System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input symptom data using an input device; means for a terminal to transmit the input symptom data to a server; means for the server to analyze the received symptom data and determine an urgency level; means for the server to generate an appropriate response message based on the determined urgency level; and means for the terminal to display the response message from the server to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Parents of children who suddenly become injured or ill often have difficulty quickly determining the urgency of the condition. In particular, when parents lack medical knowledge or the condition is highly urgent, they may delay their decision to take appropriate action, risking a deterioration in the child's health. Incorrect judgments can also lead to visits to emergency medical facilities for non-emergency situations, or to overlook visits to medical facilities where they should have been seen. To solve these problems, a system is needed that can quickly determine the urgency and prompt appropriate action. [Means for solving the problem]

[0005] The present invention provides a means for users to input their child's symptoms using an input device and send the information to a server. The server analyzes the received symptom data and uses an AI engine to determine the level of urgency. Based on the results of the determination, the server generates an appropriate response message, such as information on nearby emergency medical facilities, information on medical facilities with opening hours, or home care methods. This response message is sent to the device and displayed to the user. This allows parents to quickly and accurately understand what action to take and provides support for appropriately managing their child's health.

[0006] "User" refers to the individual who uses the system to enter their child's symptoms and receive the results.

[0007] An "input device" is a device that allows a user to input data, and specifically includes a smartphone, a computer keyboard, a touch screen, etc.

[0008] "Symptom data" refers to specific information about a child's physical condition, including details of symptoms such as fever, cough, and loss of consciousness.

[0009] "Terminal" is a general term for input devices and devices that display information used by users, and specifically includes smartphones, tablets, computers, etc.

[0010] "Server" refers to a computer system that receives, analyzes, and processes data sent from a terminal.

[0011] "Analysis" refers to the calculation and analysis performed by the server to determine the urgency of the symptoms based on the symptom data received.

[0012] "AI engine" refers to an artificial intelligence model that uses machine learning and deep learning technologies to determine the level of urgency based on symptom data.

[0013] "Urgency" indicates the severity of the symptoms and the degree of urgency required, and is classified into three levels: high, medium, and low.

[0014] The "response message" refers to a message that is generated based on the result of the urgency determination and includes specific instructions and guidance for the user.

[0015] "Nearby emergency medical facility" refers to the medical facility that is closest to the user's current location and that can respond in an emergency.

[0016] "Medical facilities open during business hours" refers to general medical facilities that are open during normal business hours.

[0017] "Home care instructions" refers to instructions containing specific treatments to be performed at home for mild symptoms.

[0018] "JSON format" is a standard format for structuring and expressing data, and is an abbreviation for JavaScript (registered trademark) Object Notation.

[0019] The "HTTPS protocol" is a protocol for secure data communication and is an abbreviation for HyperText Transfer Protocol Secure. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a 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.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0034] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The present invention is a system in which a user inputs a child's symptoms using an input device, analyzes the information, determines the urgency of the symptoms, and prompts appropriate action based on the results. Specifically, it is composed of a terminal, a server, an AI engine, and an interface for displaying a response message to the user. An embodiment for actually implementing this system is described in detail below.

[0042] Basic system configuration

[0043] 1. User Input

[0044] Users use a smartphone or computer to enter their child's symptoms through a dedicated application or web interface, and the symptom data is entered in detailed text format.

[0045] For example, enter "My child has a high fever and a persistent cough."

[0046] 2. Data transmission by the terminal

[0047] The device first converts the input symptom data into JSON format and then securely sends it to the server using the HTTPS protocol.

[0048] 3. Data analysis and urgency determination by the server

[0049] The server receives the symptom data sent from the terminal and analyzes it.

[0050] An AI engine is used for analysis and determines the level of urgency based on the received data. The AI ​​engine has learned from past medical data and cases, enabling highly accurate diagnoses.

[0051] Urgency is classified into three levels: high, medium, and low.

[0052] 4. Server Generation of Response Message

[0053] After the AI ​​engine determines the urgency, the server generates an appropriate response message based on the urgency.

[0054] If the emergency is severe, information about the nearest emergency medical facility will be provided.

[0055] If the emergency is moderate, information on medical facilities that are open will be provided.

[0056] If the condition is less urgent, home care instructions will be provided.

[0057] 5. Receiving and displaying data by the device

[0058] The server generates a response message and sends it to the terminal in JSON format.

[0059] The terminal interprets the data it receives and displays it in a user-friendly format.

[0060] Specific example explanation

[0061] Example 1: High Urgency

[0062] The user enters the child's symptom as "lost consciousness."

[0063] The terminal sends this information to the server.

[0064] The server uses an AI engine to determine the urgency as high.

[0065] The server generates a message such as "Please contact the nearest emergency hospital immediately."

[0066] The terminal displays this message to the user.

[0067] Example 2: Medium Urgency

[0068] The user types, "I have a fever of 37.5 degrees and a cough."

[0069] The terminal sends this information to the server.

[0070] The server determines the urgency to be medium and generates a message such as "Please visit XX Clinic during opening hours."

[0071] The terminal displays this message to the user.

[0072] Example 3: Low Urgency

[0073] The user types, "I have some minor scratches."

[0074] The terminal sends this information to the server.

[0075] The server determines the urgency to be low and generates a message such as "Please take care at home by cooling the area and keeping it clean."

[0076] The terminal displays this message to the user.

[0077] This allows users to take prompt and appropriate action against their child's symptoms and prevent their health from worsening. This system can be a powerful support tool for parents.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] The user starts up the device (smartphone or computer) and accesses a dedicated application or web interface.

[0081] Step 2:

[0082] The user enters the child's symptom data into a form displayed on the screen.

[0083] For example, enter "My child has a high fever and a persistent cough."

[0084] Step 3:

[0085] The user checks the input information and presses the send button.

[0086] Step 4:

[0087] The terminal converts the data entered by the user into JSON format.

[0088] Step 5:

[0089] The terminal sends the converted JSON data to the server using the HTTPS protocol.

[0090] API endpoint: https: / / example.com / api / diagnose

[0091] Step 6:

[0092] The server receives the request from the terminal and prepares the data for analysis.

[0093] Step 7:

[0094] The server interprets the received JSON data and extracts the required fields.

[0095] Step 8:

[0096] The symptom data extracted by the server is input into an AI engine to determine the level of urgency.

[0097] The AI ​​engine uses a model trained on a large amount of medical data to classify the level of urgency into three levels: high, medium, and low.

[0098] Step 9:

[0099] The server receives the urgency determination result and generates an appropriate response message based on the result.

[0100] Step 10:

[0101] If the urgency is high, the server searches a database of nearby emergency medical facilities to obtain information on the most suitable medical facility.

[0102] Example: Generate a message that reads, "This is a high urgency. Please visit XX Emergency Hospital (Address: XXX, Phone: YYY) immediately."

[0103] Step 11:

[0104] If the urgency is medium, the server suggests medical facilities that are open.

[0105] Example: Generates a message saying, "The emergency is moderate. Please visit XX Clinic during business hours."

[0106] Step 12:

[0107] If the emergency is low, the server will suggest home care methods.

[0108] Example: Generate a message that says, "Symptoms are mild. Please take care of yourself at home by cooling your body, keeping it clean, etc."

[0109] Step 13:

[0110] Convert the response message generated by the server into JSON format.

[0111] Step 14:

[0112] The server sends the converted JSON data to the terminal using the HTTPS protocol.

[0113] Step 15:

[0114] The device interprets the JSON data it receives and converts it into a human-readable format.

[0115] Step 16:

[0116] The terminal displays a response message to the user.

[0117] Step 17:

[0118] The user checks the response message displayed on the terminal and performs the suggested action.

[0119] Example 1

[0120] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0121] In today's world, accurately and quickly determining the urgency of a child's symptoms is an important challenge for parents and medical institutions. Urgent symptoms require immediate action, especially for those with high-urgency symptoms. However, parents generally lack easy-to-use methods that do not require specialized knowledge and allow accurate diagnoses and appropriate treatment. Furthermore, there is a need to provide appropriate care methods that minimize the consumption of medical resources, even in cases of mild symptoms.

[0122] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0123] In this invention, the server includes: means for a user to input symptom data using an input device; means for a terminal to convert the input symptom data into JSON format and send it to the server using the HTTPS protocol; means for the server to analyze the received symptom data and use an AI engine to determine the level of urgency; means for the server to generate an appropriate response message based on the determined level of urgency; and means for the terminal to receive the response message from the server in JSON format, interpret it, and display it in a human-readable format. This allows the user to quickly and accurately determine the level of urgency of their child's symptoms, enabling them to provide appropriate first aid or see a doctor at a medical institution.

[0124] "User" refers to a person who uses the system to enter a child's symptom data.

[0125] "Input device" refers to hardware and software used to input data into a system.

[0126] "Symptom Data" refers to information that includes textual descriptions of a child's physical condition or health status.

[0127] "Terminal" refers to a device that is operated by a user to input symptom data and communicate with a server.

[0128] "Server" refers to a computer system that receives input symptom data, performs analysis, and generates an appropriate response message.

[0129] "JSON format" stands for JavaScript Object Notation and refers to a lightweight text format for structuring data.

[0130] The "HTTPS protocol" is an abbreviation for HyperText Transfer Protocol Secure, and refers to a communication protocol for securely transmitting data over the Internet.

[0131] "AI engine" refers to software that uses artificial intelligence technology to analyze data and determine the level of urgency.

[0132] A "response message" refers to a message generated by the server based on the analysis of the symptom data.

[0133] "Database Management System" means a software system for managing, storing, and retrieving data efficiently and securely.

[0134] "Urgency" refers to the criteria for determining the severity and urgency of symptoms.

[0135] A "template engine" refers to software for dynamically generating messages from a standardized format.

[0136] "Analysis" refers to the act of examining input data in detail, understanding its contents, and drawing specific conclusions.

[0137] "Display" refers to the act of visually providing information on a terminal in a form that can be read by a user.

[0138] The present invention is a system that allows a user to input a child's symptoms using an input device, analyzes the information, determines the urgency of the symptoms, and prompts appropriate action based on the results. The system includes a terminal, a server, an AI engine, and an interface for displaying a response message to the user.

[0139] User-entered symptom data

[0140] Users use a smartphone or computer to enter detailed information about their child's symptoms through a dedicated application or web interface, and this information is entered in text format.

[0141] Example: "My child has a high fever and a persistent cough."

[0142] Data transmission by the terminal

[0143] The device converts the input symptom data into JSON format and securely sends it to the server using the HTTPS protocol. An appropriate conversion library (e.g., Jackson for Java, Gson for Java, etc.) is used to convert the data format.

[0144] Data analysis and urgency determination by server

[0145] The server holds the data received from the device and saves it in a database management system (e.g., PostgreSQL, MongoDB, etc.). The saved data is then passed to an AI engine for analysis. This AI engine has learned from past medical data and cases, and uses a model written in Python (e.g., scikit-learn, TENSORFLOW (registered trademark), etc.) to accurately determine the urgency of the case.

[0146] Server generation of response message

[0147] The server generates an appropriate response message based on the received urgency assessment result. It selects the response message based on the conditions and generates the message using a template engine (e.g., FreeMarker, Thymeleaf, etc.).

[0148] If the situation is urgent: "Contact the nearest emergency hospital immediately."

[0149] If the urgency is medium: "Please visit XX Clinic during opening hours."

[0150] If the situation is not urgent: "Take care at home by cooling the area and keeping it clean."

[0151] Receiving and displaying data by the terminal

[0152] The terminal receives the response message from the server in JSON format, converts it into a user-friendly format, and displays it using a user-friendly interface.

[0153] Examples of prompt statements

[0154] If you lose consciousness, immediately contact the nearest emergency room.

[0155] "If you have a fever of 37.5 degrees and a cough, please visit XX Clinic during business hours."

[0156] "If you suffer a minor abrasion, please take care of it at home by cooling it and keeping it clean."

[0157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0158] Step 1: User enters symptom data

[0159] Specific operation: The user inputs symptom data using a smartphone or computer input device. The input data is described in detail in text format.

[0160] Input: Text data about a child's symptoms (e.g., "My child has a high fever and a persistent cough").

[0161] Output: Symptom data in text format.

[0162] Step 2: The device converts the data into JSON format

[0163] What it does: The terminal takes the text data entered by the user and converts it to JSON format using a library such as Jackson or Gson.

[0164] Input: Symptom data in text format.

[0165] Output: Data converted to JSON format.

[0166] Step 3: The device sends the data to the server using the HTTPS protocol.

[0167] Specific operation: The terminal sends the converted JSON data to the endpoint URL via a POST request using the HTTPS protocol.

[0168] Input: Symptom data in JSON format.

[0169] Output: The HTTP request sent to the server.

[0170] Step 4: The server receives and stores the data

[0171] Specific operation: The server holds the data received from the terminal and stores it in a database management system (e.g., PostgreSQL, MongoDB).

[0172] Input: Symptom data sent from the device in JSON format.

[0173] Output: Symptom data stored in a database.

[0174] Step 5: The server analyzes the data using an AI engine

[0175] How it works: The server passes the stored data to an AI engine, which analyzes the data and determines the level of urgency. The AI ​​engine has learned from past medical data and cases.

[0176] Input: Symptom data stored in a database in JSON format.

[0177] Output: Urgency assessment result (high, medium, low).

[0178] Step 6: Server generates response message

[0179] Specific operation: The server generates an appropriate response message using a template engine based on the urgency assessment result.

[0180] Input: Urgency assessment result.

[0181] Output: A text response message (e.g., "Please contact the nearest emergency room immediately").

[0182] Step 7: The server converts the message into JSON format and sends it

[0183] Specific operation: The server converts the generated response message into JSON format and sends it to the terminal again using the HTTPS protocol.

[0184] Input: A response message in text format.

[0185] Output: The HTTP request and response message converted to JSON format.

[0186] Step 8: The device receives and displays the data

[0187] Specific operation: The device receives the response message from the server in JSON format, converts it into a format that the user can understand, and displays it.

[0188] Input: The response message in JSON format.

[0189] Output: A text response message that is displayed to the user.

[0190] (Application example 1)

[0191] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0192] In conventional factory robot maintenance and failure response, it is difficult for operators to accurately determine abnormal conditions and quickly take appropriate action. This requires a high level of specialized knowledge, which can lead to delayed responses in emergencies that require a rapid response. This can lead to the shutdown of factory production lines and further danger. This issue needs to be resolved with a new system that efficiently determines abnormal conditions and provides appropriate response actions.

[0193] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0194] In this invention, the server includes: means for a user to input status data using an input device; means for a terminal to transmit the input status data to the server; means for the server to analyze the received status data and determine the level of urgency; means for the server to generate an appropriate response message based on the determined level of urgency; means for the terminal to display the response message from the server to the user; means for a user to input abnormal status information in text format using the terminal; means for the terminal to transmit the input abnormal status data to the server and the server to analyze the level of urgency; and means for the server to generate an appropriate response action based on the result of the urgency determination and notify the user. This enables an operator to quickly and accurately report an abnormal status, and the server to automatically determine the level of urgency and provide an appropriate response action.

[0195] "User" means an individual or operator who uses the system to input status data.

[0196] An "input device" is a device for inputting status data, including, for example, a tablet, a smartphone, or a computer.

[0197] "Status data" is information entered by the user when an abnormality occurs, and is text data detailing the specific content and circumstances of the abnormality.

[0198] A "terminal" is a device on which a user inputs status data and sends it to a server, and can refer to a tablet, smartphone, or computer.

[0199] "Sending" refers to the act of transferring data from an input device to a server using a communication protocol.

[0200] The "server" is a central processing unit that receives status data sent from the terminal, analyzes it, and generates a response message.

[0201] "Analyzing" refers to the act of interpreting the content of the data received by the server, processing the information, and drawing conclusions.

[0202] "Urgency" is an evaluation criterion that determines the severity of an abnormality and the promptness of response based on status data on a three-level scale (high, medium, low).

[0203] "Determining" refers to the act of the server analyzing the status data and identifying the level of urgency.

[0204] The "response message" is the content of instructions and corresponding actions generated by the server based on the result of the urgency determination.

[0205] "Displaying" refers to the act of visually presenting to the user the response message received by the terminal from the server.

[0206] "Text format" is a data format in which the user inputs information about an abnormal state using characters or sentences.

[0207] The "urgency determination result" is the evaluation result of the urgency level derived by the server by analyzing the status data.

[0208] "Response actions" are specific measures or instructions according to the level of urgency that the server provides as a response message.

[0209] This invention is a system in which a user inputs status data using an input device, analyzes the data, determines the urgency of an abnormality, and prompts appropriate response actions. Specifically, it targets abnormal conditions related to factory robots. A detailed description of an embodiment of this system is provided below.

[0210] Basic system configuration

[0211] 1. User Input

[0212] The user (operator) uses an input device such as a tablet or smartphone to input abnormal conditions of the robot through a dedicated application or web interface. The input data is detailed in text format.

[0213] Example: Enter "The robot's motor is overheating."

[0214] 2. Data transmission by the terminal

[0215] The terminal first converts the entered status data into JSON format and then securely sends it to the server using the HTTPS protocol.

[0216] 3. Data analysis and urgency determination by the server

[0217] The server receives and analyzes the status data sent from the device. An AI engine is used for the analysis, and determines the level of urgency based on the received data. The AI ​​engine has learned past abnormal conditions and status patterns, allowing for highly accurate determinations. Urgency is classified into three levels: high, medium, and low.

[0218] 4. Server Generation of Response Message

[0219] After the AI ​​engine determines the urgency, the server generates an appropriate response message based on the urgency.

[0220] If the emergency is urgent, specific instructions will be provided, such as "Stop the robot's motors immediately and call a specialist technician."

[0221] Medium urgency: Instructions such as "Pause robot operation and monitor the situation" are provided.

[0222] Low-urgency situations: Instructions such as "Please record the situation and continue driving as normal" are provided.

[0223] 5. Receiving and displaying data by the device

[0224] The server generates a response message and sends it to the terminal in JSON format. The terminal interprets the received data and displays it in a user-friendly format.

[0225] Technology used

[0226] Hardware: tablets, smartphones, factory robots

[0227] software:

[0228] Flask (Server construction with Python)

[0229] TensorFlow or PyTorch (AI engine implementation)

[0230] JSON (data format)

[0231] Requests (HTTP communication)

[0232] React Native or Flutter (registered trademark) (mobile application development)

[0233] Specific examples

[0234] When a user inputs "The robot arm is stuck and won't move," the system analyzes the data and suggests specific actions to take, such as "Immediately stop the arm motor and call a specialist technician." This allows the user to take prompt and accurate action, improving factory efficiency and safety.

[0235] Example prompt sentence:

[0236] Enter the robot's motors getting too hot.

[0237] This system makes it possible to instantly determine the urgency of abnormal conditions involving factory robots and take appropriate action to avoid production line shutdowns and further danger.

[0238] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0239] Step 1:

[0240] A user inputs status data using an input device.

[0241] Input: Text data of abnormal conditions related to the robot (e.g., "The robot's motor is overheating")

[0242] Specific operation: Log in to a dedicated application on a smartphone or tablet and enter the abnormal condition in text format.

[0243] Output: Text data of the abnormal condition is saved in the input device.

[0244] Step 2:

[0245] The terminal converts the input status data into JSON format and sends it to the server using the HTTPS protocol.

[0246] Input: Text data of abnormal condition

[0247] Specific operation: A dedicated application converts abnormality data into JSON format and sends it securely to the server using the HTTPS protocol.

[0248] Output: JSON formatted data is sent to and received from the server.

[0249] Step 3:

[0250] The server analyzes the status data received and uses an AI engine to determine the level of urgency.

[0251] Input: State data in JSON format

[0252] Specific operation: The server parses the received JSON data and inputs it into the AI ​​engine. The AI ​​engine (using, for example, TensorFlow or PyTorch) analyzes the data and determines the urgency (high, medium, low).

[0253] Output: Data on urgency (high, medium, low) and recommended response actions

[0254] Step 4:

[0255] The server generates an appropriate response message based on the determined urgency.

[0256] Input: Urgency assessment results and recommended response actions

[0257] Specific operation: The server embeds data into a template message according to the urgency level and generates a specific response message regarding the recommended action.

[0258] Output: The generated response message

[0259] Step 5:

[0260] The terminal receives the response message from the server in JSON format and displays it to the user.

[0261] Input: Generated response message (JSON format)

[0262] Specific operation: The device parses the JSON data received from the server, converts it into a human-readable format, and displays the response message on the screen using a dedicated application.

[0263] Output: A response message that appears on the user's screen (e.g., "Immediately stop the robot's motors and call a specialist technician.")

[0264] This establishes a series of steps from inputting an abnormal condition to determining the level of urgency and displaying a response message, allowing the system to function effectively.

[0265] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0266] This system allows users to input their child's symptoms, analyzes the information, determines the level of urgency, and provides an appropriate response message. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide support tailored to the situation. This system consists of a terminal, a server, an AI engine, an emotion engine, and an interface for displaying the response message.

[0267] Basic system configuration

[0268] 1. User Input

[0269] Users use a smartphone or computer to access a dedicated application or web interface.

[0270] The user enters the child's symptom data into an on-screen form.

[0271] For example, enter "My child has a high fever and a persistent cough."

[0272] 2. Data transmission by the terminal

[0273] The device converts the entered symptom data into JSON format and sends it to the server using the HTTPS protocol.

[0274] 3. Data analysis and urgency determination by the server

[0275] The server receives the symptom data sent from the terminal and analyzes it.

[0276] An AI engine is used for analysis and determines the urgency of the received data. The AI ​​engine has learned from past medical data and cases, enabling highly accurate diagnoses.

