system

Abstract

We provide the system. [Solution] Information input means and An analytical means that analyzes the received information and estimates multiple medical conditions that can be associated with the symptoms, A generation means that generates corresponding treatment methods and advice on visiting a medical institution based on the medical condition estimated by the analysis means, A management tool for managing vaccination schedules and setting reminders, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 modern society, although the health management of children is very important, it is difficult for parents to make a judgment to visit a medical institution at an appropriate timing. Also, it is not easy to manage the schedule of preventive vaccinations and to avoid forgetting the schedule of regular health check-ups in a busy daily life. There is a demand for a system that enables parents to accurately grasp the health status of their children and receive appropriate medical support.

Means for Solving the Problems

[0005] The present invention includes an information input means for collecting data on a child's health and an analysis means for analyzing the received information. The analysis means includes a generation means for estimating multiple medical conditions associated with symptoms from the collected information and generating treatment methods and advice on visiting a medical institution based on these estimations. Furthermore, by providing a management means for managing vaccination schedules and setting reminders, the invention realizes a system that allows parents to easily manage their child's health.

[0006] "Information input means" refers to a device or interface for users to input information about a child's physical condition or symptoms.

[0007] "Analysis means" refers to a device or program for analyzing received symptom information and identifying multiple related medical conditions.

[0008] "Generating means" refers to a device or program that creates appropriate treatment methods and advice on seeking medical attention based on the medical condition identified by the analysis means.

[0009] "Management means" refers to a device or process for managing and setting reminders for vaccination and routine health checkup schedules.

[0010] A "system" is a comprehensive device or program that supports the health management of children through the coordinated operation of various means of information input, analysis, generation, and management. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The present invention is an AI healthcare assistant system for supporting the health management of children, and is implemented by combining information input means, analysis means, generation means, and management means. The embodiments thereof are described in detail below.

[0033] Users input basic information and current symptoms of their child using a dedicated terminal or application. This input is received by the terminal via an information input device, and the information is sent to the cloud or a local server.

[0034] The information that reaches the server is processed by analytical tools. Based on this information, the server uses machine learning models and pre-programmed rule-based engines to identify multiple medical conditions related to the child's symptoms. For example, if there is a fever and cough, it may increase the likelihood of influenza or a common cold.

[0035] Based on the analysis results, a generation system installed on the server generates advice for the user regarding appropriate countermeasures and the need for medical consultation. For example, it suggests home remedies for mild symptoms and recommends visiting a medical institution if the symptoms are severe.

[0036] Furthermore, the server's management system handles vaccination schedules and calculates the next vaccination date. This information is registered as a reminder and notified to the user via their device at the designated time. For example, as the scheduled MMR vaccination date approaches, the user is notified to encourage them to prepare for the vaccination.

[0037] This system allows users to properly monitor their child's health, ensure no vaccinations are missed, and seek medical attention early when necessary. Overall, it reduces parental anxiety and provides comprehensive health management support for children.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user launches the application on their device and enters the child's basic information (age, height, weight) and current symptoms (e.g., fever, cough). The device collects this information and prepares to send it to the server.

[0041] Step 2:

[0042] The terminal uses a secure communication protocol to send the entered information to the server. During this process, the information is converted to JSON format to ensure data integrity and secure transmission.

[0043] Step 3:

[0044] The server analyzes the received data. First, it performs a data integrity check to ensure the data is correctly formatted and free from missing or incorrect information.

[0045] Step 4:

[0046] The server uses an analysis tool to run a machine learning model based on the received symptom information. This model identifies potential medical conditions related to the symptoms and generates a list of these conditions.

[0047] Step 5:

[0048] The server uses a generation mechanism to create a preliminary diagnosis and corresponding treatment plan for the user, based on the listed symptoms. This information includes symptom relief measures that can be done at home and whether a visit to a medical institution is necessary.

[0049] Step 6:

[0050] The server uses administrative tools to check the child's vaccination schedule. It identifies the next scheduled vaccination date and sets a reminder based on it.

[0051] Step 7:

[0052] The server sends the generated preliminary diagnosis, treatment plan, and vaccination information to the terminal. The terminal visually displays the received information to the user and activates a reminder function to notify the user as needed.

[0053] (Example 1)

[0054] 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."

[0055] In modern society, accurately understanding a child's health condition and responding appropriately and promptly is a major challenge for parents and guardians. In particular, determining which medical condition a symptom is related to is difficult, and inappropriate responses based on incorrect self-diagnosis can harm a child's health. To solve this problem, a system that provides reliable health information and appropriate treatment methods is necessary.

[0056] 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.

[0057] In this invention, the server includes means for inputting information, means for processing information to analyze the received information and estimate multiple medical conditions associated with the symptoms, means for generating advice indicating corresponding countermeasures and the need for medical treatment at a medical institution based on the medical conditions estimated by the processing means, means for managing and adjusting vaccination schedules and setting reminders, and an information processing device that includes a function to communicate with an external database to enhance symptom analysis. This makes it possible to accurately grasp the health status of children and quickly provide appropriate medical consultations and countermeasures at home.

[0058] "Means of inputting information" refers to an interface that allows users to electronically input basic information about their child's health condition and current symptoms.

[0059] "Means of processing information" refers to digital processing technology that analyzes received information and has the function of estimating multiple medical conditions associated with a symptom.

[0060] "Means for generating advice" refers to a function that, based on the medical condition estimated by the processing means, provides suggestions regarding specific countermeasures and the necessity of visiting a medical institution.

[0061] "Means of management" refers to a schedule management function that coordinates vaccination schedules and notifies users of reminders.

[0062] An "information processing device" refers to a device or system that communicates with an external database to analyze and enhance received information.

[0063] This invention is a system for supporting the health management of children, and consists of means for inputting information, means for processing information, means for generating advice, means for managing information, and an information processing device.

[0064] Users enter their child's basic information and current symptoms using a dedicated terminal or application. This terminal receives the input information and sends the data to the server. To ensure security, the data is transmitted using the HTTPS protocol.

[0065] The server uses means to process the received information. Here, the server uses a machine learning model (e.g., TENSORFLOW®) or a rule-based engine to estimate possible medical conditions related to the symptoms. Specifically, it compares the entered symptoms with past medical condition data to determine which medical conditions they match.

[0066] Based on the analyzed information, the server uses a means to generate advice and creates advice to provide to the user. This advice allows the user to receive guidance on how to deal with symptoms at home and, if necessary, recommendations to seek medical attention.

[0067] Furthermore, through the management system, the server manages the vaccination schedule and generates reminders regarding the next vaccination date. These reminders are sent to the user's device as the vaccination date approaches.

[0068] For example, if a user enters into the application that "My child has had a fever of 38 degrees Celsius since last night and is coughing," the server will suggest the possibility of influenza and provide advice such as, "Ensure your child stays well-hydrated, and if symptoms persist, seek medical attention."

[0069] Example prompt to input into the generating AI model: "The child's current symptoms are fever and cough. Please tell me about possible illnesses and possible home remedies."

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The user enters basic information about the child (e.g., age, name) and current symptoms (e.g., fever, cough) through a dedicated terminal or application. After this information is entered, the terminal converts the data into a structured format (e.g., JSON). The converted data is encrypted using SSL / TLS and securely transmitted to the server. The input data is used in the next analysis step.

[0073] Step 2:

[0074] The server processes the data received from the terminal using analytical tools. First, the received data is fed into a machine learning model or rule-based engine. For example, it compares the entered symptoms with a past medical history dataset to check if they match known medical conditions. This analysis identifies possible medical conditions (e.g., influenza, common cold), and the results are passed on to the next generation step.

[0075] Step 3:

[0076] The server generates advice based on the analysis results. The generation mechanism constructs advice indicating appropriate treatment methods and the need for medical consultation depending on the identified medical condition. For example, if the symptoms are mild, it suggests home care methods (e.g., adequate hydration), and if they are severe, it recommends a visit to a medical institution. This generated advice is sent to the user's terminal and presented visually.

[0077] Step 4:

[0078] The server manages the vaccination schedule through its administration system. Based on the received age information, it calculates the next scheduled vaccination date and registers it in the calendar. This information is set to be notified to the user's device as a reminder at the specified date and time. For example, if the next vaccination is approaching, the server will notify the user based on this information and prompt them to plan.

[0079] Through this series of steps, users can quickly and accurately receive detailed information and advice regarding their child's health.

[0080] (Application Example 1)

[0081] 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."

[0082] In modern times, managing children's health is a crucial issue, but it can be difficult for parents to visit medical institutions at the appropriate time or manage vaccination schedules. Furthermore, there is often a lack of effective means for monitoring health within the home, which can increase anxiety. There is also a need for health management support that is easy to use and seamlessly integrated into daily life.

[0083] 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.

[0084] In this invention, the server includes means for inputting biological information, analysis means for analyzing the received biological information and estimating multiple medical conditions associated with the disease state, and generation means for generating advice on appropriate response methods and consultation with a medical institution. This makes it possible to comprehensively manage a child's health status using a home robot in daily life and reduce parental anxiety.

[0085] "Biological information" is a general term for data related to a child's health status, and includes information necessary for health management.

[0086] "Analysis means" refers to a device or software that mechanically derives the possibility of disease from received biological information.

[0087] "Generating means" refers to a device or function that provides advice on appropriate countermeasures and necessary medical consultations based on the results obtained by the analysis means.

[0088] "Management measures" refer to technologies and systems that effectively manage the timing and schedule of vaccinations and provide timely notifications.

[0089] A "home robot" is a mechanical device designed to provide support for daily life within the home, and includes those that also have health management functions.

[0090] The system to realize this application is configured as an application installed on a robot used in the home. The aim of this system is to manage a child's health and provide parents with necessary advice.

[0091] The server collects data from various sensors mounted on the robot to acquire biological information. This biological information includes health indicators such as body temperature and cough. The server uses this information and various analytical tools to analyze the received data and estimate the disease state. A learning model is used for this analysis.

[0092] Once the analysis is complete, the generation system uses it to generate advice on appropriate actions and whether a visit to a medical institution is necessary. This advice is provided to parents via audio or visual display. Furthermore, the management system manages the child's vaccination schedule and provides notifications as vaccination dates approach.

[0093] Specific hardware includes home robots equipped with sensors (e.g., mobile robots equipped with tablet devices). Software includes, for example, AI analysis libraries using Python. The analysis and generation processes involve algorithms executed via the cloud or local servers.

[0094] For example, if a child has a persistent low-grade fever and cough, the robot might advise the parents to "rest at home" and suggest that the child drink a warm beverage. This allows for comprehensive daily monitoring of the child's health.

[0095] An example of a prompt message would be: "A 5-year-old child is showing symptoms of fever and cough. As an AI chatbot, please provide appropriate health management advice to the parents."

[0096] In this way, IT technology can be used to support the creation of a safe and secure environment within the home.

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The device acquires biological information such as the child's body temperature and whether or not they have a cough in real time through sensors. This data is sent to the server as initial input.

[0100] Step 2:

[0101] The server identifies the disease state using analytical tools based on the received biological information. This process involves data analysis using a learning model. It analyzes the input body temperature and other symptom data to predict potential disease conditions. The result is the analysis output.

[0102] Step 3:

[0103] The server uses various generation methods based on the analysis output to generate advice on how to deal with the parents and whether medical consultation is necessary. During this generation process, the AI ​​model selects appropriate advice, taking the diagnostic results into consideration. The generated advice is returned to the terminal as output.

