System

Abstract

An object of a system according to an embodiment is to enable even a person without advanced medical skills to perform appropriate medical treatment in an emergency.SOLUTION: A system according to an embodiment includes a natural language processor, an image recognizer, and an arm controller. The natural language processor understands the instruction. The image recognition unit recognizes an action on the basis of the instruction understood by the natural language processing unit. The arm control unit navigates the operation recognized by the image recognition 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 the problem that it is difficult for people without advanced medical skills to provide appropriate medical treatment in an emergency.

[0005] The system according to the embodiment aims to enable even those without advanced medical skills to perform appropriate medical treatment in an emergency. [Means for solving the problem]

[0006] A system according to an embodiment includes a natural language processing unit, an image recognition unit, and an arm control unit. The natural language processing unit understands instructions. The image recognition unit recognizes actions based on the instructions understood by the natural language processing unit. The arm control unit navigates the actions recognized by the image recognition unit. [Effects of the Invention]

[0007] The system according to the embodiment allows even those without advanced medical skills to provide appropriate medical treatment in an emergency. [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 medical support system according to an embodiment of the present invention is a system that directly communicates with medical practitioners and patients, and assists in medical procedures by providing instructions and navigation using arms and images. This makes it easier for medical practitioners and patients to perform initial medical procedures, and reduces the unfortunate consequences of sudden illnesses and accidents.

[0029] The medical assistance system according to the embodiment includes a natural language processing unit, an image recognition unit, and an arm control unit. The natural language processing unit understands instructions from medical practitioners and patients. For example, the natural language processing unit uses morphological analysis to decompose sentences, perform grammatical analysis, and understand the content of the instructions through semantic analysis. The natural language processing unit can also use deep learning algorithms to understand the intentions of medical practitioners and patients with high accuracy. Furthermore, the natural language processing unit can use voice recognition technology to convert voice instructions into text and understand the instructions. The image recognition unit recognizes actions based on the instructions understood by the natural language processing unit. For example, the image recognition unit uses computer vision technology to analyze video data from a camera and recognize the actions of medical practitioners and patients. The image recognition unit can also use deep learning algorithms to extract movement features and recognize the actions. Furthermore, the image recognition unit can use object detection technology to recognize instruments and parts required for medical procedures. The arm control unit navigates the actions recognized by the image recognition unit. For example, the arm control unit accurately controls the position of the arm using PID control. The arm control unit can also use feedforward control to smoothly move the arm. Furthermore, the arm control unit can use a motion planning algorithm to generate and navigate an optimal motion pattern for the arm. This allows the medical assistance system according to the embodiment to provide instructions and navigation using the arm and images while directly communicating with medical practitioners and patients, thereby assisting in initial medical treatment. For example, medical treatments required in emergencies, such as cardiopulmonary resuscitation, hemostasis, and fixation of fractures, can be performed quickly and accurately. This is expected to reduce the unhappiness caused by sudden illnesses and accidents.

[0030] The natural language processing unit can provide individually customized advice by referring to the medical practitioner's and patient's past medical history. For example, the natural language processing unit may refer to the medical practitioner's and patient's electronic medical records and provide customized advice based on their past medical history and treatment history. For example, it may provide specific instructions on precautions to take when performing cardiopulmonary resuscitation for a patient who has previously suffered from heart disease. The natural language processing unit may also analyze the medical practitioner's and patient's past medical records and provide advice tailored to their individual health conditions. For example, it may suggest appropriate first aid based on past blood pressure data. The natural language processing unit may also refer to the medical practitioner's and patient's past communication history to provide advice tailored to individual needs. For example, it may generate more specific responses based on questions asked in past conversations. This provides individually customized advice, thereby supporting more appropriate medical practice.

[0031] The natural language processing unit can use multilingual generation AI to enable communication that can be accommodated for patients who speak different languages. The natural language processing unit, for example, uses multilingual generation AI to enable communication that can be accommodated for patients who speak different languages. For example, it generates responses in multiple languages, such as English, Spanish, and Chinese. The natural language processing unit also uses generation AI that incorporates an automatic translation function to enable real-time communication with patients who speak different languages. For example, it instantly translates patients' questions and generates appropriate responses. The natural language processing unit also uses multilingual generation AI to enable communication that can be accommodated for patients with different cultural backgrounds. For example, it generates responses that take cultural differences into consideration. This enables communication that can be accommodated for patients who speak different languages.

[0032] The natural language processing unit can analyze the tone and speed of the voices of medical practitioners and patients to determine the level of urgency and prioritize responses. The natural language processing unit, for example, analyzes the tone and speed of the voices of medical practitioners and patients to determine the level of urgency. For example, if the voice is trembling or the patient is speaking quickly, it is determined that the level of urgency is high. The natural language processing unit also builds a system that determines the priority of responses based on the tone and speed of the voice. For example, if the level of urgency is determined to be high, a response is generated immediately. The natural language processing unit also analyzes the tone and speed of the voice to generate a response according to the level of urgency. For example, if the level of urgency is high, a quick response is provided, and if the level of urgency is low, a detailed explanation is provided. This allows for a quick response by generating a response according to the level of urgency.

