelligence model that predicts the probability of headache occurrence for each user. This model uses algorithms learned from historical data to analyze headache occurrence patterns under specific conditions, as well as taking into account the user's emotional stress and its impact.

[0718] Based on the prediction results, the server generates customized self-care methods for the user. This process takes into account the user's current emotional state, and can suggest relaxation techniques if emotional stress levels are high.

[0719] The generated self-care methods and prediction results are notified from the server to the device. The device presents the received information to the user and explicitly instructs them on the necessary actions. For example, a notification such as "Your current stress level is high, so we recommend 10 minutes of meditation" might be displayed to the user on their smartphone.

[0720] Ultimately, users perform these self-care suggestions and provide feedback on the results and effects to their device. This allows the system to further improve its accuracy and provide more appropriate care methods. For example, if a user follows the suggestions and performs stretches or meditation, resulting in reduced stress and decreased headache frequency, this data will be used to predict improvements for the next time.

[0721] The following describes the processing flow.

[0722] Step 1:

[0723] The device collects the user's biometric information from wearable devices and smartphones. This includes heart rate, steps taken, and sleep data. The device also captures the user's facial expressions and voice via cameras and microphones, and an emotion engine analyzes this data to identify the user's emotional state.

[0724] Step 2:

[0725] The device transmits biometric information, emotional data, and location and weather data acquired from environmental sensors to the server in real time. Through this transmission, a comprehensive dataset of the user's state is constructed.

[0726] Step 3:

[0727] The server uses the integrated data received from the terminal to activate an artificial intelligence model. The model has learned from past data and predicts the occurrence of headaches. In doing so, the model considers not only biometric and environmental information but also emotional states to make a multifaceted assessment of factors that cause headaches.

[0728] Step 4:

[0729] Based on the predicted results, the server generates self-care methods optimized for the user. This generation also takes into account the output of the emotion engine, and includes care methods that take into account the user's mental state and mood. For example, if the stress level is high, meditation or relaxation may be suggested.

[0730] Step 5:

[0731] The server notifies the device of the self-care method it has generated. The device then displays this information to the user and suggests specific actions to take. For example, the user's device might display instructions such as "Perform deep breathing exercises for 10 minutes."

[0732] Step 6:

[0733] Users check notifications from their devices and perform the suggested self-care methods. After performing the methods, they provide feedback through their devices about the effects they experienced and the details of what they did.

[0734] Step 7:

[0735] The device collects feedback from the user and sends it to the server. The server uses this data to update the artificial intelligence model, improving the accuracy of future predictions and the quality of self-care suggestions.

[0736] (Example 2)

[0737] 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".

[0738] Conventional health management systems primarily rely on predictions based on biometric and environmental information, failing to adequately reflect the user's emotional state. As a result, the prediction accuracy of health impacts from stress and emotional factors is often insufficient. Furthermore, the lack of personalized self-care suggestions means that these systems may not lead to improvements in the user's lifestyle. Therefore, there is a need for improved prediction accuracy using multifaceted data, including emotional states, and the provision of self-care methods optimized for each user.

[0739] 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.

[0740] In this invention, the server includes means for integrating biometric information, emotional state information, and environmental information collected from the user; means for predicting the probability of developing a head disease using a generative model based on the integrated information; and means for generating a customized self-management method for the user that takes emotional state into consideration based on the prediction results. This makes it possible to provide highly accurate health predictions that take emotional state into consideration and self-care methods optimized for individual users.

[0741] "Biometric information" refers to data about the user's physical condition, such as their heart rate and sleep patterns.

[0742] "Emotional state information" refers to data that indicates the emotional state of a user, analyzed from their facial expressions and tone of voice.

[0743] "Environmental information" refers to data about external conditions, such as the user's location and weather conditions.

[0744] "Integration" refers to the process of combining various types of collected data into a single dataset.

[0745] A "generative model" refers to an artificial intelligence algorithm that learns from past data and is used to predict current and future situations.

[0746] "Probability of head-related illness" refers to a numerical representation of the likelihood that a user will experience symptoms such as headaches.

[0747] "Self-care methods" refer to specific actions and activities that users take on their own to maintain or improve their own health.

[0748] "Self-management methods" refer to suggestions of actions that users should take to control their own health and maintain optimal lifestyle habits.

