system

Abstract

We provide the system. [Solution] Means of receiving health information, A means for generating personalized health advice based on received information, A means of selecting and referring appropriate medical institutions based on the generated advice, A means of notifying the user terminal of information about the selected medical institution, A means of collecting user feedback and incorporating it into the next generation of advice, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] <^ In personal health management, it is difficult to obtain reliable health advice in real time. Also, due to insufficient information for accessing the optimal medical institutions, the effects of preventive medicine are not fully utilized. Under such circumstances, it is necessary to promptly introduce advice suitable for an individual's health condition and appropriate medical institutions to extend the user's healthy life expectancy.

Means for Solving the Problems

[0005] This invention provides a system that collects health information and generates personalized health advice based on that information. Based on the generated advice, the system selects an appropriate medical institution and notifies the user of this information on their terminal. Furthermore, it utilizes user feedback to improve the accuracy of future advice generation. In addition, it has a function to detect changes in health status in real time and promptly encourage the user to visit a medical institution as needed.

[0006] "Health information" refers to personal biometric data and lifestyle information, including heart rate, steps taken, sleep duration, and dietary content.

[0007] "Means of receiving data" refers to functions that acquire data from external devices or sensors, convert it into an appropriate format, and process it.

[0008] "Individualized health advice" refers to customized health guidelines and recommendations provided to specific individuals based on analyzed health information.

[0009] "Methods for selecting and introducing medical institutions" refers to a function that considers the user's health condition and location information to select the most suitable medical institution and present that information to the user.

[0010] "Means of notification" refers to a system for visually or audibly conveying health advice and medical information to the user's device.

[0011] "Means of collecting evaluations" refers to functions that collect user feedback and satisfaction data to be used for future service improvements.

[0012] "Real-time detection" refers to a process of immediately monitoring changes in health information and quickly taking necessary actions. [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] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

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

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system for providing personalized health advice to individual users and referring them to appropriate medical institutions. This system utilizes a server and terminals working together to continuously monitor the user's health status and provide optimal support.

[0035] First, the device acquires health information from the user. The device works in conjunction with smartwatches and fitness trackers to collect daily health data such as heart rate, steps taken, and sleep patterns. This data is transmitted to the server in real time.

[0036] Next, the server analyzes the received health information. Based on the collected data, the server assesses the user's health status and compares it to past data and general health indicators. Based on the results, the server generates personalized health advice. For example, if the user is not getting enough exercise, it might provide specific advice such as, "We recommend walking for 30 minutes every day."

[0037] In addition, the server selects a medical facility that matches the user's current health condition. The server takes the user's location into consideration to create and provide a list of nearby medical facilities and specialists. This step is crucial for enabling rapid access to medical care and helping users receive appropriate treatment.

[0038] The device then notifies the user of the generated health advice and recommended medical facilities. The user can then use this information to manage their own health. Furthermore, if the user provides feedback, the device receives it and sends it to the server, which uses this feedback to generate future health advice. This improves the overall accuracy of the system and user satisfaction.

[0039] In this way, users will be able to easily access advice and medical facilities based on their own health information. This system is expected to support daily health management and contribute to disease prevention.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device acquires health information. It collects heart rate, steps, and sleep data from the user's worn device (e.g., a smartwatch). This data is periodically stored on the device.

[0043] Step 2:

[0044] The device sends data to the server. The collected health information is transferred to the server via the internet. This transmission occurs in real time, ensuring the accuracy of the data.

[0045] Step 3:

[0046] The server receives health information and stores it in a database. The server securely stores the received data in the database in preparation for later analysis.

[0047] Step 4:

[0048] The server analyzes the data. It uses the latest health information to recognize patterns and detects anomalies by comparing them with past data. Machine learning algorithms are used for this.

[0049] Step 5:

[0050] The server generates personalized health advice. Based on the analysis results, it creates health advice tailored to the user's daily life. For example, it might provide specific instructions such as, "We recommend exercising three times a week."

[0051] Step 6:

[0052] The server generates a list of medical facilities. Considering the user's location and health status, it selects an appropriate medical facility in the vicinity.

[0053] Step 7:

[0054] The device notifies the user. It notifies the user's device of the generated health advice and information about healthcare facilities, allowing the user to review it.

[0055] Step 8:

[0056] The user provides feedback. They input an evaluation of the information received, and the device sends this to the server.

[0057] Step 9:

[0058] The server records feedback and uses it to generate advice for the next time. The recorded feedback is used to improve the system and enhance the user experience.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In modern times, many people need to efficiently manage their daily health. However, it is not easy for users to accurately understand their own health status and receive appropriate advice. Furthermore, there is a need to quickly detect changes in health status and prompt users to seek medical attention when necessary. Conventional systems struggle to meet these needs, making the provision of more effective health management systems a challenge.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for acquiring health data from the user, means for transmitting the acquired data to a processing unit in real time, and means for performing data analysis in the processing unit and generating individualized health advice. This enables personalized advice based on the user's health condition and prompt access to medical facilities.

[0064] "Means of acquiring health data from users" refers to technologies used to collect information about a user's physical condition and activity from recording devices.

[0065] "Means of transmitting data to a processing device in real time" refers to a technology that immediately sends collected data to an analysis device, enabling analysis without delay.

[0066] "A means of performing data analysis and generating individualized health advice" refers to a technology that uses collected information to evaluate a user's health status and generate appropriate advice.

[0067] A "generative AI model" is a mathematical model that utilizes artificial intelligence in data analysis and advice generation to provide users with optimal feedback.

[0068] "A means of selecting an appropriate specialized institution that takes the user's location information into consideration" refers to technology that selects the most appropriate and easily accessible medical institution based on the user's geographical location.

[0069] "Notification methods" refer to technologies that send information from a server to a terminal and inform the user of that information.

[0070] "Methods for collecting feedback and incorporating it into future generation" refers to technologies that collect user responses as data and use it to improve and optimize future advice.

[0071] This invention is a system that provides personalized health advice to individual users and refers them to appropriate professional institutions. It primarily functions through the coordinated operation of a server and terminals, monitoring the user's health status to provide optimal support.

[0072] The device utilizes recording devices worn by the user, such as smartwatches and fitness trackers, to acquire daily health data such as heart rate, steps taken, and sleep patterns. This data is transmitted to a server in real time via Bluetooth or Wi-Fi.

[0073] The server utilizes a generative AI model to analyze the received health data. Specifically, it compares the user's data with historical information and general health indicators to assess the user's health status. Based on this, the server generates personalized health advice. An example of this prompt could be the instruction, "Generate specific health advice based on the user's latest health data." The generated advice would provide the user with specific examples, such as, "You are not getting enough exercise, so we recommend a 30-minute walk every day."

[0074] Furthermore, the server selects and creates a list of appropriate nearby medical institutions based on the user's location. This allows users to quickly access medical institutions and more easily receive the necessary medical services.

[0075] The device notifies the user of generated health advice and information on recommended professional organizations. This notification is delivered via smartphone push notifications, allowing the user to immediately check the information and reflect it in their daily life and activities.

[0076] When a user provides feedback to the system, the terminal sends it to the server. The server stores this feedback in a database and considers it when generating health advice in the future. This iterative learning process can improve the overall accuracy of the system and user satisfaction.

[0077] This configuration is expected to support daily health management and prevent illness before it occurs.

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

[0079] Step 1:

[0080] The device acquires the user's health data. The input consists of biometric information from smartwatches and fitness trackers, including data such as heart rate, steps taken, and sleep patterns. This data is acquired using Bluetooth or Wi-Fi. After acquiring the data, the device transmits it to the server in real time.

[0081] Step 2:

[0082] The server receives data sent from the terminal. The received data is stored in a database and analyzed. This analysis uses a generative AI model, comparing the user's health status with past data and general health indicators. The output is an evaluation result based on the user's current health status.

[0083] Step 3:

[0084] The server generates personalized health advice based on the analysis results. The input is the result of the analyzed health status, and a generative AI model is used to generate prompt sentences. For example, advice such as "Due to lack of exercise, a 30-minute walk is recommended" might be created. The output of this process is specific health advice.

[0085] Step 4:

[0086] The server selects the appropriate medical institution based on the user's location information. Location data is obtained via GPS as input and compared against a list of nearby medical institutions. This selects the most suitable medical institution to provide to the user. The output is a list of recommended medical institutions.

[0087] Step 5:

[0088] The device notifies the user of generated health advice and recommended professional services. The input consists of health advice and healthcare information sent from the server, which is then provided to the user via push notifications on their smartphone. The output is the notification displayed on the user's device screen.

[0089] Step 6:

[0090] The device collects user feedback and sends it to the server. Input is user feedback data, including, for example, an evaluation of whether the advice provided was appropriate. The server receives this feedback and incorporates it into the next health advice generation process. Output is data aimed at improving system accuracy and user experience.

[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 modern society, individuals are expected to accurately understand their own health status and manage their health appropriately. However, many people are unable to practice proper health management due to the busyness of their daily lives and a lack of knowledge. Furthermore, a challenge remains in that they are unable to promptly seek appropriate medical attention when health problems arise. Therefore, there is a need for personalized advice tailored to each individual's health condition, as well as prompt and accurate referrals to medical institutions.

[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 receiving health information, means for generating individual health advice based on the received information, means for selecting and referring users to appropriate medical institutions based on the generated advice, means for monitoring the user's daily activities and providing activity modification advice based on supplementary information, and means for using location information to detect nearby specialized institutions and provide reservation support. This enables users to understand their own health status and manage their health appropriately, while also being able to quickly seek appropriate medical attention when problems arise.

[0096] "Health information" refers to a user's biometric and activity data, including data that indicates their individual health status, such as heart rate, steps taken, and sleep patterns.

[0097] "Individualized health advice" refers to specific advice provided based on collected health information, with the aim of improving or maintaining the user's health.

[0098] A "medical institution" is a facility or professional that provides services for the purpose of maintaining health or treating illness, and includes hospitals and clinics.

[0099] A "user terminal" is a device carried by a user, including smartphones and smart glasses, and is a device used for receiving and transmitting information.

[0100] "Ratings" refer to feedback from users indicating the usefulness and satisfaction level of health advice and medical institution recommendations they have received.

[0101] "Monitoring" is the process of continuously observing and recording changes in health information and daily activities.

[0102] "Activity modification advice" refers to specific guidance or suggestions provided to help users improve their daily activities.

[0103] "Location information" refers to data indicating the user's current location, and is coordinate information obtained using GPS technology.

[0104] "Appointment support" is a service that helps to efficiently handle the procedures necessary for visiting a medical institution, and it includes functions such as scheduling and confirming appointments on your behalf.

[0105] To implement this invention, a system is needed to receive, evaluate, and generate advice on health information. The server first receives health information transmitted from the user's terminal. This information includes data obtained from smartwatches and fitness trackers, specifically heart rate, steps taken, and sleep patterns. This data forms the basis for personalizing health information.

[0106] Next, the server analyzes the received health information. Using data analysis engines such as Python or TENSORFLOW®, it compares the collected data with historical data and general health indicators. This makes it possible to assess the user's current health status and generate personalized health advice. For example, if it is determined that the user is not getting enough exercise based on past data, specific advice such as "We recommend walking for 30 minutes every day" will be provided.

[0107] Furthermore, the server uses the user's location information to select an appropriate medical institution. Appointment support is provided to enable users to quickly visit nearby specialist institutions or clinics. GPS technology is used to obtain location information during this process. By receiving information about medical institutions, users can facilitate prompt and accurate health management.

[0108] The user terminal notifies the user of generated health advice and information on medical facilities. This function is implemented using devices such as smartphones and smart glasses, and users use this information to manage their daily health. Furthermore, the feedback provided by the user is used to generate advice for the next time. This improves the accuracy of the system and user satisfaction.

[0109] As a concrete example, a business person in their 40s using this system could receive advice on an appropriate exercise plan if their physical activity level is insufficient, and could also make a quick visit to a local clinic. An example of a prompt to input into the generating AI model would be: "Health data acquired today: 5,000 steps, 6 hours of sleep. Based on this, please suggest what kind of health advice to provide to the user."

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

[0111] Step 1:

[0112] The user's device collects health information.

[0113] As input, biometric data such as heart rate, steps taken, and sleep patterns are acquired from smartwatches and fitness trackers. As output, this health information is sent to the server. In this step, data is retrieved from the device in real time.

[0114] Step 2:

[0115] The server stores the received health information in a database and formats it for analysis.

[0116] The system receives health information obtained in Step 1 as input. This information is stored in a database and processed to convert it into a format usable by an analysis engine (e.g., TensorFlow). The output is analyzable data. The server performs data integrity checks during this process.

[0117] Step 3:

[0118] The server performs data analysis and generates personalized health advice.

[0119] The system uses formatted health information as input. A data analysis engine performs calculations, comparing the data with historical data and common health indicators, to evaluate the user's health status. Specific health advice is generated as output. This process includes determining the content of the advice using a generative AI model.

