system

Abstract

Provide a system. [Solution] a means for inputting basic patient information; a means for capturing an image of the patient; a means for analyzing the captured patient image and determining a patient ID; A means for making an initial diagnosis based on basic patient information and image analysis results; a means for prescribing medical treatment based on the diagnosis; a means for controlling the robot to perform a medical procedure; a means for monitoring the treatment process in real time and storing the data; A means for users and robots to communicate in real time, A system including:

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] In traditional medical settings, medical practitioners are required to quickly obtain appropriate information and provide accurate treatment during emergency medical procedures. However, this requires advanced skills and experience, and first-time medical practitioners can feel confused and pressured, leading to a high risk of incorrect treatment. Patient identification and confirmation of treatment history are also time-consuming and inefficient in situations with limited time. Furthermore, lack of smooth communication between medical practitioners and patients makes it difficult to provide appropriate treatment. To address these issues, a system is needed that can quickly and accurately obtain patient information and support appropriate diagnosis and treatment. [Means for solving the problem]

[0005] The present invention provides a system including a means for inputting basic patient information, a means for capturing patient images, a means for analyzing the captured patient images and identifying the patient ID, a means for performing an initial diagnosis based on the patient's basic information and the image analysis results, a means for instructing medical treatment based on the diagnosis results, a means for controlling a robot to perform the medical treatment, a means for monitoring the treatment process in real time and saving data, and a means for real-time communication between the user and the robot. This enables appropriate medical treatment to be performed quickly and accurately even in emergencies, reducing the burden on medical practitioners and ensuring patient safety. Furthermore, by further including a means for monitoring the robot's operating status in real time and suspending treatment if an abnormality is detected, further safety can be ensured. Furthermore, by further including a means for storing past treatment results in a database and using them for future diagnoses and treatments, continuous medical support can be provided.

[0006] "Basic patient information" refers to basic information required for medical treatment, such as the patient's name, age, sex, medical history, and current symptoms.

[0007] The "means for capturing images" is a function for taking images of a patient using a device such as a camera or a sensor.

[0008] "Image analysis" is the process of analyzing captured image data using algorithms to identify specific patterns or features.

[0009] A "patient ID" is an identification number or code that uniquely identifies a patient.

[0010] An "initial diagnosis" is the first diagnosis made by a medical practitioner or system based on the patient's basic information and the results of image analysis.

[0011] The means for instructing "medical treatment" is a function that gives instructions indicating the necessary treatment or measures based on the diagnosis results.

[0012] "Means for controlling a robot" refers to technologies and systems for controlling the operation of a medical robot and carrying out instructed medical procedures.

[0013] The "means for monitoring the treatment process in real time" is a function for observing the progress of medical treatment in real time and collecting data.

[0014] "Means for storing data" refers to a function for storing monitored data in a storage device or database.

[0015] "Means of real-time communication" refers to a function that allows the user and the robot to exchange information two-way using natural language processing and voice recognition.

[0016] The "means for interrupting treatment when an abnormality is detected" is a function for immediately interrupting medical treatment when an abnormality is detected in the robot's operation.

[0017] The "means for storing past treatment results in a database" is a function for recording details and results of medical treatments performed in a database to be used for future treatments. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The present invention provides a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment. The system includes a series of processes for inputting basic patient information, capturing images, and performing diagnosis and medical treatment.

[0040] The system's operation procedure begins with the user entering basic patient information into the terminal. This basic information includes the patient's name, age, gender, medical history, current symptoms, etc. The terminal then sends the information entered by the user to the server.

[0041] The user then follows the device's instructions to capture an image of the patient using the device's built-in camera. The device then adjusts the captured image and sends it to the server, where the server uses image analysis algorithms to analyze the patient's characteristics and match them with an internal database to identify the patient. The server also uses the results of the analysis to make an initial diagnosis and identify appropriate medical treatment.

[0042] The server sends the diagnosis results to the terminal, which displays them to the user. The user reviews the proposed treatment and, if they give their permission, proceeds to the next step. The terminal then controls the robot to perform the actual medical treatment. The progress of the treatment is monitored in real time, and includes a function to immediately interrupt it if an abnormality is detected.

[0043] The robot uses natural language processing to communicate with the user and responds to their questions and instructions. For example, if the user asks, "Have you finished stopping the bleeding?", the robot will reply, "Yes, I've finished stopping the bleeding. What's next, disinfection?" In this way, the user can monitor the robot's operations and give appropriate instructions.

[0044] In addition, all treatment results and progress are uploaded to a server in real time and logs are saved, making it possible to use past data for future diagnoses and treatments. The entire system supports safe and efficient medical procedures, significantly reducing the burden on medical practitioners while ensuring patient safety.

[0045] As a concrete example, consider the treatment of a cut on the arm. The user enters the patient's basic information, captures an image, and sends it to the server. The server performs an initial diagnosis and sends the diagnosis that "stopping the bleeding and disinfecting are necessary" to the terminal. After the user confirms, the terminal controls the robot to stop the bleeding and disinfect. During this time, the user and robot communicate with each other as the treatment progresses. All information is saved on the server in real time and can be used for future medical treatments.

[0046] This system enables rapid and accurate response even in emergency medical treatment, improving efficiency and safety in medical settings. In addition, the robot uses natural language processing to smoothly communicate with users, allowing them to perform medical procedures without any problems, even if it is their first time performing them.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] The user enters basic patient information into the terminal, such as the patient's name, age, sex, medical history, and current symptoms.

[0050] Step 2:

[0051] The terminal displays the entered patient information to the user in real time and asks for confirmation. After the user confirms the information, the terminal sends the information to the server.

[0052] Step 3:

[0053] The user follows the instructions on the device to capture images of the patient using the built-in camera, adjusting the angle and position as needed.

[0054] Step 4:

[0055] The device receives the captured image, adjusts the image quality and size as necessary, and sends the adjusted image to the server.

[0056] Step 5:

[0057] The server runs facial recognition and body feature analysis algorithms to analyze the received patient images and identify the patient ID against an internal database.

[0058] Step 6:

[0059] The server runs a diagnostic algorithm to make an initial diagnosis based on the patient's basic information and the image analysis results, and identifies the necessary medical treatment.

[0060] Step 7:

[0061] The server generates a diagnosis and identifies an appropriate treatment procedure, and sends the diagnosis and treatment procedure to the terminal.

[0062] Step 8:

[0063] The terminal displays the received diagnosis results and treatment procedures to the user, who then checks the contents and prepares to give permission.

[0064] Step 9:

[0065] The user checks the diagnosis and treatment details and instructs the robot to carry out the treatment via a terminal. With the user's permission, the terminal starts the robot's control program and instructs the specific medical treatment.

[0066] Step 10:

[0067] The robot performs the medical procedure: its arms take gauze to stop bleeding and apply it to the patient's cut, then apply disinfectant and proceed with the necessary treatment.

[0068] Step 11:

[0069] The terminal monitors the treatment process in real time and sends data to the server. If the terminal detects an abnormality, it immediately instructs the robot to halt treatment.

[0070] Step 12:

[0071] The user observes the robot's treatment progress and gives additional instructions via a terminal or voice command. The user can give specific instructions, such as "apply a bandage," and the robot will respond accordingly.

[0072] Step 13:

[0073] After the robot completes the entire medical procedure, the terminal sends a report of the completion of the procedure to the server, which stores all the treatment results and log data for future diagnosis and treatment.

[0074] Through the above steps, the present invention can consistently carry out the entire process from inputting basic patient information, capturing images, making an initial diagnosis, performing medical treatment, and monitoring and logging the treatment.

[0075] Example 1

[0076] 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."

[0077] In current medical settings, patient information acquisition, diagnosis, and treatment are often done manually, resulting in time-consuming processes and a high risk of human error. Furthermore, when emergency medical treatment is required, a fast and accurate response is required, but current systems have difficulty meeting this requirement. Furthermore, real-time monitoring of treatment progress and data recording are insufficient, meaning that information useful for subsequent diagnoses and treatments is not fully utilized.

[0078] 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.

[0079] In this invention, the server includes a means for inputting basic patient information, a means for acquiring patient images, a means for analyzing the acquired patient images and identifying the patient identification number, a means for making an initial diagnosis based on the patient's basic information and the image analysis results, a means for proposing medical treatment based on the diagnosis results, a means for controlling the machine to perform the medical treatment, a means for monitoring the treatment process in real time and saving data, and a means for real-time communication between the user and the machine. This enables fast and accurate medical treatment, improving efficiency and safety in medical settings. Furthermore, because treatment results and progress are monitored and recorded in real time, reliable data management is realized that can be used for subsequent diagnoses and treatments.

[0080] "Basic information" refers to basic data required for medical treatment, such as the patient's name, age, gender, medical history, and current symptoms.

[0081] "Image acquisition" refers to taking a picture of a patient's body part or symptoms using the device's camera function and acquiring the image data.

[0082] "Image analysis" refers to the process of processing acquired image data to extract and identify features. For example, this can be done using an image processing library such as OpenCV.

[0083] "Patient Identification Number" refers to a unique number assigned to an individual patient identified from analyzed images and basic information.

[0084] "Initial diagnosis" refers to the first medical diagnosis made based on the patient's basic information and the results of image analysis.

[0085] "Medical treatment" refers to the specific treatment steps that are proposed and performed based on the initial diagnosis, such as stopping bleeding and disinfection.

[0086] "Machine" refers to a robot or device that is controlled to perform a medical procedure, such as a surgical robot.

[0087] "Real-time monitoring" means monitoring the medical procedure as it progresses, and being ready to respond immediately if any abnormalities occur.

[0088] "Data storage" refers to storing information such as the progress and results of treatment in a storage device within the system for later use.

[0089] "Users" are medical professionals such as doctors and nurses who operate the system and perform medical procedures.

[0090] "Communication" refers to the exchange of information between the user and the machine, and responses to instructions and questions, for example, using natural language processing techniques.

[0091] The present invention provides a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment. This system includes a series of processes for inputting basic patient information, acquiring images, and performing diagnosis and medical treatment.

[0092] The user inputs basic information about the patient into the device. This basic information includes the patient's name, age, sex, medical history, and current symptoms. The device sends the input information to the server. Next, the user uses the device's camera to capture an image of the patient. This is done by activating the built-in camera and taking a picture of the patient's affected area. The captured image is then sent from the device to the server.

[0093] The server uses OpenCV, an open-source image analysis library, to analyze the received image data. It executes image analysis algorithms and extracts features. The results are compared with an internal database to identify the patient's identification number. Furthermore, an initial diagnosis is made based on the analysis results, and appropriate medical treatment is identified. This initial diagnosis is made based on a novel diagnostic protocol, with the server referencing the internal medical database.

[0094] The diagnosis results are sent from the server to the device, which then displays them to the user, who then confirms the proposed treatment and agrees to it.

[0095] The terminal runs a robot control program using a robotics operating system (ROS) to perform medical procedures, such as stopping bleeding and disinfecting. For example, when treating a cut on the right arm, the robot arm applies gauze to stop the bleeding and then applies disinfectant.

[0096] Communication between the user and the robot is carried out using natural language processing. When the user asks, "Have you finished stopping the bleeding?", the robot responds, "Yes, I've finished stopping the bleeding. Next is disinfection?" This communication proceeds in the form of the user asking questions and giving instructions through a voice recognition system, and the robot responding accordingly.

[0097] All treatment processes are recorded in real time on the server. The database management system MySQL (registered trademark) is used to store the data. This data will be used later for the next diagnosis or treatment. For example, the treatment details and results performed on patient ID "12345" are recorded, and when the same patient is diagnosed again, this data can be referenced to provide more accurate medical services.

[0098] Example prompt for a generative AI model:

[0099] Please explain the specific program processing flow for each step, from entering basic patient information to image capture, diagnosis, and implementation. Also, please provide a detailed description of the operation when using OpenCV for image analysis and ROS for robot control.

[0100] This system enables rapid and accurate response even in emergency medical treatment, improving efficiency and safety in medical settings. In addition, the robot uses natural language processing to smoothly communicate with users, allowing them to perform medical procedures without any problems, even if it is their first time performing them.

[0101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0102] Step 1:

[0103] The terminal inputs the patient's basic information.

[0104] Input: Data entered by the user (such as name, age, gender, medical history, current symptoms, etc.).

[0105] Data processing: The terminal converts the input data into JSON format.

[0106] Output: Patient demographics data in JSON format is generated.

[0107] Specific operation: The user opens the dedicated application on the device, enters information into the input form, and taps the "Submit" button.

[0108] Step 2:

[0109] The terminal sends the entered basic patient information to the server.

[0110] Input: Patient demographics data in JSON format.

[0111] Data processing: Send JSON data to the server via an HTTP POST request.

[0112] Output: Basic information is saved on the server.

[0113] Specific operation: The terminal sends patient data to a specific API endpoint on the server.

[0114] Step 3:

[0115] The user captures an image of the patient using the device's camera.

[0116] Input: The patient's area to be imaged.

[0117] Data processing: The device's camera app captures the image and converts it to JPEG format.

[0118] Output: Image data in JPEG format is generated.

[0119] Specific actions: The user opens the camera app, takes a picture of the patient's affected area, and taps the "Capture" button.

[0120] Step 4:

[0121] The image acquired by the terminal is sent to the server.

[0122] Input: Image data in JPEG format.

[0123] Data processing: Image data is sent to the server via an HTTP POST request.

[0124] Output: Image data is saved on the server.

[0125] Specific operation: The device sends image data to a specific API endpoint on the server.

[0126] Step 5:

[0127] The server analyzes the received image data.

[0128] Input: Image data in JPEG format.

[0129] Data processing: The server uses OpenCV to analyze the image and extract features, such as edges, color, and shape.

[0130] Output: Extracted feature data.

[0131] Specific operation: The server executes OpenCV algorithms and performs image analysis.

[0132] Step 6:

[0133] The server identifies the patient identification number based on the analysis results.

[0134] Input: Extracted feature data.

[0135] Data processing: The server checks against an internal database to identify matching patient identification numbers.

[0136] Output: Patient identification number.

[0137] Specific operation: The server compares the feature data with existing data in the database and identifies matching data.

[0138] Step 7:

[0139] The server makes an initial diagnosis based on the patient's basic information and the results of image analysis.

[0140] Input: Patient basic information data and feature data.

[0141] Data processing: The server references an internal medical database and executes diagnostic algorithms.

[0142] Output: Initial diagnostic results.

[0143] Specific operations: The server executes a diagnostic protocol using a medical database.

[0144] Step 8:

[0145] The server suggests medical treatment based on the diagnosis.

[0146] Input: Initial diagnostic results.

[0147] Data processing: The server selects recommended medical treatments based on the diagnosis results.

[0148] Output: Medical treatment suggestions.

[0149] Specific operation: The server selects an appropriate treatment method based on the diagnostic protocol.

[0150] Step 9:

[0151] The terminal displays the diagnosis and suggested medical treatment to the user.

[0152] Input: Medical procedure proposal data.

[0153] Data processing: The device analyzes the data and displays it on the screen in a visually easy-to-understand format.

[0154] Output: Diagnosis results and medical treatment suggestions displayed to the user.

[0155] What it does: The device displays the diagnosis and suggested medical treatment on the screen.

[0156] Step 10:

[0157] The terminal controls the machine to perform the medical procedure.

[0158] Input: Medical procedure proposal data.

[0159] Data processing: The terminal uses ROS to execute the robot control program.

[0160] Output: Performing a medical procedure.

[0161] Specific actions: The robotic arm performs specific medical procedures such as stopping bleeding and disinfecting.

[0162] Step 11:

[0163] The terminal monitors the treatment process in real time and stores the data.

[0164] Input: Process data.

[0165] Data processing: Data from the treatment process is collected and sent to the server in real time.

[0166] Output: Treatment data stored in real time.

[0167] Specific operation: The terminal monitors the progress of the procedure and immediately notifies if there is an abnormality.

[0168] Step 12:

[0169] The user and the machine communicate in real time.

[0170] Input: User's voice command data.

[0171] Data processing: The machine uses natural language processing to analyze the voice instructions and generate an appropriate response.

[0172] Output: Machine-generated voice response.

[0173] Specific operation: The user gives a voice command, and the machine responds by confirming the operation based on the command.

[0174] Through the above processing steps, the present invention can provide a system that supports rapid and accurate medical treatment of patients, improving efficiency and safety.

[0175] (Application example 1)

[0176] 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."

[0177] With conventional technology, factory robot maintenance required skilled technicians, and labor shortages and variations in technician skills were problems. It was also difficult to detect abnormalities in real time, quickly diagnose problems, and provide appropriate maintenance procedures, making maintenance efficiency and accuracy challenging. This led to concerns about increased factory downtime and reduced production efficiency.

[0178] 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.

[0179] In this invention, the server includes: means for inputting basic patient information; means for capturing patient images; means for analyzing the captured patient images and identifying the patient ID; means for performing an initial diagnosis based on the patient's basic information and image analysis results; means for instructing medical treatment based on the diagnosis results; means for controlling the robot to perform the medical treatment; means for monitoring the treatment process in real time and saving data; means for real-time communication between the user and the robot; means for identifying diagnosis results and appropriate maintenance procedures to support industrial equipment maintenance; and means for providing and displaying the industrial equipment maintenance procedures to the user. This enables factory robot maintenance to detect and quickly diagnose abnormalities in real time and provide appropriate maintenance procedures. This enables efficient and accurate maintenance without the need for skilled technicians, reducing factory downtime and improving production efficiency.

[0180] "Basic patient information" refers to basic information about the patient, such as the patient's name, age, sex, medical history, and current symptoms.

[0181] "Image capturing means" refers to a device or means for obtaining an image of a patient or object using a camera, sensor, or the like.

[0182] "Image analysis" refers to the process of using algorithms and software to analyze features and patterns in a captured image and extract information from it.

[0183] "Means for identifying patient ID" refers to a means for determining a patient's unique identification information based on information obtained through image analysis.

[0184] "Initial diagnosis" refers to the process of making an initial diagnosis by integrating basic patient information and image analysis results.

[0185] "Means for prescribing medical treatment based on the diagnosis results" refers to means for determining the necessary medical treatment based on the results of the initial diagnosis and prescribing the details of the treatment.

[0186] "Means for controlling a robot to perform a medical procedure" refers to means for operating and controlling a robot to actually perform a medical procedure.

[0187] "Means for monitoring the procedure in real time and storing data" refers to means for monitoring the procedure performed by the robot in real time and recording and storing the data.

[0188] "Means for real-time communication between a user and a robot" refers to a means for a user and a robot to exchange information in both directions and communicate with each other in real time.

[0189] "Means for identifying diagnostic results and appropriate maintenance procedures to support the maintenance of industrial equipment" refers to means for diagnosing abnormalities and failures in industrial equipment and determining appropriate maintenance procedures based on the results.

[0190] "Means for providing and displaying maintenance procedures for industrial equipment to users" refers to means for presenting diagnostic results and maintenance procedures to users in an easy-to-understand manner and displaying their contents.

[0191] This invention provides a system for assisting in the efficient and accurate maintenance of factory robots. The system includes a series of processes for inputting basic robot information, capturing images, and executing diagnostic and maintenance procedures.

[0192] First, the user inputs basic information about the robot using the UI of the smart glasses. This information includes the robot ID, installation location, and current problems (e.g., "abnormal noise"). The information entered by the user is sent to the server via the smart glasses.

[0193] Next, the user captures an image of the robot using the camera built into the smart glasses. The captured image is adjusted in real time and sent to the server. The server then uses image analysis algorithms (e.g., OpenCV) to analyze the robot's condition and identify any abnormalities or maintenance needs. The server also identifies appropriate maintenance procedures based on the analysis results and sends instructions to the user.

[0194] The transmitted diagnostic results and maintenance procedures are displayed on the smart glasses' display, and the user can then carry out the maintenance work accordingly. For example, if the server issues a diagnostic result indicating that a bearing needs to be replaced, the smart glasses will display the specific procedure and prompt the user to carry out the work.

[0195] The interaction between the user and the system takes place in real time, and when the user asks a question or makes a confirmation, the server responds quickly. For example, if the user asks, "What should I do next?", the server will instruct, "Next, please release the bearing lock." In this way, the user can efficiently carry out maintenance work by interacting with the server using prompts.

[0196] All maintenance progress and results are uploaded to the server in real time and saved as logs, allowing future diagnosis and troubleshooting to be performed based on past maintenance information.

[0197] Specific examples

[0198] For example, the following prompts are used to diagnose and provide maintenance procedures for a robot:

[0199] Please diagnose the cause of the strange noise my robot is making and tell me what maintenance steps I need to take.

[0200] The hardware uses smart glasses with a camera, and the software uses Python, OpenCV, and the requests library for HTTP requests, enabling efficient maintenance of factory robots.

[0201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0202] Step 1:

[0203] The user uses the UI of the smart glasses to input basic information about the robot, including the robot ID, its location, and any current issues (e.g., "abnormal noise"). The input information is converted into digital data by the smart glasses' internal processor and sent to a server via a network.

[0204] Input: User-entered robot ID, location, and problem information

[0205] Output: Basic information data sent to the server

[0206] Step 2:

[0207] The user captures an image of the robot using the camera built into the smart glasses. The software in the glasses formats the image optimally and saves it as image data. The saved image is then sent to a server over a network.

[0208] Input: An image of the robot captured by a camera

[0209] Output: Image data to be sent

[0210] Step 3:

[0211] The server analyzes the received images. Image analysis algorithms (e.g., OpenCV) are used to identify abnormalities on the robot. Features within the images are extracted and compared with past maintenance data in a database to identify abnormalities.

[0212] Input: Submitted image data

[0213] Output: Identification of abnormalities

[0214] Step 4:

[0215] The server integrates the robot's basic information with the image analysis results to perform an initial diagnosis. A diagnostic algorithm (such as a generative AI model) is used to analyze the cause of the abnormality and determine the appropriate maintenance procedure. The diagnosis results and maintenance procedure are generated.

[0216] Input: Basic information data and image analysis results

[0217] Output: Diagnostic results and maintenance procedures

[0218] Step 5:

[0219] Diagnostic results and maintenance procedures are sent from the server to the smart glasses and displayed on the glasses' display.