[0277] Urgency is classified into three levels: high, medium, and low.

[0278] 4. Emotion Recognition by Emotion Engine

[0279] The emotion engine uses a camera or microphone mounted on the user's input device to acquire the user's facial expressions and voice and recognize their emotions.

[0280] The emotion engine analyzes the user's emotion data and sends the results to the server.

[0281] 5. Server Generation of Response Messages

[0282] The server references the urgency assessment results from the AI ​​engine and the emotional data from the emotion engine to generate an appropriate response message.

[0283] If the urgency is high, the server searches a database of nearby emergency medical facilities to obtain information on the most suitable medical facility.

[0284] If the urgency is medium, the server provides information on medical facilities that are open.

[0285] If the emergency is less urgent, the server will provide home care instructions.

[0286] Based on the results of the emotion engine, users with high stress levels are provided with more detailed explanations and additional support information.

[0287] 6. Receiving and displaying data by the device

[0288] The server generates a response message and sends it to the terminal in JSON format.

[0289] The terminal interprets the data it receives and displays it in a user-friendly format.

[0290] Specific example explanation

[0291] Example 1: High Urgency

[0292] The user enters the child's symptom as "lost consciousness."

[0293] The terminal sends this information to the server.

[0294] The server uses an AI engine to determine the urgency as high.

[0295] The emotion engine analyzes the user's facial expressions and voice and recognizes that they are in a state of strong anxiety or panic.

[0296] The server provides the message "Contact the nearest emergency room immediately" along with additional details to reassure the user.

[0297] The terminal displays this message to the user.

[0298] Example 2: Medium Urgency

[0299] The user types, "I have a fever of 37.5 degrees and a cough."

[0300] The terminal sends this information to the server.

[0301] The server determines the urgency to be medium and generates a message saying, "Please visit XX Clinic during opening hours."

[0302] The emotion engine analyzes the user's emotions and determines that the stress level is medium.

[0303] The server provides additional detailed instructions, including specific appointment times and clinic contact information.

[0304] The terminal displays this message to the user.

[0305] Example 3: Low Urgency

[0306] The user types, "I have some minor scratches."

[0307] The terminal sends this information to the server.

[0308] The server determines the urgency to be low and generates a message saying, "Please take care at home by cooling the area and keeping it clean."

[0309] The emotion engine analyzes the user's emotions and determines that there are no particular signs of high stress or anxiety.

[0310] The server provides only basic care instructions.

[0311] The terminal displays this message to the user.

[0312] This allows users to respond quickly and appropriately to their child's symptoms and receive support tailored to their emotional state. This system can be a powerful support tool for parents.

[0313] The processing flow will be explained below.

[0314] Step 1:

[0315] The user starts up the device (smartphone or computer) and accesses a dedicated application or web interface.

[0316] Step 2:

[0317] The user enters their child's symptom data into an on-screen form. For example, "My child has a high fever and a persistent cough."

[0318] Step 3:

[0319] The user presses the submit button to confirm the input.

[0320] Step 4:

[0321] The terminal receives the user's input and converts it into JSON format.

[0322] Step 5:

[0323] The terminal sends the converted JSON data to the server using the HTTPS protocol.

[0324] API endpoint: https: / / example.com / api / diagnose

[0325] Step 6:

[0326] The server receives the JSON data sent from the device, parses it, and extracts the necessary fields.

[0327] Step 7:

[0328] The server inputs the extracted symptom data into an AI engine, which analyzes the symptom data and compares it with past medical data to determine the urgency (high, medium, low).

[0329] Step 8:

[0330] The server receives the AI ​​engine's judgment result and proceeds to the next step.

[0331] Step 9:

[0332] The emotion engine uses the device's built-in camera or microphone to capture the user's facial expressions and voice in real time.

[0333] Step 10:

[0334] The emotion engine analyzes the acquired data and determines the user's emotion (calm, anxious, panic, etc.).

[0335] Step 11:

[0336] The server combines the urgency assessment results of the AI ​​engine and the emotion assessment results of the emotion engine to generate an appropriate response message.

[0337] Step 12:

[0338] If the emergency is high, the server searches the database for nearby emergency medical facilities and generates a message to provide information on the most appropriate medical facility. Example: "As the emergency is high, please visit the nearest emergency hospital (Address: XXX, Phone: YYY) immediately."

[0339] Step 13:

[0340] If the urgency is medium, the server obtains information about medical facilities that are open and generates a message instructing the patient to visit the hospital. Example: "The urgency is medium. Please visit XX Clinic during opening hours."

[0341] Step 14:

[0342] If the level of urgency is low, the server generates a message suggesting home care methods, for example, "Your symptoms are mild. Please take care of yourself at home by cooling your body, keeping it clean, etc."

[0343] Step 15:

[0344] Convert the response message generated by the server into JSON format.

[0345] Step 16:

[0346] The server sends the converted JSON data to the terminal using the HTTPS protocol.

[0347] Step 17:

[0348] The device interprets the JSON data it receives and converts it into a human-readable format.

[0349] Step 18:

[0350] The terminal displays a response message to the user.

[0351] Step 19:

[0352] The user reviews the response message displayed on the device and takes the suggested action, such as contacting the nearest emergency facility, visiting a doctor, or providing home care, if necessary.

[0353] Example 2

[0354] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0355] The present invention relates to a system that allows users to quickly and accurately report their child's symptoms, analyzes the data, and immediately provides appropriate instructions for a response. In particular, the system aims to provide a more user-friendly response by taking the user's emotional state into consideration when determining the level of urgency.

[0356] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for using an emotion recognition engine to acquire and analyze emotion data of a user, means for the server to customize a response message based on the results of the urgency determination and the emotion recognition results, means for the terminal to convert input symptom data into JSON format and transmit it to the server using the HTTPS protocol, means for the server to input the received JSON data into an artificial intelligence engine and determine the urgency, means for the server to convert the generated response message into JSON format and transmit it to the terminal using the HTTPS protocol, and means for the terminal to interpret the received JSON data and display it in a human-readable format. This makes it possible to immediately determine the urgency of the symptoms reported by the user and to provide an optimal response message depending on the user's emotional state.

[0357] "User" refers to the person who uses the terminal to input the child's symptom data and other information.

[0358] "Input device" refers to a hardware or software interface through which a user inputs symptom data, etc.

[0359] "Symptom Data" refers to data that contains specific information about a child's symptoms.

[0360] The term "terminal" refers to an electronic device that transmits symptom data entered by a user to a server and displays a response message received from the server.

[0361] "Server" refers to a central processing unit used to receive and analyze data sent from terminals and generate response messages.

[0362] "Data analysis" refers to the process of using an AI engine or similar to make judgments about urgency and other factors based on input data.

[0363] "Urgency" is an indicator of the seriousness of the symptoms and is classified into three levels: high, medium, and low.

[0364] "Response message" refers to a message generated by a server, sent to a terminal, and containing instructions or information that is displayed to a user.

[0365] "Emotion recognition engine" refers to a software module or hardware device for acquiring and analyzing a user's emotion data.

[0366] "Emotion data" is data that indicates the user's emotional state, and is obtained from facial expressions, voice, and the like.

[0367] An "artificial intelligence engine" refers to a software module that performs highly accurate analysis and judgments based on input data.

[0368] "JSON format" is a lightweight data description format used for data exchange and is an abbreviation for JavaScript Object Notation.

[0369] The "HTTPS protocol" is an abbreviation for Hypertext Transfer Protocol Secure, which is used for secure communication.

[0370] This invention relates to a system that allows users to input their child's symptoms, analyzes the information, determines the level of urgency, and provides an appropriate response message. Furthermore, by incorporating an emotion recognition engine that recognizes the user's emotions, it is possible to provide support tailored to the situation. This system is comprised of a user, a terminal, a server, an AI engine, an emotion recognition engine, and an interface for displaying the response message.

[0371] Using a smartphone or computer, users access a dedicated application or web interface that provides a form for them to enter their child's symptoms, such as "My child has a high fever and a persistent cough."

[0372] The device converts the entered symptom data into JSON format and sends it to the server using the HTTPS protocol. The device uses end-to-end encryption for data transmission to enhance security. The device also supports batch processing to periodically send the entered symptom data to the server.

[0373] The server receives symptom data sent from the device. The server passes the received data to an artificial intelligence engine (AI engine), which analyzes it and determines the level of urgency. The AI ​​engine has learned from past medical data and cases, enabling highly accurate analysis. Urgency is classified into three levels: high, medium, and low.

[0374] The server also accesses the emotion recognition engine to acquire the user's emotional data. The emotion recognition engine uses the camera and microphone on the user's device to capture the user's facial expressions and voice and recognize their emotions. For example, it can detect emotions such as "anxiety" and "panic." The emotional data is sent to the server and taken into consideration along with the urgency assessment results from the AI ​​engine.

[0375] The server generates an appropriate response message based on the urgency assessment and emotion recognition results. If the urgency is high, the server provides information on nearby emergency medical facilities and includes detailed instructions to ease the user's anxiety. If the urgency is medium, the server provides information on medical facilities that are open. If the urgency is low, the server shows how to provide care at home.

[0376] The device receives the response message sent from the server and displays it in a format that is easy for the user to read. For example, it may display a message on the smartphone screen saying, "Please visit a nearby hospital during opening hours," and provide specific opening hours and contact information. This message is customized according to the user's stress level.

[0377] Specific example explanation

[0378] Example 1: High Urgency

[0379] 1. The user enters "lost consciousness" as the child's symptom.

[0380] 2. The device sends this information to the server.

[0381] 3. The server uses an AI engine to determine the urgency as high.

[0382] 4. The emotion recognition engine analyzes the user's facial expressions and voice and determines whether they are in a state of strong anxiety or panic.

[0383] 5. The server displays the message "Contact the nearest emergency medical facility immediately" along with additional details to reassure the user.

[0384] 6. The terminal displays this message to the user.

[0385] Example 2: Medium Urgency

[0386] 1. The user enters, "I have a fever of 37.5 degrees and a cough."

[0387] 2. The device sends this information to the server.

[0388] 3. The server determines the urgency to be medium and generates a message saying, "Please visit a nearby medical facility during business hours."

[0389] 4. The emotion recognition engine analyzes the user's emotions and determines that the stress level is medium.

[0390] 5. The server provides additional information, including detailed instructions, including specific appointment times and contact information for the medical facility.

[0391] 6. The terminal displays this message to the user.

[0392] Example 3: Low Urgency

[0393] 1. The user types, "I have a minor scratch."

[0394] 2. The device sends this information to the server.

[0395] 3. The server determines the urgency to be low and generates a message saying, "Please take care at home by cooling the area and keeping it clean."

[0396] 4. The emotion recognition engine analyzes the user's emotions and determines that there are no particular signs of high stress or anxiety.

[0397] 5. The server provides only basic care instructions.

[0398] 6. The terminal displays this message to the user.

[0399] Example prompts to input to the generative AI model

[0400] "If a child's symptoms are a cough accompanied by a fever, please determine the level of urgency and generate an appropriate response message. Also, please provide examples of how to respond if the user is in a high-stress state."

[0401] "Please tell me the analysis procedure and how to generate a response message when a child is unconscious."

[0402] "Please give me an example of additional support information when the emotion recognition engine's emotion analysis result indicates high stress."

[0403] This allows the system to provide a fast and appropriate response to the symptoms reported by the user, and also to provide customized support based on the user's emotional state.

[0404] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0405] Step 1:

[0406] The user enters the symptom data.

[0407] A user accesses a dedicated application or web interface using a smartphone or computer. The user enters their child's symptom data into an on-screen form. For example, they enter information such as "My child has a high fever and a persistent cough." This input data becomes the basic input information for subsequent processing.

[0408] Step 2:

[0409] The device converts the symptom data into JSON format and sends it to the server.

[0410] The device converts the symptom data entered by the user into JSON format, for example, the data looks like this:

[0411] json

[0412] {

[0413] "temperature": "high",

[0414] "symptoms": ["Uncontrollable cough"]

[0415] }

[0416] The terminal sends this JSON data to the server using the HTTPS protocol. The input here is the symptom data entered by the user, and the output is JSON format data.

[0417] Step 3:

[0418] The server receives the symptom data and performs data analysis.

[0419] The server receives the JSON data sent from the device. The server passes the received data to an artificial intelligence engine (AI engine) and begins data analysis. The input is the received JSON data, and the output is the analysis results. Data analysis refers to past medical data and case studies to determine the urgency of the symptoms. This determination is classified into three levels: high, medium, and low. For example, based on the input data of "high temperature" and "unstoppable cough," the AI ​​engine determines the urgency to be "medium."

[0420] Step 4:

[0421] The server uses an emotion recognition engine to acquire and analyze the user's emotion data.

[0422] The server accesses the emotion recognition engine and instructs it to acquire the user's emotional data. The emotion recognition engine uses the camera and microphone equipped on the user's device to acquire the user's facial expressions and voice, and recognizes the emotion. For example, it may recognize "anxiety" from the facial expression. This result is sent to the server. The input here is the user's image data and voice data, and the output is the emotion analysis result.

[0423] Step 5:

[0424] The server generates a response message based on the urgency determination result and emotion recognition result.

[0425] The server generates an appropriate response message based on the urgency assessment result from the AI ​​engine and the emotional data from the emotion recognition engine. If the urgency is high, for example, a message such as "Please contact the nearest emergency hospital immediately" is generated. If the emotion analysis result indicates "anxiety," additional detailed instructions such as "Ask an adult nearby for help" are included. The inputs to this process are the urgency assessment result and the emotion recognition result, and the output is the response message.

[0426] Step 6:

[0427] The terminal receives the response message from the server and displays it to the user.

[0428] The server generates a response message and sends it to the terminal in JSON format. The terminal interprets the received JSON data and displays it in a format that is easy for the user to read. For example, a message might appear on a smartphone screen saying, "Please visit a nearby hospital during opening hours," and also provide specific opening hours and contact information. The input here is the JSON data received from the server, and the output is the response message that is displayed to the user.

[0429] (Application example 2)

[0430] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0431] Conventional symptom data input systems determine the level of urgency based on the symptom data entered by the user, but do not generate response messages that take the user's emotional state into account, resulting in insufficient provision of appropriate support. Furthermore, in public places such as brick-and-mortar stores, there is a lack of mechanisms for promptly notifying store staff in the event of an emergency. This makes it difficult for users to feel at ease and take prompt, appropriate action.

[0432] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0433] In this invention, the server includes: means for a user to input symptom data using an input device; means for a terminal to transmit the input symptom data to the server; means for the server to analyze the received symptom data and determine the level of urgency; means for the server to generate an appropriate response message based on the determined level of urgency; means for the terminal to display the response message from the server to the user; means for recognizing the user's emotion using an emotion engine and transmitting the emotion data to the server; means for the server to analyze the received emotion data and reflect it in the response message; means for notifying staff at a physical store according to the user's level of urgency; and means for providing information about nearby medical facilities using the in-store server. This enables support tailored to the user's emotional state, enabling prompt and appropriate responses even in physical stores.

[0434] "User input device" refers to a tool used by a user to input information about themselves, such as a smartphone, smart glasses, tablet, or PC.

[0435] "Symptom data" refers to information about the child's physical condition that is input by the user through an input device, and specifically includes details of the symptoms and the time of onset.

[0436] "Terminal" refers to a device for inputting and transmitting symptom data, and specifically includes portable electronic devices such as smartphones and smart glasses.

[0437] A "server" refers to a computer system that receives and analyzes input data, generates an appropriate response message based on the results, and sends it back to the terminal.

[0438] "Urgency" refers to an indicator that expresses the priority and importance of the response based on the symptom data entered, and is classified into three levels: high, medium, and low.

[0439] A "response message" refers to a message that the server generates based on the analysis results and other information and provides to the user, and specifically includes information such as directions to medical facilities and home care methods.

[0440] "Emotion engine" refers to software and algorithms that analyze a user's facial expressions, tone of voice, etc. to determine their emotional state.

[0441] "Emotion data" refers to data obtained as a result of analyzing the user's emotional state using an emotion engine.

[0442] "Brick and mortar store" refers to a physical store facility, specifically a public location, including a children's goods store or a family restaurant.

[0443] "Means for notifying store staff" refers to systems and methods for quickly notifying store staff in an emergency.

[0444] "Medical facility information" refers to detailed information such as the location, contact information, and opening hours of nearby hospitals and clinics.

[0445] "JSON format" refers to a lightweight data interchange format for storing and exchanging data, and is an abbreviation for JavaScript Object Notation.

[0446] The "HTTPS protocol" refers to a communication protocol for securely sending and receiving data, and is an abbreviation for Hypertext Transfer Protocol Secure.

[0447] "AI engine" is a general term for software and algorithms that refer to artificial intelligence technology that analyzes and makes decisions based on input data.

[0448] The system for implementing this invention begins when a user inputs symptom data using an input device such as a smartphone or smart glasses and sends the data to a server. The server, which plays a central role in the system, analyzes the received symptom data and determines the level of urgency, while also acquiring and analyzing the user's emotion data using an emotion engine.

[0449] First, the user uses a dedicated application to input information about their child's symptoms. For example, they might input information such as "The child looks pale and complains of nausea," "The child says he has a stomachache," or "He has a fever and is listless." The input information is converted into JSON format by the device and then sent to the server using the HTTPS protocol.

[0450] The server analyzes the received symptom data using an AI engine (using, for example, TensorFlow or PyTorch) and classifies the urgency into three levels: "high," "medium," or "low." An emotion engine (using, for example, Affectiva API or Microsoft® Azure® Emotion API) also obtains emotional data based on the user's facial expressions and tone of voice, and sends this data to the server. This also analyzes the level of stress and anxiety the user is experiencing.

[0451] The server then generates an appropriate response message based on the urgency assessment and emotional data. If the urgency is high, the server provides information on nearby emergency medical facilities. If the urgency is medium, it provides information on medical facilities with opening hours, and if the urgency is low, it provides home care instructions. This response message reflects the user's emotional state, and if the user is feeling highly stressed, it provides more detailed explanations or additional support information.

[0452] The response message generated by the server is again converted to JSON format and sent to the terminal via the HTTPS protocol. The terminal interprets the received data and displays it to the user in an easy-to-read format. This response message contains specific instructions for the user to take action quickly. At the same time, if the situation is urgent, a notification can be sent to the store staff, enabling a prompt response.

[0453] In this way, by combining symptom data and emotion data, this invention provides users with more accurate, situation-specific support. It also enables quick and accurate response in physical stores, allowing users to spend time in the store with peace of mind. The goal of this system is to reduce users' anxiety and enable prompt medical treatment.

[0454] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0455] Step 1:

[0456] The user uses a dedicated application to input the child's symptom data. This input data includes specific details of the symptoms and is entered in text format. For example, the user might enter, "My child looks pale and complains of nausea."

[0457] Step 2:

[0458] The device converts the input symptom data into JSON format. Converting to JSON format defines the data structure, making it easier to exchange data during communication. Specifically, the input data is converted into key-value pairs in the format "symptoms: 'The child looks pale and complains of nausea'".

[0459] Step 3:

[0460] The device sends the converted JSON data to the server using the HTTPS protocol. This communication protocol ensures secure data transfer and prevents unauthorized access and data leaks. An example of the data sent is "{"symptoms": "The child looks pale and complains of nausea"}".

[0461] Step 4:

[0462] The server analyzes the symptom data it receives. Using an AI engine (using TensorFlow or PyTorch), the server inputs the symptom data and determines the level of urgency. Specifically, it analyzes past medical data and case studies to determine the severity of the symptoms on a three-level scale: high, medium, or low. For example, if data is sent stating that "a child's complexion is pale and he is complaining of nausea," the level of urgency is determined to be "high."

[0463] Step 5:

[0464] The server uses an emotion engine to acquire the user's emotional data. This process involves analyzing the user's facial expressions and tone of voice via the user's camera and microphone to determine their emotional state. The acquired emotional data indicates the user's level of stress or anxiety. For example, if the user appears very anxious, the data is sent to the server as "high stress."

[0465] Step 6:

[0466] The server generates a response message based on the urgency and emotion data. If the urgency is high, the server creates a message providing information on nearby emergency medical facilities; if the urgency is medium, the server creates a message providing information on medical facilities with opening hours; and if the urgency is low, the server creates a message providing home care methods. In addition, if the stress level is high, the server generates a response message including a detailed explanation and additional support information. For example, if the assessment result is "high urgency" and "high stress," the server generates a message saying, "Immediately contact the nearest emergency hospital. Please remain calm."

[0467] Step 7:

[0468] The response message generated by the server is converted to JSON format and sent to the device using the HTTPS protocol. This allows the data to be sent back to the device safely and efficiently. An example of the data sent is "{"message": "Please contact the nearest emergency hospital immediately. Please remain calm."}".

[0469] Step 8:

[0470] The device interprets the received JSON data and displays it in a human-readable format. The device then displays the received response message on the screen as is, allowing the user to take action immediately. For example, if the received message is "Please contact the nearest emergency hospital immediately. Please remain calm and act accordingly," the device will display that message to the user as is.

[0471] Step 9:

[0472] If the device detects a high level of urgency, it will notify store staff. This notification will be displayed on a dedicated staff application, encouraging appropriate action. For example, in a high-level urgency case, a notification will be sent to store staff stating, "We recommend that you immediately contact a medical institution and take emergency measures."

[0473] This allows both the user and the store staff to respond quickly and appropriately.