[0104] Step 4:

[0105] The device presents the generated advice to the parent as audio or visual display. This advice includes specific actions such as how to lower a child's temperature or how to deal with a cough.

[0106] Step 5:

[0107] The server uses management tools to monitor the vaccination schedule and sends notifications to devices as vaccination dates approach. This notification system allows parents to know when the next vaccination is due and make the necessary preparations.

[0108] 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.

[0109] This invention provides interaction tailored to the user's emotional state by combining an emotion recognition function with an AI healthcare assistant system that supports children's health management. This system is implemented with an emotion engine in addition to information input means, analysis means, generation means, and management means.

[0110] When a user enters information and symptoms of their child via a terminal, the system uses the camera and microphone to transmit the user's facial expressions and voice data to the emotion engine. The emotion engine analyzes this data in real time to recognize the user's emotional state (e.g., reassured, worried, irritated). This emotional information is sent to a server and processed by an analysis tool.

[0111] The server uses emotional information obtained by the emotion engine to adjust the advice provided by the generation system. For example, if the server determines that the user is feeling anxious, it will provide advice that is more polite and reassuring. Also, if the user is showing signs of frustration, the server will take steps to reduce the user's stress by providing concise and clear information.

[0112] Furthermore, the management system takes into account the user's emotional state and adjusts the timing and method of vaccination and routine check-up reminder notifications. For example, for users with high stress levels, reminder notifications are delivered in a gentler manner to help them relax and prepare for vaccination.

[0113] This system allows users to monitor their child's health in an accurate and user-friendly way and receive the most appropriate medical services when needed. Furthermore, it provides a better user experience by offering interactions tailored to individual emotional states.

[0114] The following describes the processing flow.

[0115] Step 1:

[0116] The user uses their device to launch the application and enter basic information and symptoms of their child. This prepares the device to send the collected information, along with the user's facial expressions and voice data, to the server.

[0117] Step 2:

[0118] The terminal converts the data to an appropriate format and sends it to the server using a secure communication method. This data includes not only symptom information but also the user's facial expressions and voice data.

[0119] Step 3:

[0120] The server processes symptom information from the received data using analytical tools. Machine learning is used to identify potentially related medical conditions based on the received symptom information.

[0121] Step 4:

[0122] The server uses an emotion engine to analyze received facial and audio data to recognize the user's emotional state. Based on this information, it determines whether the user is experiencing anxiety, relief, frustration, or other emotional states.

[0123] Step 5:

[0124] The server generates a preliminary diagnosis and advice on countermeasures for the user based on the analyzed symptom data and emotional state. The information is presented in a way that is appropriate to the user's emotional state.

[0125] Step 6:

[0126] The server uses management tools to set necessary reminders based on the vaccination schedule. These reminders are flexibly adjusted in content and timing, taking into account the user's emotional state.

[0127] Step 7:

[0128] The server sends generated advice and vaccination reminder information to the device, which then displays this information visually to the user. Based on the displayed information, the user can take necessary measures to manage their child's health.

[0129] (Example 2)

[0130] 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".

[0131] In traditional healthcare systems, information regarding children's health is often one-sided, and there is insufficient consideration for the psychological state of parents. Furthermore, the anxieties and frustrations felt by parents are not adequately reflected, which puts a burden on decision-making regarding their children's health and access to medical services.

[0132] 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.

[0133] In this invention, the server includes means for inputting information, analysis means for analyzing the received information and estimating multiple states associated with symptoms, and generation means for generating relevant treatment methods and advice on visiting a medical institution based on the medical condition estimated by the analysis means, and further adjusting the content of the advice considering the user's emotional state. This enables flexible and user-friendly interaction that responds to the user's emotional state, and provides optimal health management support.

[0134] "Means for inputting information" refers to a device or mechanism that provides an interface for users to input data regarding a child's health status and symptoms.

[0135] "Analysis means" refers to a device or software equipped with the function of analyzing a child's health status and symptoms using input data and estimating multiple related conditions.

[0136] A "generation means" is a device or system that generates advice on appropriate countermeasures and institutions to consult based on information obtained by an analysis means, and adjusts the content of that advice taking into account the user's emotional state.

[0137] A "management device" is a device or program that manages vaccination and regular check-up schedules and adjusts the timing and method of reminders, taking into account the user's emotional state.

[0138] An "emotion engine" is software or a system that analyzes a user's facial expressions and voice data in real time, identifies their emotional state, and provides that information.

[0139] A "generative AI model" is an artificial intelligence model that automatically generates advice and information in natural language based on user input and analyzed data.

[0140] A "prompt statement" is a text that describes the instructions or requirements for input to the generating AI model, and the AI ​​model generates appropriate output based on this input.

[0141] This invention is a system for supporting children's health management, achieving more flexible and user-friendly interaction by providing advice tailored to the user's emotional state. This system is implemented by combining the following key elements:

[0142] The user uses the device to input information about the child's health status and symptoms. The device is connected to a camera and microphone, which collect the user's facial expression and voice data. This data is sent from the device to an emotion engine, which analyzes the user's emotional state in real time. Based on the collected data, the emotion engine detects what emotional state the user is in, such as "reassured," "anxious," or "frustrated."

[0143] The analyzed emotional information is sent to the server. The server uses a generative AI model based on the emotional information to generate prompt statements and adjust the advice provided to the user based on those prompts. The generative AI model is trained to consider the user's emotional state and generate appropriate and reassuring advice. For example, if the user is feeling anxious, the server optimizes the advice provided by using a prompt statement such as, "Generate reassuring advice when the user is feeling anxious."

[0144] For example, if a user inputs information such as "My child has been coughing since this morning and I'm worried," the emotion engine detects the user's emotional state of "worry." At this point, the server generates a prompt message for the generating AI model, such as "Please provide advice about the symptoms in a gentle tone," and retrieves new advice.

[0145] Furthermore, the management system includes features for scheduling vaccinations and regular checkups, and for adjusting the timing and method of reminder notifications according to the user's emotional state. For example, reminders for highly stressed users might be sent in a softer tone, ensuring that users receive medical services in a more relaxed state.

[0146] This allows users to receive emotional support while more effectively managing their child's health and accessing appropriate medical services in a timely manner.

[0147] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0148] Step 1:

[0149] The user uses the device to input information about the child's health condition. Specifically, they input text data about symptoms and physical condition, and the camera and microphone are automatically activated to collect the user's facial expression and voice data. The entered health information and emotional data are temporarily stored on the device.

[0150] Step 2:

[0151] The device transmits the collected facial expression and voice data to the emotion engine. The emotion engine analyzes this data to estimate the user's emotional state. In doing so, it analyzes the pitch and tempo of the voice, the movement of facial muscles, etc., to identify emotions such as "reassurance," "anxiety," and "irritation." The analysis results are output as the emotional state.

[0152] Step 3:

[0153] The emotion information generated by the emotion engine is sent to the server. The server receives this data and uses it as basic information to determine what kind of advice to generate. The emotion information is used to construct the prompt statement corresponding to the generated advice.

[0154] Step 4:

[0155] The server sends a prompt message to the generative AI model. This prompt message includes instructions to appropriately adjust the advice based on the user's emotional state. For example, it might say, "The user is feeling anxious, so please generate reassuring advice." The generative AI model then generates the adjusted advice in response to the prompt and outputs it to the server.

[0156] Step 5:

[0157] The server sends the generated advice to the terminal. The terminal receives this and presents it to the user visually, and provides voice guidance as needed. This allows the user to receive appropriate guidance regarding their child's health.

[0158] Step 6:

[0159] Reminders for vaccinations and routine checkups are managed through a system. This system, located on the server, sets the timing and method of reminders based on the user's emotional state and sends them to the device. For example, a user experiencing stress might receive a reminder with a gentle notification sound.

[0160] (Application Example 2)

[0161] 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".

[0162] Conventional medical support systems often fail to adequately consider the emotional state of users, resulting in insufficient psychological support for those receiving assistance. Furthermore, the lack of means to provide specific advice and notifications that reflect emotional states in real time contributes to increased stress for users.

[0163] 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.

[0164] In this invention, the server includes an information acquisition device, an analysis device that analyzes the acquired information and estimates multiple situations related to the user's health status, and an emotion recognition device. This makes it possible to provide appropriate support according to the user's emotional state and to offer policies to reduce stress.

[0165] An "information acquisition device" is a device used to collect input information from users and data from sensors.

[0166] An "analysis device" is a device that analyzes health and emotional states based on acquired information and has the function of estimating the situation.

[0167] A "generator" is a device that creates policies and advice regarding visits to medical facilities based on the situation obtained through analysis.

[0168] A "device that performs emotion recognition" is a device that analyzes the user's voice and facial expression data to identify their emotional state.

[0169] A "management device" is a device that adjusts notification content based on acquired emotional information and conveys it to the user in an appropriate manner.

[0170] The following describes embodiments for carrying out the present invention. This system aims to manage the user's health and emotional state in real time and provide appropriate advice.

[0171] The system primarily consists of an information acquisition device, an analysis device, a generation device, an emotion recognition device, and a management device. The information acquisition device uses sensors such as cameras and microphones to collect the user's facial expressions and voice. This provides the basic information necessary to analyze the acquired data.

[0172] The analysis device uses the collected data to analyze the user's health and emotional state. Here, a learning model and emotion analysis engine are used to associate the obtained data with specific health conditions and emotions. Based on these analysis results, the generation device generates appropriate policies and recommendations for visits to medical facilities.

[0173] The emotion recognition device is designed to analyze the user's emotions in real time and adjust advice according to their emotional state. By providing information tailored to the user's emotions, this device creates a more empathetic and less stressful experience.

[0174] The management system adjusts generated notifications according to the user's emotions and delivers them at the appropriate time and in the appropriate manner. This reduces the user's psychological burden and ensures a comfortable user experience.

[0175] As a concrete example, if the system determines that a user is feeling anxious, it will provide a "special reassuring message" or "suggestion of relaxing music." Furthermore, it will provide prompts such as, "What advice would you give to calm an elderly person who is feeling stressed?" as input to the generative AI model.

[0176] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0177] Step 1:

[0178] The device uses a camera and microphone to collect the user's facial expressions and voice data. This provides basic data for emotion recognition. The acquired data is then sent to a server for further processing.

[0179] Step 2:

[0180] The server sends the received facial expression and voice data to an emotion recognition device. The emotion recognition device analyzes this input data and identifies the user's emotional state (e.g., reassurance, worry, irritation). The analysis device receives the emotional state information.

[0181] Step 3:

[0182] The analysis device estimates the user's health status based on emotional state information and other health-related data, and identifies specific situations associated with it. This data processing outputs a comprehensive health and emotional profile of the user.

[0183] Step 4:

[0184] The generator constructs policies and advice based on the analysis results. Depending on the obtained health and emotional state, it generates advice on specific medical policies and relaxation techniques. This advice is presented to the user in the form of information.

[0185] Step 5:

[0186] The management device adjusts the generated policies and advice according to the user's emotional state. It reconfigures the policies to ensure notifications are delivered at the appropriate time and determines the output format of the notifications. Finally, it sends the adjusted notifications to the terminal and presents them to the user.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Second Embodiment]

[0191] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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".

