System

Abstract

An object of a system according to an embodiment is to facilitate communication between a doctor and a patient.SOLUTION: A system includes a symptom analysis unit, an explanation generation unit, and a medical examination support unit. The condition analysis unit analyzes a condition using the generated AI. The explanation generation unit generates an explanation based on the symptom analyzed by the symptom analysis unit. The medical examination support unit supports a medical examination using the explanation generated by the explanation generation 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 techniques have had the problem of insufficient communication between doctors and patients regarding symptoms and explanations.

[0005] The system according to the embodiment aims to facilitate communication between doctors and patients. [Means for solving the problem]

[0006] The system according to the embodiment includes a symptom analysis unit, an explanation generation unit, and a medical examination support unit. The symptom analysis unit analyzes symptoms using a generation AI. The explanation generation unit generates an explanation based on the symptoms analyzed by the symptom analysis unit. The medical examination support unit supports the medical examination using the explanation generated by the explanation generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate communication between doctors and patients. [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) A communication support system according to an embodiment of the present invention is a system that uses generative AI to solve problems related to communication between doctors and patients. As a result, the communication support system can facilitate smooth communication between doctors and patients and support appropriate medical examinations.

[0029] A communication support system according to an embodiment includes a symptom analysis unit, an explanation generation unit, and a medical examination support unit. The symptom analysis unit analyzes a patient's symptoms using a generative AI. For example, the symptom analysis unit analyzes the patient's words, facial expressions, and movements to estimate the location and severity of pain. The symptom analysis unit can also analyze the patient's tone and rhythm of voice to estimate the intensity and type of pain. Furthermore, the symptom analysis unit can identify the cause of the pain by comparing the patient's past medical records with cases with similar symptoms. The explanation generation unit generates an explanation based on the symptoms analyzed by the symptom analysis unit. For example, the explanation generation unit converts a doctor's explanation into an easy-to-understand explanation for elderly patients. The explanation generation unit can also convert medical terms into everyday analogies to enable patients to intuitively understand. Furthermore, the explanation generation unit can use an emotion estimation function to analyze the emotional reactions of elderly patients when receiving an explanation and evaluate their level of understanding. The medical examination support unit supports the medical examination using the explanation generated by the explanation generation unit. For example, the medical examination support unit explains the procedure of the medical examination using gentle words and characters for small children. The examination support unit can also analyze the child's reactions and evaluate the stress level during the examination. Furthermore, the examination support unit can use an emotion estimation function to estimate the child's emotions from their facial expressions and voice and adjust the progress of the examination. This allows the communication support system according to the embodiment to facilitate communication between doctors and patients and support appropriate examinations. For example, the generation AI can analyze a patient's symptoms and help doctors provide appropriate explanations to elderly patients and small children. The generation AI can also generate appropriate answers to the patient's anxieties and questions, reassuring the patient.

[0030] The symptom analysis unit can analyze the patient's movements and estimate the location and level of pain. For example, if a patient rubs their body while saying, "This hurts," the symptom analysis unit analyzes their movements and estimates the location and level of pain. The symptom analysis unit can also build a system that analyzes the patient's movements in real time and estimates the location and level of pain. For example, it can analyze the patient's movements using motion capture technology and identify the location of pain. The symptom analysis unit can also analyze the patient's movements using video analysis technology and evaluate the level of pain. This allows for an accurate understanding of the location and level of pain in the patient.

[0031] The explanation generation unit can convert a doctor's explanation into something easier to understand for elderly patients. For example, if a doctor explains, "This medicine is an antibiotic," the explanation generation unit converts it into something easier to understand, such as, "This medicine is used to kill bacteria." The explanation generation unit will also develop a generative AI algorithm that automatically adjusts the level of detail in the explanation according to the elderly patient's level of understanding. For example, it will adjust the length of the explanation and the use of technical terms according to the level of understanding. The explanation generation unit will also build a system that analyzes the elderly patient's level of understanding in real time and automatically adjusts the level of detail in the explanation. For example, it will simplify the content of the explanation according to the level of understanding. This will enable elderly patients to receive explanations that are easy to understand.

[0032] The examination support unit can explain the flow of an examination to small children using gentle words and characters. For example, the examination support unit might explain things like, "I'm going to touch your stomach a little now and see if there are any small worms inside." The examination support unit will also develop a generative AI algorithm that uses characters and animations that children like to explain the flow of an examination. For example, it will generate videos in which characters explain the steps of an examination. The examination support unit will also build a system that uses characters that children like to explain the flow of an examination. For example, a character will show the steps of an examination in animation. This will allow small children to receive examinations without feeling scared.

[0033] The symptom analysis unit can analyze the tone and rhythm of a patient's voice to infer the intensity and type of pain. For example, the symptom analysis unit analyzes the tone and rhythm of a patient's voice in real time when they speak and develops an algorithm that infers the intensity and type of pain. For example, it evaluates the intensity of pain based on changes in voice pitch and speed. The symptom analysis unit also analyzes the tone and rhythm of a patient's voice to develop a generative AI algorithm that identifies the type of pain. For example, it detects voice tremors and interruptions to infer the type of pain. The symptom analysis unit also analyzes the tone and rhythm of a patient's voice and builds a system that provides feedback on the intensity and type of pain to doctors in real time. For example, it displays changes in voice in a graph to visually indicate the intensity of pain. This makes it possible to infer the intensity and type of pain from the tone and rhythm of a patient's voice.