[0220] Input: Server-generated diagnostic results and maintenance procedures

[0221] Output: Diagnostic results and maintenance procedures displayed to the user

[0222] Step 6:

[0223] The user follows the instructions on the smart glasses to perform maintenance tasks. If they have questions or need confirmation on the next step, they can contact the server using voice commands or UI operations. The server responds to the user's questions in real time.

[0224] Input: Questions and inquiries from users

[0225] Output: Real-time response from the server

[0226] Step 7:

[0227] The progress and results of maintenance are uploaded in real time from the smart glasses to a server and stored as logs, which can be used for future diagnostics and troubleshooting.

[0228] Input: User maintenance progress and results

[0229] Output: Progress and result logs stored on the server

[0230] This system will enable efficient and accurate maintenance of factory robots.

[0231] 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.

[0232] The present invention is a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment, and by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more advanced medical support. This system includes a series of processes for inputting basic patient information, capturing images, and performing diagnosis and medical treatment.

[0233] The system's operation procedure begins with the user entering basic patient information into the terminal. This basic information includes the patient's name, age, gender, medical history, current symptoms, etc. The terminal then sends the information entered by the user to the server.

[0234] The user then follows the device's instructions to capture an image of the patient using the device's built-in camera. The device then adjusts the captured image and sends it to the server, where the server uses image analysis algorithms to analyze the patient's characteristics and match them with an internal database to identify the patient. The server also uses the results of the analysis to make an initial diagnosis and identify appropriate medical treatment.

[0235] The server sends the diagnosis results to the terminal, which displays them to the user. The user reviews the proposed treatment and, if they give their permission, proceeds to the next step. The terminal then controls the robot to perform the actual medical treatment. The progress of the treatment is monitored in real time, and includes a function to immediately interrupt it if an abnormality is detected.

[0236] The system also incorporates an emotion engine, which recognizes emotions from the user's facial expressions, tone of voice, and content of words, and adjusts the robot's response accordingly. For example, if the user is nervous, the robot can respond in a calm tone, saying, "It's okay, let's stay calm and proceed."

[0237] As a concrete example, consider the case of treating a cut on an arm using an emotion engine. The user enters the patient's basic information, captures an image, and sends it to the server. The server performs an initial diagnosis and sends the diagnosis result, "The bleeding needs to be stopped and disinfected," to the terminal. After the user confirms, the terminal controls the robot to stop the bleeding and disinfect the wound.

[0238] During this process, let's say the user says, "I'm worried about whether the bleeding is being stopped properly." The emotion engine detects anxiety from the user's tone of voice and the content of their words, and the robot responds, "Don't worry. The procedure is proceeding correctly." In this way, appropriate communication is carried out according to the user's emotions.

[0239] In addition, all treatment results and progress are uploaded to a server in real time and logs are saved, making it possible to use past data for future diagnoses and treatments. The entire system supports safe and efficient medical procedures, significantly reducing the burden on medical practitioners while ensuring patient safety.

[0240] Furthermore, the emotion engine can be used to assess the user's stress level and interrupt or change treatment if a high stress state is detected, thereby protecting the health and safety of medical personnel. This system can provide more advanced medical support and further improve efficiency and safety in medical settings.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] The user enters basic patient information into the terminal, such as the patient's name, age, sex, medical history, and current symptoms.

[0244] Step 2:

[0245] The terminal displays the entered patient information to the user in real time and asks for confirmation. After the user confirms the information, the terminal sends the information to the server.

[0246] Step 3:

[0247] The user follows the instructions on the device to capture images of the patient using the built-in camera, adjusting the angle and position as needed.

[0248] Step 4:

[0249] The device receives the captured image, adjusts the image quality and size as necessary, and sends the adjusted image to the server.

[0250] Step 5:

[0251] The server runs facial recognition and body feature analysis algorithms to analyze the received patient images and identify the patient ID against an internal database.

[0252] Step 6:

[0253] The server runs a diagnostic algorithm to make an initial diagnosis based on the patient's basic information and the image analysis results, and identifies the necessary medical treatment.

[0254] Step 7:

[0255] The server generates a diagnosis and identifies an appropriate treatment procedure, and sends the diagnosis and treatment procedure to the terminal.

[0256] Step 8:

[0257] The terminal displays the received diagnosis results and treatment procedures to the user, who then checks the contents and prepares to give permission.

[0258] Step 9:

[0259] The user checks the diagnosis and treatment details and instructs the robot to carry out the treatment via a terminal. With the user's permission, the terminal starts the robot's control program and instructs the specific medical treatment.

[0260] Step 10:

[0261] The robot performs the medical procedure: its arms take gauze to stop bleeding and apply it to the patient's cut, then apply disinfectant and proceed with the necessary treatment.

[0262] Step 11:

[0263] The terminal monitors the treatment process in real time and sends data to the server. If the terminal detects an abnormality, it immediately instructs the robot to halt treatment.

[0264] Step 12:

[0265] The user observes the robot's treatment progress and gives additional instructions via a terminal or voice command. The user can give specific instructions, such as "apply a bandage," and the robot will respond accordingly.

[0266] Step 13:

[0267] The emotion engine analyzes the user's facial expressions, tone of voice, and the content of their words to recognize their emotions. For example, if the user says something like "I'm worried," the emotion engine will detect anxiety.

[0268] Step 14:

[0269] Based on the analysis results of the emotion engine, the robot will provide appropriate feedback to the user, for example, "Don't worry, the treatment is going well."

[0270] Step 15:

[0271] If the emotion engine determines that the user's stress level is high, the device will temporarily suspend treatment or issue instructions to change the treatment procedure, thereby protecting the user's mental state.

[0272] Step 16:

[0273] After the robot completes the entire medical procedure, the terminal sends a report of the completion of the procedure to the server, which stores all the treatment results and log data for future diagnosis and treatment.

[0274] Through the above steps, the present invention can seamlessly perform the entire process, from entering basic patient information, capturing images, making an initial diagnosis, performing medical procedures, monitoring and logging the procedures, and even recognizing and providing feedback to the user. This system enables fast and accurate responses even in emergency medical situations, improving efficiency and safety in medical settings. Furthermore, the introduction of an emotion engine reduces the user's psychological burden, allowing for safe and secure medical procedures.

[0275] Example 2

[0276] 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."

[0277] Conventional medical systems face the challenge of quickly and accurately obtaining basic patient information and images, and then performing diagnosis and treatment. The process involves time-consuming tasks such as inputting information and confirming treatment. Furthermore, because treatment is performed without consideration for the user's feelings, medical practitioners often feel stressed, posing a risk to the quality and safety of medical care. There is a need to solve these problems and provide effective and safe medical care to patients while reducing the burden on medical practitioners.

[0278] 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 inputting basic patient information, means for acquiring patient images, means for analyzing the acquired patient images and identifying the patient ID, means for making an initial diagnosis based on the patient's basic information and image analysis results, means for instructing medical treatment based on the diagnosis results, means for controlling the robot to perform the medical treatment, means for monitoring the treatment process in real time and saving data, means for analyzing the user's facial expressions, tone of voice, and content of words to recognize emotions, means for adjusting the robot's responses based on the recognized emotions, and means for communicating between the user and the robot in real time. This enables quick and accurate acquisition of the patient's basic information and images, execution of initial diagnosis and appropriate medical treatment, and flexible response according to the user's emotions, thereby providing more effective and safer medical care.

[0279] "Means for inputting basic patient information" refers to an interface that allows a user to input information about a patient, such as the patient's name, age, sex, medical history, and current symptoms.

[0280] "Means for obtaining images of a patient" refers to devices, including cameras and scanning devices, for capturing images of a patient's body or condition.

[0281] "Means for analyzing acquired patient images and identifying patient IDs" refers to algorithms or programs that recognize and analyze specific features of a patient and verify them against a database to determine the patient ID.

[0282] "Means for making an initial diagnosis based on the patient's basic information and image analysis results" refers to a diagnostic algorithm or program for making an initial diagnosis based on the input basic information and analyzed image information.

[0283] "Means for instructing medical treatment based on diagnostic results" refers to mechanisms or software for determining specific medical treatment based on the results of an initial diagnosis and instructing this to the next process.

[0284] "Means for controlling the robot to perform a medical procedure" refers to a control system for operating the robot based on programmed instructions to perform a specified medical procedure.

[0285] "Means for monitoring the treatment process in real time and storing data" refers to sensors and data storage devices for monitoring the progress of medical treatment in real time and recording and storing that data.

[0286] "Means for analyzing a user's facial expressions, tone of voice, and spoken content to recognize emotions" refers to a program or device that reads and analyzes emotions from a user's facial expressions, tone of voice, and spoken content.

[0287] "Means for adjusting the robot's response based on the recognized emotion" refers to a control program or system for changing the robot's response in response to the analyzed emotion of the user.

[0288] "Means for real-time communication between users and robots" refers to interfaces and network systems that allow users and robots to instantly exchange and share information and instructions.

[0289] The present invention is a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment, and by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more advanced medical support. This system includes a series of processes for inputting basic patient information, capturing images, and performing diagnosis and medical treatment.

[0290] The system's operation procedure begins with the user entering basic patient information into the terminal. This basic information includes the patient's name, age, gender, medical history, current symptoms, etc. The terminal then sends the information entered by the user to the server.

[0291] The user then follows the device's instructions to capture an image of the patient using the device's built-in camera. The device then adjusts the captured image and sends it to the server, where the server uses image analysis algorithms to analyze the patient's characteristics and match them with an internal database to identify the patient. The server also uses the results of the analysis to make an initial diagnosis and identify appropriate medical treatment.

[0292] The server sends the diagnosis results to the terminal, which displays them to the user. The user reviews the proposed treatment and, if they give their permission, proceeds to the next step. The terminal then controls the robot to perform the actual medical treatment. The progress of the treatment is monitored in real time, and includes a function to immediately interrupt it if an abnormality is detected.

[0293] The system also incorporates an emotion engine, which recognizes emotions from the user's facial expressions, tone of voice, and content of words, and adjusts the robot's response accordingly. For example, if the user is nervous, the robot can respond in a calm tone, saying, "It's okay, let's stay calm and proceed."

[0294] As a concrete example, consider the case of treating a cut on an arm using an emotion engine. The user enters the patient's basic information, captures an image, and sends it to the server. The server performs an initial diagnosis and sends the diagnosis result, "The bleeding needs to be stopped and disinfected," to the terminal. After the user confirms, the terminal controls the robot to stop the bleeding and disinfect the wound.

[0295] During this process, let's say the user says, "I'm worried about whether the bleeding is being stopped properly." The emotion engine detects anxiety from the user's tone of voice and the content of their words, and the robot responds, "Don't worry. The procedure is proceeding correctly." In this way, appropriate communication is carried out according to the user's emotions.

[0296] In addition, all treatment results and progress are uploaded to a server in real time and logs are saved, making it possible to use past data for future diagnoses and treatments. The entire system supports safe and efficient medical procedures, significantly reducing the burden on medical practitioners while ensuring patient safety.

[0297] Furthermore, the emotion engine can be used to assess the user's stress level and interrupt or change treatment if a high stress state is detected, thereby protecting the health and safety of medical personnel. This system can provide more advanced medical support and further improve efficiency and safety in medical settings.

[0298] An example of a prompt might be, "The basic information about this patient is as follows: Name: XX, Age: XX years, Gender: XX, Medical history: XX, Current symptoms: XX. Please suggest an initial diagnosis and appropriate medical treatment."

[0299] In this way, the system performs a series of steps from inputting patient information to diagnosis, performing treatment, and analyzing the user's emotions, providing safe and efficient medical support.

[0300] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0301] Step 1:

[0302] The user enters the patient's basic information (such as name, age, sex, medical history, and current symptoms) into the terminal.

[0303] Input: Patient demographic information from the user

[0304] Output: Patient demographics sent to the server

[0305] Specific operation: The user enters information into the input form of the dedicated application and presses the "Submit" button. The device then sends this information in JSON format to the server.

[0306] Step 2:

[0307] The user captures an image of the patient using the device's built-in camera.

[0308] Input: User operations based on device instructions

[0309] Output: Patient images sent to the server

[0310] Specific operation: The user points the device camera at the patient and presses the capture button. The device optimizes the image resolution, removes noise, and sends it to the server in JPEG image format.

[0311] Step 3:

[0312] The server processes the received images with image analysis algorithms to analyze the patient's characteristics.

[0313] Input: Patient images sent to the server

[0314] Output: Patient characteristics and patient ID

[0315] How it works: The server uses image analysis algorithms such as ResNet and VGG16 to extract features of the patient's face and wound from the image, then compares them with an internal database to identify the patient ID.

[0316] Step 4:

[0317] The server performs an initial diagnosis based on the image analysis results and the basic information entered, and sends the results to the terminal.

[0318] Input: Patient basic information and image analysis results

[0319] Output: Initial diagnostic results

[0320] Specific operation: The server performs an initial diagnosis using a machine learning model based on basic information and image analysis results. It generates a diagnosis such as "Bleeding control and disinfection are required" and sends this to the device.

[0321] Step 5:

[0322] The user checks the diagnosis results and treatment details displayed on the terminal and gives permission.

[0323] Input: Display of diagnostic results and user confirmation operations

[0324] Output: Permission input

[0325] Specific operation: The user checks the diagnostic results displayed on the device screen and taps the "Allow" button. This action sends permission information from the device to the server.

[0326] Step 6:

[0327] The terminal controls the robot with the user's permission and performs the necessary medical procedures.

[0328] Input: Diagnostic results and user permission

[0329] Output: Medical procedure performed

[0330] Specific operation: Based on the certified diagnosis results and authorization information, the terminal sends instructions to the robot to stop bleeding and disinfect. The robot then begins treatment according to these instructions and reports its progress to the terminal in real time.

[0331] Step 7:

[0332] The device uses an emotion engine to analyze the user's facial expressions, tone of voice, and the content of their words to recognize their emotions.

[0333] Input: User's facial expressions, voice, and words

[0334] Output: Recognized emotion

[0335] Specific operation: The device captures the user's voice and facial expressions using a camera and microphone, and uses an emotion analysis algorithm to recognize emotions such as "anxiety" and "tension."

[0336] Step 8:

[0337] The device adjusts the robot's response based on the recognized emotion.

[0338] Input: Perceived user emotion

[0339] Output: Robot response

[0340] Specific operation: If the terminal detects "anxiety," it will have the robot respond with "Don't worry. The procedure is proceeding correctly." This voice message will be played from the robot.

[0341] Step 9:

[0342] The server stores all treatment results and progress as logs, which can be used for future diagnosis and treatment.

[0343] Input: Medical procedure progress and results

[0344] Output: Saved log data

[0345] Specific operation: The server stores the progress and results of the treatment in a database in real time, making them available for future diagnosis and treatment.

[0346] (Application example 2)

[0347] 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."

[0348] Conventional medical support systems are capable of providing diagnosis and treatment based on basic patient information and image data, but they do not take the user's emotions into consideration, making it difficult to alleviate the user's anxiety and stress. It is also difficult to properly manage the impact of emotions on work efficiency and safety when workers perform precise work. Therefore, there is a need to provide appropriate communication and work support based on the user's emotions.

[0349] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting basic patient information, means for capturing an image of the patient, means for analyzing the captured patient image and identifying the patient ID, means for making an initial diagnosis based on the patient's basic information and the image analysis results, means for instructing medical treatment based on the diagnosis results, means for controlling the robot to perform the medical treatment, means for monitoring the treatment process in real time and saving data, means for real-time communication between the user and the robot, means for recognizing the user's emotions and adjusting the robot's responses based on the emotions, and means for generating and displaying work instructions and support content based on the worker's emotions. This makes it possible to provide appropriate medical support and work support that takes the user's emotions into consideration, thereby reducing the user's stress and improving work efficiency.

[0350] "Means for inputting basic patient information" refers to a device or software for inputting the patient's name, age, sex, medical history, current symptoms, etc.

[0351] A "means for capturing an image of a patient" is a camera or other imaging device for taking an image of a patient in a medical setting.

[0352] "Means for analyzing captured patient images and identifying patient IDs" refers to algorithms or software that perform image analysis using captured images and identify specific patients based on that information.

[0353] "Means for making an initial diagnosis based on basic patient information and image analysis results" refers to a system or algorithm for making an initial diagnosis using input basic information and the results of analyzed patient images.

[0354] The "means for instructing medical treatment based on the diagnostic results" refers to a device or program for proposing or instructing appropriate medical treatment based on the diagnostic results.

[0355] The "means for controlling the robot to perform a medical procedure" refers to a control system for operating the medical robot and causing it to perform a prescribed medical procedure.

[0356] The "means for monitoring the treatment process in real time and storing data" refers to a monitoring system and storage device for observing the progress of a medical treatment in real time and recording the data.

[0357] "Means for real-time communication between the user and the robot" refers to an interface that allows the user and the robot to exchange information in real time via voice or text.

[0358] "Means for recognizing the user's emotions and adjusting the robot's responses based on those emotions" refers to a system that analyzes the user's voice and facial expressions and appropriately changes the robot's behavior and responses based on the results.

[0359] "Means for generating and displaying work instructions and support content based on the worker's emotions" refers to devices or software that recognize the worker's emotional state and create and display appropriate work instructions and support content based on that.

[0360] This system allows users to input basic patient information, capture images, and perform diagnosis and medical treatment. Furthermore, by incorporating an emotion engine, it can recognize the user's emotions and respond appropriately. It can also be used to support factory work.

[0361] System Configuration

[0362] The system includes the following main means:

[0363] 1. Enter patient information by:

[0364] The user enters basic information such as the patient's name, age, sex, medical history, current symptoms, etc. This information is collected by the terminal and sent to the server.

[0365] 2. Means of capturing patient images:

[0366] Using the device's built-in camera, the user captures an image of the patient, which is also sent to the server.

[0367] 3. A means of analyzing captured patient images and determining patient ID:

[0368] The server analyzes the captured images using image analysis algorithms and identifies the patient ID by matching it with an internal database.

[0369] 4. Methods for making an initial diagnosis based on patient demographics and imaging analysis:

[0370] The server performs an initial diagnosis based on the patient's basic information and image analysis results, and uses an AI model to suggest appropriate diagnoses and treatments.

[0371] 5. Means of prescribing medical treatment based on diagnostic findings:

[0372] The server then recommends appropriate medical treatment based on the diagnosis and sends the information to the terminal, where the user follows the instructions.

[0373] 6. Means of controlling robots to perform medical procedures:

[0374] Based on the diagnosis, the robot is controlled to automatically perform medical procedures, and the robot's movements are monitored in real time on a terminal.

[0375] 7. Means for monitoring the treatment process in real time and storing data:

[0376] The device monitors the progress of medical procedures in real time and stores the data on a server, allowing past data to be used for future diagnoses and treatments.

[0377] 8. Means for real-time communication between user and robot:

[0378] The user and the robot can communicate in real time via the terminal, for example, by issuing instructions to the robot using voice commands or text messages.

[0379] 9. Means of recognizing user emotions and adjusting the robot's responses based on the emotions:

[0380] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize their emotions, and adjusts the robot's response based on the recognized emotions.

[0381] 10. Means for generating and displaying work instructions and support content based on worker emotions:

[0382] The server uses an emotion engine to evaluate the worker's emotions, generate appropriate work instructions and support content, and display them on the terminal.

[0383] Specific examples

[0384] 1. Medical application examples:

[0385] The user enters basic patient information, captures images, and sends them to the server, which performs an initial diagnosis and suggests appropriate medical treatment, which the robot then automatically performs.

[0386] Example prompt sentence:

[0387] Please enter the patient's basic information.

[0388] "Capture an image of the patient."

[0389] 2. Factory work support:

[0390] A worker wearing smart glasses inputs basic information, captures the work environment, and sends it to a server, which analyzes emotions and generates appropriate work instructions, which are displayed on the glasses' display.

[0391] Example prompt sentence:

[0392] Please enter the worker's basic information.

[0393] "Capture an image of your work environment."

[0394] Hardware and software used

[0395] Hardware:

[0396] Smart glasses or smartphones (with built-in camera, microphone, and display)

[0397] medical robots

[0398] Server (data storage and analysis)

[0399] software:

[0400] Python (data processing, image capture, server communication)

[0401] OpenCV (image capture and processing)

[0402] emotion_recognition library (emotion recognition)

[0403] server_communication library (data communication with the server)

[0404] The above configuration provides appropriate medical and work support that takes into account the user's emotions, making it possible to reduce the user's stress and improve work efficiency.

[0405] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0406] Step 1:

[0407] The user uses the terminal to input basic information about the patient (such as name, age, sex, medical history, and current symptoms).

[0408] Input: Patient's basic information (name, age, sex, medical history, current symptoms)

[0409] Action: Enter the required information into the device's input form.

[0410] Output: The basic information entered is saved on the device and then sent to the server.

[0411] Step 2:

[0412] The user captures an image of the patient using the device's built-in camera.

[0413] Input: Start capture command

[0414] Action: Activates the device's camera and takes an image of the patient.

[0415] Output: The captured image data is saved on the device and then sent to the server.

[0416] Step 3:

[0417] The server analyzes the captured image and matches it with an internal database to identify the patient ID.

[0418] Input: Photographed image data

[0419] How it works: Uses image analysis algorithms to recognize faces and compare them with an internal database.

[0420] Output: Identified patient ID

[0421] Step 4:

[0422] The server makes an initial diagnosis based on the patient's basic information and the results of image analysis.

[0423] Input: Patient basic information, specific patient ID

[0424] How it works: It uses AI models to analyze a patient's symptoms and make an initial diagnosis.

[0425] Output: Initial diagnosis result

[0426] Step 5:

[0427] The server prescribes appropriate medical treatment based on the diagnosis results and transmits the information to the terminal.

[0428] Input: Initial diagnosis result

[0429] Operation: Generates treatment contents based on the diagnosis results and sends them to the terminal.

[0430] Output: Medical treatment instructions

[0431] Step 6:

[0432] The terminal controls the robot based on the diagnosis results and performs medical treatment.

[0433] Input: Medical treatment instructions

[0434] Operation: Send instructions from the terminal to the robot to perform medical procedures.

[0435] Output: Progress information of medical procedures performed

[0436] Step 7:

[0437] The terminal monitors the treatment process in real time and stores the data on a server.

[0438] Input: Robot operation status, data from sensors

[0439] How it works: The monitoring system monitors the progress of the procedure in real time and notifies you of any abnormalities.