[0203] The present invention is an AI healthcare assistant system for supporting the health management of children, and is implemented by combining information input means, analysis means, generation means, and management means. The embodiments thereof are described in detail below.

[0204] Users input basic information and current symptoms of their child using a dedicated terminal or application. This input is received by the terminal via an information input device, and the information is sent to the cloud or a local server.

[0205] The information that reaches the server is processed by analytical tools. Based on this information, the server uses machine learning models and pre-programmed rule-based engines to identify multiple medical conditions related to the child's symptoms. For example, if there is a fever and cough, it may increase the likelihood of influenza or a common cold.

[0206] Based on the analysis results, a generation system installed on the server generates advice for the user regarding appropriate countermeasures and the need for medical consultation. For example, it suggests home remedies for mild symptoms and recommends visiting a medical institution if the symptoms are severe.

[0207] Furthermore, the server's management system handles vaccination schedules and calculates the next vaccination date. This information is registered as a reminder and notified to the user via their device at the designated time. For example, as the scheduled MMR vaccination date approaches, the user is notified to encourage them to prepare for the vaccination.

[0208] This system allows users to properly monitor their child's health, ensure no vaccinations are missed, and seek medical attention early when necessary. Overall, it reduces parental anxiety and provides comprehensive health management support for children.

[0209] The following describes the processing flow.

[0210] Step 1:

[0211] The user launches the application on their device and enters the child's basic information (age, height, weight) and current symptoms (e.g., fever, cough). The device collects this information and prepares to send it to the server.

[0212] Step 2:

[0213] The terminal uses a secure communication protocol to send the entered information to the server. During this process, the information is converted to JSON format to ensure data integrity and secure transmission.

[0214] Step 3:

[0215] The server analyzes the received data. First, it performs a data integrity check to ensure the data is correctly formatted and free from missing or incorrect information.

[0216] Step 4:

[0217] The server uses an analysis tool to run a machine learning model based on the received symptom information. This model identifies potential medical conditions related to the symptoms and generates a list of these conditions.

[0218] Step 5:

[0219] The server uses a generation mechanism to create a preliminary diagnosis and corresponding treatment plan for the user, based on the listed symptoms. This information includes symptom relief measures that can be done at home and whether a visit to a medical institution is necessary.

[0220] Step 6:

[0221] The server uses administrative tools to check the child's vaccination schedule. It identifies the next scheduled vaccination date and sets a reminder based on it.

[0222] Step 7:

[0223] The server sends the generated preliminary diagnosis, treatment plan, and vaccination information to the terminal. The terminal visually displays the received information to the user and activates a reminder function to notify the user as needed.

[0224] (Example 1)

[0225] 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."

[0226] In modern society, accurately understanding a child's health condition and responding appropriately and promptly is a major challenge for parents and guardians. In particular, determining which medical condition a symptom is related to is difficult, and inappropriate responses based on incorrect self-diagnosis can harm a child's health. To solve this problem, a system that provides reliable health information and appropriate treatment methods is necessary.

[0227] 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.

[0228] In this invention, the server includes means for inputting information, means for processing information to analyze the received information and estimate multiple medical conditions associated with the symptoms, means for generating advice indicating corresponding countermeasures and the need for medical treatment at a medical institution based on the medical conditions estimated by the processing means, means for managing and adjusting vaccination schedules and setting reminders, and an information processing device that includes a function to communicate with an external database to enhance symptom analysis. This makes it possible to accurately grasp the health status of children and quickly provide appropriate medical consultations and countermeasures at home.

[0229] "Means of inputting information" refers to an interface that allows users to electronically input basic information about their child's health condition and current symptoms.

[0230] "Means of processing information" refers to digital processing technology that analyzes received information and has the function of estimating multiple medical conditions associated with a symptom.

[0231] "Means for generating advice" refers to a function that, based on the medical condition estimated by the processing means, provides suggestions regarding specific countermeasures and the necessity of visiting a medical institution.

[0232] "Means of management" refers to a schedule management function that coordinates vaccination schedules and notifies users of reminders.

[0233] An "information processing device" refers to a device or system that communicates with an external database to analyze and enhance received information.

[0234] This invention is a system for supporting the health management of children, and consists of means for inputting information, means for processing information, means for generating advice, means for managing information, and an information processing device.

[0235] Users enter their child's basic information and current symptoms using a dedicated terminal or application. This terminal receives the input information and sends the data to the server. To ensure security, the data is transmitted using the HTTPS protocol.

[0236] The server uses means to process the received information. Here, the server uses a machine learning model (e.g., TensorFlow) or a rule-based engine to estimate possible medical conditions related to the symptoms. Specifically, it compares the input symptoms with past medical condition data to determine which medical conditions they correspond to.

[0237] Based on the analyzed information, the server uses a means to generate advice and creates advice to provide to the user. This advice allows the user to receive guidance on how to deal with symptoms at home and, if necessary, recommendations to seek medical attention.

[0238] Furthermore, through the management system, the server manages the vaccination schedule and generates reminders regarding the next vaccination date. These reminders are sent to the user's device as the vaccination date approaches.

[0239] For example, if a user enters into the application that "My child has had a fever of 38 degrees Celsius since last night and is coughing," the server will suggest the possibility of influenza and provide advice such as, "Ensure your child stays well-hydrated, and if symptoms persist, seek medical attention."

[0240] Example prompt to input into the generating AI model: "The child's current symptoms are fever and cough. Please tell me about possible illnesses and possible home remedies."

[0241] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0242] Step 1:

[0243] The user enters basic information about the child (e.g., age, name) and current symptoms (e.g., fever, cough) through a dedicated terminal or application. After this information is entered, the terminal converts the data into a structured format (e.g., JSON). The converted data is encrypted using SSL / TLS and securely transmitted to the server. The input data is used in the next analysis step.

[0244] Step 2:

[0245] The server processes the data received from the terminal using analytical tools. First, the received data is fed into a machine learning model or rule-based engine. For example, it compares the entered symptoms with a past medical history dataset to check if they match known medical conditions. This analysis identifies possible medical conditions (e.g., influenza, common cold), and the results are passed on to the next generation step.

[0246] Step 3:

[0247] The server generates advice based on the analysis results. The generation mechanism constructs advice indicating appropriate treatment methods and the need for medical consultation depending on the identified medical condition. For example, if the symptoms are mild, it suggests home care methods (e.g., adequate hydration), and if they are severe, it recommends a visit to a medical institution. This generated advice is sent to the user's terminal and presented visually.

[0248] Step 4:

[0249] The server manages the vaccination schedule through its administration system. Based on the received age information, it calculates the next scheduled vaccination date and registers it in the calendar. This information is set to be notified to the user's device as a reminder at the specified date and time. For example, if the next vaccination is approaching, the server will notify the user based on this information and prompt them to plan.

[0250] Through this series of steps, users can quickly and accurately receive detailed information and advice regarding their child's health.

[0251] (Application Example 1)

[0252] 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."

[0253] In modern times, managing children's health is a crucial issue, but it can be difficult for parents to visit medical institutions at the appropriate time or manage vaccination schedules. Furthermore, there is often a lack of effective means for monitoring health within the home, which can increase anxiety. There is also a need for health management support that is easy to use and seamlessly integrated into daily life.

[0254] 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.

[0255] In this invention, the server includes means for inputting biological information, analysis means for analyzing the received biological information and estimating multiple medical conditions associated with the disease state, and generation means for generating advice on appropriate response methods and consultation with a medical institution. This makes it possible to comprehensively manage a child's health status using a home robot in daily life and reduce parental anxiety.

[0256] "Biological information" is a general term for data related to a child's health status, and includes information necessary for health management.

[0257] "Analysis means" refers to a device or software that mechanically derives the possibility of disease from received biological information.

[0258] "Generating means" refers to a device or function that provides advice on appropriate countermeasures and necessary medical consultations based on the results obtained by the analysis means.

[0259] "Management measures" refer to technologies and systems that effectively manage the timing and schedule of vaccinations and provide timely notifications.

[0260] A "home robot" is a mechanical device designed to provide support for daily life within the home, and includes those that also have health management functions.

[0261] The system to realize this application is configured as an application installed on a robot used in the home. The aim of this system is to manage a child's health and provide parents with necessary advice.

[0262] The server collects data from various sensors mounted on the robot to acquire biological information. This biological information includes health indicators such as body temperature and cough. The server uses this information and various analytical tools to analyze the received data and estimate the disease state. A learning model is used for this analysis.

[0263] Once the analysis is complete, the generation system uses it to generate advice on appropriate actions and whether a visit to a medical institution is necessary. This advice is provided to parents via audio or visual display. Furthermore, the management system manages the child's vaccination schedule and provides notifications as vaccination dates approach.

[0264] Specific hardware includes home robots equipped with sensors (e.g., mobile robots equipped with tablet devices). Software includes, for example, AI analysis libraries using Python. The analysis and generation processes involve algorithms executed via the cloud or local servers.

[0265] For example, if a child has a persistent low-grade fever and cough, the robot might advise the parents to "rest at home" and suggest that the child drink a warm beverage. This allows for comprehensive daily monitoring of the child's health.

[0266] An example of a prompt message would be: "A 5-year-old child is showing symptoms of fever and cough. As an AI chatbot, please provide appropriate health management advice to the parents."

[0267] In this way, IT technology can be used to support the creation of a safe and secure environment within the home.

[0268] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0269] Step 1:

[0270] The device acquires biological information such as the child's body temperature and whether or not they have a cough in real time through sensors. This data is sent to the server as initial input.

[0271] Step 2:

[0272] The server identifies the disease state using analytical tools based on the received biological information. This process involves data analysis using a learning model. It analyzes the input body temperature and other symptom data to predict potential disease conditions. The result is the analysis output.

[0273] Step 3:

[0274] The server uses various generation methods based on the analysis output to generate advice on how to deal with the parents and whether medical consultation is necessary. During this generation process, the AI ​​model selects appropriate advice, taking the diagnostic results into consideration. The generated advice is returned to the terminal as output.

[0275] Step 4:

[0276] The device presents the generated advice to the parent as audio or visual display. This advice includes specific actions such as how to lower a child's temperature or how to deal with a cough.

[0277] Step 5:

[0278] The server uses management tools to monitor the vaccination schedule and sends notifications to devices as vaccination dates approach. This notification system allows parents to know when the next vaccination is due and make the necessary preparations.

[0279] 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.

[0280] This invention provides interaction tailored to the user's emotional state by combining an emotion recognition function with an AI healthcare assistant system that supports children's health management. This system is implemented with an emotion engine in addition to information input means, analysis means, generation means, and management means.

[0281] When a user enters information and symptoms of their child via a terminal, the system uses the camera and microphone to transmit the user's facial expressions and voice data to the emotion engine. The emotion engine analyzes this data in real time to recognize the user's emotional state (e.g., reassured, worried, irritated). This emotional information is sent to a server and processed by an analysis tool.

[0282] The server uses the emotion information obtained by the emotion engine to adjust the advice content provided by the generation means. For example, when it is determined that the user is feeling anxious, the server provides advice that has been changed to a more polite and reassuring expression. Also, when the user shows impatience, the server takes measures to reduce the user's stress by presenting concise and clear information.

[0283] Furthermore, the management means takes into account the user's emotional state and adjusts the timing and method of reminder notifications for vaccinations and regular medical check-ups. For example, for users with a high stress level, the reminder notification is conveyed in a gentle manner to support the user in relaxing and preparing for the vaccination.

