System

Abstract

An object of a system according to an embodiment is to express a state of a patient in a visually easy-to-understand manner based on medical data and medical photographs of the patient.SOLUTION: A system includes a medical data input unit, a medical photograph input unit, a comment input unit, and a generation unit. The medical data input unit inputs medical data of a patient. The medical photograph input unit inputs a medical photograph. The comment input unit inputs a doctor's judgment comment or instruction. The generation unit generates a photograph, a 3D model, and a moving image representing the condition of the patient based on the data input by the medical data input unit, the medical photograph input unit, and the comment input unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to visually express a patient's condition in an easy-to-understand manner based on the patient's medical data and medical photographs.

[0005] The system according to the embodiment aims to visually represent the condition of a patient in an easily understandable manner based on the patient's medical data and medical photographs. [Means for solving the problem]

[0006] The system according to the embodiment includes a medical data input unit, a medical photo input unit, a comment input unit, and a generation unit. The medical data input unit inputs the patient's medical data. The medical photo input unit inputs medical photos. The comment input unit inputs the doctor's comments and instructions. The generation unit generates photos, 3D models, and videos that represent the patient's condition based on the data input by the medical data input unit, medical photo input unit, and comment input unit. [Effects of the Invention]

[0007] The system according to the embodiment can visually and easily represent the condition of a patient based on the patient's medical data and medical photographs. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to an embodiment of the present invention is a system that converts a patient's medical data and medical photographs into easily understandable photographs, 3D models, and videos that express the patient's condition by inputting a doctor's judgment, comments, and instructions based on the patient's medical data and medical photographs. This makes it possible for the system to make technical medical terms and complex images easier to understand, enabling patients to accurately understand their own health condition.

[0029] The system according to the embodiment includes a medical data input unit, a medical photo input unit, a comment input unit, and a generation unit. The medical data input unit inputs a patient's medical data. For example, it inputs data such as health checkup results and blood test results. The medical data input unit can also acquire data from an electronic medical record system. For example, it can work in conjunction with the electronic medical record system to automatically acquire a patient's medical history and test results. The medical photo input unit inputs medical photos. For example, it inputs X-rays, CT scans, and MRI images. The medical photo input unit can also acquire image data directly from medical equipment. For example, it can acquire image data directly from a CT scan device or an MRI device. The comment input unit inputs a doctor's comments and instructions. For example, it can input specific comments and instructions such as "inflammation is observed in the shoulder joint" or "high LDL cholesterol level." The comment input unit can also input comments and instructions using various methods, such as voice input or handwriting input. The generation unit generates photos, 3D models, and videos representing the patient's condition based on the data input by the medical data input unit, medical photo input unit, and comment input unit. For example, the system generates a 3D model and video showing the condition of the shoulder joint of a patient with a shoulder injury. The generation unit can also generate a video showing the state of the blood vessels of a patient with a high LDL cholesterol level. The generation unit can also generate a 3D model and video that allows the progress of rehabilitation to be visually confirmed. This makes it easier to visually understand the patient's condition.

[0030] The generation unit can generate 3D models and videos showing the state of the shoulder joint of a patient with a shoulder injury. The generation unit, for example, generates a 3D model showing the state of the shoulder joint of a patient with a shoulder injury. For example, the generation AI creates a 3D model based on the anatomical structure of the shoulder joint. The generation unit can also generate videos showing the state of the shoulder joint. For example, the generation AI simulates the movement of the shoulder joint and creates a video showing that movement. The generation unit can also generate still images showing the state of the shoulder joint. For example, the generation AI creates still images that highlight specific parts of the shoulder joint. This makes it easier to visually understand the state of a shoulder injury.

[0031] The generation unit can generate a video showing the state of blood vessels in a patient with high LDL cholesterol levels. The generation unit generates a video showing the state of blood vessels in a patient with high LDL cholesterol levels, for example. For example, the generation AI creates a video based on the internal structure of blood vessels. The generation unit can also generate a 3D model showing the state of blood vessels. For example, the generation AI creates a 3D model based on a cross-section of a blood vessel. The generation unit can also generate a still image showing the state of blood vessels. For example, the generation AI creates a still image that highlights specific parts of the blood vessels. This makes it easier to visually understand the state of blood vessels in a patient with high LDL cholesterol levels.

[0032] The generation unit can generate 3D models and videos that allow the patient's rehabilitation progress to be visually confirmed. The generation unit generates, for example, a 3D model that allows the patient's rehabilitation progress to be visually confirmed. For example, the generation AI compares the patient's condition before and after rehabilitation and creates a 3D model that shows the progress. The generation unit can also generate videos that show the rehabilitation progress. For example, the generation AI simulates the rehabilitation process and creates a video that shows the process. The generation unit can also generate still images that show the rehabilitation progress. For example, the generation AI creates still images that highlight specific rehabilitation areas. This makes it possible to visually confirm the rehabilitation progress.

[0033] The generation unit can generate photos, 3D models, and videos to motivate patients to work on improving their diet and exercise. The generation unit, for example, generates photos to motivate patients to work on improving their diet and exercise. For example, the generation AI creates photos showing what a healthy diet and exercise look like. The generation unit can also generate 3D models to motivate patients to work on improving their diet and exercise. For example, the generation AI creates a 3D model showing an ideal body shape. The generation unit can also generate videos to motivate patients to work on improving their diet and exercise. For example, the generation AI creates a video showing the effects of exercise. This can motivate patients to work on improving their diet and exercise.