[0034] The symptom analysis unit can refer to the patient's past medical records and compare them with cases with similar symptoms to identify the cause of pain. For example, the symptom analysis unit stores the patient's past medical records in a database and adds a function that allows the generation AI to refer to that data and compare it with cases with similar symptoms. For example, it identifies the cause of pain from past medical records. The symptom analysis unit also develops a function for the generation AI that analyzes past medical records and compares them with data from patients with similar symptoms to identify the cause of pain. For example, it analyzes pain patterns based on past medical data. The symptom analysis unit also develops an algorithm for the generation AI that refers to the patient's past medical records and compares them with cases with similar symptoms to identify the cause of pain. For example, it infers the cause of pain based on past medical data. This makes it possible to identify the cause of pain by referring to past medical records.

[0035] In addition to analyzing the patient's movements, the symptom analysis unit uses sensors to measure skin temperature and sweat rate, allowing for more accurate estimation of the location and level of pain. For example, the symptom analysis unit uses sensors to measure skin temperature and sweat rate in addition to analyzing the patient's movements, and builds a system that estimates the location and level of pain. For example, it uses temperature sensors and sweat sensors. The symptom analysis unit also measures skin temperature and sweat rate with sensors to develop a generative AI algorithm that more accurately estimates the location and level of pain. For example, it analyzes data on temperature changes and sweat rate. In addition to analyzing the patient's movements, the symptom analysis unit also measures skin temperature and sweat rate in real time, and builds a system that estimates the location and level of pain. For example, it inputs data from sensors into the generative AI. In this way, by measuring skin temperature and sweat rate, it is possible to more accurately estimate the location and level of pain.

[0036] The symptom analysis unit can display the area of ​​pain in a 3D model, making it easier for doctors to understand visually. For example, the symptom analysis unit adds a generative AI function that displays the area of ​​pain in a 3D model, making it easier for doctors to understand visually. For example, the area of ​​pain is highlighted on the 3D model. The symptom analysis unit also builds a system that displays the area of ​​pain in a patient in a 3D model, making it easier for doctors to understand visually. For example, the area of ​​pain is displayed in different colors on the 3D model. The symptom analysis unit also develops a generative AI algorithm that displays the area of ​​pain in a 3D model, making it easier for doctors to understand visually. For example, the area of ​​pain is animated on the 3D model. This makes it easier for doctors to understand visually by displaying the area of ​​pain in a 3D model.

[0037] The explanation generation unit can automatically adjust the level of detail of the explanation according to the elderly patient's level of comprehension. The explanation generation unit, for example, develops a generative AI algorithm that automatically adjusts the level of detail of the explanation according to the elderly patient's level of comprehension. For example, the length of the explanation and the use of technical terms are adjusted according to the level of comprehension. The explanation generation unit also builds a system that analyzes the elderly patient's level of comprehension in real time and automatically adjusts the level of detail of the explanation. For example, the content of the explanation is simplified according to the level of comprehension. The explanation generation unit also develops a generative AI algorithm that automatically adjusts the level of detail of the explanation based on the elderly patient's level of comprehension. For example, the order and emphasis of the explanation are changed according to the level of comprehension. This makes it possible to automatically adjust the level of detail of the explanation according to the elderly patient's level of comprehension.

[0038] The explanation generation unit can convert medical terminology into everyday analogies, allowing patients to intuitively understand. For example, the explanation generation unit adds a generative AI function that converts medical terminology into everyday analogies, allowing patients to intuitively understand. For example, it converts "antibiotics" into "drugs that kill bacteria." The explanation generation unit also develops a generative AI algorithm that converts medical terminology into everyday analogies, making it easier for patients to understand. For example, it converts "MRI" into "a machine that takes pictures of the inside of the body." The explanation generation unit also builds a system that converts medical terminology into everyday analogies, allowing patients to intuitively understand. For example, it converts "blood pressure" into "strength of blood flow." In this way, by converting medical terminology into everyday analogies, patients can intuitively understand.

[0039] The explanation generation unit can automatically generate audio explanations for elderly patients and provide them in a form that is easy to understand auditorily. The explanation generation unit, for example, builds a system that automatically generates audio explanations for elderly patients and provides them in a form that is easy to understand auditorily. For example, a generation AI converts medical terms into simpler words and provides audio explanations. The explanation generation unit also develops an algorithm for a generation AI that automatically generates audio explanations and provides them in a form that is easy to understand auditorily for elderly patients. For example, an explanation is generated using voice synthesis technology. The explanation generation unit also develops a system that automatically generates audio explanations for elderly patients and provides them in a form that is easy to understand auditorily. For example, an audio guide function is added. This makes it possible to automatically generate audio explanations for elderly patients and provide them in a form that is easy to understand auditorily.

[0040] The explanation generation unit can visually complement explanations of medical terms using animations and illustrations. For example, the explanation generation unit adds a generative AI function that visually complements explanations of medical terms using animations and illustrations. For example, it could show the effects of a medication using animation. The explanation generation unit also develops a generative AI algorithm that automatically generates animations and illustrations to visually complement explanations of medical terms. For example, it could explain the flow of surgery using illustrations. The explanation generation unit also builds a system that visually complements explanations of medical terms using animations and illustrations. For example, it could show the progression of a disease using animation. In this way, visually complementing explanations of medical terms with animations and illustrations makes it easier for patients to understand.