[0749] This system aims to predict headaches and suggest self-care methods, providing technology for acquiring and processing the user's biometric information, emotional state information, and environmental information. The system implementation utilizes the following hardware and software:

[0750] The system collects the user's biometric information using a wearable device and a smartphone. The wearable device is equipped with a heart rate sensor and an accelerometer, which are used to detect heart rate, sleep patterns, and other data. The smartphone is equipped with a camera and microphone, which capture the user's facial expressions and voice, and analyze their emotional state. Facial recognition software and voice analysis software assist in this process.

[0751] The device can also acquire location information using GPS functionality and obtain weather condition data from external information provision systems via an internet connection.

[0752] All collected data is sent to the server in real time. The server integrates this information and performs data processing and analysis. A generative AI model is used to predict the probability of each user developing a head disorder. This model learns from historical data using machine learning algorithms, achieving highly accurate predictions.

[0753] Taking the user's emotional state into account, the server generates customized self-care methods based on the prediction results. For example, if a high stress level is detected, it can suggest a program to encourage deep breathing or a short meditation session.

[0754] The generated self-care method is notified from the server to the terminal and presented to the user along with specific instructions. The user can then perform the self-care based on this and provide feedback on the results to the terminal. This feedback information is sent back to the server and contributes to further improving the accuracy of the generated AI model.

[0755] A specific example of a prompt message would be, "Propose a headache prediction and self-care method that takes into account the user's biometric and emotional data."

[0756] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0757] Step 1:

[0758] The device uses sensors from wearable devices and smartphones to acquire the user's heart rate and sleep data. This includes capturing real-time biometric information using heart rate sensors and accelerometers. The input is sensor data, and the output is biometric information formatted as heart rate and sleep patterns.

[0759] Step 2:

[0760] The device analyzes the user's emotional state using the smartphone's camera and voice input functions. Specifically, it analyzes facial expression data captured by facial recognition software and voice tone collected by voice analysis software to determine the user's emotions. The input consists of image data and audio data, and the output is information indicating the user's emotional state.

[0761] Step 3:

[0762] The device obtains location and weather data from external sources via GPS and internet connectivity. This allows for the collection of the user's geographical location and weather information. Inputs are GPS coordinates and data from weather services, while outputs are the user's location information and weather conditions.

[0763] Step 4:

[0764] The device transmits biometric information, emotional state information, and environmental information acquired in steps 1-3 to the server. The server stores this data in an integrated database. The input here is all the data before integration, and the output is the integrated dataset.

[0765] Step 5:

[0766] The server inputs the integrated dataset into a generating AI model. This model learns from historical data and predicts the probability of a user developing a head disorder. The input is the integrated dataset, and the output is probability information about the occurrence of head disorders.

[0767] Step 6:

[0768] The server generates customized self-care methods for the user based on predicted probabilities. This process suggests relaxation and exercise methods that take emotional states into account, based on the output from the generating AI model. The input is the prediction result, and the output is the suggested self-care method.

[0769] Step 7:

[0770] The server notifies the terminal of the generated self-care methods and predicted results. The terminal presents this to the user visually or audibly, prompting specific actions. The input is the notification content from the server, and the output is the presentation of information as instructions to the user.

[0771] Step 8:

[0772] The user performs the suggested self-care method and provides feedback on the results to the device. The device sends this feedback information to a server, which is used to improve the accuracy of predictions by the AI ​​model for future use. The input is the user's performance results, and the output is the feedback data.

[0773] (Application Example 2)

[0774] 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".

[0775] In modern society, health problems caused by lifestyle stress and irregular habits are increasing. Headaches, in particular, are common, and there is a need for more personalized self-care suggestions to prevent their occurrence. However, conventional methods do not provide precise predictions and suggestions that take emotional states into account, making it difficult for users to find appropriate self-care methods. This invention aims to solve these problems and provide users with a more effective and personalized means of health management.

[0776] 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.

[0777] In this invention, the server includes means for integrating biometric information, environmental information, and emotional information collected from the user; means for predicting the occurrence of headaches using an artificial intelligence model generated based on the integrated information; and means for generating a customized self-care method based on the prediction results and the user's emotional state. This makes it possible to predict headaches and propose personalized self-care that takes into account multiple factors, including emotional state.