[0034] When medical data is input into the medical data input unit, the generation AI automatically evaluates the quality of the data and filters out low-quality data. For example, when medical data is input into the medical data input unit, the generation AI automatically evaluates the resolution and noise level of the data and excludes low-quality data. For example, it automatically detects and filters out low-resolution images and noisy data. The medical data input unit also checks the consistency of the input medical data and automatically filters out abnormal values ​​and inconsistent data. For example, it detects and excludes abnormal values ​​and inconsistent data in blood test results. The medical data input unit also evaluates the clarity and contrast of medical photographs and automatically filters out low-quality images. For example, it excludes X-ray images with low clarity and MRI images with insufficient contrast. This makes it possible to automatically filter out low-quality data.

[0035] When medical data is input, the medical data input unit allows the generation AI to compare it with past data and automatically detect outliers and abnormal images. For example, the medical data input unit compares the input medical data with past data and automatically detects outliers. For example, it compares it with past blood test results to detect abnormal values ​​and notify the doctor. The medical data input unit also compares medical photographs with past images and automatically detects abnormal changes and abnormal images. For example, it compares it with past CT images to detect new lesions. The medical data input unit also integrates past data with current data and automatically detects abnormal patterns and trends. For example, it compares past diagnostic data with current data to detect abnormal trends. This makes it possible to automatically detect outliers and abnormal images.

[0036] The medical data input unit can enable the input of medical data and medical photos through a variety of methods, such as voice input or gesture input. The medical data input unit, for example, builds a system that enables the input of medical data and medical photos through voice input. For example, a doctor gives instructions through voice, and a generation AI analyzes the instructions and inputs data. The medical data input unit also develops a system that enables the input of medical data and medical photos using gesture input. For example, a doctor gives instructions through hand movements, and a generation AI analyzes the movements and inputs data. The medical data input unit also builds a system that integrates a variety of input methods to enable the input of medical data and medical photos more intuitively. For example, data can be input by combining voice input and gesture input. This makes it possible to input medical data and medical photos through a variety of methods.

[0037] The medical data input unit can integrate data from different medical institutions so that the generation AI can analyze it centrally. The medical data input unit, for example, builds a system that integrates data from different medical institutions so that the generation AI can analyze it centrally. For example, diagnostic data from multiple hospitals is integrated and analyzed by the generation AI. The medical data input unit also develops a system that collects data from different medical institutions in real time so that the generation AI can analyze it centrally. For example, data from each medical institution is automatically collected and analyzed by the generation AI. The medical data input unit also builds a system that standardizes data from different medical institutions so that the generation AI can analyze it centrally. For example, data in different formats is unified and analyzed by the generation AI. This makes it possible to analyze data from different medical institutions centrally.

[0038] The comment input unit will develop a system in which, when a doctor's judgment comments or instructions are input, the generation AI will automatically convert technical terms into general terms, making them easier for patients to understand. For example, the comment input unit will build a system in which, when a doctor's judgment comments or instructions are input, the generation AI will automatically convert technical terms into general terms. For example, it will convert "high blood pressure" into "a state of high blood pressure." The comment input unit will also develop an algorithm in which the generation AI will analyze the doctor's comments and instructions and convert technical terms into general terms. For example, it will convert "myocardial infarction" into "a disease in which the blood vessels of the heart are clogged." The comment input unit will also develop a system in which, when a doctor's judgment comments or instructions are input, the generation AI will automatically convert technical terms into general terms, making them easier for patients to understand. For example, it will convert "diabetes" into "a disease in which blood sugar levels are high." This will convert technical terms into general terms, making them easier for patients to understand.

[0039] The comment input unit is capable of building a system in which, when a doctor's judgment comments or instructions are input, the generation AI compares them with past diagnostic data and automatically detects inconsistencies or errors. For example, the comment input unit compares past diagnostic data with current comments to detect inconsistencies. The comment input unit also develops an algorithm in which the generation AI analyzes the doctor's comments and instructions and compares them with past diagnostic data to automatically detect errors. For example, it compares past diagnostic data with current instructions to detect errors. The comment input unit also develops a system in which, when a doctor's judgment comments or instructions are input, the generation AI compares them with past diagnostic data and automatically detects inconsistencies or errors. For example, it compares past diagnostic data with current comments to detect inconsistencies. This makes it possible to automatically detect inconsistencies and errors.

[0040] The comment input unit can automatically translate the doctor's judgment comments and instructions into different languages, making it possible to accommodate international patients. The comment input unit, for example, builds a system that automatically translates the doctor's judgment comments and instructions into different languages, making it possible to accommodate international patients. For example, translating into English, French, Chinese, etc. The comment input unit also develops an algorithm that uses a generative AI to analyze the doctor's comments and instructions and automatically translate them into different languages. For example, accurately translating medical terminology. The comment input unit also develops a system that automatically translates the doctor's judgment comments and instructions into different languages, making it possible to accommodate international patients. For example, providing the translated comments and instructions to the patient. This makes it possible to automatically translate into different languages, making it possible to accommodate international patients.