[0120] Step 4:

[0121] The server selects the appropriate medical facility based on location information.

[0122] As input, the system obtains the user's location information using GPS technology and references the generated health advice. Using the location information, it lists nearby medical institutions and obtains the data necessary for appointment scheduling. As output, information on medical institutions suitable for the user is selected. In this step, filtering appropriate facilities based on location information is crucial.

[0123] Step 5:

[0124] The user's device will notify them of health advice and information about medical facilities.

[0125] The system receives health advice and medical institution information sent from the server as input. The output is that this information is displayed to the user. The user terminal then uses its notification function to communicate this information to the user.

[0126] Step 6:

[0127] Collect user feedback and send it to the server.

[0128] As input, users provide evaluations regarding health advice and recommendations for medical institutions. As output, the feedback is stored on a server for use in generating future advice. This process includes appropriately capturing user evaluations and recording them in a database.

[0129] 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.

[0130] This invention combines a conventional system that acquires and analyzes health information in real time with an emotion engine that recognizes the user's emotional state. As a result, the health advice and medical referrals provided become more personalized.

[0131] First, the device acquires health information from the user. Simultaneously, the device uses its camera and microphone to collect emotional information such as facial expressions and tone of voice to determine the user's emotional state. This data is analyzed via an emotion engine to identify the user's current emotional state (e.g., normal, stressed, happy).

[0132] Next, the server integrates and analyzes the received health and emotional information. During this analysis, the emotional information is compared with the health information to help determine what kind of advice is most appropriate. For example, if the user's emotional state is stressed, the server will generate health advice that focuses on stress reduction.

[0133] The server also selects appropriate medical institutions based on the user's emotional state. If negative emotions are detected, it prioritizes referring users to mental health specialists and institutions offering counseling services. In this way, it selects the types of medical institutions to provide and creates a list.

[0134] The device then notifies the user of the generated health advice and information about selected medical institutions. This allows the user to manage their health appropriately in relation to their emotional state.

[0135] Furthermore, the system is designed to be continuously improved by collecting feedback from users. User comments and ratings are sent to the server and incorporated into subsequent analyses and suggestions. This enhances the system's accuracy and reliability, and provides more personalized support for each user.

[0136] This invention allows users to enjoy more sophisticated health management that takes their emotional state into account, enabling them to respond to situations more quickly and appropriately.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The device acquires health and emotional information. The user's smart device collects biometric data such as heart rate and steps. At the same time, the device captures emotional information from facial expressions and tone of voice through its camera and microphone.

[0140] Step 2:

[0141] The device sends data to the server. Acquired health and emotional information is sent to the server in real time. This prepares the server to perform analysis based on the latest data.

[0142] Step 3:

[0143] The server receives and stores the data. The server receives health and emotional information sent from the terminal and records it in a secure database.

[0144] Step 4:

[0145] The server analyzes health and emotional information. It uses an emotion engine to identify emotional states and analyzes them in combination with health information. For example, if a user is experiencing stress, the server analyzes the cause of that stress by relating it to health data.

[0146] Step 5:

[0147] The server generates personalized health advice. Based on the analysis results, it creates health advice that takes into account the user's physical and emotional state. For example, it might generate a recommendation such as, "You appear to be stressed, so please try some relaxation exercises."

[0148] Step 6:

[0149] The server selects appropriate medical institutions. Especially if the user's emotional state is unstable, it selects and lists mental health specialists who can provide the necessary support. This list is optimized based on the user's current location.

[0150] Step 7:

[0151] The device notifies the user. The generated health advice and healthcare information are sent to the user via the device. The user receives the notification and can use it to take further action.

[0152] Step 8:

[0153] Users provide feedback. They evaluate their satisfaction with and the effectiveness of the health advice and referrals to medical institutions they receive, and input their feedback into the device.

[0154] Step 9:

[0155] The server analyzes the feedback and uses it to generate advice for the next time. The collected feedback helps improve the system's accuracy and enhance the user experience.

[0156] (Example 2)

[0157] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0158] Conventional health information management systems have faced challenges in providing health advice that adequately considers the user's emotional state. Furthermore, they have been insufficient in selecting appropriate medical institutions that take into account real-time changes in emotional state, and in improving health advice based on individual user feedback.

[0159] 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.

[0160] In this invention, the server includes means for integrating and analyzing health information and emotional information, means for generating health advice based on the obtained analysis results, and means for selecting a medical institution considering the emotional information. This enables more precise health management and medical institution recommendations tailored to the user's individual emotional state.

[0161] "Health information" refers to data that indicates the user's physical condition, including physiological indicators such as heart rate, activity level, and blood pressure.

[0162] "Emotional information" refers to data that indicates the user's psychological state, including analysis results from facial expressions and voice.

[0163] A "device" refers to a device that directly collects and processes health and emotional information from users.

[0164] A "server" refers to a computer system that receives and analyzes data collected from terminals.

[0165] "Health advice" refers to guidance provided to users for maintaining and improving their health, based on analyzed health and emotional information.

[0166] "Medical institution" refers to a facility or professional that provides medical services.

[0167] "Feedback" refers to information provided by users indicating their experience and evaluation of the system, and is used to improve the service in the future.

[0168] This invention is a system that integrates and analyzes a user's health and emotional information to provide personalized health advice and information on medical institutions. The following hardware and software are used as embodiments of this system.

[0169] Device Settings: The device consists of personal devices such as smartphones and smartwatches. These devices acquire health information such as heart rate and activity levels using sensors. They also acquire the user's facial expressions and voice data using the camera and microphone built into the device. Facial expression analysis software captures the movement of facial muscles, and voice analysis software analyzes voice tone through the microphone.

[0170] Server Role: The server resides in the cloud and receives health and emotional information transmitted from terminals. It stores this information in a database and analyzes the data using a generative AI model. The AI ​​analysis engine integrates health and emotional information to generate personalized health advice for each user. It also uses emotional information to select appropriate medical institutions.

[0171] User Interaction: Users receive notifications from their devices and view generated health advice and information about healthcare facilities. Notifications are delivered visually or audibly through the device's UI (user interface). Furthermore, users input feedback into their devices, which is received by the server and used to improve the analysis process.

[0172] Example: For instance, if a user experiences stress during their morning commute, the device recognizes an increased heart rate and emotional patterns indicating anxiety. The server analyzes this data and sends notifications to the device, including advice on breathing techniques to reduce stress and referrals to nearby counseling facilities. This system allows users to receive immediate support tailored to their physical and mental state.

[0173] Example of a prompt:

[0174] "What is your current emotional state, and provide the most appropriate health advice based on that."

[0175] This invention aims to provide more personalized services to individual users by collecting user feedback and improving the system based on that feedback.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The device acquires the user's physical information (heart rate, activity level, etc.) using health sensors. It receives raw data from the sensors as input and generates processed health information as output. Specific operations include data acquisition from a smartwatch and noise reduction through signal processing.

[0179] Step 2:

[0180] The device collects user emotion information using a camera and microphone. Input is the user's facial image and voice tone, and output is the analyzed emotion information. The camera captures facial expressions, facial recognition software analyzes the image data, and voice recognition software processes the audio obtained from the microphone.

[0181] Step 3:

[0182] The device transmits acquired health and emotional information to a server. The input is health and emotional data stored on the device, and the output is data upload to a cloud server. Specifically, the device securely uploads the data via Wi-Fi or mobile data communication.

[0183] Step 4:

[0184] The server analyzes the received data and generates personalized health advice. It integrates health and emotional information received as input and generates health advice as output. It uses a generative AI model to analyze the data and predict recommended actions based on the user's current state.

[0185] Step 5:

[0186] The server selects appropriate medical institutions based on emotional information. The input is analyzed emotional information, and the output is a list of selected medical institutions. The system refers to a database of medical institutions and executes an algorithm to select mental health services as needed.

[0187] Step 6:

[0188] The device notifies the user of health advice and healthcare information received from the server. Input is notification data from the server, and output is visual or auditory notifications to the user. The device's UI displays the information and provides trackable links as needed.

[0189] Step 7:

[0190] Users enter feedback into their devices, which is then analyzed on a server. Input consists of user ratings or comments, while output is data used to generate improved health advice for future sessions. Specific actions include filling out a feedback form and sending captured data to the server.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] Modern health management systems often only provide advice based on health data, making it difficult to offer individualized support that takes into account the user's emotional state. Similarly, in brick-and-mortar stores, customer service and product recommendations often fail to consider emotions, resulting in low customer satisfaction. There is a need to address these challenges and deliver more personalized and accurate healthcare and customer service.

[0194] 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.

[0195] In this invention, the server includes a device for receiving health data, a device for analyzing the user's facial expressions and tone of voice to determine their emotional state, and a device for selecting and suggesting products or services based on the analyzed emotional state. This enables personalized health guidance and product / service suggestions that take into account both the user's health and emotional state.

[0196] "Health data" refers to information that indicates the user's physical condition, including measurements such as blood pressure, heart rate, and steps taken.

[0197] "Health guidance" is the process of generating personalized advice for lifestyle improvement and health maintenance based on received health data.

[0198] A "medical facility" refers to a hospital, clinic, counseling center, or other facility where users can visit for medical consultation or advice related to health guidance.

[0199] "Facial expressions and tone of voice" refer to nonverbal communication methods such as changes in facial expressions and tone and rhythm of voice, which are used to identify the emotional state of a user.

[0200] "Emotional state" refers to the psychological state a user is experiencing, such as stress, joy, or anger, and is inferred through analysis of facial expressions and tone of voice.

[0201] "Product or service recommendations" refer to activities that recommend products or services that are deemed optimal for the user based on their analyzed emotional state.

[0202] "User equipment" refers to electronic devices used by a user, such as computer terminals, smartphones, or tablet devices.

[0203] "Evaluation" refers to feedback from users regarding their reactions and satisfaction levels with the health guidance, products, and services provided.

[0204] "Based on analyzed emotional state" refers to a method of determining the next steps or options using the emotional state inferred from the user's facial expressions and tone of voice.

[0205] This invention provides an integrated system for health management and emotion analysis. The server first receives health data from the user's device. This health data includes data such as blood pressure, heart rate, and steps taken, which are acquired through sensors and user input.

[0206] Next, the camera and microphone on the user's device are used to capture the user's facial expressions and tone of voice in real time, and their emotional state is analyzed. A generative AI model running in the cloud is used for the emotional analysis, classifying the emotional state into categories such as "stressed," "happy," and "normal."

[0207] The server then integrates the received health data with the analyzed emotional state to generate personalized health guidance and product / service recommendations. For example, if the analysis indicates the user is stressed, it will suggest products and services with relaxation effects. It will also select medical facilities based on the emotional state and notify the user of the necessary facility information.

[0208] The hardware used will consist of common user devices such as smartphones and tablets. Sentiment analysis and data integration will be performed on servers in the cloud. Specifically, Azure's speech recognition API will be used for speech analysis, and OpenCV and TensorFlow will be used for image analysis.

[0209] For example, if a customer starts a conversation with a robot in the store and says, "I've been feeling stressed lately," the system can detect this and immediately suggest, "How about a relaxing herbal tea?" An example of the prompt would be as follows:

[0210] "When a customer starts speaking in front of the camera, the system captures their facial expressions and records their voice, sends the data to an emotion analysis engine, suggests products that match their emotional state, and communicates the suggestions to the customer."

[0211] This system improves the accuracy of individualized instruction and enhances personalized support based on user feedback.

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The device collects the user's health data. Inputs include numerical data such as blood pressure, heart rate, and steps, obtained through sensors or manual user input. This data is temporarily stored on the device and then sent to the server in the next step.

[0215] Step 2:

[0216] The user provides facial expressions and audio through the device, which then receives them. Input includes video data of facial expressions captured by the camera and audio data recorded by the microphone. The device converts this data into a format suitable for emotion analysis and sends it to the server in real time.

[0217] Step 3:

[0218] The server receives health and emotional data and stores it in a database. The input consists of video and audio data sent in the previous step, which is then passed to an emotion analysis model in the cloud for analysis. The model analyzes the voice and facial expressions and classifies the user's emotional state into categories such as "normal," "stressed," and "happy."

[0219] Step 4:

[0220] The server generates optimal health guidance and product or service recommendations based on analyzed emotional states and health data. The input consists of the analysis results and the user's health information, which the AI ​​algorithm uses to determine personalized guidance. These recommendations are then generated as output.

[0221] Step 5:

[0222] The server sends the generated health guidance and suggestions to the user's terminal. The input is the output from step 4. The terminal notifies the user of the received information via voice or screen display.

[0223] Step 6:

[0224] Users input their evaluations of the guidance and suggestions provided into a terminal. This input constitutes user feedback. This feedback is sent from the terminal to the server and stored in a database to improve the accuracy of future guidance and suggestions.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention is a system for providing personalized health advice to individual users and referring them to appropriate medical institutions. This system utilizes a server and terminals working together to continuously monitor the user's health status and provide optimal support.