[0041] The examination support unit can explain the flow of the examination using characters and animation that children like. The examination support unit, for example, develops a generative AI algorithm that explains the flow of the examination using characters and animation that children like. For example, it generates a video in which a character explains the procedure of the examination. The examination support unit also builds a system that explains the flow of the examination using characters that children like. For example, a character shows the procedure of the examination in an animation. The examination support unit also develops a generative AI algorithm that explains the flow of the examination using animation that children like. For example, it generates a story in which an animated character explains the procedure of the examination. In this way, by explaining the flow of the examination using characters and animation that children like, children can receive the examination without being scared.

[0042] The examination support unit can analyze the child's reactions and evaluate the stress level during the examination. For example, the examination support unit adds a generative AI function that analyzes the child's reactions in real time and evaluates the stress level during the examination. For example, it analyzes facial expressions and movements to evaluate the stress level. The examination support unit also develops a generative AI algorithm that analyzes the child's reactions and evaluates the stress level during the examination. For example, it analyzes the heart rate and respiratory rate to evaluate the stress level. The examination support unit also builds a system that analyzes the child's reactions in real time and evaluates the stress level during the examination. For example, it displays the stress level in a graph and provides feedback to the doctor. This makes it possible to analyze the child's reactions and evaluate the stress level during the examination.

[0043] The examination support unit customizes the examination room environment for children, and the generation AI can automatically adjust the music and video as the examination progresses. For example, the examination support unit builds a system that customizes the examination room environment for children and the generation AI automatically adjusts the music and video as the examination progresses. For example, playing relaxing music as the examination progresses. The examination support unit also develops an algorithm that customizes the examination room environment for children and the generation AI automatically adjusts the video as the examination progresses. For example, playing animation as the examination progresses. The examination support unit also customizes the examination room environment for children and builds a system that customizes the music and video in real time as the examination progresses. For example, switching the video as the examination progresses. This makes it possible to customize the examination room environment for children and automatically adjust the music and video as the examination progresses.

[0044] The medical examination support unit can simulate the flow of a medical examination using virtual reality (VR) when a child receives a medical examination. For example, the medical examination support unit adds a generative AI function that simulates the flow of a medical examination using virtual reality (VR) when a child receives a medical examination. For example, it allows the child to experience the flow of a medical examination using VR goggles. The medical examination support unit also develops a generative AI algorithm that allows a child to simulate the flow of a medical examination using virtual reality (VR). For example, it reproduces the procedure of a medical examination in VR. The medical examination support unit also builds a system that simulates the flow of a medical examination using virtual reality (VR) when a child receives a medical examination. For example, it explains the flow of the medical examination in a VR environment. This makes it possible to simulate the flow of a medical examination using virtual reality (VR) when a child receives a medical examination.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The communication support system may further include a lifestyle analysis unit that analyzes the patient's lifestyle. The lifestyle analysis unit may analyze, for example, the patient's diet, exercise, and sleep patterns to identify factors that affect their health. The lifestyle analysis unit may also acquire data from the patient's smartwatch or fitness tracker to evaluate their daily activity level. Furthermore, the lifestyle analysis unit may provide specific advice for improving their health based on the patient's lifestyle. This allows for a comprehensive analysis of the patient's lifestyle and supports health management.

[0047] The communication support system may further include a social background analysis unit that analyzes the patient's social background. The social background analysis unit may analyze, for example, the patient's occupation, home environment, and social network to identify factors that affect the patient's health condition. The social background analysis unit may also evaluate the patient's stress level and the presence or absence of social support. Furthermore, the social background analysis unit may provide appropriate support and resources based on the patient's social background. This allows for a comprehensive analysis of the patient's social background and supports health management.

[0048] The communication support system can further include a genetic information analysis unit that analyzes the patient's genetic information. The genetic information analysis unit, for example, analyzes the patient's genetic data and identifies genetic risk factors. The genetic information analysis unit can also refer to the patient's family history to evaluate the risk of genetic diseases. Furthermore, the genetic information analysis unit can also propose individualized preventive measures and treatments based on the genetic information. This makes it possible to comprehensively analyze the patient's genetic information and provide individualized medical care.

[0049] The communication support system may further include an environmental factor analysis unit that analyzes the patient's environmental factors. The environmental factor analysis unit may analyze, for example, the patient's living environment, work environment, and local environment to identify factors that affect the patient's health condition. The environmental factor analysis unit may also evaluate the air quality and noise level in the patient's living environment. Furthermore, the environmental factor analysis unit may provide specific advice for reducing health risks based on the environmental factors. This allows for a comprehensive analysis of the patient's environmental factors and supports health management.