[0041] The comment input unit can enable doctors to enter their judgments and instructions using a variety of methods, such as voice input or handwriting input. The comment input unit, for example, builds a system that enables doctors to enter their judgments and instructions using voice input. For example, the doctor gives instructions via voice, and the generation AI analyzes the instructions and inputs the data. The comment input unit can also develop a system that enables doctors to enter their judgments and instructions using handwriting input. For example, the doctor enters comments by hand, and the generation AI analyzes the content. The comment input unit can also integrate a variety of input methods to build a system that enables doctors to enter their judgments and instructions more intuitively. For example, data can be entered by combining voice input and handwriting input. This makes it possible to enter doctors' judgments and instructions using a variety of methods.

[0042] The generation unit can add a function to show progress by comparing with past data when generating photos, 3D models, and videos that represent a patient's condition. For example, the generation unit builds a system that adds a function to show progress by comparing with past data when the generation AI generates photos, 3D models, and videos that represent a patient's condition. For example, it compares past CT images with current CT images and shows progress. The generation unit also develops an algorithm that shows progress by comparing with past data when the generation AI generates photos, 3D models, and videos that represent a patient's condition. For example, it compares past MRI images with current MRI images and shows progress. The generation unit also develops a system that adds a function to show progress by comparing with past data when the generation AI generates photos, 3D models, and videos that represent a patient's condition. For example, it compares past X-ray images with current X-ray images and shows progress. This makes it possible to add a function to show progress by comparing with past data.

[0043] The generation unit can reflect a patient's lifestyle and environmental factors in the photos, 3D models, and videos it generates, thereby enabling more realistic representations. For example, the generation unit builds a system that reflects a patient's lifestyle and environmental factors in the photos, 3D models, and videos it generates using the generation AI. For example, it generates a 3D model that reflects the patient's diet and exercise habits. The generation unit also develops an algorithm that enables the generation AI to analyze a patient's lifestyle and environmental factors and generate photos, 3D models, and videos that reflect the analysis. For example, it generates a 3D model of the lungs that reflects the patient's smoking habits. The generation unit also develops a system that reflects a patient's lifestyle and environmental factors in the photos, 3D models, and videos it generates using the generation AI, thereby enabling more realistic representations. For example, it generates a video of the heart that reflects the patient's stress level. This allows for more realistic representations by reflecting the patient's lifestyle and environmental factors.

[0044] The generation unit can make the generated photos, 3D models, and videos displayable on different devices. The generation unit, for example, builds a system that enables the generated photos, 3D models, and videos to be displayed on a smartphone. For example, a smartphone app is developed to display the generated data. The generation unit also develops a system that enables the generated photos, 3D models, and videos to be displayed on a tablet. For example, a tablet app is developed to display the generated data. The generation unit also builds a system that enables the generated photos, 3D models, and videos to be displayed on a VR headset. For example, a VR app is developed to display the generated data. This makes it possible to display the generated data on different devices.

[0045] The generation unit can share the generated photos, 3D models, and videos with different medical professionals to create a comprehensive treatment plan. The generation unit, for example, shares the generated photos, 3D models, and videos with a physical therapist to build a system for creating a comprehensive treatment plan. For example, a physical therapist creates a rehabilitation plan based on the generated data. The generation unit also develops a system for sharing the generated photos, 3D models, and videos with a nutritionist to create a comprehensive treatment plan. For example, a nutritionist creates a meal plan based on the generated data. The generation unit also builds a system for sharing the generated photos, 3D models, and videos with different medical professionals to create a comprehensive treatment plan. For example, a doctor, physical therapist, and nutritionist can jointly create a treatment plan. This allows the data to be shared with different medical professionals to create a comprehensive treatment plan.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The medical data input unit can also input lifestyle data of the patient. For example, it inputs data such as the patient's diet record, exercise record, and sleep pattern. The medical data input unit can also acquire data from wearable devices. For example, it can automatically acquire heart rate, step count, and sleep data from a smartwatch or fitness tracker. Furthermore, the medical data input unit can also input data of the patient's living environment. For example, it inputs data such as the patient's living environment and workplace stress level. This makes it possible to input medical data that takes into account the patient's lifestyle and environmental factors.

[0048] The generation unit can provide views from different perspectives when generating photographs, 3D models, and videos that show the patient's condition. For example, it can generate a 3D model that shows the internal structure of the patient's body from different angles. The generation unit can also add a zoom-in or zoom-out function to videos that show the patient's condition. Furthermore, the generation unit can add a function to enlarge specific areas in still images that show the patient's condition. This allows the patient's condition to be visually confirmed in more detail.

[0049] The generator can add interactive elements when generating photos, 3D models, and videos that represent the patient's condition. For example, it can provide an interface that allows the patient to rotate the 3D model or zoom in and out. The generator can also add a function that allows the patient to click on a specific part of a video to display more detailed information. Furthermore, the generator can add a function that allows the patient to tap on a specific part of a still image to display an explanation about that part. This makes it possible to provide interactive content that allows patients to review information at their own pace.

[0050] The medical data input unit can add a function to strengthen data privacy protection when entering patient medical data. For example, a data encryption function can be added to securely protect the entered data. The medical data input unit can also add a function to manage data access permissions. For example, the medical data input unit can allow only specific medical professionals to access the data. Furthermore, the medical data input unit can also add an audit log function to prevent data tampering. This allows the privacy of patient medical data to be protected and managed safely.