[0284] With this system, the user can monitor the health status of their child in an accurate and friendly manner and receive the optimal medical service when needed. Also, by providing an interaction according to the individual emotional state, a better user experience is realized.

[0285] The processing flow will be described below.

[0286] Step 1:

[0287] The user uses the terminal to launch the application and enter the basic information and symptoms of the child. Thereby, the terminal prepares to send the collected information and the user's facial expression and voice data to the server.

[0288] Step 2:

[0289] The terminal converts the transmission data into an appropriate format and sends it to the server using secure communication means. This data includes not only the symptom information but also the user's facial expression and voice data.

[0290] Step 3:

[0291] The server processes symptom information from the received data using analytical tools. Machine learning is used to identify potentially related medical conditions based on the received symptom information.

[0292] Step 4:

[0293] The server uses an emotion engine to analyze received facial and audio data to recognize the user's emotional state. Based on this information, it determines whether the user is experiencing anxiety, relief, frustration, or other emotional states.

[0294] Step 5:

[0295] The server generates a preliminary diagnosis and advice on countermeasures for the user based on the analyzed symptom data and emotional state. The information is presented in a way that is appropriate to the user's emotional state.

[0296] Step 6:

[0297] The server uses management tools to set necessary reminders based on the vaccination schedule. These reminders are flexibly adjusted in content and timing, taking into account the user's emotional state.

[0298] Step 7:

[0299] The server sends generated advice and vaccination reminder information to the device, which then displays this information visually to the user. Based on the displayed information, the user can take necessary measures to manage their child's health.

[0300] (Example 2)

[0301] 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".

[0302] In a conventional healthcare system, the provision of information regarding a child's health condition is one-way, and the response considering the parent's mental state is not sufficient. Also, the anxiety and frustration felt by the parent are not appropriately reflected, and there was a problem in that it imposed a burden on the decision-making regarding the child's health and medical service visits.

[0303] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Example 2 is realized by the following respective means.

[0304] In this invention, the server includes means for inputting information, analysis means for analyzing the received information to estimate a plurality of states associated with symptoms, generation means for generating advice on relevant countermeasures and institution visits based on the medical condition estimated by the analysis means, and further adjusting the advice content in consideration of the user's emotional state. Thereby, a flexible and friendly interaction according to the user's emotional state becomes possible, and optimal health management support can be provided.

[0305] The "means for inputting information" is a device or mechanism that provides an interface for a user to input data regarding a child's health condition and symptoms.

[0306] The "analysis means" is a device or software having a function of analyzing a child's health condition and symptoms using the input data and estimating a plurality of related states.

[0307] The "generation means" is a device or system having a function of generating advice on appropriate countermeasures and institutions to visit based on the information obtained by the analysis means and adjusting the content in consideration of the user's emotional state.

[0308] The "management means" is a device or program having a function of managing the schedule of vaccinations and regular check-ups and adjusting the timing and method of reminders in consideration of the user's emotional state.

[0309] An "emotion engine" is software or a system that analyzes a user's facial expressions and voice data in real time, identifies their emotional state, and provides that information.

[0310] A "generative AI model" is an artificial intelligence model that automatically generates advice and information in natural language based on user input and analyzed data.

[0311] A "prompt statement" is a text that describes the instructions or requirements for input to the generating AI model, and the AI ​​model generates appropriate output based on this input.

[0312] This invention is a system for supporting children's health management, achieving more flexible and user-friendly interaction by providing advice tailored to the user's emotional state. This system is implemented by combining the following key elements:

[0313] The user uses the device to input information about the child's health status and symptoms. The device is connected to a camera and microphone, which collect the user's facial expression and voice data. This data is sent from the device to an emotion engine, which analyzes the user's emotional state in real time. Based on the collected data, the emotion engine detects what emotional state the user is in, such as "reassured," "anxious," or "frustrated."

[0314] The analyzed emotional information is sent to the server. The server uses a generative AI model based on the emotional information to generate prompt statements and adjust the advice provided to the user based on those prompts. The generative AI model is trained to consider the user's emotional state and generate appropriate and reassuring advice. For example, if the user is feeling anxious, the server optimizes the advice provided by using a prompt statement such as, "Generate reassuring advice when the user is feeling anxious."

[0315] For example, if a user inputs information such as "My child has been coughing since this morning and I'm worried," the emotion engine detects the user's emotional state of "worry." At this point, the server generates a prompt message for the generating AI model, such as "Please provide advice about the symptoms in a gentle tone," and retrieves new advice.

[0316] Furthermore, the management system includes features for scheduling vaccinations and regular checkups, and for adjusting the timing and method of reminder notifications according to the user's emotional state. For example, reminders for highly stressed users might be sent in a softer tone, ensuring that users receive medical services in a more relaxed state.

[0317] This allows users to receive emotional support while more effectively managing their child's health and accessing appropriate medical services in a timely manner.

[0318] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0319] Step 1:

[0320] The user uses the device to input information about the child's health condition. Specifically, they input text data about symptoms and physical condition, and the camera and microphone are automatically activated to collect the user's facial expression and voice data. The entered health information and emotional data are temporarily stored on the device.

[0321] Step 2:

[0322] The device transmits the collected facial expression and voice data to the emotion engine. The emotion engine analyzes this data to estimate the user's emotional state. In doing so, it analyzes the pitch and tempo of the voice, the movement of facial muscles, etc., to identify emotions such as "reassurance," "anxiety," and "irritation." The analysis results are output as the emotional state.

[0323] Step 3:

[0324] The emotion information generated by the emotion engine is sent to the server. The server receives this data and uses it as basic information to determine what kind of advice to generate. The emotion information is used to construct the prompt statement corresponding to the generated advice.

[0325] Step 4:

[0326] The server sends a prompt message to the generative AI model. This prompt message includes instructions to appropriately adjust the advice based on the user's emotional state. For example, it might say, "The user is feeling anxious, so please generate reassuring advice." The generative AI model then generates the adjusted advice in response to the prompt and outputs it to the server.

[0327] Step 5:

[0328] The server sends the generated advice to the terminal. The terminal receives this and presents it to the user visually, and provides voice guidance as needed. This allows the user to receive appropriate guidance regarding their child's health.

[0329] Step 6:

[0330] Reminders for vaccinations and routine checkups are managed through a system. This system, located on the server, sets the timing and method of reminders based on the user's emotional state and sends them to the device. For example, a user experiencing stress might receive a reminder with a gentle notification sound.

[0331] (Application Example 2)

[0332] 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."

[0333] Conventional medical support systems often fail to adequately consider the emotional state of users, resulting in insufficient psychological support for those receiving assistance. Furthermore, the lack of means to provide specific advice and notifications that reflect emotional states in real time contributes to increased stress for users.

[0334] 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.

[0335] In this invention, the server includes an information acquisition device, an analysis device that analyzes the acquired information and estimates multiple situations related to the user's health status, and an emotion recognition device. This makes it possible to provide appropriate support according to the user's emotional state and to offer policies to reduce stress.

[0336] An "information acquisition device" is a device used to collect input information from users and data from sensors.

[0337] An "analysis device" is a device that analyzes health and emotional states based on acquired information and has the function of estimating the situation.

[0338] A "generator" is a device that creates policies and advice regarding visits to medical facilities based on the situation obtained through analysis.

[0339] A "device that performs emotion recognition" is a device that analyzes the user's voice and facial expression data to identify their emotional state.

[0340] A "management device" is a device that adjusts notification content based on acquired emotional information and conveys it to the user in an appropriate manner.

[0341] The following describes embodiments for carrying out the present invention. This system aims to manage the user's health and emotional state in real time and provide appropriate advice.

[0342] The system primarily consists of an information acquisition device, an analysis device, a generation device, an emotion recognition device, and a management device. The information acquisition device uses sensors such as cameras and microphones to collect the user's facial expressions and voice. This provides the basic information necessary to analyze the acquired data.

[0343] The analysis device uses the collected data to analyze the user's health and emotional state. Here, a learning model and emotion analysis engine are used to associate the obtained data with specific health conditions and emotions. Based on these analysis results, the generation device generates appropriate policies and recommendations for visits to medical facilities.

[0344] The emotion recognition device is designed to analyze the user's emotions in real time and adjust advice according to their emotional state. By providing information tailored to the user's emotions, this device creates a more empathetic and less stressful experience.

[0345] The management system adjusts generated notifications according to the user's emotions and delivers them at the appropriate time and in the appropriate manner. This reduces the user's psychological burden and ensures a comfortable user experience.

[0346] As a concrete example, if the system determines that a user is feeling anxious, it will provide a "special reassuring message" or "suggestion of relaxing music." Furthermore, it will provide prompts such as, "What advice would you give to calm an elderly person who is feeling stressed?" as input to the generative AI model.

[0347] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0348] Step 1:

[0349] The device uses a camera and microphone to collect the user's facial expressions and voice data. This provides basic data for emotion recognition. The acquired data is then sent to a server for further processing.

[0350] Step 2:

[0351] The server sends the received facial expression and voice data to an emotion recognition device. The emotion recognition device analyzes this input data and identifies the user's emotional state (e.g., reassurance, worry, irritation). The analysis device receives the emotional state information.

[0352] Step 3:

[0353] The analysis device estimates the user's health status based on emotional state information and other health-related data, and identifies specific situations associated with it. This data processing outputs a comprehensive health and emotional profile of the user.

[0354] Step 4:

[0355] The generator constructs policies and advice based on the analysis results. Depending on the obtained health and emotional state, it generates advice on specific medical policies and relaxation techniques. This advice is presented to the user in the form of information.

[0356] Step 5:

[0357] The management device adjusts the generated policies and advice according to the user's emotional state. It reconfigures the policies to ensure notifications are delivered at the appropriate time and determines the output format of the notifications. Finally, it sends the adjusted notifications to the terminal and presents them to the user.

[0358] 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.

[0359] 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.

[0360] 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.

[0361] [Third Embodiment]

[0362] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0363] 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.

[0364] 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).

[0365] 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.

[0366] 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.

[0367] 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).

[0368] 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.

[0369] 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.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] 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".

[0374] The present invention is an AI healthcare assistant system for supporting the health management of children, and is implemented by combining information input means, analysis means, generation means, and management means. The embodiments thereof are described in detail below.

[0375] Users input basic information and current symptoms of their child using a dedicated terminal or application. This input is received by the terminal via an information input device, and the information is sent to the cloud or a local server.

[0376] The information that reaches the server is processed by analytical tools. Based on this information, the server uses machine learning models and pre-programmed rule-based engines to identify multiple medical conditions related to the child's symptoms. For example, if there is a fever and cough, it may increase the likelihood of influenza or a common cold.

[0377] Based on the analysis results, a generation system installed on the server generates advice for the user regarding appropriate countermeasures and the need for medical consultation. For example, it suggests home remedies for mild symptoms and recommends visiting a medical institution if the symptoms are severe.

[0378] Furthermore, the server's management system handles vaccination schedules and calculates the next vaccination date. This information is registered as a reminder and notified to the user via their device at the designated time. For example, as the scheduled MMR vaccination date approaches, the user is notified to encourage them to prepare for the vaccination.

[0379] This system allows users to properly monitor their child's health, ensure no vaccinations are missed, and seek medical attention early when necessary. Overall, it reduces parental anxiety and provides comprehensive health management support for children.