[0050] The communication support system can further include a drug response analysis unit that analyzes the patient's drug response. The drug response analysis unit, for example, analyzes data related to the patient's drug metabolism and predicts the effects and side effects of drugs. The drug response analysis unit can also refer to the patient's genetic information and past drug use history to propose individualized drug treatment. Furthermore, the drug response analysis unit can evaluate drug interactions and propose appropriate drug combinations. This allows for a comprehensive analysis of the patient's drug response and supports safe and effective drug treatment.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The symptom analysis unit uses generative AI to analyze the patient's symptoms. For example, the symptom analysis unit analyzes the patient's words, facial expressions, and movements to infer the location and severity of the pain. It can also analyze the tone and rhythm of the patient's voice to infer the intensity and type of pain. It can also refer to the patient's past medical records and compare them with cases with similar symptoms to identify the cause of the pain. Step 2: The explanation generator generates an explanation based on the symptoms analyzed by the symptom analyzer. For example, the explanation generator converts a doctor's explanation into something that is easy to understand for elderly patients. It can also convert medical terms into everyday analogies that patients can intuitively understand. Furthermore, it can use the emotion estimation function to analyze the emotional reactions of elderly patients when they receive an explanation and evaluate their level of understanding. Step 3: The medical examination support unit supports the medical examination using the explanation generated by the explanation generation unit. For example, the medical examination support unit can explain the procedure of the medical examination to small children using gentle words and characters. It can also analyze the child's reactions and evaluate their stress level during the examination. Furthermore, it can use an emotion estimation function to estimate the child's emotions from their facial expressions and voice and adjust the progress of the examination.

[0053] (Example 2) A communication support system according to an embodiment of the present invention is a system that uses generative AI to solve problems related to communication between doctors and patients. As a result, the communication support system can facilitate smooth communication between doctors and patients and support appropriate medical examinations.

[0054] A communication support system according to an embodiment includes a symptom analysis unit, an explanation generation unit, and a medical examination support unit. The symptom analysis unit analyzes a patient's symptoms using a generative AI. For example, the symptom analysis unit analyzes the patient's words, facial expressions, and movements to estimate the location and severity of pain. The symptom analysis unit can also analyze the patient's tone and rhythm of voice to estimate the intensity and type of pain. Furthermore, the symptom analysis unit can identify the cause of the pain by comparing the patient's past medical records with cases with similar symptoms. The explanation generation unit generates an explanation based on the symptoms analyzed by the symptom analysis unit. For example, the explanation generation unit converts a doctor's explanation into an easy-to-understand explanation for elderly patients. The explanation generation unit can also convert medical terms into everyday analogies to enable patients to intuitively understand. Furthermore, the explanation generation unit can use an emotion estimation function to analyze the emotional reactions of elderly patients when receiving an explanation and evaluate their level of understanding. The medical examination support unit supports the medical examination using the explanation generated by the explanation generation unit. For example, the medical examination support unit explains the procedure of the medical examination using gentle words and characters for small children. The examination support unit can also analyze the child's reactions and evaluate the stress level during the examination. Furthermore, the examination support unit can use an emotion estimation function to estimate the child's emotions from their facial expressions and voice and adjust the progress of the examination. This allows the communication support system according to the embodiment to facilitate communication between doctors and patients and support appropriate examinations. For example, the generation AI can analyze a patient's symptoms and help doctors provide appropriate explanations to elderly patients and small children. The generation AI can also generate appropriate answers to the patient's anxieties and questions, reassuring the patient.

[0055] The symptom analysis unit can analyze the patient's movements and estimate the location and level of pain. For example, if a patient rubs their body while saying, "This hurts," the symptom analysis unit analyzes their movements and estimates the location and level of pain. The symptom analysis unit can also build a system that analyzes the patient's movements in real time and estimates the location and level of pain. For example, it can analyze the patient's movements using motion capture technology and identify the location of pain. The symptom analysis unit can also analyze the patient's movements using video analysis technology and evaluate the level of pain. This allows for an accurate understanding of the location and level of pain in the patient.

[0056] The explanation generation unit can convert a doctor's explanation into something easier to understand for elderly patients. For example, if a doctor explains, "This medicine is an antibiotic," the explanation generation unit converts it into something easier to understand, such as, "This medicine is used to kill bacteria." The explanation generation unit will also develop a generative AI algorithm that automatically adjusts the level of detail in the explanation according to the elderly patient's level of understanding. For example, it will adjust the length of the explanation and the use of technical terms according to the level of understanding. The explanation generation unit will also build a system that analyzes the elderly patient's level of understanding in real time and automatically adjusts the level of detail in the explanation. For example, it will simplify the content of the explanation according to the level of understanding. This will enable elderly patients to receive explanations that are easy to understand.

[0057] The examination support unit can explain the flow of an examination to small children using gentle words and characters. For example, the examination support unit might explain things like, "I'm going to touch your stomach a little now and see if there are any small worms inside." The examination support unit will also develop a generative AI algorithm that uses characters and animations that children like to explain the flow of an examination. For example, it will generate videos in which characters explain the steps of an examination. The examination support unit will also build a system that uses characters that children like to explain the flow of an examination. For example, a character will show the steps of an examination in animation. This will allow small children to receive examinations without feeling scared.

[0058] The symptom analysis unit can analyze the tone and rhythm of a patient's voice to infer the intensity and type of pain. For example, the symptom analysis unit analyzes the tone and rhythm of a patient's voice in real time when they speak and develops an algorithm that infers the intensity and type of pain. For example, it evaluates the intensity of pain based on changes in voice pitch and speed. The symptom analysis unit also analyzes the tone and rhythm of a patient's voice to develop a generative AI algorithm that identifies the type of pain. For example, it detects voice tremors and interruptions to infer the type of pain. The symptom analysis unit also analyzes the tone and rhythm of a patient's voice and builds a system that provides feedback on the intensity and type of pain to doctors in real time. For example, it displays changes in voice in a graph to visually indicate the intensity of pain. This makes it possible to infer the intensity and type of pain from the tone and rhythm of a patient's voice.