[0440] Output: Monitoring data is stored on the server.

[0441] Step 8:

[0442] The server recognizes the user's emotions and generates an appropriate response.

[0443] Input: User's voice and facial expression data

[0444] How it works: The emotion engine analyzes the user's emotions and generates a response based on the results.

[0445] Output: The generated response

[0446] Step 9:

[0447] The terminal generates and displays work instructions and support content based on the worker's emotions.

[0448] Input: Emotion engine analysis results, work environment data

[0449] Operation: Based on the results of emotion analysis, appropriate work instructions and assistance content are generated and displayed on a display such as smart glasses.

[0450] Output: Work instructions and support details

[0451] 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.

[0452] 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.

[0453] 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.

[0454] [Second embodiment]

[0455] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] In the smart glasses 214, the 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.

[0466] 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."

[0467] The present invention provides a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment. The system includes a series of processes for inputting basic patient information, capturing images, and performing diagnosis and medical treatment.

[0468] The system's operation procedure begins with the user entering basic patient information into the terminal. This basic information includes the patient's name, age, gender, medical history, current symptoms, etc. The terminal then sends the information entered by the user to the server.

[0469] The user then follows the device's instructions to capture an image of the patient using the device's built-in camera. The device then adjusts the captured image and sends it to the server, where the server uses image analysis algorithms to analyze the patient's characteristics and match them with an internal database to identify the patient. The server also uses the results of the analysis to make an initial diagnosis and identify appropriate medical treatment.

[0470] The server sends the diagnosis results to the terminal, which displays them to the user. The user reviews the proposed treatment and, if they give their permission, proceeds to the next step. The terminal then controls the robot to perform the actual medical treatment. The progress of the treatment is monitored in real time, and includes a function to immediately interrupt it if an abnormality is detected.

[0471] The robot uses natural language processing to communicate with the user and responds to their questions and instructions. For example, if the user asks, "Have you finished stopping the bleeding?", the robot will reply, "Yes, I've finished stopping the bleeding. What's next, disinfection?" In this way, the user can monitor the robot's operations and give appropriate instructions.

[0472] In addition, all treatment results and progress are uploaded to a server in real time and logs are saved, making it possible to use past data for future diagnoses and treatments. The entire system supports safe and efficient medical procedures, significantly reducing the burden on medical practitioners while ensuring patient safety.

[0473] As a concrete example, consider the treatment of a cut on the arm. The user enters the patient's basic information, captures an image, and sends it to the server. The server performs an initial diagnosis and sends the diagnosis that "stopping the bleeding and disinfecting are necessary" to the terminal. After the user confirms, the terminal controls the robot to stop the bleeding and disinfect. During this time, the user and robot communicate with each other as the treatment progresses. All information is saved on the server in real time and can be used for future medical treatments.

[0474] This system enables rapid and accurate response even in emergency medical treatment, improving efficiency and safety in medical settings. In addition, the robot uses natural language processing to smoothly communicate with users, allowing them to perform medical procedures without any problems, even if it is their first time performing them.

[0475] The processing flow will be explained below.

[0476] Step 1:

[0477] The user enters basic patient information into the terminal, such as the patient's name, age, sex, medical history, and current symptoms.

[0478] Step 2:

[0479] The terminal displays the entered patient information to the user in real time and asks for confirmation. After the user confirms the information, the terminal sends the information to the server.

[0480] Step 3:

[0481] The user follows the instructions on the device to capture images of the patient using the built-in camera, adjusting the angle and position as needed.

[0482] Step 4:

[0483] The device receives the captured image, adjusts the image quality and size as necessary, and sends the adjusted image to the server.

[0484] Step 5:

[0485] The server runs facial recognition and body feature analysis algorithms to analyze the received patient images and identify the patient ID against an internal database.

[0486] Step 6:

[0487] The server runs a diagnostic algorithm to make an initial diagnosis based on the patient's basic information and the image analysis results, and identifies the necessary medical treatment.

[0488] Step 7:

[0489] The server generates a diagnosis and identifies an appropriate treatment procedure, and sends the diagnosis and treatment procedure to the terminal.

[0490] Step 8:

[0491] The terminal displays the received diagnosis results and treatment procedures to the user, who then checks the contents and prepares to give permission.

[0492] Step 9:

[0493] The user checks the diagnosis and treatment details and instructs the robot to carry out the treatment via a terminal. With the user's permission, the terminal starts the robot's control program and instructs the specific medical treatment.

[0494] Step 10:

[0495] The robot performs the medical procedure: its arms take gauze to stop bleeding and apply it to the patient's cut, then apply disinfectant and proceed with the necessary treatment.

[0496] Step 11:

[0497] The terminal monitors the treatment process in real time and sends data to the server. If the terminal detects an abnormality, it immediately instructs the robot to halt treatment.

[0498] Step 12:

[0499] The user observes the robot's treatment progress and gives additional instructions via a terminal or voice command. The user can give specific instructions, such as "apply a bandage," and the robot will respond accordingly.

[0500] Step 13:

[0501] After the robot completes the entire medical procedure, the terminal sends a report of the completion of the procedure to the server, which stores all the treatment results and log data for future diagnosis and treatment.

[0502] Through the above steps, the present invention can consistently carry out the entire process from inputting basic patient information, capturing images, making an initial diagnosis, performing medical treatment, and monitoring and logging the treatment.

[0503] Example 1

[0504] 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."

[0505] In current medical settings, patient information acquisition, diagnosis, and treatment are often done manually, resulting in time-consuming processes and a high risk of human error. Furthermore, when emergency medical treatment is required, a fast and accurate response is required, but current systems have difficulty meeting this requirement. Furthermore, real-time monitoring of treatment progress and data recording are insufficient, meaning that information useful for subsequent diagnoses and treatments is not fully utilized.

[0506] 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.

[0507] In this invention, the server includes a means for inputting basic patient information, a means for acquiring patient images, a means for analyzing the acquired patient images and identifying the patient identification number, a means for making an initial diagnosis based on the patient's basic information and the image analysis results, a means for proposing medical treatment based on the diagnosis results, a means for controlling the machine to perform the medical treatment, a means for monitoring the treatment process in real time and saving data, and a means for real-time communication between the user and the machine. This enables fast and accurate medical treatment, improving efficiency and safety in medical settings. Furthermore, because treatment results and progress are monitored and recorded in real time, reliable data management is realized that can be used for subsequent diagnoses and treatments.

[0508] "Basic information" refers to basic data required for medical treatment, such as the patient's name, age, gender, medical history, and current symptoms.

[0509] "Image acquisition" refers to taking a picture of a patient's body part or symptoms using the device's camera function and acquiring the image data.

[0510] "Image analysis" refers to the process of processing acquired image data to extract and identify features. For example, this can be done using an image processing library such as OpenCV.

[0511] "Patient Identification Number" refers to a unique number assigned to an individual patient identified from analyzed images and basic information.

[0512] "Initial diagnosis" refers to the first medical diagnosis made based on the patient's basic information and the results of image analysis.

[0513] "Medical treatment" refers to the specific treatment steps that are proposed and performed based on the initial diagnosis, such as stopping bleeding and disinfection.

[0514] "Machine" refers to a robot or device that is controlled to perform a medical procedure, such as a surgical robot.

[0515] "Real-time monitoring" means monitoring the medical procedure as it progresses, and being ready to respond immediately if any abnormalities occur.

[0516] "Data storage" refers to storing information such as the progress and results of treatment in a storage device within the system for later use.

[0517] "Users" are medical professionals such as doctors and nurses who operate the system and perform medical procedures.

[0518] "Communication" refers to the exchange of information between the user and the machine, and responses to instructions and questions, for example, using natural language processing techniques.

[0519] The present invention provides a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment. This system includes a series of processes for inputting basic patient information, acquiring images, and performing diagnosis and medical treatment.

[0520] The user inputs basic information about the patient into the device. This basic information includes the patient's name, age, sex, medical history, and current symptoms. The device sends the input information to the server. Next, the user uses the device's camera to capture an image of the patient. This is done by activating the built-in camera and taking a picture of the patient's affected area. The captured image is then sent from the device to the server.

[0521] The server uses OpenCV, an open-source image analysis library, to analyze the received image data. It executes image analysis algorithms and extracts features. The results are compared with an internal database to identify the patient's identification number. Furthermore, an initial diagnosis is made based on the analysis results, and appropriate medical treatment is identified. This initial diagnosis is made based on a novel diagnostic protocol, with the server referencing the internal medical database.

[0522] The diagnosis results are sent from the server to the device, which then displays them to the user, who then confirms the proposed treatment and agrees to it.

[0523] The terminal runs a robot control program using a robotics operating system (ROS) to perform medical procedures, such as stopping bleeding and disinfecting. For example, when treating a cut on the right arm, the robot arm applies gauze to stop the bleeding and then applies disinfectant.

[0524] Communication between the user and the robot is carried out using natural language processing. When the user asks, "Have you finished stopping the bleeding?", the robot responds, "Yes, I've finished stopping the bleeding. Next is disinfection?" This communication proceeds in the form of the user asking questions and giving instructions through a voice recognition system, and the robot responding accordingly.

[0525] All treatment processes are recorded in real time on the server. The database management system MySQL is used to store the data. This data will be used later for the next diagnosis and treatment. For example, the treatment details and results performed on patient ID "12345" are recorded, and when the same patient is diagnosed again, this data can be referenced to provide more accurate medical services.

[0526] Example prompt for a generative AI model:

[0527] Please explain the specific program processing flow for each step, from entering basic patient information to image capture, diagnosis, and implementation. Also, please provide a detailed description of the operation when using OpenCV for image analysis and ROS for robot control.

[0528] This system enables rapid and accurate response even in emergency medical treatment, improving efficiency and safety in medical settings. In addition, the robot uses natural language processing to smoothly communicate with users, allowing them to perform medical procedures without any problems, even if it is their first time performing them.

[0529] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0530] Step 1:

[0531] The terminal inputs the patient's basic information.

[0532] Input: Data entered by the user (such as name, age, gender, medical history, current symptoms, etc.).

[0533] Data processing: The terminal converts the input data into JSON format.

[0534] Output: Patient demographics data in JSON format is generated.

[0535] Specific operation: The user opens the dedicated application on the device, enters information into the input form, and taps the "Submit" button.

[0536] Step 2:

[0537] The terminal sends the entered basic patient information to the server.

[0538] Input: Patient demographics data in JSON format.

[0539] Data processing: Send JSON data to the server via an HTTP POST request.

[0540] Output: Basic information is saved on the server.

[0541] Specific operation: The terminal sends patient data to a specific API endpoint on the server.

[0542] Step 3:

[0543] The user captures an image of the patient using the device's camera.

[0544] Input: The patient's area to be imaged.

[0545] Data processing: The device's camera app captures the image and converts it to JPEG format.

[0546] Output: Image data in JPEG format is generated.

[0547] Specific actions: The user opens the camera app, takes a picture of the patient's affected area, and taps the "Capture" button.

[0548] Step 4:

[0549] The image acquired by the terminal is sent to the server.

[0550] Input: Image data in JPEG format.

[0551] Data processing: Image data is sent to the server via an HTTP POST request.

[0552] Output: Image data is saved on the server.

[0553] Specific operation: The device sends image data to a specific API endpoint on the server.

[0554] Step 5:

[0555] The server analyzes the received image data.

[0556] Input: Image data in JPEG format.

[0557] Data processing: The server uses OpenCV to analyze the image and extract features, such as edges, color, and shape.

[0558] Output: Extracted feature data.

[0559] Specific operation: The server executes OpenCV algorithms and performs image analysis.

[0560] Step 6:

[0561] The server identifies the patient identification number based on the analysis results.

[0562] Input: Extracted feature data.

[0563] Data processing: The server checks against an internal database to identify matching patient identification numbers.

[0564] Output: Patient identification number.

[0565] Specific operation: The server compares the feature data with existing data in the database and identifies matching data.

[0566] Step 7:

[0567] The server makes an initial diagnosis based on the patient's basic information and the results of image analysis.

[0568] Input: Patient basic information data and feature data.

[0569] Data processing: The server references an internal medical database and executes diagnostic algorithms.

[0570] Output: Initial diagnostic results.

[0571] Specific operations: The server executes a diagnostic protocol using a medical database.

[0572] Step 8:

[0573] The server suggests medical treatment based on the diagnosis.

[0574] Input: Initial diagnostic results.

[0575] Data processing: The server selects recommended medical treatments based on the diagnosis results.

[0576] Output: Medical treatment suggestions.

[0577] Specific operation: The server selects an appropriate treatment method based on the diagnostic protocol.

[0578] Step 9:

[0579] The terminal displays the diagnosis and suggested medical treatment to the user.

[0580] Input: Medical procedure proposal data.

[0581] Data processing: The device analyzes the data and displays it on the screen in a visually easy-to-understand format.

[0582] Output: Diagnosis results and medical treatment suggestions displayed to the user.

[0583] What it does: The device displays the diagnosis and suggested medical treatment on the screen.

[0584] Step 10:

[0585] The terminal controls the machine to perform the medical procedure.

[0586] Input: Medical procedure proposal data.

[0587] Data processing: The terminal uses ROS to execute the robot control program.

[0588] Output: Performing a medical procedure.

[0589] Specific actions: The robotic arm performs specific medical procedures such as stopping bleeding and disinfecting.

[0590] Step 11:

[0591] The terminal monitors the treatment process in real time and stores the data.

[0592] Input: Process data.

[0593] Data processing: Data from the treatment process is collected and sent to the server in real time.

[0594] Output: Treatment data stored in real time.

[0595] Specific operation: The terminal monitors the progress of the procedure and immediately notifies if there is an abnormality.

[0596] Step 12:

[0597] The user and the machine communicate in real time.

[0598] Input: User's voice command data.

[0599] Data processing: The machine uses natural language processing to analyze the voice instructions and generate an appropriate response.

[0600] Output: Machine-generated voice response.

[0601] Specific operation: The user gives a voice command, and the machine responds by confirming the operation based on the command.

[0602] Through the above processing steps, the present invention can provide a system that supports rapid and accurate medical treatment of patients, improving efficiency and safety.

[0603] (Application example 1)

[0604] 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."

[0605] With conventional technology, factory robot maintenance required skilled technicians, and labor shortages and variations in technician skills were problems. It was also difficult to detect abnormalities in real time, quickly diagnose problems, and provide appropriate maintenance procedures, making maintenance efficiency and accuracy challenging. This led to concerns about increased factory downtime and reduced production efficiency.

[0606] 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.

[0607] In this invention, the server includes: means for inputting basic patient information; means for capturing patient images; means for analyzing the captured patient images and identifying the patient ID; means for performing an initial diagnosis based on the patient's basic information and image analysis results; means for instructing medical treatment based on the diagnosis results; means for controlling the robot to perform the medical treatment; means for monitoring the treatment process in real time and saving data; means for real-time communication between the user and the robot; means for identifying diagnosis results and appropriate maintenance procedures to support industrial equipment maintenance; and means for providing and displaying the industrial equipment maintenance procedures to the user. This enables factory robot maintenance to detect and quickly diagnose abnormalities in real time and provide appropriate maintenance procedures. This enables efficient and accurate maintenance without the need for skilled technicians, reducing factory downtime and improving production efficiency.

[0608] "Basic patient information" refers to basic information about the patient, such as the patient's name, age, sex, medical history, and current symptoms.

[0609] "Image capturing means" refers to a device or means for obtaining an image of a patient or object using a camera, sensor, or the like.

[0610] "Image analysis" refers to the process of using algorithms and software to analyze features and patterns in a captured image and extract information from it.

[0611] "Means for identifying patient ID" refers to a means for determining a patient's unique identification information based on information obtained through image analysis.

[0612] "Initial diagnosis" refers to the process of making an initial diagnosis by integrating basic patient information and image analysis results.

[0613] "Means for prescribing medical treatment based on the diagnosis results" refers to means for determining the necessary medical treatment based on the results of the initial diagnosis and prescribing the details of the treatment.

[0614] "Means for controlling a robot to perform a medical procedure" refers to means for operating and controlling a robot to actually perform a medical procedure.

[0615] "Means for monitoring the procedure in real time and storing data" refers to means for monitoring the procedure performed by the robot in real time and recording and storing the data.

[0616] "Means for real-time communication between a user and a robot" refers to a means for a user and a robot to exchange information in both directions and communicate with each other in real time.

[0617] "Means for identifying diagnostic results and appropriate maintenance procedures to support the maintenance of industrial equipment" refers to means for diagnosing abnormalities and failures in industrial equipment and determining appropriate maintenance procedures based on the results.

[0618] "Means for providing and displaying maintenance procedures for industrial equipment to users" refers to means for presenting diagnostic results and maintenance procedures to users in an easy-to-understand manner and displaying their contents.

[0619] This invention provides a system for assisting in the efficient and accurate maintenance of factory robots. The system includes a series of processes for inputting basic robot information, capturing images, and executing diagnostic and maintenance procedures.

[0620] First, the user inputs basic information about the robot using the UI of the smart glasses. This information includes the robot ID, installation location, and current problems (e.g., "abnormal noise"). The information entered by the user is sent to the server via the smart glasses.

[0621] Next, the user captures an image of the robot using the camera built into the smart glasses. The captured image is adjusted in real time and sent to the server. The server then uses image analysis algorithms (e.g., OpenCV) to analyze the robot's condition and identify any abnormalities or maintenance needs. The server also identifies appropriate maintenance procedures based on the analysis results and sends instructions to the user.

[0622] The transmitted diagnostic results and maintenance procedures are displayed on the smart glasses' display, and the user can then carry out the maintenance work accordingly. For example, if the server issues a diagnostic result indicating that a bearing needs to be replaced, the smart glasses will display the specific procedure and prompt the user to carry out the work.

[0623] The interaction between the user and the system takes place in real time, and when the user asks a question or makes a confirmation, the server responds quickly. For example, if the user asks, "What should I do next?", the server will instruct, "Next, please release the bearing lock." In this way, the user can efficiently carry out maintenance work by interacting with the server using prompts.

[0624] All maintenance progress and results are uploaded to the server in real time and saved as logs, allowing future diagnosis and troubleshooting to be performed based on past maintenance information.

[0625] Specific examples

[0626] For example, the following prompts are used to diagnose and provide maintenance procedures for a robot:

[0627] Please diagnose the cause of the strange noise my robot is making and tell me what maintenance steps I need to take.

[0628] The hardware uses smart glasses with a camera, and the software uses Python, OpenCV, and the requests library for HTTP requests, enabling efficient maintenance of factory robots.

[0629] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0630] Step 1:

[0631] The user uses the UI of the smart glasses to input basic information about the robot, including the robot ID, its location, and any current issues (e.g., "abnormal noise"). The input information is converted into digital data by the smart glasses' internal processor and sent to a server via a network.

[0632] Input: User-entered robot ID, location, and problem information

[0633] Output: Basic information data sent to the server

[0634] Step 2:

[0635] The user captures an image of the robot using the camera built into the smart glasses. The software in the glasses formats the image optimally and saves it as image data. The saved image is then sent to a server over a network.

[0636] Input: An image of the robot captured by a camera

[0637] Output: Image data to be sent

[0638] Step 3:

[0639] The server analyzes the received images. Image analysis algorithms (e.g., OpenCV) are used to identify abnormalities on the robot. Features within the images are extracted and compared with past maintenance data in a database to identify abnormalities.

[0640] Input: Submitted image data

[0641] Output: Identification of abnormalities

[0642] Step 4:

[0643] The server integrates the robot's basic information with the image analysis results to perform an initial diagnosis. A diagnostic algorithm (such as a generative AI model) is used to analyze the cause of the abnormality and determine the appropriate maintenance procedure. The diagnosis results and maintenance procedure are generated.

[0644] Input: Basic information data and image analysis results

[0645] Output: Diagnostic results and maintenance procedures

[0646] Step 5:

[0647] Diagnostic results and maintenance procedures are sent from the server to the smart glasses and displayed on the glasses' display.

[0648] Input: Server-generated diagnostic results and maintenance procedures

[0649] Output: Diagnostic results and maintenance procedures displayed to the user

[0650] Step 6:

[0651] The user follows the instructions on the smart glasses to perform maintenance tasks. If they have questions or need confirmation on the next step, they can contact the server using voice commands or UI operations. The server responds to the user's questions in real time.

[0652] Input: Questions and inquiries from users

[0653] Output: Real-time response from the server

[0654] Step 7:

[0655] The progress and results of maintenance are uploaded in real time from the smart glasses to a server and stored as logs, which can be used for future diagnostics and troubleshooting.

[0656] Input: User maintenance progress and results

[0657] Output: Progress and result logs stored on the server

[0658] This system will enable efficient and accurate maintenance of factory robots.

[0659] 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.

[0660] The present invention is a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment, and by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more advanced medical support. This system includes a series of processes for inputting basic patient information, capturing images, and performing diagnosis and medical treatment.

[0661] The system's operation procedure begins with the user entering basic patient information into the terminal. This basic information includes the patient's name, age, gender, medical history, current symptoms, etc. The terminal then sends the information entered by the user to the server.

[0662] The user then follows the device's instructions to capture an image of the patient using the device's built-in camera. The device then adjusts the captured image and sends it to the server, where the server uses image analysis algorithms to analyze the patient's characteristics and match them with an internal database to identify the patient. The server also uses the results of the analysis to make an initial diagnosis and identify appropriate medical treatment.

[0663] The server sends the diagnosis results to the terminal, which displays them to the user. The user reviews the proposed treatment and, if they give their permission, proceeds to the next step. The terminal then controls the robot to perform the actual medical treatment. The progress of the treatment is monitored in real time, and includes a function to immediately interrupt it if an abnormality is detected.

[0664] The system also incorporates an emotion engine, which recognizes emotions from the user's facial expressions, tone of voice, and content of words, and adjusts the robot's response accordingly. For example, if the user is nervous, the robot can respond in a calm tone, saying, "It's okay, let's stay calm and proceed."

[0665] As a concrete example, consider the case of treating a cut on an arm using an emotion engine. The user enters the patient's basic information, captures an image, and sends it to the server. The server performs an initial diagnosis and sends the diagnosis result, "The bleeding needs to be stopped and disinfected," to the terminal. After the user confirms, the terminal controls the robot to stop the bleeding and disinfect the wound.