[0474] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0475] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0476] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0477] [Second embodiment]

[0478] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0479] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0480] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0481] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0482] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0483] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0484] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0485] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0486] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0487] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0488] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0489] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0490] The present invention is a system in which a user inputs a child's symptoms using an input device, analyzes the information, determines the urgency of the symptoms, and prompts appropriate action based on the results. Specifically, it is composed of a terminal, a server, an AI engine, and an interface for displaying a response message to the user. An embodiment for actually implementing this system is described in detail below.

[0491] Basic system configuration

[0492] 1. User Input

[0493] Users use a smartphone or computer to enter their child's symptoms through a dedicated application or web interface, and the symptom data is entered in detailed text format.

[0494] For example, enter "My child has a high fever and a persistent cough."

[0495] 2. Data transmission by the terminal

[0496] The device first converts the input symptom data into JSON format and then securely sends it to the server using the HTTPS protocol.

[0497] 3. Data analysis and urgency determination by the server

[0498] The server receives the symptom data sent from the terminal and analyzes it.

[0499] An AI engine is used for analysis and determines the level of urgency based on the received data. The AI ​​engine has learned from past medical data and cases, enabling highly accurate diagnoses.

[0500] Urgency is classified into three levels: high, medium, and low.

[0501] 4. Server Generation of Response Message

[0502] After the AI ​​engine determines the urgency, the server generates an appropriate response message based on the urgency.

[0503] If the emergency is severe, information about the nearest emergency medical facility will be provided.

[0504] If the emergency is moderate, information on medical facilities that are open will be provided.

[0505] If the condition is less urgent, home care instructions will be provided.

[0506] 5. Receiving and displaying data by the device

[0507] The server generates a response message and sends it to the terminal in JSON format.

[0508] The terminal interprets the data it receives and displays it in a user-friendly format.

[0509] Specific example explanation

[0510] Example 1: High Urgency

[0511] The user enters the child's symptom as "lost consciousness."

[0512] The terminal sends this information to the server.

[0513] The server uses an AI engine to determine the urgency as high.

[0514] The server generates a message such as "Please contact the nearest emergency hospital immediately."

[0515] The terminal displays this message to the user.

[0516] Example 2: Medium Urgency

[0517] The user types, "I have a fever of 37.5 degrees and a cough."

[0518] The terminal sends this information to the server.

[0519] The server determines the urgency to be medium and generates a message such as "Please visit XX Clinic during opening hours."

[0520] The terminal displays this message to the user.

[0521] Example 3: Low Urgency

[0522] The user types, "I have some minor scratches."

[0523] The terminal sends this information to the server.

[0524] The server determines the urgency to be low and generates a message such as "Please take care at home by cooling the area and keeping it clean."

[0525] The terminal displays this message to the user.

[0526] This allows users to take prompt and appropriate action against their child's symptoms and prevent their health from worsening. This system can be a powerful support tool for parents.

[0527] The processing flow will be explained below.

[0528] Step 1:

[0529] The user starts up the device (smartphone or computer) and accesses a dedicated application or web interface.

[0530] Step 2:

[0531] The user enters the child's symptom data into a form displayed on the screen.

[0532] For example, enter "My child has a high fever and a persistent cough."

[0533] Step 3:

[0534] The user checks the input information and presses the send button.

[0535] Step 4:

[0536] The terminal converts the data entered by the user into JSON format.

[0537] Step 5:

[0538] The terminal sends the converted JSON data to the server using the HTTPS protocol.

[0539] API endpoint: https: / / example.com / api / diagnose

[0540] Step 6:

[0541] The server receives the request from the terminal and prepares the data for analysis.

[0542] Step 7:

[0543] The server interprets the received JSON data and extracts the required fields.

[0544] Step 8:

[0545] The symptom data extracted by the server is input into an AI engine to determine the level of urgency.

[0546] The AI ​​engine uses a model trained on a large amount of medical data to classify the level of urgency into three levels: high, medium, and low.

[0547] Step 9:

[0548] The server receives the urgency determination result and generates an appropriate response message based on the result.

[0549] Step 10:

[0550] If the urgency is high, the server searches a database of nearby emergency medical facilities to obtain information on the most suitable medical facility.

[0551] Example: Generate a message that reads, "This is a high urgency. Please visit XX Emergency Hospital (Address: XXX, Phone: YYY) immediately."

[0552] Step 11:

[0553] If the urgency is medium, the server suggests medical facilities that are open.

[0554] Example: Generates a message saying, "The emergency is moderate. Please visit XX Clinic during business hours."

[0555] Step 12:

[0556] If the emergency is low, the server will suggest home care methods.

[0557] Example: Generate a message that says, "Symptoms are mild. Please take care of yourself at home by cooling your body, keeping it clean, etc."

[0558] Step 13:

[0559] Convert the response message generated by the server into JSON format.

[0560] Step 14:

[0561] The server sends the converted JSON data to the terminal using the HTTPS protocol.

[0562] Step 15:

[0563] The device interprets the JSON data it receives and converts it into a human-readable format.

[0564] Step 16:

[0565] The terminal displays a response message to the user.

[0566] Step 17:

[0567] The user checks the response message displayed on the terminal and performs the suggested action.

[0568] Example 1

[0569] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0570] In today's world, accurately and quickly determining the urgency of a child's symptoms is an important challenge for parents and medical institutions. Urgent symptoms require immediate action, especially for those with high-urgency symptoms. However, parents generally lack easy-to-use methods that do not require specialized knowledge and allow accurate diagnoses and appropriate treatment. Furthermore, there is a need to provide appropriate care methods that minimize the consumption of medical resources, even in cases of mild symptoms.

[0571] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0572] In this invention, the server includes: means for a user to input symptom data using an input device; means for a terminal to convert the input symptom data into JSON format and send it to the server using the HTTPS protocol; means for the server to analyze the received symptom data and use an AI engine to determine the level of urgency; means for the server to generate an appropriate response message based on the determined level of urgency; and means for the terminal to receive the response message from the server in JSON format, interpret it, and display it in a human-readable format. This allows the user to quickly and accurately determine the level of urgency of their child's symptoms, enabling them to provide appropriate first aid or see a doctor at a medical institution.

[0573] "User" refers to a person who uses the system to enter a child's symptom data.

[0574] "Input device" refers to hardware and software used to input data into a system.

[0575] "Symptom Data" refers to information that includes textual descriptions of a child's physical condition or health status.

[0576] "Terminal" refers to a device that is operated by a user to input symptom data and communicate with a server.

[0577] "Server" refers to a computer system that receives input symptom data, performs analysis, and generates an appropriate response message.

[0578] "JSON format" stands for JavaScript Object Notation and refers to a lightweight text format for structuring data.

[0579] The "HTTPS protocol" is an abbreviation for HyperText Transfer Protocol Secure, and refers to a communication protocol for securely transmitting data over the Internet.

[0580] "AI engine" refers to software that uses artificial intelligence technology to analyze data and determine the level of urgency.

[0581] A "response message" refers to a message generated by the server based on the analysis of the symptom data.

[0582] "Database Management System" means a software system for managing, storing, and retrieving data efficiently and securely.

[0583] "Urgency" refers to the criteria for determining the severity and urgency of symptoms.

[0584] A "template engine" refers to software for dynamically generating messages from a standardized format.

[0585] "Analysis" refers to the act of examining input data in detail, understanding its contents, and drawing specific conclusions.

[0586] "Display" refers to the act of visually providing information on a terminal in a form that can be read by a user.

[0587] The present invention is a system that allows a user to input a child's symptoms using an input device, analyzes the information, determines the urgency of the symptoms, and prompts appropriate action based on the results. The system includes a terminal, a server, an AI engine, and an interface for displaying a response message to the user.

[0588] User-entered symptom data

[0589] Users use a smartphone or computer to enter detailed information about their child's symptoms through a dedicated application or web interface, and this information is entered in text format.

[0590] Example: "My child has a high fever and a persistent cough."

[0591] Data transmission by the terminal

[0592] The terminal converts the input symptom data into JSON format and sends it securely to the server using the HTTPS protocol. An appropriate conversion library (e.g., Jackson for Java, Gson for Java, etc.) is used to convert the data format.

[0593] Data analysis and urgency determination by server

[0594] The server retains the data received from the device and saves it in a database management system (e.g., PostgreSQL, MongoDB, etc.). The saved data is then passed to an AI engine for analysis. This AI engine has learned from past medical data and cases, and uses a model written in Python (e.g., scikit-learn, TensorFlow, etc.) to accurately determine the urgency of the case.

[0595] Server generation of response message

[0596] The server generates an appropriate response message based on the received urgency assessment result. It selects the response message based on the conditions and generates the message using a template engine (e.g., FreeMarker, Thymeleaf, etc.).

[0597] If the situation is urgent: "Contact the nearest emergency hospital immediately."

[0598] If the urgency is medium: "Please visit XX Clinic during opening hours."

[0599] If the situation is not urgent: "Take care at home by cooling the area and keeping it clean."

[0600] Receiving and displaying data by the terminal

[0601] The terminal receives the response message from the server in JSON format, converts it into a user-friendly format, and displays it using a user-friendly interface.

[0602] Examples of prompt statements

[0603] If you lose consciousness, immediately contact the nearest emergency room.

[0604] "If you have a fever of 37.5 degrees and a cough, please visit XX Clinic during business hours."

[0605] "If you suffer a minor abrasion, please take care of it at home by cooling it and keeping it clean."

[0606] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0607] Step 1: User enters symptom data

[0608] Specific operation: The user inputs symptom data using a smartphone or computer input device. The input data is described in detail in text format.

[0609] Input: Text data about a child's symptoms (e.g., "My child has a high fever and a persistent cough").

[0610] Output: Symptom data in text format.

[0611] Step 2: The device converts the data into JSON format

[0612] What it does: The terminal takes the text data entered by the user and converts it to JSON format using a library such as Jackson or Gson.

[0613] Input: Symptom data in text format.

[0614] Output: Data converted to JSON format.

[0615] Step 3: The device sends the data to the server using the HTTPS protocol.

[0616] Specific operation: The terminal sends the converted JSON data to the endpoint URL via a POST request using the HTTPS protocol.

[0617] Input: Symptom data in JSON format.

[0618] Output: The HTTP request sent to the server.

[0619] Step 4: The server receives and stores the data

[0620] Specific operation: The server holds the data received from the terminal and stores it in a database management system (e.g., PostgreSQL, MongoDB).

[0621] Input: Symptom data sent from the device in JSON format.

[0622] Output: Symptom data stored in a database.

[0623] Step 5: The server analyzes the data using an AI engine

[0624] How it works: The server passes the stored data to an AI engine, which analyzes the data and determines the level of urgency. The AI ​​engine has learned from past medical data and cases.

[0625] Input: Symptom data stored in a database in JSON format.

[0626] Output: Urgency assessment result (high, medium, low).

[0627] Step 6: Server generates response message

[0628] Specific operation: The server generates an appropriate response message using a template engine based on the urgency assessment result.

[0629] Input: Urgency assessment result.

[0630] Output: A text response message (e.g., "Please contact the nearest emergency room immediately").

[0631] Step 7: The server converts the message into JSON format and sends it

[0632] Specific operation: The server converts the generated response message into JSON format and sends it to the terminal again using the HTTPS protocol.

[0633] Input: A response message in text format.

[0634] Output: The HTTP request and response message converted to JSON format.

[0635] Step 8: The device receives and displays the data

[0636] Specific operation: The device receives the response message from the server in JSON format, converts it into a format that the user can understand, and displays it.

[0637] Input: The response message in JSON format.

[0638] Output: A text response message that is displayed to the user.

[0639] (Application example 1)

[0640] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0641] In conventional factory robot maintenance and failure response, it is difficult for operators to accurately determine abnormal conditions and quickly take appropriate action. This requires a high level of specialized knowledge, which can lead to delayed responses in emergencies that require a rapid response. This can lead to the shutdown of factory production lines and further danger. This issue needs to be resolved with a new system that efficiently determines abnormal conditions and provides appropriate response actions.

[0642] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0643] In this invention, the server includes: means for a user to input status data using an input device; means for a terminal to transmit the input status data to the server; means for the server to analyze the received status data and determine the level of urgency; means for the server to generate an appropriate response message based on the determined level of urgency; means for the terminal to display the response message from the server to the user; means for a user to input abnormal status information in text format using the terminal; means for the terminal to transmit the input abnormal status data to the server and the server to analyze the level of urgency; and means for the server to generate an appropriate response action based on the result of the urgency determination and notify the user. This enables an operator to quickly and accurately report an abnormal status, and the server to automatically determine the level of urgency and provide an appropriate response action.

[0644] "User" means an individual or operator who uses the system to input status data.

[0645] An "input device" is a device for inputting status data, including, for example, a tablet, a smartphone, or a computer.

[0646] "Status data" is information entered by the user when an abnormality occurs, and is text data detailing the specific content and circumstances of the abnormality.

[0647] A "terminal" is a device on which a user inputs status data and sends it to a server, and can refer to a tablet, smartphone, or computer.

[0648] "Sending" refers to the act of transferring data from an input device to a server using a communication protocol.

[0649] The "server" is a central processing unit that receives status data sent from the terminal, analyzes it, and generates a response message.

[0650] "Analyzing" refers to the act of interpreting the content of the data received by the server, processing the information, and drawing conclusions.

[0651] "Urgency" is an evaluation criterion that determines the severity of an abnormality and the promptness of response based on status data on a three-level scale (high, medium, low).

[0652] "Determining" refers to the act of the server analyzing the status data and identifying the level of urgency.

[0653] The "response message" is the content of instructions and corresponding actions generated by the server based on the result of the urgency determination.

[0654] "Displaying" refers to the act of visually presenting to the user the response message received by the terminal from the server.

[0655] "Text format" is a data format in which the user inputs information about an abnormal state using characters or sentences.

[0656] The "urgency determination result" is the evaluation result of the urgency level derived by the server by analyzing the status data.

[0657] "Response actions" are specific measures or instructions according to the level of urgency that the server provides as a response message.

[0658] This invention is a system in which a user inputs status data using an input device, analyzes the data, determines the urgency of an abnormality, and prompts appropriate response actions. Specifically, it targets abnormal conditions related to factory robots. A detailed description of an embodiment of this system is provided below.

[0659] Basic system configuration

[0660] 1. User Input

[0661] The user (operator) uses an input device such as a tablet or smartphone to input abnormal conditions of the robot through a dedicated application or web interface. The input data is detailed in text format.

[0662] Example: Enter "The robot's motor is overheating."

[0663] 2. Data transmission by the terminal

[0664] The terminal first converts the entered status data into JSON format and then securely sends it to the server using the HTTPS protocol.

[0665] 3. Data analysis and urgency determination by the server

[0666] The server receives and analyzes the status data sent from the device. An AI engine is used for the analysis, and determines the level of urgency based on the received data. The AI ​​engine has learned past abnormal conditions and status patterns, allowing for highly accurate determinations. Urgency is classified into three levels: high, medium, and low.

[0667] 4. Server Generation of Response Message

[0668] After the AI ​​engine determines the urgency, the server generates an appropriate response message based on the urgency.

[0669] If the emergency is urgent, specific instructions will be provided, such as "Stop the robot's motors immediately and call a specialist technician."

[0670] Medium urgency: Instructions such as "Pause robot operation and monitor the situation" are provided.

[0671] Low-urgency situations: Instructions such as "Please record the situation and continue driving as normal" are provided.

[0672] 5. Receiving and displaying data by the device

[0673] The server generates a response message and sends it to the terminal in JSON format. The terminal interprets the received data and displays it in a user-friendly format.

[0674] Technology used

[0675] Hardware: tablets, smartphones, factory robots

[0676] software:

[0677] Flask (Server construction with Python)

[0678] TensorFlow or PyTorch (AI engine implementation)

[0679] JSON (data format)

[0680] Requests (HTTP communication)

[0681] React Native or Flutter (Mobile Application Development)

[0682] Specific examples

[0683] When a user inputs "The robot arm is stuck and won't move," the system analyzes the data and suggests specific actions to take, such as "Immediately stop the arm motor and call a specialist technician." This allows the user to take prompt and accurate action, improving factory efficiency and safety.

[0684] Example prompt sentence:

[0685] Enter the robot's motors getting too hot.

[0686] This system makes it possible to instantly determine the urgency of abnormal conditions involving factory robots and take appropriate action to avoid production line shutdowns and further danger.

[0687] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0688] Step 1:

[0689] A user inputs status data using an input device.

[0690] Input: Text data of abnormal conditions related to the robot (e.g., "The robot's motor is overheating")

[0691] Specific operation: Log in to a dedicated application on a smartphone or tablet and enter the abnormal condition in text format.

[0692] Output: Text data of the abnormal condition is saved in the input device.

[0693] Step 2:

[0694] The terminal converts the input status data into JSON format and sends it to the server using the HTTPS protocol.

[0695] Input: Text data of abnormal condition

[0696] Specific operation: A dedicated application converts abnormality data into JSON format and sends it securely to the server using the HTTPS protocol.

[0697] Output: JSON formatted data is sent to and received from the server.

[0698] Step 3:

[0699] The server analyzes the status data received and uses an AI engine to determine the level of urgency.

[0700] Input: State data in JSON format

[0701] Specific operation: The server parses the received JSON data and inputs it into the AI ​​engine. The AI ​​engine (using, for example, TensorFlow or PyTorch) analyzes the data and determines the urgency (high, medium, low).

[0702] Output: Data on urgency (high, medium, low) and recommended response actions

[0703] Step 4:

[0704] The server generates an appropriate response message based on the determined urgency.

[0705] Input: Urgency assessment results and recommended response actions

[0706] Specific operation: The server embeds data into a template message according to the urgency level and generates a specific response message regarding the recommended action.

[0707] Output: The generated response message

[0708] Step 5:

[0709] The terminal receives the response message from the server in JSON format and displays it to the user.

[0710] Input: Generated response message (JSON format)

[0711] Specific operation: The device parses the JSON data received from the server, converts it into a human-readable format, and displays the response message on the screen using a dedicated application.

[0712] Output: A response message that appears on the user's screen (e.g., "Immediately stop the robot's motors and call a specialist technician.")

[0713] This establishes a series of steps from inputting an abnormal condition to determining the level of urgency and displaying a response message, allowing the system to function effectively.

[0714] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0715] This system allows users to input their child's symptoms, analyzes the information, determines the level of urgency, and provides an appropriate response message. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide support tailored to the situation. This system consists of a terminal, a server, an AI engine, an emotion engine, and an interface for displaying the response message.

[0716] Basic system configuration

[0717] 1. User Input

[0718] Users use a smartphone or computer to access a dedicated application or web interface.

[0719] The user enters the child's symptom data into an on-screen form.

[0720] For example, enter "My child has a high fever and a persistent cough."

[0721] 2. Data transmission by the terminal

[0722] The device converts the entered symptom data into JSON format and sends it to the server using the HTTPS protocol.

[0723] 3. Data analysis and urgency determination by the server

[0724] The server receives the symptom data sent from the terminal and analyzes it.

[0725] An AI engine is used for analysis and determines the urgency of the received data. The AI ​​engine has learned from past medical data and cases, enabling highly accurate diagnoses.

[0726] Urgency is classified into three levels: high, medium, and low.

[0727] 4. Emotion Recognition by Emotion Engine

[0728] The emotion engine uses a camera or microphone mounted on the user's input device to acquire the user's facial expressions and voice and recognize their emotions.

[0729] The emotion engine analyzes the user's emotion data and sends the results to the server.

[0730] 5. Server Generation of Response Messages

[0731] The server references the urgency assessment results from the AI ​​engine and the emotional data from the emotion engine to generate an appropriate response message.

[0732] If the urgency is high, the server searches a database of nearby emergency medical facilities to obtain information on the most suitable medical facility.

[0733] If the urgency is medium, the server provides information on medical facilities that are open.

[0734] If the emergency is less urgent, the server will provide home care instructions.

[0735] Based on the results of the emotion engine, users with high stress levels are provided with more detailed explanations and additional support information.

[0736] 6. Receiving and displaying data by the device

[0737] The server generates a response message and sends it to the terminal in JSON format.

[0738] The terminal interprets the data it receives and displays it in a user-friendly format.

[0739] Specific example explanation

[0740] Example 1: High Urgency

[0741] The user enters the child's symptom as "lost consciousness."

[0742] The terminal sends this information to the server.

[0743] The server uses an AI engine to determine the urgency as high.

[0744] The emotion engine analyzes the user's facial expressions and voice and recognizes that they are in a state of strong anxiety or panic.

[0745] The server provides the message "Contact the nearest emergency room immediately" along with additional details to reassure the user.

[0746] The terminal displays this message to the user.

[0747] Example 2: Medium Urgency

[0748] The user types, "I have a fever of 37.5 degrees and a cough."

[0749] The terminal sends this information to the server.

[0750] The server determines the urgency to be medium and generates a message saying, "Please visit XX Clinic during opening hours."

[0751] The emotion engine analyzes the user's emotions and determines that the stress level is medium.

[0752] The server provides additional detailed instructions, including specific appointment times and clinic contact information.

[0753] The terminal displays this message to the user.

[0754] Example 3: Low Urgency

[0755] The user types, "I have some minor scratches."

[0756] The terminal sends this information to the server.

[0757] The server determines the urgency to be low and generates a message saying, "Please take care at home by cooling the area and keeping it clean."

[0758] The emotion engine analyzes the user's emotions and determines that there are no particular signs of high stress or anxiety.

[0759] The server provides only basic care instructions.

[0760] The terminal displays this message to the user.

[0761] This allows users to respond quickly and appropriately to their child's symptoms and receive support tailored to their emotional state. This system can be a powerful support tool for parents.

[0762] The processing flow will be explained below.

[0763] Step 1:

[0764] The user starts up the device (smartphone or computer) and accesses a dedicated application or web interface.