[0242] First, the device acquires health information from the user. The device works in conjunction with smartwatches and fitness trackers to collect daily health data such as heart rate, steps taken, and sleep patterns. This data is transmitted to the server in real time.

[0243] Next, the server analyzes the received health information. Based on the collected data, the server assesses the user's health status and compares it to past data and general health indicators. Based on the results, the server generates personalized health advice. For example, if the user is not getting enough exercise, it might provide specific advice such as, "We recommend walking for 30 minutes every day."

[0244] In addition, the server selects a medical facility that matches the user's current health condition. The server takes the user's location into consideration to create and provide a list of nearby medical facilities and specialists. This step is crucial for enabling rapid access to medical care and helping users receive appropriate treatment.

[0245] The device then notifies the user of the generated health advice and recommended medical facilities. The user can then use this information to manage their own health. Furthermore, if the user provides feedback, the device receives it and sends it to the server, which uses this feedback to generate future health advice. This improves the overall accuracy of the system and user satisfaction.

[0246] In this way, users will be able to easily access advice and medical facilities based on their own health information. This system is expected to support daily health management and contribute to disease prevention.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] The device acquires health information. It collects heart rate, steps, and sleep data from the user's worn device (e.g., a smartwatch). This data is periodically stored on the device.

[0250] Step 2:

[0251] The device sends data to the server. The collected health information is transferred to the server via the internet. This transmission occurs in real time, ensuring the accuracy of the data.

[0252] Step 3:

[0253] The server receives health information and stores it in a database. The server securely stores the received data in the database in preparation for later analysis.

[0254] Step 4:

[0255] The server analyzes the data. It uses the latest health information to recognize patterns and detects anomalies by comparing them with past data. Machine learning algorithms are used for this.

[0256] Step 5:

[0257] The server generates personalized health advice. Based on the analysis results, it creates health advice tailored to the user's daily life. For example, it might provide specific instructions such as, "We recommend exercising three times a week."

[0258] Step 6:

[0259] The server generates a list of medical facilities. Considering the user's location and health status, it selects an appropriate medical facility in the vicinity.

[0260] Step 7:

[0261] The device notifies the user. It notifies the user's device of the generated health advice and information about healthcare facilities, allowing the user to review it.

[0262] Step 8:

[0263] The user provides feedback. They input an evaluation of the information received, and the device sends this to the server.

[0264] Step 9:

[0265] The server records feedback and uses it to generate advice for the next time. The recorded feedback is used to improve the system and enhance the user experience.

[0266] (Example 1)

[0267] 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."

[0268] In modern times, many people need to efficiently manage their daily health. However, it is not easy for users to accurately understand their own health status and receive appropriate advice. Furthermore, there is a need to quickly detect changes in health status and prompt users to seek medical attention when necessary. Conventional systems struggle to meet these needs, making the provision of more effective health management systems a challenge.

[0269] 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.

[0270] In this invention, the server includes means for acquiring health data from the user, means for transmitting the acquired data to a processing unit in real time, and means for performing data analysis in the processing unit and generating individualized health advice. This enables personalized advice based on the user's health condition and prompt access to medical facilities.

[0271] "Means of acquiring health data from users" refers to technologies used to collect information about a user's physical condition and activity from recording devices.

[0272] "Means of transmitting data to a processing device in real time" refers to a technology that immediately sends collected data to an analysis device, enabling analysis without delay.

[0273] "A means of performing data analysis and generating individualized health advice" refers to a technology that uses collected information to evaluate a user's health status and generate appropriate advice.

[0274] A "generative AI model" is a mathematical model that utilizes artificial intelligence in data analysis and advice generation to provide users with optimal feedback.

[0275] "A means of selecting an appropriate specialized institution that takes the user's location information into consideration" refers to technology that selects the most appropriate and easily accessible medical institution based on the user's geographical location.

[0276] "Notification methods" refer to technologies that send information from a server to a terminal and inform the user of that information.

[0277] "Methods for collecting feedback and incorporating it into future generation" refers to technologies that collect user responses as data and use it to improve and optimize future advice.

[0278] This invention is a system that provides personalized health advice to individual users and refers them to appropriate professional institutions. It primarily functions through the coordinated operation of a server and terminals, monitoring the user's health status to provide optimal support.

[0279] The device utilizes recording devices worn by the user, such as smartwatches and fitness trackers, to acquire daily health data such as heart rate, steps taken, and sleep patterns. This data is transmitted to a server in real time via Bluetooth or Wi-Fi.

[0280] When analyzing the received health data, the server utilizes a generative AI model. Specifically, it compares the user's data with past information and general health metrics to evaluate the user's health status. Based on this, the server generates personalized health advice. As an example of this prompt text, an instruction such as "Please generate specific health advice based on the user's latest health data" can be used. The generated advice provides the user with specific examples such as "You are lacking in exercise, so I recommend 30 minutes of walking every day."

[0281] Furthermore, the server selects and creates a list of appropriate local specialized institutions based on the user's location information. This enables the user to quickly access specialized institutions and easily receive necessary medical services as needed.

[0282] The terminal notifies the user of the generated health advice and information on the recommended specialized institutions. This notification is via push notifications on the smartphone, enabling the user to immediately check the information and reflect it in their life and activities.

[0283] When the user provides feedback to the system, the terminal sends this to the server. The server saves this feedback in the database and considers it when generating the next health advice. This iterative learning process can improve the overall accuracy and user satisfaction of the system.

[0284] With such a configuration, it is expected to support daily health management and prevent diseases in advance.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The device acquires the user's health data. The input consists of biometric information from smartwatches and fitness trackers, including data such as heart rate, steps taken, and sleep patterns. This data is acquired using Bluetooth or Wi-Fi. After acquiring the data, the device transmits it to the server in real time.

[0288] Step 2:

[0289] The server receives data sent from the terminal. The received data is stored in a database and analyzed. This analysis uses a generative AI model, comparing the user's health status with past data and general health indicators. The output is an evaluation result based on the user's current health status.

[0290] Step 3:

[0291] The server generates personalized health advice based on the analysis results. The input is the result of the analyzed health status, and a generative AI model is used to generate prompt sentences. For example, advice such as "Due to lack of exercise, a 30-minute walk is recommended" might be created. The output of this process is specific health advice.

[0292] Step 4:

[0293] The server selects the appropriate medical institution based on the user's location information. Location data is obtained via GPS as input and compared against a list of nearby medical institutions. This selects the most suitable medical institution to provide to the user. The output is a list of recommended medical institutions.

[0294] Step 5:

[0295] The device notifies the user of generated health advice and recommended professional services. The input consists of health advice and healthcare information sent from the server, which is then provided to the user via push notifications on their smartphone. The output is the notification displayed on the user's device screen.

[0296] Step 6:

[0297] The device collects user feedback and sends it to the server. Input is user feedback data, including, for example, an evaluation of whether the advice provided was appropriate. The server receives this feedback and incorporates it into the next health advice generation process. Output is data aimed at improving system accuracy and user experience.

[0298] (Application Example 1)

[0299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0300] In modern society, individuals are expected to accurately understand their own health status and manage their health appropriately. However, many people are unable to practice proper health management due to the busyness of their daily lives and a lack of knowledge. Furthermore, a challenge remains in that they are unable to promptly seek appropriate medical attention when health problems arise. Therefore, there is a need for personalized advice tailored to each individual's health condition, as well as prompt and accurate referrals to medical institutions.

[0301] 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.

[0302] In this invention, the server includes means for receiving health information, means for generating individual health advice based on the received information, means for selecting and referring users to appropriate medical institutions based on the generated advice, means for monitoring the user's daily activities and providing activity modification advice based on supplementary information, and means for using location information to detect nearby specialized institutions and provide reservation support. This enables users to understand their own health status and manage their health appropriately, while also being able to quickly seek appropriate medical attention when problems arise.

[0303] "Health information" refers to the user's biological data and activity data, which are data indicating individual health conditions including heart rate, number of steps, sleep status, etc.

[0304] "Individual health advice" is specific advice provided based on the collected health information for the purpose of improving and maintaining the user's health status.

[0305] "Medical institution" refers to a facility or professional that provides services for the purpose of health maintenance and disease treatment, including hospitals and clinics.

[0306] "User terminal" is a device carried by the user, including smartphones and smart glasses, and is a device for receiving and transmitting information.

[0307] "Evaluation" is feedback indicating the usefulness and satisfaction regarding the health advice received by the user and the recommendations of medical institutions.

[0308] "Monitoring" is a process of continuously observing and recording changes in health information and daily activities.

[0309] "Activity modification advice" is specific guidance or suggestions provided to assist in improving the user's daily activities.

[0310] "Location information" is data indicating the user's current location, which is coordinate information obtained using GPS technology.

[0311] "Reservation support" is a service for efficiently performing the procedures required for visiting a medical institution, and is a function that substitutes for adjusting the date and time and pre-checking.

[0312] To implement this invention, a system is needed to receive, evaluate, and generate advice on health information. The server first receives health information transmitted from the user's terminal. This information includes data obtained from smartwatches and fitness trackers, specifically heart rate, steps taken, and sleep patterns. This data forms the basis for personalizing health information.

[0313] Next, the server analyzes the received health information. Using data analysis engines such as Python or TensorFlow, it compares the collected data with historical data and general health indicators. This makes it possible to assess the user's current health status and generate personalized health advice. For example, if it is determined that the user is not getting enough exercise based on past data, specific advice such as "We recommend walking for 30 minutes every day" will be provided.

[0314] Furthermore, the server uses the user's location information to select an appropriate medical institution. Appointment support is provided to enable users to quickly visit nearby specialist institutions or clinics. GPS technology is used to obtain location information during this process. By receiving information about medical institutions, users can facilitate prompt and accurate health management.

[0315] The user terminal notifies the user of generated health advice and information on medical facilities. This function is implemented using devices such as smartphones and smart glasses, and users use this information to manage their daily health. Furthermore, the feedback provided by the user is used to generate advice for the next time. This improves the accuracy of the system and user satisfaction.

[0316] As a concrete example, a business person in their 40s using this system could receive advice on an appropriate exercise plan if their physical activity level is insufficient, and could also make a quick visit to a local clinic. An example of a prompt to input into the generating AI model would be: "Health data acquired today: 5,000 steps, 6 hours of sleep. Based on this, please suggest what kind of health advice to provide to the user."

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The user's device collects health information.

[0320] As input, biometric data such as heart rate, steps taken, and sleep patterns are acquired from smartwatches and fitness trackers. As output, this health information is sent to the server. In this step, data is retrieved from the device in real time.

[0321] Step 2:

[0322] The server stores the received health information in a database and formats it for analysis.

[0323] The system receives health information obtained in Step 1 as input. This information is stored in a database and processed to convert it into a format usable by an analysis engine (e.g., TensorFlow). The output is analyzable data. The server performs data integrity checks during this process.

[0324] Step 3:

[0325] The server performs data analysis and generates personalized health advice.

[0326] The system uses formatted health information as input. A data analysis engine performs calculations, comparing the data with historical data and common health indicators, to evaluate the user's health status. Specific health advice is generated as output. This process includes determining the content of the advice using a generative AI model.

[0327] Step 4:

[0328] The server selects the appropriate medical facility based on location information.

[0329] As input, the system obtains the user's location information using GPS technology and references the generated health advice. Using the location information, it lists nearby medical institutions and obtains the data necessary for appointment scheduling. As output, information on medical institutions suitable for the user is selected. In this step, filtering appropriate facilities based on location information is crucial.

[0330] Step 5:

[0331] The user's device will notify them of health advice and information about medical facilities.

[0332] The system receives health advice and medical institution information sent from the server as input. The output is that this information is displayed to the user. The user terminal then uses its notification function to communicate this information to the user.

[0333] Step 6:

[0334] Collect user feedback and send it to the server.

[0335] As input, users provide evaluations regarding health advice and recommendations for medical institutions. As output, the feedback is stored on a server for use in generating future advice. This process includes appropriately capturing user evaluations and recording them in a database.

[0336] 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.

[0337] This invention combines a conventional system that acquires and analyzes health information in real time with an emotion engine that recognizes the user's emotional state. As a result, the health advice and medical referrals provided become more personalized.

[0338] First, the device acquires health information from the user. Simultaneously, the device uses its camera and microphone to collect emotional information such as facial expressions and tone of voice to determine the user's emotional state. This data is analyzed via an emotion engine to identify the user's current emotional state (e.g., normal, stressed, happy).

[0339] Next, the server integrates and analyzes the received health and emotional information. During this analysis, the emotional information is compared with the health information to help determine what kind of advice is most appropriate. For example, if the user's emotional state is stressed, the server will generate health advice that focuses on stress reduction.

[0340] The server also selects appropriate medical institutions based on the user's emotional state. If negative emotions are detected, it prioritizes referring users to mental health specialists and institutions offering counseling services. In this way, it selects the types of medical institutions to provide and creates a list.