[0059] The symptom analysis unit can refer to the patient's past medical records and compare them with cases with similar symptoms to identify the cause of pain. For example, the symptom analysis unit stores the patient's past medical records in a database and adds a function that allows the generation AI to refer to that data and compare it with cases with similar symptoms. For example, it identifies the cause of pain from past medical records. The symptom analysis unit also develops a function for the generation AI that analyzes past medical records and compares them with data from patients with similar symptoms to identify the cause of pain. For example, it analyzes pain patterns based on past medical data. The symptom analysis unit also develops an algorithm for the generation AI that refers to the patient's past medical records and compares them with cases with similar symptoms to identify the cause of pain. For example, it infers the cause of pain based on past medical data. This makes it possible to identify the cause of pain by referring to past medical records.

[0060] The symptom analysis unit can use the emotion estimation function to estimate emotions from the patient's facial expression and voice, and evaluate the degree of pain and emotional stress. For example, the symptom analysis unit uses the emotion estimation function to estimate emotions from the patient's facial expression and voice, and build a system to evaluate the degree of pain and emotional stress. For example, emotions are estimated by analyzing facial expressions and tone of voice. The symptom analysis unit also develops a generative AI algorithm that estimates emotions from the patient's facial expression and voice, and evaluates the degree of pain and emotional stress. For example, emotions are analyzed using facial recognition technology. The symptom analysis unit also uses the emotion estimation function to estimate emotions from the patient's facial expression and voice in real time, and build a system that provides feedback on the degree of pain and emotional stress to the doctor. For example, an emotion score is displayed. This makes it possible to estimate emotions from the patient's facial expression and voice, and evaluate the degree of pain and emotional stress.

[0061] In addition to analyzing the patient's movements, the symptom analysis unit uses sensors to measure skin temperature and sweat rate, allowing for more accurate estimation of the location and level of pain. For example, the symptom analysis unit uses sensors to measure skin temperature and sweat rate in addition to analyzing the patient's movements, and builds a system that estimates the location and level of pain. For example, it uses temperature sensors and sweat sensors. The symptom analysis unit also measures skin temperature and sweat rate with sensors to develop a generative AI algorithm that more accurately estimates the location and level of pain. For example, it analyzes data on temperature changes and sweat rate. In addition to analyzing the patient's movements, the symptom analysis unit also measures skin temperature and sweat rate in real time, and builds a system that estimates the location and level of pain. For example, it inputs data from sensors into the generative AI. In this way, by measuring skin temperature and sweat rate, it is possible to more accurately estimate the location and level of pain.

[0062] The symptom analysis unit can display the area of ​​pain in a 3D model, making it easier for doctors to understand visually. For example, the symptom analysis unit adds a generative AI function that displays the area of ​​pain in a 3D model, making it easier for doctors to understand visually. For example, the area of ​​pain is highlighted on the 3D model. The symptom analysis unit also builds a system that displays the area of ​​pain in a patient in a 3D model, making it easier for doctors to understand visually. For example, the area of ​​pain is displayed in different colors on the 3D model. The symptom analysis unit also develops a generative AI algorithm that displays the area of ​​pain in a 3D model, making it easier for doctors to understand visually. For example, the area of ​​pain is animated on the 3D model. This makes it easier for doctors to understand visually by displaying the area of ​​pain in a 3D model.

[0063] The symptom analysis unit can use the emotion estimation function to monitor the emotions of a patient when they feel pain in real time and provide feedback to a doctor. For example, the symptom analysis unit uses the emotion estimation function to build a system that monitors the emotions of a patient when they feel pain in real time and provides feedback to a doctor. For example, it displays an emotion score in real time. The symptom analysis unit also develops a generative AI algorithm that monitors the emotions of a patient when they feel pain in real time and provides feedback to a doctor. For example, it analyzes emotion estimation data in real time. The symptom analysis unit also uses the emotion estimation function to develop a system that monitors the emotions of a patient when they feel pain in real time and provides feedback to a doctor. For example, it displays an emotion score in a graph. This makes it possible to monitor the emotions of a patient when they feel pain in real time and provide feedback to a doctor.

[0064] The explanation generation unit can automatically adjust the level of detail of the explanation according to the elderly patient's level of comprehension. The explanation generation unit, for example, develops a generative AI algorithm that automatically adjusts the level of detail of the explanation according to the elderly patient's level of comprehension. For example, the length of the explanation and the use of technical terms are adjusted according to the level of comprehension. The explanation generation unit also builds a system that analyzes the elderly patient's level of comprehension in real time and automatically adjusts the level of detail of the explanation. For example, the content of the explanation is simplified according to the level of comprehension. The explanation generation unit also develops a generative AI algorithm that automatically adjusts the level of detail of the explanation based on the elderly patient's level of comprehension. For example, the order and emphasis of the explanation are changed according to the level of comprehension. This makes it possible to automatically adjust the level of detail of the explanation according to the elderly patient's level of comprehension.