[0051] The medical data input unit can be added with a function to prevent data input errors when entering patient medical data. For example, it can check entered data in real time and automatically detect abnormal values ​​or inconsistent data. The medical data input unit can also be added with a function to display guidelines or checklists when entering data. For example, it can display necessary information for each input item to prevent input errors. Furthermore, the medical data input unit can be added with a function to display a confirmation screen after data entry to reconfirm the entered content. This prevents data entry errors and enables accurate medical data to be entered.

[0052] The medical data input unit can add a function to improve the efficiency of data input when inputting patient medical data. For example, data can be input quickly using voice input or gesture input. The medical data input unit can also add a function to simplify data input using templates or presets. For example, frequently used data items can be saved as templates and reused. Furthermore, the medical data input unit can add a predictive input function to automatically complete input content when inputting data. This can improve the efficiency of data input.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The medical data input unit inputs the patient's medical data. For example, it inputs data such as health checkup results and blood test results. It can also acquire data from an electronic medical record system. For example, it can link with the electronic medical record system to automatically acquire the patient's medical history and test results. Step 2: The medical image input unit inputs medical images. For example, X-rays, CT scans, MRI images, etc. Image data can also be acquired directly from medical equipment. For example, image data can be acquired directly from a CT scan device or an MRI device. Step 3: The comment input section inputs the doctor's judgment and instructions. For example, specific comments and instructions such as "inflammation is observed in the shoulder joint" or "high LDL cholesterol level" can be input. Comments and instructions can also be input in a variety of ways, such as voice input or handwriting input. Step 4: The generation unit generates photos, 3D models, and videos that represent the patient's condition based on the data input by the medical data input unit, medical photo input unit, and comment input unit. For example, it can generate a 3D model and video that shows the condition of the shoulder joint of a patient with a shoulder injury. It can also generate a video that shows the state of the blood vessels of a patient with high LDL cholesterol levels. It can also generate 3D models and videos that allow the progress of rehabilitation to be visually confirmed.

[0055] (Example 2) The system according to an embodiment of the present invention is a system that converts a patient's medical data and medical photographs into easily understandable photographs, 3D models, and videos that express the patient's condition by inputting a doctor's judgment, comments, and instructions based on the patient's medical data and medical photographs. This makes it possible for the system to make technical medical terms and complex images easier to understand, enabling patients to accurately understand their own health condition.

[0056] The system according to the embodiment includes a medical data input unit, a medical photo input unit, a comment input unit, and a generation unit. The medical data input unit inputs a patient's medical data. For example, it inputs data such as health checkup results and blood test results. The medical data input unit can also acquire data from an electronic medical record system. For example, it can work in conjunction with the electronic medical record system to automatically acquire a patient's medical history and test results. The medical photo input unit inputs medical photos. For example, it inputs X-rays, CT scans, and MRI images. The medical photo input unit can also acquire image data directly from medical equipment. For example, it can acquire image data directly from a CT scan device or an MRI device. The comment input unit inputs a doctor's comments and instructions. For example, it can input specific comments and instructions such as "inflammation is observed in the shoulder joint" or "high LDL cholesterol level." The comment input unit can also input comments and instructions using various methods, such as voice input or handwriting input. The generation unit generates photos, 3D models, and videos representing the patient's condition based on the data input by the medical data input unit, medical photo input unit, and comment input unit. For example, the system generates a 3D model and video showing the condition of the shoulder joint of a patient with a shoulder injury. The generation unit can also generate a video showing the state of the blood vessels of a patient with a high LDL cholesterol level. The generation unit can also generate a 3D model and video that allows the progress of rehabilitation to be visually confirmed. This makes it easier to visually understand the patient's condition.

[0057] The generation unit can generate 3D models and videos showing the state of the shoulder joint of a patient with a shoulder injury. The generation unit, for example, generates a 3D model showing the state of the shoulder joint of a patient with a shoulder injury. For example, the generation AI creates a 3D model based on the anatomical structure of the shoulder joint. The generation unit can also generate videos showing the state of the shoulder joint. For example, the generation AI simulates the movement of the shoulder joint and creates a video showing that movement. The generation unit can also generate still images showing the state of the shoulder joint. For example, the generation AI creates still images that highlight specific parts of the shoulder joint. This makes it easier to visually understand the state of a shoulder injury.

[0058] The generation unit can generate a video showing the state of blood vessels in a patient with high LDL cholesterol levels. The generation unit generates a video showing the state of blood vessels in a patient with high LDL cholesterol levels, for example. For example, the generation AI creates a video based on the internal structure of blood vessels. The generation unit can also generate a 3D model showing the state of blood vessels. For example, the generation AI creates a 3D model based on a cross-section of a blood vessel. The generation unit can also generate a still image showing the state of blood vessels. For example, the generation AI creates a still image that highlights specific parts of the blood vessels. This makes it easier to visually understand the state of blood vessels in a patient with high LDL cholesterol levels.

[0059] The generation unit can generate 3D models and videos that allow the patient's rehabilitation progress to be visually confirmed. The generation unit generates, for example, a 3D model that allows the patient's rehabilitation progress to be visually confirmed. For example, the generation AI compares the patient's condition before and after rehabilitation and creates a 3D model that shows the progress. The generation unit can also generate videos that show the rehabilitation progress. For example, the generation AI simulates the rehabilitation process and creates a video that shows the process. The generation unit can also generate still images that show the rehabilitation progress. For example, the generation AI creates still images that highlight specific rehabilitation areas. This makes it possible to visually confirm the rehabilitation progress.