[0033] The natural language processing unit can analyze the gestures and facial expressions of medical practitioners and patients and incorporate non-verbal communication. The natural language processing unit, for example, uses a camera to analyze the gestures and facial expressions of medical practitioners and patients and incorporates non-verbal communication. For example, it estimates emotions based on hand movements and facial expressions and generates appropriate responses. The natural language processing unit also develops algorithms to analyze gestures and facial expressions and realizes non-verbal communication. For example, it detects the movement of raising a hand and determines that a question is asked. The natural language processing unit also builds a database of gestures and facial expressions to incorporate non-verbal communication. For example, it analyzes facial expressions such as smiling and nodding and generates responses according to the emotions. In this way, incorporating non-verbal communication enables more natural communication.

[0034] The image recognition unit can detect subtle differences in the movements of the medical practitioner and the patient and provide more precise instructions. The image recognition unit, for example, uses a camera to analyze the movements of the medical practitioner and the patient in real time and detects subtle differences. For example, it can provide instructions to fine-tune the position or angle of the hands. The image recognition unit also uses image recognition technology to analyze the movements of the medical practitioner and the patient and provide precise instructions. For example, it can provide instructions to adjust the position or pressure of the hands during cardiopulmonary resuscitation. The image recognition unit also develops an algorithm to detect subtle differences in movements and provides precise instructions. For example, it can provide specific instructions on how to wrap a bandage or the angle of an injection. This allows the unit to detect subtle differences in movements and provide precise instructions, thereby improving the accuracy of medical procedures.

[0035] The image recognition unit uses generative AI to reproduce the movements of the medical practitioner and patient in 3D models, thereby providing visually easy-to-understand instructions. The image recognition unit, for example, uses generative AI to reproduce the movements of the medical practitioner and patient in 3D models. For example, cardiopulmonary resuscitation procedures are displayed in 3D models, providing visually easy-to-understand instructions. The image recognition unit also uses 3D models to reproduce the movements of the medical practitioner and patient, providing visually easy-to-understand instructions. For example, 3D models are used to show how to wrap a bandage or how to administer an injection. The image recognition unit also uses generative AI to convert the movements of the medical practitioner and patient into 3D models in real time, providing visually easy-to-understand instructions. For example, fine-tuning of movements is shown in 3D models. This helps understand medical procedures by providing visually easy-to-understand instructions using 3D models.

[0036] The image recognition unit can analyze the movements of medical practitioners and patients in real time and provide appropriate feedback immediately. The image recognition unit, for example, uses a camera to analyze the movements of medical practitioners and patients in real time and provide immediate feedback. For example, it monitors cardiopulmonary resuscitation procedures in real time and provides appropriate feedback. The image recognition unit also uses a motion analysis algorithm to analyze the movements of medical practitioners and patients in real time and provide feedback. For example, it provides instructions on how to wrap a bandage or the procedure for administering an injection in real time. The image recognition unit also builds a system that analyzes movements in real time and provides appropriate feedback. For example, it provides instructions on fine-tuning movements in real time to support accurate medical procedures. As a result, real-time feedback is provided, improving the accuracy of medical procedures.

[0037] The image recognition unit can evaluate the movements of the medical practitioner and the patient and specifically point out areas for improvement. The image recognition unit, for example, uses image recognition technology to evaluate the movements of the medical practitioner and the patient and specifically point out areas for improvement. For example, it evaluates cardiopulmonary resuscitation procedures and instructs them to adjust hand position and pressure. The image recognition unit also develops a movement evaluation algorithm to evaluate the movements of the medical practitioner and the patient. For example, it evaluates how bandages are applied or injection procedures and specifically points out areas for improvement. The image recognition unit also uses image recognition technology to evaluate the movements of the medical practitioner and the patient in real time and point out areas for improvement. For example, it instructs them to fine-tune their movements in real time, supporting accurate medical treatment. In this way, the evaluation of movements and the indication of areas for improvement improves the accuracy of medical treatment.

[0038] The arm control unit can control the movement of the arm in synchronization with the movements of the medical practitioner and the patient. For example, the arm control unit analyzes the movements of the medical practitioner and the patient in real time and synchronizes the movement of the arm. For example, the arm moves in synchronization with the timing of the medical practitioner's hand movements. The arm control unit also develops a movement synchronization algorithm to synchronize the movement of the arm with the movements of the medical practitioner and the patient. For example, the arm moves in synchronization with the movement of the medical practitioner's hand when wrapping a bandage. The arm control unit also monitors the movements of the medical practitioner and the patient in real time and adjusts the movement of the arm. For example, the arm moves in synchronization with the direction in which the medical practitioner moves their hand. In this way, controlling the movement of the arm in synchronization with the movements of the medical practitioner and the patient provides more natural navigation.