[0065] The explanation generation unit can convert medical terminology into everyday analogies, allowing patients to intuitively understand. For example, the explanation generation unit adds a generative AI function that converts medical terminology into everyday analogies, allowing patients to intuitively understand. For example, it converts "antibiotics" into "drugs that kill bacteria." The explanation generation unit also develops a generative AI algorithm that converts medical terminology into everyday analogies, making it easier for patients to understand. For example, it converts "MRI" into "a machine that takes pictures of the inside of the body." The explanation generation unit also builds a system that converts medical terminology into everyday analogies, allowing patients to intuitively understand. For example, it converts "blood pressure" into "strength of blood flow." In this way, by converting medical terminology into everyday analogies, patients can intuitively understand.

[0066] The explanation generation unit can use the emotion estimation function to analyze the emotional reactions of elderly patients when they receive an explanation and evaluate their level of understanding. The explanation generation unit, for example, uses the emotion estimation function to analyze the emotional reactions of elderly patients when they receive an explanation and build a system to evaluate their level of understanding. For example, it analyzes facial expressions and tone of voice to estimate the level of understanding. The explanation generation unit also develops a generation AI algorithm that analyzes the emotional reactions of elderly patients when they receive an explanation in real time and evaluates their level of understanding. For example, it evaluates the level of understanding based on an emotion score. The explanation generation unit also uses the emotion estimation function to analyze the emotional reactions of elderly patients when they receive an explanation and develops a system to evaluate their level of understanding. For example, it provides feedback on the level of understanding based on emotion data. This makes it possible to analyze the emotional reactions of elderly patients when they receive an explanation and evaluate their level of understanding.

[0067] The explanation generation unit can automatically generate audio explanations for elderly patients and provide them in a form that is easy to understand auditorily. The explanation generation unit, for example, builds a system that automatically generates audio explanations for elderly patients and provides them in a form that is easy to understand auditorily. For example, a generation AI converts medical terms into simpler words and provides audio explanations. The explanation generation unit also develops an algorithm for a generation AI that automatically generates audio explanations and provides them in a form that is easy to understand auditorily for elderly patients. For example, an explanation is generated using voice synthesis technology. The explanation generation unit also develops a system that automatically generates audio explanations for elderly patients and provides them in a form that is easy to understand auditorily. For example, an audio guide function is added. This makes it possible to automatically generate audio explanations for elderly patients and provide them in a form that is easy to understand auditorily.

[0068] The explanation generation unit can visually complement explanations of medical terms using animations and illustrations. For example, the explanation generation unit adds a generative AI function that visually complements explanations of medical terms using animations and illustrations. For example, it could show the effects of a medication using animation. The explanation generation unit also develops a generative AI algorithm that automatically generates animations and illustrations to visually complement explanations of medical terms. For example, it could explain the flow of surgery using illustrations. The explanation generation unit also builds a system that visually complements explanations of medical terms using animations and illustrations. For example, it could show the progression of a disease using animation. In this way, visually complementing explanations of medical terms with animations and illustrations makes it easier for patients to understand.

[0069] The explanation generation unit uses the emotion estimation function to monitor the emotions of elderly patients when receiving an explanation in real time, and can repeat the explanation depending on the level of understanding. For example, the explanation generation unit uses the emotion estimation function to add a function to monitor the emotions of elderly patients when receiving an explanation in real time, and repeat the explanation depending on the level of understanding. For example, if the level of understanding is low, the explanation generation unit develops a generation AI algorithm that monitors the emotions of elderly patients when receiving an explanation in real time, and repeats the explanation depending on the level of understanding. For example, the explanation is re-explanatory based on the emotion score. The explanation generation unit also uses the emotion estimation function to build a system that monitors the emotions of elderly patients when receiving an explanation in real time, and repeats the explanation depending on the level of understanding. For example, the explanation is re-explanatory based on emotion data. This makes it possible to monitor the emotions of elderly patients when receiving an explanation in real time, and repeat the explanation depending on the level of understanding.

[0070] The examination support unit can explain the flow of the examination using characters and animation that children like. The examination support unit, for example, develops a generative AI algorithm that explains the flow of the examination using characters and animation that children like. For example, it generates a video in which a character explains the procedure of the examination. The examination support unit also builds a system that explains the flow of the examination using characters that children like. For example, a character shows the procedure of the examination in an animation. The examination support unit also develops a generative AI algorithm that explains the flow of the examination using animation that children like. For example, it generates a story in which an animated character explains the procedure of the examination. In this way, by explaining the flow of the examination using characters and animation that children like, children can receive the examination without being scared.

[0071] The examination support unit can analyze the child's reactions and evaluate the stress level during the examination. For example, the examination support unit adds a generative AI function that analyzes the child's reactions in real time and evaluates the stress level during the examination. For example, it analyzes facial expressions and movements to evaluate the stress level. The examination support unit also develops a generative AI algorithm that analyzes the child's reactions and evaluates the stress level during the examination. For example, it analyzes the heart rate and respiratory rate to evaluate the stress level. The examination support unit also builds a system that analyzes the child's reactions in real time and evaluates the stress level during the examination. For example, it displays the stress level in a graph and provides feedback to the doctor. This makes it possible to analyze the child's reactions and evaluate the stress level during the examination.

