System

Abstract

An object of a system according to an embodiment is to improve the effectiveness of telephone consultants by utilizing AI.SOLUTION: A system includes a symptom analysis part, an advice providing part, a hospital selection part, and a voice guidance providing part. The condition analysis unit receives telephone consultation from the user and analyzes the condition of the user. The advice providing unit provides advice based on the symptom analyzed by the symptom analysis unit. The hospital selecting unit selects a hospital based on the advice provided by the advice providing unit. The voice guidance providing unit provides voice guidance related to the hospital selected by the hospital selecting 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] With conventional technology, there was a problem that the number of telephone consultation centers was limited, and the burden on counselors was heavy.

[0005] The system according to the embodiment aims to improve the efficiency of telephone consultation services by utilizing AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a symptom analysis unit, an advice providing unit, a hospital selection unit, and an audio guidance providing unit. The symptom analysis unit receives a telephone consultation from a user and analyzes the user's symptoms. The advice providing unit provides advice based on the symptoms analyzed by the symptom analysis unit. The hospital selection unit selects a hospital based on the advice provided by the advice providing unit. The audio guidance providing unit provides audio guidance regarding the hospital selected by the hospital selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of telephone consultation services by utilizing AI. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The emergency consultation system according to the embodiment of the present invention is a system that provides appropriate advice according to the user's symptoms, selects hospitals, and provides voice guidance. As a result, the emergency consultation system provides appropriate advice and hospital selection according to the user's symptoms, enabling a quick response.

[0029] An emergency consultation system according to an embodiment includes a symptom analysis unit, an advice providing unit, a hospital selection unit, and a voice guidance providing unit. The symptom analysis unit receives a telephone consultation from a user and analyzes the user's symptoms. For example, when a user complains of a headache, the symptom analysis unit analyzes the symptoms and identifies possible causes and solutions. The symptom analysis unit converts the user's speech into text data using, for example, speech recognition technology and analyzes the symptoms using natural language processing technology. The advice providing unit provides advice based on the symptoms analyzed by the symptom analysis unit. For example, the advice providing unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to provide the user with specific advice such as "If your headache persists, apply ice or take a painkiller." The generation AI receives a prompt containing information about the user's symptoms as input and generates appropriate advice based on the prompt. The hospital selection unit selects a hospital based on the advice provided by the advice providing unit. For example, if a user complains of "stomach pain," the hospital selection unit selects a hospital appropriate for the user's symptoms and provides information such as, "The nearest internal medicine hospital is XX. Its office hours are △△." The hospital selection unit inputs a prompt including the user's symptoms and location information into the generation AI, and selects a hospital based on the prompt. The voice guidance providing unit provides voice guidance regarding the hospital selected by the hospital selection unit. For example, the voice guidance providing unit uses the generation AI to provide voice guidance to the user such as, "The nearest internal medicine hospital is XX. Its office hours are △△." This enables the emergency consultation system according to the embodiment to provide appropriate advice and hospital selection according to the user's symptoms, enabling a prompt response.

[0030] The symptom analysis unit can refer to the user's past medical history and reflect it in the symptom analysis. For example, the symptom analysis unit retrieves the user's past medical history from a database and reflects it in the current symptom analysis. For example, if a user who was previously diagnosed with a headache complains of a headache again, advice is provided taking into account the past diagnosis results and prescribed medications. The symptom analysis unit also refers to the user's past medical history and allergy information and reflects it in the symptom analysis. For example, it may advise the user to avoid medications that have caused allergic reactions in the past. The symptom analysis unit also analyzes the progression of symptoms and the effectiveness of treatment based on the user's past medical records and provides optimal advice for the current symptoms. For example, if a past treatment was effective, it may recommend that treatment be repeated. This makes it possible to provide personalized advice that takes into account the user's past medical history.

[0031] The symptom analysis unit analyzes changes in the user's voice tone and speaking style, and can detect symptoms of high urgency early. The symptom analysis unit, for example, analyzes changes in the user's voice tone and speaking style in real time to detect symptoms of high urgency early. For example, it determines the level of urgency when the user's breathing becomes rough or the voice is trembling. The symptom analysis unit also uses voice analysis technology to detect changes in the speed and rhythm of the user's speech and identify symptoms of high urgency. For example, it issues a warning if the user's speaking speed suddenly slows down. The symptom analysis unit also analyzes the user's voice data to detect signs of stress or anxiety. For example, it determines the level of urgency when the user's voice tone becomes higher or the user stumbles over words. This enables early detection of symptoms of high urgency and enables prompt response.