[0380] The following describes the processing flow.

[0381] Step 1:

[0382] The user launches the application on their device and enters the child's basic information (age, height, weight) and current symptoms (e.g., fever, cough). The device collects this information and prepares to send it to the server.

[0383] Step 2:

[0384] The terminal uses a secure communication protocol to send the entered information to the server. During this process, the information is converted to JSON format to ensure data integrity and secure transmission.

[0385] Step 3:

[0386] The server analyzes the received data. First, it performs a data integrity check to ensure the data is correctly formatted and free from missing or incorrect information.

[0387] Step 4:

[0388] The server uses an analysis tool to run a machine learning model based on the received symptom information. This model identifies potential medical conditions related to the symptoms and generates a list of these conditions.

[0389] Step 5:

[0390] The server uses a generation mechanism to create a preliminary diagnosis and corresponding treatment plan for the user, based on the listed symptoms. This information includes symptom relief measures that can be done at home and whether a visit to a medical institution is necessary.

[0391] Step 6:

[0392] The server uses administrative tools to check the child's vaccination schedule. It identifies the next scheduled vaccination date and sets a reminder based on it.

[0393] Step 7:

[0394] The server sends the generated preliminary diagnosis, treatment plan, and vaccination information to the terminal. The terminal visually displays the received information to the user and activates a reminder function to notify the user as needed.

[0395] (Example 1)

[0396] 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."

[0397] In modern society, accurately understanding a child's health condition and responding appropriately and promptly is a major challenge for parents and guardians. In particular, determining which medical condition a symptom is related to is difficult, and inappropriate responses based on incorrect self-diagnosis can harm a child's health. To solve this problem, a system that provides reliable health information and appropriate treatment methods is necessary.

[0398] 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.

[0399] In this invention, the server includes means for inputting information, means for processing information to analyze the received information and estimate multiple medical conditions associated with the symptoms, means for generating advice indicating corresponding countermeasures and the need for medical treatment at a medical institution based on the medical conditions estimated by the processing means, means for managing and adjusting vaccination schedules and setting reminders, and an information processing device that includes a function to communicate with an external database to enhance symptom analysis. This makes it possible to accurately grasp the health status of children and quickly provide appropriate medical consultations and countermeasures at home.

[0400] "Means of inputting information" refers to an interface that allows users to electronically input basic information about their child's health condition and current symptoms.

[0401] "Means of processing information" refers to digital processing technology that analyzes received information and has the function of estimating multiple medical conditions associated with a symptom.

[0402] "Means for generating advice" refers to a function that, based on the medical condition estimated by the processing means, provides suggestions regarding specific countermeasures and the necessity of visiting a medical institution.

[0403] "Means of management" refers to a schedule management function that coordinates vaccination schedules and notifies users of reminders.

[0404] An "information processing device" refers to a device or system that communicates with an external database to analyze and enhance received information.

[0405] This invention is a system for supporting the health management of children, and consists of means for inputting information, means for processing information, means for generating advice, means for managing information, and an information processing device.

[0406] Users enter their child's basic information and current symptoms using a dedicated terminal or application. This terminal receives the input information and sends the data to the server. To ensure security, the data is transmitted using the HTTPS protocol.

[0407] The server uses means to process the received information. Here, the server uses a machine learning model (e.g., TensorFlow) or a rule-based engine to estimate possible medical conditions related to the symptoms. Specifically, it compares the input symptoms with past medical condition data to determine which medical conditions they correspond to.

[0408] Based on the analyzed information, the server uses a means to generate advice and creates advice to provide to the user. This advice allows the user to receive guidance on how to deal with symptoms at home and, if necessary, recommendations to seek medical attention.

[0409] Furthermore, through the management system, the server manages the vaccination schedule and generates reminders regarding the next vaccination date. These reminders are sent to the user's device as the vaccination date approaches.

[0410] For example, if a user enters into the application that "My child has had a fever of 38 degrees Celsius since last night and is coughing," the server will suggest the possibility of influenza and provide advice such as, "Ensure your child stays well-hydrated, and if symptoms persist, seek medical attention."

[0411] Example prompt to input into the generating AI model: "The child's current symptoms are fever and cough. Please tell me about possible illnesses and possible home remedies."

[0412] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0413] Step 1:

[0414] The user enters basic information about the child (e.g., age, name) and current symptoms (e.g., fever, cough) through a dedicated terminal or application. After this information is entered, the terminal converts the data into a structured format (e.g., JSON). The converted data is encrypted using SSL / TLS and securely transmitted to the server. The input data is used in the next analysis step.

[0415] Step 2:

[0416] The server processes the data received from the terminal using analytical tools. First, the received data is fed into a machine learning model or rule-based engine. For example, it compares the entered symptoms with a past medical history dataset to check if they match known medical conditions. This analysis identifies possible medical conditions (e.g., influenza, common cold), and the results are passed on to the next generation step.

[0417] Step 3:

[0418] The server generates advice based on the analysis results. The generation mechanism constructs advice indicating appropriate treatment methods and the need for medical consultation depending on the identified medical condition. For example, if the symptoms are mild, it suggests home care methods (e.g., adequate hydration), and if they are severe, it recommends a visit to a medical institution. This generated advice is sent to the user's terminal and presented visually.

[0419] Step 4:

[0420] The server manages the vaccination schedule through its administration system. Based on the received age information, it calculates the next scheduled vaccination date and registers it in the calendar. This information is set to be notified to the user's device as a reminder at the specified date and time. For example, if the next vaccination is approaching, the server will notify the user based on this information and prompt them to plan.

[0421] Through this series of steps, users can quickly and accurately receive detailed information and advice regarding their child's health.

[0422] (Application Example 1)

[0423] 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."

[0424] In modern times, managing children's health is a crucial issue, but it can be difficult for parents to visit medical institutions at the appropriate time or manage vaccination schedules. Furthermore, there is often a lack of effective means for monitoring health within the home, which can increase anxiety. There is also a need for health management support that is easy to use and seamlessly integrated into daily life.

[0425] 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.

[0426] In this invention, the server includes means for inputting biological information, analysis means for analyzing the received biological information and estimating multiple medical conditions associated with the disease state, and generation means for generating advice on appropriate response methods and consultation with a medical institution. This makes it possible to comprehensively manage a child's health status using a home robot in daily life and reduce parental anxiety.

[0427] "Biological information" is a general term for data related to a child's health status, and includes information necessary for health management.

[0428] "Analysis means" refers to a device or software that mechanically derives the possibility of disease from received biological information.

[0429] "Generating means" refers to a device or function that provides advice on appropriate countermeasures and necessary medical consultations based on the results obtained by the analysis means.

[0430] "Management measures" refer to technologies and systems that effectively manage the timing and schedule of vaccinations and provide timely notifications.

[0431] A "home robot" is a mechanical device designed to provide support for daily life within the home, and includes those that also have health management functions.

[0432] The system to realize this application is configured as an application installed on a robot used in the home. The aim of this system is to manage a child's health and provide parents with necessary advice.

[0433] The server collects data from various sensors mounted on the robot to acquire biological information. This biological information includes health indicators such as body temperature and cough. The server uses this information and various analytical tools to analyze the received data and estimate the disease state. A learning model is used for this analysis.

[0434] Once the analysis is complete, the generation system uses it to generate advice on appropriate actions and whether a visit to a medical institution is necessary. This advice is provided to parents via audio or visual display. Furthermore, the management system manages the child's vaccination schedule and provides notifications as vaccination dates approach.

[0435] Specific hardware includes home robots equipped with sensors (e.g., mobile robots equipped with tablet devices). Software includes, for example, AI analysis libraries using Python. The analysis and generation processes involve algorithms executed via the cloud or local servers.

[0436] For example, if a child has a persistent low-grade fever and cough, the robot might advise the parents to "rest at home" and suggest that the child drink a warm beverage. This allows for comprehensive daily monitoring of the child's health.

[0437] An example of a prompt message would be: "A 5-year-old child is showing symptoms of fever and cough. As an AI chatbot, please provide appropriate health management advice to the parents."

[0438] In this way, IT technology can be used to support the creation of a safe and secure environment within the home.

[0439] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0440] Step 1:

[0441] The device acquires biological information such as the child's body temperature and whether or not they have a cough in real time through sensors. This data is sent to the server as initial input.

[0442] Step 2:

[0443] The server identifies the disease state using analytical tools based on the received biological information. This process involves data analysis using a learning model. It analyzes the input body temperature and other symptom data to predict potential disease conditions. The result is the analysis output.

[0444] Step 3:

[0445] The server uses various generation methods based on the analysis output to generate advice on how to deal with the parents and whether medical consultation is necessary. During this generation process, the AI ​​model selects appropriate advice, taking the diagnostic results into consideration. The generated advice is returned to the terminal as output.

[0446] Step 4:

[0447] The device presents the generated advice to the parent as audio or visual display. This advice includes specific actions such as how to lower a child's temperature or how to deal with a cough.

[0448] Step 5:

[0449] The server uses management tools to monitor the vaccination schedule and sends notifications to devices as vaccination dates approach. This notification system allows parents to know when the next vaccination is due and make the necessary preparations.

[0450] 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.

[0451] This invention provides interaction tailored to the user's emotional state by combining an emotion recognition function with an AI healthcare assistant system that supports children's health management. This system is implemented with an emotion engine in addition to information input means, analysis means, generation means, and management means.

[0452] When a user enters information and symptoms of their child via a terminal, the system uses the camera and microphone to transmit the user's facial expressions and voice data to the emotion engine. The emotion engine analyzes this data in real time to recognize the user's emotional state (e.g., reassured, worried, irritated). This emotional information is sent to a server and processed by an analysis tool.

[0453] The server uses emotional information obtained by the emotion engine to adjust the advice provided by the generation system. For example, if the server determines that the user is feeling anxious, it will provide advice that is more polite and reassuring. Also, if the user is showing signs of frustration, the server will take steps to reduce the user's stress by providing concise and clear information.

[0454] Furthermore, the management system takes into account the user's emotional state and adjusts the timing and method of vaccination and routine check-up reminder notifications. For example, for users with high stress levels, reminder notifications are delivered in a gentler manner to help them relax and prepare for vaccination.

[0455] This system allows users to monitor their child's health in an accurate and user-friendly way and receive the most appropriate medical services when needed. Furthermore, it provides a better user experience by offering interactions tailored to individual emotional states.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] The user uses their device to launch the application and enter basic information and symptoms of their child. This prepares the device to send the collected information, along with the user's facial expressions and voice data, to the server.

[0459] Step 2:

[0460] The terminal converts the data to an appropriate format and sends it to the server using a secure communication method. This data includes not only symptom information but also the user's facial expressions and voice data.

[0461] Step 3:

[0462] The server processes symptom information from the received data using analytical tools. Machine learning is used to identify potentially related medical conditions based on the received symptom information.

[0463] Step 4:

[0464] The server uses an emotion engine to analyze received facial and audio data to recognize the user's emotional state. Based on this information, it determines whether the user is experiencing anxiety, relief, frustration, or other emotional states.

[0465] Step 5:

[0466] The server generates a preliminary diagnosis and advice on countermeasures for the user based on the analyzed symptom data and emotional state. The information is presented in a way that is appropriate to the user's emotional state.

[0467] Step 6:

[0468] The server uses management tools to set necessary reminders based on the vaccination schedule. These reminders are flexibly adjusted in content and timing, taking into account the user's emotional state.