[0666] During this process, let's say the user says, "I'm worried about whether the bleeding is being stopped properly." The emotion engine detects anxiety from the user's tone of voice and the content of their words, and the robot responds, "Don't worry. The procedure is proceeding correctly." In this way, appropriate communication is carried out according to the user's emotions.

[0667] In addition, all treatment results and progress are uploaded to a server in real time and logs are saved, making it possible to use past data for future diagnoses and treatments. The entire system supports safe and efficient medical procedures, significantly reducing the burden on medical practitioners while ensuring patient safety.

[0668] Furthermore, the emotion engine can be used to assess the user's stress level and interrupt or change treatment if a high stress state is detected, thereby protecting the health and safety of medical personnel. This system can provide more advanced medical support and further improve efficiency and safety in medical settings.

[0669] The processing flow will be explained below.

[0670] Step 1:

[0671] The user enters basic patient information into the terminal, such as the patient's name, age, sex, medical history, and current symptoms.

[0672] Step 2:

[0673] The terminal displays the entered patient information to the user in real time and asks for confirmation. After the user confirms the information, the terminal sends the information to the server.

[0674] Step 3:

[0675] The user follows the instructions on the device to capture images of the patient using the built-in camera, adjusting the angle and position as needed.

[0676] Step 4:

[0677] The device receives the captured image, adjusts the image quality and size as necessary, and sends the adjusted image to the server.

[0678] Step 5:

[0679] The server runs facial recognition and body feature analysis algorithms to analyze the received patient images and identify the patient ID against an internal database.

[0680] Step 6:

[0681] The server runs a diagnostic algorithm to make an initial diagnosis based on the patient's basic information and the image analysis results, and identifies the necessary medical treatment.

[0682] Step 7:

[0683] The server generates a diagnosis and identifies an appropriate treatment procedure, and sends the diagnosis and treatment procedure to the terminal.

[0684] Step 8:

[0685] The terminal displays the received diagnosis results and treatment procedures to the user, who then checks the contents and prepares to give permission.

[0686] Step 9:

[0687] The user checks the diagnosis and treatment details and instructs the robot to carry out the treatment via a terminal. With the user's permission, the terminal starts the robot's control program and instructs the specific medical treatment.

[0688] Step 10:

[0689] The robot performs the medical procedure: its arms take gauze to stop bleeding and apply it to the patient's cut, then apply disinfectant and proceed with the necessary treatment.

[0690] Step 11:

[0691] The terminal monitors the treatment process in real time and sends data to the server. If the terminal detects an abnormality, it immediately instructs the robot to halt treatment.

[0692] Step 12:

[0693] The user observes the robot's treatment progress and gives additional instructions via a terminal or voice command. The user can give specific instructions, such as "apply a bandage," and the robot will respond accordingly.

[0694] Step 13:

[0695] The emotion engine analyzes the user's facial expressions, tone of voice, and the content of their words to recognize their emotions. For example, if the user says something like "I'm worried," the emotion engine will detect anxiety.

[0696] Step 14:

[0697] Based on the analysis results of the emotion engine, the robot will provide appropriate feedback to the user, for example, "Don't worry, the treatment is going well."

[0698] Step 15:

[0699] If the emotion engine determines that the user's stress level is high, the device will temporarily suspend treatment or issue instructions to change the treatment procedure, thereby protecting the user's mental state.

[0700] Step 16:

[0701] After the robot completes the entire medical procedure, the terminal sends a report of the completion of the procedure to the server, which stores all the treatment results and log data for future diagnosis and treatment.

[0702] Through the above steps, the present invention can seamlessly perform the entire process, from entering basic patient information, capturing images, making an initial diagnosis, performing medical procedures, monitoring and logging the procedures, and even recognizing and providing feedback to the user. This system enables fast and accurate responses even in emergency medical situations, improving efficiency and safety in medical settings. Furthermore, the introduction of an emotion engine reduces the user's psychological burden, allowing for safe and secure medical procedures.

[0703] Example 2

[0704] 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."

[0705] Conventional medical systems face the challenge of quickly and accurately obtaining basic patient information and images, and then performing diagnosis and treatment. The process involves time-consuming tasks such as inputting information and confirming treatment. Furthermore, because treatment is performed without consideration for the user's feelings, medical practitioners often feel stressed, posing a risk to the quality and safety of medical care. There is a need to solve these problems and provide effective and safe medical care to patients while reducing the burden on medical practitioners.

[0706] 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 inputting basic patient information, means for acquiring patient images, means for analyzing the acquired patient images and identifying the patient ID, means for making an initial diagnosis based on the patient's basic information and image analysis results, means for instructing medical treatment based on the diagnosis results, means for controlling the robot to perform the medical treatment, means for monitoring the treatment process in real time and saving data, means for analyzing the user's facial expressions, tone of voice, and content of words to recognize emotions, means for adjusting the robot's responses based on the recognized emotions, and means for communicating between the user and the robot in real time. This enables quick and accurate acquisition of the patient's basic information and images, execution of initial diagnosis and appropriate medical treatment, and flexible response according to the user's emotions, thereby providing more effective and safer medical care.

[0707] "Means for inputting basic patient information" refers to an interface that allows a user to input information about a patient, such as the patient's name, age, sex, medical history, and current symptoms.

[0708] "Means for obtaining images of a patient" refers to devices, including cameras and scanning devices, for capturing images of a patient's body or condition.

[0709] "Means for analyzing acquired patient images and identifying patient IDs" refers to algorithms or programs that recognize and analyze specific features of a patient and verify them against a database to determine the patient ID.

[0710] "Means for making an initial diagnosis based on the patient's basic information and image analysis results" refers to a diagnostic algorithm or program for making an initial diagnosis based on the input basic information and analyzed image information.

[0711] "Means for instructing medical treatment based on diagnostic results" refers to mechanisms or software for determining specific medical treatment based on the results of an initial diagnosis and instructing this to the next process.

[0712] "Means for controlling the robot to perform a medical procedure" refers to a control system for operating the robot based on programmed instructions to perform a specified medical procedure.

[0713] "Means for monitoring the treatment process in real time and storing data" refers to sensors and data storage devices for monitoring the progress of medical treatment in real time and recording and storing that data.

[0714] "Means for analyzing a user's facial expressions, tone of voice, and spoken content to recognize emotions" refers to a program or device that reads and analyzes emotions from a user's facial expressions, tone of voice, and spoken content.

[0715] "Means for adjusting the robot's response based on the recognized emotion" refers to a control program or system for changing the robot's response in response to the analyzed emotion of the user.

[0716] "Means for real-time communication between users and robots" refers to interfaces and network systems that allow users and robots to instantly exchange and share information and instructions.

[0717] The present invention is a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment, and by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more advanced medical support. This system includes a series of processes for inputting basic patient information, capturing images, and performing diagnosis and medical treatment.

[0718] The system's operation procedure begins with the user entering basic patient information into the terminal. This basic information includes the patient's name, age, gender, medical history, current symptoms, etc. The terminal then sends the information entered by the user to the server.

[0719] The user then follows the device's instructions to capture an image of the patient using the device's built-in camera. The device then adjusts the captured image and sends it to the server, where the server uses image analysis algorithms to analyze the patient's characteristics and match them with an internal database to identify the patient. The server also uses the results of the analysis to make an initial diagnosis and identify appropriate medical treatment.

[0720] The server sends the diagnosis results to the terminal, which displays them to the user. The user reviews the proposed treatment and, if they give their permission, proceeds to the next step. The terminal then controls the robot to perform the actual medical treatment. The progress of the treatment is monitored in real time, and includes a function to immediately interrupt it if an abnormality is detected.

[0721] The system also incorporates an emotion engine, which recognizes emotions from the user's facial expressions, tone of voice, and content of words, and adjusts the robot's response accordingly. For example, if the user is nervous, the robot can respond in a calm tone, saying, "It's okay, let's stay calm and proceed."

[0722] As a concrete example, consider the case of treating a cut on an arm using an emotion engine. The user enters the patient's basic information, captures an image, and sends it to the server. The server performs an initial diagnosis and sends the diagnosis result, "The bleeding needs to be stopped and disinfected," to the terminal. After the user confirms, the terminal controls the robot to stop the bleeding and disinfect the wound.

[0723] During this process, let's say the user says, "I'm worried about whether the bleeding is being stopped properly." The emotion engine detects anxiety from the user's tone of voice and the content of their words, and the robot responds, "Don't worry. The procedure is proceeding correctly." In this way, appropriate communication is carried out according to the user's emotions.

[0724] In addition, all treatment results and progress are uploaded to a server in real time and logs are saved, making it possible to use past data for future diagnoses and treatments. The entire system supports safe and efficient medical procedures, significantly reducing the burden on medical practitioners while ensuring patient safety.

[0725] Furthermore, the emotion engine can be used to assess the user's stress level and interrupt or change treatment if a high stress state is detected, thereby protecting the health and safety of medical personnel. This system can provide more advanced medical support and further improve efficiency and safety in medical settings.

[0726] An example of a prompt might be, "The basic information about this patient is as follows: Name: XX, Age: XX years, Gender: XX, Medical history: XX, Current symptoms: XX. Please suggest an initial diagnosis and appropriate medical treatment."

[0727] In this way, the system performs a series of steps from inputting patient information to diagnosis, performing treatment, and analyzing the user's emotions, providing safe and efficient medical support.

[0728] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0729] Step 1:

[0730] The user enters the patient's basic information (such as name, age, sex, medical history, and current symptoms) into the terminal.

[0731] Input: Patient demographic information from the user

[0732] Output: Patient demographics sent to the server

[0733] Specific operation: The user enters information into the input form of the dedicated application and presses the "Submit" button. The device then sends this information in JSON format to the server.

[0734] Step 2:

[0735] The user captures an image of the patient using the device's built-in camera.

[0736] Input: User operations based on device instructions

[0737] Output: Patient images sent to the server

[0738] Specific operation: The user points the device camera at the patient and presses the capture button. The device optimizes the image resolution, removes noise, and sends it to the server in JPEG image format.

[0739] Step 3:

[0740] The server processes the received images with image analysis algorithms to analyze the patient's characteristics.

[0741] Input: Patient images sent to the server

[0742] Output: Patient characteristics and patient ID

[0743] How it works: The server uses image analysis algorithms such as ResNet and VGG16 to extract features of the patient's face and wound from the image, then compares them with an internal database to identify the patient ID.

[0744] Step 4:

[0745] The server performs an initial diagnosis based on the image analysis results and the basic information entered, and sends the results to the terminal.

[0746] Input: Patient basic information and image analysis results

[0747] Output: Initial diagnostic results

[0748] Specific operation: The server performs an initial diagnosis using a machine learning model based on basic information and image analysis results. It generates a diagnosis such as "Bleeding control and disinfection are required" and sends this to the device.

[0749] Step 5:

[0750] The user checks the diagnosis results and treatment details displayed on the terminal and gives permission.

[0751] Input: Display of diagnostic results and user confirmation operations

[0752] Output: Permission input

[0753] Specific operation: The user checks the diagnostic results displayed on the device screen and taps the "Allow" button. This action sends permission information from the device to the server.

[0754] Step 6:

[0755] The terminal controls the robot with the user's permission and performs the necessary medical procedures.

[0756] Input: Diagnostic results and user permission

[0757] Output: Medical procedure performed

[0758] Specific operation: Based on the certified diagnosis results and authorization information, the terminal sends instructions to the robot to stop bleeding and disinfect. The robot then begins treatment according to these instructions and reports its progress to the terminal in real time.

[0759] Step 7:

[0760] The device uses an emotion engine to analyze the user's facial expressions, tone of voice, and the content of their words to recognize their emotions.

[0761] Input: User's facial expressions, voice, and words

[0762] Output: Recognized emotion

[0763] Specific operation: The device captures the user's voice and facial expressions using a camera and microphone, and uses an emotion analysis algorithm to recognize emotions such as "anxiety" and "tension."

[0764] Step 8:

[0765] The device adjusts the robot's response based on the recognized emotion.

[0766] Input: Perceived user emotion

[0767] Output: Robot response

[0768] Specific operation: If the terminal detects "anxiety," it will have the robot respond with "Don't worry. The procedure is proceeding correctly." This voice message will be played from the robot.

[0769] Step 9:

[0770] The server stores all treatment results and progress as logs, which can be used for future diagnosis and treatment.

[0771] Input: Medical procedure progress and results

[0772] Output: Saved log data

[0773] Specific operation: The server stores the progress and results of the treatment in a database in real time, making them available for future diagnosis and treatment.

[0774] (Application example 2)

[0775] 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."

[0776] Conventional medical support systems are capable of providing diagnosis and treatment based on basic patient information and image data, but they do not take the user's emotions into consideration, making it difficult to alleviate the user's anxiety and stress. It is also difficult to properly manage the impact of emotions on work efficiency and safety when workers perform precise work. Therefore, there is a need to provide appropriate communication and work support based on the user's emotions.

[0777] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting basic patient information, means for capturing an image of the patient, means for analyzing the captured patient image and identifying the patient ID, means for making an initial diagnosis based on the patient's basic information and the image analysis results, means for instructing medical treatment based on the diagnosis results, means for controlling the robot to perform the medical treatment, means for monitoring the treatment process in real time and saving data, means for real-time communication between the user and the robot, means for recognizing the user's emotions and adjusting the robot's responses based on the emotions, and means for generating and displaying work instructions and support content based on the worker's emotions. This makes it possible to provide appropriate medical support and work support that takes the user's emotions into consideration, thereby reducing the user's stress and improving work efficiency.

[0778] "Means for inputting basic patient information" refers to a device or software for inputting the patient's name, age, sex, medical history, current symptoms, etc.

[0779] A "means for capturing an image of a patient" is a camera or other imaging device for taking an image of a patient in a medical setting.

[0780] "Means for analyzing captured patient images and identifying patient IDs" refers to algorithms or software that perform image analysis using captured images and identify specific patients based on that information.

[0781] "Means for making an initial diagnosis based on basic patient information and image analysis results" refers to a system or algorithm for making an initial diagnosis using input basic information and the results of analyzed patient images.

[0782] The "means for instructing medical treatment based on the diagnostic results" refers to a device or program for proposing or instructing appropriate medical treatment based on the diagnostic results.

[0783] The "means for controlling the robot to perform a medical procedure" refers to a control system for operating the medical robot and causing it to perform a prescribed medical procedure.

[0784] The "means for monitoring the treatment process in real time and storing data" refers to a monitoring system and storage device for observing the progress of a medical treatment in real time and recording the data.

[0785] "Means for real-time communication between the user and the robot" refers to an interface that allows the user and the robot to exchange information in real time via voice or text.

[0786] "Means for recognizing the user's emotions and adjusting the robot's responses based on those emotions" refers to a system that analyzes the user's voice and facial expressions and appropriately changes the robot's behavior and responses based on the results.

[0787] "Means for generating and displaying work instructions and support content based on the worker's emotions" refers to devices or software that recognize the worker's emotional state and create and display appropriate work instructions and support content based on that.

[0788] This system allows users to input basic patient information, capture images, and perform diagnosis and medical treatment. Furthermore, by incorporating an emotion engine, it can recognize the user's emotions and respond appropriately. It can also be used to support factory work.

[0789] System Configuration

[0790] The system includes the following main means:

[0791] 1. Enter patient information by:

[0792] The user enters basic information such as the patient's name, age, sex, medical history, current symptoms, etc. This information is collected by the terminal and sent to the server.

[0793] 2. Means of capturing patient images:

[0794] Using the device's built-in camera, the user captures an image of the patient, which is also sent to the server.

[0795] 3. A means of analyzing captured patient images and determining patient ID:

[0796] The server analyzes the captured images using image analysis algorithms and identifies the patient ID by matching it with an internal database.

[0797] 4. Methods for making an initial diagnosis based on patient demographics and imaging analysis:

[0798] The server performs an initial diagnosis based on the patient's basic information and image analysis results, and uses an AI model to suggest appropriate diagnoses and treatments.

[0799] 5. Means of prescribing medical treatment based on diagnostic findings:

[0800] The server then recommends appropriate medical treatment based on the diagnosis and sends the information to the terminal, where the user follows the instructions.

[0801] 6. Means of controlling robots to perform medical procedures:

[0802] Based on the diagnosis, the robot is controlled to automatically perform medical procedures, and the robot's movements are monitored in real time on a terminal.

[0803] 7. Means for monitoring the treatment process in real time and storing data:

[0804] The device monitors the progress of medical procedures in real time and stores the data on a server, allowing past data to be used for future diagnoses and treatments.

[0805] 8. Means for real-time communication between user and robot:

[0806] The user and the robot can communicate in real time via the terminal, for example, by issuing instructions to the robot using voice commands or text messages.

[0807] 9. Means of recognizing user emotions and adjusting the robot's responses based on the emotions:

[0808] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize their emotions, and adjusts the robot's response based on the recognized emotions.

[0809] 10. Means for generating and displaying work instructions and support content based on worker emotions:

[0810] The server uses an emotion engine to evaluate the worker's emotions, generate appropriate work instructions and support content, and display them on the terminal.

[0811] Specific examples

[0812] 1. Medical application examples:

[0813] The user enters basic patient information, captures images, and sends them to the server, which performs an initial diagnosis and suggests appropriate medical treatment, which the robot then automatically performs.

[0814] Example prompt sentence:

[0815] Please enter the patient's basic information.

[0816] "Capture an image of the patient."

[0817] 2. Factory work support:

[0818] A worker wearing smart glasses inputs basic information, captures the work environment, and sends it to a server, which analyzes emotions and generates appropriate work instructions, which are displayed on the glasses' display.

[0819] Example prompt sentence:

[0820] Please enter the worker's basic information.

[0821] "Capture an image of your work environment."

[0822] Hardware and software used

[0823] Hardware:

[0824] Smart glasses or smartphones (with built-in camera, microphone, and display)

[0825] medical robots

[0826] Server (data storage and analysis)

[0827] software:

[0828] Python (data processing, image capture, server communication)

[0829] OpenCV (image capture and processing)

[0830] emotion_recognition library (emotion recognition)

[0831] server_communication library (data communication with the server)

[0832] The above configuration provides appropriate medical and work support that takes into account the user's emotions, making it possible to reduce the user's stress and improve work efficiency.

[0833] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0834] Step 1:

[0835] The user uses the terminal to input basic information about the patient (such as name, age, sex, medical history, and current symptoms).

[0836] Input: Patient's basic information (name, age, sex, medical history, current symptoms)

[0837] Action: Enter the required information into the device's input form.

[0838] Output: The basic information entered is saved on the device and then sent to the server.

[0839] Step 2:

[0840] The user captures an image of the patient using the device's built-in camera.

[0841] Input: Start capture command

[0842] Action: Activates the device's camera and takes an image of the patient.

[0843] Output: The captured image data is saved on the device and then sent to the server.

[0844] Step 3:

[0845] The server analyzes the captured image and matches it with an internal database to identify the patient ID.

[0846] Input: Photographed image data

[0847] How it works: Uses image analysis algorithms to recognize faces and compare them with an internal database.

[0848] Output: Identified patient ID

[0849] Step 4:

[0850] The server makes an initial diagnosis based on the patient's basic information and the results of image analysis.

[0851] Input: Patient basic information, specific patient ID

[0852] How it works: It uses AI models to analyze a patient's symptoms and make an initial diagnosis.

[0853] Output: Initial diagnosis result

[0854] Step 5:

[0855] The server prescribes appropriate medical treatment based on the diagnosis results and transmits the information to the terminal.

[0856] Input: Initial diagnosis result

[0857] Operation: Generates treatment contents based on the diagnosis results and sends them to the terminal.

[0858] Output: Medical treatment instructions

[0859] Step 6:

[0860] The terminal controls the robot based on the diagnosis results and performs medical treatment.

[0861] Input: Medical treatment instructions

[0862] Operation: Send instructions from the terminal to the robot to perform medical procedures.

[0863] Output: Progress information of medical procedures performed

[0864] Step 7:

[0865] The terminal monitors the treatment process in real time and stores the data on a server.

[0866] Input: Robot operation status, data from sensors

[0867] How it works: The monitoring system monitors the progress of the procedure in real time and notifies you of any abnormalities.

[0868] Output: Monitoring data is stored on the server.

[0869] Step 8:

[0870] The server recognizes the user's emotions and generates an appropriate response.

[0871] Input: User's voice and facial expression data

[0872] How it works: The emotion engine analyzes the user's emotions and generates a response based on the results.

[0873] Output: The generated response

[0874] Step 9:

[0875] The terminal generates and displays work instructions and support content based on the worker's emotions.

[0876] Input: Emotion engine analysis results, work environment data

[0877] Operation: Based on the results of emotion analysis, appropriate work instructions and assistance content are generated and displayed on a display such as smart glasses.

[0878] Output: Work instructions and support details

[0879] 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.

[0880] 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.

[0881] 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.

[0882] [Third embodiment]

[0883] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0884] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0885] 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).

[0886] 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.

[0887] 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.

[0888] 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).

[0889] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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."

[0895] The present invention provides a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment. The system includes a series of processes for inputting basic patient information, capturing images, and performing diagnosis and medical treatment.

[0896] The system's operation procedure begins with the user entering basic patient information into the terminal. This basic information includes the patient's name, age, gender, medical history, current symptoms, etc. The terminal then sends the information entered by the user to the server.

[0897] The user then follows the device's instructions to capture an image of the patient using the device's built-in camera. The device then adjusts the captured image and sends it to the server, where the server uses image analysis algorithms to analyze the patient's characteristics and match them with an internal database to identify the patient. The server also uses the results of the analysis to make an initial diagnosis and identify appropriate medical treatment.

[0898] The server sends the diagnosis results to the terminal, which displays them to the user. The user reviews the proposed treatment and, if they give their permission, proceeds to the next step. The terminal then controls the robot to perform the actual medical treatment. The progress of the treatment is monitored in real time, and includes a function to immediately interrupt it if an abnormality is detected.

[0899] The robot uses natural language processing to communicate with the user and responds to their questions and instructions. For example, if the user asks, "Have you finished stopping the bleeding?", the robot will reply, "Yes, I've finished stopping the bleeding. What's next, disinfection?" In this way, the user can monitor the robot's operations and give appropriate instructions.