[0341] The device then notifies the user of the generated health advice and information about selected medical institutions. This allows the user to manage their health appropriately in relation to their emotional state.

[0342] Furthermore, the system is designed to be continuously improved by collecting feedback from users. User comments and ratings are sent to the server and incorporated into subsequent analyses and suggestions. This enhances the system's accuracy and reliability, and provides more personalized support for each user.

[0343] This invention allows users to enjoy more sophisticated health management that takes their emotional state into account, enabling them to respond to situations more quickly and appropriately.

[0344] The following describes the processing flow.

[0345] Step 1:

[0346] The device acquires health and emotional information. The user's smart device collects biometric data such as heart rate and steps. At the same time, the device captures emotional information from facial expressions and tone of voice through its camera and microphone.

[0347] Step 2:

[0348] The device sends data to the server. Acquired health and emotional information is sent to the server in real time. This prepares the server to perform analysis based on the latest data.

[0349] Step 3:

[0350] The server receives and stores the data. The server receives health and emotional information sent from the terminal and records it in a secure database.

[0351] Step 4:

[0352] The server analyzes health and emotional information. It uses an emotion engine to identify emotional states and analyzes them in combination with health information. For example, if a user is experiencing stress, the server analyzes the cause of that stress by relating it to health data.

[0353] Step 5:

[0354] The server generates personalized health advice. Based on the analysis results, it creates health advice that takes into account the user's physical and emotional state. For example, it might generate a recommendation such as, "You appear to be stressed, so please try some relaxation exercises."

[0355] Step 6:

[0356] The server selects appropriate medical institutions. Especially if the user's emotional state is unstable, it selects and lists mental health specialists who can provide the necessary support. This list is optimized based on the user's current location.

[0357] Step 7:

[0358] The device notifies the user. The generated health advice and healthcare information are sent to the user via the device. The user receives the notification and can use it to take further action.

[0359] Step 8:

[0360] Users provide feedback. They evaluate their satisfaction with and the effectiveness of the health advice and referrals to medical institutions they receive, and input their feedback into the device.

[0361] Step 9:

[0362] The server analyzes the feedback and uses it to generate advice for the next time. The collected feedback helps improve the system's accuracy and enhance the user experience.

[0363] (Example 2)

[0364] 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".

[0365] Conventional health information management systems have faced challenges in providing health advice that adequately considers the user's emotional state. Furthermore, they have been insufficient in selecting appropriate medical institutions that take into account real-time changes in emotional state, and in improving health advice based on individual user feedback.

[0366] 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.

[0367] In this invention, the server includes means for integrating and analyzing health information and emotional information, means for generating health advice based on the obtained analysis results, and means for selecting a medical institution considering the emotional information. This enables more precise health management and medical institution recommendations tailored to the user's individual emotional state.

[0368] "Health information" refers to data that indicates the user's physical condition, including physiological indicators such as heart rate, activity level, and blood pressure.

[0369] "Emotional information" refers to data that indicates the user's psychological state, including analysis results from facial expressions and voice.

[0370] A "device" refers to a device that directly collects and processes health and emotional information from users.

[0371] A "server" refers to a computer system that receives and analyzes data collected from terminals.

[0372] "Health advice" refers to guidance provided to users for maintaining and improving their health, based on analyzed health and emotional information.

[0373] "Medical institution" refers to a facility or professional that provides medical services.

[0374] "Feedback" refers to information provided by users indicating their experience and evaluation of the system, and is used to improve the service in the future.

[0375] This invention is a system that integrates and analyzes a user's health and emotional information to provide personalized health advice and information on medical institutions. The following hardware and software are used as embodiments of this system.

[0376] Device Settings: The device consists of personal devices such as smartphones and smartwatches. These devices acquire health information such as heart rate and activity levels using sensors. They also acquire the user's facial expressions and voice data using the camera and microphone built into the device. Facial expression analysis software captures the movement of facial muscles, and voice analysis software analyzes voice tone through the microphone.

[0377] Server Role: The server resides in the cloud and receives health and emotional information transmitted from terminals. It stores this information in a database and analyzes the data using a generative AI model. The AI ​​analysis engine integrates health and emotional information to generate personalized health advice for each user. It also uses emotional information to select appropriate medical institutions.

[0378] User Interaction: Users receive notifications from their devices and view generated health advice and information about healthcare facilities. Notifications are delivered visually or audibly through the device's UI (user interface). Furthermore, users input feedback into their devices, which is received by the server and used to improve the analysis process.

[0379] Example: For instance, if a user experiences stress during their morning commute, the device recognizes an increased heart rate and emotional patterns indicating anxiety. The server analyzes this data and sends notifications to the device, including advice on breathing techniques to reduce stress and referrals to nearby counseling facilities. This system allows users to receive immediate support tailored to their physical and mental state.

[0380] Example of a prompt:

[0381] "What is your current emotional state, and provide the most appropriate health advice based on that."

[0382] This invention aims to provide more personalized services to individual users by collecting user feedback and improving the system based on that feedback.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The device acquires the user's physical information (heart rate, activity level, etc.) using health sensors. It receives raw data from the sensors as input and generates processed health information as output. Specific operations include data acquisition from a smartwatch and noise reduction through signal processing.

[0386] Step 2:

[0387] The device collects user emotion information using a camera and microphone. Input is the user's facial image and voice tone, and output is the analyzed emotion information. The camera captures facial expressions, facial recognition software analyzes the image data, and voice recognition software processes the audio obtained from the microphone.

[0388] Step 3:

[0389] The device transmits acquired health and emotional information to a server. The input is health and emotional data stored on the device, and the output is data upload to a cloud server. Specifically, the device securely uploads the data via Wi-Fi or mobile data communication.

[0390] Step 4:

[0391] The server analyzes the received data and generates personalized health advice. It integrates health and emotional information received as input and generates health advice as output. It uses a generative AI model to analyze the data and predict recommended actions based on the user's current state.

[0392] Step 5:

[0393] The server selects appropriate medical institutions based on emotional information. The input is analyzed emotional information, and the output is a list of selected medical institutions. The system refers to a database of medical institutions and executes an algorithm to select mental health services as needed.

[0394] Step 6:

[0395] The device notifies the user of health advice and healthcare information received from the server. Input is notification data from the server, and output is visual or auditory notifications to the user. The device's UI displays the information and provides trackable links as needed.

[0396] Step 7:

[0397] Users enter feedback into their devices, which is then analyzed on a server. Input consists of user ratings or comments, while output is data used to generate improved health advice for future sessions. Specific actions include filling out a feedback form and sending captured data to the server.

[0398] (Application Example 2)

[0399] 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."

[0400] Modern health management systems often only provide advice based on health data, making it difficult to offer individualized support that takes into account the user's emotional state. Similarly, in brick-and-mortar stores, customer service and product recommendations often fail to consider emotions, resulting in low customer satisfaction. There is a need to address these challenges and deliver more personalized and accurate healthcare and customer service.

[0401] 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.

[0402] In this invention, the server includes a device for receiving health data, a device for analyzing the user's facial expressions and tone of voice to determine their emotional state, and a device for selecting and suggesting products or services based on the analyzed emotional state. This enables personalized health guidance and product / service suggestions that take into account both the user's health and emotional state.

[0403] "Health data" refers to information that indicates the user's physical condition, including measurements such as blood pressure, heart rate, and steps taken.

[0404] "Health guidance" is the process of generating personalized advice for lifestyle improvement and health maintenance based on received health data.

[0405] A "medical facility" refers to a hospital, clinic, counseling center, or other facility where users can visit for medical consultation or advice related to health guidance.

[0406] "Facial expressions and tone of voice" refer to nonverbal communication methods such as changes in facial expressions and tone and rhythm of voice, which are used to identify the emotional state of a user.

[0407] "Emotional state" refers to the psychological state a user is experiencing, such as stress, joy, or anger, and is inferred through analysis of facial expressions and tone of voice.

[0408] "Product or service recommendations" refer to activities that recommend products or services that are deemed optimal for the user based on their analyzed emotional state.

[0409] "User equipment" refers to electronic devices used by a user, such as computer terminals, smartphones, or tablet devices.

[0410] "Evaluation" refers to feedback from users regarding their reactions and satisfaction levels with the health guidance, products, and services provided.

[0411] "Based on analyzed emotional state" refers to a method of determining the next steps or options using the emotional state inferred from the user's facial expressions and tone of voice.

[0412] This invention provides an integrated system for health management and emotion analysis. The server first receives health data from the user's device. This health data includes data such as blood pressure, heart rate, and steps taken, which are acquired through sensors and user input.

[0413] Next, the camera and microphone on the user's device are used to capture the user's facial expressions and tone of voice in real time, and their emotional state is analyzed. A generative AI model running in the cloud is used for the emotional analysis, classifying the emotional state into categories such as "stressed," "happy," and "normal."

[0414] The server then integrates the received health data with the analyzed emotional state to generate personalized health guidance and product / service recommendations. For example, if the analysis indicates the user is stressed, it will suggest products and services with relaxation effects. It will also select medical facilities based on the emotional state and notify the user of the necessary facility information.

[0415] The hardware used will consist of common user devices such as smartphones and tablets. Sentiment analysis and data integration will be performed on servers in the cloud. Specifically, Azure's speech recognition API will be used for speech analysis, and OpenCV and TensorFlow will be used for image analysis.

[0416] For example, if a customer starts a conversation with a robot in the store and says, "I've been feeling stressed lately," the system can detect this and immediately suggest, "How about a relaxing herbal tea?" An example of the prompt would be as follows:

[0417] "When a customer starts speaking in front of the camera, the system captures their facial expressions and records their voice, sends the data to an emotion analysis engine, suggests products that match their emotional state, and communicates the suggestions to the customer."

[0418] This system improves the accuracy of individualized instruction and enhances personalized support based on user feedback.

[0419] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0420] Step 1:

[0421] The device collects the user's health data. Inputs include numerical data such as blood pressure, heart rate, and steps, obtained through sensors or manual user input. This data is temporarily stored on the device and then sent to the server in the next step.

[0422] Step 2:

[0423] The user provides facial expressions and audio through the device, which then receives them. Input includes video data of facial expressions captured by the camera and audio data recorded by the microphone. The device converts this data into a format suitable for emotion analysis and sends it to the server in real time.

[0424] Step 3:

[0425] The server receives health and emotional data and stores it in a database. The input consists of video and audio data sent in the previous step, which is then passed to an emotion analysis model in the cloud for analysis. The model analyzes the voice and facial expressions and classifies the user's emotional state into categories such as "normal," "stressed," and "happy."

[0426] Step 4:

[0427] The server generates optimal health guidance and product or service recommendations based on analyzed emotional states and health data. The input consists of the analysis results and the user's health information, which the AI ​​algorithm uses to determine personalized guidance. These recommendations are then generated as output.

[0428] Step 5:

[0429] The server sends the generated health guidance and suggestions to the user's terminal. The input is the output from step 4. The terminal notifies the user of the received information via voice or screen display.

[0430] Step 6:

[0431] Users input their evaluations of the guidance and suggestions provided into a terminal. This input constitutes user feedback. This feedback is sent from the terminal to the server and stored in a database to improve the accuracy of future guidance and suggestions.

[0432] 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.

[0433] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] 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.

[0435] [Third Embodiment]

[0436] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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".

[0448] This invention is a system for providing personalized health advice to individual users and referring them to appropriate medical institutions. This system utilizes a server and terminals working together to continuously monitor the user's health status and provide optimal support.

[0449] First, the device acquires health information from the user. The device works in conjunction with smartwatches and fitness trackers to collect daily health data such as heart rate, steps taken, and sleep patterns. This data is transmitted to the server in real time.

[0450] Next, the server analyzes the received health information. Based on the collected data, the server assesses the user's health status and compares it to past data and general health indicators. Based on the results, the server generates personalized health advice. For example, if the user is not getting enough exercise, it might provide specific advice such as, "We recommend walking for 30 minutes every day."

[0451] In addition, the server selects a medical facility that matches the user's current health condition. The server takes the user's location into consideration to create and provide a list of nearby medical facilities and specialists. This step is crucial for enabling rapid access to medical care and helping users receive appropriate treatment.

[0452] The device then notifies the user of the generated health advice and recommended medical facilities. The user can then use this information to manage their own health. Furthermore, if the user provides feedback, the device receives it and sends it to the server, which uses this feedback to generate future health advice. This improves the overall accuracy of the system and user satisfaction.

[0453] In this way, users will be able to easily access advice and medical facilities based on their own health information. This system is expected to support daily health management and contribute to disease prevention.

[0454] The following describes the processing flow.

[0455] Step 1:

[0456] The device acquires health information. It collects heart rate, steps, and sleep data from the user's worn device (e.g., a smartwatch). This data is periodically stored on the device.

[0457] Step 2:

[0458] The device sends data to the server. The collected health information is transferred to the server via the internet. This transmission occurs in real time, ensuring the accuracy of the data.