[0060] The generation unit can generate photos, 3D models, and videos to motivate patients to work on improving their diet and exercise. The generation unit, for example, generates photos to motivate patients to work on improving their diet and exercise. For example, the generation AI creates photos showing what a healthy diet and exercise look like. The generation unit can also generate 3D models to motivate patients to work on improving their diet and exercise. For example, the generation AI creates a 3D model showing an ideal body shape. The generation unit can also generate videos to motivate patients to work on improving their diet and exercise. For example, the generation AI creates a video showing the effects of exercise. This can motivate patients to work on improving their diet and exercise.

[0061] When medical data is input into the medical data input unit, the generation AI automatically evaluates the quality of the data and filters out low-quality data. For example, when medical data is input into the medical data input unit, the generation AI automatically evaluates the resolution and noise level of the data and excludes low-quality data. For example, it automatically detects and filters out low-resolution images and noisy data. The medical data input unit also checks the consistency of the input medical data and automatically filters out abnormal values ​​and inconsistent data. For example, it detects and excludes abnormal values ​​and inconsistent data in blood test results. The medical data input unit also evaluates the clarity and contrast of medical photographs and automatically filters out low-quality images. For example, it excludes X-ray images with low clarity and MRI images with insufficient contrast. This makes it possible to automatically filter out low-quality data.

[0062] When medical data is input, the medical data input unit allows the generation AI to compare it with past data and automatically detect outliers and abnormal images. For example, the medical data input unit compares the input medical data with past data and automatically detects outliers. For example, it compares it with past blood test results to detect abnormal values ​​and notify the doctor. The medical data input unit also compares medical photographs with past images and automatically detects abnormal changes and abnormal images. For example, it compares it with past CT images to detect new lesions. The medical data input unit also integrates past data with current data and automatically detects abnormal patterns and trends. For example, it compares past diagnostic data with current data to detect abnormal trends. This makes it possible to automatically detect outliers and abnormal images.

[0063] The medical data input unit can use the emotion estimation function to provide an interface for reducing the anxiety and stress felt by patients when entering data. For example, the medical data input unit can use the emotion estimation function to analyze the anxiety and stress felt by patients when entering data in real time and provide a relaxing interface. For example, it can display calming music or visuals with a relaxing effect. The medical data input unit can also analyze the patient's emotional state and provide guidance and support messages to reduce anxiety and stress. For example, it can display encouraging messages or simple explanations. The medical data input unit can also use the emotion estimation function to provide an interactive interface for reducing the anxiety and stress felt by patients when entering data. For example, it can provide games or activities with a relaxing effect. In this way, it is possible to provide an interface that reduces the anxiety and stress felt by patients.

[0064] The medical data input unit can enable the input of medical data and medical photos through a variety of methods, such as voice input or gesture input. The medical data input unit, for example, builds a system that enables the input of medical data and medical photos through voice input. For example, a doctor gives instructions through voice, and a generation AI analyzes the instructions and inputs data. The medical data input unit also develops a system that enables the input of medical data and medical photos using gesture input. For example, a doctor gives instructions through hand movements, and a generation AI analyzes the movements and inputs data. The medical data input unit also builds a system that integrates a variety of input methods to enable the input of medical data and medical photos more intuitively. For example, data can be input by combining voice input and gesture input. This makes it possible to input medical data and medical photos through a variety of methods.

[0065] The medical data input unit can integrate data from different medical institutions so that the generation AI can analyze it centrally. The medical data input unit, for example, builds a system that integrates data from different medical institutions so that the generation AI can analyze it centrally. For example, diagnostic data from multiple hospitals is integrated and analyzed by the generation AI. The medical data input unit also develops a system that collects data from different medical institutions in real time so that the generation AI can analyze it centrally. For example, data from each medical institution is automatically collected and analyzed by the generation AI. The medical data input unit also builds a system that standardizes data from different medical institutions so that the generation AI can analyze it centrally. For example, data in different formats is unified and analyzed by the generation AI. This makes it possible to analyze data from different medical institutions centrally.

[0066] The medical data input unit can use the emotion estimation function to monitor the emotions felt by patients when entering data in real time and provide positive feedback. The medical data input unit, for example, uses the emotion estimation function to build a system that monitors the emotions felt by patients when entering data in real time and provides positive feedback. For example, an encouraging message is displayed if the patient feels anxious. The medical data input unit also develops an interface that analyzes the patient's emotional state and provides positive feedback. For example, a visual with a relaxing effect is displayed if the patient feels stressed. The medical data input unit also uses the emotion estimation function to develop a system that monitors the emotions felt by patients when entering data in real time and provides positive feedback. For example, a compliment is displayed if the patient feels happy. This makes it possible to monitor the patient's emotions in real time and provide positive feedback.

[0067] The comment input unit will develop a system in which, when a doctor's judgment comments or instructions are input, the generation AI will automatically convert technical terms into general terms, making them easier for patients to understand. For example, the comment input unit will build a system in which, when a doctor's judgment comments or instructions are input, the generation AI will automatically convert technical terms into general terms. For example, it will convert "high blood pressure" into "a state of high blood pressure." The comment input unit will also develop an algorithm in which the generation AI will analyze the doctor's comments and instructions and convert technical terms into general terms. For example, it will convert "myocardial infarction" into "a disease in which the blood vessels of the heart are clogged." The comment input unit will also develop a system in which, when a doctor's judgment comments or instructions are input, the generation AI will automatically convert technical terms into general terms, making them easier for patients to understand. For example, it will convert "diabetes" into "a disease in which blood sugar levels are high." This will convert technical terms into general terms, making them easier for patients to understand.