[0765] Step 2:

[0766] The user enters their child's symptom data into an on-screen form. For example, "My child has a high fever and a persistent cough."

[0767] Step 3:

[0768] The user presses the submit button to confirm the input.

[0769] Step 4:

[0770] The terminal receives the user's input and converts it into JSON format.

[0771] Step 5:

[0772] The terminal sends the converted JSON data to the server using the HTTPS protocol.

[0773] API endpoint: https: / / example.com / api / diagnose

[0774] Step 6:

[0775] The server receives the JSON data sent from the device, parses it, and extracts the necessary fields.

[0776] Step 7:

[0777] The server inputs the extracted symptom data into an AI engine, which analyzes the symptom data and compares it with past medical data to determine the urgency (high, medium, low).

[0778] Step 8:

[0779] The server receives the AI ​​engine's judgment result and proceeds to the next step.

[0780] Step 9:

[0781] The emotion engine uses the device's built-in camera or microphone to capture the user's facial expressions and voice in real time.

[0782] Step 10:

[0783] The emotion engine analyzes the acquired data and determines the user's emotion (calm, anxious, panic, etc.).

[0784] Step 11:

[0785] The server combines the urgency assessment results of the AI ​​engine and the emotion assessment results of the emotion engine to generate an appropriate response message.

[0786] Step 12:

[0787] If the emergency is high, the server searches the database for nearby emergency medical facilities and generates a message to provide information on the most appropriate medical facility. Example: "As the emergency is high, please visit the nearest emergency hospital (Address: XXX, Phone: YYY) immediately."

[0788] Step 13:

[0789] If the urgency is medium, the server obtains information about medical facilities that are open and generates a message instructing the patient to visit the hospital. Example: "The urgency is medium. Please visit XX Clinic during opening hours."

[0790] Step 14:

[0791] If the level of urgency is low, the server generates a message suggesting home care methods, for example, "Your symptoms are mild. Please take care of yourself at home by cooling your body, keeping it clean, etc."

[0792] Step 15:

[0793] Convert the response message generated by the server into JSON format.

[0794] Step 16:

[0795] The server sends the converted JSON data to the terminal using the HTTPS protocol.

[0796] Step 17:

[0797] The device interprets the JSON data it receives and converts it into a human-readable format.

[0798] Step 18:

[0799] The terminal displays a response message to the user.

[0800] Step 19:

[0801] The user reviews the response message displayed on the device and takes the suggested action, such as contacting the nearest emergency facility, visiting a doctor, or providing home care, if necessary.

[0802] Example 2

[0803] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0804] The present invention relates to a system that allows users to quickly and accurately report their child's symptoms, analyzes the data, and immediately provides appropriate instructions for a response. In particular, the system aims to provide a more user-friendly response by taking the user's emotional state into consideration when determining the level of urgency.

[0805] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for using an emotion recognition engine to acquire and analyze emotion data of a user, means for the server to customize a response message based on the results of the urgency determination and the emotion recognition results, means for the terminal to convert input symptom data into JSON format and transmit it to the server using the HTTPS protocol, means for the server to input the received JSON data into an artificial intelligence engine and determine the urgency, means for the server to convert the generated response message into JSON format and transmit it to the terminal using the HTTPS protocol, and means for the terminal to interpret the received JSON data and display it in a human-readable format. This makes it possible to immediately determine the urgency of the symptoms reported by the user and to provide an optimal response message depending on the user's emotional state.

[0806] "User" refers to the person who uses the terminal to input the child's symptom data and other information.

[0807] "Input device" refers to a hardware or software interface through which a user inputs symptom data, etc.

[0808] "Symptom Data" refers to data that contains specific information about a child's symptoms.

[0809] The term "terminal" refers to an electronic device that transmits symptom data entered by a user to a server and displays a response message received from the server.

[0810] "Server" refers to a central processing unit used to receive and analyze data sent from terminals and generate response messages.

[0811] "Data analysis" refers to the process of using an AI engine or similar to make judgments about urgency and other factors based on input data.

[0812] "Urgency" is an indicator of the seriousness of the symptoms and is classified into three levels: high, medium, and low.

[0813] "Response message" refers to a message generated by a server, sent to a terminal, and containing instructions or information that is displayed to a user.

[0814] "Emotion recognition engine" refers to a software module or hardware device for acquiring and analyzing a user's emotion data.

[0815] "Emotion data" is data that indicates the user's emotional state, and is obtained from facial expressions, voice, and the like.

[0816] An "artificial intelligence engine" refers to a software module that performs highly accurate analysis and judgments based on input data.

[0817] "JSON format" is a lightweight data description format used for data exchange and is an abbreviation for JavaScript Object Notation.

[0818] The "HTTPS protocol" is an abbreviation for Hypertext Transfer Protocol Secure, which is used for secure communication.

[0819] This invention relates to a system that allows users to input their child's symptoms, analyzes the information, determines the level of urgency, and provides an appropriate response message. Furthermore, by incorporating an emotion recognition engine that recognizes the user's emotions, it is possible to provide support tailored to the situation. This system is comprised of a user, a terminal, a server, an AI engine, an emotion recognition engine, and an interface for displaying the response message.

[0820] Using a smartphone or computer, users access a dedicated application or web interface that provides a form for them to enter their child's symptoms, such as "My child has a high fever and a persistent cough."

[0821] The device converts the entered symptom data into JSON format and sends it to the server using the HTTPS protocol. The device uses end-to-end encryption for data transmission to enhance security. The device also supports batch processing to periodically send the entered symptom data to the server.

[0822] The server receives symptom data sent from the device. The server passes the received data to an artificial intelligence engine (AI engine), which analyzes it and determines the level of urgency. The AI ​​engine has learned from past medical data and cases, enabling highly accurate analysis. Urgency is classified into three levels: high, medium, and low.

[0823] The server also accesses the emotion recognition engine to acquire the user's emotional data. The emotion recognition engine uses the camera and microphone on the user's device to capture the user's facial expressions and voice and recognize their emotions. For example, it can detect emotions such as "anxiety" and "panic." The emotional data is sent to the server and taken into consideration along with the urgency assessment results from the AI ​​engine.

[0824] The server generates an appropriate response message based on the urgency assessment and emotion recognition results. If the urgency is high, the server provides information on nearby emergency medical facilities and includes detailed instructions to ease the user's anxiety. If the urgency is medium, the server provides information on medical facilities that are open. If the urgency is low, the server shows how to provide care at home.

[0825] The device receives the response message sent from the server and displays it in a format that is easy for the user to read. For example, it may display a message on the smartphone screen saying, "Please visit a nearby hospital during opening hours," and provide specific opening hours and contact information. This message is customized according to the user's stress level.

[0826] Specific example explanation

[0827] Example 1: High Urgency

[0828] 1. The user enters "lost consciousness" as the child's symptom.

[0829] 2. The device sends this information to the server.

[0830] 3. The server uses an AI engine to determine the urgency as high.

[0831] 4. The emotion recognition engine analyzes the user's facial expressions and voice and determines whether they are in a state of strong anxiety or panic.

[0832] 5. The server displays the message "Contact the nearest emergency medical facility immediately" along with additional details to reassure the user.

[0833] 6. The terminal displays this message to the user.

[0834] Example 2: Medium Urgency

[0835] 1. The user enters, "I have a fever of 37.5 degrees and a cough."

[0836] 2. The device sends this information to the server.

[0837] 3. The server determines the urgency to be medium and generates a message saying, "Please visit a nearby medical facility during business hours."

[0838] 4. The emotion recognition engine analyzes the user's emotions and determines that the stress level is medium.

[0839] 5. The server provides additional information, including detailed instructions, including specific appointment times and contact information for the medical facility.

[0840] 6. The terminal displays this message to the user.

[0841] Example 3: Low Urgency

[0842] 1. The user types, "I have a minor scratch."

[0843] 2. The device sends this information to the server.

[0844] 3. The server determines the urgency to be low and generates a message saying, "Please take care at home by cooling the area and keeping it clean."

[0845] 4. The emotion recognition engine analyzes the user's emotions and determines that there are no particular signs of high stress or anxiety.

[0846] 5. The server provides only basic care instructions.

[0847] 6. The terminal displays this message to the user.

[0848] Example prompts to input to the generative AI model

[0849] "If a child's symptoms are a cough accompanied by a fever, please determine the level of urgency and generate an appropriate response message. Also, please provide examples of how to respond if the user is in a high-stress state."

[0850] "Please tell me the analysis procedure and how to generate a response message when a child is unconscious."

[0851] "Please give me an example of additional support information when the emotion recognition engine's emotion analysis result indicates high stress."

[0852] This allows the system to provide a fast and appropriate response to the symptoms reported by the user, and also to provide customized support based on the user's emotional state.

[0853] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0854] Step 1:

[0855] The user enters the symptom data.

[0856] A user accesses a dedicated application or web interface using a smartphone or computer. The user enters their child's symptom data into an on-screen form. For example, they enter information such as "My child has a high fever and a persistent cough." This input data becomes the basic input information for subsequent processing.

[0857] Step 2:

[0858] The device converts the symptom data into JSON format and sends it to the server.

[0859] The device converts the symptom data entered by the user into JSON format, for example, the data looks like this:

[0860] json

[0861] {

[0862] "temperature": "high",

[0863] "symptoms": ["Uncontrollable cough"]

[0864] }

[0865] The terminal sends this JSON data to the server using the HTTPS protocol. The input here is the symptom data entered by the user, and the output is JSON format data.

[0866] Step 3:

[0867] The server receives the symptom data and performs data analysis.

[0868] The server receives the JSON data sent from the device. The server passes the received data to an artificial intelligence engine (AI engine) and begins data analysis. The input is the received JSON data, and the output is the analysis results. Data analysis refers to past medical data and case studies to determine the urgency of the symptoms. This determination is classified into three levels: high, medium, and low. For example, based on the input data of "high temperature" and "unstoppable cough," the AI ​​engine determines the urgency to be "medium."

[0869] Step 4:

[0870] The server uses an emotion recognition engine to acquire and analyze the user's emotion data.

[0871] The server accesses the emotion recognition engine and instructs it to acquire the user's emotional data. The emotion recognition engine uses the camera and microphone equipped on the user's device to acquire the user's facial expressions and voice, and recognizes the emotion. For example, it may recognize "anxiety" from the facial expression. This result is sent to the server. The input here is the user's image data and voice data, and the output is the emotion analysis result.

[0872] Step 5:

[0873] The server generates a response message based on the urgency determination result and emotion recognition result.

[0874] The server generates an appropriate response message based on the urgency assessment result from the AI ​​engine and the emotional data from the emotion recognition engine. If the urgency is high, for example, a message such as "Please contact the nearest emergency hospital immediately" is generated. If the emotion analysis result indicates "anxiety," additional detailed instructions such as "Ask an adult nearby for help" are included. The inputs to this process are the urgency assessment result and the emotion recognition result, and the output is the response message.

[0875] Step 6:

[0876] The terminal receives the response message from the server and displays it to the user.

[0877] The server generates a response message and sends it to the terminal in JSON format. The terminal interprets the received JSON data and displays it in a format that is easy for the user to read. For example, a message might appear on a smartphone screen saying, "Please visit a nearby hospital during opening hours," and also provide specific opening hours and contact information. The input here is the JSON data received from the server, and the output is the response message that is displayed to the user.

[0878] (Application example 2)

[0879] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0880] Conventional symptom data input systems determine the level of urgency based on the symptom data entered by the user, but do not generate response messages that take the user's emotional state into account, resulting in insufficient provision of appropriate support. Furthermore, in public places such as brick-and-mortar stores, there is a lack of mechanisms for promptly notifying store staff in the event of an emergency. This makes it difficult for users to feel at ease and take prompt, appropriate action.

[0881] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0882] In this invention, the server includes: means for a user to input symptom data using an input device; means for a terminal to transmit the input symptom data to the server; means for the server to analyze the received symptom data and determine the level of urgency; means for the server to generate an appropriate response message based on the determined level of urgency; means for the terminal to display the response message from the server to the user; means for recognizing the user's emotion using an emotion engine and transmitting the emotion data to the server; means for the server to analyze the received emotion data and reflect it in the response message; means for notifying staff at a physical store according to the user's level of urgency; and means for providing information about nearby medical facilities using the in-store server. This enables support tailored to the user's emotional state, enabling prompt and appropriate responses even in physical stores.

[0883] "User input device" refers to a tool used by a user to input information about themselves, such as a smartphone, smart glasses, tablet, or PC.

[0884] "Symptom data" refers to information about the child's physical condition that is input by the user through an input device, and specifically includes details of the symptoms and the time of onset.

[0885] "Terminal" refers to a device for inputting and transmitting symptom data, and specifically includes portable electronic devices such as smartphones and smart glasses.

[0886] A "server" refers to a computer system that receives and analyzes input data, generates an appropriate response message based on the results, and sends it back to the terminal.

[0887] "Urgency" refers to an indicator that expresses the priority and importance of the response based on the symptom data entered, and is classified into three levels: high, medium, and low.

[0888] A "response message" refers to a message that the server generates based on the analysis results and other information and provides to the user, and specifically includes information such as directions to medical facilities and home care methods.

[0889] "Emotion engine" refers to software and algorithms that analyze a user's facial expressions, tone of voice, etc. to determine their emotional state.

[0890] "Emotion data" refers to data obtained as a result of analyzing the user's emotional state using an emotion engine.

[0891] "Brick and mortar store" refers to a physical store facility, specifically a public location, including a children's goods store or a family restaurant.

[0892] "Means for notifying store staff" refers to systems and methods for quickly notifying store staff in an emergency.

[0893] "Medical facility information" refers to detailed information such as the location, contact information, and opening hours of nearby hospitals and clinics.

[0894] "JSON format" refers to a lightweight data interchange format for storing and exchanging data, and is an abbreviation for JavaScript Object Notation.

[0895] The "HTTPS protocol" refers to a communication protocol for securely sending and receiving data, and is an abbreviation for Hypertext Transfer Protocol Secure.

[0896] "AI engine" is a general term for software and algorithms that refer to artificial intelligence technology that analyzes and makes decisions based on input data.

[0897] The system for implementing this invention begins when a user inputs symptom data using an input device such as a smartphone or smart glasses and sends the data to a server. The server, which plays a central role in the system, analyzes the received symptom data and determines the level of urgency, while also acquiring and analyzing the user's emotion data using an emotion engine.

[0898] First, the user uses a dedicated application to input information about their child's symptoms. For example, they might input information such as "The child looks pale and complains of nausea," "The child says he has a stomachache," or "He has a fever and is listless." The input information is converted into JSON format by the device and then sent to the server using the HTTPS protocol.

[0899] The server analyzes the received symptom data using an AI engine (using, for example, TensorFlow or PyTorch) and classifies the urgency into three levels: "high," "medium," or "low." An emotion engine (using, for example, the Affectiva API or Microsoft Azure Emotion API) then obtains emotional data based on the user's facial expressions and tone of voice, and sends that data to the server. This also analyzes the level of stress and anxiety the user is experiencing.

[0900] The server then generates an appropriate response message based on the urgency assessment and emotional data. If the urgency is high, the server provides information on nearby emergency medical facilities. If the urgency is medium, it provides information on medical facilities with opening hours, and if the urgency is low, it provides home care instructions. This response message reflects the user's emotional state, and if the user is feeling highly stressed, it provides more detailed explanations or additional support information.

[0901] The response message generated by the server is again converted to JSON format and sent to the terminal via the HTTPS protocol. The terminal interprets the received data and displays it to the user in an easy-to-read format. This response message contains specific instructions for the user to take action quickly. At the same time, if the situation is urgent, a notification can be sent to the store staff, enabling a prompt response.

[0902] In this way, by combining symptom data and emotion data, this invention provides users with more accurate, situation-specific support. It also enables quick and accurate response in physical stores, allowing users to spend time in the store with peace of mind. The goal of this system is to reduce users' anxiety and enable prompt medical treatment.

[0903] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0904] Step 1:

[0905] The user uses a dedicated application to input the child's symptom data. This input data includes specific details of the symptoms and is entered in text format. For example, the user might enter, "My child looks pale and complains of nausea."

[0906] Step 2:

[0907] The device converts the input symptom data into JSON format. Converting to JSON format defines the data structure, making it easier to exchange data during communication. Specifically, the input data is converted into key-value pairs in the format "symptoms: 'The child looks pale and complains of nausea'".

[0908] Step 3:

[0909] The device sends the converted JSON data to the server using the HTTPS protocol. This communication protocol ensures secure data transfer and prevents unauthorized access and data leaks. An example of the data sent is "{"symptoms": "The child looks pale and complains of nausea"}".

[0910] Step 4:

[0911] The server analyzes the symptom data it receives. Using an AI engine (using TensorFlow or PyTorch), the server inputs the symptom data and determines the level of urgency. Specifically, it analyzes past medical data and case studies to determine the severity of the symptoms on a three-level scale: high, medium, or low. For example, if data is sent stating that "a child's complexion is pale and he is complaining of nausea," the level of urgency is determined to be "high."

[0912] Step 5:

[0913] The server uses an emotion engine to acquire the user's emotional data. This process involves analyzing the user's facial expressions and tone of voice via the user's camera and microphone to determine their emotional state. The acquired emotional data indicates the user's level of stress or anxiety. For example, if the user appears very anxious, the data is sent to the server as "high stress."

[0914] Step 6:

[0915] The server generates a response message based on the urgency and emotion data. If the urgency is high, the server creates a message providing information on nearby emergency medical facilities; if the urgency is medium, the server creates a message providing information on medical facilities with opening hours; and if the urgency is low, the server creates a message providing home care methods. In addition, if the stress level is high, the server generates a response message including a detailed explanation and additional support information. For example, if the assessment result is "high urgency" and "high stress," the server generates a message saying, "Immediately contact the nearest emergency hospital. Please remain calm."

[0916] Step 7:

[0917] The response message generated by the server is converted to JSON format and sent to the device using the HTTPS protocol. This allows the data to be sent back to the device safely and efficiently. An example of the data sent is "{"message": "Please contact the nearest emergency hospital immediately. Please remain calm."}".

[0918] Step 8:

[0919] The device interprets the received JSON data and displays it in a human-readable format. The device then displays the received response message on the screen as is, allowing the user to take action immediately. For example, if the received message is "Please contact the nearest emergency hospital immediately. Please remain calm and act accordingly," the device will display that message to the user as is.

[0920] Step 9:

[0921] If the device detects a high level of urgency, it will notify store staff. This notification will be displayed on a dedicated staff application, encouraging appropriate action. For example, in a high-level urgency case, a notification will be sent to store staff stating, "We recommend that you immediately contact a medical institution and take emergency measures."

[0922] This allows both the user and the store staff to respond quickly and appropriately.

[0923] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0924] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0925] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0926] [Third embodiment]

[0927] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0928] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0929] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0930] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0931] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0932] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0933] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0934] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0935] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0936] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0937] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0938] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0939] The present invention is a system in which a user inputs a child's symptoms using an input device, analyzes the information, determines the urgency of the symptoms, and prompts appropriate action based on the results. Specifically, it is composed of a terminal, a server, an AI engine, and an interface for displaying a response message to the user. An embodiment for actually implementing this system is described in detail below.

[0940] Basic system configuration

[0941] 1. User Input

[0942] Users use a smartphone or computer to enter their child's symptoms through a dedicated application or web interface, and the symptom data is entered in detailed text format.

[0943] For example, enter "My child has a high fever and a persistent cough."

[0944] 2. Data transmission by the terminal

[0945] The device first converts the input symptom data into JSON format and then securely sends it to the server using the HTTPS protocol.

[0946] 3. Data analysis and urgency determination by the server

[0947] The server receives the symptom data sent from the terminal and analyzes it.

[0948] An AI engine is used for analysis and determines the level of urgency based on the received data. The AI ​​engine has learned from past medical data and cases, enabling highly accurate diagnoses.

[0949] Urgency is classified into three levels: high, medium, and low.

[0950] 4. Server Generation of Response Message

[0951] After the AI ​​engine determines the urgency, the server generates an appropriate response message based on the urgency.

[0952] If the emergency is severe, information about the nearest emergency medical facility will be provided.

[0953] If the emergency is moderate, information on medical facilities that are open will be provided.

[0954] If the condition is less urgent, home care instructions will be provided.

[0955] 5. Receiving and displaying data by the device

[0956] The server generates a response message and sends it to the terminal in JSON format.

[0957] The terminal interprets the data it receives and displays it in a user-friendly format.

[0958] Specific example explanation

[0959] Example 1: High Urgency

[0960] The user enters the child's symptom as "lost consciousness."

[0961] The terminal sends this information to the server.

[0962] The server uses an AI engine to determine the urgency as high.

[0963] The server generates a message such as "Please contact the nearest emergency hospital immediately."

[0964] The terminal displays this message to the user.

[0965] Example 2: Medium Urgency

[0966] The user types, "I have a fever of 37.5 degrees and a cough."

[0967] The terminal sends this information to the server.

[0968] The server determines the urgency to be medium and generates a message such as "Please visit XX Clinic during opening hours."

[0969] The terminal displays this message to the user.

[0970] Example 3: Low Urgency

[0971] The user types, "I have some minor scratches."

[0972] The terminal sends this information to the server.

[0973] The server determines the urgency to be low and generates a message such as "Please take care at home by cooling the area and keeping it clean."

[0974] The terminal displays this message to the user.

[0975] This allows users to take prompt and appropriate action against their child's symptoms and prevent their health from worsening. This system can be a powerful support tool for parents.

[0976] The processing flow will be explained below.

[0977] Step 1:

[0978] The user starts up the device (smartphone or computer) and accesses a dedicated application or web interface.