[0459] Step 3:

[0460] The server receives health information and stores it in a database. The server securely stores the received data in the database in preparation for later analysis.

[0461] Step 4:

[0462] The server analyzes the data. It uses the latest health information to recognize patterns and detects anomalies by comparing them with past data. Machine learning algorithms are used for this.

[0463] Step 5:

[0464] The server generates personalized health advice. Based on the analysis results, it creates health advice tailored to the user's daily life. For example, it might provide specific instructions such as, "We recommend exercising three times a week."

[0465] Step 6:

[0466] The server generates a list of medical facilities. Considering the user's location and health status, it selects an appropriate medical facility in the vicinity.

[0467] Step 7:

[0468] The device notifies the user. It notifies the user's device of the generated health advice and information about healthcare facilities, allowing the user to review it.

[0469] Step 8:

[0470] The user provides feedback. They input an evaluation of the information received, and the device sends this to the server.

[0471] Step 9:

[0472] The server records feedback and uses it to generate advice for the next time. The recorded feedback is used to improve the system and enhance the user experience.

[0473] (Example 1)

[0474] 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."

[0475] In modern times, many people need to efficiently manage their daily health. However, it is not easy for users to accurately understand their own health status and receive appropriate advice. Furthermore, there is a need to quickly detect changes in health status and prompt users to seek medical attention when necessary. Conventional systems struggle to meet these needs, making the provision of more effective health management systems a challenge.

[0476] 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.

[0477] In this invention, the server includes means for acquiring health data from the user, means for transmitting the acquired data to a processing unit in real time, and means for performing data analysis in the processing unit and generating individualized health advice. This enables personalized advice based on the user's health condition and prompt access to medical facilities.

[0478] "Means of acquiring health data from users" refers to technologies used to collect information about a user's physical condition and activity from recording devices.

[0479] "Means of transmitting data to a processing device in real time" refers to a technology that immediately sends collected data to an analysis device, enabling analysis without delay.

[0480] "A means of performing data analysis and generating individualized health advice" refers to a technology that uses collected information to evaluate a user's health status and generate appropriate advice.

[0481] A "generative AI model" is a mathematical model that utilizes artificial intelligence in data analysis and advice generation to provide users with optimal feedback.

[0482] "A means of selecting an appropriate specialized institution that takes the user's location information into consideration" refers to technology that selects the most appropriate and easily accessible medical institution based on the user's geographical location.

[0483] "Notification methods" refer to technologies that send information from a server to a terminal and inform the user of that information.

[0484] "Methods for collecting feedback and incorporating it into future generation" refers to technologies that collect user responses as data and use it to improve and optimize future advice.

[0485] This invention is a system that provides personalized health advice to individual users and refers them to appropriate professional institutions. It primarily functions through the coordinated operation of a server and terminals, monitoring the user's health status to provide optimal support.

[0486] The device utilizes recording devices worn by the user, such as smartwatches and fitness trackers, to acquire daily health data such as heart rate, steps taken, and sleep patterns. This data is transmitted to a server in real time via Bluetooth or Wi-Fi.

[0487] The server utilizes a generative AI model to analyze the received health data. Specifically, it compares the user's data with historical information and general health indicators to assess the user's health status. Based on this, the server generates personalized health advice. An example of this prompt could be the instruction, "Generate specific health advice based on the user's latest health data." The generated advice would provide the user with specific examples, such as, "You are not getting enough exercise, so we recommend a 30-minute walk every day."

[0488] Furthermore, the server selects and creates a list of appropriate nearby medical institutions based on the user's location. This allows users to quickly access medical institutions and more easily receive the necessary medical services.

[0489] The device notifies the user of generated health advice and information on recommended professional organizations. This notification is delivered via smartphone push notifications, allowing the user to immediately check the information and reflect it in their daily life and activities.

[0490] When a user provides feedback to the system, the terminal sends it to the server. The server stores this feedback in a database and considers it when generating health advice in the future. This iterative learning process can improve the overall accuracy of the system and user satisfaction.

[0491] This configuration is expected to support daily health management and prevent illness before it occurs.

[0492] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0493] Step 1:

[0494] The device acquires the user's health data. The input consists of biometric information from smartwatches and fitness trackers, including data such as heart rate, steps taken, and sleep patterns. This data is acquired using Bluetooth or Wi-Fi. After acquiring the data, the device transmits it to the server in real time.

[0495] Step 2:

[0496] The server receives data sent from the terminal. The received data is stored in a database and analyzed. This analysis uses a generative AI model, comparing the user's health status with past data and general health indicators. The output is an evaluation result based on the user's current health status.

[0497] Step 3:

[0498] The server generates personalized health advice based on the analysis results. The input is the result of the analyzed health status, and a generative AI model is used to generate prompt sentences. For example, advice such as "Due to lack of exercise, a 30-minute walk is recommended" might be created. The output of this process is specific health advice.

[0499] Step 4:

[0500] The server selects the appropriate medical institution based on the user's location information. Location data is obtained via GPS as input and compared against a list of nearby medical institutions. This selects the most suitable medical institution to provide to the user. The output is a list of recommended medical institutions.

[0501] Step 5:

[0502] The device notifies the user of generated health advice and recommended professional services. The input consists of health advice and healthcare information sent from the server, which is then provided to the user via push notifications on their smartphone. The output is the notification displayed on the user's device screen.

[0503] Step 6:

[0504] The device collects user feedback and sends it to the server. Input is user feedback data, including, for example, an evaluation of whether the advice provided was appropriate. The server receives this feedback and incorporates it into the next health advice generation process. Output is data aimed at improving system accuracy and user experience.

[0505] (Application Example 1)

[0506] 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."

[0507] In modern society, individuals are expected to accurately understand their own health status and manage their health appropriately. However, many people are unable to practice proper health management due to the busyness of their daily lives and a lack of knowledge. Furthermore, a challenge remains in that they are unable to promptly seek appropriate medical attention when health problems arise. Therefore, there is a need for personalized advice tailored to each individual's health condition, as well as prompt and accurate referrals to medical institutions.

[0508] 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.

[0509] In this invention, the server includes means for receiving health information, means for generating individual health advice based on the received information, means for selecting and referring users to appropriate medical institutions based on the generated advice, means for monitoring the user's daily activities and providing activity modification advice based on supplementary information, and means for using location information to detect nearby specialized institutions and provide reservation support. This enables users to understand their own health status and manage their health appropriately, while also being able to quickly seek appropriate medical attention when problems arise.

[0510] "Health information" refers to a user's biometric and activity data, including data that indicates their individual health status, such as heart rate, steps taken, and sleep patterns.

[0511] "Individualized health advice" refers to specific advice provided based on collected health information, with the aim of improving or maintaining the user's health.

[0512] A "medical institution" is a facility or professional that provides services for the purpose of maintaining health or treating illness, and includes hospitals and clinics.

[0513] A "user terminal" is a device carried by a user, including smartphones and smart glasses, and is a device used for receiving and transmitting information.

[0514] "Ratings" refer to feedback from users indicating the usefulness and satisfaction level of health advice and medical institution recommendations they have received.

[0515] "Monitoring" is the process of continuously observing and recording changes in health information and daily activities.

[0516] "Activity modification advice" refers to specific guidance or suggestions provided to help users improve their daily activities.

[0517] "Location information" refers to data indicating the user's current location, and is coordinate information obtained using GPS technology.

[0518] "Appointment support" is a service that helps to efficiently handle the procedures necessary for visiting a medical institution, and it includes functions such as scheduling and confirming appointments on your behalf.

[0519] To implement this invention, a system is needed to receive, evaluate, and generate advice on health information. The server first receives health information transmitted from the user's terminal. This information includes data obtained from smartwatches and fitness trackers, specifically heart rate, steps taken, and sleep patterns. This data forms the basis for personalizing health information.

[0520] Next, the server analyzes the received health information. Using data analysis engines such as Python or TensorFlow, it compares the collected data with historical data and general health indicators. This makes it possible to assess the user's current health status and generate personalized health advice. For example, if it is determined that the user is not getting enough exercise based on past data, specific advice such as "We recommend walking for 30 minutes every day" will be provided.

[0521] Furthermore, the server uses the user's location information to select an appropriate medical institution. Appointment support is provided to enable users to quickly visit nearby specialist institutions or clinics. GPS technology is used to obtain location information during this process. By receiving information about medical institutions, users can facilitate prompt and accurate health management.

[0522] The user terminal notifies the user of generated health advice and information on medical facilities. This function is implemented using devices such as smartphones and smart glasses, and users use this information to manage their daily health. Furthermore, the feedback provided by the user is used to generate advice for the next time. This improves the accuracy of the system and user satisfaction.

[0523] As a concrete example, a business person in their 40s using this system could receive advice on an appropriate exercise plan if their physical activity level is insufficient, and could also make a quick visit to a local clinic. An example of a prompt to input into the generating AI model would be: "Health data acquired today: 5,000 steps, 6 hours of sleep. Based on this, please suggest what kind of health advice to provide to the user."

[0524] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0525] Step 1:

[0526] The user's device collects health information.

[0527] As input, biometric data such as heart rate, steps taken, and sleep patterns are acquired from smartwatches and fitness trackers. As output, this health information is sent to the server. In this step, data is retrieved from the device in real time.

[0528] Step 2:

[0529] The server stores the received health information in a database and formats it for analysis.

[0530] The system receives health information obtained in Step 1 as input. This information is stored in a database and processed to convert it into a format usable by an analysis engine (e.g., TensorFlow). The output is analyzable data. The server performs data integrity checks during this process.

[0531] Step 3:

[0532] The server performs data analysis and generates personalized health advice.

[0533] The system uses formatted health information as input. A data analysis engine performs calculations, comparing the data with historical data and common health indicators, to evaluate the user's health status. Specific health advice is generated as output. This process includes determining the content of the advice using a generative AI model.

[0534] Step 4:

[0535] The server selects the appropriate medical facility based on location information.

[0536] As input, the system obtains the user's location information using GPS technology and references the generated health advice. Using the location information, it lists nearby medical institutions and obtains the data necessary for appointment scheduling. As output, information on medical institutions suitable for the user is selected. In this step, filtering appropriate facilities based on location information is crucial.

[0537] Step 5:

[0538] The user's device will notify them of health advice and information about medical facilities.

[0539] The system receives health advice and medical institution information sent from the server as input. The output is that this information is displayed to the user. The user terminal then uses its notification function to communicate this information to the user.

[0540] Step 6:

[0541] Collect user feedback and send it to the server.

[0542] As input, users provide evaluations regarding health advice and recommendations for medical institutions. As output, the feedback is stored on a server for use in generating future advice. This process includes appropriately capturing user evaluations and recording them in a database.

[0543] 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.

[0544] This invention combines a conventional system that acquires and analyzes health information in real time with an emotion engine that recognizes the user's emotional state. As a result, the health advice and medical referrals provided become more personalized.

[0545] First, the device acquires health information from the user. Simultaneously, the device uses its camera and microphone to collect emotional information such as facial expressions and tone of voice to determine the user's emotional state. This data is analyzed via an emotion engine to identify the user's current emotional state (e.g., normal, stressed, happy).

[0546] Next, the server integrates and analyzes the received health and emotional information. During this analysis, the emotional information is compared with the health information to help determine what kind of advice is most appropriate. For example, if the user's emotional state is stressed, the server will generate health advice that focuses on stress reduction.

[0547] The server also selects appropriate medical institutions based on the user's emotional state. If negative emotions are detected, it prioritizes referring users to mental health specialists and institutions offering counseling services. In this way, it selects the types of medical institutions to provide and creates a list.

[0548] The device then notifies the user of the generated health advice and information about selected medical institutions. This allows the user to manage their health appropriately in relation to their emotional state.

[0549] Furthermore, the system is designed to be continuously improved by collecting feedback from users. User comments and ratings are sent to the server and incorporated into subsequent analyses and suggestions. This enhances the system's accuracy and reliability, and provides more personalized support for each user.

[0550] This invention allows users to enjoy more sophisticated health management that takes their emotional state into account, enabling them to respond to situations more quickly and appropriately.

[0551] The following describes the processing flow.

[0552] Step 1:

[0553] The device acquires health and emotional information. The user's smart device collects biometric data such as heart rate and steps. At the same time, the device captures emotional information from facial expressions and tone of voice through its camera and microphone.

[0554] Step 2:

[0555] The device sends data to the server. Acquired health and emotional information is sent to the server in real time. This prepares the server to perform analysis based on the latest data.

[0556] Step 3:

[0557] The server receives and stores the data. The server receives health and emotional information sent from the terminal and records it in a secure database.

[0558] Step 4:

[0559] The server analyzes health and emotional information. It uses an emotion engine to identify emotional states and analyzes them in combination with health information. For example, if a user is experiencing stress, the server analyzes the cause of that stress by relating it to health data.

[0560] Step 5:

[0561] The server generates personalized health advice. Based on the analysis results, it creates health advice that takes into account the user's physical and emotional state. For example, it might generate a recommendation such as, "You appear to be stressed, so please try some relaxation exercises."