[0068] The comment input unit is capable of building a system in which, when a doctor's judgment comments or instructions are input, the generation AI compares them with past diagnostic data and automatically detects inconsistencies or errors. For example, the comment input unit compares past diagnostic data with current comments to detect inconsistencies. The comment input unit also develops an algorithm in which the generation AI analyzes the doctor's comments and instructions and compares them with past diagnostic data to automatically detect errors. For example, it compares past diagnostic data with current instructions to detect errors. The comment input unit also develops a system in which, when a doctor's judgment comments or instructions are input, the generation AI compares them with past diagnostic data and automatically detects inconsistencies or errors. For example, it compares past diagnostic data with current comments to detect inconsistencies. This makes it possible to automatically detect inconsistencies and errors.

[0069] The comment input unit can use the emotion estimation function to evaluate the emotional impact that a doctor's comments or instructions have on a patient and correct the expressions to an appropriate one. The comment input unit, for example, uses the emotion estimation function to build a system that evaluates the emotional impact that a doctor's comments or instructions have on a patient and corrects the expressions to an appropriate one. For example, negative expressions are corrected to positive expressions. The comment input unit also analyzes the doctor's comments and instructions and uses the emotion estimation function to develop an algorithm that evaluates the emotional impact on a patient. For example, expressions that make the patient feel anxious are corrected. The comment input unit also uses the emotion estimation function to develop a system that evaluates the emotional impact that a doctor's comments or instructions have on a patient and corrects the expressions to an appropriate one. For example, expressions that make the patient feel reassured are corrected. In this way, the emotional impact on a patient can be evaluated and corrected to an appropriate one.

[0070] The comment input unit can automatically translate the doctor's judgment comments and instructions into different languages, making it possible to accommodate international patients. The comment input unit, for example, builds a system that automatically translates the doctor's judgment comments and instructions into different languages, making it possible to accommodate international patients. For example, translating into English, French, Chinese, etc. The comment input unit also develops an algorithm that uses a generative AI to analyze the doctor's comments and instructions and automatically translate them into different languages. For example, accurately translating medical terminology. The comment input unit also develops a system that automatically translates the doctor's judgment comments and instructions into different languages, making it possible to accommodate international patients. For example, providing the translated comments and instructions to the patient. This makes it possible to automatically translate into different languages, making it possible to accommodate international patients.

[0071] The comment input unit can enable doctors to enter their judgments and instructions using a variety of methods, such as voice input or handwriting input. The comment input unit, for example, builds a system that enables doctors to enter their judgments and instructions using voice input. For example, the doctor gives instructions via voice, and the generation AI analyzes the instructions and inputs the data. The comment input unit can also develop a system that enables doctors to enter their judgments and instructions using handwriting input. For example, the doctor enters comments by hand, and the generation AI analyzes the content. The comment input unit can also integrate a variety of input methods to build a system that enables doctors to enter their judgments and instructions more intuitively. For example, data can be entered by combining voice input and handwriting input. This makes it possible to enter doctors' judgments and instructions using a variety of methods.

[0072] The comment input unit uses the emotion estimation function to monitor the emotional impact of the doctor's comments and instructions on the patient in real time and provide positive feedback. The comment input unit, for example, uses the emotion estimation function to build a system that monitors the emotional impact of the doctor's comments and instructions on the patient in real time and provides positive feedback. For example, an encouraging message is displayed if the patient feels anxious. The comment input unit also analyzes the doctor's comments and instructions and uses the emotion estimation function to develop an algorithm that monitors the emotional impact on the patient in real time. For example, a visual with a relaxing effect is displayed if the patient feels stressed. The comment input unit also uses the emotion estimation function to develop a system that monitors the emotional impact of the doctor's comments and instructions on the patient in real time and provides positive feedback. For example, a compliment is displayed if the patient feels happy. This makes it possible to monitor the emotional impact on the patient in real time and provide positive feedback.

[0073] The generation unit can add a function to show progress by comparing with past data when generating photos, 3D models, and videos that represent a patient's condition. For example, the generation unit builds a system that adds a function to show progress by comparing with past data when the generation AI generates photos, 3D models, and videos that represent a patient's condition. For example, it compares past CT images with current CT images and shows progress. The generation unit also develops an algorithm that shows progress by comparing with past data when the generation AI generates photos, 3D models, and videos that represent a patient's condition. For example, it compares past MRI images with current MRI images and shows progress. The generation unit also develops a system that adds a function to show progress by comparing with past data when the generation AI generates photos, 3D models, and videos that represent a patient's condition. For example, it compares past X-ray images with current X-ray images and shows progress. This makes it possible to add a function to show progress by comparing with past data.