[0039] The arm control unit can simulate arm movements using generative AI and learn optimal movement patterns in advance. The arm control unit, for example, uses generative AI to simulate arm movements and learn optimal movement patterns. For example, it simulates cardiopulmonary resuscitation procedures and learns optimal movements. The arm control unit also simulates arm movements and builds a system that learns optimal movement patterns in advance. For example, it simulates how to wrap a bandage or the procedure for giving an injection and learns optimal movements. The arm control unit also uses generative AI to simulate arm movements in real time and learn optimal movement patterns. For example, it simulates fine-tuning of movements and learns optimal movements. In this way, by simulating arm movements and learning optimal movement patterns, the accuracy of medical procedures is improved.

[0040] The arm control unit can adjust the movement of the arm in real time in accordance with the movements of the medical practitioner and the patient. For example, the arm control unit analyzes the movements of the medical practitioner and the patient in real time and adjusts the movement of the arm. For example, the arm moves in accordance with the timing of the medical practitioner's hand movements. The arm control unit also develops a movement adjustment algorithm to match the movement of the arm to the movements of the medical practitioner and the patient. For example, the arm moves in accordance with the movement of the medical practitioner's hand when wrapping a bandage. The arm control unit also monitors the movements of the medical practitioner and the patient in real time and adjusts the movement of the arm. For example, the arm moves in accordance with the direction in which the medical practitioner moves their hand. In this way, optimal support can be provided by adjusting the movement of the arm in real time in accordance with the movements of the medical practitioner and the patient.

[0041] The arm control unit can customize the arm movement based on the movement history of the medical practitioner and the patient, and provide individually optimized navigation. The arm control unit, for example, analyzes the movement history of the medical practitioner and the patient and customizes the arm movement. For example, it generates an optimal movement pattern based on past movement data. The arm control unit also builds a system that individually optimizes the arm movement based on the movement history. For example, it adjusts the arm movement based on past movement data. The arm control unit also analyzes the movement history of the medical practitioner and the patient in real time and customizes the arm movement. For example, it generates an optimal movement pattern based on past movement data. In this way, individually optimized navigation is provided by customizing the arm movement based on the movement history.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The medical assistance system may further include an environmental recognition unit. The environmental recognition unit analyzes the environment in which medical procedures are performed in real time and provides appropriate instructions. For example, it may monitor the temperature and humidity of the operating room and issue instructions to maintain optimal environmental conditions. The environmental recognition unit may also analyze the placement of items in the operating room, identify the location of instruments needed by medical practitioners, and provide them promptly. Furthermore, the environmental recognition unit may adjust the lighting conditions in the operating room to ensure optimal visibility for medical practitioners. This is expected to optimize the environment in which medical procedures are performed and reduce the burden on medical practitioners.

[0044] The medical assistance system can further include a vital sign monitoring unit. The vital sign monitoring unit monitors the patient's vital signs, such as heart rate, blood pressure, and body temperature, in real time and notifies medical personnel if an abnormality is detected. For example, if the heart rate suddenly increases, an alert is issued to prompt a prompt response. The vital sign monitoring unit can also analyze the patient's vital sign data and track changes in long-term health conditions. Furthermore, the vital sign monitoring unit can suggest appropriate medical treatments based on the patient's vital sign data. This is expected to enable a real-time understanding of the patient's health condition and enable prompt and appropriate medical treatment.

[0045] The medical assistance system can further include a rehabilitation support unit. The rehabilitation support unit manages the patient's rehabilitation program and suggests appropriate exercises and training. For example, it evaluates the patient's muscle strength and flexibility and provides an individually customized rehabilitation plan. The rehabilitation support unit can also analyze the patient's exercise data and monitor their progress. Furthermore, the rehabilitation support unit can analyze the patient's movements during rehabilitation in real time and provide appropriate feedback. This is expected to effectively support the patient's rehabilitation and promote recovery.

[0046] The medical assistance system can further include a remote medical support unit. The remote medical support unit enables medical practitioners to provide medical support to patients in remote locations. For example, the remote medical support unit can check the patient's condition via video call and provide appropriate medical advice. The remote medical support unit can also share the patient's vital sign data in real time, allowing medical practitioners in remote locations to respond quickly. Furthermore, the remote medical support unit allows medical practitioners to remotely operate medical instruments for patients in remote locations. This is expected to provide medical support beyond geographical constraints and improve access to medical care.