[0562] Step 6:

[0563] The server selects appropriate medical institutions. Especially if the user's emotional state is unstable, it selects and lists mental health specialists who can provide the necessary support. This list is optimized based on the user's current location.

[0564] Step 7:

[0565] The device notifies the user. The generated health advice and healthcare information are sent to the user via the device. The user receives the notification and can use it to take further action.

[0566] Step 8:

[0567] Users provide feedback. They evaluate their satisfaction with and the effectiveness of the health advice and referrals to medical institutions they receive, and input their feedback into the device.

[0568] Step 9:

[0569] The server analyzes the feedback and uses it to generate advice for the next time. The collected feedback helps improve the system's accuracy and enhance the user experience.

[0570] (Example 2)

[0571] 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."

[0572] Conventional health information management systems have faced challenges in providing health advice that adequately considers the user's emotional state. Furthermore, they have been insufficient in selecting appropriate medical institutions that take into account real-time changes in emotional state, and in improving health advice based on individual user feedback.

[0573] 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.

[0574] In this invention, the server includes means for integrating and analyzing health information and emotional information, means for generating health advice based on the obtained analysis results, and means for selecting a medical institution considering the emotional information. This enables more precise health management and medical institution recommendations tailored to the user's individual emotional state.

[0575] "Health information" refers to data that indicates the user's physical condition, including physiological indicators such as heart rate, activity level, and blood pressure.

[0576] "Emotional information" refers to data that indicates the user's psychological state, including analysis results from facial expressions and voice.

[0577] A "device" refers to a device that directly collects and processes health and emotional information from users.

[0578] A "server" refers to a computer system that receives and analyzes data collected from terminals.

[0579] "Health advice" refers to guidance provided to users for maintaining and improving their health, based on analyzed health and emotional information.

[0580] "Medical institution" refers to a facility or professional that provides medical services.

[0581] "Feedback" refers to information provided by users indicating their experience and evaluation of the system, and is used to improve the service in the future.

[0582] This invention is a system that integrates and analyzes a user's health and emotional information to provide personalized health advice and information on medical institutions. The following hardware and software are used as embodiments of this system.

[0583] Device Settings: The device consists of personal devices such as smartphones and smartwatches. These devices acquire health information such as heart rate and activity levels using sensors. They also acquire the user's facial expressions and voice data using the camera and microphone built into the device. Facial expression analysis software captures the movement of facial muscles, and voice analysis software analyzes voice tone through the microphone.

[0584] Server Role: The server resides in the cloud and receives health and emotional information transmitted from terminals. It stores this information in a database and analyzes the data using a generative AI model. The AI ​​analysis engine integrates health and emotional information to generate personalized health advice for each user. It also uses emotional information to select appropriate medical institutions.

[0585] User Interaction: Users receive notifications from their devices and view generated health advice and information about healthcare facilities. Notifications are delivered visually or audibly through the device's UI (user interface). Furthermore, users input feedback into their devices, which is received by the server and used to improve the analysis process.

[0586] Example: For instance, if a user experiences stress during their morning commute, the device recognizes an increased heart rate and emotional patterns indicating anxiety. The server analyzes this data and sends notifications to the device, including advice on breathing techniques to reduce stress and referrals to nearby counseling facilities. This system allows users to receive immediate support tailored to their physical and mental state.

[0587] Example of a prompt:

[0588] "What is your current emotional state, and provide the most appropriate health advice based on that."

[0589] This invention aims to provide more personalized services to individual users by collecting user feedback and improving the system based on that feedback.

[0590] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0591] Step 1:

[0592] The device acquires the user's physical information (heart rate, activity level, etc.) using health sensors. It receives raw data from the sensors as input and generates processed health information as output. Specific operations include data acquisition from a smartwatch and noise reduction through signal processing.

[0593] Step 2:

[0594] The device collects user emotion information using a camera and microphone. Input is the user's facial image and voice tone, and output is the analyzed emotion information. The camera captures facial expressions, facial recognition software analyzes the image data, and voice recognition software processes the audio obtained from the microphone.

[0595] Step 3:

[0596] The device transmits acquired health and emotional information to a server. The input is health and emotional data stored on the device, and the output is data upload to a cloud server. Specifically, the device securely uploads the data via Wi-Fi or mobile data communication.

[0597] Step 4:

[0598] The server analyzes the received data and generates personalized health advice. It integrates health and emotional information received as input and generates health advice as output. It uses a generative AI model to analyze the data and predict recommended actions based on the user's current state.

[0599] Step 5:

[0600] The server selects appropriate medical institutions based on emotional information. The input is analyzed emotional information, and the output is a list of selected medical institutions. The system refers to a database of medical institutions and executes an algorithm to select mental health services as needed.

[0601] Step 6:

[0602] The device notifies the user of health advice and healthcare information received from the server. Input is notification data from the server, and output is visual or auditory notifications to the user. The device's UI displays the information and provides trackable links as needed.

[0603] Step 7:

[0604] Users enter feedback into their devices, which is then analyzed on a server. Input consists of user ratings or comments, while output is data used to generate improved health advice for future sessions. Specific actions include filling out a feedback form and sending captured data to the server.

[0605] (Application Example 2)

[0606] 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."

[0607] Modern health management systems often only provide advice based on health data, making it difficult to offer individualized support that takes into account the user's emotional state. Similarly, in brick-and-mortar stores, customer service and product recommendations often fail to consider emotions, resulting in low customer satisfaction. There is a need to address these challenges and deliver more personalized and accurate healthcare and customer service.

[0608] 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.

[0609] In this invention, the server includes a device for receiving health data, a device for analyzing the user's facial expressions and tone of voice to determine their emotional state, and a device for selecting and suggesting products or services based on the analyzed emotional state. This enables personalized health guidance and product / service suggestions that take into account both the user's health and emotional state.

[0610] "Health data" refers to information that indicates the user's physical condition, including measurements such as blood pressure, heart rate, and steps taken.

[0611] "Health guidance" is the process of generating personalized advice for lifestyle improvement and health maintenance based on received health data.

[0612] A "medical facility" refers to a hospital, clinic, counseling center, or other facility where users can visit for medical consultation or advice related to health guidance.

[0613] "Facial expressions and tone of voice" refer to nonverbal communication methods such as changes in facial expressions and tone and rhythm of voice, which are used to identify the emotional state of a user.

[0614] "Emotional state" refers to the psychological state a user is experiencing, such as stress, joy, or anger, and is inferred through analysis of facial expressions and tone of voice.

[0615] "Product or service recommendations" refer to activities that recommend products or services that are deemed optimal for the user based on their analyzed emotional state.

[0616] "User equipment" refers to electronic devices used by a user, such as computer terminals, smartphones, or tablet devices.

[0617] "Evaluation" refers to feedback from users regarding their reactions and satisfaction levels with the health guidance, products, and services provided.

[0618] "Based on analyzed emotional state" refers to a method of determining the next steps or options using the emotional state inferred from the user's facial expressions and tone of voice.

[0619] This invention provides an integrated system for health management and emotion analysis. The server first receives health data from the user's device. This health data includes data such as blood pressure, heart rate, and steps taken, which are acquired through sensors and user input.

[0620] Next, the camera and microphone on the user's device are used to capture the user's facial expressions and tone of voice in real time, and their emotional state is analyzed. A generative AI model running in the cloud is used for the emotional analysis, classifying the emotional state into categories such as "stressed," "happy," and "normal."

[0621] The server then integrates the received health data with the analyzed emotional state to generate personalized health guidance and product / service recommendations. For example, if the analysis indicates the user is stressed, it will suggest products and services with relaxation effects. It will also select medical facilities based on the emotional state and notify the user of the necessary facility information.

[0622] The hardware used will consist of common user devices such as smartphones and tablets. Sentiment analysis and data integration will be performed on servers in the cloud. Specifically, Azure's speech recognition API will be used for speech analysis, and OpenCV and TensorFlow will be used for image analysis.

[0623] For example, if a customer starts a conversation with a robot in the store and says, "I've been feeling stressed lately," the system can detect this and immediately suggest, "How about a relaxing herbal tea?" An example of the prompt would be as follows:

[0624] "When a customer starts speaking in front of the camera, the system captures their facial expressions and records their voice, sends the data to an emotion analysis engine, suggests products that match their emotional state, and communicates the suggestions to the customer."

[0625] This system improves the accuracy of individualized instruction and enhances personalized support based on user feedback.

[0626] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0627] Step 1:

[0628] The device collects the user's health data. Inputs include numerical data such as blood pressure, heart rate, and steps, obtained through sensors or manual user input. This data is temporarily stored on the device and then sent to the server in the next step.

[0629] Step 2:

[0630] The user provides facial expressions and audio through the device, which then receives them. Input includes video data of facial expressions captured by the camera and audio data recorded by the microphone. The device converts this data into a format suitable for emotion analysis and sends it to the server in real time.

[0631] Step 3:

[0632] The server receives health and emotional data and stores it in a database. The input consists of video and audio data sent in the previous step, which is then passed to an emotion analysis model in the cloud for analysis. The model analyzes the voice and facial expressions and classifies the user's emotional state into categories such as "normal," "stressed," and "happy."

[0633] Step 4:

[0634] The server generates optimal health guidance and product or service recommendations based on analyzed emotional states and health data. The input consists of the analysis results and the user's health information, which the AI ​​algorithm uses to determine personalized guidance. These recommendations are then generated as output.

[0635] Step 5:

[0636] The server sends the generated health guidance and suggestions to the user's terminal. The input is the output from step 4. The terminal notifies the user of the received information via voice or screen display.

[0637] Step 6:

[0638] Users input their evaluations of the guidance and suggestions provided into a terminal. This input constitutes user feedback. This feedback is sent from the terminal to the server and stored in a database to improve the accuracy of future guidance and suggestions.

[0639] 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.

[0640] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0641] 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.

[0642] [Fourth Embodiment]

[0643] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0644] 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.

[0645] 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).

[0646] 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.

[0647] 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.

[0648] 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).

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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".

[0656] This invention is a system for providing personalized health advice to individual users and referring them to appropriate medical institutions. This system utilizes a server and terminals working together to continuously monitor the user's health status and provide optimal support.

[0657] First, the device acquires health information from the user. The device works in conjunction with smartwatches and fitness trackers to collect daily health data such as heart rate, steps taken, and sleep patterns. This data is transmitted to the server in real time.

[0658] Next, the server analyzes the received health information. Based on the collected data, the server assesses the user's health status and compares it to past data and general health indicators. Based on the results, the server generates personalized health advice. For example, if the user is not getting enough exercise, it might provide specific advice such as, "We recommend walking for 30 minutes every day."

[0659] In addition, the server selects a medical facility that matches the user's current health condition. The server takes the user's location into consideration to create and provide a list of nearby medical facilities and specialists. This step is crucial for enabling rapid access to medical care and helping users receive appropriate treatment.

[0660] The device then notifies the user of the generated health advice and recommended medical facilities. The user can then use this information to manage their own health. Furthermore, if the user provides feedback, the device receives it and sends it to the server, which uses this feedback to generate future health advice. This improves the overall accuracy of the system and user satisfaction.

[0661] In this way, users will be able to easily access advice and medical facilities based on their own health information. This system is expected to support daily health management and contribute to disease prevention.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The device acquires health information. It collects heart rate, steps, and sleep data from the user's worn device (e.g., a smartwatch). This data is periodically stored on the device.

[0665] Step 2:

[0666] The device sends data to the server. The collected health information is transferred to the server via the internet. This transmission occurs in real time, ensuring the accuracy of the data.

[0667] Step 3:

[0668] The server receives health information and stores it in a database. The server securely stores the received data in the database in preparation for later analysis.

[0669] Step 4:

[0670] The server analyzes the data. It uses the latest health information to recognize patterns and detects anomalies by comparing them with past data. Machine learning algorithms are used for this.

[0671] Step 5:

[0672] The server generates personalized health advice. Based on the analysis results, it creates health advice tailored to the user's daily life. For example, it might provide specific instructions such as, "We recommend exercising three times a week."

[0673] Step 6:

[0674] The server generates a list of medical facilities. Considering the user's location and health status, it selects an appropriate medical facility in the vicinity.

[0675] Step 7:

[0676] The device notifies the user. It notifies the user's device of the generated health advice and information about healthcare facilities, allowing the user to review it.

[0677] Step 8:

[0678] The user provides feedback. They input an evaluation of the information received, and the device sends this to the server.

[0679] Step 9:

[0680] The server records feedback and uses it to generate advice for the next time. The recorded feedback is used to improve the system and enhance the user experience.

[0681] (Example 1)

[0682] 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".

[0683] In modern times, many people need to efficiently manage their daily health. However, it is not easy for users to accurately understand their own health status and receive appropriate advice. Furthermore, there is a need to quickly detect changes in health status and prompt users to seek medical attention when necessary. Conventional systems struggle to meet these needs, making the provision of more effective health management systems a challenge.