[0469] Step 7:

[0470] The server sends generated advice and vaccination reminder information to the device, which then displays this information visually to the user. Based on the displayed information, the user can take necessary measures to manage their child's health.

[0471] (Example 2)

[0472] 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."

[0473] In traditional healthcare systems, information regarding children's health is often one-sided, and there is insufficient consideration for the psychological state of parents. Furthermore, the anxieties and frustrations felt by parents are not adequately reflected, which puts a burden on decision-making regarding their children's health and access to medical services.

[0474] 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.

[0475] In this invention, the server includes means for inputting information, analysis means for analyzing the received information and estimating multiple states associated with symptoms, and generation means for generating relevant treatment methods and advice on visiting a medical institution based on the medical condition estimated by the analysis means, and further adjusting the content of the advice considering the user's emotional state. This enables flexible and user-friendly interaction that responds to the user's emotional state, and provides optimal health management support.

[0476] "Means for inputting information" refers to a device or mechanism that provides an interface for users to input data regarding a child's health status and symptoms.

[0477] "Analysis means" refers to a device or software equipped with the function of analyzing a child's health status and symptoms using input data and estimating multiple related conditions.

[0478] A "generation means" is a device or system that generates advice on appropriate countermeasures and institutions to consult based on information obtained by an analysis means, and adjusts the content of that advice taking into account the user's emotional state.

[0479] A "management device" is a device or program that manages vaccination and regular check-up schedules and adjusts the timing and method of reminders, taking into account the user's emotional state.

[0480] An "emotion engine" is software or a system that analyzes a user's facial expressions and voice data in real time, identifies their emotional state, and provides that information.

[0481] A "generative AI model" is an artificial intelligence model that automatically generates advice and information in natural language based on user input and analyzed data.

[0482] A "prompt statement" is a text that describes the instructions or requirements for input to the generating AI model, and the AI ​​model generates appropriate output based on this input.

[0483] This invention is a system for supporting children's health management, achieving more flexible and user-friendly interaction by providing advice tailored to the user's emotional state. This system is implemented by combining the following key elements:

[0484] The user uses the device to input information about the child's health status and symptoms. The device is connected to a camera and microphone, which collect the user's facial expression and voice data. This data is sent from the device to an emotion engine, which analyzes the user's emotional state in real time. Based on the collected data, the emotion engine detects what emotional state the user is in, such as "reassured," "anxious," or "frustrated."

[0485] The analyzed emotional information is sent to the server. The server uses a generative AI model based on the emotional information to generate prompt statements and adjust the advice provided to the user based on those prompts. The generative AI model is trained to consider the user's emotional state and generate appropriate and reassuring advice. For example, if the user is feeling anxious, the server optimizes the advice provided by using a prompt statement such as, "Generate reassuring advice when the user is feeling anxious."

[0486] For example, if a user inputs information such as "My child has been coughing since this morning and I'm worried," the emotion engine detects the user's emotional state of "worry." At this point, the server generates a prompt message for the generating AI model, such as "Please provide advice about the symptoms in a gentle tone," and retrieves new advice.

[0487] Furthermore, the management system includes features for scheduling vaccinations and regular checkups, and for adjusting the timing and method of reminder notifications according to the user's emotional state. For example, reminders for highly stressed users might be sent in a softer tone, ensuring that users receive medical services in a more relaxed state.

[0488] This allows users to receive emotional support while more effectively managing their child's health and accessing appropriate medical services in a timely manner.

[0489] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0490] Step 1:

[0491] The user uses the device to input information about the child's health condition. Specifically, they input text data about symptoms and physical condition, and the camera and microphone are automatically activated to collect the user's facial expression and voice data. The entered health information and emotional data are temporarily stored on the device.

[0492] Step 2:

[0493] The device transmits the collected facial expression and voice data to the emotion engine. The emotion engine analyzes this data to estimate the user's emotional state. In doing so, it analyzes the pitch and tempo of the voice, the movement of facial muscles, etc., to identify emotions such as "reassurance," "anxiety," and "irritation." The analysis results are output as the emotional state.

[0494] Step 3:

[0495] The emotion information generated by the emotion engine is sent to the server. The server receives this data and uses it as basic information to determine what kind of advice to generate. The emotion information is used to construct the prompt statement corresponding to the generated advice.

[0496] Step 4:

[0497] The server sends a prompt message to the generative AI model. This prompt message includes instructions to appropriately adjust the advice based on the user's emotional state. For example, it might say, "The user is feeling anxious, so please generate reassuring advice." The generative AI model then generates the adjusted advice in response to the prompt and outputs it to the server.

[0498] Step 5:

[0499] The server sends the generated advice to the terminal. The terminal receives this and presents it to the user visually, and provides voice guidance as needed. This allows the user to receive appropriate guidance regarding their child's health.

[0500] Step 6:

[0501] Reminders for vaccinations and routine checkups are managed through a system. This system, located on the server, sets the timing and method of reminders based on the user's emotional state and sends them to the device. For example, a user experiencing stress might receive a reminder with a gentle notification sound.

[0502] (Application Example 2)

[0503] 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."

[0504] Conventional medical support systems often fail to adequately consider the emotional state of users, resulting in insufficient psychological support for those receiving assistance. Furthermore, the lack of means to provide specific advice and notifications that reflect emotional states in real time contributes to increased stress for users.

[0505] 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.

[0506] In this invention, the server includes an information acquisition device, an analysis device that analyzes the acquired information and estimates multiple situations related to the user's health status, and an emotion recognition device. This makes it possible to provide appropriate support according to the user's emotional state and to offer policies to reduce stress.

[0507] An "information acquisition device" is a device used to collect input information from users and data from sensors.

[0508] An "analysis device" is a device that analyzes health and emotional states based on acquired information and has the function of estimating the situation.

[0509] A "generator" is a device that creates policies and advice regarding visits to medical facilities based on the situation obtained through analysis.

[0510] A "device that performs emotion recognition" is a device that analyzes the user's voice and facial expression data to identify their emotional state.

[0511] A "management device" is a device that adjusts notification content based on acquired emotional information and conveys it to the user in an appropriate manner.

[0512] The following describes embodiments for carrying out the present invention. This system aims to manage the user's health and emotional state in real time and provide appropriate advice.

[0513] The system primarily consists of an information acquisition device, an analysis device, a generation device, an emotion recognition device, and a management device. The information acquisition device uses sensors such as cameras and microphones to collect the user's facial expressions and voice. This provides the basic information necessary to analyze the acquired data.

[0514] The analysis device uses the collected data to analyze the user's health and emotional state. Here, a learning model and emotion analysis engine are used to associate the obtained data with specific health conditions and emotions. Based on these analysis results, the generation device generates appropriate policies and recommendations for visits to medical facilities.

[0515] The emotion recognition device is designed to analyze the user's emotions in real time and adjust advice according to their emotional state. By providing information tailored to the user's emotions, this device creates a more empathetic and less stressful experience.

[0516] The management system adjusts generated notifications according to the user's emotions and delivers them at the appropriate time and in the appropriate manner. This reduces the user's psychological burden and ensures a comfortable user experience.

[0517] As a concrete example, if the system determines that a user is feeling anxious, it will provide a "special reassuring message" or "suggestion of relaxing music." Furthermore, it will provide prompts such as, "What advice would you give to calm an elderly person who is feeling stressed?" as input to the generative AI model.

[0518] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0519] Step 1:

[0520] The device uses a camera and microphone to collect the user's facial expressions and voice data. This provides basic data for emotion recognition. The acquired data is then sent to a server for further processing.

[0521] Step 2:

[0522] The server sends the received facial expression and voice data to an emotion recognition device. The emotion recognition device analyzes this input data and identifies the user's emotional state (e.g., reassurance, worry, irritation). The analysis device receives the emotional state information.

[0523] Step 3:

[0524] The analysis device estimates the user's health status based on emotional state information and other health-related data, and identifies specific situations associated with it. This data processing outputs a comprehensive health and emotional profile of the user.

[0525] Step 4:

[0526] The generator constructs policies and advice based on the analysis results. Depending on the obtained health and emotional state, it generates advice on specific medical policies and relaxation techniques. This advice is presented to the user in the form of information.

[0527] Step 5:

[0528] The management device adjusts the generated policies and advice according to the user's emotional state. It reconfigures the policies to ensure notifications are delivered at the appropriate time and determines the output format of the notifications. Finally, it sends the adjusted notifications to the terminal and presents them to the user.

[0529] 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.

[0530] 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.

[0531] 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.

[0532] [Fourth Embodiment]

[0533] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0534] 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.

[0535] 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).

[0536] 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.

[0537] 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.

[0538] 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).

[0539] 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.

[0540] 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.

[0541] 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.

[0542] 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.

[0543] 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.

[0544] 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.

[0545] 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".

[0546] The present invention is an AI healthcare assistant system for supporting the health management of children, and is implemented by combining information input means, analysis means, generation means, and management means. The embodiments thereof are described in detail below.

[0547] Users input basic information and current symptoms of their child using a dedicated terminal or application. This input is received by the terminal via an information input device, and the information is sent to the cloud or a local server.

[0548] The information that reaches the server is processed by analytical tools. Based on this information, the server uses machine learning models and pre-programmed rule-based engines to identify multiple medical conditions related to the child's symptoms. For example, if there is a fever and cough, it may increase the likelihood of influenza or a common cold.

[0549] Based on the analysis results, a generation system installed on the server generates advice for the user regarding appropriate countermeasures and the need for medical consultation. For example, it suggests home remedies for mild symptoms and recommends visiting a medical institution if the symptoms are severe.

[0550] Furthermore, the server's management system handles vaccination schedules and calculates the next vaccination date. This information is registered as a reminder and notified to the user via their device at the designated time. For example, as the scheduled MMR vaccination date approaches, the user is notified to encourage them to prepare for the vaccination.

[0551] This system allows users to properly monitor their child's health, ensure no vaccinations are missed, and seek medical attention early when necessary. Overall, it reduces parental anxiety and provides comprehensive health management support for children.

[0552] The following describes the processing flow.

[0553] Step 1:

[0554] The user launches the application on their device and enters the child's basic information (age, height, weight) and current symptoms (e.g., fever, cough). The device collects this information and prepares to send it to the server.

[0555] Step 2:

[0556] The terminal uses a secure communication protocol to send the entered information to the server. During this process, the information is converted to JSON format to ensure data integrity and secure transmission.

[0557] Step 3:

[0558] The server analyzes the received data. First, it performs a data integrity check to ensure the data is correctly formatted and free from missing or incorrect information.

[0559] Step 4:

[0560] The server uses an analysis tool to run a machine learning model based on the received symptom information. This model identifies potential medical conditions related to the symptoms and generates a list of these conditions.

[0561] Step 5:

[0562] The server uses a generation mechanism to create a preliminary diagnosis and corresponding treatment plan for the user, based on the listed symptoms. This information includes symptom relief measures that can be done at home and whether a visit to a medical institution is necessary.

[0563] Step 6:

[0564] The server uses administrative tools to check the child's vaccination schedule. It identifies the next scheduled vaccination date and sets a reminder based on it.

[0565] Step 7:

[0566] The server sends the generated preliminary diagnosis, treatment plan, and vaccination information to the terminal. The terminal visually displays the received information to the user and activates a reminder function to notify the user as needed.