[0900] In addition, all treatment results and progress are uploaded to a server in real time and logs are saved, making it possible to use past data for future diagnoses and treatments. The entire system supports safe and efficient medical procedures, significantly reducing the burden on medical practitioners while ensuring patient safety.

[0901] As a concrete example, consider the treatment of a cut on the arm. The user enters the patient's basic information, captures an image, and sends it to the server. The server performs an initial diagnosis and sends the diagnosis that "stopping the bleeding and disinfecting are necessary" to the terminal. After the user confirms, the terminal controls the robot to stop the bleeding and disinfect. During this time, the user and robot communicate with each other as the treatment progresses. All information is saved on the server in real time and can be used for future medical treatments.

[0902] This system enables rapid and accurate response even in emergency medical treatment, improving efficiency and safety in medical settings. In addition, the robot uses natural language processing to smoothly communicate with users, allowing them to perform medical procedures without any problems, even if it is their first time performing them.

[0903] The processing flow will be explained below.

[0904] Step 1:

[0905] The user enters basic patient information into the terminal, such as the patient's name, age, sex, medical history, and current symptoms.

[0906] Step 2:

[0907] The terminal displays the entered patient information to the user in real time and asks for confirmation. After the user confirms the information, the terminal sends the information to the server.

[0908] Step 3:

[0909] The user follows the instructions on the device to capture images of the patient using the built-in camera, adjusting the angle and position as needed.

[0910] Step 4:

[0911] The device receives the captured image, adjusts the image quality and size as necessary, and sends the adjusted image to the server.

[0912] Step 5:

[0913] The server runs facial recognition and body feature analysis algorithms to analyze the received patient images and identify the patient ID against an internal database.

[0914] Step 6:

[0915] The server runs a diagnostic algorithm to make an initial diagnosis based on the patient's basic information and the image analysis results, and identifies the necessary medical treatment.

[0916] Step 7:

[0917] The server generates a diagnosis and identifies an appropriate treatment procedure, and sends the diagnosis and treatment procedure to the terminal.

[0918] Step 8:

[0919] The terminal displays the received diagnosis results and treatment procedures to the user, who then checks the contents and prepares to give permission.

[0920] Step 9:

[0921] The user checks the diagnosis and treatment details and instructs the robot to carry out the treatment via a terminal. With the user's permission, the terminal starts the robot's control program and instructs the specific medical treatment.

[0922] Step 10:

[0923] The robot performs the medical procedure: its arms take gauze to stop bleeding and apply it to the patient's cut, then apply disinfectant and proceed with the necessary treatment.

[0924] Step 11:

[0925] The terminal monitors the treatment process in real time and sends data to the server. If the terminal detects an abnormality, it immediately instructs the robot to halt treatment.

[0926] Step 12:

[0927] The user observes the robot's treatment progress and gives additional instructions via a terminal or voice command. The user can give specific instructions, such as "apply a bandage," and the robot will respond accordingly.

[0928] Step 13:

[0929] After the robot completes the entire medical procedure, the terminal sends a report of the completion of the procedure to the server, which stores all the treatment results and log data for future diagnosis and treatment.

[0930] Through the above steps, the present invention can consistently carry out the entire process from inputting basic patient information, capturing images, making an initial diagnosis, performing medical treatment, and monitoring and logging the treatment.

[0931] Example 1

[0932] 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."

[0933] In current medical settings, patient information acquisition, diagnosis, and treatment are often done manually, resulting in time-consuming processes and a high risk of human error. Furthermore, when emergency medical treatment is required, a fast and accurate response is required, but current systems have difficulty meeting this requirement. Furthermore, real-time monitoring of treatment progress and data recording are insufficient, meaning that information useful for subsequent diagnoses and treatments is not fully utilized.

[0934] 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.

[0935] In this invention, the server includes a means for inputting basic patient information, a means for acquiring patient images, a means for analyzing the acquired patient images and identifying the patient identification number, a means for making an initial diagnosis based on the patient's basic information and the image analysis results, a means for proposing medical treatment based on the diagnosis results, a means for controlling the machine to perform the medical treatment, a means for monitoring the treatment process in real time and saving data, and a means for real-time communication between the user and the machine. This enables fast and accurate medical treatment, improving efficiency and safety in medical settings. Furthermore, because treatment results and progress are monitored and recorded in real time, reliable data management is realized that can be used for subsequent diagnoses and treatments.

[0936] "Basic information" refers to basic data required for medical treatment, such as the patient's name, age, gender, medical history, and current symptoms.

[0937] "Image acquisition" refers to taking a picture of a patient's body part or symptoms using the device's camera function and acquiring the image data.

[0938] "Image analysis" refers to the process of processing acquired image data to extract and identify features. For example, this can be done using an image processing library such as OpenCV.

[0939] "Patient Identification Number" refers to a unique number assigned to an individual patient identified from analyzed images and basic information.

[0940] "Initial diagnosis" refers to the first medical diagnosis made based on the patient's basic information and the results of image analysis.

[0941] "Medical treatment" refers to the specific treatment steps that are proposed and performed based on the initial diagnosis, such as stopping bleeding and disinfection.

[0942] "Machine" refers to a robot or device that is controlled to perform a medical procedure, such as a surgical robot.

[0943] "Real-time monitoring" means monitoring the medical procedure as it progresses, and being ready to respond immediately if any abnormalities occur.

[0944] "Data storage" refers to storing information such as the progress and results of treatment in a storage device within the system for later use.

[0945] "Users" are medical professionals such as doctors and nurses who operate the system and perform medical procedures.

[0946] "Communication" refers to the exchange of information between the user and the machine, and responses to instructions and questions, for example, using natural language processing techniques.

[0947] The present invention provides a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment. This system includes a series of processes for inputting basic patient information, acquiring images, and performing diagnosis and medical treatment.

[0948] The user inputs basic information about the patient into the device. This basic information includes the patient's name, age, sex, medical history, and current symptoms. The device sends the input information to the server. Next, the user uses the device's camera to capture an image of the patient. This is done by activating the built-in camera and taking a picture of the patient's affected area. The captured image is then sent from the device to the server.

[0949] The server uses OpenCV, an open-source image analysis library, to analyze the received image data. It executes image analysis algorithms and extracts features. The results are compared with an internal database to identify the patient's identification number. Furthermore, an initial diagnosis is made based on the analysis results, and appropriate medical treatment is identified. This initial diagnosis is made based on a novel diagnostic protocol, with the server referencing the internal medical database.

[0950] The diagnosis results are sent from the server to the device, which then displays them to the user, who then confirms the proposed treatment and agrees to it.

[0951] The terminal runs a robot control program using a robotics operating system (ROS) to perform medical procedures, such as stopping bleeding and disinfecting. For example, when treating a cut on the right arm, the robot arm applies gauze to stop the bleeding and then applies disinfectant.

[0952] Communication between the user and the robot is carried out using natural language processing. When the user asks, "Have you finished stopping the bleeding?", the robot responds, "Yes, I've finished stopping the bleeding. Next is disinfection?" This communication proceeds in the form of the user asking questions and giving instructions through a voice recognition system, and the robot responding accordingly.

[0953] All treatment processes are recorded in real time on the server. The database management system MySQL is used to store the data. This data will be used later for the next diagnosis and treatment. For example, the treatment details and results performed on patient ID "12345" are recorded, and when the same patient is diagnosed again, this data can be referenced to provide more accurate medical services.

[0954] Example prompt for a generative AI model:

[0955] Please explain the specific program processing flow for each step, from entering basic patient information to image capture, diagnosis, and implementation. Also, please provide a detailed description of the operation when using OpenCV for image analysis and ROS for robot control.

[0956] This system enables rapid and accurate response even in emergency medical treatment, improving efficiency and safety in medical settings. In addition, the robot uses natural language processing to smoothly communicate with users, allowing them to perform medical procedures without any problems, even if it is their first time performing them.

[0957] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0958] Step 1:

[0959] The terminal inputs the patient's basic information.

[0960] Input: Data entered by the user (such as name, age, gender, medical history, current symptoms, etc.).

[0961] Data processing: The terminal converts the input data into JSON format.

[0962] Output: Patient demographics data in JSON format is generated.

[0963] Specific operation: The user opens the dedicated application on the device, enters information into the input form, and taps the "Submit" button.

[0964] Step 2:

[0965] The terminal sends the entered basic patient information to the server.

[0966] Input: Patient demographics data in JSON format.

[0967] Data processing: Send JSON data to the server via an HTTP POST request.

[0968] Output: Basic information is saved on the server.

[0969] Specific operation: The terminal sends patient data to a specific API endpoint on the server.

[0970] Step 3:

[0971] The user captures an image of the patient using the device's camera.

[0972] Input: The patient's area to be imaged.

[0973] Data processing: The device's camera app captures the image and converts it to JPEG format.

[0974] Output: Image data in JPEG format is generated.

[0975] Specific actions: The user opens the camera app, takes a picture of the patient's affected area, and taps the "Capture" button.

[0976] Step 4:

[0977] The image acquired by the terminal is sent to the server.

[0978] Input: Image data in JPEG format.

[0979] Data processing: Image data is sent to the server via an HTTP POST request.

[0980] Output: Image data is saved on the server.

[0981] Specific operation: The device sends image data to a specific API endpoint on the server.

[0982] Step 5:

[0983] The server analyzes the received image data.

[0984] Input: Image data in JPEG format.

[0985] Data processing: The server uses OpenCV to analyze the image and extract features, such as edges, color, and shape.

[0986] Output: Extracted feature data.

[0987] Specific operation: The server executes OpenCV algorithms and performs image analysis.

[0988] Step 6:

[0989] The server identifies the patient identification number based on the analysis results.

[0990] Input: Extracted feature data.

[0991] Data processing: The server checks against an internal database to identify matching patient identification numbers.

[0992] Output: Patient identification number.

[0993] Specific operation: The server compares the feature data with existing data in the database and identifies matching data.

[0994] Step 7:

[0995] The server makes an initial diagnosis based on the patient's basic information and the results of image analysis.

[0996] Input: Patient basic information data and feature data.

[0997] Data processing: The server references an internal medical database and executes diagnostic algorithms.

[0998] Output: Initial diagnostic results.

[0999] Specific operations: The server executes a diagnostic protocol using a medical database.

[1000] Step 8:

[1001] The server suggests medical treatment based on the diagnosis.

[1002] Input: Initial diagnostic results.

[1003] Data processing: The server selects recommended medical treatments based on the diagnosis results.

[1004] Output: Medical treatment suggestions.

[1005] Specific operation: The server selects an appropriate treatment method based on the diagnostic protocol.

[1006] Step 9:

[1007] The terminal displays the diagnosis and suggested medical treatment to the user.

[1008] Input: Medical procedure proposal data.

[1009] Data processing: The device analyzes the data and displays it on the screen in a visually easy-to-understand format.

[1010] Output: Diagnosis results and medical treatment suggestions displayed to the user.

[1011] What it does: The device displays the diagnosis and suggested medical treatment on the screen.

[1012] Step 10:

[1013] The terminal controls the machine to perform the medical procedure.

[1014] Input: Medical procedure proposal data.

[1015] Data processing: The terminal uses ROS to execute the robot control program.

[1016] Output: Performing a medical procedure.

[1017] Specific actions: The robotic arm performs specific medical procedures such as stopping bleeding and disinfecting.

[1018] Step 11:

[1019] The terminal monitors the treatment process in real time and stores the data.

[1020] Input: Process data.

[1021] Data processing: Data from the treatment process is collected and sent to the server in real time.

[1022] Output: Treatment data stored in real time.

[1023] Specific operation: The terminal monitors the progress of the procedure and immediately notifies if there is an abnormality.

[1024] Step 12:

[1025] The user and the machine communicate in real time.

[1026] Input: User's voice command data.

[1027] Data processing: The machine uses natural language processing to analyze the voice instructions and generate an appropriate response.

[1028] Output: Machine-generated voice response.

[1029] Specific operation: The user gives a voice command, and the machine responds by confirming the operation based on the command.

[1030] Through the above processing steps, the present invention can provide a system that supports rapid and accurate medical treatment of patients, improving efficiency and safety.

[1031] (Application example 1)

[1032] 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."

[1033] With conventional technology, factory robot maintenance required skilled technicians, and labor shortages and variations in technician skills were problems. It was also difficult to detect abnormalities in real time, quickly diagnose problems, and provide appropriate maintenance procedures, making maintenance efficiency and accuracy challenging. This led to concerns about increased factory downtime and reduced production efficiency.

[1034] 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.

[1035] In this invention, the server includes: means for inputting basic patient information; means for capturing patient images; means for analyzing the captured patient images and identifying the patient ID; means for performing an initial diagnosis based on the patient's basic information and image analysis results; means for instructing medical treatment based on the diagnosis results; means for controlling the robot to perform the medical treatment; means for monitoring the treatment process in real time and saving data; means for real-time communication between the user and the robot; means for identifying diagnosis results and appropriate maintenance procedures to support industrial equipment maintenance; and means for providing and displaying the industrial equipment maintenance procedures to the user. This enables factory robot maintenance to detect and quickly diagnose abnormalities in real time and provide appropriate maintenance procedures. This enables efficient and accurate maintenance without the need for skilled technicians, reducing factory downtime and improving production efficiency.

[1036] "Basic patient information" refers to basic information about the patient, such as the patient's name, age, sex, medical history, and current symptoms.

[1037] "Image capturing means" refers to a device or means for obtaining an image of a patient or object using a camera, sensor, or the like.

[1038] "Image analysis" refers to the process of using algorithms and software to analyze features and patterns in a captured image and extract information from it.

[1039] "Means for identifying patient ID" refers to a means for determining a patient's unique identification information based on information obtained through image analysis.

[1040] "Initial diagnosis" refers to the process of making an initial diagnosis by integrating basic patient information and image analysis results.

[1041] "Means for prescribing medical treatment based on the diagnosis results" refers to means for determining the necessary medical treatment based on the results of the initial diagnosis and prescribing the details of the treatment.

[1042] "Means for controlling a robot to perform a medical procedure" refers to means for operating and controlling a robot to actually perform a medical procedure.

[1043] "Means for monitoring the procedure in real time and storing data" refers to means for monitoring the procedure performed by the robot in real time and recording and storing the data.

[1044] "Means for real-time communication between a user and a robot" refers to a means for a user and a robot to exchange information in both directions and communicate with each other in real time.

[1045] "Means for identifying diagnostic results and appropriate maintenance procedures to support the maintenance of industrial equipment" refers to means for diagnosing abnormalities and failures in industrial equipment and determining appropriate maintenance procedures based on the results.

[1046] "Means for providing and displaying maintenance procedures for industrial equipment to users" refers to means for presenting diagnostic results and maintenance procedures to users in an easy-to-understand manner and displaying their contents.

[1047] This invention provides a system for assisting in the efficient and accurate maintenance of factory robots. The system includes a series of processes for inputting basic robot information, capturing images, and executing diagnostic and maintenance procedures.

[1048] First, the user inputs basic information about the robot using the UI of the smart glasses. This information includes the robot ID, installation location, and current problems (e.g., "abnormal noise"). The information entered by the user is sent to the server via the smart glasses.

[1049] Next, the user captures an image of the robot using the camera built into the smart glasses. The captured image is adjusted in real time and sent to the server. The server then uses image analysis algorithms (e.g., OpenCV) to analyze the robot's condition and identify any abnormalities or maintenance needs. The server also identifies appropriate maintenance procedures based on the analysis results and sends instructions to the user.

[1050] The transmitted diagnostic results and maintenance procedures are displayed on the smart glasses' display, and the user can then carry out the maintenance work accordingly. For example, if the server issues a diagnostic result indicating that a bearing needs to be replaced, the smart glasses will display the specific procedure and prompt the user to carry out the work.

[1051] The interaction between the user and the system takes place in real time, and when the user asks a question or makes a confirmation, the server responds quickly. For example, if the user asks, "What should I do next?", the server will instruct, "Next, please release the bearing lock." In this way, the user can efficiently carry out maintenance work by interacting with the server using prompts.

[1052] All maintenance progress and results are uploaded to the server in real time and saved as logs, allowing future diagnosis and troubleshooting to be performed based on past maintenance information.

[1053] Specific examples

[1054] For example, the following prompts are used to diagnose and provide maintenance procedures for a robot:

[1055] Please diagnose the cause of the strange noise my robot is making and tell me what maintenance steps I need to take.

[1056] The hardware uses smart glasses with a camera, and the software uses Python, OpenCV, and the requests library for HTTP requests, enabling efficient maintenance of factory robots.

[1057] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1058] Step 1:

[1059] The user uses the UI of the smart glasses to input basic information about the robot, including the robot ID, its location, and any current issues (e.g., "abnormal noise"). The input information is converted into digital data by the smart glasses' internal processor and sent to a server via a network.

[1060] Input: User-entered robot ID, location, and problem information

[1061] Output: Basic information data sent to the server

[1062] Step 2:

[1063] The user captures an image of the robot using the camera built into the smart glasses. The software in the glasses formats the image optimally and saves it as image data. The saved image is then sent to a server over a network.

[1064] Input: An image of the robot captured by a camera

[1065] Output: Image data to be sent

[1066] Step 3:

[1067] The server analyzes the received images. Image analysis algorithms (e.g., OpenCV) are used to identify abnormalities on the robot. Features within the images are extracted and compared with past maintenance data in a database to identify abnormalities.

[1068] Input: Submitted image data

[1069] Output: Identification of abnormalities

[1070] Step 4:

[1071] The server integrates the robot's basic information with the image analysis results to perform an initial diagnosis. A diagnostic algorithm (such as a generative AI model) is used to analyze the cause of the abnormality and determine the appropriate maintenance procedure. The diagnosis results and maintenance procedure are generated.

[1072] Input: Basic information data and image analysis results

[1073] Output: Diagnostic results and maintenance procedures

[1074] Step 5:

[1075] Diagnostic results and maintenance procedures are sent from the server to the smart glasses and displayed on the glasses' display.

[1076] Input: Server-generated diagnostic results and maintenance procedures

[1077] Output: Diagnostic results and maintenance procedures displayed to the user

[1078] Step 6:

[1079] The user follows the instructions on the smart glasses to perform maintenance tasks. If they have questions or need confirmation on the next step, they can contact the server using voice commands or UI operations. The server responds to the user's questions in real time.

[1080] Input: Questions and inquiries from users

[1081] Output: Real-time response from the server

[1082] Step 7:

[1083] The progress and results of maintenance are uploaded in real time from the smart glasses to a server and stored as logs, which can be used for future diagnostics and troubleshooting.

[1084] Input: User maintenance progress and results

[1085] Output: Progress and result logs stored on the server

[1086] This system will enable efficient and accurate maintenance of factory robots.

[1087] 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.

[1088] The present invention is a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment, and by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more advanced medical support. This system includes a series of processes for inputting basic patient information, capturing images, and performing diagnosis and medical treatment.

[1089] The system's operation procedure begins with the user entering basic patient information into the terminal. This basic information includes the patient's name, age, gender, medical history, current symptoms, etc. The terminal then sends the information entered by the user to the server.

[1090] The user then follows the device's instructions to capture an image of the patient using the device's built-in camera. The device then adjusts the captured image and sends it to the server, where the server uses image analysis algorithms to analyze the patient's characteristics and match them with an internal database to identify the patient. The server also uses the results of the analysis to make an initial diagnosis and identify appropriate medical treatment.

[1091] The server sends the diagnosis results to the terminal, which displays them to the user. The user reviews the proposed treatment and, if they give their permission, proceeds to the next step. The terminal then controls the robot to perform the actual medical treatment. The progress of the treatment is monitored in real time, and includes a function to immediately interrupt it if an abnormality is detected.

[1092] The system also incorporates an emotion engine, which recognizes emotions from the user's facial expressions, tone of voice, and content of words, and adjusts the robot's response accordingly. For example, if the user is nervous, the robot can respond in a calm tone, saying, "It's okay, let's stay calm and proceed."

[1093] As a concrete example, consider the case of treating a cut on an arm using an emotion engine. The user enters the patient's basic information, captures an image, and sends it to the server. The server performs an initial diagnosis and sends the diagnosis result, "The bleeding needs to be stopped and disinfected," to the terminal. After the user confirms, the terminal controls the robot to stop the bleeding and disinfect the wound.

[1094] During this process, let's say the user says, "I'm worried about whether the bleeding is being stopped properly." The emotion engine detects anxiety from the user's tone of voice and the content of their words, and the robot responds, "Don't worry. The procedure is proceeding correctly." In this way, appropriate communication is carried out according to the user's emotions.

[1095] In addition, all treatment results and progress are uploaded to a server in real time and logs are saved, making it possible to use past data for future diagnoses and treatments. The entire system supports safe and efficient medical procedures, significantly reducing the burden on medical practitioners while ensuring patient safety.

[1096] Furthermore, the emotion engine can be used to assess the user's stress level and interrupt or change treatment if a high stress state is detected, thereby protecting the health and safety of medical personnel. This system can provide more advanced medical support and further improve efficiency and safety in medical settings.

[1097] The processing flow will be explained below.

[1098] Step 1:

[1099] The user enters basic patient information into the terminal, such as the patient's name, age, sex, medical history, and current symptoms.

[1100] Step 2:

[1101] The terminal displays the entered patient information to the user in real time and asks for confirmation. After the user confirms the information, the terminal sends the information to the server.

[1102] Step 3:

[1103] The user follows the instructions on the device to capture images of the patient using the built-in camera, adjusting the angle and position as needed.

[1104] Step 4:

[1105] The device receives the captured image, adjusts the image quality and size as necessary, and sends the adjusted image to the server.

[1106] Step 5:

[1107] The server runs facial recognition and body feature analysis algorithms to analyze the received patient images and identify the patient ID against an internal database.

[1108] Step 6:

[1109] The server runs a diagnostic algorithm to make an initial diagnosis based on the patient's basic information and the image analysis results, and identifies the necessary medical treatment.

[1110] Step 7:

[1111] The server generates a diagnosis and identifies an appropriate treatment procedure, and sends the diagnosis and treatment procedure to the terminal.

[1112] Step 8:

[1113] The terminal displays the received diagnosis results and treatment procedures to the user, who then checks the contents and prepares to give permission.