[0072] The medical examination support unit can use the emotion estimation function to infer emotions from the child's facial expressions and voice and adjust the progress of the examination. For example, the medical examination support unit uses the emotion estimation function to build a system that infers emotions from the child's facial expressions and voice and adjusts the progress of the examination. For example, it adjusts the pace of the examination based on the emotion score. The medical examination support unit also develops a generative AI algorithm that infers emotions from the child's facial expressions and voice and adjusts the progress of the examination. For example, it analyzes emotions using facial expression recognition technology. The medical examination support unit also uses the emotion estimation function to develop a system that infers emotions from the child's facial expressions and voice in real time and adjusts the progress of the examination. For example, it adjusts the content of the examination based on the emotion data. This makes it possible to infer emotions from the child's facial expressions and voice and adjust the progress of the examination.

[0073] The examination support unit customizes the examination room environment for children, and the generation AI can automatically adjust the music and video as the examination progresses. For example, the examination support unit builds a system that customizes the examination room environment for children and the generation AI automatically adjusts the music and video as the examination progresses. For example, playing relaxing music as the examination progresses. The examination support unit also develops an algorithm that customizes the examination room environment for children and the generation AI automatically adjusts the video as the examination progresses. For example, playing animation as the examination progresses. The examination support unit also customizes the examination room environment for children and builds a system that customizes the music and video in real time as the examination progresses. For example, switching the video as the examination progresses. This makes it possible to customize the examination room environment for children and automatically adjust the music and video as the examination progresses.

[0074] The medical examination support unit can simulate the flow of a medical examination using virtual reality (VR) when a child receives a medical examination. For example, the medical examination support unit adds a generative AI function that simulates the flow of a medical examination using virtual reality (VR) when a child receives a medical examination. For example, it allows the child to experience the flow of a medical examination using VR goggles. The medical examination support unit also develops a generative AI algorithm that allows a child to simulate the flow of a medical examination using virtual reality (VR). For example, it reproduces the procedure of a medical examination in VR. The medical examination support unit also builds a system that simulates the flow of a medical examination using virtual reality (VR) when a child receives a medical examination. For example, it explains the flow of the medical examination in a VR environment. This makes it possible to simulate the flow of a medical examination using virtual reality (VR) when a child receives a medical examination.

[0075] The medical examination support unit can use the emotion estimation function to monitor the emotions of children when they are examined in real time and provide feedback to the doctor. For example, the medical examination support unit uses the emotion estimation function to build a system that monitors the emotions of children when they are examined in real time and provides feedback to the doctor. For example, it displays emotion scores in real time. The medical examination support unit also develops a generative AI algorithm that monitors the emotions of children when they are examined in real time and provides feedback to the doctor. For example, it analyzes emotion estimation data in real time. The medical examination support unit also uses the emotion estimation function to develop a system that monitors the emotions of children when they are examined in real time and provides feedback to the doctor. For example, it displays emotion scores in a graph. This makes it possible to monitor the emotions of children when they are examined in real time and provide feedback to the doctor.

[0076] The explanation generation unit generates appropriate answers to patients' anxieties and questions, thereby reassuring them. For example, the explanation generation unit adds a generative AI function that generates appropriate answers to patients' anxieties and questions, thereby reassuring them. For example, it generates specific answers to patients' questions. The explanation generation unit also develops a generative AI algorithm that analyzes patients' anxieties and questions and generates appropriate answers. For example, it generates answers based on an FAQ database. The explanation generation unit also builds a system that generates appropriate answers to patients' anxieties and questions, thereby reassuring them. For example, it generates answers using natural language generation technology. This allows appropriate answers to patients' anxieties and questions to be generated, thereby reassuring them.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The communication support system may further include a lifestyle analysis unit that analyzes the patient's lifestyle. The lifestyle analysis unit may analyze, for example, the patient's diet, exercise, and sleep patterns to identify factors that affect their health. The lifestyle analysis unit may also acquire data from the patient's smartwatch or fitness tracker to evaluate their daily activity level. Furthermore, the lifestyle analysis unit may provide specific advice for improving their health based on the patient's lifestyle. This allows for a comprehensive analysis of the patient's lifestyle and supports health management.

[0079] The communication support system may further include a social background analysis unit that analyzes the patient's social background. The social background analysis unit may analyze, for example, the patient's occupation, home environment, and social network to identify factors that affect the patient's health condition. The social background analysis unit may also evaluate the patient's stress level and the presence or absence of social support. Furthermore, the social background analysis unit may provide appropriate support and resources based on the patient's social background. This allows for a comprehensive analysis of the patient's social background and supports health management.

[0080] The communication support system can further include a genetic information analysis unit that analyzes the patient's genetic information. The genetic information analysis unit, for example, analyzes the patient's genetic data and identifies genetic risk factors. The genetic information analysis unit can also refer to the patient's family history to evaluate the risk of genetic diseases. Furthermore, the genetic information analysis unit can also propose individualized preventive measures and treatments based on the genetic information. This makes it possible to comprehensively analyze the patient's genetic information and provide individualized medical care.

[0081] The communication support system may further include an environmental factor analysis unit that analyzes the patient's environmental factors. The environmental factor analysis unit may analyze, for example, the patient's living environment, work environment, and local environment to identify factors that affect the patient's health condition. The environmental factor analysis unit may also evaluate the air quality and noise level in the patient's living environment. Furthermore, the environmental factor analysis unit may provide specific advice for reducing health risks based on the environmental factors. This allows for a comprehensive analysis of the patient's environmental factors and supports health management.

