system

Abstract

We provide the system. [Solution] Means for acquiring diverse biological information, A means of integrating and analyzing acquired biological information, A means for generating health-related risk diagnoses and advice based on analysis results, A means of suggesting the most suitable medical institution based on the generated advice, A means of collecting user feedback and improving the analysis results and suggestions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

[0006] "Diverse biometric information" is a general term for various physiological data obtained from users, such as heart rate, body temperature, oxygen saturation, health checkup results, and exercise information.

[0007] "Means of acquisition" refers to the process of devices and software used to electronically collect biometric information from users.

[0008] "Means for integration and analysis" refers to a system that has the function of centrally organizing collected biological information and analyzing it using technologies such as artificial intelligence.

[0009] "Health-related risk assessment" is the process of predicting diseases and health risks that a user may be susceptible to in the future, based on analyzed biometric information.

[0010] "Means of generating advice" refers to the process of creating specific instructions and suggestions for maintaining or improving the user's health based on the analysis results.

[0011] "A means of suggesting the most suitable medical institution" refers to a system that selects accessible medical institutions based on the user's health condition and provides that information to the user.

[0012] "Means of collecting feedback and improving analysis results and suggestions" refers to the process of gathering opinions and reactions from users regarding the service and using that to improve the accuracy of the system's analysis and the content of the advice provided. [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, when an emotion engine is combined. [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] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0016] In the following embodiments, a 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, a 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, a 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 relates to a system that provides users with customized health risk diagnoses and advice by aggregating and analyzing diverse biometric information. The invention is implemented by the user providing data through a terminal and a server processing that data.

[0035] First, users provide biometric information such as heart rate, body temperature, eating and drinking history, and exercise records using wearable devices and smartphone applications. For example, users input their daily meal details into the app, and this information is then incorporated into the system. Wearable devices automatically measure daily exercise levels and heart rate and transmit the data to the device.

[0036] Next, the device formats the collected biometric information into the necessary format for transmission to the server and securely transfers it. This transfer process encrypts the data to ensure user privacy is protected.

[0037] The server then integrates and centrally manages the diverse biometric information it receives. Using AI technology, the server analyzes the data to comprehensively assess the user's health status. This analysis identifies potential health risks the user may face in the future and generates specific advice on preventative and corrective measures. For example, if the data analysis indicates a risk of high blood pressure, the user will be notified with advice to reduce sodium intake.

[0038] The server also generates suggestions for medical institutions suitable for the user's health condition. It lists the most suitable hospitals based on the user's place of residence and health status, and provides this information to the user via their device. Furthermore, it has a mechanism to continuously improve the system's accuracy and user satisfaction by utilizing user feedback.

[0039] In this way, the present invention can provide individual users with a more appropriate and personalized health management experience. This system helps users maintain a healthy and effective daily life.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users input biometric information such as heart rate, body temperature, diet, and exercise data using wearable devices and smartphone apps, and collect data. For example, they might manually enter their daily meal details into the app and automatically record heart rate data through the device.

[0043] Step 2:

[0044] The device processes the collected biometric information and converts it into a standard format. This format conversion ensures that the data is available for subsequent processing in a consistent manner. The device also encrypts the obtained data before transmitting it to the server, ensuring privacy.

[0045] Step 3:

[0046] The server receives biometric information transmitted from the terminal and stores it centrally in a database. The stored data, combined with data from different sources, enables more comprehensive analysis.

[0047] Step 4:

[0048] The server uses AI technology to analyze integrated data and assess the user's health status. This identifies disease risks and health concerns. Based on the analysis results, it generates risk assessments and specific advice on lifestyle improvements.

[0049] Step 5:

[0050] The server selects the most suitable medical institution for the user based on the analysis results and organizes the information. It generates a list of appropriate hospitals and clinics, taking into account the user's place of residence and identified health risks.

[0051] Step 6:

[0052] The device notifies the user of health advice and suggestions for medical institutions received from the server. This allows the user to receive a specific action plan based on their current health status.

[0053] Step 7:

[0054] Users provide feedback through the app on the advice and medical institution suggestions they receive. This feedback includes their reactions to the usefulness of the advice and their choice of medical institution.

[0055] Step 8:

[0056] The server analyzes user feedback data to improve overall system performance. Based on the feedback, it adjusts AI algorithms and suggestions, and implements improvements to enhance the accuracy of future advice and suggestions.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In modern society, managing diverse biometric information and understanding one's health status is increasingly important, but effective systems to address this are still lacking. In particular, there is a need to support users in leading healthy lives by identifying individual health risks and providing appropriate advice and suggesting medical facilities. Existing systems face challenges in collecting and analyzing sufficient data and providing appropriate actionable guidelines based on the results.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for acquiring diverse biometric information, means for securely transmitting the acquired biometric information via a communication terminal, means for integrating and analyzing the transmitted biometric information, means for generating health-related risk diagnoses and advice based on the analysis results, means for suggesting the most suitable medical institution based on the generated advice, and means for collecting user feedback and improving the analysis results and suggestions. This enables personalized health management for individual users.

[0062] "Diverse biometric information" is a general term for various types of data that indicate an individual's health status, such as heart rate, body temperature, eating and drinking history, and exercise records.

[0063] "Communication terminals" refer to electronic devices such as smartphones and tablets that can send and receive data over a network.

[0064] "Integration" means centrally gathering acquired biometric information and processing it as a single piece of information.

[0065] "Analysis" refers to data processing operations performed using collected data to evaluate and predict specific health conditions or risks.

[0066] "Health-related risk assessment" refers to identifying potential or future health problems in an individual that have been revealed through analysis.

[0067] "Generating advice" refers to providing users with behavioral guidelines and suggestions for lifestyle improvements based on health-related risk assessments.

[0068] "Suggesting the most suitable medical institution" means listing facilities that can provide appropriate medical services based on the user's health condition and place of residence, and providing this information.

[0069] "Feedback" refers to reactions and opinions from users regarding the system's performance and the advice they have provided.

[0070] This invention is a system that provides users with customized health risk diagnoses and advice by aggregating and analyzing diverse biological information. The invention is implemented when the user provides data through a terminal, and a server processes that data.

[0071] First, users acquire biometric data using wearable technology. For example, they use wearable devices to measure their heart rate and body temperature, and input medical information such as their eating and drinking history and exercise records into a smartphone application. This allows users to easily understand their daily health status.

[0072] Next, when the device provides the collected biometric information to the server, it formats it into the required format and transfers it securely. The technologies used include the SSL / TLS protocol for encrypting and transmitting the data. This process is essential to protect the user's private information.

[0073] The server then integrates the diverse data it receives and stores it in a self-managing database. The server uses a generative AI model to analyze this data and comprehensively evaluate the user's health status. The generative AI model, for example, uses machine learning algorithms and, based on the insights gained from the data analysis, can identify health risks and suggest preventative measures for the user.

[0074] Furthermore, when suggesting medical facilities, the server considers the user's health status and location information to identify and suggest the most suitable facility. It also incorporates a mechanism to continuously improve the analysis results and suggestions by utilizing user feedback.

[0075] As a specific example, the prompt text to be entered into the system is as follows:

[0076] "A 35-year-old male with an average heart rate of 75 bpm, primarily a desk job, and runs three times a week. Please assess this user's future health risks and provide necessary advice."

[0077] In this way, the system provides users with a personalized health management experience, contributing to maintaining good health.

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

[0079] Step 1:

[0080] Users collect biometric information using wearable technology and smartphone apps. The inputs include a variety of data such as heart rate, body temperature, exercise records, and eating and drinking history. For example, a user might enter details of their breakfast into the app, and the wearable device automatically measures their heart rate. This input data is temporarily stored on the device for later analysis.

[0081] Step 2:

[0082] The device formats the collected biometric information and prepares it for transmission to the server. During this process, the input data is encrypted and formatted in a secure manner. Specifically, the device converts the data to JSON format and outputs it after encrypting it using the SSL / TLS protocol. This output is then ready to be transferred to the server.

[0083] Step 3:

[0084] The server integrates data received from terminals and centrally manages it in a database. It receives encrypted JSON data as input, decrypts it, and then organizes it in the storage system. Specifically, it stores user IDs and associated biometric information in the database and outputs it in a format suitable for future analysis.

[0085] Step 4:

[0086] The server analyzes data using a generative AI model. This analysis provides a multifaceted assessment of the user's health status. Specifically, it applies a random forest algorithm to identify health risks from the input data. This output is then prepared as specific advice for the user.

[0087] Step 5:

[0088] The server generates health advice based on the analysis results and suggests the most suitable medical facilities. Here, based on the risk assessment generated from the analysis output, it creates health guidelines that are beneficial to the user. Specifically, if a risk of hypertension is identified, it will formulate advice to reduce sodium intake for the user and output a list of appropriate medical facilities.

[0089] Step 6:

[0090] Users submit feedback on the advice and suggestions provided, and the system uses this information to make improvements. As input, users fill out a feedback form with their opinions. Specifically, users enter comments on the usefulness of the advice, and this information is transmitted to the server. This output is used to improve the system's algorithms and services.

[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] In recent years, the importance of health management has increased, but there are insufficient systems to provide personalized health risk assessments and recommend appropriate medical institutions for individual users. Furthermore, there is a lack of means to monitor users' biometric information in real time and respond quickly to abnormal situations.

[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 acquiring diverse biometric information, means for integrating and analyzing the acquired biometric information, and means for generating health-related risk diagnoses and advice based on the analysis results. This makes it possible to notify users of health risks in real time and prompt them to contact necessary medical institutions.

[0096] "Diverse biometric information" refers to various physiological data necessary to understand the user's health status, such as heart rate, body temperature, exercise level, and eating and drinking history.

[0097] "Means of integration and analysis" refers to a method for evaluating a user's health status by centrally managing collected biometric information and analyzing the data using AI technology.

[0098] A "means for generating health-related risk assessments and advice" refers to a method that evaluates the health risks of individual users based on analyzed data and provides specific preventive and corrective measures.

[0099] "A method for proposing the most suitable medical facility" refers to a method of selecting and presenting appropriate medical institutions to users based on their place of residence and health condition.

[0100] "Means of notifying the user via voice or display device" refers to a method of informing the user in real time, using speech synthesis technology or a display, when an abnormality in biometric information is detected.

[0101] "Means of prompting contact with medical institutions as needed" refers to a method of instructing users to contact appropriate medical institutions when an abnormality in their health condition is detected as a result of the analysis.

[0102] To implement this invention, a user collects biometric information using a wearable device or smartphone and transmits that data to a terminal. This terminal encrypts the information and transfers it to a server. The server integrates the data and performs analysis using AI technology.

[0103] The hardware required includes a wearable device (e.g., a smartwatch) and a communication terminal carried by the user. This communication terminal connects with the wearable device via Bluetooth or Wi-Fi to receive biometric information. The received information is encrypted to protect privacy before being sent to a server.

[0104] The server processes data using software such as Python and TENSORFLOW®. The analyzed results are used to notify the user of their health status and potential risks in real time. Furthermore, if an abnormality is detected, the system uses speech synthesis technology and a display to notify the user and, if necessary, prompts them to contact an appropriate medical facility.

[0105] For example, if a user's heart rate shows an abnormal value while running, the server will analyze the results and notify the user via voice message on their device saying, "Your heart rate is higher than normal. Take a break." Depending on the situation, it may also send a follow-up message saying, "You need to find the nearest medical facility."

[0106] An example of a prompt message for a generative AI model is, "Based on the acquired biometric information, analyze the current health status and generate advice."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] Users collect biometric information using wearable devices.

[0110] Specifically, the device measures heart rate, body temperature, and activity level, and transmits this data to the user's communication terminal via Bluetooth. The input is biometric data from the wearable device, and the output is the data received by the communication terminal.

[0111] Step 2:

[0112] The device formats the biometric information it collects and transfers it to the server.

[0113] The specific operation involves formatting the received data and encrypting the information. A secure communication protocol is used to send the data to the server while protecting privacy. The input is biometric data received by the terminal, and the output is encrypted data.

[0114] Step 3:

[0115] The server receives the transferred data, integrates it, and analyzes it.

[0116] The server utilizes Python and TensorFlow to perform analysis by comparing it with historical data stored in a database. The input is encrypted biometric data, and the output is the result of the analyzed health status assessment.

[0117] Step 4:

[0118] The server evaluates the user's health risks based on the analysis results and generates advice.

[0119] Using a generative AI model, it outputs immediately applicable advice to the user. The selected advice is tailored to the user's health condition. The input is the analysis result, and the output is the generated advice.

[0120] Step 5:

[0121] The server generates advice and notifies the user via the terminal.