[0047] The medical assistance system can further include a preventive medical support unit. The preventive medical support unit analyzes the patient's health data and supports the prevention and early detection of disease. For example, it can evaluate health risks based on the patient's lifestyle data and suggest appropriate preventive measures. The preventive medical support unit can also encourage regular health checks and notify medical professionals early if an abnormality is detected. Furthermore, the preventive medical support unit can provide individually customized health management plans based on the patient's health data. This is expected to support the prevention and early detection of disease and promote the maintenance of patients' health.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The natural language processing unit understands instructions from medical practitioners and patients. For example, it uses morphological analysis to break down sentences, performs grammatical analysis, and understands the content of instructions through semantic analysis. It can also use deep learning algorithms to understand the intentions of medical practitioners and patients with high accuracy. It can also use voice recognition technology to convert voice instructions into text and understand instructions. Step 2: The image recognition unit recognizes actions based on the instructions understood by the natural language processing unit. For example, it uses computer vision technology to analyze video data from a camera and recognize the actions of medical practitioners and patients. It can also use deep learning algorithms to extract movement characteristics and recognize movements. It can also use object detection technology to recognize instruments and parts of the body required for medical procedures. Step 3: The arm control unit navigates the movement recognized by the image recognition unit. For example, it uses PID control to accurately control the position of the arm. It can also use feedforward control to smoothly move the arm. It can also use a motion planning algorithm to generate and navigate the optimal movement pattern for the arm.

[0050] (Example 2) The medical support system according to an embodiment of the present invention is a system that directly communicates with medical practitioners and patients, and assists in medical procedures by providing instructions and navigation using arms and images. This makes it easier for medical practitioners and patients to perform initial medical procedures, and reduces the unfortunate consequences of sudden illnesses and accidents.

[0051] The medical assistance system according to the embodiment includes a natural language processing unit, an image recognition unit, and an arm control unit. The natural language processing unit understands instructions from medical practitioners and patients. For example, the natural language processing unit uses morphological analysis to decompose sentences, perform grammatical analysis, and understand the content of the instructions through semantic analysis. The natural language processing unit can also use deep learning algorithms to understand the intentions of medical practitioners and patients with high accuracy. Furthermore, the natural language processing unit can use voice recognition technology to convert voice instructions into text and understand the instructions. The image recognition unit recognizes actions based on the instructions understood by the natural language processing unit. For example, the image recognition unit uses computer vision technology to analyze video data from a camera and recognize the actions of medical practitioners and patients. The image recognition unit can also use deep learning algorithms to extract movement features and recognize the actions. Furthermore, the image recognition unit can use object detection technology to recognize instruments and parts required for medical procedures. The arm control unit navigates the actions recognized by the image recognition unit. For example, the arm control unit accurately controls the position of the arm using PID control. The arm control unit can also use feedforward control to smoothly move the arm. Furthermore, the arm control unit can use a motion planning algorithm to generate and navigate an optimal motion pattern for the arm. This allows the medical assistance system according to the embodiment to provide instructions and navigation using the arm and images while directly communicating with medical practitioners and patients, thereby assisting in initial medical treatment. For example, medical treatments required in emergencies, such as cardiopulmonary resuscitation, hemostasis, and fixation of fractures, can be performed quickly and accurately. This is expected to reduce the unhappiness caused by sudden illnesses and accidents.

[0052] The natural language processing unit can estimate the emotions of medical practitioners and patients and generate responses according to those emotions. For example, the natural language processing unit analyzes voice data of medical practitioners and patients to estimate their emotions. For example, it identifies emotions based on the tone, speed, and volume of their voices and generates responses that provide a sense of security. The natural language processing unit also captures the facial expressions of medical practitioners and patients with a camera to estimate their emotions. For example, it analyzes changes in facial expressions such as smiling and furrowing the brow and generates responses according to those emotions. The natural language processing unit also tracks changes in emotions by referencing the past conversation history of medical practitioners and patients. For example, it estimates current emotions based on emotional data from past conversations and generates appropriate responses. This enables communication that provides a sense of security to medical practitioners and patients.

[0053] The natural language processing unit can provide individually customized advice by referring to the medical practitioner's and patient's past medical history. For example, the natural language processing unit may refer to the medical practitioner's and patient's electronic medical records and provide customized advice based on their past medical history and treatment history. For example, it may provide specific instructions on precautions to take when performing cardiopulmonary resuscitation for a patient who has previously suffered from heart disease. The natural language processing unit may also analyze the medical practitioner's and patient's past medical records and provide advice tailored to their individual health conditions. For example, it may suggest appropriate first aid based on past blood pressure data. The natural language processing unit may also refer to the medical practitioner's and patient's past communication history to provide advice tailored to individual needs. For example, it may generate more specific responses based on questions asked in past conversations. This provides individually customized advice, thereby supporting more appropriate medical practice.