[0567] (Example 1)

[0568] 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".

[0569] In modern society, accurately understanding a child's health condition and responding appropriately and promptly is a major challenge for parents and guardians. In particular, determining which medical condition a symptom is related to is difficult, and inappropriate responses based on incorrect self-diagnosis can harm a child's health. To solve this problem, a system that provides reliable health information and appropriate treatment methods is necessary.

[0570] 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.

[0571] In this invention, the server includes means for inputting information, means for processing information to analyze the received information and estimate multiple medical conditions associated with the symptoms, means for generating advice indicating corresponding countermeasures and the need for medical treatment at a medical institution based on the medical conditions estimated by the processing means, means for managing and adjusting vaccination schedules and setting reminders, and an information processing device that includes a function to communicate with an external database to enhance symptom analysis. This makes it possible to accurately grasp the health status of children and quickly provide appropriate medical consultations and countermeasures at home.

[0572] "Means of inputting information" refers to an interface that allows users to electronically input basic information about their child's health condition and current symptoms.

[0573] "Means of processing information" refers to digital processing technology that analyzes received information and has the function of estimating multiple medical conditions associated with a symptom.

[0574] "Means for generating advice" refers to a function that, based on the medical condition estimated by the processing means, provides suggestions regarding specific countermeasures and the necessity of visiting a medical institution.

[0575] "Means of management" refers to a schedule management function that coordinates vaccination schedules and notifies users of reminders.

[0576] An "information processing device" refers to a device or system that communicates with an external database to analyze and enhance received information.

[0577] This invention is a system for supporting the health management of children, and consists of means for inputting information, means for processing information, means for generating advice, means for managing information, and an information processing device.

[0578] Users enter their child's basic information and current symptoms using a dedicated terminal or application. This terminal receives the input information and sends the data to the server. To ensure security, the data is transmitted using the HTTPS protocol.

[0579] The server uses means to process the received information. Here, the server uses a machine learning model (e.g., TensorFlow) or a rule-based engine to estimate possible medical conditions related to the symptoms. Specifically, it compares the input symptoms with past medical condition data to determine which medical conditions they correspond to.

[0580] Based on the analyzed information, the server uses a means to generate advice and creates advice to provide to the user. This advice allows the user to receive guidance on how to deal with symptoms at home and, if necessary, recommendations to seek medical attention.

[0581] Furthermore, through the management system, the server manages the vaccination schedule and generates reminders regarding the next vaccination date. These reminders are sent to the user's device as the vaccination date approaches.

[0582] For example, if a user enters into the application that "My child has had a fever of 38 degrees Celsius since last night and is coughing," the server will suggest the possibility of influenza and provide advice such as, "Ensure your child stays well-hydrated, and if symptoms persist, seek medical attention."

[0583] Example prompt to input into the generating AI model: "The child's current symptoms are fever and cough. Please tell me about possible illnesses and possible home remedies."

[0584] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0585] Step 1:

[0586] The user enters basic information about the child (e.g., age, name) and current symptoms (e.g., fever, cough) through a dedicated terminal or application. After this information is entered, the terminal converts the data into a structured format (e.g., JSON). The converted data is encrypted using SSL / TLS and securely transmitted to the server. The input data is used in the next analysis step.

[0587] Step 2:

[0588] The server processes the data received from the terminal using analytical tools. First, the received data is fed into a machine learning model or rule-based engine. For example, it compares the entered symptoms with a past medical history dataset to check if they match known medical conditions. This analysis identifies possible medical conditions (e.g., influenza, common cold), and the results are passed on to the next generation step.

[0589] Step 3:

[0590] The server generates advice based on the analysis results. The generation mechanism constructs advice indicating appropriate treatment methods and the need for medical consultation depending on the identified medical condition. For example, if the symptoms are mild, it suggests home care methods (e.g., adequate hydration), and if they are severe, it recommends a visit to a medical institution. This generated advice is sent to the user's terminal and presented visually.

[0591] Step 4:

[0592] The server manages the vaccination schedule through its administration system. Based on the received age information, it calculates the next scheduled vaccination date and registers it in the calendar. This information is set to be notified to the user's device as a reminder at the specified date and time. For example, if the next vaccination is approaching, the server will notify the user based on this information and prompt them to plan.

[0593] Through this series of steps, users can quickly and accurately receive detailed information and advice regarding their child's health.

[0594] (Application Example 1)

[0595] 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".

[0596] In modern times, managing children's health is a crucial issue, but it can be difficult for parents to visit medical institutions at the appropriate time or manage vaccination schedules. Furthermore, there is often a lack of effective means for monitoring health within the home, which can increase anxiety. There is also a need for health management support that is easy to use and seamlessly integrated into daily life.

[0597] 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.

[0598] In this invention, the server includes means for inputting biological information, analysis means for analyzing the received biological information and estimating multiple medical conditions associated with the disease state, and generation means for generating advice on appropriate response methods and consultation with a medical institution. This makes it possible to comprehensively manage a child's health status using a home robot in daily life and reduce parental anxiety.

[0599] "Biological information" is a general term for data related to a child's health status, and includes information necessary for health management.

[0600] "Analysis means" refers to a device or software that mechanically derives the possibility of disease from received biological information.

[0601] "Generating means" refers to a device or function that provides advice on appropriate countermeasures and necessary medical consultations based on the results obtained by the analysis means.

[0602] "Management measures" refer to technologies and systems that effectively manage the timing and schedule of vaccinations and provide timely notifications.

[0603] A "home robot" is a mechanical device designed to provide support for daily life within the home, and includes those that also have health management functions.

[0604] The system to realize this application is configured as an application installed on a robot used in the home. The aim of this system is to manage a child's health and provide parents with necessary advice.

[0605] The server collects data from various sensors mounted on the robot to acquire biological information. This biological information includes health indicators such as body temperature and cough. The server uses this information and various analytical tools to analyze the received data and estimate the disease state. A learning model is used for this analysis.

[0606] Once the analysis is complete, the generation system uses it to generate advice on appropriate actions and whether a visit to a medical institution is necessary. This advice is provided to parents via audio or visual display. Furthermore, the management system manages the child's vaccination schedule and provides notifications as vaccination dates approach.

[0607] Specific hardware includes home robots equipped with sensors (e.g., mobile robots equipped with tablet devices). Software includes, for example, AI analysis libraries using Python. The analysis and generation processes involve algorithms executed via the cloud or local servers.

[0608] For example, if a child has a persistent low-grade fever and cough, the robot might advise the parents to "rest at home" and suggest that the child drink a warm beverage. This allows for comprehensive daily monitoring of the child's health.

[0609] An example of a prompt message would be: "A 5-year-old child is showing symptoms of fever and cough. As an AI chatbot, please provide appropriate health management advice to the parents."

[0610] In this way, IT technology can be used to support the creation of a safe and secure environment within the home.

[0611] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0612] Step 1:

[0613] The device acquires biological information such as the child's body temperature and whether or not they have a cough in real time through sensors. This data is sent to the server as initial input.

[0614] Step 2:

[0615] The server identifies the disease state using analytical tools based on the received biological information. This process involves data analysis using a learning model. It analyzes the input body temperature and other symptom data to predict potential disease conditions. The result is the analysis output.

[0616] Step 3:

[0617] The server uses various generation methods based on the analysis output to generate advice on how to deal with the parents and whether medical consultation is necessary. During this generation process, the AI ​​model selects appropriate advice, taking the diagnostic results into consideration. The generated advice is returned to the terminal as output.

[0618] Step 4:

[0619] The device presents the generated advice to the parent as audio or visual display. This advice includes specific actions such as how to lower a child's temperature or how to deal with a cough.

[0620] Step 5:

[0621] The server uses management tools to monitor the vaccination schedule and sends notifications to devices as vaccination dates approach. This notification system allows parents to know when the next vaccination is due and make the necessary preparations.

[0622] 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.

[0623] This invention provides interaction tailored to the user's emotional state by combining an emotion recognition function with an AI healthcare assistant system that supports children's health management. This system is implemented with an emotion engine in addition to information input means, analysis means, generation means, and management means.

[0624] When a user enters information and symptoms of their child via a terminal, the system uses the camera and microphone to transmit the user's facial expressions and voice data to the emotion engine. The emotion engine analyzes this data in real time to recognize the user's emotional state (e.g., reassured, worried, irritated). This emotional information is sent to a server and processed by an analysis tool.

[0625] The server uses emotional information obtained by the emotion engine to adjust the advice provided by the generation system. For example, if the server determines that the user is feeling anxious, it will provide advice that is more polite and reassuring. Also, if the user is showing signs of frustration, the server will take steps to reduce the user's stress by providing concise and clear information.

[0626] Furthermore, the management system takes into account the user's emotional state and adjusts the timing and method of vaccination and routine check-up reminder notifications. For example, for users with high stress levels, reminder notifications are delivered in a gentler manner to help them relax and prepare for vaccination.

[0627] This system allows users to monitor their child's health in an accurate and user-friendly way and receive the most appropriate medical services when needed. Furthermore, it provides a better user experience by offering interactions tailored to individual emotional states.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The user uses their device to launch the application and enter basic information and symptoms of their child. This prepares the device to send the collected information, along with the user's facial expressions and voice data, to the server.

[0631] Step 2:

[0632] The terminal converts the data to an appropriate format and sends it to the server using a secure communication method. This data includes not only symptom information but also the user's facial expressions and voice data.

[0633] Step 3:

[0634] The server processes symptom information from the received data using analytical tools. Machine learning is used to identify potentially related medical conditions based on the received symptom information.

[0635] Step 4:

[0636] The server uses an emotion engine to analyze received facial and audio data to recognize the user's emotional state. Based on this information, it determines whether the user is experiencing anxiety, relief, frustration, or other emotional states.

[0637] Step 5:

[0638] The server generates a preliminary diagnosis and advice on countermeasures for the user based on the analyzed symptom data and emotional state. The information is presented in a way that is appropriate to the user's emotional state.

[0639] Step 6:

[0640] The server uses management tools to set necessary reminders based on the vaccination schedule. These reminders are flexibly adjusted in content and timing, taking into account the user's emotional state.

[0641] Step 7:

[0642] The server sends generated advice and vaccination reminder information to the device, which then displays this information visually to the user. Based on the displayed information, the user can take necessary measures to manage their child's health.

[0643] (Example 2)

[0644] 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".

[0645] In traditional healthcare systems, information regarding children's health is often one-sided, and there is insufficient consideration for the psychological state of parents. Furthermore, the anxieties and frustrations felt by parents are not adequately reflected, which puts a burden on decision-making regarding their children's health and access to medical services.

[0646] 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.

[0647] In this invention, the server includes means for inputting information, analysis means for analyzing the received information and estimating multiple states associated with symptoms, and generation means for generating relevant treatment methods and advice on visiting a medical institution based on the medical condition estimated by the analysis means, and further adjusting the content of the advice considering the user's emotional state. This enables flexible and user-friendly interaction that responds to the user's emotional state, and provides optimal health management support.

[0648] "Means for inputting information" refers to a device or mechanism that provides an interface for users to input data regarding a child's health status and symptoms.

[0649] "Analysis means" refers to a device or software equipped with the function of analyzing a child's health status and symptoms using input data and estimating multiple related conditions.

[0650] A "generation means" is a device or system that generates advice on appropriate countermeasures and institutions to consult based on information obtained by an analysis means, and adjusts the content of that advice taking into account the user's emotional state.