[0082] The communication support system can further include a drug response analysis unit that analyzes the patient's drug response. The drug response analysis unit, for example, analyzes data related to the patient's drug metabolism and predicts the effects and side effects of drugs. The drug response analysis unit can also refer to the patient's genetic information and past drug use history to propose individualized drug treatment. Furthermore, the drug response analysis unit can evaluate drug interactions and propose appropriate drug combinations. This allows for a comprehensive analysis of the patient's drug response and supports safe and effective drug treatment.

[0083] The communication support system can further include an emotion adjustment unit that estimates the patient's emotions and adjusts the progress of the examination based on the estimated emotions. The emotion adjustment unit, for example, analyzes the patient's facial expressions and tone of voice to estimate the emotions. The emotion adjustment unit can also adjust the pace of the examination and the content of the explanation based on the estimated emotions. Furthermore, the emotion adjustment unit can provide relaxing music or images depending on the patient's emotions. This makes it possible to monitor the patient's emotions in real time and adjust the progress of the examination.

[0084] The communication support system may further include an emotion explanation unit that estimates the patient's emotion and adjusts the content of the explanation based on the estimated emotion. The emotion explanation unit may, for example, analyze the patient's facial expression or tone of voice to estimate the emotion. The emotion explanation unit may also adjust the level of detail and wording of the explanation based on the estimated emotion. Furthermore, the emotion explanation unit may change the order and emphasis of the explanation depending on the patient's emotion. This makes it possible to monitor the patient's emotion in real time and adjust the content of the explanation.

[0085] The communication support system may further include an emotional environment unit that estimates the patient's emotions and adjusts the examination environment based on the estimated emotions. The emotional environment unit may, for example, analyze the patient's facial expressions and tone of voice to estimate the patient's emotions. The emotional environment unit may also adjust the lighting and temperature of the examination room based on the estimated emotions. Furthermore, the emotional environment unit may provide relaxing music and images according to the patient's emotions. This allows the patient's emotions to be monitored in real time and the examination environment to be adjusted.

[0086] The communication support system may further include an emotion feedback unit that estimates the patient's emotion and provides consultation feedback based on the estimated emotion. The emotion feedback unit may, for example, analyze the patient's facial expression or tone of voice to estimate the emotion. The emotion feedback unit may also adjust the progress of the consultation or the content of the explanation based on the estimated emotion. Furthermore, the emotion feedback unit may provide feedback to the doctor in real time according to the patient's emotion. This makes it possible to monitor the patient's emotion in real time and provide consultation feedback.

[0087] The communication support system can further include an emotional stress reduction unit that estimates the patient's emotions and reduces the stress of the examination based on the estimated emotions. The emotional stress reduction unit, for example, analyzes the patient's facial expressions and tone of voice to estimate the emotions. The emotional stress reduction unit can also adjust the progress of the examination and the content of the explanation based on the estimated emotions. Furthermore, the emotional stress reduction unit can provide relaxing music or images according to the patient's emotions. In this way, the patient's emotions can be monitored in real time and the stress of the examination can be reduced.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The symptom analysis unit uses generative AI to analyze the patient's symptoms. For example, the symptom analysis unit analyzes the patient's words, facial expressions, and movements to infer the location and severity of the pain. It can also analyze the tone and rhythm of the patient's voice to infer the intensity and type of pain. It can also refer to the patient's past medical records and compare them with cases with similar symptoms to identify the cause of the pain. Step 2: The explanation generator generates an explanation based on the symptoms analyzed by the symptom analyzer. For example, the explanation generator converts a doctor's explanation into something that is easy to understand for elderly patients. It can also convert medical terms into everyday analogies that patients can intuitively understand. Furthermore, it can use the emotion estimation function to analyze the emotional reactions of elderly patients when they receive an explanation and evaluate their level of understanding. Step 3: The medical examination support unit supports the medical examination using the explanation generated by the explanation generation unit. For example, the medical examination support unit can explain the procedure of the medical examination to small children using gentle words and characters. It can also analyze the child's reactions and evaluate their stress level during the examination. Furthermore, it can use an emotion estimation function to estimate the child's emotions from their facial expressions and voice and adjust the progress of the examination.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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]

[0157] 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 symptom analysis unit using generative AI, an explanation generation unit that generates an explanation based on the symptom analyzed by the symptom analysis unit; a medical examination support unit that supports medical examinations using the explanation generated by the explanation generation unit; A system characterized by:

2. The symptom analysis unit Analyzing the patient's movements to estimate the location and severity of the pain 2. The system of claim 1.

3. The explanation generation unit Converting doctors' explanations into easy-to-understand information for elderly patients 2. The system of claim 1.

4. The medical examination support unit Explain the procedure for the examination to the small child using the easy-to-understand words and characters 2. The system of claim 1.

5. The symptom analysis unit Analyzing the tone and rhythm of the patient's voice to estimate the intensity and type of pain 2. The system of claim 1.

6. The symptom analysis unit Review the patient's past medical records and compare them with cases with similar symptoms to identify the cause of the pain 2. The system of claim 1.

7. The symptom analysis unit Estimate the patient's emotions from their facial expressions and voice, and evaluate the degree of pain and emotional stress.

2. The system of claim 1.

8. The symptom analysis unit In addition to analyzing the patient's movements, sensors are used to measure skin temperature and sweating rate to more accurately estimate the location and severity of the pain.

2. The system of claim 1.

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