[0074] The generation unit can reflect a patient's lifestyle and environmental factors in the photos, 3D models, and videos it generates, thereby enabling more realistic representations. For example, the generation unit builds a system that reflects a patient's lifestyle and environmental factors in the photos, 3D models, and videos it generates using the generation AI. For example, it generates a 3D model that reflects the patient's diet and exercise habits. The generation unit also develops an algorithm that enables the generation AI to analyze a patient's lifestyle and environmental factors and generate photos, 3D models, and videos that reflect the analysis. For example, it generates a 3D model of the lungs that reflects the patient's smoking habits. The generation unit also develops a system that reflects a patient's lifestyle and environmental factors in the photos, 3D models, and videos it generates using the generation AI, thereby enabling more realistic representations. For example, it generates a video of the heart that reflects the patient's stress level. This allows for more realistic representations by reflecting the patient's lifestyle and environmental factors.

[0075] The generation unit can use the emotion estimation function to evaluate the emotional impact that the generated photos, 3D models, and videos have on the patient and correct them to positive expressions. For example, the generation unit uses the emotion estimation function to build a system that evaluates the emotional impact that the generated photos, 3D models, and videos have on the patient and corrects them to positive expressions. For example, negative expressions are corrected to positive expressions. The generation unit also analyzes the generated photos, 3D models, and videos and develops an algorithm that uses the emotion estimation function to evaluate the emotional impact on the patient. For example, expressions that make the patient feel anxious are corrected. The generation unit also uses the emotion estimation function to develop a system that evaluates the emotional impact that the generated photos, 3D models, and videos have on the patient and corrects them to positive expressions. For example, expressions that make the patient feel reassured are corrected. In this way, the emotional impact on the patient can be evaluated and corrected to positive expressions.

[0076] The generation unit can make the generated photos, 3D models, and videos displayable on different devices. The generation unit, for example, builds a system that enables the generated photos, 3D models, and videos to be displayed on a smartphone. For example, a smartphone app is developed to display the generated data. The generation unit also develops a system that enables the generated photos, 3D models, and videos to be displayed on a tablet. For example, a tablet app is developed to display the generated data. The generation unit also builds a system that enables the generated photos, 3D models, and videos to be displayed on a VR headset. For example, a VR app is developed to display the generated data. This makes it possible to display the generated data on different devices.

[0077] The generation unit can share the generated photos, 3D models, and videos with different medical professionals to create a comprehensive treatment plan. The generation unit, for example, shares the generated photos, 3D models, and videos with a physical therapist to build a system for creating a comprehensive treatment plan. For example, a physical therapist creates a rehabilitation plan based on the generated data. The generation unit also develops a system for sharing the generated photos, 3D models, and videos with a nutritionist to create a comprehensive treatment plan. For example, a nutritionist creates a meal plan based on the generated data. The generation unit also builds a system for sharing the generated photos, 3D models, and videos with different medical professionals to create a comprehensive treatment plan. For example, a doctor, physical therapist, and nutritionist can jointly create a treatment plan. This allows the data to be shared with different medical professionals to create a comprehensive treatment plan.

[0078] The generation unit uses the emotion estimation function to monitor the emotional impact of the generated photos, 3D models, and videos on patients in real time and provide positive feedback. For example, the generation unit uses the emotion estimation function to build a system that monitors the emotional impact of the generated photos, 3D models, and videos on patients in real time and provides positive feedback. For example, if the patient feels anxious, an encouraging message is displayed. The generation unit also analyzes the generated photos, 3D models, and videos and develops an algorithm that uses the emotion estimation function to monitor the emotional impact on patients in real time. For example, if the patient feels stressed, a visual with a relaxing effect is displayed. The generation unit also uses the emotion estimation function to develop a system that monitors the emotional impact of the generated photos, 3D models, and videos on patients in real time and provides positive feedback. For example, if the patient feels happy, a compliment is displayed. This makes it possible to monitor the emotional impact on patients in real time and provide positive feedback.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The medical data input unit can also input lifestyle data of the patient. For example, it inputs data such as the patient's diet record, exercise record, and sleep pattern. The medical data input unit can also acquire data from wearable devices. For example, it can automatically acquire heart rate, step count, and sleep data from a smartwatch or fitness tracker. Furthermore, the medical data input unit can also input data of the patient's living environment. For example, it inputs data such as the patient's living environment and workplace stress level. This makes it possible to input medical data that takes into account the patient's lifestyle and environmental factors.

[0081] The generation unit can reflect the patient's emotional state when generating photos, 3D models, and videos that represent the patient's condition. For example, if the patient is relaxed, a video incorporating calming colors and music can be generated. Also, if the patient is feeling anxious, a 3D model including visuals and messages that provide a sense of security can be generated. Furthermore, the generation unit can generate photos including messages of encouragement and comfort according to the patient's emotional state. This makes it possible to provide visual content that reflects the patient's emotional state.

[0082] The generation unit can provide views from different perspectives when generating photographs, 3D models, and videos that show the patient's condition. For example, it can generate a 3D model that shows the internal structure of the patient's body from different angles. The generation unit can also add a zoom-in or zoom-out function to videos that show the patient's condition. Furthermore, the generation unit can add a function to enlarge specific areas in still images that show the patient's condition. This allows the patient's condition to be visually confirmed in more detail.

[0083] The generator can add interactive elements when generating photos, 3D models, and videos that represent the patient's condition. For example, it can provide an interface that allows the patient to rotate the 3D model or zoom in and out. The generator can also add a function that allows the patient to click on a specific part of a video to display more detailed information. Furthermore, the generator can add a function that allows the patient to tap on a specific part of a still image to display an explanation about that part. This makes it possible to provide interactive content that allows patients to review information at their own pace.