[0054] The natural language processing unit can use multilingual generation AI to enable communication that can be accommodated for patients who speak different languages. The natural language processing unit, for example, uses multilingual generation AI to enable communication that can be accommodated for patients who speak different languages. For example, it generates responses in multiple languages, such as English, Spanish, and Chinese. The natural language processing unit also uses generation AI that incorporates an automatic translation function to enable real-time communication with patients who speak different languages. For example, it instantly translates patients' questions and generates appropriate responses. The natural language processing unit also uses multilingual generation AI to enable communication that can be accommodated for patients with different cultural backgrounds. For example, it generates responses that take cultural differences into consideration. This enables communication that can be accommodated for patients who speak different languages.

[0055] The natural language processing unit can analyze the tone and speed of the voices of medical practitioners and patients to determine the level of urgency and prioritize responses. The natural language processing unit, for example, analyzes the tone and speed of the voices of medical practitioners and patients to determine the level of urgency. For example, if the voice is trembling or the patient is speaking quickly, it is determined that the level of urgency is high. The natural language processing unit also builds a system that determines the priority of responses based on the tone and speed of the voice. For example, if the level of urgency is determined to be high, a response is generated immediately. The natural language processing unit also analyzes the tone and speed of the voice to generate a response according to the level of urgency. For example, if the level of urgency is high, a quick response is provided, and if the level of urgency is low, a detailed explanation is provided. This allows for a quick response by generating a response according to the level of urgency.

[0056] The natural language processing unit can analyze the gestures and facial expressions of medical practitioners and patients and incorporate non-verbal communication. The natural language processing unit, for example, uses a camera to analyze the gestures and facial expressions of medical practitioners and patients and incorporates non-verbal communication. For example, it estimates emotions based on hand movements and facial expressions and generates appropriate responses. The natural language processing unit also develops algorithms to analyze gestures and facial expressions and realizes non-verbal communication. For example, it detects the movement of raising a hand and determines that a question is asked. The natural language processing unit also builds a database of gestures and facial expressions to incorporate non-verbal communication. For example, it analyzes facial expressions such as smiling and nodding and generates responses according to the emotions. In this way, incorporating non-verbal communication enables more natural communication.

[0057] The natural language processing unit can use the emotion estimation function to monitor the stress levels of medical practitioners and patients in real time and suggest appropriate relaxation methods. The natural language processing unit, for example, analyzes the voices and facial expressions of medical practitioners and patients to monitor stress levels in real time. For example, it detects voice tremors and facial tension to estimate stress levels. The natural language processing unit also builds a system that suggests appropriate relaxation methods based on stress levels. For example, it suggests deep breathing or playing relaxing music. The natural language processing unit also monitors stress levels in real time and suggests relaxation methods. For example, if stress is determined to be high, it provides advice on how to relax. In this way, stress levels are monitored and appropriate relaxation methods are suggested, thereby reducing stress for medical practitioners and patients.

[0058] The image recognition unit can estimate the emotions of the medical practitioner and the patient and generate visual instructions according to the emotions. The image recognition unit, for example, uses a camera to analyze the facial expressions of the medical practitioner and the patient to estimate their emotions. For example, it detects smiling or nervous expressions and generates visual instructions according to the emotions. The image recognition unit also uses the emotion estimation function to generate visual instructions according to the emotions of the medical practitioner and the patient. For example, it displays visual guides to encourage a relaxed expression. The image recognition unit also monitors the emotions of the medical practitioner and the patient in real time and generates visual instructions according to the emotions. For example, it suggests visual relaxation methods to relieve tension. In this way, generating visual instructions according to the emotions gives the medical practitioner and the patient a sense of security.

[0059] The image recognition unit can detect subtle differences in the movements of the medical practitioner and the patient and provide more precise instructions. The image recognition unit, for example, uses a camera to analyze the movements of the medical practitioner and the patient in real time and detects subtle differences. For example, it can provide instructions to fine-tune the position or angle of the hands. The image recognition unit also uses image recognition technology to analyze the movements of the medical practitioner and the patient and provide precise instructions. For example, it can provide instructions to adjust the position or pressure of the hands during cardiopulmonary resuscitation. The image recognition unit also develops an algorithm to detect subtle differences in movements and provides precise instructions. For example, it can provide specific instructions on how to wrap a bandage or the angle of an injection. This allows the unit to detect subtle differences in movements and provide precise instructions, thereby improving the accuracy of medical procedures.

[0060] The image recognition unit uses generative AI to reproduce the movements of the medical practitioner and patient in 3D models, thereby providing visually easy-to-understand instructions. The image recognition unit, for example, uses generative AI to reproduce the movements of the medical practitioner and patient in 3D models. For example, cardiopulmonary resuscitation procedures are displayed in 3D models, providing visually easy-to-understand instructions. The image recognition unit also uses 3D models to reproduce the movements of the medical practitioner and patient, providing visually easy-to-understand instructions. For example, 3D models are used to show how to wrap a bandage or how to administer an injection. The image recognition unit also uses generative AI to convert the movements of the medical practitioner and patient into 3D models in real time, providing visually easy-to-understand instructions. For example, fine-tuning of movements is shown in 3D models. This helps understand medical procedures by providing visually easy-to-understand instructions using 3D models.