[1114] Step 9:

[1115] The user checks the diagnosis and treatment details and instructs the robot to carry out the treatment via a terminal. With the user's permission, the terminal starts the robot's control program and instructs the specific medical treatment.

[1116] Step 10:

[1117] The robot performs the medical procedure: its arms take gauze to stop bleeding and apply it to the patient's cut, then apply disinfectant and proceed with the necessary treatment.

[1118] Step 11:

[1119] The terminal monitors the treatment process in real time and sends data to the server. If the terminal detects an abnormality, it immediately instructs the robot to halt treatment.

[1120] Step 12:

[1121] The user observes the robot's treatment progress and gives additional instructions via a terminal or voice command. The user can give specific instructions, such as "apply a bandage," and the robot will respond accordingly.

[1122] Step 13:

[1123] The emotion engine analyzes the user's facial expressions, tone of voice, and the content of their words to recognize their emotions. For example, if the user says something like "I'm worried," the emotion engine will detect anxiety.

[1124] Step 14:

[1125] Based on the analysis results of the emotion engine, the robot will provide appropriate feedback to the user, for example, "Don't worry, the treatment is going well."

[1126] Step 15:

[1127] If the emotion engine determines that the user's stress level is high, the device will temporarily suspend treatment or issue instructions to change the treatment procedure, thereby protecting the user's mental state.

[1128] Step 16:

[1129] After the robot completes the entire medical procedure, the terminal sends a report of the completion of the procedure to the server, which stores all the treatment results and log data for future diagnosis and treatment.

[1130] Through the above steps, the present invention can seamlessly perform the entire process, from entering basic patient information, capturing images, making an initial diagnosis, performing medical procedures, monitoring and logging the procedures, and even recognizing and providing feedback to the user. This system enables fast and accurate responses even in emergency medical situations, improving efficiency and safety in medical settings. Furthermore, the introduction of an emotion engine reduces the user's psychological burden, allowing for safe and secure medical procedures.

[1131] Example 2

[1132] 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."

[1133] Conventional medical systems face the challenge of quickly and accurately obtaining basic patient information and images, and then performing diagnosis and treatment. The process involves time-consuming tasks such as inputting information and confirming treatment. Furthermore, because treatment is performed without consideration for the user's feelings, medical practitioners often feel stressed, posing a risk to the quality and safety of medical care. There is a need to solve these problems and provide effective and safe medical care to patients while reducing the burden on medical practitioners.

[1134] 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 inputting basic patient information, means for acquiring patient images, means for analyzing the acquired patient images and identifying the patient ID, means for making an initial diagnosis based on the patient's basic information and image analysis results, means for instructing medical treatment based on the diagnosis results, means for controlling the robot to perform the medical treatment, means for monitoring the treatment process in real time and saving data, means for analyzing the user's facial expressions, tone of voice, and content of words to recognize emotions, means for adjusting the robot's responses based on the recognized emotions, and means for communicating between the user and the robot in real time. This enables quick and accurate acquisition of the patient's basic information and images, execution of initial diagnosis and appropriate medical treatment, and flexible response according to the user's emotions, thereby providing more effective and safer medical care.

[1135] "Means for inputting basic patient information" refers to an interface that allows a user to input information about a patient, such as the patient's name, age, sex, medical history, and current symptoms.

[1136] "Means for obtaining images of a patient" refers to devices, including cameras and scanning devices, for capturing images of a patient's body or condition.

[1137] "Means for analyzing acquired patient images and identifying patient IDs" refers to algorithms or programs that recognize and analyze specific features of a patient and verify them against a database to determine the patient ID.

[1138] "Means for making an initial diagnosis based on the patient's basic information and image analysis results" refers to a diagnostic algorithm or program for making an initial diagnosis based on the input basic information and analyzed image information.

[1139] "Means for instructing medical treatment based on diagnostic results" refers to mechanisms or software for determining specific medical treatment based on the results of an initial diagnosis and instructing this to the next process.

[1140] "Means for controlling the robot to perform a medical procedure" refers to a control system for operating the robot based on programmed instructions to perform a specified medical procedure.

[1141] "Means for monitoring the treatment process in real time and storing data" refers to sensors and data storage devices for monitoring the progress of medical treatment in real time and recording and storing that data.

[1142] "Means for analyzing a user's facial expressions, tone of voice, and spoken content to recognize emotions" refers to a program or device that reads and analyzes emotions from a user's facial expressions, tone of voice, and spoken content.

[1143] "Means for adjusting the robot's response based on the recognized emotion" refers to a control program or system for changing the robot's response in response to the analyzed emotion of the user.

[1144] "Means for real-time communication between users and robots" refers to interfaces and network systems that allow users and robots to instantly exchange and share information and instructions.

[1145] The present invention is a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment, and by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more advanced medical support. This system includes a series of processes for inputting basic patient information, capturing images, and performing diagnosis and medical treatment.

[1146] The system's operation procedure begins with the user entering basic patient information into the terminal. This basic information includes the patient's name, age, gender, medical history, current symptoms, etc. The terminal then sends the information entered by the user to the server.

[1147] The user then follows the device's instructions to capture an image of the patient using the device's built-in camera. The device then adjusts the captured image and sends it to the server, where the server uses image analysis algorithms to analyze the patient's characteristics and match them with an internal database to identify the patient. The server also uses the results of the analysis to make an initial diagnosis and identify appropriate medical treatment.

[1148] The server sends the diagnosis results to the terminal, which displays them to the user. The user reviews the proposed treatment and, if they give their permission, proceeds to the next step. The terminal then controls the robot to perform the actual medical treatment. The progress of the treatment is monitored in real time, and includes a function to immediately interrupt it if an abnormality is detected.

[1149] The system also incorporates an emotion engine, which recognizes emotions from the user's facial expressions, tone of voice, and content of words, and adjusts the robot's response accordingly. For example, if the user is nervous, the robot can respond in a calm tone, saying, "It's okay, let's stay calm and proceed."

[1150] As a concrete example, consider the case of treating a cut on an arm using an emotion engine. The user enters the patient's basic information, captures an image, and sends it to the server. The server performs an initial diagnosis and sends the diagnosis result, "The bleeding needs to be stopped and disinfected," to the terminal. After the user confirms, the terminal controls the robot to stop the bleeding and disinfect the wound.

[1151] During this process, let's say the user says, "I'm worried about whether the bleeding is being stopped properly." The emotion engine detects anxiety from the user's tone of voice and the content of their words, and the robot responds, "Don't worry. The procedure is proceeding correctly." In this way, appropriate communication is carried out according to the user's emotions.

[1152] In addition, all treatment results and progress are uploaded to a server in real time and logs are saved, making it possible to use past data for future diagnoses and treatments. The entire system supports safe and efficient medical procedures, significantly reducing the burden on medical practitioners while ensuring patient safety.

[1153] Furthermore, the emotion engine can be used to assess the user's stress level and interrupt or change treatment if a high stress state is detected, thereby protecting the health and safety of medical personnel. This system can provide more advanced medical support and further improve efficiency and safety in medical settings.

[1154] An example of a prompt might be, "The basic information about this patient is as follows: Name: XX, Age: XX years, Gender: XX, Medical history: XX, Current symptoms: XX. Please suggest an initial diagnosis and appropriate medical treatment."

[1155] In this way, the system performs a series of steps from inputting patient information to diagnosis, performing treatment, and analyzing the user's emotions, providing safe and efficient medical support.

[1156] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1157] Step 1:

[1158] The user enters the patient's basic information (such as name, age, sex, medical history, and current symptoms) into the terminal.

[1159] Input: Patient demographic information from the user

[1160] Output: Patient demographics sent to the server

[1161] Specific operation: The user enters information into the input form of the dedicated application and presses the "Submit" button. The device then sends this information in JSON format to the server.

[1162] Step 2:

[1163] The user captures an image of the patient using the device's built-in camera.

[1164] Input: User operations based on device instructions

[1165] Output: Patient images sent to the server

[1166] Specific operation: The user points the device camera at the patient and presses the capture button. The device optimizes the image resolution, removes noise, and sends it to the server in JPEG image format.

[1167] Step 3:

[1168] The server processes the received images with image analysis algorithms to analyze the patient's characteristics.

[1169] Input: Patient images sent to the server

[1170] Output: Patient characteristics and patient ID

[1171] How it works: The server uses image analysis algorithms such as ResNet and VGG16 to extract features of the patient's face and wound from the image, then compares them with an internal database to identify the patient ID.

[1172] Step 4:

[1173] The server performs an initial diagnosis based on the image analysis results and the basic information entered, and sends the results to the terminal.

[1174] Input: Patient basic information and image analysis results

[1175] Output: Initial diagnostic results

[1176] Specific operation: The server performs an initial diagnosis using a machine learning model based on basic information and image analysis results. It generates a diagnosis such as "Bleeding control and disinfection are required" and sends this to the device.

[1177] Step 5:

[1178] The user checks the diagnosis results and treatment details displayed on the terminal and gives permission.

[1179] Input: Display of diagnostic results and user confirmation operations

[1180] Output: Permission input

[1181] Specific operation: The user checks the diagnostic results displayed on the device screen and taps the "Allow" button. This action sends permission information from the device to the server.

[1182] Step 6:

[1183] The terminal controls the robot with the user's permission and performs the necessary medical procedures.

[1184] Input: Diagnostic results and user permission

[1185] Output: Medical procedure performed

[1186] Specific operation: Based on the certified diagnosis results and authorization information, the terminal sends instructions to the robot to stop bleeding and disinfect. The robot then begins treatment according to these instructions and reports its progress to the terminal in real time.

[1187] Step 7:

[1188] The device uses an emotion engine to analyze the user's facial expressions, tone of voice, and the content of their words to recognize their emotions.

[1189] Input: User's facial expressions, voice, and words

[1190] Output: Recognized emotion

[1191] Specific operation: The device captures the user's voice and facial expressions using a camera and microphone, and uses an emotion analysis algorithm to recognize emotions such as "anxiety" and "tension."

[1192] Step 8:

[1193] The device adjusts the robot's response based on the recognized emotion.

[1194] Input: Perceived user emotion

[1195] Output: Robot response

[1196] Specific operation: If the terminal detects "anxiety," it will have the robot respond with "Don't worry. The procedure is proceeding correctly." This voice message will be played from the robot.

[1197] Step 9:

[1198] The server stores all treatment results and progress as logs, which can be used for future diagnosis and treatment.

[1199] Input: Medical procedure progress and results

[1200] Output: Saved log data

[1201] Specific operation: The server stores the progress and results of the treatment in a database in real time, making them available for future diagnosis and treatment.

[1202] (Application example 2)

[1203] 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."

[1204] Conventional medical support systems are capable of providing diagnosis and treatment based on basic patient information and image data, but they do not take the user's emotions into consideration, making it difficult to alleviate the user's anxiety and stress. It is also difficult to properly manage the impact of emotions on work efficiency and safety when workers perform precise work. Therefore, there is a need to provide appropriate communication and work support based on the user's emotions.

[1205] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting basic patient information, means for capturing an image of the patient, means for analyzing the captured patient image and identifying the patient ID, means for making an initial diagnosis based on the patient's basic information and the image analysis results, means for instructing medical treatment based on the diagnosis results, means for controlling the robot to perform the medical treatment, means for monitoring the treatment process in real time and saving data, means for real-time communication between the user and the robot, means for recognizing the user's emotions and adjusting the robot's responses based on the emotions, and means for generating and displaying work instructions and support content based on the worker's emotions. This makes it possible to provide appropriate medical support and work support that takes the user's emotions into consideration, thereby reducing the user's stress and improving work efficiency.

[1206] "Means for inputting basic patient information" refers to a device or software for inputting the patient's name, age, sex, medical history, current symptoms, etc.

[1207] A "means for capturing an image of a patient" is a camera or other imaging device for taking an image of a patient in a medical setting.

[1208] "Means for analyzing captured patient images and identifying patient IDs" refers to algorithms or software that perform image analysis using captured images and identify specific patients based on that information.

[1209] "Means for making an initial diagnosis based on basic patient information and image analysis results" refers to a system or algorithm for making an initial diagnosis using input basic information and the results of analyzed patient images.

[1210] The "means for instructing medical treatment based on the diagnostic results" refers to a device or program for proposing or instructing appropriate medical treatment based on the diagnostic results.

[1211] The "means for controlling the robot to perform a medical procedure" refers to a control system for operating the medical robot and causing it to perform a prescribed medical procedure.

[1212] The "means for monitoring the treatment process in real time and storing data" refers to a monitoring system and storage device for observing the progress of a medical treatment in real time and recording the data.

[1213] "Means for real-time communication between the user and the robot" refers to an interface that allows the user and the robot to exchange information in real time via voice or text.

[1214] "Means for recognizing the user's emotions and adjusting the robot's responses based on those emotions" refers to a system that analyzes the user's voice and facial expressions and appropriately changes the robot's behavior and responses based on the results.

[1215] "Means for generating and displaying work instructions and support content based on the worker's emotions" refers to devices or software that recognize the worker's emotional state and create and display appropriate work instructions and support content based on that.

[1216] This system allows users to input basic patient information, capture images, and perform diagnosis and medical treatment. Furthermore, by incorporating an emotion engine, it can recognize the user's emotions and respond appropriately. It can also be used to support factory work.

[1217] System Configuration

[1218] The system includes the following main means:

[1219] 1. Enter patient information by:

[1220] The user enters basic information such as the patient's name, age, sex, medical history, current symptoms, etc. This information is collected by the terminal and sent to the server.

[1221] 2. Means of capturing patient images:

[1222] Using the device's built-in camera, the user captures an image of the patient, which is also sent to the server.

[1223] 3. A means of analyzing captured patient images and determining patient ID:

[1224] The server analyzes the captured images using image analysis algorithms and identifies the patient ID by matching it with an internal database.

[1225] 4. Methods for making an initial diagnosis based on patient demographics and imaging analysis:

[1226] The server performs an initial diagnosis based on the patient's basic information and image analysis results, and uses an AI model to suggest appropriate diagnoses and treatments.

[1227] 5. Means of prescribing medical treatment based on diagnostic findings:

[1228] The server then recommends appropriate medical treatment based on the diagnosis and sends the information to the terminal, where the user follows the instructions.

[1229] 6. Means of controlling robots to perform medical procedures:

[1230] Based on the diagnosis, the robot is controlled to automatically perform medical procedures, and the robot's movements are monitored in real time on a terminal.

[1231] 7. Means for monitoring the treatment process in real time and storing data:

[1232] The device monitors the progress of medical procedures in real time and stores the data on a server, allowing past data to be used for future diagnoses and treatments.

[1233] 8. Means for real-time communication between user and robot:

[1234] The user and the robot can communicate in real time via the terminal, for example, by issuing instructions to the robot using voice commands or text messages.

[1235] 9. Means of recognizing user emotions and adjusting the robot's responses based on the emotions:

[1236] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize their emotions, and adjusts the robot's response based on the recognized emotions.

[1237] 10. Means for generating and displaying work instructions and support content based on worker emotions:

[1238] The server uses an emotion engine to evaluate the worker's emotions, generate appropriate work instructions and support content, and display them on the terminal.

[1239] Specific examples

[1240] 1. Medical application examples:

[1241] The user enters basic patient information, captures images, and sends them to the server, which performs an initial diagnosis and suggests appropriate medical treatment, which the robot then automatically performs.

[1242] Example prompt sentence:

[1243] Please enter the patient's basic information.

[1244] "Capture an image of the patient."

[1245] 2. Factory work support:

[1246] A worker wearing smart glasses inputs basic information, captures the work environment, and sends it to a server, which analyzes emotions and generates appropriate work instructions, which are displayed on the glasses' display.

[1247] Example prompt sentence:

[1248] Please enter the worker's basic information.

[1249] "Capture an image of your work environment."

[1250] Hardware and software used

[1251] Hardware:

[1252] Smart glasses or smartphones (with built-in camera, microphone, and display)

[1253] medical robots

[1254] Server (data storage and analysis)

[1255] software:

[1256] Python (data processing, image capture, server communication)

[1257] OpenCV (image capture and processing)

[1258] emotion_recognition library (emotion recognition)

[1259] server_communication library (data communication with the server)

[1260] The above configuration provides appropriate medical and work support that takes into account the user's emotions, making it possible to reduce the user's stress and improve work efficiency.

[1261] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1262] Step 1:

[1263] The user uses the terminal to input basic information about the patient (such as name, age, sex, medical history, and current symptoms).

[1264] Input: Patient's basic information (name, age, sex, medical history, current symptoms)

[1265] Action: Enter the required information into the device's input form.

[1266] Output: The basic information entered is saved on the device and then sent to the server.

[1267] Step 2:

[1268] The user captures an image of the patient using the device's built-in camera.

[1269] Input: Start capture command

[1270] Action: Activates the device's camera and takes an image of the patient.

[1271] Output: The captured image data is saved on the device and then sent to the server.

[1272] Step 3:

[1273] The server analyzes the captured image and matches it with an internal database to identify the patient ID.

[1274] Input: Photographed image data

[1275] How it works: Uses image analysis algorithms to recognize faces and compare them with an internal database.

[1276] Output: Identified patient ID

[1277] Step 4:

[1278] The server makes an initial diagnosis based on the patient's basic information and the results of image analysis.

[1279] Input: Patient basic information, specific patient ID

[1280] How it works: It uses AI models to analyze a patient's symptoms and make an initial diagnosis.

[1281] Output: Initial diagnosis result

[1282] Step 5:

[1283] The server prescribes appropriate medical treatment based on the diagnosis results and transmits the information to the terminal.

[1284] Input: Initial diagnosis result

[1285] Operation: Generates treatment contents based on the diagnosis results and sends them to the terminal.

[1286] Output: Medical treatment instructions

[1287] Step 6:

[1288] The terminal controls the robot based on the diagnosis results and performs medical treatment.

[1289] Input: Medical treatment instructions

[1290] Operation: Send instructions from the terminal to the robot to perform medical procedures.

[1291] Output: Progress information of medical procedures performed

[1292] Step 7:

[1293] The terminal monitors the treatment process in real time and stores the data on a server.

[1294] Input: Robot operation status, data from sensors

[1295] How it works: The monitoring system monitors the progress of the procedure in real time and notifies you of any abnormalities.

[1296] Output: Monitoring data is stored on the server.

[1297] Step 8:

[1298] The server recognizes the user's emotions and generates an appropriate response.

[1299] Input: User's voice and facial expression data

[1300] How it works: The emotion engine analyzes the user's emotions and generates a response based on the results.

[1301] Output: The generated response

[1302] Step 9:

[1303] The terminal generates and displays work instructions and support content based on the worker's emotions.

[1304] Input: Emotion engine analysis results, work environment data

[1305] Operation: Based on the results of emotion analysis, appropriate work instructions and assistance content are generated and displayed on a display such as smart glasses.

[1306] Output: Work instructions and support details

[1307] 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.

[1308] 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.

[1309] 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.

[1310] [Fourth embodiment]

[1311] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1312] 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.

[1313] 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).

[1314] 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.

[1315] 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.

[1316] 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).

[1317] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1318] 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.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] 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.

[1323] 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."

[1324] The present invention provides a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment. The system includes a series of processes for inputting basic patient information, capturing images, and performing diagnosis and medical treatment.

[1325] The system's operation procedure begins with the user entering basic patient information into the terminal. This basic information includes the patient's name, age, gender, medical history, current symptoms, etc. The terminal then sends the information entered by the user to the server.

[1326] The user then follows the device's instructions to capture an image of the patient using the device's built-in camera. The device then adjusts the captured image and sends it to the server, where the server uses image analysis algorithms to analyze the patient's characteristics and match them with an internal database to identify the patient. The server also uses the results of the analysis to make an initial diagnosis and identify appropriate medical treatment.

[1327] The server sends the diagnosis results to the terminal, which displays them to the user. The user reviews the proposed treatment and, if they give their permission, proceeds to the next step. The terminal then controls the robot to perform the actual medical treatment. The progress of the treatment is monitored in real time, and includes a function to immediately interrupt it if an abnormality is detected.

[1328] The robot uses natural language processing to communicate with the user and responds to their questions and instructions. For example, if the user asks, "Have you finished stopping the bleeding?", the robot will reply, "Yes, I've finished stopping the bleeding. What's next, disinfection?" In this way, the user can monitor the robot's operations and give appropriate instructions.

[1329] In addition, all treatment results and progress are uploaded to a server in real time and logs are saved, making it possible to use past data for future diagnoses and treatments. The entire system supports safe and efficient medical procedures, significantly reducing the burden on medical practitioners while ensuring patient safety.

[1330] As a concrete example, consider the treatment of a cut on the arm. The user enters the patient's basic information, captures an image, and sends it to the server. The server performs an initial diagnosis and sends the diagnosis that "stopping the bleeding and disinfecting are necessary" to the terminal. After the user confirms, the terminal controls the robot to stop the bleeding and disinfect. During this time, the user and robot communicate with each other as the treatment progresses. All information is saved on the server in real time and can be used for future medical treatments.

[1331] This system enables rapid and accurate response even in emergency medical treatment, improving efficiency and safety in medical settings. In addition, the robot uses natural language processing to smoothly communicate with users, allowing them to perform medical procedures without any problems, even if it is their first time performing them.

[1332] The processing flow will be explained below.

[1333] Step 1:

[1334] The user enters basic patient information into the terminal, such as the patient's name, age, sex, medical history, and current symptoms.

[1335] Step 2:

[1336] The terminal displays the entered patient information to the user in real time and asks for confirmation. After the user confirms the information, the terminal sends the information to the server.

[1337] Step 3:

[1338] The user follows the instructions on the device to capture images of the patient using the built-in camera, adjusting the angle and position as needed.

[1339] Step 4:

[1340] The device receives the captured image, adjusts the image quality and size as necessary, and sends the adjusted image to the server.

[1341] Step 5:

[1342] The server runs facial recognition and body feature analysis algorithms to analyze the received patient images and identify the patient ID against an internal database.

[1343] Step 6:

[1344] The server runs a diagnostic algorithm to make an initial diagnosis based on the patient's basic information and the image analysis results, and identifies the necessary medical treatment.

[1345] Step 7:

[1346] The server generates a diagnosis and identifies an appropriate treatment procedure, and sends the diagnosis and treatment procedure to the terminal.