[0979] Step 2:

[0980] The user enters the child's symptom data into a form displayed on the screen.

[0981] For example, enter "My child has a high fever and a persistent cough."

[0982] Step 3:

[0983] The user checks the input information and presses the send button.

[0984] Step 4:

[0985] The terminal converts the data entered by the user into JSON format.

[0986] Step 5:

[0987] The terminal sends the converted JSON data to the server using the HTTPS protocol.

[0988] API endpoint: https: / / example.com / api / diagnose

[0989] Step 6:

[0990] The server receives the request from the terminal and prepares the data for analysis.

[0991] Step 7:

[0992] The server interprets the received JSON data and extracts the required fields.

[0993] Step 8:

[0994] The symptom data extracted by the server is input into an AI engine to determine the level of urgency.

[0995] The AI ​​engine uses a model trained on a large amount of medical data to classify the level of urgency into three levels: high, medium, and low.

[0996] Step 9:

[0997] The server receives the urgency determination result and generates an appropriate response message based on the result.

[0998] Step 10:

[0999] If the urgency is high, the server searches a database of nearby emergency medical facilities to obtain information on the most suitable medical facility.

[1000] Example: Generate a message that reads, "This is a high urgency. Please visit XX Emergency Hospital (Address: XXX, Phone: YYY) immediately."

[1001] Step 11:

[1002] If the urgency is medium, the server suggests medical facilities that are open.

[1003] Example: Generates a message saying, "The emergency is moderate. Please visit XX Clinic during business hours."

[1004] Step 12:

[1005] If the emergency is low, the server will suggest home care methods.

[1006] Example: Generate a message that says, "Symptoms are mild. Please take care of yourself at home by cooling your body, keeping it clean, etc."

[1007] Step 13:

[1008] Convert the response message generated by the server into JSON format.

[1009] Step 14:

[1010] The server sends the converted JSON data to the terminal using the HTTPS protocol.

[1011] Step 15:

[1012] The device interprets the JSON data it receives and converts it into a human-readable format.

[1013] Step 16:

[1014] The terminal displays a response message to the user.

[1015] Step 17:

[1016] The user checks the response message displayed on the terminal and performs the suggested action.

[1017] Example 1

[1018] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1019] In today's world, accurately and quickly determining the urgency of a child's symptoms is an important challenge for parents and medical institutions. Urgent symptoms require immediate action, especially for those with high-urgency symptoms. However, parents generally lack easy-to-use methods that do not require specialized knowledge and allow accurate diagnoses and appropriate treatment. Furthermore, there is a need to provide appropriate care methods that minimize the consumption of medical resources, even in cases of mild symptoms.

[1020] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1021] In this invention, the server includes: means for a user to input symptom data using an input device; means for a terminal to convert the input symptom data into JSON format and send it to the server using the HTTPS protocol; means for the server to analyze the received symptom data and use an AI engine to determine the level of urgency; means for the server to generate an appropriate response message based on the determined level of urgency; and means for the terminal to receive the response message from the server in JSON format, interpret it, and display it in a human-readable format. This allows the user to quickly and accurately determine the level of urgency of their child's symptoms, enabling them to provide appropriate first aid or see a doctor at a medical institution.

[1022] "User" refers to a person who uses the system to enter a child's symptom data.

[1023] "Input device" refers to hardware and software used to input data into a system.

[1024] "Symptom Data" refers to information that includes textual descriptions of a child's physical condition or health status.

[1025] "Terminal" refers to a device that is operated by a user to input symptom data and communicate with a server.

[1026] "Server" refers to a computer system that receives input symptom data, performs analysis, and generates an appropriate response message.

[1027] "JSON format" stands for JavaScript Object Notation and refers to a lightweight text format for structuring data.

[1028] The "HTTPS protocol" is an abbreviation for HyperText Transfer Protocol Secure, and refers to a communication protocol for securely transmitting data over the Internet.

[1029] "AI engine" refers to software that uses artificial intelligence technology to analyze data and determine the level of urgency.

[1030] A "response message" refers to a message generated by the server based on the analysis of the symptom data.

[1031] "Database Management System" means a software system for managing, storing, and retrieving data efficiently and securely.

[1032] "Urgency" refers to the criteria for determining the severity and urgency of symptoms.

[1033] A "template engine" refers to software for dynamically generating messages from a standardized format.

[1034] "Analysis" refers to the act of examining input data in detail, understanding its contents, and drawing specific conclusions.

[1035] "Display" refers to the act of visually providing information on a terminal in a form that can be read by a user.

[1036] The present invention is a system that allows a user to input a child's symptoms using an input device, analyzes the information, determines the urgency of the symptoms, and prompts appropriate action based on the results. The system includes a terminal, a server, an AI engine, and an interface for displaying a response message to the user.

[1037] User-entered symptom data

[1038] Users use a smartphone or computer to enter detailed information about their child's symptoms through a dedicated application or web interface, and this information is entered in text format.

[1039] Example: "My child has a high fever and a persistent cough."

[1040] Data transmission by the terminal

[1041] The terminal converts the input symptom data into JSON format and sends it securely to the server using the HTTPS protocol. An appropriate conversion library (e.g., Jackson for Java, Gson for Java, etc.) is used to convert the data format.

[1042] Data analysis and urgency determination by server

[1043] The server retains the data received from the device and saves it in a database management system (e.g., PostgreSQL, MongoDB, etc.). The saved data is then passed to an AI engine for analysis. This AI engine has learned from past medical data and cases, and uses a model written in Python (e.g., scikit-learn, TensorFlow, etc.) to accurately determine the urgency of the case.

[1044] Server generation of response message

[1045] The server generates an appropriate response message based on the received urgency assessment result. It selects the response message based on the conditions and generates the message using a template engine (e.g., FreeMarker, Thymeleaf, etc.).

[1046] If the situation is urgent: "Contact the nearest emergency hospital immediately."

[1047] If the urgency is medium: "Please visit XX Clinic during opening hours."

[1048] If the situation is not urgent: "Take care at home by cooling the area and keeping it clean."

[1049] Receiving and displaying data by the terminal

[1050] The terminal receives the response message from the server in JSON format, converts it into a user-friendly format, and displays it using a user-friendly interface.

[1051] Examples of prompt statements

[1052] If you lose consciousness, immediately contact the nearest emergency room.

[1053] "If you have a fever of 37.5 degrees and a cough, please visit XX Clinic during business hours."

[1054] "If you suffer a minor abrasion, please take care of it at home by cooling it and keeping it clean."

[1055] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1056] Step 1: User enters symptom data

[1057] Specific operation: The user inputs symptom data using a smartphone or computer input device. The input data is described in detail in text format.

[1058] Input: Text data about a child's symptoms (e.g., "My child has a high fever and a persistent cough").

[1059] Output: Symptom data in text format.

[1060] Step 2: The device converts the data into JSON format

[1061] What it does: The terminal takes the text data entered by the user and converts it to JSON format using a library such as Jackson or Gson.

[1062] Input: Symptom data in text format.

[1063] Output: Data converted to JSON format.

[1064] Step 3: The device sends the data to the server using the HTTPS protocol.

[1065] Specific operation: The terminal sends the converted JSON data to the endpoint URL via a POST request using the HTTPS protocol.

[1066] Input: Symptom data in JSON format.

[1067] Output: The HTTP request sent to the server.

[1068] Step 4: The server receives and stores the data

[1069] Specific operation: The server holds the data received from the terminal and stores it in a database management system (e.g., PostgreSQL, MongoDB).

[1070] Input: Symptom data sent from the device in JSON format.

[1071] Output: Symptom data stored in a database.

[1072] Step 5: The server analyzes the data using an AI engine

[1073] How it works: The server passes the stored data to an AI engine, which analyzes the data and determines the level of urgency. The AI ​​engine has learned from past medical data and cases.

[1074] Input: Symptom data stored in a database in JSON format.

[1075] Output: Urgency assessment result (high, medium, low).

[1076] Step 6: Server generates response message

[1077] Specific operation: The server generates an appropriate response message using a template engine based on the urgency assessment result.

[1078] Input: Urgency assessment result.

[1079] Output: A text response message (e.g., "Please contact the nearest emergency room immediately").

[1080] Step 7: The server converts the message into JSON format and sends it

[1081] Specific operation: The server converts the generated response message into JSON format and sends it to the terminal again using the HTTPS protocol.

[1082] Input: A response message in text format.

[1083] Output: The HTTP request and response message converted to JSON format.

[1084] Step 8: The device receives and displays the data

[1085] Specific operation: The device receives the response message from the server in JSON format, converts it into a format that the user can understand, and displays it.

[1086] Input: The response message in JSON format.

[1087] Output: A text response message that is displayed to the user.

[1088] (Application example 1)

[1089] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1090] In conventional factory robot maintenance and failure response, it is difficult for operators to accurately determine abnormal conditions and quickly take appropriate action. This requires a high level of specialized knowledge, which can lead to delayed responses in emergencies that require a rapid response. This can lead to the shutdown of factory production lines and further danger. This issue needs to be resolved with a new system that efficiently determines abnormal conditions and provides appropriate response actions.

[1091] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1092] In this invention, the server includes: means for a user to input status data using an input device; means for a terminal to transmit the input status data to the server; means for the server to analyze the received status data and determine the level of urgency; means for the server to generate an appropriate response message based on the determined level of urgency; means for the terminal to display the response message from the server to the user; means for a user to input abnormal status information in text format using the terminal; means for the terminal to transmit the input abnormal status data to the server and the server to analyze the level of urgency; and means for the server to generate an appropriate response action based on the result of the urgency determination and notify the user. This enables an operator to quickly and accurately report an abnormal status, and the server to automatically determine the level of urgency and provide an appropriate response action.

[1093] "User" means an individual or operator who uses the system to input status data.

[1094] An "input device" is a device for inputting status data, including, for example, a tablet, a smartphone, or a computer.

[1095] "Status data" is information entered by the user when an abnormality occurs, and is text data detailing the specific content and circumstances of the abnormality.

[1096] A "terminal" is a device on which a user inputs status data and sends it to a server, and can refer to a tablet, smartphone, or computer.

[1097] "Sending" refers to the act of transferring data from an input device to a server using a communication protocol.

[1098] The "server" is a central processing unit that receives status data sent from the terminal, analyzes it, and generates a response message.

[1099] "Analyzing" refers to the act of interpreting the content of the data received by the server, processing the information, and drawing conclusions.

[1100] "Urgency" is an evaluation criterion that determines the severity of an abnormality and the promptness of response based on status data on a three-level scale (high, medium, low).

[1101] "Determining" refers to the act of the server analyzing the status data and identifying the level of urgency.

[1102] The "response message" is the content of instructions and corresponding actions generated by the server based on the result of the urgency determination.

[1103] "Displaying" refers to the act of visually presenting to the user the response message received by the terminal from the server.

[1104] "Text format" is a data format in which the user inputs information about an abnormal state using characters or sentences.

[1105] The "urgency determination result" is the evaluation result of the urgency level derived by the server by analyzing the status data.

[1106] "Response actions" are specific measures or instructions according to the level of urgency that the server provides as a response message.

[1107] This invention is a system in which a user inputs status data using an input device, analyzes the data, determines the urgency of an abnormality, and prompts appropriate response actions. Specifically, it targets abnormal conditions related to factory robots. A detailed description of an embodiment of this system is provided below.

[1108] Basic system configuration

[1109] 1. User Input

[1110] The user (operator) uses an input device such as a tablet or smartphone to input abnormal conditions of the robot through a dedicated application or web interface. The input data is detailed in text format.

[1111] Example: Enter "The robot's motor is overheating."

[1112] 2. Data transmission by the terminal

[1113] The terminal first converts the entered status data into JSON format and then securely sends it to the server using the HTTPS protocol.

[1114] 3. Data analysis and urgency determination by the server

[1115] The server receives and analyzes the status data sent from the device. An AI engine is used for the analysis, and determines the level of urgency based on the received data. The AI ​​engine has learned past abnormal conditions and status patterns, allowing for highly accurate determinations. Urgency is classified into three levels: high, medium, and low.

[1116] 4. Server Generation of Response Message

[1117] After the AI ​​engine determines the urgency, the server generates an appropriate response message based on the urgency.

[1118] If the emergency is urgent, specific instructions will be provided, such as "Stop the robot's motors immediately and call a specialist technician."

[1119] Medium urgency: Instructions such as "Pause robot operation and monitor the situation" are provided.

[1120] Low-urgency situations: Instructions such as "Please record the situation and continue driving as normal" are provided.

[1121] 5. Receiving and displaying data by the device

[1122] The server generates a response message and sends it to the terminal in JSON format. The terminal interprets the received data and displays it in a user-friendly format.

[1123] Technology used

[1124] Hardware: tablets, smartphones, factory robots

[1125] software:

[1126] Flask (Server construction with Python)

[1127] TensorFlow or PyTorch (AI engine implementation)

[1128] JSON (data format)

[1129] Requests (HTTP communication)

[1130] React Native or Flutter (Mobile Application Development)

[1131] Specific examples

[1132] When a user inputs "The robot arm is stuck and won't move," the system analyzes the data and suggests specific actions to take, such as "Immediately stop the arm motor and call a specialist technician." This allows the user to take prompt and accurate action, improving factory efficiency and safety.

[1133] Example prompt sentence:

[1134] Enter the robot's motors getting too hot.

[1135] This system makes it possible to instantly determine the urgency of abnormal conditions involving factory robots and take appropriate action to avoid production line shutdowns and further danger.

[1136] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1137] Step 1:

[1138] A user inputs status data using an input device.

[1139] Input: Text data of abnormal conditions related to the robot (e.g., "The robot's motor is overheating")

[1140] Specific operation: Log in to a dedicated application on a smartphone or tablet and enter the abnormal condition in text format.

[1141] Output: Text data of the abnormal condition is saved in the input device.

[1142] Step 2:

[1143] The terminal converts the input status data into JSON format and sends it to the server using the HTTPS protocol.

[1144] Input: Text data of abnormal condition

[1145] Specific operation: A dedicated application converts abnormality data into JSON format and sends it securely to the server using the HTTPS protocol.

[1146] Output: JSON formatted data is sent to and received from the server.

[1147] Step 3:

[1148] The server analyzes the status data received and uses an AI engine to determine the level of urgency.

[1149] Input: State data in JSON format

[1150] Specific operation: The server parses the received JSON data and inputs it into the AI ​​engine. The AI ​​engine (using, for example, TensorFlow or PyTorch) analyzes the data and determines the urgency (high, medium, low).

[1151] Output: Data on urgency (high, medium, low) and recommended response actions

[1152] Step 4:

[1153] The server generates an appropriate response message based on the determined urgency.

[1154] Input: Urgency assessment results and recommended response actions

[1155] Specific operation: The server embeds data into a template message according to the urgency level and generates a specific response message regarding the recommended action.

[1156] Output: The generated response message

[1157] Step 5:

[1158] The terminal receives the response message from the server in JSON format and displays it to the user.

[1159] Input: Generated response message (JSON format)

[1160] Specific operation: The device parses the JSON data received from the server, converts it into a human-readable format, and displays the response message on the screen using a dedicated application.

[1161] Output: A response message that appears on the user's screen (e.g., "Immediately stop the robot's motors and call a specialist technician.")

[1162] This establishes a series of steps from inputting an abnormal condition to determining the level of urgency and displaying a response message, allowing the system to function effectively.

[1163] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1164] This system allows users to input their child's symptoms, analyzes the information, determines the level of urgency, and provides an appropriate response message. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide support tailored to the situation. This system consists of a terminal, a server, an AI engine, an emotion engine, and an interface for displaying the response message.

[1165] Basic system configuration

[1166] 1. User Input

[1167] Users use a smartphone or computer to access a dedicated application or web interface.

[1168] The user enters the child's symptom data into an on-screen form.

[1169] For example, enter "My child has a high fever and a persistent cough."

[1170] 2. Data transmission by the terminal

[1171] The device converts the entered symptom data into JSON format and sends it to the server using the HTTPS protocol.

[1172] 3. Data analysis and urgency determination by the server

[1173] The server receives the symptom data sent from the terminal and analyzes it.

[1174] An AI engine is used for analysis and determines the urgency of the received data. The AI ​​engine has learned from past medical data and cases, enabling highly accurate diagnoses.

[1175] Urgency is classified into three levels: high, medium, and low.

[1176] 4. Emotion Recognition by Emotion Engine

[1177] The emotion engine uses a camera or microphone mounted on the user's input device to acquire the user's facial expressions and voice and recognize their emotions.

[1178] The emotion engine analyzes the user's emotion data and sends the results to the server.

[1179] 5. Server Generation of Response Messages

[1180] The server references the urgency assessment results from the AI ​​engine and the emotional data from the emotion engine to generate an appropriate response message.

[1181] If the urgency is high, the server searches a database of nearby emergency medical facilities to obtain information on the most suitable medical facility.

[1182] If the urgency is medium, the server provides information on medical facilities that are open.

[1183] If the emergency is less urgent, the server will provide home care instructions.

[1184] Based on the results of the emotion engine, users with high stress levels are provided with more detailed explanations and additional support information.

[1185] 6. Receiving and displaying data by the device

[1186] The server generates a response message and sends it to the terminal in JSON format.

[1187] The terminal interprets the data it receives and displays it in a user-friendly format.

[1188] Specific example explanation

[1189] Example 1: High Urgency

[1190] The user enters the child's symptom as "lost consciousness."

[1191] The terminal sends this information to the server.

[1192] The server uses an AI engine to determine the urgency as high.

[1193] The emotion engine analyzes the user's facial expressions and voice and recognizes that they are in a state of strong anxiety or panic.

[1194] The server provides the message "Contact the nearest emergency room immediately" along with additional details to reassure the user.

[1195] The terminal displays this message to the user.

[1196] Example 2: Medium Urgency

[1197] The user types, "I have a fever of 37.5 degrees and a cough."

[1198] The terminal sends this information to the server.

[1199] The server determines the urgency to be medium and generates a message saying, "Please visit XX Clinic during opening hours."

[1200] The emotion engine analyzes the user's emotions and determines that the stress level is medium.

[1201] The server provides additional detailed instructions, including specific appointment times and clinic contact information.

[1202] The terminal displays this message to the user.

[1203] Example 3: Low Urgency

[1204] The user types, "I have some minor scratches."

[1205] The terminal sends this information to the server.

[1206] The server determines the urgency to be low and generates a message saying, "Please take care at home by cooling the area and keeping it clean."

[1207] The emotion engine analyzes the user's emotions and determines that there are no particular signs of high stress or anxiety.

[1208] The server provides only basic care instructions.

[1209] The terminal displays this message to the user.

[1210] This allows users to respond quickly and appropriately to their child's symptoms and receive support tailored to their emotional state. This system can be a powerful support tool for parents.

[1211] The processing flow will be explained below.

[1212] Step 1:

[1213] The user starts up the device (smartphone or computer) and accesses a dedicated application or web interface.

[1214] Step 2:

[1215] The user enters their child's symptom data into an on-screen form. For example, "My child has a high fever and a persistent cough."

[1216] Step 3:

[1217] The user presses the submit button to confirm the input.

[1218] Step 4:

[1219] The terminal receives the user's input and converts it into JSON format.

[1220] Step 5:

[1221] The terminal sends the converted JSON data to the server using the HTTPS protocol.

[1222] API endpoint: https: / / example.com / api / diagnose

[1223] Step 6:

[1224] The server receives the JSON data sent from the device, parses it, and extracts the necessary fields.

[1225] Step 7:

[1226] The server inputs the extracted symptom data into an AI engine, which analyzes the symptom data and compares it with past medical data to determine the urgency (high, medium, low).

[1227] Step 8:

[1228] The server receives the AI ​​engine's judgment result and proceeds to the next step.

[1229] Step 9:

[1230] The emotion engine uses the device's built-in camera or microphone to capture the user's facial expressions and voice in real time.

[1231] Step 10:

[1232] The emotion engine analyzes the acquired data and determines the user's emotion (calm, anxious, panic, etc.).

[1233] Step 11:

[1234] The server combines the urgency assessment results of the AI ​​engine and the emotion assessment results of the emotion engine to generate an appropriate response message.

[1235] Step 12:

[1236] If the emergency is high, the server searches the database for nearby emergency medical facilities and generates a message to provide information on the most appropriate medical facility. Example: "As the emergency is high, please visit the nearest emergency hospital (Address: XXX, Phone: YYY) immediately."

[1237] Step 13:

[1238] If the urgency is medium, the server obtains information about medical facilities that are open and generates a message instructing the patient to visit the hospital. Example: "The urgency is medium. Please visit XX Clinic during opening hours."

[1239] Step 14:

[1240] If the level of urgency is low, the server generates a message suggesting home care methods, for example, "Your symptoms are mild. Please take care of yourself at home by cooling your body, keeping it clean, etc."

[1241] Step 15:

[1242] Convert the response message generated by the server into JSON format.

[1243] Step 16:

[1244] The server sends the converted JSON data to the terminal using the HTTPS protocol.

[1245] Step 17:

[1246] The device interprets the JSON data it receives and converts it into a human-readable format.

[1247] Step 18:

[1248] The terminal displays a response message to the user.

[1249] Step 19:

[1250] The user reviews the response message displayed on the device and takes the suggested action, such as contacting the nearest emergency facility, visiting a doctor, or providing home care, if necessary.

[1251] Example 2

[1252] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1253] The present invention relates to a system that allows users to quickly and accurately report their child's symptoms, analyzes the data, and immediately provides appropriate instructions for a response. In particular, the system aims to provide a more user-friendly response by taking the user's emotional state into consideration when determining the level of urgency.