[0122] Specifically, it utilizes speech synthesis and display technology to provide users with information in real time. If necessary, it may also prompt users to contact medical institutions. The input is generated advice, and the output is a notification to the user.

[0123] 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.

[0124] This invention relates to a system that provides individually customized health advice by integrating and analyzing diverse biometric information and user emotional data. A new function incorporating an emotional engine plays a crucial role in the implementation of this system.

[0125] First, users collect daily biometric data through wearable devices and smartphones. This includes heart rate, body temperature, diet, and exercise levels. Additionally, user emotional data is acquired through emotion recognition algorithms using the smartphone's camera and voice input. For example, when a user keeps a diary on their smartphone and uses voice input, their emotions are analyzed from that voice data.

[0126] The collected data is converted to a standard format on the device and encrypted. This encrypted data is then sent to the server via secure communication.

[0127] The server integrates diverse biometric and emotional data and stores it in a database. AI is used to analyze this integrated data, assessing the user's health status and correlating emotional changes with their health condition. The results of this analysis are used to generate more comprehensive health risk assessments and advice that consider both the user's physical and emotional health.

[0128] For example, if a user frequently experiences stress, the server will take that emotional state into consideration and suggest relaxation techniques or refer users to specialized medical institutions. In addition, emotional data will be used to ensure that advice is delivered in a style that is best suited to the user. If a depressed mood is detected, advice will be provided using uplifting language.

[0129] Furthermore, by receiving feedback from users, this system continuously improves its analytical accuracy and the quality of its advice. Based on this feedback, the server implements measures to improve the content and emotional response of the advice it provides.

[0130] In this way, the present invention, which utilizes an emotion engine, aims to support both the emotional and physical aspects of the user. This enables the user to maintain a balanced approach to health management.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] Users collect biometric and emotional data using wearable devices and smartphone apps. Heart rate and body temperature are automatically acquired from the wearable device. Emotional data is obtained by taking a picture of the user's face with the smartphone camera and analyzing it using facial recognition technology.

[0134] Step 2:

[0135] The device converts collected biometric and emotional data into a unified format. This conversion process adjusts the data to a format suitable for analysis. Simultaneously, the data is encrypted to ensure security.

[0136] Step 3:

[0137] The terminal sends the format-converted and encrypted data to the server. Since the transmission is done via a secure communication protocol, the risk of user information leakage is reduced.

[0138] Step 4:

[0139] The server stores the received data in a database and integrates diverse biometric and emotional data. This integrated data is then prepared for use in subsequent analysis steps.

[0140] Step 5:

[0141] The server uses AI algorithms to analyze the integrated data. This analysis assesses the user's health status and determines how detected emotions relate to health risks.

[0142] Step 6:

[0143] The server performs a health risk assessment based on the analysis results and generates specific improvement advice. Based on emotional data, feedback is provided that takes into account the user's emotional state. For example, if strong stress is detected, advice that helps reduce stress will be prioritized.

[0144] Step 7:

[0145] The server generates recommendations for the most suitable medical institutions. It combines the user's health status and emotional data to select and present information on the most appropriate hospitals and facilities to the user.

[0146] Step 8:

[0147] The device notifies the user of advice from the server and suggestions from medical institutions. Using the app's notification function, it provides real-time information so that the user can check it immediately.

[0148] Step 9:

[0149] Users can provide feedback on the advice and suggestions offered through the app. Emotional responses can also be provided in a similar manner.

[0150] Step 10:

[0151] The server aggregates feedback data and uses it to provide advice and improve the overall system. This will enable improved analytical accuracy and user experience in the future.

[0152] (Example 2)

[0153] 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."

[0154] In modern society, it is crucial to comprehensively manage individual health and emotional states and provide personalized health guidance. However, conventional systems struggle to efficiently collect and analyze this information, resulting in the inability to provide effective advice tailored to individual needs. Furthermore, methods for continuously improving the system using feedback have not yet been established.

[0155] 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.

[0156] In this invention, the server includes means for collecting biometric and emotional information from users, means for integrating and encrypting the collected biometric and emotional information, and means for a central processing unit to analyze diverse information, evaluate health status, and associate it with emotional status. This makes it possible to provide individually customized health guidance in appropriate expressions according to the user's emotional state.

[0157] A "user" is an individual who provides biometric and emotional information using the system.

[0158] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate and body temperature.

[0159] "Emotional information" refers to data that indicates the user's emotional state, acquired through camera or voice input.

[0160] "Integration" is a method of centralizing data in order to combine and process biometric and emotional information.

[0161] "Encryption" is a security technology that transforms data in a way that prevents unauthorized use by third parties.

[0162] A "central processing unit" is a computer system that analyzes and evaluates data and executes processing based on the analysis results.

[0163] "Health guidance" refers to advice and suggestions provided to improve the health condition of users.

[0164] "Feedback" refers to the act of users returning their evaluations and responses to the health guidance provided to the system.

[0165] "Personalized health guidance" refers to specific advice and suggestions tailored to the individual user's condition.

[0166] This invention is a system that provides individually customized health guidance by integrating and analyzing the user's biometric and emotional information. In implementation, the user utilizes a wearable device and a smartphone to measure their physical condition on a daily basis. This includes functions to record data such as heart rate, body temperature, and exercise level. In addition, emotion recognition technology using the smartphone's camera and microphone acquires the user's emotional information.

[0167] Specifically, when users leave voice diaries on their smartphones, the audio data is collected and sentiment analysis is performed. A voice sentiment recognition algorithm is used in this process. The collected data is converted to a standard format on the device, its security is enhanced through encryption, and then it is transmitted to the server via a secure communication protocol.

[0168] The server integrates the received data into a database and analyzes it using a generative AI model. This allows for a comprehensive assessment of the user's physical and emotional state. Based on the generated analysis results, the server creates personalized health guidance tailored to each user's situation. The advice is delivered using language that is appropriate to the user's emotional state. For example, if stress is prominent, relaxation methods may be suggested. Furthermore, user feedback allows the server to continuously improve the accuracy and quality of the guidance it provides.

[0169] An example of a prompt message is, "Based on recent emotional changes and heart rate data, please create advice for stress reduction." In this way, the present invention aims to address both the physical and emotional aspects of the user, providing a means to effectively manage health in daily life.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] Users collect biometric and emotional information using wearable devices and smartphones. Specifically, they wear smartwatches to measure heart rate and body temperature, and acquire emotional information using their smartphone's camera and microphone. Inputs include data on physical condition and voice data, and the output is this raw data.

[0173] Step 2:

[0174] The device converts the collected biometric and emotional information into a standard format and encrypts it. Specifically, it uses data format conversion software to convert the data into a unified format and applies an encryption algorithm. The input for this step is raw biometric and emotional data, and the output is encrypted, unified-format data.

[0175] Step 3:

[0176] The terminal sends encrypted data to the server using a secure communication protocol. Specifically, data is transferred to the server via secure communication using SSL / TLS. The input is encrypted data, and the output is the arrival of the data at the server.

[0177] Step 4:

[0178] The server integrates the received data and stores it in a database. Specifically, it performs a data cleansing process and records the clean data in the database. The input for this step is encrypted data, and the output is the integrated data in the database.

[0179] Step 5:

[0180] The server analyzes the integrated data using a generative AI model. The AI ​​algorithm correlates health status assessments and emotional states to generate personalized health guidance. Specifically, it inputs data into the AI ​​model and outputs analysis results of health risks and status. The input for this step is integrated data, and the output is the analysis results and personalized advice.

[0181] Step 6:

[0182] The server provides health guidance generated based on the analysis results in a format suitable for the user. For example, if stress is detected, relaxation techniques will be suggested. The input is the analysis results, and the output is health guidance customized for the user.

[0183] Step 7:

[0184] Users provide feedback on the advice given, and this feedback is sent to the server. The server analyzes the feedback and takes action to improve the system's analysis accuracy and the quality of health guidance. The input is the user's feedback, and the output is the improved analysis model and advice quality.

[0185] (Application Example 2)

[0186] 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."

[0187] In modern times, many people are exposed to various health risks, and there is a need for personalized health management methods. However, existing health management systems do not adequately consider the emotional state of users and may not provide integrated support. Therefore, there is a need for means to provide health advice that takes into account the emotional state of users and to improve their physical and emotional health.

[0188] 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.

[0189] In this invention, the server includes means for acquiring, integrating, and analyzing diverse biometric and emotional information; means for generating health-related risk diagnoses and advice in a style appropriate to the user's emotional state based on the analysis results; and means for suggesting the most suitable medical institution based on the generated advice. This enables comprehensive and personalized health management that takes into account the user's emotional state.

[0190] "Diverse biometric information" refers to different types of data about the user's body, such as heart rate, body temperature, exercise level, and diet.

[0191] "Emotional information" refers to data about the user's emotional state, analyzed from their facial expressions and voice.

[0192] "Means of integration and analysis" refers to a function that combines diverse biometric and emotional information acquired into a single dataset and evaluates the user's health and emotional state from that data.

[0193] "Means for generating risk assessments and advice" refers to a function that identifies health risks based on analysis results and proposes specific health management strategies tailored to those risks.

[0194] "Means of communicating in a style that suits the user's emotional state" refers to a function that optimizes the method of delivering advice based on the user's current emotions.

[0195] "A means of suggesting the most suitable medical institution" refers to a function that recommends necessary medical institutions and specialists to the user based on the generated health advice.

[0196] "Means of collecting feedback" refers to a function that gathers user opinions and suggestions for improvement, and uses that information to improve the accuracy of the system and the quality of the advice provided.

[0197] This invention provides a system to support users' daily health management. This system functions in conjunction with wearable devices and smart devices used by the user. The terminal collects the user's diverse biometric and emotional information in real time and converts this data into a standard format. The converted data is then encrypted and securely transmitted to a server.

[0198] The server utilizes AI-powered software to integrate and analyze the received data. Specifically, it comprehensively analyzes the user's biometric and emotional data using the aforementioned "integration and analysis method," and then provides concrete suggestions using the "risk diagnosis and advice generation method." By combining heart rate, emotional patterns, and other factors in the analysis, a more comprehensive assessment of health and emotional state becomes possible.

[0199] For example, the server can provide users with appropriate advice by utilizing a "means of communication tailored to their emotional state." If data detects that the user is experiencing stress, the server can offer advice in a warm tone about relaxation techniques and the importance of rest. In addition, a "means of suggesting the most suitable medical institution" provides users with information on medical institutions that match their specific health condition.

[0200] By collecting user feedback, the server will improve the quality of the healthcare advice it provides in the future. The feedback provided by users will contribute to improving the overall system performance and will be a crucial factor in enhancing the accuracy of future advice.

[0201] A concrete example of a prompt is, "Generate daily health advice based on the user's heart rate and emotional data." This prompt provides the AI ​​model with the necessary information to make appropriate suggestions to the user.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The device acquires biometric information from the user, such as heart rate, body temperature, exercise level, and dietary information, via a wearable device. The input data consists of this biometric information, and the output data is provided in a standard data format. The device also collects the user's emotional data using a camera and microphone and analyzes it with an emotion recognition algorithm.

[0205] Step 2:

[0206] The device encrypts the collected biometric and emotional data and transfers it to the server. The input is formatted biometric and emotional data, and the output generates securely transferable encrypted data. This ensures data security.

[0207] Step 3:

[0208] The server decrypts the received encrypted data and integrates diverse biometric and emotional information. The input is encrypted biometric and emotional data, and the output is an integrated dataset. The server then uses an AI model to prepare this data for analysis.

[0209] Step 4:

[0210] The server analyzes the integrated dataset using a generative AI model to assess the user's health and emotional state. The input is the integrated dataset, and the output generates a risk assessment and health advice. This prepares the system to comprehensively understand the user's condition and provide appropriate advice.

[0211] Step 5:

[0212] The server sends the generated risk assessment and health advice to the user's terminal in a format tailored to their emotional state. The input is the risk assessment and advice information, and the output is advice expressed in a way that is optimized for the user.

[0213] Step 6:

[0214] Users receive the advice provided and use it to guide their actions. They can also send feedback about the content and wording of the advice to the server via their device. Based on this input, the server uses the feedback information to improve the quality of the advice.

[0215] Step 7:

[0216] The server analyzes user feedback and uses it to improve the overall advice provided by the system. The input is user feedback, and the output is an improved advice strategy. This enables continuous system growth and an enhanced user experience.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention relates to a system that provides users with customized health risk diagnoses and advice by aggregating and analyzing diverse biometric information. The invention is implemented by the user providing data through a terminal and a server processing that data.