[1347] Step 8:

[1348] The terminal displays the received diagnosis results and treatment procedures to the user, who then checks the contents and prepares to give permission.

[1349] Step 9:

[1350] The user checks the diagnosis and treatment details and instructs the robot to carry out the treatment via a terminal. With the user's permission, the terminal starts the robot's control program and instructs the specific medical treatment.

[1351] Step 10:

[1352] The robot performs the medical procedure: its arms take gauze to stop bleeding and apply it to the patient's cut, then apply disinfectant and proceed with the necessary treatment.

[1353] Step 11:

[1354] The terminal monitors the treatment process in real time and sends data to the server. If the terminal detects an abnormality, it immediately instructs the robot to halt treatment.

[1355] Step 12:

[1356] The user observes the robot's treatment progress and gives additional instructions via a terminal or voice command. The user can give specific instructions, such as "apply a bandage," and the robot will respond accordingly.

[1357] Step 13:

[1358] After the robot completes the entire medical procedure, the terminal sends a report of the completion of the procedure to the server, which stores all the treatment results and log data for future diagnosis and treatment.

[1359] Through the above steps, the present invention can consistently carry out the entire process from inputting basic patient information, capturing images, making an initial diagnosis, performing medical treatment, and monitoring and logging the treatment.

[1360] Example 1

[1361] 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."

[1362] In current medical settings, patient information acquisition, diagnosis, and treatment are often done manually, resulting in time-consuming processes and a high risk of human error. Furthermore, when emergency medical treatment is required, a fast and accurate response is required, but current systems have difficulty meeting this requirement. Furthermore, real-time monitoring of treatment progress and data recording are insufficient, meaning that information useful for subsequent diagnoses and treatments is not fully utilized.

[1363] 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.

[1364] In this invention, the server includes a means for inputting basic patient information, a means for acquiring patient images, a means for analyzing the acquired patient images and identifying the patient identification number, a means for making an initial diagnosis based on the patient's basic information and the image analysis results, a means for proposing medical treatment based on the diagnosis results, a means for controlling the machine to perform the medical treatment, a means for monitoring the treatment process in real time and saving data, and a means for real-time communication between the user and the machine. This enables fast and accurate medical treatment, improving efficiency and safety in medical settings. Furthermore, because treatment results and progress are monitored and recorded in real time, reliable data management is realized that can be used for subsequent diagnoses and treatments.

[1365] "Basic information" refers to basic data required for medical treatment, such as the patient's name, age, gender, medical history, and current symptoms.

[1366] "Image acquisition" refers to taking a picture of a patient's body part or symptoms using the device's camera function and acquiring the image data.

[1367] "Image analysis" refers to the process of processing acquired image data to extract and identify features. For example, this can be done using an image processing library such as OpenCV.

[1368] "Patient Identification Number" refers to a unique number assigned to an individual patient identified from analyzed images and basic information.

[1369] "Initial diagnosis" refers to the first medical diagnosis made based on the patient's basic information and the results of image analysis.

[1370] "Medical treatment" refers to the specific treatment steps that are proposed and performed based on the initial diagnosis, such as stopping bleeding and disinfection.

[1371] "Machine" refers to a robot or device that is controlled to perform a medical procedure, such as a surgical robot.

[1372] "Real-time monitoring" means monitoring the medical procedure as it progresses, and being ready to respond immediately if any abnormalities occur.

[1373] "Data storage" refers to storing information such as the progress and results of treatment in a storage device within the system for later use.

[1374] "Users" are medical professionals such as doctors and nurses who operate the system and perform medical procedures.

[1375] "Communication" refers to the exchange of information between the user and the machine, and responses to instructions and questions, for example, using natural language processing techniques.

[1376] The present invention provides a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment. This system includes a series of processes for inputting basic patient information, acquiring images, and performing diagnosis and medical treatment.

[1377] The user inputs basic information about the patient into the device. This basic information includes the patient's name, age, sex, medical history, and current symptoms. The device sends the input information to the server. Next, the user uses the device's camera to capture an image of the patient. This is done by activating the built-in camera and taking a picture of the patient's affected area. The captured image is then sent from the device to the server.

[1378] The server uses OpenCV, an open-source image analysis library, to analyze the received image data. It executes image analysis algorithms and extracts features. The results are compared with an internal database to identify the patient's identification number. Furthermore, an initial diagnosis is made based on the analysis results, and appropriate medical treatment is identified. This initial diagnosis is made based on a novel diagnostic protocol, with the server referencing the internal medical database.

[1379] The diagnosis results are sent from the server to the device, which then displays them to the user, who then confirms the proposed treatment and agrees to it.

[1380] The terminal runs a robot control program using a robotics operating system (ROS) to perform medical procedures, such as stopping bleeding and disinfecting. For example, when treating a cut on the right arm, the robot arm applies gauze to stop the bleeding and then applies disinfectant.

[1381] Communication between the user and the robot is carried out using natural language processing. When the user asks, "Have you finished stopping the bleeding?", the robot responds, "Yes, I've finished stopping the bleeding. Next is disinfection?" This communication proceeds in the form of the user asking questions and giving instructions through a voice recognition system, and the robot responding accordingly.

[1382] All treatment processes are recorded in real time on the server. The database management system MySQL is used to store the data. This data will be used later for the next diagnosis and treatment. For example, the treatment details and results performed on patient ID "12345" are recorded, and when the same patient is diagnosed again, this data can be referenced to provide more accurate medical services.

[1383] Example prompt for a generative AI model:

[1384] Please explain the specific program processing flow for each step, from entering basic patient information to image capture, diagnosis, and implementation. Also, please provide a detailed description of the operation when using OpenCV for image analysis and ROS for robot control.

[1385] This system enables rapid and accurate response even in emergency medical treatment, improving efficiency and safety in medical settings. In addition, the robot uses natural language processing to smoothly communicate with users, allowing them to perform medical procedures without any problems, even if it is their first time performing them.

[1386] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1387] Step 1:

[1388] The terminal inputs the patient's basic information.

[1389] Input: Data entered by the user (such as name, age, gender, medical history, current symptoms, etc.).

[1390] Data processing: The terminal converts the input data into JSON format.

[1391] Output: Patient demographics data in JSON format is generated.

[1392] Specific operation: The user opens the dedicated application on the device, enters information into the input form, and taps the "Submit" button.

[1393] Step 2:

[1394] The terminal sends the entered basic patient information to the server.

[1395] Input: Patient demographics data in JSON format.

[1396] Data processing: Send JSON data to the server via an HTTP POST request.

[1397] Output: Basic information is saved on the server.

[1398] Specific operation: The terminal sends patient data to a specific API endpoint on the server.

[1399] Step 3:

[1400] The user captures an image of the patient using the device's camera.

[1401] Input: The patient's area to be imaged.

[1402] Data processing: The device's camera app captures the image and converts it to JPEG format.

[1403] Output: Image data in JPEG format is generated.

[1404] Specific actions: The user opens the camera app, takes a picture of the patient's affected area, and taps the "Capture" button.

[1405] Step 4:

[1406] The image acquired by the terminal is sent to the server.

[1407] Input: Image data in JPEG format.

[1408] Data processing: Image data is sent to the server via an HTTP POST request.

[1409] Output: Image data is saved on the server.

[1410] Specific operation: The device sends image data to a specific API endpoint on the server.

[1411] Step 5:

[1412] The server analyzes the received image data.

[1413] Input: Image data in JPEG format.

[1414] Data processing: The server uses OpenCV to analyze the image and extract features, such as edges, color, and shape.

[1415] Output: Extracted feature data.

[1416] Specific operation: The server executes OpenCV algorithms and performs image analysis.

[1417] Step 6:

[1418] The server identifies the patient identification number based on the analysis results.

[1419] Input: Extracted feature data.

[1420] Data processing: The server checks against an internal database to identify matching patient identification numbers.

[1421] Output: Patient identification number.

[1422] Specific operation: The server compares the feature data with existing data in the database and identifies matching data.

[1423] Step 7:

[1424] The server makes an initial diagnosis based on the patient's basic information and the results of image analysis.

[1425] Input: Patient basic information data and feature data.

[1426] Data processing: The server references an internal medical database and executes diagnostic algorithms.

[1427] Output: Initial diagnostic results.

[1428] Specific operations: The server executes a diagnostic protocol using a medical database.

[1429] Step 8:

[1430] The server suggests medical treatment based on the diagnosis.

[1431] Input: Initial diagnostic results.

[1432] Data processing: The server selects recommended medical treatments based on the diagnosis results.

[1433] Output: Medical treatment suggestions.

[1434] Specific operation: The server selects an appropriate treatment method based on the diagnostic protocol.

[1435] Step 9:

[1436] The terminal displays the diagnosis and suggested medical treatment to the user.

[1437] Input: Medical procedure proposal data.

[1438] Data processing: The device analyzes the data and displays it on the screen in a visually easy-to-understand format.

[1439] Output: Diagnosis results and medical treatment suggestions displayed to the user.

[1440] What it does: The device displays the diagnosis and suggested medical treatment on the screen.

[1441] Step 10:

[1442] The terminal controls the machine to perform the medical procedure.

[1443] Input: Medical procedure proposal data.

[1444] Data processing: The terminal uses ROS to execute the robot control program.

[1445] Output: Performing a medical procedure.

[1446] Specific actions: The robotic arm performs specific medical procedures such as stopping bleeding and disinfecting.

[1447] Step 11:

[1448] The terminal monitors the treatment process in real time and stores the data.

[1449] Input: Process data.

[1450] Data processing: Data from the treatment process is collected and sent to the server in real time.

[1451] Output: Treatment data stored in real time.

[1452] Specific operation: The terminal monitors the progress of the procedure and immediately notifies if there is an abnormality.

[1453] Step 12:

[1454] The user and the machine communicate in real time.

[1455] Input: User's voice command data.

[1456] Data processing: The machine uses natural language processing to analyze the voice instructions and generate an appropriate response.

[1457] Output: Machine-generated voice response.

[1458] Specific operation: The user gives a voice command, and the machine responds by confirming the operation based on the command.

[1459] Through the above processing steps, the present invention can provide a system that supports rapid and accurate medical treatment of patients, improving efficiency and safety.

[1460] (Application example 1)

[1461] 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."

[1462] With conventional technology, factory robot maintenance required skilled technicians, and labor shortages and variations in technician skills were problems. It was also difficult to detect abnormalities in real time, quickly diagnose problems, and provide appropriate maintenance procedures, making maintenance efficiency and accuracy challenging. This led to concerns about increased factory downtime and reduced production efficiency.

[1463] 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.

[1464] In this invention, the server includes: means for inputting basic patient information; means for capturing patient images; means for analyzing the captured patient images and identifying the patient ID; means for performing an initial diagnosis based on the patient's basic information and image analysis results; means for instructing medical treatment based on the diagnosis results; means for controlling the robot to perform the medical treatment; means for monitoring the treatment process in real time and saving data; means for real-time communication between the user and the robot; means for identifying diagnosis results and appropriate maintenance procedures to support industrial equipment maintenance; and means for providing and displaying the industrial equipment maintenance procedures to the user. This enables factory robot maintenance to detect and quickly diagnose abnormalities in real time and provide appropriate maintenance procedures. This enables efficient and accurate maintenance without the need for skilled technicians, reducing factory downtime and improving production efficiency.

[1465] "Basic patient information" refers to basic information about the patient, such as the patient's name, age, sex, medical history, and current symptoms.

[1466] "Image capturing means" refers to a device or means for obtaining an image of a patient or object using a camera, sensor, or the like.

[1467] "Image analysis" refers to the process of using algorithms and software to analyze features and patterns in a captured image and extract information from it.

[1468] "Means for identifying patient ID" refers to a means for determining a patient's unique identification information based on information obtained through image analysis.

[1469] "Initial diagnosis" refers to the process of making an initial diagnosis by integrating basic patient information and image analysis results.

[1470] "Means for prescribing medical treatment based on the diagnosis results" refers to means for determining the necessary medical treatment based on the results of the initial diagnosis and prescribing the details of the treatment.

[1471] "Means for controlling a robot to perform a medical procedure" refers to means for operating and controlling a robot to actually perform a medical procedure.

[1472] "Means for monitoring the procedure in real time and storing data" refers to means for monitoring the procedure performed by the robot in real time and recording and storing the data.

[1473] "Means for real-time communication between a user and a robot" refers to a means for a user and a robot to exchange information in both directions and communicate with each other in real time.

[1474] "Means for identifying diagnostic results and appropriate maintenance procedures to support the maintenance of industrial equipment" refers to means for diagnosing abnormalities and failures in industrial equipment and determining appropriate maintenance procedures based on the results.

[1475] "Means for providing and displaying maintenance procedures for industrial equipment to users" refers to means for presenting diagnostic results and maintenance procedures to users in an easy-to-understand manner and displaying their contents.

[1476] This invention provides a system for assisting in the efficient and accurate maintenance of factory robots. The system includes a series of processes for inputting basic robot information, capturing images, and executing diagnostic and maintenance procedures.

[1477] First, the user inputs basic information about the robot using the UI of the smart glasses. This information includes the robot ID, installation location, and current problems (e.g., "abnormal noise"). The information entered by the user is sent to the server via the smart glasses.

[1478] Next, the user captures an image of the robot using the camera built into the smart glasses. The captured image is adjusted in real time and sent to the server. The server then uses image analysis algorithms (e.g., OpenCV) to analyze the robot's condition and identify any abnormalities or maintenance needs. The server also identifies appropriate maintenance procedures based on the analysis results and sends instructions to the user.

[1479] The transmitted diagnostic results and maintenance procedures are displayed on the smart glasses' display, and the user can then carry out the maintenance work accordingly. For example, if the server issues a diagnostic result indicating that a bearing needs to be replaced, the smart glasses will display the specific procedure and prompt the user to carry out the work.

[1480] The interaction between the user and the system takes place in real time, and when the user asks a question or makes a confirmation, the server responds quickly. For example, if the user asks, "What should I do next?", the server will instruct, "Next, please release the bearing lock." In this way, the user can efficiently carry out maintenance work by interacting with the server using prompts.

[1481] All maintenance progress and results are uploaded to the server in real time and saved as logs, allowing future diagnosis and troubleshooting to be performed based on past maintenance information.

[1482] Specific examples

[1483] For example, the following prompts are used to diagnose and provide maintenance procedures for a robot:

[1484] Please diagnose the cause of the strange noise my robot is making and tell me what maintenance steps I need to take.

[1485] The hardware uses smart glasses with a camera, and the software uses Python, OpenCV, and the requests library for HTTP requests, enabling efficient maintenance of factory robots.

[1486] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1487] Step 1:

[1488] The user uses the UI of the smart glasses to input basic information about the robot, including the robot ID, its location, and any current issues (e.g., "abnormal noise"). The input information is converted into digital data by the smart glasses' internal processor and sent to a server via a network.

[1489] Input: User-entered robot ID, location, and problem information

[1490] Output: Basic information data sent to the server

[1491] Step 2:

[1492] The user captures an image of the robot using the camera built into the smart glasses. The software in the glasses formats the image optimally and saves it as image data. The saved image is then sent to a server over a network.

[1493] Input: An image of the robot captured by a camera

[1494] Output: Image data to be sent

[1495] Step 3:

[1496] The server analyzes the received images. Image analysis algorithms (e.g., OpenCV) are used to identify abnormalities on the robot. Features within the images are extracted and compared with past maintenance data in a database to identify abnormalities.

[1497] Input: Submitted image data

[1498] Output: Identification of abnormalities

[1499] Step 4:

[1500] The server integrates the robot's basic information with the image analysis results to perform an initial diagnosis. A diagnostic algorithm (such as a generative AI model) is used to analyze the cause of the abnormality and determine the appropriate maintenance procedure. The diagnosis results and maintenance procedure are generated.

[1501] Input: Basic information data and image analysis results

[1502] Output: Diagnostic results and maintenance procedures

[1503] Step 5:

[1504] Diagnostic results and maintenance procedures are sent from the server to the smart glasses and displayed on the glasses' display.

[1505] Input: Server-generated diagnostic results and maintenance procedures

[1506] Output: Diagnostic results and maintenance procedures displayed to the user

[1507] Step 6:

[1508] The user follows the instructions on the smart glasses to perform maintenance tasks. If they have questions or need confirmation on the next step, they can contact the server using voice commands or UI operations. The server responds to the user's questions in real time.

[1509] Input: Questions and inquiries from users

[1510] Output: Real-time response from the server

[1511] Step 7:

[1512] The progress and results of maintenance are uploaded in real time from the smart glasses to a server and stored as logs, which can be used for future diagnostics and troubleshooting.

[1513] Input: User maintenance progress and results

[1514] Output: Progress and result logs stored on the server

[1515] This system will enable efficient and accurate maintenance of factory robots.

[1516] 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.

[1517] The present invention is a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment, and by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more advanced medical support. This system includes a series of processes for inputting basic patient information, capturing images, and performing diagnosis and medical treatment.

[1518] The system's operation procedure begins with the user entering basic patient information into the terminal. This basic information includes the patient's name, age, gender, medical history, current symptoms, etc. The terminal then sends the information entered by the user to the server.

[1519] The user then follows the device's instructions to capture an image of the patient using the device's built-in camera. The device then adjusts the captured image and sends it to the server, where the server uses image analysis algorithms to analyze the patient's characteristics and match them with an internal database to identify the patient. The server also uses the results of the analysis to make an initial diagnosis and identify appropriate medical treatment.

[1520] The server sends the diagnosis results to the terminal, which displays them to the user. The user reviews the proposed treatment and, if they give their permission, proceeds to the next step. The terminal then controls the robot to perform the actual medical treatment. The progress of the treatment is monitored in real time, and includes a function to immediately interrupt it if an abnormality is detected.

[1521] The system also incorporates an emotion engine, which recognizes emotions from the user's facial expressions, tone of voice, and content of words, and adjusts the robot's response accordingly. For example, if the user is nervous, the robot can respond in a calm tone, saying, "It's okay, let's stay calm and proceed."

[1522] As a concrete example, consider the case of treating a cut on an arm using an emotion engine. The user enters the patient's basic information, captures an image, and sends it to the server. The server performs an initial diagnosis and sends the diagnosis result, "The bleeding needs to be stopped and disinfected," to the terminal. After the user confirms, the terminal controls the robot to stop the bleeding and disinfect the wound.

[1523] During this process, let's say the user says, "I'm worried about whether the bleeding is being stopped properly." The emotion engine detects anxiety from the user's tone of voice and the content of their words, and the robot responds, "Don't worry. The procedure is proceeding correctly." In this way, appropriate communication is carried out according to the user's emotions.

[1524] In addition, all treatment results and progress are uploaded to a server in real time and logs are saved, making it possible to use past data for future diagnoses and treatments. The entire system supports safe and efficient medical procedures, significantly reducing the burden on medical practitioners while ensuring patient safety.

[1525] Furthermore, the emotion engine can be used to assess the user's stress level and interrupt or change treatment if a high stress state is detected, thereby protecting the health and safety of medical personnel. This system can provide more advanced medical support and further improve efficiency and safety in medical settings.

[1526] The processing flow will be explained below.

[1527] Step 1:

[1528] The user enters basic patient information into the terminal, such as the patient's name, age, sex, medical history, and current symptoms.

[1529] Step 2:

[1530] The terminal displays the entered patient information to the user in real time and asks for confirmation. After the user confirms the information, the terminal sends the information to the server.

[1531] Step 3:

[1532] The user follows the instructions on the device to capture images of the patient using the built-in camera, adjusting the angle and position as needed.

[1533] Step 4:

[1534] The device receives the captured image, adjusts the image quality and size as necessary, and sends the adjusted image to the server.

[1535] Step 5:

[1536] The server runs facial recognition and body feature analysis algorithms to analyze the received patient images and identify the patient ID against an internal database.

[1537] Step 6:

[1538] The server runs a diagnostic algorithm to make an initial diagnosis based on the patient's basic information and the image analysis results, and identifies the necessary medical treatment.

[1539] Step 7:

[1540] The server generates a diagnosis and identifies an appropriate treatment procedure, and sends the diagnosis and treatment procedure to the terminal.

[1541] Step 8:

[1542] The terminal displays the received diagnosis results and treatment procedures to the user, who then checks the contents and prepares to give permission.

[1543] Step 9:

[1544] The user checks the diagnosis and treatment details and instructs the robot to carry out the treatment via a terminal. With the user's permission, the terminal starts the robot's control program and instructs the specific medical treatment.

[1545] Step 10:

[1546] The robot performs the medical procedure: its arms take gauze to stop bleeding and apply it to the patient's cut, then apply disinfectant and proceed with the necessary treatment.

[1547] Step 11:

[1548] The terminal monitors the treatment process in real time and sends data to the server. If the terminal detects an abnormality, it immediately instructs the robot to halt treatment.

[1549] Step 12:

[1550] The user observes the robot's treatment progress and gives additional instructions via a terminal or voice command. The user can give specific instructions, such as "apply a bandage," and the robot will respond accordingly.

[1551] Step 13:

[1552] The emotion engine analyzes the user's facial expressions, tone of voice, and the content of their words to recognize their emotions. For example, if the user says something like "I'm worried," the emotion engine will detect anxiety.

[1553] Step 14:

[1554] Based on the analysis results of the emotion engine, the robot will provide appropriate feedback to the user, for example, "Don't worry, the treatment is going well."

[1555] Step 15:

[1556] If the emotion engine determines that the user's stress level is high, the device will temporarily suspend treatment or issue instructions to change the treatment procedure, thereby protecting the user's mental state.

[1557] Step 16:

[1558] After the robot completes the entire medical procedure, the terminal sends a report of the completion of the procedure to the server, which stores all the treatment results and log data for future diagnosis and treatment.

[1559] Through the above steps, the present invention can seamlessly perform the entire process, from entering basic patient information, capturing images, making an initial diagnosis, performing medical procedures, monitoring and logging the procedures, and even recognizing and providing feedback to the user. This system enables fast and accurate responses even in emergency medical situations, improving efficiency and safety in medical settings. Furthermore, the introduction of an emotion engine reduces the user's psychological burden, allowing for safe and secure medical procedures.

[1560] Example 2

[1561] 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."