[1254] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for using an emotion recognition engine to acquire and analyze emotion data of a user, means for the server to customize a response message based on the results of the urgency determination and the emotion recognition results, means for the terminal to convert input symptom data into JSON format and transmit it to the server using the HTTPS protocol, means for the server to input the received JSON data into an artificial intelligence engine and determine the urgency, means for the server to convert the generated response message into JSON format and transmit it to the terminal using the HTTPS protocol, and means for the terminal to interpret the received JSON data and display it in a human-readable format. This makes it possible to immediately determine the urgency of the symptoms reported by the user and to provide an optimal response message depending on the user's emotional state.

[1255] "User" refers to the person who uses the terminal to input the child's symptom data and other information.

[1256] "Input device" refers to a hardware or software interface through which a user inputs symptom data, etc.

[1257] "Symptom Data" refers to data that contains specific information about a child's symptoms.

[1258] The term "terminal" refers to an electronic device that transmits symptom data entered by a user to a server and displays a response message received from the server.

[1259] "Server" refers to a central processing unit used to receive and analyze data sent from terminals and generate response messages.

[1260] "Data analysis" refers to the process of using an AI engine or similar to make judgments about urgency and other factors based on input data.

[1261] "Urgency" is an indicator of the seriousness of the symptoms and is classified into three levels: high, medium, and low.

[1262] "Response message" refers to a message generated by a server, sent to a terminal, and containing instructions or information that is displayed to a user.

[1263] "Emotion recognition engine" refers to a software module or hardware device for acquiring and analyzing a user's emotion data.

[1264] "Emotion data" is data that indicates the user's emotional state, and is obtained from facial expressions, voice, and the like.

[1265] An "artificial intelligence engine" refers to a software module that performs highly accurate analysis and judgments based on input data.

[1266] "JSON format" is a lightweight data description format used for data exchange and is an abbreviation for JavaScript Object Notation.

[1267] The "HTTPS protocol" is an abbreviation for Hypertext Transfer Protocol Secure, which is used for secure communication.

[1268] This invention relates to a system that allows users to input their child's symptoms, analyzes the information, determines the level of urgency, and provides an appropriate response message. Furthermore, by incorporating an emotion recognition engine that recognizes the user's emotions, it is possible to provide support tailored to the situation. This system is comprised of a user, a terminal, a server, an AI engine, an emotion recognition engine, and an interface for displaying the response message.

[1269] Using a smartphone or computer, users access a dedicated application or web interface that provides a form for them to enter their child's symptoms, such as "My child has a high fever and a persistent cough."

[1270] The device converts the entered symptom data into JSON format and sends it to the server using the HTTPS protocol. The device uses end-to-end encryption for data transmission to enhance security. The device also supports batch processing to periodically send the entered symptom data to the server.

[1271] The server receives symptom data sent from the device. The server passes the received data to an artificial intelligence engine (AI engine), which analyzes it and determines the level of urgency. The AI ​​engine has learned from past medical data and cases, enabling highly accurate analysis. Urgency is classified into three levels: high, medium, and low.

[1272] The server also accesses the emotion recognition engine to acquire the user's emotional data. The emotion recognition engine uses the camera and microphone on the user's device to capture the user's facial expressions and voice and recognize their emotions. For example, it can detect emotions such as "anxiety" and "panic." The emotional data is sent to the server and taken into consideration along with the urgency assessment results from the AI ​​engine.

[1273] The server generates an appropriate response message based on the urgency assessment and emotion recognition results. If the urgency is high, the server provides information on nearby emergency medical facilities and includes detailed instructions to ease the user's anxiety. If the urgency is medium, the server provides information on medical facilities that are open. If the urgency is low, the server shows how to provide care at home.

[1274] The device receives the response message sent from the server and displays it in a format that is easy for the user to read. For example, it may display a message on the smartphone screen saying, "Please visit a nearby hospital during opening hours," and provide specific opening hours and contact information. This message is customized according to the user's stress level.

[1275] Specific example explanation

[1276] Example 1: High Urgency

[1277] 1. The user enters "lost consciousness" as the child's symptom.

[1278] 2. The device sends this information to the server.

[1279] 3. The server uses an AI engine to determine the urgency as high.

[1280] 4. The emotion recognition engine analyzes the user's facial expressions and voice and determines whether they are in a state of strong anxiety or panic.

[1281] 5. The server displays the message "Contact the nearest emergency medical facility immediately" along with additional details to reassure the user.

[1282] 6. The terminal displays this message to the user.

[1283] Example 2: Medium Urgency

[1284] 1. The user enters, "I have a fever of 37.5 degrees and a cough."

[1285] 2. The device sends this information to the server.

[1286] 3. The server determines the urgency to be medium and generates a message saying, "Please visit a nearby medical facility during business hours."

[1287] 4. The emotion recognition engine analyzes the user's emotions and determines that the stress level is medium.

[1288] 5. The server provides additional information, including detailed instructions, including specific appointment times and contact information for the medical facility.

[1289] 6. The terminal displays this message to the user.

[1290] Example 3: Low Urgency

[1291] 1. The user types, "I have a minor scratch."

[1292] 2. The device sends this information to the server.

[1293] 3. The server determines the urgency to be low and generates a message saying, "Please take care at home by cooling the area and keeping it clean."

[1294] 4. The emotion recognition engine analyzes the user's emotions and determines that there are no particular signs of high stress or anxiety.

[1295] 5. The server provides only basic care instructions.

[1296] 6. The terminal displays this message to the user.

[1297] Example prompts to input to the generative AI model

[1298] "If a child's symptoms are a cough accompanied by a fever, please determine the level of urgency and generate an appropriate response message. Also, please provide examples of how to respond if the user is in a high-stress state."

[1299] "Please tell me the analysis procedure and how to generate a response message when a child is unconscious."

[1300] "Please give me an example of additional support information when the emotion recognition engine's emotion analysis result indicates high stress."

[1301] This allows the system to provide a fast and appropriate response to the symptoms reported by the user, and also to provide customized support based on the user's emotional state.

[1302] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1303] Step 1:

[1304] The user enters the symptom data.

[1305] A user accesses a dedicated application or web interface using a smartphone or computer. The user enters their child's symptom data into an on-screen form. For example, they enter information such as "My child has a high fever and a persistent cough." This input data becomes the basic input information for subsequent processing.

[1306] Step 2:

[1307] The device converts the symptom data into JSON format and sends it to the server.

[1308] The device converts the symptom data entered by the user into JSON format, for example, the data looks like this:

[1309] json

[1310] {

[1311] "temperature": "high",

[1312] "symptoms": ["Uncontrollable cough"]

[1313] }

[1314] The terminal sends this JSON data to the server using the HTTPS protocol. The input here is the symptom data entered by the user, and the output is JSON format data.

[1315] Step 3:

[1316] The server receives the symptom data and performs data analysis.

[1317] The server receives the JSON data sent from the device. The server passes the received data to an artificial intelligence engine (AI engine) and begins data analysis. The input is the received JSON data, and the output is the analysis results. Data analysis refers to past medical data and case studies to determine the urgency of the symptoms. This determination is classified into three levels: high, medium, and low. For example, based on the input data of "high temperature" and "unstoppable cough," the AI ​​engine determines the urgency to be "medium."

[1318] Step 4:

[1319] The server uses an emotion recognition engine to acquire and analyze the user's emotion data.

[1320] The server accesses the emotion recognition engine and instructs it to acquire the user's emotional data. The emotion recognition engine uses the camera and microphone equipped on the user's device to acquire the user's facial expressions and voice, and recognizes the emotion. For example, it may recognize "anxiety" from the facial expression. This result is sent to the server. The input here is the user's image data and voice data, and the output is the emotion analysis result.

[1321] Step 5:

[1322] The server generates a response message based on the urgency determination result and emotion recognition result.

[1323] The server generates an appropriate response message based on the urgency assessment result from the AI ​​engine and the emotional data from the emotion recognition engine. If the urgency is high, for example, a message such as "Please contact the nearest emergency hospital immediately" is generated. If the emotion analysis result indicates "anxiety," additional detailed instructions such as "Ask an adult nearby for help" are included. The inputs to this process are the urgency assessment result and the emotion recognition result, and the output is the response message.

[1324] Step 6:

[1325] The terminal receives the response message from the server and displays it to the user.

[1326] The server generates a response message and sends it to the terminal in JSON format. The terminal interprets the received JSON data and displays it in a format that is easy for the user to read. For example, a message might appear on a smartphone screen saying, "Please visit a nearby hospital during opening hours," and also provide specific opening hours and contact information. The input here is the JSON data received from the server, and the output is the response message that is displayed to the user.

[1327] (Application example 2)

[1328] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1329] Conventional symptom data input systems determine the level of urgency based on the symptom data entered by the user, but do not generate response messages that take the user's emotional state into account, resulting in insufficient provision of appropriate support. Furthermore, in public places such as brick-and-mortar stores, there is a lack of mechanisms for promptly notifying store staff in the event of an emergency. This makes it difficult for users to feel at ease and take prompt, appropriate action.

[1330] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1331] In this invention, the server includes: means for a user to input symptom data using an input device; means for a terminal to transmit the input symptom data to the server; means for the server to analyze the received symptom data and determine the level of urgency; means for the server to generate an appropriate response message based on the determined level of urgency; means for the terminal to display the response message from the server to the user; means for recognizing the user's emotion using an emotion engine and transmitting the emotion data to the server; means for the server to analyze the received emotion data and reflect it in the response message; means for notifying staff at a physical store according to the user's level of urgency; and means for providing information about nearby medical facilities using the in-store server. This enables support tailored to the user's emotional state, enabling prompt and appropriate responses even in physical stores.

[1332] "User input device" refers to a tool used by a user to input information about themselves, such as a smartphone, smart glasses, tablet, or PC.

[1333] "Symptom data" refers to information about the child's physical condition that is input by the user through an input device, and specifically includes details of the symptoms and the time of onset.

[1334] "Terminal" refers to a device for inputting and transmitting symptom data, and specifically includes portable electronic devices such as smartphones and smart glasses.

[1335] A "server" refers to a computer system that receives and analyzes input data, generates an appropriate response message based on the results, and sends it back to the terminal.

[1336] "Urgency" refers to an indicator that expresses the priority and importance of the response based on the symptom data entered, and is classified into three levels: high, medium, and low.

[1337] A "response message" refers to a message that the server generates based on the analysis results and other information and provides to the user, and specifically includes information such as directions to medical facilities and home care methods.

[1338] "Emotion engine" refers to software and algorithms that analyze a user's facial expressions, tone of voice, etc. to determine their emotional state.

[1339] "Emotion data" refers to data obtained as a result of analyzing the user's emotional state using an emotion engine.

[1340] "Brick and mortar store" refers to a physical store facility, specifically a public location, including a children's goods store or a family restaurant.

[1341] "Means for notifying store staff" refers to systems and methods for quickly notifying store staff in an emergency.

[1342] "Medical facility information" refers to detailed information such as the location, contact information, and opening hours of nearby hospitals and clinics.

[1343] "JSON format" refers to a lightweight data interchange format for storing and exchanging data, and is an abbreviation for JavaScript Object Notation.

[1344] The "HTTPS protocol" refers to a communication protocol for securely sending and receiving data, and is an abbreviation for Hypertext Transfer Protocol Secure.

[1345] "AI engine" is a general term for software and algorithms that refer to artificial intelligence technology that analyzes and makes decisions based on input data.

[1346] The system for implementing this invention begins when a user inputs symptom data using an input device such as a smartphone or smart glasses and sends the data to a server. The server, which plays a central role in the system, analyzes the received symptom data and determines the level of urgency, while also acquiring and analyzing the user's emotion data using an emotion engine.

[1347] First, the user uses a dedicated application to input information about their child's symptoms. For example, they might input information such as "The child looks pale and complains of nausea," "The child says he has a stomachache," or "He has a fever and is listless." The input information is converted into JSON format by the device and then sent to the server using the HTTPS protocol.

[1348] The server analyzes the received symptom data using an AI engine (using, for example, TensorFlow or PyTorch) and classifies the urgency into three levels: "high," "medium," or "low." An emotion engine (using, for example, the Affectiva API or Microsoft Azure Emotion API) then obtains emotional data based on the user's facial expressions and tone of voice, and sends that data to the server. This also analyzes the level of stress and anxiety the user is experiencing.

[1349] The server then generates an appropriate response message based on the urgency assessment and emotional data. If the urgency is high, the server provides information on nearby emergency medical facilities. If the urgency is medium, it provides information on medical facilities with opening hours, and if the urgency is low, it provides home care instructions. This response message reflects the user's emotional state, and if the user is feeling highly stressed, it provides more detailed explanations or additional support information.

[1350] The response message generated by the server is again converted to JSON format and sent to the terminal via the HTTPS protocol. The terminal interprets the received data and displays it to the user in an easy-to-read format. This response message contains specific instructions for the user to take action quickly. At the same time, if the situation is urgent, a notification can be sent to the store staff, enabling a prompt response.

[1351] In this way, by combining symptom data and emotion data, this invention provides users with more accurate, situation-specific support. It also enables quick and accurate response in physical stores, allowing users to spend time in the store with peace of mind. The goal of this system is to reduce users' anxiety and enable prompt medical treatment.

[1352] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1353] Step 1:

[1354] The user uses a dedicated application to input the child's symptom data. This input data includes specific details of the symptoms and is entered in text format. For example, the user might enter, "My child looks pale and complains of nausea."

[1355] Step 2:

[1356] The device converts the input symptom data into JSON format. Converting to JSON format defines the data structure, making it easier to exchange data during communication. Specifically, the input data is converted into key-value pairs in the format "symptoms: 'The child looks pale and complains of nausea'".

[1357] Step 3:

[1358] The device sends the converted JSON data to the server using the HTTPS protocol. This communication protocol ensures secure data transfer and prevents unauthorized access and data leaks. An example of the data sent is "{"symptoms": "The child looks pale and complains of nausea"}".

[1359] Step 4:

[1360] The server analyzes the symptom data it receives. Using an AI engine (using TensorFlow or PyTorch), the server inputs the symptom data and determines the level of urgency. Specifically, it analyzes past medical data and case studies to determine the severity of the symptoms on a three-level scale: high, medium, or low. For example, if data is sent stating that "a child's complexion is pale and he is complaining of nausea," the level of urgency is determined to be "high."

[1361] Step 5:

[1362] The server uses an emotion engine to acquire the user's emotional data. This process involves analyzing the user's facial expressions and tone of voice via the user's camera and microphone to determine their emotional state. The acquired emotional data indicates the user's level of stress or anxiety. For example, if the user appears very anxious, the data is sent to the server as "high stress."

[1363] Step 6:

[1364] The server generates a response message based on the urgency and emotion data. If the urgency is high, the server creates a message providing information on nearby emergency medical facilities; if the urgency is medium, the server creates a message providing information on medical facilities with opening hours; and if the urgency is low, the server creates a message providing home care methods. In addition, if the stress level is high, the server generates a response message including a detailed explanation and additional support information. For example, if the assessment result is "high urgency" and "high stress," the server generates a message saying, "Immediately contact the nearest emergency hospital. Please remain calm."

[1365] Step 7:

[1366] The response message generated by the server is converted to JSON format and sent to the device using the HTTPS protocol. This allows the data to be sent back to the device safely and efficiently. An example of the data sent is "{"message": "Please contact the nearest emergency hospital immediately. Please remain calm."}".

[1367] Step 8:

[1368] The device interprets the received JSON data and displays it in a human-readable format. The device then displays the received response message on the screen as is, allowing the user to take action immediately. For example, if the received message is "Please contact the nearest emergency hospital immediately. Please remain calm and act accordingly," the device will display that message to the user as is.

[1369] Step 9:

[1370] If the device detects a high level of urgency, it will notify store staff. This notification will be displayed on a dedicated staff application, encouraging appropriate action. For example, in a high-level urgency case, a notification will be sent to store staff stating, "We recommend that you immediately contact a medical institution and take emergency measures."

[1371] This allows both the user and the store staff to respond quickly and appropriately.

[1372] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1373] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1374] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1375] [Fourth embodiment]

[1376] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1377] 7, a 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.

[1378] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1379] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1380] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1381] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1382] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1383] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1384] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1385] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1386] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1387] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1388] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1389] The present invention is a system in which a user inputs a child's symptoms using an input device, analyzes the information, determines the urgency of the symptoms, and prompts appropriate action based on the results. Specifically, it is composed of a terminal, a server, an AI engine, and an interface for displaying a response message to the user. An embodiment for actually implementing this system is described in detail below.

[1390] Basic system configuration

[1391] 1. User Input

[1392] Users use a smartphone or computer to enter their child's symptoms through a dedicated application or web interface, and the symptom data is entered in detailed text format.

[1393] For example, enter "My child has a high fever and a persistent cough."

[1394] 2. Data transmission by the terminal

[1395] The device first converts the input symptom data into JSON format and then securely sends it to the server using the HTTPS protocol.

[1396] 3. Data analysis and urgency determination by the server

[1397] The server receives the symptom data sent from the terminal and analyzes it.

[1398] An AI engine is used for analysis and determines the level of urgency based on the received data. The AI ​​engine has learned from past medical data and cases, enabling highly accurate diagnoses.

[1399] Urgency is classified into three levels: high, medium, and low.

[1400] 4. Server Generation of Response Message

[1401] After the AI ​​engine determines the urgency, the server generates an appropriate response message based on the urgency.

[1402] If the emergency is severe, information about the nearest emergency medical facility will be provided.

[1403] If the emergency is moderate, information on medical facilities that are open will be provided.

[1404] If the condition is less urgent, home care instructions will be provided.

[1405] 5. Receiving and displaying data by the device

[1406] The server generates a response message and sends it to the terminal in JSON format.

[1407] The terminal interprets the data it receives and displays it in a user-friendly format.

[1408] Specific example explanation

[1409] Example 1: High Urgency

[1410] The user enters the child's symptom as "lost consciousness."

[1411] The terminal sends this information to the server.

[1412] The server uses an AI engine to determine the urgency as high.

[1413] The server generates a message such as "Please contact the nearest emergency hospital immediately."

[1414] The terminal displays this message to the user.

[1415] Example 2: Medium Urgency

[1416] The user types, "I have a fever of 37.5 degrees and a cough."

[1417] The terminal sends this information to the server.

[1418] The server determines the urgency to be medium and generates a message such as "Please visit XX Clinic during opening hours."

[1419] The terminal displays this message to the user.

[1420] Example 3: Low Urgency

[1421] The user types, "I have some minor scratches."

[1422] The terminal sends this information to the server.

[1423] The server determines the urgency to be low and generates a message such as "Please take care at home by cooling the area and keeping it clean."

[1424] The terminal displays this message to the user.

[1425] This allows users to take prompt and appropriate action against their child's symptoms and prevent their health from worsening. This system can be a powerful support tool for parents.

[1426] The processing flow will be explained below.

[1427] Step 1:

[1428] The user starts up the device (smartphone or computer) and accesses a dedicated application or web interface.

[1429] Step 2:

[1430] The user enters the child's symptom data into a form displayed on the screen.

[1431] For example, enter "My child has a high fever and a persistent cough."

[1432] Step 3:

[1433] The user checks the input information and presses the send button.

[1434] Step 4:

[1435] The terminal converts the data entered by the user into JSON format.

[1436] Step 5:

[1437] The terminal sends the converted JSON data to the server using the HTTPS protocol.

[1438] API endpoint: https: / / example.com / api / diagnose

[1439] Step 6:

[1440] The server receives the request from the terminal and prepares the data for analysis.

[1441] Step 7:

[1442] The server interprets the received JSON data and extracts the required fields.

[1443] Step 8:

[1444] The symptom data extracted by the server is input into an AI engine to determine the level of urgency.

[1445] The AI ​​engine uses a model trained on a large amount of medical data to classify the level of urgency into three levels: high, medium, and low.

[1446] Step 9:

[1447] The server receives the urgency determination result and generates an appropriate response message based on the result.

[1448] Step 10:

[1449] If the urgency is high, the server searches a database of nearby emergency medical facilities to obtain information on the most suitable medical facility.

[1450] Example: Generate a message that reads, "This is a high urgency. Please visit XX Emergency Hospital (Address: XXX, Phone: YYY) immediately."

[1451] Step 11:

[1452] If the urgency is medium, the server suggests medical facilities that are open.

[1453] Example: Generates a message saying, "The emergency is moderate. Please visit XX Clinic during business hours."

[1454] Step 12:

[1455] If the emergency is low, the server will suggest home care methods.

[1456] Example: Generate a message that says, "Symptoms are mild. Please take care of yourself at home by cooling your body, keeping it clean, etc."

[1457] Step 13:

[1458] Convert the response message generated by the server into JSON format.

[1459] Step 14:

[1460] The server sends the converted JSON data to the terminal using the HTTPS protocol.

[1461] Step 15:

[1462] The device interprets the JSON data it receives and converts it into a human-readable format.

[1463] Step 16:

[1464] The terminal displays a response message to the user.

[1465] Step 17:

[1466] The user checks the response message displayed on the terminal and performs the suggested action.

[1467] Example 1

[1468] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1469] In today's world, accurately and quickly determining the urgency of a child's symptoms is an important challenge for parents and medical institutions. Urgent symptoms require immediate action, especially for those with high-urgency symptoms. However, parents generally lack easy-to-use methods that do not require specialized knowledge and allow accurate diagnoses and appropriate treatment. Furthermore, there is a need to provide appropriate care methods that minimize the consumption of medical resources, even in cases of mild symptoms.