[0061] The image recognition unit can analyze the movements of medical practitioners and patients in real time and provide appropriate feedback immediately. The image recognition unit, for example, uses a camera to analyze the movements of medical practitioners and patients in real time and provide immediate feedback. For example, it monitors cardiopulmonary resuscitation procedures in real time and provides appropriate feedback. The image recognition unit also uses a motion analysis algorithm to analyze the movements of medical practitioners and patients in real time and provide feedback. For example, it provides instructions on how to wrap a bandage or the procedure for administering an injection in real time. The image recognition unit also builds a system that analyzes movements in real time and provides appropriate feedback. For example, it provides instructions on fine-tuning movements in real time to support accurate medical procedures. As a result, real-time feedback is provided, improving the accuracy of medical procedures.

[0062] The image recognition unit can evaluate the movements of the medical practitioner and the patient and specifically point out areas for improvement. The image recognition unit, for example, uses image recognition technology to evaluate the movements of the medical practitioner and the patient and specifically point out areas for improvement. For example, it evaluates cardiopulmonary resuscitation procedures and instructs them to adjust hand position and pressure. The image recognition unit also develops a movement evaluation algorithm to evaluate the movements of the medical practitioner and the patient. For example, it evaluates how bandages are applied or injection procedures and specifically points out areas for improvement. The image recognition unit also uses image recognition technology to evaluate the movements of the medical practitioner and the patient in real time and point out areas for improvement. For example, it instructs them to fine-tune their movements in real time, supporting accurate medical treatment. In this way, the evaluation of movements and the indication of areas for improvement improves the accuracy of medical treatment.

[0063] The image recognition unit can use the emotion estimation function to provide visual feedback according to the emotions of the medical practitioner and the patient. The image recognition unit, for example, uses the emotion estimation function to analyze the emotions of the medical practitioner and the patient in real time and provide visual feedback. For example, it displays a visual guide to encourage a relaxed facial expression. The image recognition unit also builds a system that provides visual feedback according to the emotions of the medical practitioner and the patient. For example, it suggests a visual relaxation method to relieve tension. The image recognition unit also provides visual feedback according to the emotions of the medical practitioner and the patient based on the emotion estimation data. For example, it displays a visual guide to give a sense of security. In this way, providing visual feedback according to emotions gives a sense of security to the medical practitioner and the patient.

[0064] The arm control unit can estimate the emotions of the medical practitioner and the patient and control the movement of the arm according to the emotions. The arm control unit, for example, analyzes the emotions of the medical practitioner and the patient in real time and controls the movement of the arm according to the emotions. For example, if they are nervous, it moves slowly, giving a sense of security. The arm control unit also uses the emotion estimation function to build a system that controls the movement of the arm according to the emotions of the medical practitioner and the patient. For example, it moves to encourage a relaxed expression. The arm control unit also adjusts the movement of the arm based on the emotions of the medical practitioner and the patient. For example, it moves quickly if they are emotionally stable, and moves slowly if they are nervous. In this way, controlling the movement of the arm according to the emotions gives a sense of security to the medical practitioner and the patient.

[0065] The arm control unit can control the movement of the arm in synchronization with the movements of the medical practitioner and the patient. For example, the arm control unit analyzes the movements of the medical practitioner and the patient in real time and synchronizes the movement of the arm. For example, the arm moves in synchronization with the timing of the medical practitioner's hand movements. The arm control unit also develops a movement synchronization algorithm to synchronize the movement of the arm with the movements of the medical practitioner and the patient. For example, the arm moves in synchronization with the movement of the medical practitioner's hand when wrapping a bandage. The arm control unit also monitors the movements of the medical practitioner and the patient in real time and adjusts the movement of the arm. For example, the arm moves in synchronization with the direction in which the medical practitioner moves their hand. In this way, controlling the movement of the arm in synchronization with the movements of the medical practitioner and the patient provides more natural navigation.

[0066] The arm control unit can simulate arm movements using generative AI and learn optimal movement patterns in advance. The arm control unit, for example, uses generative AI to simulate arm movements and learn optimal movement patterns. For example, it simulates cardiopulmonary resuscitation procedures and learns optimal movements. The arm control unit also simulates arm movements and builds a system that learns optimal movement patterns in advance. For example, it simulates how to wrap a bandage or the procedure for giving an injection and learns optimal movements. The arm control unit also uses generative AI to simulate arm movements in real time and learn optimal movement patterns. For example, it simulates fine-tuning of movements and learns optimal movements. In this way, by simulating arm movements and learning optimal movement patterns, the accuracy of medical procedures is improved.