[0234] First, users provide biometric information such as heart rate, body temperature, eating and drinking history, and exercise records using wearable devices and smartphone applications. For example, users input their daily meal details into the app, and this information is then incorporated into the system. Wearable devices automatically measure daily exercise levels and heart rate and transmit the data to the device.

[0235] Next, the device formats the collected biometric information into the necessary format for transmission to the server and securely transfers it. This transfer process encrypts the data to ensure user privacy is protected.

[0236] The server then integrates and centrally manages the diverse biometric information it receives. Using AI technology, the server analyzes the data to comprehensively assess the user's health status. This analysis identifies potential health risks the user may face in the future and generates specific advice on preventative and corrective measures. For example, if the data analysis indicates a risk of high blood pressure, the user will be notified with advice to reduce sodium intake.

[0237] The server also generates suggestions for medical institutions suitable for the user's health condition. It lists the most suitable hospitals based on the user's place of residence and health status, and provides this information to the user via their device. Furthermore, it has a mechanism to continuously improve the system's accuracy and user satisfaction by utilizing user feedback.

[0238] In this way, the present invention can provide individual users with a more appropriate and personalized health management experience. This system helps users maintain a healthy and effective daily life.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] Users input biometric information such as heart rate, body temperature, diet, and exercise data using wearable devices and smartphone apps, and collect data. For example, they might manually enter their daily meal details into the app and automatically record heart rate data through the device.

[0242] Step 2:

[0243] The device processes the collected biometric information and converts it into a standard format. This format conversion ensures that the data is available for subsequent processing in a consistent manner. The device also encrypts the obtained data before transmitting it to the server, ensuring privacy.

[0244] Step 3:

[0245] The server receives biometric information transmitted from the terminal and stores it centrally in a database. The stored data, combined with data from different sources, enables more comprehensive analysis.

[0246] Step 4:

[0247] The server uses AI technology to analyze integrated data and assess the user's health status. This identifies disease risks and health concerns. Based on the analysis results, it generates risk assessments and specific advice on lifestyle improvements.

[0248] Step 5:

[0249] The server selects the most suitable medical institution for the user based on the analysis results and organizes the information. It generates a list of appropriate hospitals and clinics, taking into account the user's place of residence and identified health risks.

[0250] Step 6:

[0251] The device notifies the user of health advice and suggestions for medical institutions received from the server. This allows the user to receive a specific action plan based on their current health status.

[0252] Step 7:

[0253] Users provide feedback through the app on the advice and medical institution suggestions they receive. This feedback includes their reactions to the usefulness of the advice and their choice of medical institution.

[0254] Step 8:

[0255] The server analyzes user feedback data to improve overall system performance. Based on the feedback, it adjusts AI algorithms and suggestions, and implements improvements to enhance the accuracy of future advice and suggestions.

[0256] (Example 1)

[0257] 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."

[0258] In modern society, managing diverse biometric information and understanding one's health status is increasingly important, but effective systems to address this are still lacking. In particular, there is a need to support users in leading healthy lives by identifying individual health risks and providing appropriate advice and suggesting medical facilities. Existing systems face challenges in collecting and analyzing sufficient data and providing appropriate actionable guidelines based on the results.

[0259] 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.

[0260] In this invention, the server includes means for acquiring diverse biometric information, means for securely transmitting the acquired biometric information via a communication terminal, means for integrating and analyzing the transmitted biometric information, means for generating health-related risk diagnoses and advice based on the analysis results, means for suggesting the most suitable medical institution based on the generated advice, and means for collecting user feedback and improving the analysis results and suggestions. This enables personalized health management for individual users.

[0261] "Diverse biometric information" is a general term for various types of data that indicate an individual's health status, such as heart rate, body temperature, eating and drinking history, and exercise records.

[0262] "Communication terminals" refer to electronic devices such as smartphones and tablets that can send and receive data over a network.

[0263] "Integration" means centrally gathering acquired biometric information and processing it as a single piece of information.

[0264] "Analysis" refers to data processing operations performed using collected data to evaluate and predict specific health conditions or risks.

[0265] "Health-related risk assessment" refers to identifying potential or future health problems in an individual that have been revealed through analysis.

[0266] "Generating advice" refers to providing users with behavioral guidelines and suggestions for lifestyle improvements based on health-related risk assessments.

[0267] "Suggesting the most suitable medical institution" means listing facilities that can provide appropriate medical services based on the user's health condition and place of residence, and providing this information.

[0268] "Feedback" refers to reactions and opinions from users regarding the system's performance and the advice they have provided.

[0269] This invention is a system that provides users with customized health risk diagnoses and advice by aggregating and analyzing diverse biological information. The invention is implemented when the user provides data through a terminal, and a server processes that data.

[0270] First, users acquire biometric data using wearable technology. For example, they use wearable devices to measure their heart rate and body temperature, and input medical information such as their eating and drinking history and exercise records into a smartphone application. This allows users to easily understand their daily health status.

[0271] Next, when the device provides the collected biometric information to the server, it formats it into the required format and transfers it securely. The technologies used include the SSL / TLS protocol for encrypting and transmitting the data. This process is essential to protect the user's private information.

[0272] The server then integrates the diverse data it receives and stores it in a self-managing database. The server uses a generative AI model to analyze this data and comprehensively evaluate the user's health status. The generative AI model, for example, uses machine learning algorithms and, based on the insights gained from the data analysis, can identify health risks and suggest preventative measures for the user.

[0273] Furthermore, when suggesting medical facilities, the server considers the user's health status and location information to identify and suggest the most suitable facility. It also incorporates a mechanism to continuously improve the analysis results and suggestions by utilizing user feedback.

[0274] As a specific example, the prompt text to be entered into the system is as follows:

[0275] "A 35-year-old male with an average heart rate of 75, mainly engaged in desk work, and running three times a week. Evaluate the future health risks of this user and provide necessary advice."

[0276] In this way, the system provides a personalized health management experience for the user and realizes its contribution to maintaining health.

[0277] The flow of the specific process in Example 1 will be described using FIG. 11.

[0278] Step 1:

[0279] The user collects biometric information using wearable technology or a smartphone app. As inputs, various data such as heart rate, body temperature, exercise records, and dietary history are obtained. As a specific operation, the user enters the content of breakfast into the app, and the wearable device automatically measures the heart rate. These input data are temporarily stored in the terminal for later analysis.

[0280] Step 2:

[0281] The terminal formats the collected biometric information and prepares to send it to the server. In this process, the input data is encrypted and formatted into a secure form. As a specific operation, the terminal converts the data into JSON format and outputs it by encrypting the data using the SSL / TLS protocol. This output is ready to be transferred to the server.

[0282] Step 3: :

[0283] The server integrates the data received from the terminal and centrally manages it in the database. As an input, it receives the encrypted JSON data, decrypts it, and then arranges it in the storage system. As a specific operation, it saves the biometric information related to the user ID in the database and outputs it in a form for future analysis.

[0284] Step 4:

[0285] The server analyzes data using a generative AI model. By analyzing, it comprehensively evaluates the user's health status. As a specific operation, it applies the random forest algorithm to identify health risks from the input data. This output is prepared as specific advice provided to the user.

[0286] Step 5:

[0287] Based on the analysis results, the server generates health advice and proposes the most suitable medical institutions. Here, based on the risk diagnosis generated from the output of the analysis, beneficial health guidelines for the user are created. As a specific operation, when a risk of high blood pressure is recognized, advice to reduce sodium intake is formed for the user, and a list of appropriate medical institutions is output.

[0288] Step 6:

[0289] The user sends feedback on the provided advice and proposals, and the system uses this information to improve. As input, the user fills in their opinions in a feedback form. As a specific operation, the user enters comments on the usefulness of the advice, and this information is transmitted to the server. This output is used to improve the system's algorithms and services.

[0290] (Application Example 1)

[0291] 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".

[0292] In recent years, the importance of health management has increased, but systems that perform health risk diagnosis optimized for individual users and propose appropriate medical institutions are not sufficient. Also, there is a lack of means to monitor users' biometric information in real time and respond quickly to abnormal situations.

[0293] 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.

[0294] In this invention, the server includes means for acquiring diverse biometric information, means for integrating and analyzing the acquired biometric information, and means for generating health-related risk diagnoses and advice based on the analysis results. This makes it possible to notify users of health risks in real time and prompt them to contact necessary medical institutions.

[0295] "Diverse biometric information" refers to various physiological data necessary to understand the user's health status, such as heart rate, body temperature, exercise level, and eating and drinking history.

[0296] "Means of integration and analysis" refers to a method for evaluating a user's health status by centrally managing collected biometric information and analyzing the data using AI technology.

[0297] A "means for generating health-related risk assessments and advice" refers to a method that evaluates the health risks of individual users based on analyzed data and provides specific preventive and corrective measures.

[0298] "A method for proposing the most suitable medical facility" refers to a method of selecting and presenting appropriate medical institutions to users based on their place of residence and health condition.

[0299] "Means of notifying the user via voice or display device" refers to a method of informing the user in real time, using speech synthesis technology or a display, when an abnormality in biometric information is detected.

[0300] "Means of prompting contact with medical institutions as needed" refers to a method of instructing users to contact appropriate medical institutions when an abnormality in their health condition is detected as a result of the analysis.

[0301] To implement this invention, the user uses a wearable device or a smartphone to collect biological information and transmit the data to the terminal. This terminal encrypts the information and transfers it to the server. The server integrates the data and performs analysis using AI technology.

[0302] As hardware, a wearable device (e.g., smartwatch) and a communication terminal held by the user are required. This communication terminal communicates with the wearable device via Bluetooth or Wi-Fi to receive biological information. The received information is encrypted to protect privacy and then transmitted to the server.

[0303] At the server, the data is processed using software such as Python and TensorFlow. The analyzed results notify the user of their health status and potential risks in real time. Furthermore, if an abnormality is detected, voice synthesis technology and a display are utilized to notify the user and, in some cases, prompt contact with an appropriate medical facility.

[0304] As a specific example, when the user's heart rate shows an abnormal value during running, the server notifies the user via the terminal with a voice message such as "Your heart rate is higher than normal. Take a break." Depending on the situation, a subsequent notification may be "It may be necessary to search for the nearest medical facility."

[0305] An example of a prompt sentence for the generative AI model is "Based on the acquired biological information, analyze the current health status and generate advice."

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The user uses a wearable device to collect biological information.

[0309] Specifically, the device measures heart rate, body temperature, and activity level, and transmits this data to the user's communication terminal via Bluetooth. The input is biometric data from the wearable device, and the output is the data received by the communication terminal.

[0310] Step 2:

[0311] The device formats the biometric information it collects and transfers it to the server.

[0312] The specific operation involves formatting the received data and encrypting the information. A secure communication protocol is used to send the data to the server while protecting privacy. The input is biometric data received by the terminal, and the output is encrypted data.

[0313] Step 3:

[0314] The server receives the transferred data, integrates it, and analyzes it.

[0315] The server utilizes Python and TensorFlow to perform analysis by comparing it with historical data stored in a database. The input is encrypted biometric data, and the output is the result of the analyzed health status assessment.

[0316] Step 4:

[0317] The server evaluates the user's health risks based on the analysis results and generates advice.

[0318] Using a generative AI model, it outputs immediately applicable advice to the user. The selected advice is tailored to the user's health condition. The input is the analysis result, and the output is the generated advice.

[0319] Step 5:

[0320] The server generates advice and notifies the user via the terminal.

[0321] Specifically, it utilizes speech synthesis and display technology to provide users with information in real time. If necessary, it may also prompt users to contact medical institutions. The input is generated advice, and the output is a notification to the user.

[0322] 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.

[0323] This invention relates to a system that provides individually customized health advice by integrating and analyzing diverse biometric information and user emotional data. A new function incorporating an emotional engine plays a crucial role in the implementation of this system.

[0324] First, users collect daily biometric data through wearable devices and smartphones. This includes heart rate, body temperature, diet, and exercise levels. Additionally, user emotional data is acquired through emotion recognition algorithms using the smartphone's camera and voice input. For example, when a user keeps a diary on their smartphone and uses voice input, their emotions are analyzed from that voice data.

[0325] The collected data is converted to a standard format on the device and encrypted. This encrypted data is then sent to the server via secure communication.

[0326] The server integrates diverse biometric and emotional data and stores it in a database. AI is used to analyze this integrated data, assessing the user's health status and correlating emotional changes with their health condition. The results of this analysis are used to generate more comprehensive health risk assessments and advice that consider both the user's physical and emotional health.

[0327] For example, if a user frequently experiences stress, the server will take that emotional state into consideration and suggest relaxation techniques or refer users to specialized medical institutions. In addition, emotional data will be used to ensure that advice is delivered in a style that is best suited to the user. If a depressed mood is detected, advice will be provided using uplifting language.