[1562] Conventional medical systems face the challenge of quickly and accurately obtaining basic patient information and images, and then performing diagnosis and treatment. The process involves time-consuming tasks such as inputting information and confirming treatment. Furthermore, because treatment is performed without consideration for the user's feelings, medical practitioners often feel stressed, posing a risk to the quality and safety of medical care. There is a need to solve these problems and provide effective and safe medical care to patients while reducing the burden on medical practitioners.

[1563] 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 inputting basic patient information, means for acquiring patient images, means for analyzing the acquired patient images and identifying the patient ID, means for making an initial diagnosis based on the patient's basic information and image analysis results, means for instructing medical treatment based on the diagnosis results, means for controlling the robot to perform the medical treatment, means for monitoring the treatment process in real time and saving data, means for analyzing the user's facial expressions, tone of voice, and content of words to recognize emotions, means for adjusting the robot's responses based on the recognized emotions, and means for communicating between the user and the robot in real time. This enables quick and accurate acquisition of the patient's basic information and images, execution of initial diagnosis and appropriate medical treatment, and flexible response according to the user's emotions, thereby providing more effective and safer medical care.

[1564] "Means for inputting basic patient information" refers to an interface that allows a user to input information about a patient, such as the patient's name, age, sex, medical history, and current symptoms.

[1565] "Means for obtaining images of a patient" refers to devices, including cameras and scanning devices, for capturing images of a patient's body or condition.

[1566] "Means for analyzing acquired patient images and identifying patient IDs" refers to algorithms or programs that recognize and analyze specific features of a patient and verify them against a database to determine the patient ID.

[1567] "Means for making an initial diagnosis based on the patient's basic information and image analysis results" refers to a diagnostic algorithm or program for making an initial diagnosis based on the input basic information and analyzed image information.

[1568] "Means for instructing medical treatment based on diagnostic results" refers to mechanisms or software for determining specific medical treatment based on the results of an initial diagnosis and instructing this to the next process.

[1569] "Means for controlling the robot to perform a medical procedure" refers to a control system for operating the robot based on programmed instructions to perform a specified medical procedure.

[1570] "Means for monitoring the treatment process in real time and storing data" refers to sensors and data storage devices for monitoring the progress of medical treatment in real time and recording and storing that data.

[1571] "Means for analyzing a user's facial expressions, tone of voice, and spoken content to recognize emotions" refers to a program or device that reads and analyzes emotions from a user's facial expressions, tone of voice, and spoken content.

[1572] "Means for adjusting the robot's response based on the recognized emotion" refers to a control program or system for changing the robot's response in response to the analyzed emotion of the user.

[1573] "Means for real-time communication between users and robots" refers to interfaces and network systems that allow users and robots to instantly exchange and share information and instructions.

[1574] The present invention is a system for accurately and quickly acquiring patient information and supporting appropriate diagnosis and treatment, and by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more advanced medical support. This system includes a series of processes for inputting basic patient information, capturing images, and performing diagnosis and medical treatment.

[1575] The system's operation procedure begins with the user entering basic patient information into the terminal. This basic information includes the patient's name, age, gender, medical history, current symptoms, etc. The terminal then sends the information entered by the user to the server.

[1576] The user then follows the device's instructions to capture an image of the patient using the device's built-in camera. The device then adjusts the captured image and sends it to the server, where the server uses image analysis algorithms to analyze the patient's characteristics and match them with an internal database to identify the patient. The server also uses the results of the analysis to make an initial diagnosis and identify appropriate medical treatment.

[1577] The server sends the diagnosis results to the terminal, which displays them to the user. The user reviews the proposed treatment and, if they give their permission, proceeds to the next step. The terminal then controls the robot to perform the actual medical treatment. The progress of the treatment is monitored in real time, and includes a function to immediately interrupt it if an abnormality is detected.

[1578] The system also incorporates an emotion engine, which recognizes emotions from the user's facial expressions, tone of voice, and content of words, and adjusts the robot's response accordingly. For example, if the user is nervous, the robot can respond in a calm tone, saying, "It's okay, let's stay calm and proceed."

[1579] As a concrete example, consider the case of treating a cut on an arm using an emotion engine. The user enters the patient's basic information, captures an image, and sends it to the server. The server performs an initial diagnosis and sends the diagnosis result, "The bleeding needs to be stopped and disinfected," to the terminal. After the user confirms, the terminal controls the robot to stop the bleeding and disinfect the wound.

[1580] During this process, let's say the user says, "I'm worried about whether the bleeding is being stopped properly." The emotion engine detects anxiety from the user's tone of voice and the content of their words, and the robot responds, "Don't worry. The procedure is proceeding correctly." In this way, appropriate communication is carried out according to the user's emotions.

[1581] In addition, all treatment results and progress are uploaded to a server in real time and logs are saved, making it possible to use past data for future diagnoses and treatments. The entire system supports safe and efficient medical procedures, significantly reducing the burden on medical practitioners while ensuring patient safety.

[1582] Furthermore, the emotion engine can be used to assess the user's stress level and interrupt or change treatment if a high stress state is detected, thereby protecting the health and safety of medical personnel. This system can provide more advanced medical support and further improve efficiency and safety in medical settings.

[1583] An example of a prompt might be, "The basic information about this patient is as follows: Name: XX, Age: XX years, Gender: XX, Medical history: XX, Current symptoms: XX. Please suggest an initial diagnosis and appropriate medical treatment."

[1584] In this way, the system performs a series of steps from inputting patient information to diagnosis, performing treatment, and analyzing the user's emotions, providing safe and efficient medical support.

[1585] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1586] Step 1:

[1587] The user enters the patient's basic information (such as name, age, sex, medical history, and current symptoms) into the terminal.

[1588] Input: Patient demographic information from the user

[1589] Output: Patient demographics sent to the server

[1590] Specific operation: The user enters information into the input form of the dedicated application and presses the "Submit" button. The device then sends this information in JSON format to the server.

[1591] Step 2:

[1592] The user captures an image of the patient using the device's built-in camera.

[1593] Input: User operations based on device instructions

[1594] Output: Patient images sent to the server

[1595] Specific operation: The user points the device camera at the patient and presses the capture button. The device optimizes the image resolution, removes noise, and sends it to the server in JPEG image format.

[1596] Step 3:

[1597] The server processes the received images with image analysis algorithms to analyze the patient's characteristics.

[1598] Input: Patient images sent to the server

[1599] Output: Patient characteristics and patient ID

[1600] How it works: The server uses image analysis algorithms such as ResNet and VGG16 to extract features of the patient's face and wound from the image, then compares them with an internal database to identify the patient ID.

[1601] Step 4:

[1602] The server performs an initial diagnosis based on the image analysis results and the basic information entered, and sends the results to the terminal.

[1603] Input: Patient basic information and image analysis results

[1604] Output: Initial diagnostic results

[1605] Specific operation: The server performs an initial diagnosis using a machine learning model based on basic information and image analysis results. It generates a diagnosis such as "Bleeding control and disinfection are required" and sends this to the device.

[1606] Step 5:

[1607] The user checks the diagnosis results and treatment details displayed on the terminal and gives permission.

[1608] Input: Display of diagnostic results and user confirmation operations

[1609] Output: Permission input

[1610] Specific operation: The user checks the diagnostic results displayed on the device screen and taps the "Allow" button. This action sends permission information from the device to the server.

[1611] Step 6:

[1612] The terminal controls the robot with the user's permission and performs the necessary medical procedures.

[1613] Input: Diagnostic results and user permission

[1614] Output: Medical procedure performed

[1615] Specific operation: Based on the certified diagnosis results and authorization information, the terminal sends instructions to the robot to stop bleeding and disinfect. The robot then begins treatment according to these instructions and reports its progress to the terminal in real time.

[1616] Step 7:

[1617] The device uses an emotion engine to analyze the user's facial expressions, tone of voice, and the content of their words to recognize their emotions.

[1618] Input: User's facial expressions, voice, and words

[1619] Output: Recognized emotion

[1620] Specific operation: The device captures the user's voice and facial expressions using a camera and microphone, and uses an emotion analysis algorithm to recognize emotions such as "anxiety" and "tension."

[1621] Step 8:

[1622] The device adjusts the robot's response based on the recognized emotion.

[1623] Input: Perceived user emotion

[1624] Output: Robot response

[1625] Specific operation: If the terminal detects "anxiety," it will have the robot respond with "Don't worry. The procedure is proceeding correctly." This voice message will be played from the robot.

[1626] Step 9:

[1627] The server stores all treatment results and progress as logs, which can be used for future diagnosis and treatment.

[1628] Input: Medical procedure progress and results

[1629] Output: Saved log data

[1630] Specific operation: The server stores the progress and results of the treatment in a database in real time, making them available for future diagnosis and treatment.

[1631] (Application example 2)

[1632] 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."

[1633] Conventional medical support systems are capable of providing diagnosis and treatment based on basic patient information and image data, but they do not take the user's emotions into consideration, making it difficult to alleviate the user's anxiety and stress. It is also difficult to properly manage the impact of emotions on work efficiency and safety when workers perform precise work. Therefore, there is a need to provide appropriate communication and work support based on the user's emotions.

[1634] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting basic patient information, means for capturing an image of the patient, means for analyzing the captured patient image and identifying the patient ID, means for making an initial diagnosis based on the patient's basic information and the image analysis results, means for instructing medical treatment based on the diagnosis results, means for controlling the robot to perform the medical treatment, means for monitoring the treatment process in real time and saving data, means for real-time communication between the user and the robot, means for recognizing the user's emotions and adjusting the robot's responses based on the emotions, and means for generating and displaying work instructions and support content based on the worker's emotions. This makes it possible to provide appropriate medical support and work support that takes the user's emotions into consideration, thereby reducing the user's stress and improving work efficiency.

[1635] "Means for inputting basic patient information" refers to a device or software for inputting the patient's name, age, sex, medical history, current symptoms, etc.

[1636] A "means for capturing an image of a patient" is a camera or other imaging device for taking an image of a patient in a medical setting.

[1637] "Means for analyzing captured patient images and identifying patient IDs" refers to algorithms or software that perform image analysis using captured images and identify specific patients based on that information.

[1638] "Means for making an initial diagnosis based on basic patient information and image analysis results" refers to a system or algorithm for making an initial diagnosis using input basic information and the results of analyzed patient images.

[1639] The "means for instructing medical treatment based on the diagnostic results" refers to a device or program for proposing or instructing appropriate medical treatment based on the diagnostic results.

[1640] The "means for controlling the robot to perform a medical procedure" refers to a control system for operating the medical robot and causing it to perform a prescribed medical procedure.

[1641] The "means for monitoring the treatment process in real time and storing data" refers to a monitoring system and storage device for observing the progress of a medical treatment in real time and recording the data.

[1642] "Means for real-time communication between the user and the robot" refers to an interface that allows the user and the robot to exchange information in real time via voice or text.

[1643] "Means for recognizing the user's emotions and adjusting the robot's responses based on those emotions" refers to a system that analyzes the user's voice and facial expressions and appropriately changes the robot's behavior and responses based on the results.

[1644] "Means for generating and displaying work instructions and support content based on the worker's emotions" refers to devices or software that recognize the worker's emotional state and create and display appropriate work instructions and support content based on that.

[1645] This system allows users to input basic patient information, capture images, and perform diagnosis and medical treatment. Furthermore, by incorporating an emotion engine, it can recognize the user's emotions and respond appropriately. It can also be used to support factory work.

[1646] System Configuration

[1647] The system includes the following main means:

[1648] 1. Enter patient information by:

[1649] The user enters basic information such as the patient's name, age, sex, medical history, current symptoms, etc. This information is collected by the terminal and sent to the server.

[1650] 2. Means of capturing patient images:

[1651] Using the device's built-in camera, the user captures an image of the patient, which is also sent to the server.

[1652] 3. A means of analyzing captured patient images and determining patient ID:

[1653] The server analyzes the captured images using image analysis algorithms and identifies the patient ID by matching it with an internal database.

[1654] 4. Methods for making an initial diagnosis based on patient demographics and imaging analysis:

[1655] The server performs an initial diagnosis based on the patient's basic information and image analysis results, and uses an AI model to suggest appropriate diagnoses and treatments.

[1656] 5. Means of prescribing medical treatment based on diagnostic findings:

[1657] The server then recommends appropriate medical treatment based on the diagnosis and sends the information to the terminal, where the user follows the instructions.

[1658] 6. Means of controlling robots to perform medical procedures:

[1659] Based on the diagnosis, the robot is controlled to automatically perform medical procedures, and the robot's movements are monitored in real time on a terminal.

[1660] 7. Means for monitoring the treatment process in real time and storing data:

[1661] The device monitors the progress of medical procedures in real time and stores the data on a server, allowing past data to be used for future diagnoses and treatments.

[1662] 8. Means for real-time communication between user and robot:

[1663] The user and the robot can communicate in real time via the terminal, for example, by issuing instructions to the robot using voice commands or text messages.

[1664] 9. Means of recognizing user emotions and adjusting the robot's responses based on the emotions:

[1665] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize their emotions, and adjusts the robot's response based on the recognized emotions.

[1666] 10. Means for generating and displaying work instructions and support content based on worker emotions:

[1667] The server uses an emotion engine to evaluate the worker's emotions, generate appropriate work instructions and support content, and display them on the terminal.

[1668] Specific examples

[1669] 1. Medical application examples:

[1670] The user enters basic patient information, captures images, and sends them to the server, which performs an initial diagnosis and suggests appropriate medical treatment, which the robot then automatically performs.

[1671] Example prompt sentence:

[1672] Please enter the patient's basic information.

[1673] "Capture an image of the patient."

[1674] 2. Factory work support:

[1675] A worker wearing smart glasses inputs basic information, captures the work environment, and sends it to a server, which analyzes emotions and generates appropriate work instructions, which are displayed on the glasses' display.

[1676] Example prompt sentence:

[1677] Please enter the worker's basic information.

[1678] "Capture an image of your work environment."

[1679] Hardware and software used

[1680] Hardware:

[1681] Smart glasses or smartphones (with built-in camera, microphone, and display)

[1682] medical robots

[1683] Server (data storage and analysis)

[1684] software:

[1685] Python (data processing, image capture, server communication)

[1686] OpenCV (image capture and processing)

[1687] emotion_recognition library (emotion recognition)

[1688] server_communication library (data communication with the server)

[1689] The above configuration provides appropriate medical and work support that takes into account the user's emotions, making it possible to reduce the user's stress and improve work efficiency.

[1690] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1691] Step 1:

[1692] The user uses the terminal to input basic information about the patient (such as name, age, sex, medical history, and current symptoms).

[1693] Input: Patient's basic information (name, age, sex, medical history, current symptoms)

[1694] Action: Enter the required information into the device's input form.

[1695] Output: The basic information entered is saved on the device and then sent to the server.

[1696] Step 2:

[1697] The user captures an image of the patient using the device's built-in camera.

[1698] Input: Start capture command

[1699] Action: Activates the device's camera and takes an image of the patient.

[1700] Output: The captured image data is saved on the device and then sent to the server.

[1701] Step 3:

[1702] The server analyzes the captured image and matches it with an internal database to identify the patient ID.

[1703] Input: Photographed image data

[1704] How it works: Uses image analysis algorithms to recognize faces and compare them with an internal database.

[1705] Output: Identified patient ID

[1706] Step 4:

[1707] The server makes an initial diagnosis based on the patient's basic information and the results of image analysis.

[1708] Input: Patient basic information, specific patient ID

[1709] How it works: It uses AI models to analyze a patient's symptoms and make an initial diagnosis.

[1710] Output: Initial diagnosis result

[1711] Step 5:

[1712] The server prescribes appropriate medical treatment based on the diagnosis results and transmits the information to the terminal.

[1713] Input: Initial diagnosis result

[1714] Operation: Generates treatment contents based on the diagnosis results and sends them to the terminal.

[1715] Output: Medical treatment instructions

[1716] Step 6:

[1717] The terminal controls the robot based on the diagnosis results and performs medical treatment.

[1718] Input: Medical treatment instructions

[1719] Operation: Send instructions from the terminal to the robot to perform medical procedures.

[1720] Output: Progress information of medical procedures performed

[1721] Step 7:

[1722] The terminal monitors the treatment process in real time and stores the data on a server.

[1723] Input: Robot operation status, data from sensors

[1724] How it works: The monitoring system monitors the progress of the procedure in real time and notifies you of any abnormalities.

[1725] Output: Monitoring data is stored on the server.

[1726] Step 8:

[1727] The server recognizes the user's emotions and generates an appropriate response.

[1728] Input: User's voice and facial expression data

[1729] How it works: The emotion engine analyzes the user's emotions and generates a response based on the results.

[1730] Output: The generated response

[1731] Step 9:

[1732] The terminal generates and displays work instructions and support content based on the worker's emotions.

[1733] Input: Emotion engine analysis results, work environment data

[1734] Operation: Based on the results of emotion analysis, appropriate work instructions and assistance content are generated and displayed on a display such as smart glasses.

[1735] Output: Work instructions and support details

[1736] 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.

[1737] 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.

[1738] 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.

[1739] 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.

[1740] 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.

[1741] 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.

[1742] 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).

[1743] 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.

[1744] 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."

[1745] 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.

[1746] 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).

[1747] 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.

[1748] 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.

[1749] 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.

[1750] 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.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] 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.

[1755] 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.

[1756] 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.

[1757] The following is further disclosed regarding the above embodiment.

[1758] (Claim 1)

[1759] a means for inputting basic patient information;

[1760] a means for capturing an image of the patient;

[1761] a means for analyzing the captured patient image and determining a patient ID;

[1762] A means for making an initial diagnosis based on basic patient information and image analysis results;

[1763] a means for prescribing medical treatment based on the diagnosis;

[1764] a means for controlling the robot to perform a medical procedure;

[1765] a means for monitoring the treatment process in real time and storing the data;

[1766] A means for users and robots to communicate in real time,

[1767] A system including:

[1768] (Claim 2)

[1769] 2. The system according to claim 1, further comprising means for monitoring the operation status of the robot in real time and suspending treatment if an abnormality is detected.

[1770] (Claim 3)

[1771] 2. The system according to claim 1, further comprising means for storing past treatment results in a database and using the results for subsequent diagnosis or treatment.

[1772] "Example 1"

[1773] (Claim 1)

[1774] a means for inputting basic patient information;

[1775] a means for acquiring an image of the patient;

[1776] means for analyzing the acquired patient images and identifying a patient identification number;

[1777] A means for making an initial diagnosis based on basic patient information and image analysis results;

[1778] a means for suggesting medical treatment based on the diagnosis;

[1779] a means for controlling the machine to perform the medical procedure;

[1780] a means for monitoring the treatment process in real time and storing the data;

[1781] A means for users and machines to communicate in real time,

[1782] A system including:

[1783] (Claim 2)

[1784] 10. The system according to claim 1, further comprising means for monitoring the operating status of the machine in real time and suspending treatment if an abnormality is detected.

[1785] (Claim 3)

[1786] 2. The system according to claim 1, further comprising means for storing past treatment results in a storage device and using the results for subsequent diagnosis or treatment.

[1787] "Application Example 1"

[1788] (Claim 1)

[1789] a means for inputting basic patient information;

[1790] a means for capturing an image of the patient;

[1791] a means for analyzing the captured patient image and determining a patient ID;

[1792] A means for making an initial diagnosis based on basic patient information and image analysis results;

[1793] a means for prescribing medical treatment based on the diagnosis;

[1794] a means for controlling the robot to perform a medical procedure;

[1795] a means for monitoring the treatment process in real time and storing the data;

[1796] A means for users and robots to communicate in real time,

[1797] a means for identifying diagnostic findings and appropriate maintenance procedures to assist in the maintenance of industrial equipment;

[1798] means for providing and displaying maintenance procedures for the industrial equipment to a user;

[1799] A system including:

[1800] (Claim 2)

[1801] 2. The system according to claim 1, further comprising means for monitoring the operation status of the robot in real time and suspending treatment if an abnormality is detected.

[1802] (Claim 3)

[1803] 2. The system according to claim 1, further comprising means for storing past treatment results in a database and using the results for subsequent diagnosis or treatment.

[1804] "Example 2: Combining Emotion Engines"

[1805] (Claim 1)

[1806] a means for inputting basic patient information;

[1807] a means for acquiring an image of the patient;

[1808] a means for analyzing the acquired patient image and identifying a patient ID;

[1809] A means for making an initial diagnosis based on basic patient information and image analysis results;

[1810] a means for prescribing medical treatment based on the diagnosis;

[1811] a means for controlling the robot to perform a medical procedure;

[1812] a means for monitoring the treatment process in real time and storing the data;

[1813] A means of recognizing emotions by analyzing the user's facial expressions, tone of voice, and content of words;

[1814] means for adjusting the robot's response based on the recognized emotion;

[1815] A means for users and robots to communicate in real time,

[1816] A system including:

[1817] (Claim 2)

[1818] 2. The system according to claim 1, further comprising means for monitoring the operation status of the robot in real time and suspending treatment if an abnormality is detected.

[1819] (Claim 3)

[1820] 2. The system according to claim 1, further comprising means for storing past treatment results in a database and using the results for subsequent diagnoses or treatments.

[1821] "Application example 2 when combining emotion engines"

[1822] (Claim 1)

[1823] a means for inputting basic patient information;

[1824] a means for capturing an image of the patient;

[1825] a means for analyzing the captured patient image and determining a patient ID;

[1826] A means for making an initial diagnosis based on basic patient information and image analysis results;

[1827] a means for prescribing medical treatment based on the diagnosis;

[1828] a means for controlling the robot to perform a medical procedure;

[1829] a means for monitoring the treatment process in real time and storing the data;

[1830] A means for users and robots to communicate in real time,

[1831] means for recognizing ...

Claims

1. a means for inputting basic patient information; a means for capturing an image of the patient; a means for analyzing the captured patient image and determining a patient ID; A means for making an initial diagnosis based on basic patient information and image analysis results; a means for prescribing medical treatment based on the diagnosis; a means for controlling the robot to perform a medical procedure; a means for monitoring the treatment process in real time and storing the data; A means for users and robots to communicate in real time, A system including:

2. The system according to claim 1 , further comprising means for monitoring the operation status of the robot in real time and suspending treatment when an abnormality is detected.

3. 2. The system according to claim 1, further comprising means for storing past treatment results in a database and using the results for subsequent diagnosis or treatment.

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