[0067] The arm control unit can adjust the movement of the arm in real time in accordance with the movements of the medical practitioner and the patient. For example, the arm control unit analyzes the movements of the medical practitioner and the patient in real time and adjusts the movement of the arm. For example, the arm moves in accordance with the timing of the medical practitioner's hand movements. The arm control unit also develops a movement adjustment algorithm to match the movement of the arm to the movements of the medical practitioner and the patient. For example, the arm moves in accordance with the movement of the medical practitioner's hand when wrapping a bandage. The arm control unit also monitors the movements of the medical practitioner and the patient in real time and adjusts the movement of the arm. For example, the arm moves in accordance with the direction in which the medical practitioner moves their hand. In this way, optimal support can be provided by adjusting the movement of the arm in real time in accordance with the movements of the medical practitioner and the patient.

[0068] The arm control unit can customize the arm movement based on the movement history of the medical practitioner and the patient, and provide individually optimized navigation. The arm control unit, for example, analyzes the movement history of the medical practitioner and the patient and customizes the arm movement. For example, it generates an optimal movement pattern based on past movement data. The arm control unit also builds a system that individually optimizes the arm movement based on the movement history. For example, it adjusts the arm movement based on past movement data. The arm control unit also analyzes the movement history of the medical practitioner and the patient in real time and customizes the arm movement. For example, it generates an optimal movement pattern based on past movement data. In this way, individually optimized navigation is provided by customizing the arm movement based on the movement history.

[0069] The arm control unit can use the emotion estimation function to control the movement of the arm according to the emotions of the medical practitioner and the patient. For example, the arm control unit uses the emotion estimation function to analyze the emotions of the medical practitioner and the patient in real time and control the movement of the arm. For example, if they are nervous, it will perform slow movements to give a sense of security. The arm control unit also builds a system that controls the movement of the arm according to the emotions of the medical practitioner and the patient. For example, it will perform movements to encourage a relaxed expression. The arm control unit also adjusts the movement of the arm based on the emotion estimation data. For example, it will perform quick movements if they are emotionally stable, and slow movements if they are nervous. In this way, controlling the movement of the arm according to their emotions gives a sense of security to the medical practitioner and the patient.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The medical assistance system may further include an environmental recognition unit. The environmental recognition unit analyzes the environment in which medical procedures are performed in real time and provides appropriate instructions. For example, it may monitor the temperature and humidity of the operating room and issue instructions to maintain optimal environmental conditions. The environmental recognition unit may also analyze the placement of items in the operating room, identify the location of instruments needed by medical practitioners, and provide them promptly. Furthermore, the environmental recognition unit may adjust the lighting conditions in the operating room to ensure optimal visibility for medical practitioners. This is expected to optimize the environment in which medical procedures are performed and reduce the burden on medical practitioners.

[0072] The medical assistance system can further include a vital sign monitoring unit. The vital sign monitoring unit monitors the patient's vital signs, such as heart rate, blood pressure, and body temperature, in real time and notifies medical personnel if an abnormality is detected. For example, if the heart rate suddenly increases, an alert is issued to prompt a prompt response. The vital sign monitoring unit can also analyze the patient's vital sign data and track changes in long-term health conditions. Furthermore, the vital sign monitoring unit can suggest appropriate medical treatments based on the patient's vital sign data. This is expected to enable a real-time understanding of the patient's health condition and enable prompt and appropriate medical treatment.

[0073] The medical assistance system can further include a rehabilitation support unit. The rehabilitation support unit manages the patient's rehabilitation program and suggests appropriate exercises and training. For example, it evaluates the patient's muscle strength and flexibility and provides an individually customized rehabilitation plan. The rehabilitation support unit can also analyze the patient's exercise data and monitor their progress. Furthermore, the rehabilitation support unit can analyze the patient's movements during rehabilitation in real time and provide appropriate feedback. This is expected to effectively support the patient's rehabilitation and promote recovery.

[0074] The medical assistance system can further include a remote medical support unit. The remote medical support unit enables medical practitioners to provide medical support to patients in remote locations. For example, the remote medical support unit can check the patient's condition via video call and provide appropriate medical advice. The remote medical support unit can also share the patient's vital sign data in real time, allowing medical practitioners in remote locations to respond quickly. Furthermore, the remote medical support unit allows medical practitioners to remotely operate medical instruments for patients in remote locations. This is expected to provide medical support beyond geographical constraints and improve access to medical care.

[0075] The medical assistance system can further include a preventive medical support unit. The preventive medical support unit analyzes the patient's health data and supports the prevention and early detection of disease. For example, it can evaluate health risks based on the patient's lifestyle data and suggest appropriate preventive measures. The preventive medical support unit can also encourage regular health checks and notify medical professionals early if an abnormality is detected. Furthermore, the preventive medical support unit can provide individually customized health management plans based on the patient's health data. This is expected to support the prevention and early detection of disease and promote the maintenance of patients' health.