[0328] Furthermore, by receiving feedback from users, this system continuously improves its analytical accuracy and the quality of its advice. Based on this feedback, the server implements measures to improve the content and emotional response of the advice it provides.

[0329] In this way, the present invention, which utilizes an emotion engine, aims to support both the emotional and physical aspects of the user. This enables the user to maintain a balanced approach to health management.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] Users collect biometric and emotional data using wearable devices and smartphone apps. Heart rate and body temperature are automatically acquired from the wearable device. Emotional data is obtained by taking a picture of the user's face with the smartphone camera and analyzing it using facial recognition technology.

[0333] Step 2:

[0334] The device converts collected biometric and emotional data into a unified format. This conversion process adjusts the data to a format suitable for analysis. Simultaneously, the data is encrypted to ensure security.

[0335] Step 3:

[0336] The terminal sends the format-converted and encrypted data to the server. Since the transmission is done via a secure communication protocol, the risk of user information leakage is reduced.

[0337] Step 4:

[0338] The server stores the received data in a database and integrates diverse biometric and emotional data. This integrated data is then prepared for use in subsequent analysis steps.

[0339] Step 5:

[0340] The server uses AI algorithms to analyze the integrated data. This analysis assesses the user's health status and determines how detected emotions relate to health risks.

[0341] Step 6:

[0342] The server performs a health risk assessment based on the analysis results and generates specific improvement advice. Based on emotional data, feedback is provided that takes into account the user's emotional state. For example, if strong stress is detected, advice that helps reduce stress will be prioritized.

[0343] Step 7:

[0344] The server generates recommendations for the most suitable medical institutions. It combines the user's health status and emotional data to select and present information on the most appropriate hospitals and facilities to the user.

[0345] Step 8:

[0346] The device notifies the user of advice from the server and suggestions from medical institutions. Using the app's notification function, it provides real-time information so that the user can check it immediately.

[0347] Step 9:

[0348] Users can provide feedback on the advice and suggestions offered through the app. Emotional responses can also be provided in a similar manner.

[0349] Step 10:

[0350] The server aggregates feedback data and uses it to provide advice and improve the overall system. This will enable improved analytical accuracy and user experience in the future.

[0351] (Example 2)

[0352] 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".

[0353] In modern society, it is crucial to comprehensively manage individual health and emotional states and provide personalized health guidance. However, conventional systems struggle to efficiently collect and analyze this information, resulting in the inability to provide effective advice tailored to individual needs. Furthermore, methods for continuously improving the system using feedback have not yet been established.

[0354] 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.

[0355] In this invention, the server includes means for collecting biometric and emotional information from users, means for integrating and encrypting the collected biometric and emotional information, and means for a central processing unit to analyze diverse information, evaluate health status, and associate it with emotional status. This makes it possible to provide individually customized health guidance in appropriate expressions according to the user's emotional state.

[0356] A "user" is an individual who provides biometric and emotional information using the system.

[0357] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate and body temperature.

[0358] "Emotional information" refers to data that indicates the user's emotional state, acquired through camera or voice input.

[0359] "Integration" is a method of centralizing data in order to combine and process biometric and emotional information.

[0360] "Encryption" is a security technology that transforms data in a way that prevents unauthorized use by third parties.

[0361] A "central processing unit" is a computer system that analyzes and evaluates data and executes processing based on the analysis results.

[0362] "Health guidance" refers to advice and suggestions provided to improve the health condition of users.

[0363] "Feedback" refers to the act of users returning their evaluations and responses to the health guidance provided to the system.

[0364] "Personalized health guidance" refers to specific advice and suggestions tailored to the individual user's condition.

[0365] This invention is a system that provides individually customized health guidance by integrating and analyzing the user's biometric and emotional information. In implementation, the user utilizes a wearable device and a smartphone to measure their physical condition on a daily basis. This includes functions to record data such as heart rate, body temperature, and exercise level. In addition, emotion recognition technology using the smartphone's camera and microphone acquires the user's emotional information.

[0366] Specifically, when users leave voice diaries on their smartphones, the audio data is collected and sentiment analysis is performed. A voice sentiment recognition algorithm is used in this process. The collected data is converted to a standard format on the device, its security is enhanced through encryption, and then it is transmitted to the server via a secure communication protocol.

[0367] The server integrates the received data into a database and analyzes it using a generative AI model. This allows for a comprehensive assessment of the user's physical and emotional state. Based on the generated analysis results, the server creates personalized health guidance tailored to each user's situation. The advice is delivered using language that is appropriate to the user's emotional state. For example, if stress is prominent, relaxation methods may be suggested. Furthermore, user feedback allows the server to continuously improve the accuracy and quality of the guidance it provides.

[0368] An example of a prompt message is, "Based on recent emotional changes and heart rate data, please create advice for stress reduction." In this way, the present invention aims to address both the physical and emotional aspects of the user, providing a means to effectively manage health in daily life.

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

[0370] Step 1:

[0371] Users collect biometric and emotional information using wearable devices and smartphones. Specifically, they wear smartwatches to measure heart rate and body temperature, and acquire emotional information using their smartphone's camera and microphone. Inputs include data on physical condition and voice data, and the output is this raw data.

[0372] Step 2:

[0373] The device converts the collected biometric and emotional information into a standard format and encrypts it. Specifically, it uses data format conversion software to convert the data into a unified format and applies an encryption algorithm. The input for this step is raw biometric and emotional data, and the output is encrypted, unified-format data.

[0374] Step 3:

[0375] The terminal sends encrypted data to the server using a secure communication protocol. Specifically, data is transferred to the server via secure communication using SSL / TLS. The input is encrypted data, and the output is the arrival of the data at the server.

[0376] Step 4:

[0377] The server integrates the received data and stores it in a database. Specifically, it performs a data cleansing process and records the clean data in the database. The input for this step is encrypted data, and the output is the integrated data in the database.

[0378] Step 5:

[0379] The server analyzes the integrated data using a generative AI model. The AI ​​algorithm correlates health status assessments and emotional states to generate personalized health guidance. Specifically, it inputs data into the AI ​​model and outputs analysis results of health risks and status. The input for this step is integrated data, and the output is the analysis results and personalized advice.

[0380] Step 6:

[0381] The server provides health guidance generated based on the analysis results in a format suitable for the user. For example, if stress is detected, relaxation techniques will be suggested. The input is the analysis results, and the output is health guidance customized for the user.

[0382] Step 7:

[0383] Users provide feedback on the advice given, and this feedback is sent to the server. The server analyzes the feedback and takes action to improve the system's analysis accuracy and the quality of health guidance. The input is the user's feedback, and the output is the improved analysis model and advice quality.

[0384] (Application Example 2)

[0385] 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."

[0386] In modern times, many people are exposed to various health risks, and there is a need for personalized health management methods. However, existing health management systems do not adequately consider the emotional state of users and may not provide integrated support. Therefore, there is a need for means to provide health advice that takes into account the emotional state of users and to improve their physical and emotional health.

[0387] 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.

[0388] In this invention, the server includes means for acquiring, integrating, and analyzing diverse biometric and emotional information; means for generating health-related risk diagnoses and advice in a style appropriate to the user's emotional state based on the analysis results; and means for suggesting the most suitable medical institution based on the generated advice. This enables comprehensive and personalized health management that takes into account the user's emotional state.

[0389] "Diverse biometric information" refers to different types of data about the user's body, such as heart rate, body temperature, exercise level, and diet.

[0390] "Emotional information" refers to data about the user's emotional state, analyzed from their facial expressions and voice.

[0391] "Means of integration and analysis" refers to a function that combines diverse biometric and emotional information acquired into a single dataset and evaluates the user's health and emotional state from that data.

[0392] "Means for generating risk assessments and advice" refers to a function that identifies health risks based on analysis results and proposes specific health management strategies tailored to those risks.

[0393] "Means of communicating in a style that suits the user's emotional state" refers to a function that optimizes the method of delivering advice based on the user's current emotions.

[0394] "A means of suggesting the most suitable medical institution" refers to a function that recommends necessary medical institutions and specialists to the user based on the generated health advice.

[0395] "Means of collecting feedback" refers to a function that gathers user opinions and suggestions for improvement, and uses that information to improve the accuracy of the system and the quality of the advice provided.

[0396] This invention provides a system to support users' daily health management. This system functions in conjunction with wearable devices and smart devices used by the user. The terminal collects the user's diverse biometric and emotional information in real time and converts this data into a standard format. The converted data is then encrypted and securely transmitted to a server.

[0397] The server utilizes AI-powered software to integrate and analyze the received data. Specifically, it comprehensively analyzes the user's biometric and emotional data using the aforementioned "integration and analysis method," and then provides concrete suggestions using the "risk diagnosis and advice generation method." By combining heart rate, emotional patterns, and other factors in the analysis, a more comprehensive assessment of health and emotional state becomes possible.

[0398] For example, the server can provide users with appropriate advice by utilizing a "means of communication tailored to their emotional state." If data detects that the user is experiencing stress, the server can offer advice in a warm tone about relaxation techniques and the importance of rest. In addition, a "means of suggesting the most suitable medical institution" provides users with information on medical institutions that match their specific health condition.

[0399] By collecting user feedback, the server will improve the quality of the healthcare advice it provides in the future. The feedback provided by users will contribute to improving the overall system performance and will be a crucial factor in enhancing the accuracy of future advice.

[0400] A concrete example of a prompt is, "Generate daily health advice based on the user's heart rate and emotional data." This prompt provides the AI ​​model with the necessary information to make appropriate suggestions to the user.

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The device acquires biometric information from the user, such as heart rate, body temperature, exercise level, and dietary information, via a wearable device. The input data consists of this biometric information, and the output data is provided in a standard data format. The device also collects the user's emotional data using a camera and microphone and analyzes it with an emotion recognition algorithm.

[0404] Step 2:

[0405] The device encrypts the collected biometric and emotional data and transfers it to the server. The input is formatted biometric and emotional data, and the output generates securely transferable encrypted data. This ensures data security.

[0406] Step 3:

[0407] The server decrypts the received encrypted data and integrates diverse biometric and emotional information. The input is encrypted biometric and emotional data, and the output is an integrated dataset. The server then uses an AI model to prepare this data for analysis.

[0408] Step 4:

[0409] The server analyzes the integrated dataset using a generative AI model to assess the user's health and emotional state. The input is the integrated dataset, and the output generates a risk assessment and health advice. This prepares the system to comprehensively understand the user's condition and provide appropriate advice.

[0410] Step 5:

[0411] The server sends the generated risk assessment and health advice to the user's terminal in a format tailored to their emotional state. The input is the risk assessment and advice information, and the output is advice expressed in a way that is optimized for the user.

[0412] Step 6:

[0413] Users receive the advice provided and use it to guide their actions. They can also send feedback about the content and wording of the advice to the server via their device. Based on this input, the server uses the feedback information to improve the quality of the advice.

[0414] Step 7:

[0415] The server analyzes user feedback and uses it to improve the overall advice provided by the system. The input is user feedback, and the output is an improved advice strategy. This enables continuous system growth and an enhanced user experience.

[0416] 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.

[0417] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0418] 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.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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".

[0432] This invention relates to a system that provides users with customized health risk diagnoses and advice by aggregating and analyzing diverse biometric information. The invention is implemented by the user providing data through a terminal and a server processing that data.

[0433] First, users provide biometric information such as heart rate, body temperature, eating and drinking history, and exercise records using wearable devices and smartphone applications. For example, users input their daily meal details into the app, and this information is then incorporated into the system. Wearable devices automatically measure daily exercise levels and heart rate and transmit the data to the device.

[0434] Next, the device formats the collected biometric information into the necessary format for transmission to the server and securely transfers it. This transfer process encrypts the data to ensure user privacy is protected.

[0435] The server then integrates and centrally manages the diverse biometric information it receives. Using AI technology, the server analyzes the data to comprehensively assess the user's health status. This analysis identifies potential health risks the user may face in the future and generates specific advice on preventative and corrective measures. For example, if the data analysis indicates a risk of high blood pressure, the user will be notified with advice to reduce sodium intake.

[0436] The server also generates suggestions for medical institutions suitable for the user's health condition. It lists the most suitable hospitals based on the user's place of residence and health status, and provides this information to the user via their device. Furthermore, it has a mechanism to continuously improve the system's accuracy and user satisfaction by utilizing user feedback.