[0778] "Biometric information" refers to data related to the user's physical condition, including heart rate and sleep information.

[0779] "Environmental information" refers to information about the user's surroundings, including location information and weather data.

[0780] "Emotional information" refers to information about the user's emotional state, and is data obtained through facial recognition and voice tone analysis.

[0781] An "artificial intelligence model" is a model equipped with a learning algorithm that predicts the occurrence of headaches based on integrated information from users.

[0782] "Self-care methods" refer to actions and activities that users take to prevent or alleviate headaches, and include relaxation techniques.

[0783] A "user interface" is an interface that includes display devices and input means for a user to interact with a system.

[0784] A "user device" is an electronic device used by a user, and includes terminals such as smartphones and smart glasses.

[0785] A "notification" is a message or alert that the system uses to communicate self-care methods or product information to the user.

[0786] "Means of promoting purchase" refer to functions and processes that suggest users purchase goods or services and facilitate that purchase action.

[0787] To implement this invention, a system consisting of smart glasses, a wearable device, a smartphone, and a communication network is utilized. The user wears the smart glasses and, while going about their daily life, periodically acquires biometric information such as heart rate and sleep information from the wearable device. At the same time, the built-in camera and microphone of the smart glasses analyze the user's facial expressions and voice tone to collect emotional information. Furthermore, environmental information, including location information and weather data, is acquired using environmental sensors built into the smartphone.

[0788] The device transmits this information to the server in real time. The server uses digital processing technology to integrate this information and build a detailed user profile, including emotional information. Based on this data, an artificial intelligence model designed with machine learning frameworks such as TensorFlow and PyTorch predicts the occurrence of headaches, and based on that prediction, suggests user-specific self-care methods and health-related products.

[0789] The generated self-care suggestions are displayed on the user's device through the user interface. Specifically, a notification such as "Your current stress level is high, so we recommend 10 minutes of meditation" will appear on the smart glasses' display, along with product information that can help with relaxation.

[0790] In this system, users perform self-care methods tailored to their own health and emotional state, and input the effects as feedback into a terminal. This feedback data is used to improve the accuracy of future predictions. For example, if a user performs a suggested meditation guide and reports that their stress has been reduced, this will be reflected in the next suggestion.

[0791] An example of a prompt message to input to a generative AI model is: "The user's current stress level is high. Please suggest the most suitable self-care products."

[0792] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0793] Step 1:

[0794] Users wear smart glasses and wearable devices to collect data on a daily basis. Facial and voice data are captured through the smart glasses' camera and microphone, and heart rate and sleep information are collected from the wearable device; this data is then transferred to the terminal. At this stage, the input is the user's real-time biometric and emotional information, which forms the basis for subsequent data analysis.

[0795] Step 2:

[0796] The device utilizes the environmental sensors in the user's smartphone to collect location and weather data. This allows it to obtain data about the external environment in which the user is located. The input for this step is environmental information, which the device uses to complete a dataset that helps understand the user's overall condition.

[0797] Step 3:

[0798] The device transmits collected biometric, emotional, and environmental information to the server in real time. The server integrates this data and comprehensively analyzes the user's current health and emotional state. The output of this process is stored on the server as integrated data and input into the next predictive process.

[0799] Step 4:

[0800] The server runs an artificial intelligence model using integrated data to predict the likelihood of a user experiencing a headache. This step uses integrated data as input and performs calculations through a generative AI model based on historical datasets. The output is the headache prediction result.

[0801] Step 5:

[0802] The server generates customized self-care methods that take into account the user's emotional state based on the prediction results. Here, the AI ​​uses the prediction results (input) to suggest relaxation activities and products suitable for the user. These suggestions are output as self-care suggestions and product information.

[0803] Step 6:

[0804] The server sends self-care methods and product information to the terminal and displays it on the user interface of the user device. The terminal receives this information and displays notifications such as "We recommend meditation" or "Please try our relaxing tea" on the smart glasses' display. This step is a direct output from the system to the user.

[0805] Step 7:

[0806] The user performs the suggested self-care method and inputs feedback on its effectiveness into the device. This feedback is transmitted from the device to the server and used as new data to train the next predictive model. This step is completed by inputting the user's experience and results as feedback into the server.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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."