[0684] 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.

[0685] In this invention, the server includes means for acquiring health data from the user, means for transmitting the acquired data to a processing unit in real time, and means for performing data analysis in the processing unit and generating individualized health advice. This enables personalized advice based on the user's health condition and prompt access to medical facilities.

[0686] "Means of acquiring health data from users" refers to technologies used to collect information about a user's physical condition and activity from recording devices.

[0687] "Means of transmitting data to a processing device in real time" refers to a technology that immediately sends collected data to an analysis device, enabling analysis without delay.

[0688] "A means of performing data analysis and generating individualized health advice" refers to a technology that uses collected information to evaluate a user's health status and generate appropriate advice.

[0689] A "generative AI model" is a mathematical model that utilizes artificial intelligence in data analysis and advice generation to provide users with optimal feedback.

[0690] "A means of selecting an appropriate specialized institution that takes the user's location information into consideration" refers to technology that selects the most appropriate and easily accessible medical institution based on the user's geographical location.

[0691] "Notification methods" refer to technologies that send information from a server to a terminal and inform the user of that information.

[0692] "Methods for collecting feedback and incorporating it into future generation" refers to technologies that collect user responses as data and use it to improve and optimize future advice.

[0693] This invention is a system that provides personalized health advice to individual users and refers them to appropriate professional institutions. It primarily functions through the coordinated operation of a server and terminals, monitoring the user's health status to provide optimal support.

[0694] The device utilizes recording devices worn by the user, such as smartwatches and fitness trackers, to acquire daily health data such as heart rate, steps taken, and sleep patterns. This data is transmitted to a server in real time via Bluetooth or Wi-Fi.

[0695] The server utilizes a generative AI model to analyze the received health data. Specifically, it compares the user's data with historical information and general health indicators to assess the user's health status. Based on this, the server generates personalized health advice. An example of this prompt could be the instruction, "Generate specific health advice based on the user's latest health data." The generated advice would provide the user with specific examples, such as, "You are not getting enough exercise, so we recommend a 30-minute walk every day."

[0696] Furthermore, the server selects and creates a list of appropriate nearby medical institutions based on the user's location. This allows users to quickly access medical institutions and more easily receive the necessary medical services.

[0697] The device notifies the user of generated health advice and information on recommended professional organizations. This notification is delivered via smartphone push notifications, allowing the user to immediately check the information and reflect it in their daily life and activities.

[0698] When a user provides feedback to the system, the terminal sends it to the server. The server stores this feedback in a database and considers it when generating health advice in the future. This iterative learning process can improve the overall accuracy of the system and user satisfaction.

[0699] This configuration is expected to support daily health management and prevent illness before it occurs.

[0700] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0701] Step 1:

[0702] The device acquires the user's health data. The input consists of biometric information from smartwatches and fitness trackers, including data such as heart rate, steps taken, and sleep patterns. This data is acquired using Bluetooth or Wi-Fi. After acquiring the data, the device transmits it to the server in real time.

[0703] Step 2:

[0704] The server receives data sent from the terminal. The received data is stored in a database and analyzed. This analysis uses a generative AI model, comparing the user's health status with past data and general health indicators. The output is an evaluation result based on the user's current health status.

[0705] Step 3:

[0706] The server generates personalized health advice based on the analysis results. The input is the result of the analyzed health status, and a generative AI model is used to generate prompt sentences. For example, advice such as "Due to lack of exercise, a 30-minute walk is recommended" might be created. The output of this process is specific health advice.

[0707] Step 4:

[0708] The server selects the appropriate medical institution based on the user's location information. Location data is obtained via GPS as input and compared against a list of nearby medical institutions. This selects the most suitable medical institution to provide to the user. The output is a list of recommended medical institutions.

[0709] Step 5:

[0710] The device notifies the user of generated health advice and recommended professional services. The input consists of health advice and healthcare information sent from the server, which is then provided to the user via push notifications on their smartphone. The output is the notification displayed on the user's device screen.

[0711] Step 6:

[0712] The device collects user feedback and sends it to the server. Input is user feedback data, including, for example, an evaluation of whether the advice provided was appropriate. The server receives this feedback and incorporates it into the next health advice generation process. Output is data aimed at improving system accuracy and user experience.

[0713] (Application Example 1)

[0714] 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".

[0715] In modern society, individuals are expected to accurately understand their own health status and manage their health appropriately. However, many people are unable to practice proper health management due to the busyness of their daily lives and a lack of knowledge. Furthermore, a challenge remains in that they are unable to promptly seek appropriate medical attention when health problems arise. Therefore, there is a need for personalized advice tailored to each individual's health condition, as well as prompt and accurate referrals to medical institutions.

[0716] 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.

[0717] In this invention, the server includes means for receiving health information, means for generating individual health advice based on the received information, means for selecting and referring users to appropriate medical institutions based on the generated advice, means for monitoring the user's daily activities and providing activity modification advice based on supplementary information, and means for using location information to detect nearby specialized institutions and provide reservation support. This enables users to understand their own health status and manage their health appropriately, while also being able to quickly seek appropriate medical attention when problems arise.

[0718] "Health information" refers to a user's biometric and activity data, including data that indicates their individual health status, such as heart rate, steps taken, and sleep patterns.

[0719] "Individualized health advice" refers to specific advice provided based on collected health information, with the aim of improving or maintaining the user's health.

[0720] A "medical institution" is a facility or professional that provides services for the purpose of maintaining health or treating illness, and includes hospitals and clinics.

[0721] A "user terminal" is a device carried by a user, including smartphones and smart glasses, and is a device used for receiving and transmitting information.

[0722] "Ratings" refer to feedback from users indicating the usefulness and satisfaction level of health advice and medical institution recommendations they have received.

[0723] "Monitoring" is the process of continuously observing and recording changes in health information and daily activities.

[0724] "Activity modification advice" refers to specific guidance or suggestions provided to help users improve their daily activities.

[0725] "Location information" refers to data indicating the user's current location, and is coordinate information obtained using GPS technology.

[0726] "Appointment support" is a service that helps to efficiently handle the procedures necessary for visiting a medical institution, and it includes functions such as scheduling and confirming appointments on your behalf.

[0727] To implement this invention, a system is needed to receive, evaluate, and generate advice on health information. The server first receives health information transmitted from the user's terminal. This information includes data obtained from smartwatches and fitness trackers, specifically heart rate, steps taken, and sleep patterns. This data forms the basis for personalizing health information.

[0728] Next, the server analyzes the received health information. Using data analysis engines such as Python or TensorFlow, it compares the collected data with historical data and general health indicators. This makes it possible to assess the user's current health status and generate personalized health advice. For example, if it is determined that the user is not getting enough exercise based on past data, specific advice such as "We recommend walking for 30 minutes every day" will be provided.

[0729] Furthermore, the server uses the user's location information to select an appropriate medical institution. Appointment support is provided to enable users to quickly visit nearby specialist institutions or clinics. GPS technology is used to obtain location information during this process. By receiving information about medical institutions, users can facilitate prompt and accurate health management.

[0730] The user terminal notifies the user of generated health advice and information on medical facilities. This function is implemented using devices such as smartphones and smart glasses, and users use this information to manage their daily health. Furthermore, the feedback provided by the user is used to generate advice for the next time. This improves the accuracy of the system and user satisfaction.

[0731] As a concrete example, a business person in their 40s using this system could receive advice on an appropriate exercise plan if their physical activity level is insufficient, and could also make a quick visit to a local clinic. An example of a prompt to input into the generating AI model would be: "Health data acquired today: 5,000 steps, 6 hours of sleep. Based on this, please suggest what kind of health advice to provide to the user."

[0732] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0733] Step 1:

[0734] The user's device collects health information.

[0735] As input, biometric data such as heart rate, steps taken, and sleep patterns are acquired from smartwatches and fitness trackers. As output, this health information is sent to the server. In this step, data is retrieved from the device in real time.

[0736] Step 2:

[0737] The server stores the received health information in a database and formats it for analysis.

[0738] The system receives health information obtained in Step 1 as input. This information is stored in a database and processed to convert it into a format usable by an analysis engine (e.g., TensorFlow). The output is analyzable data. The server performs data integrity checks during this process.

[0739] Step 3:

[0740] The server performs data analysis and generates personalized health advice.

[0741] The system uses formatted health information as input. A data analysis engine performs calculations, comparing the data with historical data and common health indicators, to evaluate the user's health status. Specific health advice is generated as output. This process includes determining the content of the advice using a generative AI model.

[0742] Step 4:

[0743] The server selects the appropriate medical facility based on location information.

[0744] As input, the system obtains the user's location information using GPS technology and references the generated health advice. Using the location information, it lists nearby medical institutions and obtains the data necessary for appointment scheduling. As output, information on medical institutions suitable for the user is selected. In this step, filtering appropriate facilities based on location information is crucial.

[0745] Step 5:

[0746] The user's device will notify them of health advice and information about medical facilities.

[0747] The system receives health advice and medical institution information sent from the server as input. The output is that this information is displayed to the user. The user terminal then uses its notification function to communicate this information to the user.

[0748] Step 6:

[0749] Collect user feedback and send it to the server.

[0750] As input, users provide evaluations regarding health advice and recommendations for medical institutions. As output, the feedback is stored on a server for use in generating future advice. This process includes appropriately capturing user evaluations and recording them in a database.

[0751] 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.

[0752] This invention combines a conventional system that acquires and analyzes health information in real time with an emotion engine that recognizes the user's emotional state. As a result, the health advice and medical referrals provided become more personalized.

[0753] First, the device acquires health information from the user. Simultaneously, the device uses its camera and microphone to collect emotional information such as facial expressions and tone of voice to determine the user's emotional state. This data is analyzed via an emotion engine to identify the user's current emotional state (e.g., normal, stressed, happy).

[0754] Next, the server integrates and analyzes the received health and emotional information. During this analysis, the emotional information is compared with the health information to help determine what kind of advice is most appropriate. For example, if the user's emotional state is stressed, the server will generate health advice that focuses on stress reduction.

[0755] The server also selects appropriate medical institutions based on the user's emotional state. If negative emotions are detected, it prioritizes referring users to mental health specialists and institutions offering counseling services. In this way, it selects the types of medical institutions to provide and creates a list.

[0756] The device then notifies the user of the generated health advice and information about selected medical institutions. This allows the user to manage their health appropriately in relation to their emotional state.

[0757] Furthermore, the system is designed to be continuously improved by collecting feedback from users. User comments and ratings are sent to the server and incorporated into subsequent analyses and suggestions. This enhances the system's accuracy and reliability, and provides more personalized support for each user.

[0758] This invention allows users to enjoy more sophisticated health management that takes their emotional state into account, enabling them to respond to situations more quickly and appropriately.

[0759] The following describes the processing flow.

[0760] Step 1:

[0761] The device acquires health and emotional information. The user's smart device collects biometric data such as heart rate and steps. At the same time, the device captures emotional information from facial expressions and tone of voice through its camera and microphone.

[0762] Step 2:

[0763] The device sends data to the server. Acquired health and emotional information is sent to the server in real time. This prepares the server to perform analysis based on the latest data.

[0764] Step 3:

[0765] The server receives and stores the data. The server receives health and emotional information sent from the terminal and records it in a secure database.

[0766] Step 4:

[0767] The server analyzes health and emotional information. It uses an emotion engine to identify emotional states and analyzes them in combination with health information. For example, if a user is experiencing stress, the server analyzes the cause of that stress by relating it to health data.

[0768] Step 5:

[0769] The server generates personalized health advice. Based on the analysis results, it creates health advice that takes into account the user's physical and emotional state. For example, it might generate a recommendation such as, "You appear to be stressed, so please try some relaxation exercises."

[0770] Step 6:

[0771] The server selects appropriate medical institutions. Especially if the user's emotional state is unstable, it selects and lists mental health specialists who can provide the necessary support. This list is optimized based on the user's current location.

[0772] Step 7:

[0773] The device notifies the user. The generated health advice and healthcare information are sent to the user via the device. The user receives the notification and can use it to take further action.

[0774] Step 8:

[0775] Users provide feedback. They evaluate their satisfaction with and the effectiveness of the health advice and referrals to medical institutions they receive, and input their feedback into the device.

[0776] Step 9:

[0777] The server analyzes the feedback and uses it to generate advice for the next time. The collected feedback helps improve the system's accuracy and enhance the user experience.

[0778] (Example 2)

[0779] 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".

[0780] Conventional health information management systems have faced challenges in providing health advice that adequately considers the user's emotional state. Furthermore, they have been insufficient in selecting appropriate medical institutions that take into account real-time changes in emotional state, and in improving health advice based on individual user feedback.

[0781] 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.

[0782] In this invention, the server includes means for integrating and analyzing health information and emotional information, means for generating health advice based on the obtained analysis results, and means for selecting a medical institution considering the emotional information. This enables more precise health management and medical institution recommendations tailored to the user's individual emotional state.