[0437] In this way, the present invention can provide individual users with a more appropriate and personalized health management experience. This system helps users maintain a healthy and effective daily life.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] Users input biometric information such as heart rate, body temperature, diet, and exercise data using wearable devices and smartphone apps, and collect data. For example, they might manually enter their daily meal details into the app and automatically record heart rate data through the device.

[0441] Step 2:

[0442] The device processes the collected biometric information and converts it into a standard format. This format conversion ensures that the data is available for subsequent processing in a consistent manner. The device also encrypts the obtained data before transmitting it to the server, ensuring privacy.

[0443] Step 3:

[0444] The server receives biometric information transmitted from the terminal and stores it centrally in a database. The stored data, combined with data from different sources, enables more comprehensive analysis.

[0445] Step 4:

[0446] The server uses AI technology to analyze integrated data and assess the user's health status. This identifies disease risks and health concerns. Based on the analysis results, it generates risk assessments and specific advice on lifestyle improvements.

[0447] Step 5:

[0448] The server selects the most suitable medical institution for the user based on the analysis results and organizes the information. It generates a list of appropriate hospitals and clinics, taking into account the user's place of residence and identified health risks.

[0449] Step 6:

[0450] The device notifies the user of health advice and suggestions for medical institutions received from the server. This allows the user to receive a specific action plan based on their current health status.

[0451] Step 7:

[0452] Users provide feedback through the app on the advice and medical institution suggestions they receive. This feedback includes their reactions to the usefulness of the advice and their choice of medical institution.

[0453] Step 8:

[0454] The server analyzes user feedback data to improve overall system performance. Based on the feedback, it adjusts AI algorithms and suggestions, and implements improvements to enhance the accuracy of future advice and suggestions.

[0455] (Example 1)

[0456] 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."

[0457] In modern society, managing diverse biometric information and understanding one's health status is increasingly important, but effective systems to address this are still lacking. In particular, there is a need to support users in leading healthy lives by identifying individual health risks and providing appropriate advice and suggesting medical facilities. Existing systems face challenges in collecting and analyzing sufficient data and providing appropriate actionable guidelines based on the results.

[0458] 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.

[0459] In this invention, the server includes means for acquiring diverse biometric information, means for securely transmitting the acquired biometric information via a communication terminal, means for integrating and analyzing the transmitted biometric information, means for generating health-related risk diagnoses and advice based on the analysis results, means for suggesting the most suitable medical institution based on the generated advice, and means for collecting user feedback and improving the analysis results and suggestions. This enables personalized health management for individual users.

[0460] "Diverse biometric information" is a general term for various types of data that indicate an individual's health status, such as heart rate, body temperature, eating and drinking history, and exercise records.

[0461] "Communication terminals" refer to electronic devices such as smartphones and tablets that can send and receive data over a network.

[0462] "Integration" means centrally gathering acquired biometric information and processing it as a single piece of information.

[0463] "Analysis" refers to data processing operations performed using collected data to evaluate and predict specific health conditions or risks.

[0464] "Health-related risk assessment" refers to identifying potential or future health problems in an individual that have been revealed through analysis.

[0465] "Generating advice" refers to providing users with behavioral guidelines and suggestions for lifestyle improvements based on health-related risk assessments.

[0466] "Suggesting the most suitable medical institution" means listing facilities that can provide appropriate medical services based on the user's health condition and place of residence, and providing this information.

[0467] "Feedback" refers to reactions and opinions from users regarding the system's performance and the advice they have provided.

[0468] This invention is a system that provides users with customized health risk diagnoses and advice by aggregating and analyzing diverse biological information. The invention is implemented when the user provides data through a terminal, and a server processes that data.

[0469] First, users acquire biometric data using wearable technology. For example, they use wearable devices to measure their heart rate and body temperature, and input medical information such as their eating and drinking history and exercise records into a smartphone application. This allows users to easily understand their daily health status.

[0470] Next, when the device provides the collected biometric information to the server, it formats it into the required format and transfers it securely. The technologies used include the SSL / TLS protocol for encrypting and transmitting the data. This process is essential to protect the user's private information.

[0471] The server then integrates the diverse data it receives and stores it in a self-managing database. The server uses a generative AI model to analyze this data and comprehensively evaluate the user's health status. The generative AI model, for example, uses machine learning algorithms and, based on the insights gained from the data analysis, can identify health risks and suggest preventative measures for the user.

[0472] Furthermore, when suggesting medical facilities, the server considers the user's health status and location information to identify and suggest the most suitable facility. It also incorporates a mechanism to continuously improve the analysis results and suggestions by utilizing user feedback.

[0473] As a specific example, the prompt text to be entered into the system is as follows:

[0474] "A 35-year-old male with an average heart rate of 75 bpm, primarily a desk job, and runs three times a week. Please assess this user's future health risks and provide necessary advice."

[0475] In this way, the system provides users with a personalized health management experience, contributing to maintaining good health.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] Users collect biometric information using wearable technology and smartphone apps. The inputs include a variety of data such as heart rate, body temperature, exercise records, and eating and drinking history. For example, a user might enter details of their breakfast into the app, and the wearable device automatically measures their heart rate. This input data is temporarily stored on the device for later analysis.

[0479] Step 2:

[0480] The device formats the collected biometric information and prepares it for transmission to the server. During this process, the input data is encrypted and formatted in a secure manner. Specifically, the device converts the data to JSON format and outputs it after encrypting it using the SSL / TLS protocol. This output is then ready to be transferred to the server.

[0481] Step 3:

[0482] The server integrates data received from terminals and centrally manages it in a database. It receives encrypted JSON data as input, decrypts it, and then organizes it in the storage system. Specifically, it stores user IDs and associated biometric information in the database and outputs it in a format suitable for future analysis.

[0483] Step 4:

[0484] The server analyzes data using a generative AI model. This analysis provides a multifaceted assessment of the user's health status. Specifically, it applies a random forest algorithm to identify health risks from the input data. This output is then prepared as specific advice for the user.

[0485] Step 5:

[0486] The server generates health advice based on the analysis results and suggests the most suitable medical facilities. Here, based on the risk assessment generated from the analysis output, it creates health guidelines that are beneficial to the user. Specifically, if a risk of hypertension is identified, it will formulate advice to reduce sodium intake for the user and output a list of appropriate medical facilities.

[0487] Step 6:

[0488] Users submit feedback on the advice and suggestions provided, and the system uses this information to make improvements. As input, users fill out a feedback form with their opinions. Specifically, users enter comments on the usefulness of the advice, and this information is transmitted to the server. This output is used to improve the system's algorithms and services.

[0489] (Application Example 1)

[0490] 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."

[0491] In recent years, the importance of health management has increased, but there are insufficient systems to provide personalized health risk assessments and recommend appropriate medical institutions for individual users. Furthermore, there is a lack of means to monitor users' biometric information in real time and respond quickly to abnormal situations.

[0492] 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.

[0493] In this invention, the server includes means for acquiring diverse biometric information, means for integrating and analyzing the acquired biometric information, and means for generating health-related risk diagnoses and advice based on the analysis results. This makes it possible to notify users of health risks in real time and prompt them to contact necessary medical institutions.

[0494] "Diverse biometric information" refers to various physiological data necessary to understand the user's health status, such as heart rate, body temperature, exercise level, and eating and drinking history.

[0495] "Means of integration and analysis" refers to a method for evaluating a user's health status by centrally managing collected biometric information and analyzing the data using AI technology.

[0496] A "means for generating health-related risk assessments and advice" refers to a method that evaluates the health risks of individual users based on analyzed data and provides specific preventive and corrective measures.

[0497] "A method for proposing the most suitable medical facility" refers to a method of selecting and presenting appropriate medical institutions to users based on their place of residence and health condition.

[0498] "Means of notifying the user via voice or display device" refers to a method of informing the user in real time, using speech synthesis technology or a display, when an abnormality in biometric information is detected.

[0499] "Means of prompting contact with medical institutions as needed" refers to a method of instructing users to contact appropriate medical institutions when an abnormality in their health condition is detected as a result of the analysis.

[0500] To implement this invention, a user collects biometric information using a wearable device or smartphone and transmits that data to a terminal. This terminal encrypts the information and transfers it to a server. The server integrates the data and performs analysis using AI technology.

[0501] The hardware required includes a wearable device (e.g., a smartwatch) and a communication terminal carried by the user. This communication terminal connects with the wearable device via Bluetooth or Wi-Fi to receive biometric information. The received information is encrypted to protect privacy before being sent to a server.

[0502] The server processes data using software such as Python and TensorFlow. The analyzed results are then used to notify the user of their health status and potential risks in real time. Furthermore, if an anomaly is detected, the system uses speech synthesis technology and a display to notify the user and, if necessary, prompts them to contact an appropriate medical facility.

[0503] For example, if a user's heart rate shows an abnormal value while running, the server will analyze the results and notify the user via voice message on their device saying, "Your heart rate is higher than normal. Take a break." Depending on the situation, it may also send a follow-up message saying, "You need to find the nearest medical facility."

[0504] An example of a prompt message for a generative AI model is, "Based on the acquired biometric information, analyze the current health status and generate advice."

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] Users collect biometric information using wearable devices.

[0508] Specifically, the device measures heart rate, body temperature, and activity level, and transmits this data to the user's communication terminal via Bluetooth. The input is biometric data from the wearable device, and the output is the data received by the communication terminal.

[0509] Step 2:

[0510] The device formats the biometric information it collects and transfers it to the server.

[0511] The specific operation involves formatting the received data and encrypting the information. A secure communication protocol is used to send the data to the server while protecting privacy. The input is biometric data received by the terminal, and the output is encrypted data.

[0512] Step 3:

[0513] The server receives the transferred data, integrates it, and analyzes it.

[0514] The server utilizes Python and TensorFlow to perform analysis by comparing it with historical data stored in a database. The input is encrypted biometric data, and the output is the result of the analyzed health status assessment.

[0515] Step 4:

[0516] The server evaluates the user's health risks based on the analysis results and generates advice.

[0517] Using a generative AI model, it outputs immediately applicable advice to the user. The selected advice is tailored to the user's health condition. The input is the analysis result, and the output is the generated advice.

[0518] Step 5:

[0519] The server generates advice and notifies the user via the terminal.

[0520] Specifically, it utilizes speech synthesis and display technology to provide users with information in real time. If necessary, it may also prompt users to contact medical institutions. The input is generated advice, and the output is a notification to the user.

[0521] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0522] This invention relates to a system that provides individually customized health advice by integrating and analyzing diverse biometric information and user emotional data. A new function incorporating an emotional engine plays a crucial role in the implementation of this system.

[0523] First, users collect daily biometric data through wearable devices and smartphones. This includes heart rate, body temperature, diet, and exercise levels. Additionally, user emotional data is acquired through emotion recognition algorithms using the smartphone's camera and voice input. For example, when a user keeps a diary on their smartphone and uses voice input, their emotions are analyzed from that voice data.

[0524] The collected data is converted to a standard format on the device and encrypted. This encrypted data is then sent to the server via secure communication.

[0525] The server integrates diverse biometric and emotional data and stores it in a database. AI is used to analyze this integrated data, assessing the user's health status and correlating emotional changes with their health condition. The results of this analysis are used to generate more comprehensive health risk assessments and advice that consider both the user's physical and emotional health.

[0526] For example, if a user frequently experiences stress, the server will take that emotional state into consideration and suggest relaxation techniques or refer users to specialized medical institutions. In addition, emotional data will be used to ensure that advice is delivered in a style that is best suited to the user. If a depressed mood is detected, advice will be provided using uplifting language.

[0527] Furthermore, by receiving feedback from users, this system continuously improves its analytical accuracy and the quality of its advice. Based on this feedback, the server implements measures to improve the content and emotional response of the advice it provides.

[0528] In this way, the present invention, which utilizes an emotion engine, aims to support both the emotional and physical aspects of the user. This enables the user to maintain a balanced approach to health management.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] Users collect biometric and emotional data using wearable devices and smartphone apps. Heart rate and body temperature are automatically acquired from the wearable device. Emotional data is obtained by taking a picture of the user's face with the smartphone camera and analyzing it using facial recognition technology.

[0532] Step 2:

[0533] The device converts collected biometric and emotional data into a unified format. This conversion process adjusts the data to a format suitable for analysis. Simultaneously, the data is encrypted to ensure security.

[0534] Step 3:

[0535] The terminal sends the format-converted and encrypted data to the server. Since the transmission is done via a secure communication protocol, the risk of user information leakage is reduced.

[0536] Step 4:

[0537] The server stores the received data in a database and integrates diverse biometric and emotional data. This integrated data is then prepared for use in subsequent analysis steps.

[0538] Step 5:

[0539] The server uses AI algorithms to analyze the integrated data. This analysis assesses the user's health status and determines how detected emotions relate to health risks.