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] The following is further disclosed regarding the embodiments described above.

[0829] (Claim 1)

[0830] A means of integrating biometric and environmental information collected from users,

[0831] A means for predicting the occurrence of headaches using an artificial intelligence model based on the aforementioned integrated information,

[0832] A means for generating a self-care method customized for the user based on the aforementioned prediction results,

[0833] A system including means for notifying a user device of the aforementioned self-care method.

[0834] (Claim 2)

[0835] The system according to claim 1, wherein the aforementioned biometric information includes information acquired from a wearable device.

[0836] (Claim 3)

[0837] The system according to claim 1, wherein the environmental information includes weather data obtained from an external information provision platform.

[0838] "Example 1"

[0839] (Claim 1)

[0840] A data processing means that integrates biometric information and environmental information obtained from the user,

[0841] An analytical means that uses a generated AI model based on the aforementioned integrated data to predict the occurrence of headaches,

[0842] A customization means for generating a self-care method suitable for the user based on the aforementioned prediction results,

[0843] A display means for notifying the user of the self-care method through a user interface,

[0844] A system including a learning means for collecting user feedback and improving the accuracy of the generated AI model.

[0845] (Claim 2)

[0846] The system according to claim 1, wherein the aforementioned biometric information includes information obtained from a portable device.

[0847] (Claim 3)

[0848] The system according to claim 1, wherein the environmental information includes weather-related data obtained from an external information provision system.

[0849] "Application Example 1"

[0850] (Claim 1)

[0851] A means of integrating biometric data collected from users with external environmental data,

[0852] A means for predicting health status using a machine learning model based on the aforementioned integrated information,

[0853] Means for generating a self-management method tailored for the user based on the aforementioned prediction results,

[0854] Means for transmitting the self-management method to a display device,

[0855] The aforementioned display device includes means for displaying instructions in an intuitively understandable format,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, wherein the aforementioned biological data includes information acquired from a portable device.

[0859] (Claim 3)

[0860] The system according to claim 1, wherein the external environmental data includes weather information obtained from an external data provision platform.

[0861] "Example 2 of combining an emotion engine"

[0862] (Claim 1)

[0863] A means for integrating biometric information, emotional state information, and environmental information collected from users,

[0864] A means for predicting the probability of head disease occurrence using a generative model based on the aforementioned integrated information,

[0865] A means for generating a customized self-management method for the user that takes into account their emotional state based on the aforementioned prediction results,

[0866] A means of notifying the user terminal of the aforementioned self-management method and prompting specific actions,

[0867] A system including means for collecting data to improve the accuracy of a predictive model by providing feedback on the results of the self-management method described above.

[0868] (Claim 2)

[0869] The system according to claim 1, wherein the aforementioned biological information includes information obtained from a portable measuring instrument.

[0870] (Claim 3)

[0871] The system according to claim 1, wherein the environmental information includes weather condition data obtained from an external information provider.

[0872] "Application example 2 when combining with an emotional engine"

[0873] (Claim 1)

[0874] A means for integrating biometric information, environmental information, and emotional information collected from users,

[0875] A means for predicting the occurrence of headaches using an artificial intelligence model generated based on the aforementioned integrated information,

[0876] Means for generating a customized self-care method based on the aforementioned prediction results and the user's emotional state,

[0877] A means for displaying proposed health-related products, including the self-care method, on a user interface,

[0878] Means for notifying the user device of the aforementioned self-care method and related product information,

[0879] Means of promoting the purchase of goods or services based on the aforementioned notice,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, wherein the aforementioned biometric information includes information acquired from a portable device.

[0883] (Claim 3)

[0884] The system according to claim 1, wherein the environmental information includes weather data obtained from an external information provision platform. [Explanation of symbols]

[0885] 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 integrating biometric and environmental information collected from users, A means for predicting the occurrence of headaches using an artificial intelligence model based on the aforementioned integrated information, A means for generating a self-care method customized for the user based on the aforementioned prediction results, A system including means for notifying a user device of the aforementioned self-care method.

2. The system according to claim 1, wherein the aforementioned biometric information includes information acquired from a wearable device.

3. The system according to claim 1, wherein the environmental information includes weather data obtained from an external information provision platform.

Images