[1470] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1471] In this invention, the server includes: means for a user to input symptom data using an input device; means for a terminal to convert the input symptom data into JSON format and send it to the server using the HTTPS protocol; means for the server to analyze the received symptom data and use an AI engine to determine the level of urgency; means for the server to generate an appropriate response message based on the determined level of urgency; and means for the terminal to receive the response message from the server in JSON format, interpret it, and display it in a human-readable format. This allows the user to quickly and accurately determine the level of urgency of their child's symptoms, enabling them to provide appropriate first aid or see a doctor at a medical institution.

[1472] "User" refers to a person who uses the system to enter a child's symptom data.

[1473] "Input device" refers to hardware and software used to input data into a system.

[1474] "Symptom Data" refers to information that includes textual descriptions of a child's physical condition or health status.

[1475] "Terminal" refers to a device that is operated by a user to input symptom data and communicate with a server.

[1476] "Server" refers to a computer system that receives input symptom data, performs analysis, and generates an appropriate response message.

[1477] "JSON format" stands for JavaScript Object Notation and refers to a lightweight text format for structuring data.

[1478] The "HTTPS protocol" is an abbreviation for HyperText Transfer Protocol Secure, and refers to a communication protocol for securely transmitting data over the Internet.

[1479] "AI engine" refers to software that uses artificial intelligence technology to analyze data and determine the level of urgency.

[1480] A "response message" refers to a message generated by the server based on the analysis of the symptom data.

[1481] "Database Management System" means a software system for managing, storing, and retrieving data efficiently and securely.

[1482] "Urgency" refers to the criteria for determining the severity and urgency of symptoms.

[1483] A "template engine" refers to software for dynamically generating messages from a standardized format.

[1484] "Analysis" refers to the act of examining input data in detail, understanding its contents, and drawing specific conclusions.

[1485] "Display" refers to the act of visually providing information on a terminal in a form that can be read by a user.

[1486] The present invention is a system that allows a user to input a child's symptoms using an input device, analyzes the information, determines the urgency of the symptoms, and prompts appropriate action based on the results. The system includes a terminal, a server, an AI engine, and an interface for displaying a response message to the user.

[1487] User-entered symptom data

[1488] Users use a smartphone or computer to enter detailed information about their child's symptoms through a dedicated application or web interface, and this information is entered in text format.

[1489] Example: "My child has a high fever and a persistent cough."

[1490] Data transmission by the terminal

[1491] The terminal converts the input symptom data into JSON format and sends it securely to the server using the HTTPS protocol. An appropriate conversion library (e.g., Jackson for Java, Gson for Java, etc.) is used to convert the data format.

[1492] Data analysis and urgency determination by server

[1493] The server retains the data received from the device and saves it in a database management system (e.g., PostgreSQL, MongoDB, etc.). The saved data is then passed to an AI engine for analysis. This AI engine has learned from past medical data and cases, and uses a model written in Python (e.g., scikit-learn, TensorFlow, etc.) to accurately determine the urgency of the case.

[1494] Server generation of response message

[1495] The server generates an appropriate response message based on the received urgency assessment result. It selects the response message based on the conditions and generates the message using a template engine (e.g., FreeMarker, Thymeleaf, etc.).

[1496] If the situation is urgent: "Contact the nearest emergency hospital immediately."

[1497] If the urgency is medium: "Please visit XX Clinic during opening hours."

[1498] If the situation is not urgent: "Take care at home by cooling the area and keeping it clean."

[1499] Receiving and displaying data by the terminal

[1500] The terminal receives the response message from the server in JSON format, converts it into a user-friendly format, and displays it using a user-friendly interface.

[1501] Examples of prompt statements

[1502] If you lose consciousness, immediately contact the nearest emergency room.

[1503] "If you have a fever of 37.5 degrees and a cough, please visit XX Clinic during business hours."

[1504] "If you suffer a minor abrasion, please take care of it at home by cooling it and keeping it clean."

[1505] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1506] Step 1: User enters symptom data

[1507] Specific operation: The user inputs symptom data using a smartphone or computer input device. The input data is described in detail in text format.

[1508] Input: Text data about a child's symptoms (e.g., "My child has a high fever and a persistent cough").

[1509] Output: Symptom data in text format.

[1510] Step 2: The device converts the data into JSON format

[1511] What it does: The terminal takes the text data entered by the user and converts it to JSON format using a library such as Jackson or Gson.

[1512] Input: Symptom data in text format.

[1513] Output: Data converted to JSON format.

[1514] Step 3: The device sends the data to the server using the HTTPS protocol.

[1515] Specific operation: The terminal sends the converted JSON data to the endpoint URL via a POST request using the HTTPS protocol.

[1516] Input: Symptom data in JSON format.

[1517] Output: The HTTP request sent to the server.

[1518] Step 4: The server receives and stores the data

[1519] Specific operation: The server holds the data received from the terminal and stores it in a database management system (e.g., PostgreSQL, MongoDB).

[1520] Input: Symptom data sent from the device in JSON format.

[1521] Output: Symptom data stored in a database.

[1522] Step 5: The server analyzes the data using an AI engine

[1523] How it works: The server passes the stored data to an AI engine, which analyzes the data and determines the level of urgency. The AI ​​engine has learned from past medical data and cases.

[1524] Input: Symptom data stored in a database in JSON format.

[1525] Output: Urgency assessment result (high, medium, low).

[1526] Step 6: Server generates response message

[1527] Specific operation: The server generates an appropriate response message using a template engine based on the urgency assessment result.

[1528] Input: Urgency assessment result.

[1529] Output: A text response message (e.g., "Please contact the nearest emergency room immediately").

[1530] Step 7: The server converts the message into JSON format and sends it

[1531] Specific operation: The server converts the generated response message into JSON format and sends it to the terminal again using the HTTPS protocol.

[1532] Input: A response message in text format.

[1533] Output: The HTTP request and response message converted to JSON format.

[1534] Step 8: The device receives and displays the data

[1535] Specific operation: The device receives the response message from the server in JSON format, converts it into a format that the user can understand, and displays it.

[1536] Input: The response message in JSON format.

[1537] Output: A text response message that is displayed to the user.

[1538] (Application example 1)

[1539] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1540] In conventional factory robot maintenance and failure response, it is difficult for operators to accurately determine abnormal conditions and quickly take appropriate action. This requires a high level of specialized knowledge, which can lead to delayed responses in emergencies that require a rapid response. This can lead to the shutdown of factory production lines and further danger. This issue needs to be resolved with a new system that efficiently determines abnormal conditions and provides appropriate response actions.

[1541] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1542] In this invention, the server includes: means for a user to input status data using an input device; means for a terminal to transmit the input status data to the server; means for the server to analyze the received status data and determine the level of urgency; means for the server to generate an appropriate response message based on the determined level of urgency; means for the terminal to display the response message from the server to the user; means for a user to input abnormal status information in text format using the terminal; means for the terminal to transmit the input abnormal status data to the server and the server to analyze the level of urgency; and means for the server to generate an appropriate response action based on the result of the urgency determination and notify the user. This enables an operator to quickly and accurately report an abnormal status, and the server to automatically determine the level of urgency and provide an appropriate response action.

[1543] "User" means an individual or operator who uses the system to input status data.

[1544] An "input device" is a device for inputting status data, including, for example, a tablet, a smartphone, or a computer.

[1545] "Status data" is information entered by the user when an abnormality occurs, and is text data detailing the specific content and circumstances of the abnormality.

[1546] A "terminal" is a device on which a user inputs status data and sends it to a server, and can refer to a tablet, smartphone, or computer.

[1547] "Sending" refers to the act of transferring data from an input device to a server using a communication protocol.

[1548] The "server" is a central processing unit that receives status data sent from the terminal, analyzes it, and generates a response message.

[1549] "Analyzing" refers to the act of interpreting the content of the data received by the server, processing the information, and drawing conclusions.

[1550] "Urgency" is an evaluation criterion that determines the severity of an abnormality and the promptness of response based on status data on a three-level scale (high, medium, low).

[1551] "Determining" refers to the act of the server analyzing the status data and identifying the level of urgency.

[1552] The "response message" is the content of instructions and corresponding actions generated by the server based on the result of the urgency determination.

[1553] "Displaying" refers to the act of visually presenting to the user the response message received by the terminal from the server.

[1554] "Text format" is a data format in which the user inputs information about an abnormal state using characters or sentences.

[1555] The "urgency determination result" is the evaluation result of the urgency level derived by the server by analyzing the status data.

[1556] "Response actions" are specific measures or instructions according to the level of urgency that the server provides as a response message.

[1557] This invention is a system in which a user inputs status data using an input device, analyzes the data, determines the urgency of an abnormality, and prompts appropriate response actions. Specifically, it targets abnormal conditions related to factory robots. A detailed description of an embodiment of this system is provided below.

[1558] Basic system configuration

[1559] 1. User Input

[1560] The user (operator) uses an input device such as a tablet or smartphone to input abnormal conditions of the robot through a dedicated application or web interface. The input data is detailed in text format.

[1561] Example: Enter "The robot's motor is overheating."

[1562] 2. Data transmission by the terminal

[1563] The terminal first converts the entered status data into JSON format and then securely sends it to the server using the HTTPS protocol.

[1564] 3. Data analysis and urgency determination by the server

[1565] The server receives and analyzes the status data sent from the device. An AI engine is used for the analysis, and determines the level of urgency based on the received data. The AI ​​engine has learned past abnormal conditions and status patterns, allowing for highly accurate determinations. Urgency is classified into three levels: high, medium, and low.

[1566] 4. Server Generation of Response Message

[1567] After the AI ​​engine determines the urgency, the server generates an appropriate response message based on the urgency.

[1568] If the emergency is urgent, specific instructions will be provided, such as "Stop the robot's motors immediately and call a specialist technician."

[1569] Medium urgency: Instructions such as "Pause robot operation and monitor the situation" are provided.

[1570] Low-urgency situations: Instructions such as "Please record the situation and continue driving as normal" are provided.

[1571] 5. Receiving and displaying data by the device

[1572] The server generates a response message and sends it to the terminal in JSON format. The terminal interprets the received data and displays it in a user-friendly format.

[1573] Technology used

[1574] Hardware: tablets, smartphones, factory robots

[1575] software:

[1576] Flask (Server construction with Python)

[1577] TensorFlow or PyTorch (AI engine implementation)

[1578] JSON (data format)

[1579] Requests (HTTP communication)

[1580] React Native or Flutter (Mobile Application Development)

[1581] Specific examples

[1582] When a user inputs "The robot arm is stuck and won't move," the system analyzes the data and suggests specific actions to take, such as "Immediately stop the arm motor and call a specialist technician." This allows the user to take prompt and accurate action, improving factory efficiency and safety.

[1583] Example prompt sentence:

[1584] Enter the robot's motors getting too hot.

[1585] This system makes it possible to instantly determine the urgency of abnormal conditions involving factory robots and take appropriate action to avoid production line shutdowns and further danger.

[1586] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1587] Step 1:

[1588] A user inputs status data using an input device.

[1589] Input: Text data of abnormal conditions related to the robot (e.g., "The robot's motor is overheating")

[1590] Specific operation: Log in to a dedicated application on a smartphone or tablet and enter the abnormal condition in text format.

[1591] Output: Text data of the abnormal condition is saved in the input device.

[1592] Step 2:

[1593] The terminal converts the input status data into JSON format and sends it to the server using the HTTPS protocol.

[1594] Input: Text data of abnormal condition

[1595] Specific operation: A dedicated application converts abnormality data into JSON format and sends it securely to the server using the HTTPS protocol.

[1596] Output: JSON formatted data is sent to and received from the server.

[1597] Step 3:

[1598] The server analyzes the status data received and uses an AI engine to determine the level of urgency.

[1599] Input: State data in JSON format

[1600] Specific operation: The server parses the received JSON data and inputs it into the AI ​​engine. The AI ​​engine (using, for example, TensorFlow or PyTorch) analyzes the data and determines the urgency (high, medium, low).

[1601] Output: Data on urgency (high, medium, low) and recommended response actions

[1602] Step 4:

[1603] The server generates an appropriate response message based on the determined urgency.

[1604] Input: Urgency assessment results and recommended response actions

[1605] Specific operation: The server embeds data into a template message according to the urgency level and generates a specific response message regarding the recommended action.

[1606] Output: The generated response message

[1607] Step 5:

[1608] The terminal receives the response message from the server in JSON format and displays it to the user.

[1609] Input: Generated response message (JSON format)

[1610] Specific operation: The device parses the JSON data received from the server, converts it into a human-readable format, and displays the response message on the screen using a dedicated application.

[1611] Output: A response message that appears on the user's screen (e.g., "Immediately stop the robot's motors and call a specialist technician.")

[1612] This establishes a series of steps from inputting an abnormal condition to determining the level of urgency and displaying a response message, allowing the system to function effectively.

[1613] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1614] This system allows users to input their child's symptoms, analyzes the information, determines the level of urgency, and provides an appropriate response message. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide support tailored to the situation. This system consists of a terminal, a server, an AI engine, an emotion engine, and an interface for displaying the response message.

[1615] Basic system configuration

[1616] 1. User Input

[1617] Users use a smartphone or computer to access a dedicated application or web interface.

[1618] The user enters the child's symptom data into an on-screen form.

[1619] For example, enter "My child has a high fever and a persistent cough."

[1620] 2. Data transmission by the terminal

[1621] The device converts the entered symptom data into JSON format and sends it to the server using the HTTPS protocol.

[1622] 3. Data analysis and urgency determination by the server

[1623] The server receives the symptom data sent from the terminal and analyzes it.

[1624] An AI engine is used for analysis and determines the urgency of the received data. The AI ​​engine has learned from past medical data and cases, enabling highly accurate diagnoses.

[1625] Urgency is classified into three levels: high, medium, and low.

[1626] 4. Emotion Recognition by Emotion Engine

[1627] The emotion engine uses a camera or microphone mounted on the user's input device to acquire the user's facial expressions and voice and recognize their emotions.

[1628] The emotion engine analyzes the user's emotion data and sends the results to the server.

[1629] 5. Server Generation of Response Messages

[1630] The server references the urgency assessment results from the AI ​​engine and the emotional data from the emotion engine to generate an appropriate response message.

[1631] If the urgency is high, the server searches a database of nearby emergency medical facilities to obtain information on the most suitable medical facility.

[1632] If the urgency is medium, the server provides information on medical facilities that are open.

[1633] If the emergency is less urgent, the server will provide home care instructions.

[1634] Based on the results of the emotion engine, users with high stress levels are provided with more detailed explanations and additional support information.

[1635] 6. Receiving and displaying data by the device

[1636] The server generates a response message and sends it to the terminal in JSON format.

[1637] The terminal interprets the data it receives and displays it in a user-friendly format.

[1638] Specific example explanation

[1639] Example 1: High Urgency

[1640] The user enters the child's symptom as "lost consciousness."

[1641] The terminal sends this information to the server.

[1642] The server uses an AI engine to determine the urgency as high.

[1643] The emotion engine analyzes the user's facial expressions and voice and recognizes that they are in a state of strong anxiety or panic.

[1644] The server provides the message "Contact the nearest emergency room immediately" along with additional details to reassure the user.

[1645] The terminal displays this message to the user.

[1646] Example 2: Medium Urgency

[1647] The user types, "I have a fever of 37.5 degrees and a cough."

[1648] The terminal sends this information to the server.

[1649] The server determines the urgency to be medium and generates a message saying, "Please visit XX Clinic during opening hours."

[1650] The emotion engine analyzes the user's emotions and determines that the stress level is medium.

[1651] The server provides additional detailed instructions, including specific appointment times and clinic contact information.

[1652] The terminal displays this message to the user.

[1653] Example 3: Low Urgency

[1654] The user types, "I have some minor scratches."

[1655] The terminal sends this information to the server.

[1656] The server determines the urgency to be low and generates a message saying, "Please take care at home by cooling the area and keeping it clean."

[1657] The emotion engine analyzes the user's emotions and determines that there are no particular signs of high stress or anxiety.

[1658] The server provides only basic care instructions.

[1659] The terminal displays this message to the user.

[1660] This allows users to respond quickly and appropriately to their child's symptoms and receive support tailored to their emotional state. This system can be a powerful support tool for parents.

[1661] The processing flow will be explained below.

[1662] Step 1:

[1663] The user starts up the device (smartphone or computer) and accesses a dedicated application or web interface.

[1664] Step 2:

[1665] The user enters their child's symptom data into an on-screen form. For example, "My child has a high fever and a persistent cough."

[1666] Step 3:

[1667] The user presses the submit button to confirm the input.

[1668] Step 4:

[1669] The terminal receives the user's input and converts it into JSON format.

[1670] Step 5:

[1671] The terminal sends the converted JSON data to the server using the HTTPS protocol.

[1672] API endpoint: https: / / example.com / api / diagnose

[1673] Step 6:

[1674] The server receives the JSON data sent from the device, parses it, and extracts the necessary fields.

[1675] Step 7:

[1676] The server inputs the extracted symptom data into an AI engine, which analyzes the symptom data and compares it with past medical data to determine the urgency (high, medium, low).

[1677] Step 8:

[1678] The server receives the AI ​​engine's judgment result and proceeds to the next step.

[1679] Step 9:

[1680] The emotion engine uses the device's built-in camera or microphone to capture the user's facial expressions and voice in real time.

[1681] Step 10:

[1682] The emotion engine analyzes the acquired data and determines the user's emotion (calm, anxious, panic, etc.).

[1683] Step 11:

[1684] The server combines the urgency assessment results of the AI ​​engine and the emotion assessment results of the emotion engine to generate an appropriate response message.

[1685] Step 12:

[1686] If the emergency is high, the server searches the database for nearby emergency medical facilities and generates a message to provide information on the most appropriate medical facility. Example: "As the emergency is high, please visit the nearest emergency hospital (Address: XXX, Phone: YYY) immediately."

[1687] Step 13:

[1688] If the urgency is medium, the server obtains information about medical facilities that are open and generates a message instructing the patient to visit the hospital. Example: "The urgency is medium. Please visit XX Clinic during opening hours."

[1689] Step 14:

[1690] If the level of urgency is low, the server generates a message suggesting home care methods, for example, "Your symptoms are mild. Please take care of yourself at home by cooling your body, keeping it clean, etc."

[1691] Step 15:

[1692] Convert the response message generated by the server into JSON format.

[1693] Step 16:

[1694] The server sends the converted JSON data to the terminal using the HTTPS protocol.

[1695] Step 17:

[1696] The device interprets the JSON data it receives and converts it into a human-readable format.

[1697] Step 18:

[1698] The terminal displays a response message to the user.

[1699] Step 19:

[1700] The user reviews the response message displayed on the device and takes the suggested action, such as contacting the nearest emergency facility, visiting a doctor, or providing home care, if necessary.

[1701] Example 2

[1702] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1703] The present invention relates to a system that allows users to quickly and accurately report their child's symptoms, analyzes the data, and immediately provides appropriate instructions for a response. In particular, the system aims to provide a more user-friendly response by taking the user's emotional state into consideration when determining the level of urgency.

[1704] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for using an emotion recognition engine to acquire and analyze emotion data of a user, means for the server to customize a response message based on the results of the urgency determination and the emotion recognition results, means for the terminal to convert input symptom data into JSON format and transmit it to the server using the HTTPS protocol, means for the server to input the received JSON data into an artificial intelligence engine and determine the urgency, means for the server to convert the generated response message into JSON format and transmit it to the terminal using the HTTPS protocol, and means for the terminal to interpret the received JSON data and display it in a human-readable format. This makes it possible to immediately determine the urgency of the symptoms reported by the user and to provide an optimal response message depending on the user's emotional state.

[1705] "User" refers to the person who uses the terminal to input the child's symptom data and other information.

[1706] "Input device" refers to a hardware or software interface through which a user inputs symptom data, etc.

[1707] "Symptom Data" refers to data that contains specific information about a child's symptoms.

[1708] The term "terminal" refers to an electronic device that transmits symptom data entered by a user to a server and displays a response message received from the server.

[1709] "Server" refers to a central processing unit used to receive and analyze data sent from terminals and generate response messages.

[1710] "Data analysis" refers to the process of using an AI engine or similar to make judgments about urgency and other factors based on input data.

[1711] "Urgency" is an indicator of the seriousness of the symptoms and is classified into three levels: high, medium, and low.

[1712] "Response message" refers to a message generated by a server, sent to a terminal, and containing instructions or information that is displayed to a user.

[1713] "Emotion recognition engine" refers to a software module or hardware device for acquiring and analyzing a user's emotion data.

[1714] "Emotion data" is data that indicates the user's emotional state, and is obtained from facial expressions, voice, and the like.

[1715] An "artificial intelligence engine" refers to a software module that performs highly accurate analysis and judgments based on input data.

[1716] "JSON format" is a lightweight data description format used for data exchange and is an abbreviation for JavaScript Object Notation.

[1717] The "HTTPS protocol" is an abbreviation for Hypertext Transfer Protocol Secure, which is used for secure communication.

[1718] This invention relates to a system that allows users to input their child's symptoms, analyzes the information, determines the level of urgency, and provides an appropriate response message. Furthermore, by incorporating an emotion recognition engine that recognizes the user's emotions, it is possible to provide support tailored to the situation. This system is comprised of a user, a terminal, a server, an AI engine, an emotion recognition engine, and an interface for displaying the response message.

[1719] Using a smartphone or computer, users access a dedicated application or web interface that provides a form for them to enter their child's symptoms, such as "My child has a high fever and a persistent cough."

[1720] The device converts the entered symptom data into JSON format and sends it to the server using the HTTPS protocol. The device uses end-to-end encryption for data transmission to enhance security. The device also supports batch processing to periodically send the entered symptom data to the server.