[0540] Step 6:

[0541] The server performs a health risk assessment based on the analysis results and generates specific improvement advice. Based on emotional data, feedback is provided that takes into account the user's emotional state. For example, if strong stress is detected, advice that helps reduce stress will be prioritized.

[0542] Step 7:

[0543] The server generates recommendations for the most suitable medical institutions. It combines the user's health status and emotional data to select and present information on the most appropriate hospitals and facilities to the user.

[0544] Step 8:

[0545] The device notifies the user of advice from the server and suggestions from medical institutions. Using the app's notification function, it provides real-time information so that the user can check it immediately.

[0546] Step 9:

[0547] Users can provide feedback on the advice and suggestions offered through the app. Emotional responses can also be provided in a similar manner.

[0548] Step 10:

[0549] The server aggregates feedback data and uses it to provide advice and improve the overall system. This will enable improved analytical accuracy and user experience in the future.

[0550] (Example 2)

[0551] 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."

[0552] In modern society, it is crucial to comprehensively manage individual health and emotional states and provide personalized health guidance. However, conventional systems struggle to efficiently collect and analyze this information, resulting in the inability to provide effective advice tailored to individual needs. Furthermore, methods for continuously improving the system using feedback have not yet been established.

[0553] 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.

[0554] In this invention, the server includes means for collecting biometric and emotional information from users, means for integrating and encrypting the collected biometric and emotional information, and means for a central processing unit to analyze diverse information, evaluate health status, and associate it with emotional status. This makes it possible to provide individually customized health guidance in appropriate expressions according to the user's emotional state.

[0555] A "user" is an individual who provides biometric and emotional information using the system.

[0556] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate and body temperature.

[0557] "Emotional information" refers to data that indicates the user's emotional state, acquired through camera or voice input.

[0558] "Integration" is a method of centralizing data in order to combine and process biometric and emotional information.

[0559] "Encryption" is a security technology that transforms data in a way that prevents unauthorized use by third parties.

[0560] A "central processing unit" is a computer system that analyzes and evaluates data and executes processing based on the analysis results.

[0561] "Health guidance" refers to advice and suggestions provided to improve the health condition of users.

[0562] "Feedback" refers to the act of users returning their evaluations and responses to the health guidance provided to the system.

[0563] "Personalized health guidance" refers to specific advice and suggestions tailored to the individual user's condition.

[0564] This invention is a system that provides individually customized health guidance by integrating and analyzing the user's biometric and emotional information. In implementation, the user utilizes a wearable device and a smartphone to measure their physical condition on a daily basis. This includes functions to record data such as heart rate, body temperature, and exercise level. In addition, emotion recognition technology using the smartphone's camera and microphone acquires the user's emotional information.

[0565] Specifically, when users leave voice diaries on their smartphones, the audio data is collected and sentiment analysis is performed. A voice sentiment recognition algorithm is used in this process. The collected data is converted to a standard format on the device, its security is enhanced through encryption, and then it is transmitted to the server via a secure communication protocol.

[0566] The server integrates the received data into a database and analyzes it using a generative AI model. This allows for a comprehensive assessment of the user's physical and emotional state. Based on the generated analysis results, the server creates personalized health guidance tailored to each user's situation. The advice is delivered using language that is appropriate to the user's emotional state. For example, if stress is prominent, relaxation methods may be suggested. Furthermore, user feedback allows the server to continuously improve the accuracy and quality of the guidance it provides.

[0567] An example of a prompt message is, "Based on recent emotional changes and heart rate data, please create advice for stress reduction." In this way, the present invention aims to address both the physical and emotional aspects of the user, providing a means to effectively manage health in daily life.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] Users collect biometric and emotional information using wearable devices and smartphones. Specifically, they wear smartwatches to measure heart rate and body temperature, and acquire emotional information using their smartphone's camera and microphone. Inputs include data on physical condition and voice data, and the output is this raw data.

[0571] Step 2:

[0572] The device converts the collected biometric and emotional information into a standard format and encrypts it. Specifically, it uses data format conversion software to convert the data into a unified format and applies an encryption algorithm. The input for this step is raw biometric and emotional data, and the output is encrypted, unified-format data.

[0573] Step 3:

[0574] The terminal sends encrypted data to the server using a secure communication protocol. Specifically, data is transferred to the server via secure communication using SSL / TLS. The input is encrypted data, and the output is the arrival of the data at the server.

[0575] Step 4:

[0576] The server integrates the received data and stores it in a database. Specifically, it performs a data cleansing process and records the clean data in the database. The input for this step is encrypted data, and the output is the integrated data in the database.

[0577] Step 5:

[0578] The server analyzes the integrated data using a generative AI model. The AI ​​algorithm correlates health status assessments and emotional states to generate personalized health guidance. Specifically, it inputs data into the AI ​​model and outputs analysis results of health risks and status. The input for this step is integrated data, and the output is the analysis results and personalized advice.

[0579] Step 6:

[0580] The server provides health guidance generated based on the analysis results in a format suitable for the user. For example, if stress is detected, relaxation techniques will be suggested. The input is the analysis results, and the output is health guidance customized for the user.

[0581] Step 7:

[0582] Users provide feedback on the advice given, and this feedback is sent to the server. The server analyzes the feedback and takes action to improve the system's analysis accuracy and the quality of health guidance. The input is the user's feedback, and the output is the improved analysis model and advice quality.

[0583] (Application Example 2)

[0584] 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."

[0585] In modern times, many people are exposed to various health risks, and there is a need for personalized health management methods. However, existing health management systems do not adequately consider the emotional state of users and may not provide integrated support. Therefore, there is a need for means to provide health advice that takes into account the emotional state of users and to improve their physical and emotional health.

[0586] 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.

[0587] In this invention, the server includes means for acquiring, integrating, and analyzing diverse biometric and emotional information; means for generating health-related risk diagnoses and advice in a style appropriate to the user's emotional state based on the analysis results; and means for suggesting the most suitable medical institution based on the generated advice. This enables comprehensive and personalized health management that takes into account the user's emotional state.

[0588] "Diverse biometric information" refers to different types of data about the user's body, such as heart rate, body temperature, exercise level, and diet.

[0589] "Emotional information" refers to data about the user's emotional state, analyzed from their facial expressions and voice.

[0590] "Means of integration and analysis" refers to a function that combines diverse biometric and emotional information acquired into a single dataset and evaluates the user's health and emotional state from that data.

[0591] "Means for generating risk assessments and advice" refers to a function that identifies health risks based on analysis results and proposes specific health management strategies tailored to those risks.

[0592] "Means of communicating in a style that suits the user's emotional state" refers to a function that optimizes the method of delivering advice based on the user's current emotions.

[0593] "A means of suggesting the most suitable medical institution" refers to a function that recommends necessary medical institutions and specialists to the user based on the generated health advice.

[0594] "Means of collecting feedback" refers to a function that gathers user opinions and suggestions for improvement, and uses that information to improve the accuracy of the system and the quality of the advice provided.

[0595] This invention provides a system to support users' daily health management. This system functions in conjunction with wearable devices and smart devices used by the user. The terminal collects the user's diverse biometric and emotional information in real time and converts this data into a standard format. The converted data is then encrypted and securely transmitted to a server.

[0596] The server utilizes AI-powered software to integrate and analyze the received data. Specifically, it comprehensively analyzes the user's biometric and emotional data using the aforementioned "integration and analysis method," and then provides concrete suggestions using the "risk diagnosis and advice generation method." By combining heart rate, emotional patterns, and other factors in the analysis, a more comprehensive assessment of health and emotional state becomes possible.

[0597] For example, the server can provide users with appropriate advice by utilizing a "means of communication tailored to their emotional state." If data detects that the user is experiencing stress, the server can offer advice in a warm tone about relaxation techniques and the importance of rest. In addition, a "means of suggesting the most suitable medical institution" provides users with information on medical institutions that match their specific health condition.

[0598] By collecting user feedback, the server will improve the quality of the healthcare advice it provides in the future. The feedback provided by users will contribute to improving the overall system performance and will be a crucial factor in enhancing the accuracy of future advice.

[0599] A concrete example of a prompt is, "Generate daily health advice based on the user's heart rate and emotional data." This prompt provides the AI ​​model with the necessary information to make appropriate suggestions to the user.

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The device acquires biometric information from the user, such as heart rate, body temperature, exercise level, and dietary information, via a wearable device. The input data consists of this biometric information, and the output data is provided in a standard data format. The device also collects the user's emotional data using a camera and microphone and analyzes it with an emotion recognition algorithm.

[0603] Step 2:

[0604] The device encrypts the collected biometric and emotional data and transfers it to the server. The input is formatted biometric and emotional data, and the output generates securely transferable encrypted data. This ensures data security.

[0605] Step 3:

[0606] The server decrypts the received encrypted data and integrates diverse biometric and emotional information. The input is encrypted biometric and emotional data, and the output is an integrated dataset. The server then uses an AI model to prepare this data for analysis.

[0607] Step 4:

[0608] The server analyzes the integrated dataset using a generative AI model to assess the user's health and emotional state. The input is the integrated dataset, and the output generates a risk assessment and health advice. This prepares the system to comprehensively understand the user's condition and provide appropriate advice.

[0609] Step 5:

[0610] The server sends the generated risk assessment and health advice to the user's terminal in a format tailored to their emotional state. The input is the risk assessment and advice information, and the output is advice expressed in a way that is optimized for the user.

[0611] Step 6:

[0612] Users receive the advice provided and use it to guide their actions. They can also send feedback about the content and wording of the advice to the server via their device. Based on this input, the server uses the feedback information to improve the quality of the advice.

[0613] Step 7:

[0614] The server analyzes user feedback and uses it to improve the overall advice provided by the system. The input is user feedback, and the output is an improved advice strategy. This enables continuous system growth and an enhanced user experience.

[0615] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0616] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0617] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0618] [Fourth Embodiment]

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

[0620] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0621] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0622] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0623] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0624] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0625] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0626] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0627] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0628] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0629] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0630] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0631] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0632] This invention relates to a system that provides users with customized health risk diagnoses and advice by aggregating and analyzing diverse biometric information. The invention is implemented by the user providing data through a terminal and a server processing that data.

[0633] First, users provide biometric information such as heart rate, body temperature, eating and drinking history, and exercise records using wearable devices and smartphone applications. For example, users input their daily meal details into the app, and this information is then incorporated into the system. Wearable devices automatically measure daily exercise levels and heart rate and transmit the data to the device.

[0634] Next, the device formats the collected biometric information into the necessary format for transmission to the server and securely transfers it. This transfer process encrypts the data to ensure user privacy is protected.

[0635] The server then integrates and centrally manages the diverse biometric information it receives. Using AI technology, the server analyzes the data to comprehensively assess the user's health status. This analysis identifies potential health risks the user may face in the future and generates specific advice on preventative and corrective measures. For example, if the data analysis indicates a risk of high blood pressure, the user will be notified with advice to reduce sodium intake.

[0636] The server also generates suggestions for medical institutions suitable for the user's health condition. It lists the most suitable hospitals based on the user's place of residence and health status, and provides this information to the user via their device. Furthermore, it has a mechanism to continuously improve the system's accuracy and user satisfaction by utilizing user feedback.

[0637] In this way, the present invention can provide individual users with a more appropriate and personalized health management experience. This system helps users maintain a healthy and effective daily life.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] Users input biometric information such as heart rate, body temperature, diet, and exercise data using wearable devices and smartphone apps, and collect data. For example, they might manually enter their daily meal details into the app and automatically record heart rate data through the device.

[0641] Step 2:

[0642] The device processes the collected biometric information and converts it into a standard format. This format conversion ensures that the data is available for subsequent processing in a consistent manner. The device also encrypts the obtained data before transmitting it to the server, ensuring privacy.

[0643] Step 3:

[0644] The server receives biometric information transmitted from the terminal and stores it centrally in a database. The stored data, combined with data from different sources, enables more comprehensive analysis.

[0645] Step 4:

[0646] The server uses AI technology to analyze integrated data and assess the user's health status. This identifies disease risks and health concerns. Based on the analysis results, it generates risk assessments and specific advice on lifestyle improvements.

[0647] Step 5:

[0648] The server selects the most suitable medical institution for the user based on the analysis results and organizes the information. It generates a list of appropriate hospitals and clinics, taking into account the user's place of residence and identified health risks.

[0649] Step 6:

[0650] The device notifies the user of health advice and suggestions for medical institutions received from the server. This allows the user to receive a specific action plan based on their current health status.