[0084] When generating photos, 3D models, and videos depicting the patient's condition, the generator can add a function for sharing them with the patient's family and friends. For example, the generator can generate a link that allows the generated content to be easily shared. The generator can also provide an interface that allows the patient's family and friends to leave comments and feedback. Furthermore, when the patient's family and friends view the generated content, the generator can use an emotion estimation function to analyze their emotional state and provide appropriate feedback. This allows the patient's condition to be shared with family and friends and receive their support.

[0085] The medical data input unit can add a function to strengthen data privacy protection when entering patient medical data. For example, a data encryption function can be added to securely protect the entered data. The medical data input unit can also add a function to manage data access permissions. For example, the medical data input unit can allow only specific medical professionals to access the data. Furthermore, the medical data input unit can also add an audit log function to prevent data tampering. This allows the privacy of patient medical data to be protected and managed safely.

[0086] The medical data input unit can be added with a function to prevent data input errors when entering patient medical data. For example, it can check entered data in real time and automatically detect abnormal values ​​or inconsistent data. The medical data input unit can also be added with a function to display guidelines or checklists when entering data. For example, it can display necessary information for each input item to prevent input errors. Furthermore, the medical data input unit can be added with a function to display a confirmation screen after data entry to reconfirm the entered content. This prevents data entry errors and enables accurate medical data to be entered.

[0087] The medical data input unit can use the emotion estimation function to analyze the emotions felt by the patient when entering data and personalize the input process. For example, if the patient is feeling stressed, the input process can be simplified and a relaxing interface can be provided. Also, if the patient is feeling anxious, reassuring messages and visuals can be displayed. Furthermore, the medical data input unit can add a function to progress the input process step by step depending on the patient's emotional state. This makes it possible to provide a personalized input process that corresponds to the patient's emotional state.

[0088] The medical data input unit can add a function to improve the efficiency of data input when inputting patient medical data. For example, data can be input quickly using voice input or gesture input. The medical data input unit can also add a function to simplify data input using templates or presets. For example, frequently used data items can be saved as templates and reused. Furthermore, the medical data input unit can add a predictive input function to automatically complete input content when inputting data. This can improve the efficiency of data input.

[0089] The medical data input unit can use its emotion estimation function to analyze the emotions felt by the patient when entering data and guide them through the input process. For example, if the patient is feeling anxious, it can display a step-by-step guide to guide them through the input process. Also, if the patient is feeling stressed, it can display music or visuals that have a relaxing effect. Furthermore, the medical data input unit can add a function to customize the input process according to the patient's emotional state. This allows it to provide guidance that is appropriate for the patient's emotional state and smoothly progress through the input process.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The medical data input unit inputs the patient's medical data. For example, it inputs data such as health checkup results and blood test results. It can also acquire data from an electronic medical record system. For example, it can link with the electronic medical record system to automatically acquire the patient's medical history and test results. Step 2: The medical image input unit inputs medical images. For example, X-rays, CT scans, MRI images, etc. Image data can also be acquired directly from medical equipment. For example, image data can be acquired directly from a CT scan device or an MRI device. Step 3: The comment input section inputs the doctor's judgment and instructions. For example, specific comments and instructions such as "inflammation is observed in the shoulder joint" or "high LDL cholesterol level" can be input. Comments and instructions can also be input in a variety of ways, such as voice input or handwriting input. Step 4: The generation unit generates photos, 3D models, and videos that represent the patient's condition based on the data input by the medical data input unit, medical photo input unit, and comment input unit. For example, it can generate a 3D model and video that shows the condition of the shoulder joint of a patient with a shoulder injury. It can also generate a video that shows the state of the blood vessels of a patient with high LDL cholesterol levels. It can also generate 3D models and videos that allow the progress of rehabilitation to be visually confirmed.

[0092] 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.

[0093] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0098] 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.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0113] 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.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0128] 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] 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.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0133] 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.

[0134] 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.

[0135] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] 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.

[0142] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0143] 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.

[0144] 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).

[0145] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] 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."

[0147] 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.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0153] The hardware resource that executes the specific process 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 process may be a single processor.

[0154] 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.

[0155] 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.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] 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.

[0158] 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. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a medical data input unit for inputting medical data of a patient; a medical photo input unit for inputting medical photos; a comment input section for inputting doctor's judgment comments and instructions; a generation unit that generates a photograph, a 3D model, and a video that represent the condition of a patient based on the data input by the medical data input unit, the medical photo input unit, and the comment input unit. A system characterized by:

2. The generation unit Generate the 3D model and video showing the state of the shoulder joint of a patient with a shoulder injury.

2. The system of claim 1.

3. The generation unit generating the video showing the state of blood vessels of the patient with high LDL cholesterol levels; 2. The system of claim 1.

4. The generation unit Generate the 3D model and the video to visually monitor the patient's rehabilitation progress.

2. The system of claim 1.

5. The generation unit Generate the photograph, the 3D model, and the video to motivate the patient to improve their diet and exercise.

2. The system of claim 1.

6. The medical data input unit When the medical data is input, the generation AI automatically evaluates the quality of the data and filters out low-quality data.

2. The system of claim 1.

7. The medical data input unit When the medical data is input, the generating AI compares it with past data and automatically detects abnormal values ​​and abnormal images.

2. The system of claim 1.

8. The medical data input unit To provide an interface that reduces the anxiety and stress felt by the patient when entering data.

2. The system of claim 1.

Images