[1721] The server receives symptom data sent from the device. The server passes the received data to an artificial intelligence engine (AI engine), which analyzes it and determines the level of urgency. The AI ​​engine has learned from past medical data and cases, enabling highly accurate analysis. Urgency is classified into three levels: high, medium, and low.

[1722] The server also accesses the emotion recognition engine to acquire the user's emotional data. The emotion recognition engine uses the camera and microphone on the user's device to capture the user's facial expressions and voice and recognize their emotions. For example, it can detect emotions such as "anxiety" and "panic." The emotional data is sent to the server and taken into consideration along with the urgency assessment results from the AI ​​engine.

[1723] The server generates an appropriate response message based on the urgency assessment and emotion recognition results. If the urgency is high, the server provides information on nearby emergency medical facilities and includes detailed instructions to ease the user's anxiety. If the urgency is medium, the server provides information on medical facilities that are open. If the urgency is low, the server shows how to provide care at home.

[1724] The device receives the response message sent from the server and displays it in a format that is easy for the user to read. For example, it may display a message on the smartphone screen saying, "Please visit a nearby hospital during opening hours," and provide specific opening hours and contact information. This message is customized according to the user's stress level.

[1725] Specific example explanation

[1726] Example 1: High Urgency

[1727] 1. The user enters "lost consciousness" as the child's symptom.

[1728] 2. The device sends this information to the server.

[1729] 3. The server uses an AI engine to determine the urgency as high.

[1730] 4. The emotion recognition engine analyzes the user's facial expressions and voice and determines whether they are in a state of strong anxiety or panic.

[1731] 5. The server displays the message "Contact the nearest emergency medical facility immediately" along with additional details to reassure the user.

[1732] 6. The terminal displays this message to the user.

[1733] Example 2: Medium Urgency

[1734] 1. The user enters, "I have a fever of 37.5 degrees and a cough."

[1735] 2. The device sends this information to the server.

[1736] 3. The server determines the urgency to be medium and generates a message saying, "Please visit a nearby medical facility during business hours."

[1737] 4. The emotion recognition engine analyzes the user's emotions and determines that the stress level is medium.

[1738] 5. The server provides additional information, including detailed instructions, including specific appointment times and contact information for the medical facility.

[1739] 6. The terminal displays this message to the user.

[1740] Example 3: Low Urgency

[1741] 1. The user types, "I have a minor scratch."

[1742] 2. The device sends this information to the server.

[1743] 3. The server determines the urgency to be low and generates a message saying, "Please take care at home by cooling the area and keeping it clean."

[1744] 4. The emotion recognition engine analyzes the user's emotions and determines that there are no particular signs of high stress or anxiety.

[1745] 5. The server provides only basic care instructions.

[1746] 6. The terminal displays this message to the user.

[1747] Example prompts to input to the generative AI model

[1748] "If a child's symptoms are a cough accompanied by a fever, please determine the level of urgency and generate an appropriate response message. Also, please provide examples of how to respond if the user is in a high-stress state."

[1749] "Please tell me the analysis procedure and how to generate a response message when a child is unconscious."

[1750] "Please give me an example of additional support information when the emotion recognition engine's emotion analysis result indicates high stress."

[1751] This allows the system to provide a fast and appropriate response to the symptoms reported by the user, and also to provide customized support based on the user's emotional state.

[1752] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1753] Step 1:

[1754] The user enters the symptom data.

[1755] A user accesses a dedicated application or web interface using a smartphone or computer. The user enters their child's symptom data into an on-screen form. For example, they enter information such as "My child has a high fever and a persistent cough." This input data becomes the basic input information for subsequent processing.

[1756] Step 2:

[1757] The device converts the symptom data into JSON format and sends it to the server.

[1758] The device converts the symptom data entered by the user into JSON format, for example, the data looks like this:

[1759] json

[1760] {

[1761] "temperature": "high",

[1762] "symptoms": ["Uncontrollable cough"]

[1763] }

[1764] The terminal sends this JSON data to the server using the HTTPS protocol. The input here is the symptom data entered by the user, and the output is JSON format data.

[1765] Step 3:

[1766] The server receives the symptom data and performs data analysis.

[1767] The server receives the JSON data sent from the device. The server passes the received data to an artificial intelligence engine (AI engine) and begins data analysis. The input is the received JSON data, and the output is the analysis results. Data analysis refers to past medical data and case studies to determine the urgency of the symptoms. This determination is classified into three levels: high, medium, and low. For example, based on the input data of "high temperature" and "unstoppable cough," the AI ​​engine determines the urgency to be "medium."

[1768] Step 4:

[1769] The server uses an emotion recognition engine to acquire and analyze the user's emotion data.

[1770] The server accesses the emotion recognition engine and instructs it to acquire the user's emotional data. The emotion recognition engine uses the camera and microphone equipped on the user's device to acquire the user's facial expressions and voice, and recognizes the emotion. For example, it may recognize "anxiety" from the facial expression. This result is sent to the server. The input here is the user's image data and voice data, and the output is the emotion analysis result.

[1771] Step 5:

[1772] The server generates a response message based on the urgency determination result and emotion recognition result.

[1773] The server generates an appropriate response message based on the urgency assessment result from the AI ​​engine and the emotional data from the emotion recognition engine. If the urgency is high, for example, a message such as "Please contact the nearest emergency hospital immediately" is generated. If the emotion analysis result indicates "anxiety," additional detailed instructions such as "Ask an adult nearby for help" are included. The inputs to this process are the urgency assessment result and the emotion recognition result, and the output is the response message.

[1774] Step 6:

[1775] The terminal receives the response message from the server and displays it to the user.

[1776] The server generates a response message and sends it to the terminal in JSON format. The terminal interprets the received JSON data and displays it in a format that is easy for the user to read. For example, a message might appear on a smartphone screen saying, "Please visit a nearby hospital during opening hours," and also provide specific opening hours and contact information. The input here is the JSON data received from the server, and the output is the response message that is displayed to the user.

[1777] (Application example 2)

[1778] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1779] Conventional symptom data input systems determine the level of urgency based on the symptom data entered by the user, but do not generate response messages that take the user's emotional state into account, resulting in insufficient provision of appropriate support. Furthermore, in public places such as brick-and-mortar stores, there is a lack of mechanisms for promptly notifying store staff in the event of an emergency. This makes it difficult for users to feel at ease and take prompt, appropriate action.

[1780] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1781] In this invention, the server includes: means for a user to input symptom data using an input device; means for a terminal to transmit the input symptom data to the server; means for the server to analyze the received symptom data and determine the level of urgency; means for the server to generate an appropriate response message based on the determined level of urgency; means for the terminal to display the response message from the server to the user; means for recognizing the user's emotion using an emotion engine and transmitting the emotion data to the server; means for the server to analyze the received emotion data and reflect it in the response message; means for notifying staff at a physical store according to the user's level of urgency; and means for providing information about nearby medical facilities using the in-store server. This enables support tailored to the user's emotional state, enabling prompt and appropriate responses even in physical stores.

[1782] "User input device" refers to a tool used by a user to input information about themselves, such as a smartphone, smart glasses, tablet, or PC.

[1783] "Symptom data" refers to information about the child's physical condition that is input by the user through an input device, and specifically includes details of the symptoms and the time of onset.

[1784] "Terminal" refers to a device for inputting and transmitting symptom data, and specifically includes portable electronic devices such as smartphones and smart glasses.

[1785] A "server" refers to a computer system that receives and analyzes input data, generates an appropriate response message based on the results, and sends it back to the terminal.

[1786] "Urgency" refers to an indicator that expresses the priority and importance of the response based on the symptom data entered, and is classified into three levels: high, medium, and low.

[1787] A "response message" refers to a message that the server generates based on the analysis results and other information and provides to the user, and specifically includes information such as directions to medical facilities and home care methods.

[1788] "Emotion engine" refers to software and algorithms that analyze a user's facial expressions, tone of voice, etc. to determine their emotional state.

[1789] "Emotion data" refers to data obtained as a result of analyzing the user's emotional state using an emotion engine.

[1790] "Brick and mortar store" refers to a physical store facility, specifically a public location, including a children's goods store or a family restaurant.

[1791] "Means for notifying store staff" refers to systems and methods for quickly notifying store staff in an emergency.

[1792] "Medical facility information" refers to detailed information such as the location, contact information, and opening hours of nearby hospitals and clinics.

[1793] "JSON format" refers to a lightweight data interchange format for storing and exchanging data, and is an abbreviation for JavaScript Object Notation.

[1794] The "HTTPS protocol" refers to a communication protocol for securely sending and receiving data, and is an abbreviation for Hypertext Transfer Protocol Secure.

[1795] "AI engine" is a general term for software and algorithms that refer to artificial intelligence technology that analyzes and makes decisions based on input data.

[1796] The system for implementing this invention begins when a user inputs symptom data using an input device such as a smartphone or smart glasses and sends the data to a server. The server, which plays a central role in the system, analyzes the received symptom data and determines the level of urgency, while also acquiring and analyzing the user's emotion data using an emotion engine.

[1797] First, the user uses a dedicated application to input information about their child's symptoms. For example, they might input information such as "The child looks pale and complains of nausea," "The child says he has a stomachache," or "He has a fever and is listless." The input information is converted into JSON format by the device and then sent to the server using the HTTPS protocol.

[1798] The server analyzes the received symptom data using an AI engine (using, for example, TensorFlow or PyTorch) and classifies the urgency into three levels: "high," "medium," or "low." An emotion engine (using, for example, the Affectiva API or Microsoft Azure Emotion API) then obtains emotional data based on the user's facial expressions and tone of voice, and sends that data to the server. This also analyzes the level of stress and anxiety the user is experiencing.

[1799] The server then generates an appropriate response message based on the urgency assessment and emotional data. If the urgency is high, the server provides information on nearby emergency medical facilities. If the urgency is medium, it provides information on medical facilities with opening hours, and if the urgency is low, it provides home care instructions. This response message reflects the user's emotional state, and if the user is feeling highly stressed, it provides more detailed explanations or additional support information.

[1800] The response message generated by the server is again converted to JSON format and sent to the terminal via the HTTPS protocol. The terminal interprets the received data and displays it to the user in an easy-to-read format. This response message contains specific instructions for the user to take action quickly. At the same time, if the situation is urgent, a notification can be sent to the store staff, enabling a prompt response.

[1801] In this way, by combining symptom data and emotion data, this invention provides users with more accurate, situation-specific support. It also enables quick and accurate response in physical stores, allowing users to spend time in the store with peace of mind. The goal of this system is to reduce users' anxiety and enable prompt medical treatment.

[1802] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1803] Step 1:

[1804] The user uses a dedicated application to input the child's symptom data. This input data includes specific details of the symptoms and is entered in text format. For example, the user might enter, "My child looks pale and complains of nausea."

[1805] Step 2:

[1806] The device converts the input symptom data into JSON format. Converting to JSON format defines the data structure, making it easier to exchange data during communication. Specifically, the input data is converted into key-value pairs in the format "symptoms: 'The child looks pale and complains of nausea'".

[1807] Step 3:

[1808] The device sends the converted JSON data to the server using the HTTPS protocol. This communication protocol ensures secure data transfer and prevents unauthorized access and data leaks. An example of the data sent is "{"symptoms": "The child looks pale and complains of nausea"}".

[1809] Step 4:

[1810] The server analyzes the symptom data it receives. Using an AI engine (using TensorFlow or PyTorch), the server inputs the symptom data and determines the level of urgency. Specifically, it analyzes past medical data and case studies to determine the severity of the symptoms on a three-level scale: high, medium, or low. For example, if data is sent stating that "a child's complexion is pale and he is complaining of nausea," the level of urgency is determined to be "high."

[1811] Step 5:

[1812] The server uses an emotion engine to acquire the user's emotional data. This process involves analyzing the user's facial expressions and tone of voice via the user's camera and microphone to determine their emotional state. The acquired emotional data indicates the user's level of stress or anxiety. For example, if the user appears very anxious, the data is sent to the server as "high stress."

[1813] Step 6:

[1814] The server generates a response message based on the urgency and emotion data. If the urgency is high, the server creates a message providing information on nearby emergency medical facilities; if the urgency is medium, the server creates a message providing information on medical facilities with opening hours; and if the urgency is low, the server creates a message providing home care methods. In addition, if the stress level is high, the server generates a response message including a detailed explanation and additional support information. For example, if the assessment result is "high urgency" and "high stress," the server generates a message saying, "Immediately contact the nearest emergency hospital. Please remain calm."

[1815] Step 7:

[1816] The response message generated by the server is converted to JSON format and sent to the device using the HTTPS protocol. This allows the data to be sent back to the device safely and efficiently. An example of the data sent is "{"message": "Please contact the nearest emergency hospital immediately. Please remain calm."}".

[1817] Step 8:

[1818] The device interprets the received JSON data and displays it in a human-readable format. The device then displays the received response message on the screen as is, allowing the user to take action immediately. For example, if the received message is "Please contact the nearest emergency hospital immediately. Please remain calm and act accordingly," the device will display that message to the user as is.

[1819] Step 9:

[1820] If the device detects a high level of urgency, it will notify store staff. This notification will be displayed on a dedicated staff application, encouraging appropriate action. For example, in a high-level urgency case, a notification will be sent to store staff stating, "We recommend that you immediately contact a medical institution and take emergency measures."

[1821] This allows both the user and the store staff to respond quickly and appropriately.

[1822] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1823] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1824] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1825] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1826] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1827] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1828] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1829] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1830] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1831] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1832] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1833] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1834] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1835] 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.

[1836] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1837] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1838] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1839] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1840] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1841] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1842] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1843] The following is further disclosed regarding the above embodiment.

[1844] (Claim 1)

[1845] means for a user to input symptom data using an input device;

[1846] means for transmitting the input symptom data from the terminal to a server;

[1847] A means for analyzing the symptom data received by the server and determining the degree of urgency;

[1848] means for the server to generate an appropriate response message based on the determined urgency;

[1849] means for the terminal to display a response message from the server to the user;

[1850] A system including:

[1851] (Claim 2)

[1852] A means for the server to classify symptom data entered by the user into high, medium, or low urgency levels;

[1853] A means for providing information on nearby emergency medical facilities when the server indicates a high level of emergency;

[1854] In the case of a moderate emergency, a means for providing information on medical facilities that are open for business;

[1855] A means of providing home care in less urgent cases;

[1856] 10. The system of claim 1, comprising:

[1857] (Claim 3)

[1858] A means for converting the input symptom data into JSON format and transmitting it to a server using the HTTPS protocol;

[1859] A means for inputting the JSON data received by the server into an AI engine to determine the urgency of the data;

[1860] A means for converting the response message generated by the server into a JSON format and transmitting the JSON format to the terminal using the HTTPS protocol;

[1861] A means for the device to interpret the JSON data it receives and display it in a human-readable format;

[1862] 10. The system of claim 1, comprising:

[1863] "Example 1"

[1864] (Claim 1)

[1865] means for a user to input symptom data using an input device;

[1866] A means for converting the input symptom data into JSON format and transmitting it to a server using the HTTPS protocol;

[1867] A means for using an AI engine to analyze the symptom data received by the server and determine the urgency of the symptom data;

[1868] means for the server to generate an appropriate response message based on the determined urgency;

[1869] A means for the terminal to receive, interpret, and display response messages from the server in JSON format in a human-readable format;

[1870] A system including:

[1871] (Claim 2)

[1872] A means for the server to classify symptom data entered by the user into high, medium, or low urgency levels;

[1873] A means for providing information on nearby emergency medical facilities when the server indicates a high level of emergency;

[1874] In the case of a moderate emergency, a means for providing information on medical facilities that are open for business;

[1875] A means of providing home care in less urgent cases;

[1876] 10. The system of claim 1, comprising:

[1877] (Claim 3)

[1878] The server stores the received data in a database management system and inputs it into an AI engine to determine the level of urgency.

[1879] A means for converting the response message generated by the server into a JSON format and transmitting the JSON format to the terminal using the HTTPS protocol;

[1880] A means for the device to interpret the JSON data it receives and display it in a human-readable format;

[1881] 10. The system of claim 1, comprising:

[1882] "Application Example 1"

[1883] (Claim 1)

[1884] means for a user to input status data using an input device;

[1885] means for transmitting input status data from the terminal to the server;

[1886] A means for analyzing the status data received by the server and determining the level of urgency;

[1887] means for the server to generate an appropriate response message based on the determined urgency;

[1888] means for the terminal to display a response message from the server to the user;

[1889] A means for a user to input abnormal state information in text format using a terminal;

[1890] a means for transmitting the abnormal state data input by the terminal to a server, and for the server to analyze the degree of urgency;

[1891] A means for the server to generate an appropriate response action based on the result of the urgency determination and notify the user;

[1892] A system including:

[1893] (Claim 2)

[1894] A means for the server to classify symptom data entered by the user into high, medium, or low urgency levels;

[1895] When the server is in a high emergency state, a means for providing information on nearby specialized facilities is provided.

[1896] In the case of a medium emergency, a means for providing information on available facilities;

[1897] A means for providing self-help methods in low-urgency cases;

[1898] 10. The system of claim 1, further comprising means for providing detailed information about the corresponding action generated by the server.

[1899] (Claim 3)

[1900] A means for converting the input symptom data into JSON format and transmitting it to a server using the HTTPS protocol;

[1901] A means for inputting the JSON data received by the server into an AI engine to determine the urgency of the data;

[1902] A means for converting the response message generated by the server into a JSON format and transmitting the JSON format to the terminal using the HTTPS protocol;

[1903] A means for the device to interpret the JSON data it receives and display it in a human-readable format;

[1904] 2. The system according to claim 1, further comprising means for the server to classify the status data entered by the operator into a specific response action and present instructions.

[1905] "Example 2: Combining Emotion Engines"

[1906] (Claim 1)

[1907] means for a user to input symptom data using an input device;

[1908] means for transmitting the input symptom data from the terminal to a server;

[1909] A means for analyzing the symptom data received by the server and determining the degree of urgency;

[1910] means for the server to generate an appropriate response message based on the determined urgency;

[1911] means for the terminal to display a response message from the server to the user;

[1912] A system including:

[1913] (Claim 2)

[1914] a means for acquiring and analyzing user emotion data using an emotion recognition engine;

[1915] a means for the server to customize a response message based on the result of the urgency determination and the result of the emotion recognition;

[1916] 10. The system of claim 1, comprising:

[1917] (Claim 3)

[1918] A means for converting the input symptom data into JSON format and transmitting it to a server using the HTTPS protocol;

[1919] A means for inputting the JSON data received by the server into an artificial intelligence engine to determine the urgency of the data;

[1920] A means for converting the response message generated by the server into a JSON format and transmitting the JSON format to the terminal using the HTTPS protocol;

[1921] A means for the device to interpret the JSON data it receives and display it in a human-readable format;

[1922] 10. The system of claim 1, comprising:

[1923] "Application example 2 when combining emotion engines"

[1924] (Claim 1)

[1925] means for a user to input symptom data using an input device;

[1926] means for transmitting the input symptom data from the terminal to a server;

[1927] A means for analyzing the symptom data received by the server and determining the degree of urgency;

[1928] means for the server to generate an appropriate response message based on the determined urgency;

[1929] means for the terminal to display a response message from the server to the user;

[1930] Furthermore, a means for recognizing the user's emotion using an emotion engine and transmitting the emotion data to a server;

[1931] A means for the server to analyze the received emotion data and reflect it in a response message;

[1932] A means for notifying store staff depending on the user's urgency;

[1933] a means for providing information about nearby medical facilities using a server in the store;

[1934] A system including:

[1935] (Claim 2)

[1936] A means for the server to classify symptom data entered by the user into high, medium, or low urgency levels;

[1937] A means for providing information on nearby emergency medical facilities when the server indicates a high level of emergency;

[1938] In the case of a moderate emergency, a means for providing information on medical facilities that are open for business;

[1939] A means of providing home care in less urgent cases;

[1940] Furthermore, means for adjusting the response message according to the emotional state of the user;

[1941] 10. The system of claim 1, comprising:

[1942] (Claim 3)

[1943] A means for converting the input symptom data into JSON format and transmitting it to a server using the HTTPS protocol;

[1944] A means for inputting the JSON data received by the server into an AI engine to determine the urgency of the data;

[1945] means for transmitting the emotion data acquired by the emotion engine to a server via HTTPS protocol;

[1946] The server generates a response message based on the urgency assessment result and emotion data, converts it into JSON format, and sends it to the terminal using the HTTPS protocol;

[1947] A means for the device to interpret the JSON data it receives and display it in a human-readable format;

[1948] A means of sending appropriate notifications to store staff depending on the level of urgency;

[1949] 10. The system of claim 1, comprising: [Explanation of symbols]

[1950] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for a user to input symptom data using an input device; means for transmitting the input symptom data from the terminal to a server; A means for analyzing the symptom data received by the server and determining the degree of urgency; means for the server to generate an appropriate response message based on the determined urgency; means for the terminal to display a response message from the server to the user; A system including:

2. A means for the server to classify symptom data entered by the user into high, medium, or low urgency levels; A means for providing information on nearby emergency medical facilities when the server indicates a high level of emergency; In the case of a moderate emergency, a means for providing information on medical facilities that are open for business; A means of providing home care in less urgent cases; The system of claim 1 , comprising:

3. A means for converting the input symptom data into JSON format and transmitting it to a server using the HTTPS protocol; A means for inputting the JSON data received by the server into an AI engine to determine the urgency of the data; A means for converting the response message generated by the server into a JSON format and transmitting the JSON format to the terminal using the HTTPS protocol; A means for the device to interpret the JSON data it receives and display it in a human-readable format; The system of claim 1 , comprising:

Images