[0651] Step 7:

[0652] Users provide feedback through the app on the advice and medical institution suggestions they receive. This feedback includes their reactions to the usefulness of the advice and their choice of medical institution.

[0653] Step 8:

[0654] The server analyzes user feedback data to improve overall system performance. Based on the feedback, it adjusts AI algorithms and suggestions, and implements improvements to enhance the accuracy of future advice and suggestions.

[0655] (Example 1)

[0656] 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".

[0657] In modern society, managing diverse biometric information and understanding one's health status is increasingly important, but effective systems to address this are still lacking. In particular, there is a need to support users in leading healthy lives by identifying individual health risks and providing appropriate advice and suggesting medical facilities. Existing systems face challenges in collecting and analyzing sufficient data and providing appropriate actionable guidelines based on the results.

[0658] 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.

[0659] In this invention, the server includes means for acquiring diverse biometric information, means for securely transmitting the acquired biometric information via a communication terminal, means for integrating and analyzing the transmitted biometric information, means for generating health-related risk diagnoses and advice based on the analysis results, means for suggesting the most suitable medical institution based on the generated advice, and means for collecting user feedback and improving the analysis results and suggestions. This enables personalized health management for individual users.

[0660] "Diverse biometric information" is a general term for various types of data that indicate an individual's health status, such as heart rate, body temperature, eating and drinking history, and exercise records.

[0661] "Communication terminals" refer to electronic devices such as smartphones and tablets that can send and receive data over a network.

[0662] "Integration" means centrally gathering acquired biometric information and processing it as a single piece of information.

[0663] "Analysis" refers to data processing operations performed using collected data to evaluate and predict specific health conditions or risks.

[0664] "Health-related risk assessment" refers to identifying potential or future health problems in an individual that have been revealed through analysis.

[0665] "Generating advice" refers to providing users with behavioral guidelines and suggestions for lifestyle improvements based on health-related risk assessments.

[0666] "Suggesting the most suitable medical institution" means listing facilities that can provide appropriate medical services based on the user's health condition and place of residence, and providing this information.

[0667] "Feedback" refers to reactions and opinions from users regarding the system's performance and the advice they have provided.

[0668] This invention is a system that provides users with customized health risk diagnoses and advice by aggregating and analyzing diverse biological information. The invention is implemented when the user provides data through a terminal, and a server processes that data.

[0669] First, users acquire biometric data using wearable technology. For example, they use wearable devices to measure their heart rate and body temperature, and input medical information such as their eating and drinking history and exercise records into a smartphone application. This allows users to easily understand their daily health status.

[0670] Next, when the device provides the collected biometric information to the server, it formats it into the required format and transfers it securely. The technologies used include the SSL / TLS protocol for encrypting and transmitting the data. This process is essential to protect the user's private information.

[0671] The server then integrates the diverse data it receives and stores it in a self-managing database. The server uses a generative AI model to analyze this data and comprehensively evaluate the user's health status. The generative AI model, for example, uses machine learning algorithms and, based on the insights gained from the data analysis, can identify health risks and suggest preventative measures for the user.

[0672] Furthermore, when suggesting medical facilities, the server considers the user's health status and location information to identify and suggest the most suitable facility. It also incorporates a mechanism to continuously improve the analysis results and suggestions by utilizing user feedback.

[0673] As a specific example, the prompt text to be entered into the system is as follows:

[0674] "A 35-year-old male with an average heart rate of 75 bpm, primarily a desk job, and runs three times a week. Please assess this user's future health risks and provide necessary advice."

[0675] In this way, the system provides users with a personalized health management experience, contributing to maintaining good health.

[0676] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0677] Step 1:

[0678] Users collect biometric information using wearable technology and smartphone apps. The inputs include a variety of data such as heart rate, body temperature, exercise records, and eating and drinking history. For example, a user might enter details of their breakfast into the app, and the wearable device automatically measures their heart rate. This input data is temporarily stored on the device for later analysis.

[0679] Step 2:

[0680] The device formats the collected biometric information and prepares it for transmission to the server. During this process, the input data is encrypted and formatted in a secure manner. Specifically, the device converts the data to JSON format and outputs it after encrypting it using the SSL / TLS protocol. This output is then ready to be transferred to the server.

[0681] Step 3:

[0682] The server integrates data received from terminals and centrally manages it in a database. It receives encrypted JSON data as input, decrypts it, and then organizes it in the storage system. Specifically, it stores user IDs and associated biometric information in the database and outputs it in a format suitable for future analysis.

[0683] Step 4:

[0684] The server analyzes data using a generative AI model. This analysis provides a multifaceted assessment of the user's health status. Specifically, it applies a random forest algorithm to identify health risks from the input data. This output is then prepared as specific advice for the user.

[0685] Step 5:

[0686] The server generates health advice based on the analysis results and suggests the most suitable medical facilities. Here, based on the risk assessment generated from the analysis output, it creates health guidelines that are beneficial to the user. Specifically, if a risk of hypertension is identified, it will formulate advice to reduce sodium intake for the user and output a list of appropriate medical facilities.

[0687] Step 6:

[0688] Users submit feedback on the advice and suggestions provided, and the system uses this information to make improvements. As input, users fill out a feedback form with their opinions. Specifically, users enter comments on the usefulness of the advice, and this information is transmitted to the server. This output is used to improve the system's algorithms and services.

[0689] (Application Example 1)

[0690] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] In recent years, the importance of health management has increased, but there are insufficient systems to provide personalized health risk assessments and recommend appropriate medical institutions for individual users. Furthermore, there is a lack of means to monitor users' biometric information in real time and respond quickly to abnormal situations.

[0692] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0693] In this invention, the server includes means for acquiring diverse biometric information, means for integrating and analyzing the acquired biometric information, and means for generating health-related risk diagnoses and advice based on the analysis results. This makes it possible to notify users of health risks in real time and prompt them to contact necessary medical institutions.

[0694] "Diverse biometric information" refers to various physiological data necessary to understand the user's health status, such as heart rate, body temperature, exercise level, and eating and drinking history.

[0695] "Means of integration and analysis" refers to a method for evaluating a user's health status by centrally managing collected biometric information and analyzing the data using AI technology.

[0696] A "means for generating health-related risk assessments and advice" refers to a method that evaluates the health risks of individual users based on analyzed data and provides specific preventive and corrective measures.

[0697] "A method for proposing the most suitable medical facility" refers to a method of selecting and presenting appropriate medical institutions to users based on their place of residence and health condition.

[0698] "Means of notifying the user via voice or display device" refers to a method of informing the user in real time, using speech synthesis technology or a display, when an abnormality in biometric information is detected.

[0699] "Means of prompting contact with medical institutions as needed" refers to a method of instructing users to contact appropriate medical institutions when an abnormality in their health condition is detected as a result of the analysis.

[0700] To implement this invention, a user collects biometric information using a wearable device or smartphone and transmits that data to a terminal. This terminal encrypts the information and transfers it to a server. The server integrates the data and performs analysis using AI technology.

[0701] The hardware required includes a wearable device (e.g., a smartwatch) and a communication terminal carried by the user. This communication terminal connects with the wearable device via Bluetooth or Wi-Fi to receive biometric information. The received information is encrypted to protect privacy before being sent to a server.

[0702] The server processes data using software such as Python and TensorFlow. The analyzed results are then used to notify the user of their health status and potential risks in real time. Furthermore, if an anomaly is detected, the system uses speech synthesis technology and a display to notify the user and, if necessary, prompts them to contact an appropriate medical facility.

[0703] For example, if a user's heart rate shows an abnormal value while running, the server will analyze the results and notify the user via voice message on their device saying, "Your heart rate is higher than normal. Take a break." Depending on the situation, it may also send a follow-up message saying, "You need to find the nearest medical facility."

[0704] An example of a prompt message for a generative AI model is, "Based on the acquired biometric information, analyze the current health status and generate advice."

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

[0706] Step 1:

[0707] Users collect biometric information using wearable devices.

[0708] Specifically, the device measures heart rate, body temperature, and activity level, and transmits this data to the user's communication terminal via Bluetooth. The input is biometric data from the wearable device, and the output is the data received by the communication terminal.

[0709] Step 2:

[0710] The device formats the biometric information it collects and transfers it to the server.

[0711] The specific operation involves formatting the received data and encrypting the information. A secure communication protocol is used to send the data to the server while protecting privacy. The input is biometric data received by the terminal, and the output is encrypted data.

[0712] Step 3:

[0713] The server receives the transferred data, integrates it, and analyzes it.

[0714] The server utilizes Python and TensorFlow to perform analysis by comparing it with historical data stored in a database. The input is encrypted biometric data, and the output is the result of the analyzed health status assessment.

[0715] Step 4:

[0716] The server evaluates the user's health risks based on the analysis results and generates advice.

[0717] Using a generative AI model, it outputs immediately applicable advice to the user. The selected advice is tailored to the user's health condition. The input is the analysis result, and the output is the generated advice.

[0718] Step 5:

[0719] The server generates advice and notifies the user via the terminal.

[0720] Specifically, it utilizes speech synthesis and display technology to provide users with information in real time. If necessary, it may also prompt users to contact medical institutions. The input is generated advice, and the output is a notification to the user.

[0721] 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.

[0722] This invention relates to a system that provides individually customized health advice by integrating and analyzing diverse biometric information and user emotional data. A new function incorporating an emotional engine plays a crucial role in the implementation of this system.

[0723] First, users collect daily biometric data through wearable devices and smartphones. This includes heart rate, body temperature, diet, and exercise levels. Additionally, user emotional data is acquired through emotion recognition algorithms using the smartphone's camera and voice input. For example, when a user keeps a diary on their smartphone and uses voice input, their emotions are analyzed from that voice data.

[0724] The collected data is converted to a standard format on the device and encrypted. This encrypted data is then sent to the server via secure communication.

[0725] The server integrates diverse biometric and emotional data and stores it in a database. AI is used to analyze this integrated data, assessing the user's health status and correlating emotional changes with their health condition. The results of this analysis are used to generate more comprehensive health risk assessments and advice that consider both the user's physical and emotional health.

[0726] For example, if a user frequently experiences stress, the server will take that emotional state into consideration and suggest relaxation techniques or refer users to specialized medical institutions. In addition, emotional data will be used to ensure that advice is delivered in a style that is best suited to the user. If a depressed mood is detected, advice will be provided using uplifting language.

[0727] Furthermore, by receiving feedback from users, this system continuously improves its analytical accuracy and the quality of its advice. Based on this feedback, the server implements measures to improve the content and emotional response of the advice it provides.

[0728] In this way, the present invention, which utilizes an emotion engine, aims to support both the emotional and physical aspects of the user. This enables the user to maintain a balanced approach to health management.

[0729] The following describes the processing flow.

[0730] Step 1:

[0731] Users collect biometric and emotional data using wearable devices and smartphone apps. Heart rate and body temperature are automatically acquired from the wearable device. Emotional data is obtained by taking a picture of the user's face with the smartphone camera and analyzing it using facial recognition technology.

[0732] Step 2:

[0733] The device converts collected biometric and emotional data into a unified format. This conversion process adjusts the data to a format suitable for analysis. Simultaneously, the data is encrypted to ensure security.

[0734] Step 3:

[0735] The terminal sends the format-converted and encrypted data to the server. Since the transmission is done via a secure communication protocol, the risk of user information leakage is reduced.

[0736] Step 4:

[0737] The server stores the received data in a database and integrates diverse biometric and emotional data. This integrated data is then prepared for use in subsequent analysis steps.

[0738] Step 5:

[0739] The server uses AI algorithms to analyze the integrated data. This analysis assesses the user's health status and determines how detected emotions relate to health risks.

[0740] Step 6:

[0741] The server performs a health risk assessment based on the analysis results and generates specific improvement advice. Based on emotional data, feedback is provided that takes into account the user's emotional state. For example, if strong stress is detected, advice that helps reduce stress will be prioritized.

[0742] Step 7:

[0743] The server generates recommendations for the most suitable medical institutions. It combines the user's health status and emotional data to select and present information on the most appropriate hospitals and facilities to the user.

[0744] Step 8:

[0745] The device notifies the user of advice from the server and suggestions from medical institutions. Using the app's notification function, it provides real-time information so that the user can check it immediately.

[0746] Step 9:

[0747] Users can provide feedback on the advice and suggestions offered through the app. Emotional responses can also be provided in a similar manner.

[0748] Step 10:

[0749] The server aggregates feedback data and uses it to provide advice and improve the overall system. This will enable improved analytical accuracy and user experience in the future.

[0750] (Example 2)

[0751] 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".