[0076] The medical assistance system can further use the emotion estimation function to enhance emotion-based communication between medical practitioners and patients. For example, if a medical practitioner is nervous, the system can provide advice to help them relax. Also, if a patient is feeling anxious, the system can generate a response to reassure them. Furthermore, the emotion estimation function can be used to monitor changes in the emotions of medical practitioners and patients in real time and take appropriate measures. This is expected to enhance emotion-based communication and reduce the psychological burden on medical practitioners and patients.

[0077] The medical assistance system can also use the emotion estimation function to provide feedback based on the emotions of the medical practitioner and patient. For example, if the medical practitioner is feeling stressed, the system can suggest ways to relax. If the patient is feeling fearful, the system can display visual guidance to reassure them. Furthermore, the emotion estimation function can also be used to monitor changes in the emotions of the medical practitioner and patient in real time and provide appropriate feedback. This is expected to provide emotion-based feedback and reduce the psychological burden on the medical practitioner and patient.

[0078] The medical assistance system can further use the emotion estimation function to control the arm movements based on the emotions of the medical practitioner and the patient. For example, if the medical practitioner is nervous, the arm movements can be made slowly, providing a sense of security. On the other hand, if the patient is relaxed, the arm movements can be made quickly. Furthermore, the emotion estimation function can be used to monitor changes in the emotions of the medical practitioner and the patient in real time and adjust the arm movements appropriately. This is expected to control the arm movements based on emotions and provide a sense of security to the medical practitioner and the patient.

[0079] The medical assistance system can further use the emotion estimation function to provide visual feedback based on the emotions of the medical practitioner and the patient. For example, if the medical practitioner is feeling stressed, the system can display a visual guide to help them relax. Also, if the patient is feeling anxious, the system can provide visual feedback to give them a sense of security. Furthermore, the emotion estimation function can be used to monitor changes in the emotions of the medical practitioner and the patient in real time and provide appropriate visual feedback. This is expected to provide visual feedback based on emotions and reduce the psychological burden on the medical practitioner and the patient.

[0080] The medical assistance system can further use the emotion estimation function to provide voice feedback based on the emotions of the medical practitioner and the patient. For example, if the medical practitioner is nervous, the system can provide voice guidance to help them relax. Also, if the patient is feeling scared, the system can provide voice feedback to reassure them. Furthermore, the emotion estimation function can be used to monitor changes in the emotions of the medical practitioner and the patient in real time and provide appropriate voice feedback. This is expected to provide voice feedback based on emotions and reduce the psychological burden on the medical practitioner and the patient.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The natural language processing unit understands instructions from medical practitioners and patients. For example, it uses morphological analysis to break down sentences, performs grammatical analysis, and understands the content of instructions through semantic analysis. It can also use deep learning algorithms to understand the intentions of medical practitioners and patients with high accuracy. It can also use voice recognition technology to convert voice instructions into text and understand instructions. Step 2: The image recognition unit recognizes actions based on the instructions understood by the natural language processing unit. For example, it uses computer vision technology to analyze video data from a camera and recognize the actions of medical practitioners and patients. It can also use deep learning algorithms to extract movement characteristics and recognize movements. It can also use object detection technology to recognize instruments and parts of the body required for medical procedures. Step 3: The arm control unit navigates the movement recognized by the image recognition unit. For example, it uses PID control to accurately control the position of the arm. It can also use feedforward control to smoothly move the arm. It can also use a motion planning algorithm to generate and navigate the optimal movement pattern for the arm.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 natural language processing unit; an image recognition unit that recognizes an action based on an instruction understood by the natural language processing unit; an arm control unit that navigates the movement recognized by the image recognition unit; A system characterized by:

2. The natural language processing unit Estimating the emotions of the medical practitioner and the patient and generating a response according to the emotions 2. The system of claim 1.

3. The natural language processing unit Referencing the medical practitioner's and patient's past medical history to provide individually tailored advice 2. The system of claim 1.

4. The natural language processing unit Using multilingual generative AI, we can communicate with patients who speak different languages.

2. The system of claim 1.

5. The natural language processing unit Analyze the tone and speed of voice of the medical practitioner and patient to determine the urgency and prioritize the response.

2. The system of claim 1.

6. The natural language processing unit Analyze the gestures and facial expressions of the medical practitioner and patient, and incorporate non-verbal communication.

2. The system of claim 1.

7. The natural language processing unit Monitoring the stress levels of the medical practitioner and patient in real time and suggesting appropriate relaxation methods 2. The system of claim 1.

8. The image recognition unit Estimating the emotions of the medical practitioner and the patient and generating visual instructions according to the emotions.

2. The system of claim 1.

Images