[0783] "Health information" refers to data that indicates the user's physical condition, including physiological indicators such as heart rate, activity level, and blood pressure.

[0784] "Emotional information" refers to data that indicates the user's psychological state, including analysis results from facial expressions and voice.

[0785] A "device" refers to a device that directly collects and processes health and emotional information from users.

[0786] A "server" refers to a computer system that receives and analyzes data collected from terminals.

[0787] "Health advice" refers to guidance provided to users for maintaining and improving their health, based on analyzed health and emotional information.

[0788] "Medical institution" refers to a facility or professional that provides medical services.

[0789] "Feedback" refers to information provided by users indicating their experience and evaluation of the system, and is used to improve the service in the future.

[0790] This invention is a system that integrates and analyzes a user's health and emotional information to provide personalized health advice and information on medical institutions. The following hardware and software are used as embodiments of this system.

[0791] Device Settings: The device consists of personal devices such as smartphones and smartwatches. These devices acquire health information such as heart rate and activity levels using sensors. They also acquire the user's facial expressions and voice data using the camera and microphone built into the device. Facial expression analysis software captures the movement of facial muscles, and voice analysis software analyzes voice tone through the microphone.

[0792] Server Role: The server resides in the cloud and receives health and emotional information transmitted from terminals. It stores this information in a database and analyzes the data using a generative AI model. The AI ​​analysis engine integrates health and emotional information to generate personalized health advice for each user. It also uses emotional information to select appropriate medical institutions.

[0793] User Interaction: Users receive notifications from their devices and view generated health advice and information about healthcare facilities. Notifications are delivered visually or audibly through the device's UI (user interface). Furthermore, users input feedback into their devices, which is received by the server and used to improve the analysis process.

[0794] Example: For instance, if a user experiences stress during their morning commute, the device recognizes an increased heart rate and emotional patterns indicating anxiety. The server analyzes this data and sends notifications to the device, including advice on breathing techniques to reduce stress and referrals to nearby counseling facilities. This system allows users to receive immediate support tailored to their physical and mental state.

[0795] Example of a prompt:

[0796] "What is your current emotional state, and provide the most appropriate health advice based on that."

[0797] This invention aims to provide more personalized services to individual users by collecting user feedback and improving the system based on that feedback.

[0798] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0799] Step 1:

[0800] The device acquires the user's physical information (heart rate, activity level, etc.) using health sensors. It receives raw data from the sensors as input and generates processed health information as output. Specific operations include data acquisition from a smartwatch and noise reduction through signal processing.

[0801] Step 2:

[0802] The device collects user emotion information using a camera and microphone. Input is the user's facial image and voice tone, and output is the analyzed emotion information. The camera captures facial expressions, facial recognition software analyzes the image data, and voice recognition software processes the audio obtained from the microphone.

[0803] Step 3:

[0804] The device transmits acquired health and emotional information to a server. The input is health and emotional data stored on the device, and the output is data upload to a cloud server. Specifically, the device securely uploads the data via Wi-Fi or mobile data communication.

[0805] Step 4:

[0806] The server analyzes the received data and generates personalized health advice. It integrates health and emotional information received as input and generates health advice as output. It uses a generative AI model to analyze the data and predict recommended actions based on the user's current state.

[0807] Step 5:

[0808] The server selects appropriate medical institutions based on emotional information. The input is analyzed emotional information, and the output is a list of selected medical institutions. The system refers to a database of medical institutions and executes an algorithm to select mental health services as needed.

[0809] Step 6:

[0810] The device notifies the user of health advice and healthcare information received from the server. Input is notification data from the server, and output is visual or auditory notifications to the user. The device's UI displays the information and provides trackable links as needed.

[0811] Step 7:

[0812] Users enter feedback into their devices, which is then analyzed on a server. Input consists of user ratings or comments, while output is data used to generate improved health advice for future sessions. Specific actions include filling out a feedback form and sending captured data to the server.

[0813] (Application Example 2)

[0814] 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".

[0815] Modern health management systems often only provide advice based on health data, making it difficult to offer individualized support that takes into account the user's emotional state. Similarly, in brick-and-mortar stores, customer service and product recommendations often fail to consider emotions, resulting in low customer satisfaction. There is a need to address these challenges and deliver more personalized and accurate healthcare and customer service.

[0816] 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.

[0817] In this invention, the server includes a device for receiving health data, a device for analyzing the user's facial expressions and tone of voice to determine their emotional state, and a device for selecting and suggesting products or services based on the analyzed emotional state. This enables personalized health guidance and product / service suggestions that take into account both the user's health and emotional state.

[0818] "Health data" refers to information that indicates the user's physical condition, including measurements such as blood pressure, heart rate, and steps taken.

[0819] "Health guidance" is the process of generating personalized advice for lifestyle improvement and health maintenance based on received health data.

[0820] A "medical facility" refers to a hospital, clinic, counseling center, or other facility where users can visit for medical consultation or advice related to health guidance.

[0821] "Facial expressions and tone of voice" refer to nonverbal communication methods such as changes in facial expressions and tone and rhythm of voice, which are used to identify the emotional state of a user.

[0822] "Emotional state" refers to the psychological state a user is experiencing, such as stress, joy, or anger, and is inferred through analysis of facial expressions and tone of voice.

[0823] "Product or service recommendations" refer to activities that recommend products or services that are deemed optimal for the user based on their analyzed emotional state.

[0824] "User equipment" refers to electronic devices used by a user, such as computer terminals, smartphones, or tablet devices.

[0825] "Evaluation" refers to feedback from users regarding their reactions and satisfaction levels with the health guidance, products, and services provided.

[0826] "Based on analyzed emotional state" refers to a method of determining the next steps or options using the emotional state inferred from the user's facial expressions and tone of voice.

[0827] This invention provides an integrated system for health management and emotion analysis. The server first receives health data from the user's device. This health data includes data such as blood pressure, heart rate, and steps taken, which are acquired through sensors and user input.

[0828] Next, the camera and microphone on the user's device are used to capture the user's facial expressions and tone of voice in real time, and their emotional state is analyzed. A generative AI model running in the cloud is used for the emotional analysis, classifying the emotional state into categories such as "stressed," "happy," and "normal."

[0829] The server then integrates the received health data with the analyzed emotional state to generate personalized health guidance and product / service recommendations. For example, if the analysis indicates the user is stressed, it will suggest products and services with relaxation effects. It will also select medical facilities based on the emotional state and notify the user of the necessary facility information.

[0830] The hardware used will consist of common user devices such as smartphones and tablets. Sentiment analysis and data integration will be performed on servers in the cloud. Specifically, Azure's speech recognition API will be used for speech analysis, and OpenCV and TensorFlow will be used for image analysis.

[0831] For example, if a customer starts a conversation with a robot in the store and says, "I've been feeling stressed lately," the system can detect this and immediately suggest, "How about a relaxing herbal tea?" An example of the prompt would be as follows:

[0832] "When a customer starts speaking in front of the camera, the system captures their facial expressions and records their voice, sends the data to an emotion analysis engine, suggests products that match their emotional state, and communicates the suggestions to the customer."

[0833] This system improves the accuracy of individualized instruction and enhances personalized support based on user feedback.

[0834] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0835] Step 1:

[0836] The device collects the user's health data. Inputs include numerical data such as blood pressure, heart rate, and steps, obtained through sensors or manual user input. This data is temporarily stored on the device and then sent to the server in the next step.

[0837] Step 2:

[0838] The user provides facial expressions and audio through the device, which then receives them. Input includes video data of facial expressions captured by the camera and audio data recorded by the microphone. The device converts this data into a format suitable for emotion analysis and sends it to the server in real time.

[0839] Step 3:

[0840] The server receives health and emotional data and stores it in a database. The input consists of video and audio data sent in the previous step, which is then passed to an emotion analysis model in the cloud for analysis. The model analyzes the voice and facial expressions and classifies the user's emotional state into categories such as "normal," "stressed," and "happy."

[0841] Step 4:

[0842] The server generates optimal health guidance and product or service recommendations based on analyzed emotional states and health data. The input consists of the analysis results and the user's health information, which the AI ​​algorithm uses to determine personalized guidance. These recommendations are then generated as output.

[0843] Step 5:

[0844] The server sends the generated health guidance and suggestions to the user's terminal. The input is the output from step 4. The terminal notifies the user of the received information via voice or screen display.

[0845] Step 6:

[0846] Users input their evaluations of the guidance and suggestions provided into a terminal. This input constitutes user feedback. This feedback is sent from the terminal to the server and stored in a database to improve the accuracy of future guidance and suggestions.

[0847] 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.

[0848] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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."

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] The following is further disclosed regarding the embodiments described above.

[0869] (Claim 1)

[0870] Means of receiving health information,

[0871] A means for generating personalized health advice based on received information,

[0872] A means of selecting and referring appropriate medical institutions based on the generated advice,

[0873] A means of notifying the user terminal of information about the selected medical institution,

[0874] A means of collecting user feedback and incorporating it into the next generation of advice,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, which adjusts health advice in consideration of feedback on the analysis of health status.

[0878] (Claim 3)

[0879] The system according to claim 1, which detects changes in health status in real time and promptly prompts the user to seek medical attention as needed.

[0880] "Example 1"

[0881] (Claim 1)

[0882] Means of obtaining health data from users,

[0883] A means for transmitting acquired data to a processing unit in real time,

[0884] A means for performing data analysis in a processing device and generating individual health advice,

[0885] A means of comparing health indicators using generative AI models and customizing advice,

[0886] Based on the generated advice, a means of selecting an appropriate professional organization that takes the user's location information into consideration,

[0887] A means of notifying the user terminal of information from the selected specialized organization,

[0888] A means of collecting user feedback and incorporating it into the next generation,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, which optimizes the next health advice taking into account the analysis results and feedback.

[0892] (Claim 3)

[0893] The system according to claim 1, which detects changes in a user's biometric information in real time and promptly suggests that the user seek medical attention from a specialist.

[0894] "Application Example 1"

[0895] (Claim 1)

[0896] Means of receiving health information,

[0897] A means for generating personalized health advice based on received information,

[0898] A means of selecting and referring appropriate medical institutions based on the generated advice,

[0899] A means of notifying the user terminal of information about the selected medical institution,

[0900] A means of collecting user feedback and incorporating it into the next generation of advice,

[0901] A means of monitoring the user's daily activities and providing activity modification advice based on supplementary information,

[0902] A method that utilizes location information to detect nearby specialized agencies and provide reservation support,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, which adjusts health advice in consideration of feedback on the analysis of health status.

[0906] (Claim 3)

[0907] The system according to claim 1, which detects changes in health status in real time and promptly prompts the user to seek medical attention as needed.

[0908] "Example 2 of combining an emotion engine"

[0909] (Claim 1)

[0910] A terminal for acquiring health information,

[0911] A terminal that analyzes the user's facial expressions and voice to collect emotional information,

[0912] A server that receives data integrating acquired health information and emotional information,

[0913] A means for generating appropriate health advice based on received data,

[0914] A method for selecting and referring medical institutions while considering the user's emotional information,

[0915] A means of notifying the user terminal of the selected medical institution information,

[0916] A means of collecting user feedback and incorporating it into the next generation of advice,

[0917] A system that includes this.

[0918] (Claim 2)

[0919] The system according to claim 1, which adjusts health advice in consideration of feedback on the analysis of health information and emotional information.

[0920] (Claim 3)

[0921] The system according to claim 1, which analyzes health information and emotional information in real time and promptly prompts the user to seek medical attention as needed.

[0922] "Application example 2 when combining with an emotional engine"

[0923] (Claim 1)

[0924] A device that receives health data,

[0925] A device that generates individualized health guidance based on received data,

[0926] A device that selects and refers to an appropriate medical facility based on the generated guidance,

[0927] A device that analyzes the user's facial expressions and tone of voice to determine their emotional state,

[0928] A device that selects and proposes products or services based on analyzed emotional states,

[0929] A device that notifies the user's device of the selected medical facility and the proposed product or service information,

[0930] A device that collects user feedback and incorporates it into the generation of future guidance and suggestions,

[0931] A system that includes this.

[0932] (Claim 2)

[0933] The system according to claim 1, which adjusts health guidance and suggestions in consideration of feedback on the analysis of health status and emotional state.

[0934] (Claim 3)

[0935] The system according to claim 1, which detects changes in health and emotional state in real time and promptly prompts the user to seek medical attention as needed. [Explanation of Symbols]

[0936] 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 of receiving health information, A means for generating personalized health advice based on received information, A means of selecting and referring appropriate medical institutions based on the generated advice, A means of notifying the user terminal of information about the selected medical institution, A means of collecting user feedback and incorporating it into the next generation of advice, A system that includes this.

2. The system according to claim 1, which adjusts health advice in consideration of feedback on the analysis of health status.

3. The system according to claim 1, which detects changes in health status in real time and promptly prompts the user to seek medical attention as needed.

Images