[0752] In modern society, it is crucial to comprehensively manage individual health and emotional states and provide personalized health guidance. However, conventional systems struggle to efficiently collect and analyze this information, resulting in the inability to provide effective advice tailored to individual needs. Furthermore, methods for continuously improving the system using feedback have not yet been established.

[0753] 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.

[0754] In this invention, the server includes means for collecting biometric and emotional information from users, means for integrating and encrypting the collected biometric and emotional information, and means for a central processing unit to analyze diverse information, evaluate health status, and associate it with emotional status. This makes it possible to provide individually customized health guidance in appropriate expressions according to the user's emotional state.

[0755] A "user" is an individual who provides biometric and emotional information using the system.

[0756] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate and body temperature.

[0757] "Emotional information" refers to data that indicates the user's emotional state, acquired through camera or voice input.

[0758] "Integration" is a method of centralizing data in order to combine and process biometric and emotional information.

[0759] "Encryption" is a security technology that transforms data in a way that prevents unauthorized use by third parties.

[0760] A "central processing unit" is a computer system that analyzes and evaluates data and executes processing based on the analysis results.

[0761] "Health guidance" refers to advice and suggestions provided to improve the health condition of users.

[0762] "Feedback" refers to the act of users returning their evaluations and responses to the health guidance provided to the system.

[0763] "Personalized health guidance" refers to specific advice and suggestions tailored to the individual user's condition.

[0764] This invention is a system that provides individually customized health guidance by integrating and analyzing the user's biometric and emotional information. In implementation, the user utilizes a wearable device and a smartphone to measure their physical condition on a daily basis. This includes functions to record data such as heart rate, body temperature, and exercise level. In addition, emotion recognition technology using the smartphone's camera and microphone acquires the user's emotional information.

[0765] Specifically, when users leave voice diaries on their smartphones, the audio data is collected and sentiment analysis is performed. A voice sentiment recognition algorithm is used in this process. The collected data is converted to a standard format on the device, its security is enhanced through encryption, and then it is transmitted to the server via a secure communication protocol.

[0766] The server integrates the received data into a database and analyzes it using a generative AI model. This allows for a comprehensive assessment of the user's physical and emotional state. Based on the generated analysis results, the server creates personalized health guidance tailored to each user's situation. The advice is delivered using language that is appropriate to the user's emotional state. For example, if stress is prominent, relaxation methods may be suggested. Furthermore, user feedback allows the server to continuously improve the accuracy and quality of the guidance it provides.

[0767] An example of a prompt message is, "Based on recent emotional changes and heart rate data, please create advice for stress reduction." In this way, the present invention aims to address both the physical and emotional aspects of the user, providing a means to effectively manage health in daily life.

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] Users collect biometric and emotional information using wearable devices and smartphones. Specifically, they wear smartwatches to measure heart rate and body temperature, and acquire emotional information using their smartphone's camera and microphone. Inputs include data on physical condition and voice data, and the output is this raw data.

[0771] Step 2:

[0772] The device converts the collected biometric and emotional information into a standard format and encrypts it. Specifically, it uses data format conversion software to convert the data into a unified format and applies an encryption algorithm. The input for this step is raw biometric and emotional data, and the output is encrypted, unified-format data.

[0773] Step 3:

[0774] The terminal sends encrypted data to the server using a secure communication protocol. Specifically, data is transferred to the server via secure communication using SSL / TLS. The input is encrypted data, and the output is the arrival of the data at the server.

[0775] Step 4:

[0776] The server integrates the received data and stores it in a database. Specifically, it performs a data cleansing process and records the clean data in the database. The input for this step is encrypted data, and the output is the integrated data in the database.

[0777] Step 5:

[0778] The server analyzes the integrated data using a generative AI model. The AI ​​algorithm correlates health status assessments and emotional states to generate personalized health guidance. Specifically, it inputs data into the AI ​​model and outputs analysis results of health risks and status. The input for this step is integrated data, and the output is the analysis results and personalized advice.

[0779] Step 6:

[0780] The server provides health guidance generated based on the analysis results in a format suitable for the user. For example, if stress is detected, relaxation techniques will be suggested. The input is the analysis results, and the output is health guidance customized for the user.

[0781] Step 7:

[0782] Users provide feedback on the advice given, and this feedback is sent to the server. The server analyzes the feedback and takes action to improve the system's analysis accuracy and the quality of health guidance. The input is the user's feedback, and the output is the improved analysis model and advice quality.

[0783] (Application Example 2)

[0784] 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".

[0785] In modern times, many people are exposed to various health risks, and there is a need for personalized health management methods. However, existing health management systems do not adequately consider the emotional state of users and may not provide integrated support. Therefore, there is a need for means to provide health advice that takes into account the emotional state of users and to improve their physical and emotional health.

[0786] 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.

[0787] In this invention, the server includes means for acquiring, integrating, and analyzing diverse biometric and emotional information; means for generating health-related risk diagnoses and advice in a style appropriate to the user's emotional state based on the analysis results; and means for suggesting the most suitable medical institution based on the generated advice. This enables comprehensive and personalized health management that takes into account the user's emotional state.

[0788] "Diverse biometric information" refers to different types of data about the user's body, such as heart rate, body temperature, exercise level, and diet.

[0789] "Emotional information" refers to data about the user's emotional state, analyzed from their facial expressions and voice.

[0790] "Means of integration and analysis" refers to a function that combines diverse biometric and emotional information acquired into a single dataset and evaluates the user's health and emotional state from that data.

[0791] "Means for generating risk assessments and advice" refers to a function that identifies health risks based on analysis results and proposes specific health management strategies tailored to those risks.

[0792] "Means of communicating in a style that suits the user's emotional state" refers to a function that optimizes the method of delivering advice based on the user's current emotions.

[0793] "A means of suggesting the most suitable medical institution" refers to a function that recommends necessary medical institutions and specialists to the user based on the generated health advice.

[0794] "Means of collecting feedback" refers to a function that gathers user opinions and suggestions for improvement, and uses that information to improve the accuracy of the system and the quality of the advice provided.

[0795] This invention provides a system to support users' daily health management. This system functions in conjunction with wearable devices and smart devices used by the user. The terminal collects the user's diverse biometric and emotional information in real time and converts this data into a standard format. The converted data is then encrypted and securely transmitted to a server.

[0796] The server utilizes AI-powered software to integrate and analyze the received data. Specifically, it comprehensively analyzes the user's biometric and emotional data using the aforementioned "integration and analysis method," and then provides concrete suggestions using the "risk diagnosis and advice generation method." By combining heart rate, emotional patterns, and other factors in the analysis, a more comprehensive assessment of health and emotional state becomes possible.

[0797] For example, the server can provide users with appropriate advice by utilizing a "means of communication tailored to their emotional state." If data detects that the user is experiencing stress, the server can offer advice in a warm tone about relaxation techniques and the importance of rest. In addition, a "means of suggesting the most suitable medical institution" provides users with information on medical institutions that match their specific health condition.

[0798] By collecting user feedback, the server will improve the quality of the healthcare advice it provides in the future. The feedback provided by users will contribute to improving the overall system performance and will be a crucial factor in enhancing the accuracy of future advice.

[0799] A concrete example of a prompt is, "Generate daily health advice based on the user's heart rate and emotional data." This prompt provides the AI ​​model with the necessary information to make appropriate suggestions to the user.

[0800] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0801] Step 1:

[0802] The device acquires biometric information from the user, such as heart rate, body temperature, exercise level, and dietary information, via a wearable device. The input data consists of this biometric information, and the output data is provided in a standard data format. The device also collects the user's emotional data using a camera and microphone and analyzes it with an emotion recognition algorithm.

[0803] Step 2:

[0804] The device encrypts the collected biometric and emotional data and transfers it to the server. The input is formatted biometric and emotional data, and the output generates securely transferable encrypted data. This ensures data security.

[0805] Step 3:

[0806] The server decrypts the received encrypted data and integrates diverse biometric and emotional information. The input is encrypted biometric and emotional data, and the output is an integrated dataset. The server then uses an AI model to prepare this data for analysis.

[0807] Step 4:

[0808] The server analyzes the integrated dataset using a generative AI model to assess the user's health and emotional state. The input is the integrated dataset, and the output generates a risk assessment and health advice. This prepares the system to comprehensively understand the user's condition and provide appropriate advice.

[0809] Step 5:

[0810] The server sends the generated risk assessment and health advice to the user's terminal in a format tailored to their emotional state. The input is the risk assessment and advice information, and the output is advice expressed in a way that is optimized for the user.

[0811] Step 6:

[0812] Users receive the advice provided and use it to guide their actions. They can also send feedback about the content and wording of the advice to the server via their device. Based on this input, the server uses the feedback information to improve the quality of the advice.

[0813] Step 7:

[0814] The server analyzes user feedback and uses it to improve the overall advice provided by the system. The input is user feedback, and the output is an improved advice strategy. This enables continuous system growth and an enhanced user experience.

[0815] 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.

[0816] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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."

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] The following is further disclosed regarding the embodiments described above.

[0837] (Claim 1)

[0838] Means for acquiring diverse biological information,

[0839] A means of integrating and analyzing acquired biological information,

[0840] A means for generating health-related risk diagnoses and advice based on analysis results,

[0841] A means of suggesting the most suitable medical institution based on the generated advice,

[0842] A means of collecting user feedback and improving the analysis results and suggestions,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising means for collecting operation data entered by a user.

[0846] (Claim 3)

[0847] The system according to claim 1, comprising means for receiving data from a medical device and processing it using the integration and analysis means.

[0848] "Example 1"

[0849] (Claim 1)

[0850] Means for acquiring diverse biological information,

[0851] A means of securely transmitting acquired biometric information via a communication terminal,

[0852] A means for integrating and analyzing transmitted biometric information,

[0853] A means for generating health-related risk diagnoses and advice based on analysis results,

[0854] A means of suggesting the most suitable medical institution based on the generated advice,

[0855] A means of collecting user feedback and improving the analysis results and suggestions,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, comprising means for collecting activity data entered by a user.

[0859] (Claim 3)

[0860] The system according to claim 1, comprising means for receiving information from a medical device and processing it using the integration and analysis means.

[0861] "Application Example 1"

[0862] (Claim 1)

[0863] Means for acquiring diverse biological information,

[0864] A means of integrating and analyzing acquired biological information,

[0865] A means for generating health-related risk diagnoses and advice based on analysis results,

[0866] A means of suggesting the most suitable medical facility based on the generated advice,

[0867] A means of collecting user feedback and improving the analysis results and suggestions,

[0868] A means of detecting abnormalities in biometric information in real time, notifying the user via voice or display device, and prompting them to contact a medical institution if necessary.

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, comprising means for collecting user-inputted behavioral data.

[0872] (Claim 3)

[0873] The system according to claim 1, comprising means for receiving data from a medical device and processing it using the integration and analysis means.

[0874] "Example 2 of combining an emotion engine"

[0875] (Claim 1)

[0876] A means of collecting biometric and emotional information from users,

[0877] A means of integrating and encrypting collected biometric and emotional information,

[0878] A means for transmitting encrypted information to a central processing unit via a communication network,

[0879] A central processing unit analyzes diverse information and provides means for evaluating health status and relating it to emotional state,

[0880] A means of generating customized health guidance based on analysis results and providing it in appropriate language according to the emotional state,

[0881] A means of collecting user feedback and continuously improving the analysis results and content of health guidance,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, comprising means for identifying emotions entered by a user.

[0885] (Claim 3)

[0886] The system according to claim 1, comprising means for receiving biometric information from wearable technology and processing it using the integration and analysis means.

[0887] "Application example 2 when combining with an emotional engine"

[0888] (Claim 1)

[0889] Means for acquiring diverse biological information,

[0890] A means of integrating and analyzing acquired biometric and emotional information,

[0891] A means of generating health-related risk diagnoses and advice based on analysis results, and communicating them in a style that suits the user's emotional state,

[0892] A means of suggesting the most suitable medical institution based on the generated advice,

[0893] A means of collecting user feedback and improving the analysis results and suggestions,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, comprising means for collecting motion data and emotion data entered by the user.

[0897] (Claim 3)

[0898] The system according to claim 1, comprising means for receiving data from an information processing device and processing it using the integration and analysis means. [Explanation of Symbols]

[0899] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for acquiring diverse biological information, A means of integrating and analyzing acquired biological information, A means for generating health-related risk diagnoses and advice based on analysis results, A means of suggesting the most suitable medical institution based on the generated advice, A means of collecting user feedback and improving the analysis results and suggestions, A system that includes this.

2. The system according to claim 1, further comprising means for collecting operation data entered by a user.

3. The system according to claim 1, comprising means for receiving data from a medical device and processing it using the integration and analysis means.

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