[0651] A "management device" is a device or program that manages vaccination and regular check-up schedules and adjusts the timing and method of reminders, taking into account the user's emotional state.

[0652] An "emotion engine" is software or a system that analyzes a user's facial expressions and voice data in real time, identifies their emotional state, and provides that information.

[0653] A "generative AI model" is an artificial intelligence model that automatically generates advice and information in natural language based on user input and analyzed data.

[0654] A "prompt statement" is a text that describes the instructions or requirements for input to the generating AI model, and the AI ​​model generates appropriate output based on this input.

[0655] This invention is a system for supporting children's health management, achieving more flexible and user-friendly interaction by providing advice tailored to the user's emotional state. This system is implemented by combining the following key elements:

[0656] The user uses the device to input information about the child's health status and symptoms. The device is connected to a camera and microphone, which collect the user's facial expression and voice data. This data is sent from the device to an emotion engine, which analyzes the user's emotional state in real time. Based on the collected data, the emotion engine detects what emotional state the user is in, such as "reassured," "anxious," or "frustrated."

[0657] The analyzed emotional information is sent to the server. The server uses a generative AI model based on the emotional information to generate prompt statements and adjust the advice provided to the user based on those prompts. The generative AI model is trained to consider the user's emotional state and generate appropriate and reassuring advice. For example, if the user is feeling anxious, the server optimizes the advice provided by using a prompt statement such as, "Generate reassuring advice when the user is feeling anxious."

[0658] For example, if a user inputs information such as "My child has been coughing since this morning and I'm worried," the emotion engine detects the user's emotional state of "worry." At this point, the server generates a prompt message for the generating AI model, such as "Please provide advice about the symptoms in a gentle tone," and retrieves new advice.

[0659] Furthermore, the management system includes features for scheduling vaccinations and regular checkups, and for adjusting the timing and method of reminder notifications according to the user's emotional state. For example, reminders for highly stressed users might be sent in a softer tone, ensuring that users receive medical services in a more relaxed state.

[0660] This allows users to receive emotional support while more effectively managing their child's health and accessing appropriate medical services in a timely manner.

[0661] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0662] Step 1:

[0663] The user uses the device to input information about the child's health condition. Specifically, they input text data about symptoms and physical condition, and the camera and microphone are automatically activated to collect the user's facial expression and voice data. The entered health information and emotional data are temporarily stored on the device.

[0664] Step 2:

[0665] The device transmits the collected facial expression and voice data to the emotion engine. The emotion engine analyzes this data to estimate the user's emotional state. In doing so, it analyzes the pitch and tempo of the voice, the movement of facial muscles, etc., to identify emotions such as "reassurance," "anxiety," and "irritation." The analysis results are output as the emotional state.

[0666] Step 3:

[0667] The emotion information generated by the emotion engine is sent to the server. The server receives this data and uses it as basic information to determine what kind of advice to generate. The emotion information is used to construct the prompt statement corresponding to the generated advice.

[0668] Step 4:

[0669] The server sends a prompt message to the generative AI model. This prompt message includes instructions to appropriately adjust the advice based on the user's emotional state. For example, it might say, "The user is feeling anxious, so please generate reassuring advice." The generative AI model then generates the adjusted advice in response to the prompt and outputs it to the server.

[0670] Step 5:

[0671] The server sends the generated advice to the terminal. The terminal receives this and presents it to the user visually, and provides voice guidance as needed. This allows the user to receive appropriate guidance regarding their child's health.

[0672] Step 6:

[0673] Reminders for vaccinations and routine checkups are managed through a system. This system, located on the server, sets the timing and method of reminders based on the user's emotional state and sends them to the device. For example, a user experiencing stress might receive a reminder with a gentle notification sound.

[0674] (Application Example 2)

[0675] 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".

[0676] Conventional medical support systems often fail to adequately consider the emotional state of users, resulting in insufficient psychological support for those receiving assistance. Furthermore, the lack of means to provide specific advice and notifications that reflect emotional states in real time contributes to increased stress for users.

[0677] 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.

[0678] In this invention, the server includes an information acquisition device, an analysis device that analyzes the acquired information and estimates multiple situations related to the user's health status, and an emotion recognition device. This makes it possible to provide appropriate support according to the user's emotional state and to offer policies to reduce stress.

[0679] An "information acquisition device" is a device used to collect input information from users and data from sensors.

[0680] An "analysis device" is a device that analyzes health and emotional states based on acquired information and has the function of estimating the situation.

[0681] A "generator" is a device that creates policies and advice regarding visits to medical facilities based on the situation obtained through analysis.

[0682] A "device that performs emotion recognition" is a device that analyzes the user's voice and facial expression data to identify their emotional state.

[0683] A "management device" is a device that adjusts notification content based on acquired emotional information and conveys it to the user in an appropriate manner.

[0684] The following describes embodiments for carrying out the present invention. This system aims to manage the user's health and emotional state in real time and provide appropriate advice.

[0685] The system primarily consists of an information acquisition device, an analysis device, a generation device, an emotion recognition device, and a management device. The information acquisition device uses sensors such as cameras and microphones to collect the user's facial expressions and voice. This provides the basic information necessary to analyze the acquired data.

[0686] The analysis device uses the collected data to analyze the user's health and emotional state. Here, a learning model and emotion analysis engine are used to associate the obtained data with specific health conditions and emotions. Based on these analysis results, the generation device generates appropriate policies and recommendations for visits to medical facilities.

[0687] The emotion recognition device is designed to analyze the user's emotions in real time and adjust advice according to their emotional state. By providing information tailored to the user's emotions, this device creates a more empathetic and less stressful experience.

[0688] The management system adjusts generated notifications according to the user's emotions and delivers them at the appropriate time and in the appropriate manner. This reduces the user's psychological burden and ensures a comfortable user experience.

[0689] As a concrete example, if the system determines that a user is feeling anxious, it will provide a "special reassuring message" or "suggestion of relaxing music." Furthermore, it will provide prompts such as, "What advice would you give to calm an elderly person who is feeling stressed?" as input to the generative AI model.

[0690] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0691] Step 1:

[0692] The device uses a camera and microphone to collect the user's facial expressions and voice data. This provides basic data for emotion recognition. The acquired data is then sent to a server for further processing.

[0693] Step 2:

[0694] The server sends the received facial expression and voice data to an emotion recognition device. The emotion recognition device analyzes this input data and identifies the user's emotional state (e.g., reassurance, worry, irritation). The analysis device receives the emotional state information.

[0695] Step 3:

[0696] The analysis device estimates the user's health status based on emotional state information and other health-related data, and identifies specific situations associated with it. This data processing outputs a comprehensive health and emotional profile of the user.

[0697] Step 4:

[0698] The generator constructs policies and advice based on the analysis results. Depending on the obtained health and emotional state, it generates advice on specific medical policies and relaxation techniques. This advice is presented to the user in the form of information.

[0699] Step 5:

[0700] The management device adjusts the generated policies and advice according to the user's emotional state. It reconfigures the policies to ensure notifications are delivered at the appropriate time and determines the output format of the notifications. Finally, it sends the adjusted notifications to the terminal and presents them to the user.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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."

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] The following is further disclosed regarding the embodiments described above.

[0723] (Claim 1)

[0724] Information input means and

[0725] An analytical means that analyzes the received information and estimates multiple medical conditions that can be associated with the symptoms,

[0726] A generation means that generates corresponding treatment methods and advice on visiting a medical institution based on the medical condition estimated by the analysis means,

[0727] A management tool for managing vaccination schedules and setting reminders,

[0728] A system that includes this.

[0729] (Claim 2)

[0730] The system according to claim 1, wherein the analysis means analyzes symptom data using a machine learning model.

[0731] (Claim 3)

[0732] The system according to claim 1, comprising a display means for visually presenting the generated preliminary diagnosis and advice.

[0733] "Example 1"

[0734] (Claim 1)

[0735] Means of inputting information,

[0736] A means for processing information to analyze received information and estimate multiple medical conditions associated with the symptoms,

[0737] A means for generating advice indicating the appropriate course of action and the need for medical treatment at a medical institution based on the medical condition estimated by the processing means,

[0738] A means of managing and scheduling vaccination appointments and setting reminders,

[0739] An information processing device that includes a function to enhance symptom analysis by communicating with an external database,

[0740] A system that includes this.

[0741] (Claim 2)

[0742] The system according to claim 1, wherein the processing means analyzes symptom data using a machine learning model.

[0743] (Claim 3)

[0744] The system according to claim 1, comprising means for visually presenting the generated preliminary diagnosis and advice and for displaying a warning to the user.

[0745] "Application Example 1"

[0746] (Claim 1)

[0747] A means for inputting biological information,

[0748] An analytical means for analyzing received biological information and estimating multiple disease conditions associated with a disease state,

[0749] A generation means that generates appropriate response methods and advice on seeking medical attention based on the medical condition estimated by the analysis means,

[0750] A management system for managing vaccination schedules and setting up notifications,

[0751] Implementing healthcare functions into home robots, and using them as a means of daily life,

[0752] A system that includes this.

[0753] (Claim 2)

[0754] The system according to claim 1, wherein the analysis means analyzes biological information using a learning model.

[0755] (Claim 3)

[0756] The system according to claim 1, comprising means for providing the generated provisional diagnosis and advice by visual means.

[0757] "Example 2 of combining an emotion engine"

[0758] (Claim 1)

[0759] Means of inputting information,

[0760] An analytical means for analyzing received information and estimating multiple conditions that can be associated with symptoms,

[0761] A generation means that generates relevant coping strategies and advice on seeking medical attention based on the medical condition estimated by the analysis means, and further adjusts the content of the advice considering the user's emotional state.

[0762] A management system for managing vaccination schedules and adjusting the timing and method of reminder notifications according to the user's emotional state,

[0763] An emotion engine that analyzes the user's emotional state in real time,

[0764] A system that includes this.

[0765] (Claim 2)

[0766] The system according to claim 1, wherein the analysis means and emotion engine analyze data using a machine learning model and generate advice using a generative AI model.

[0767] (Claim 3)

[0768] The system according to claim 1, comprising display means for presenting the generated preliminary diagnosis and advice in a visual and emotionally sensitive manner.

[0769] "Application example 2 when combining with an emotional engine"

[0770] (Claim 1)

[0771] Information acquisition device and

[0772] An analysis device that analyzes acquired information to estimate multiple situations that can be associated with health status,

[0773] A generating device that generates corresponding policies and advice on visits to medical facilities based on the situation estimated by the analysis device,

[0774] A device for recognizing emotions,

[0775] A management device that adjusts notification content according to emotional information,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, wherein the analysis device analyzes symptom information using a learning model.

[0779] (Claim 3)

[0780] The system according to claim 1, comprising a display device for visually presenting the generated provisional diagnosis and advice. [Explanation of Symbols]

[0781] 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. Information input means and An analytical means that analyzes the received information and estimates multiple medical conditions that can be associated with the symptoms, A generation means that generates corresponding treatment methods and advice on visiting a medical institution based on the medical condition estimated by the analysis means, A management tool for managing vaccination schedules and setting reminders, A system that includes this.

2. The system according to claim 1, wherein the analysis means analyzes symptom data using a machine learning model.

3. The system according to claim 1, comprising a display means for visually presenting the generated preliminary diagnosis and advice.

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