[0032] The advice providing unit can notify the user of the symptom analysis results in real time to promote self-management. For example, the advice providing unit can notify the user of the symptom analysis results in real time to the smartphone app, allowing the user to self-manage. For example, the cause of a headache and how to deal with it can be displayed in the app. The advice providing unit can also notify the user of the symptom analysis results in the smartphone app, setting a reminder for self-management. For example, it can notify the user of the time to take medicine. The advice providing unit can also provide the user with the symptom analysis results through the smartphone app to promote self-management. For example, it can recommend a doctor's consultation if symptoms do not improve. This promotes self-management by the user, improving health management.

[0033] The advice providing unit can automatically recommend appropriate self-care videos and articles based on the user's symptoms. The advice providing unit, for example, builds a system that automatically recommends appropriate self-care videos and articles based on the user's symptoms. For example, it recommends videos on how to deal with headaches. The advice providing unit also automatically provides self-care content appropriate for the user based on the symptom analysis results. For example, it displays articles introducing stretching and massage techniques. The advice providing unit also develops an algorithm that recommends self-care videos and articles according to the user's symptoms. For example, it automatically displays videos on how to deal with stomach pain. This makes it possible to provide the user with information to perform appropriate self-care.

[0034] The hospital selection unit can analyze hospital congestion and waiting times in real time and select the most suitable hospital. The hospital selection unit, for example, analyzes hospital congestion and waiting times in real time and builds a system to select the most suitable hospital for the user. For example, it may preferentially recommend hospitals with short waiting times. The hospital selection unit also develops an algorithm to select the most suitable hospital based on the user's location information and hospital congestion. For example, it may recommend a nearby hospital with less availability. The hospital selection unit also introduces a system that analyzes real-time hospital data and selects the most suitable hospital for the user. For example, it may recommend a hospital taking into account times when congestion is less. This allows the most suitable hospital to be selected for the user and shortens waiting times.

[0035] The hospital selection unit can recommend the most accessible hospital by taking into account the user's means of transportation and travel time. The hospital selection unit, for example, builds a system that recommends the most accessible hospital by taking into account the user's means of transportation and travel time. For example, it selects hospitals based on the usage of public transportation. The hospital selection unit also develops an algorithm that recommends the most accessible hospital based on the user's location information and means of transportation. For example, it selects hospitals by taking into account travel time by car. The hospital selection unit also introduces a system that analyzes the user's means of transportation and travel time in real time and recommends the most suitable hospital. For example, it recommends hospitals that are easily accessible on foot. This makes it possible to recommend the most accessible hospital to the user.

[0036] The hospital selection unit can link the results of hospital selection with the user's calendar app to automatically set appointments and reminders. For example, the hospital selection unit can link the results of hospital selection with the user's calendar app to build a system that automatically sets appointments and reminders. For example, it can automatically add medical appointments to the calendar. The hospital selection unit can also link with the user's calendar app to set reminders based on the results of hospital selection. For example, it can send notifications before the appointment starts. The hospital selection unit can also reflect the results of hospital selection in the calendar app, allowing users to smoothly manage appointments and reminders. For example, it can allow users to check and change medical appointments from the calendar. This allows users to smoothly manage appointments and reminders.

[0037] The hospital selection unit can provide online medical consultation options based on the user's symptoms, enabling remote consultations. The hospital selection unit, for example, builds a system that provides online medical consultation options based on the user's symptoms. For example, it may recommend online medical consultations in cases where the symptoms are mild. The hospital selection unit also provides online medical consultation options to the user based on the results of symptom analysis. For example, it may suggest a consultation via video call. The hospital selection unit also develops an algorithm that provides online medical consultation options according to the user's symptoms. For example, it may recommend online medical consultations to users who live in remote locations. This allows the user to receive medical consultations remotely.

[0038] The Counselor Support Department can learn from the past response history of counselors and propose the optimal response method. For example, the Counselor Support Department will build a system in which AI learns from the past response history of counselors and proposes the optimal response method. For example, it will propose a response method based on past successful cases. The Counselor Support Department will also analyze the past response history of counselors and have AI propose the optimal response method in real time. For example, it will refer to past responses to similar symptoms. The Counselor Support Department will also develop an algorithm in which AI learns from the response history of counselors and proposes the optimal response method. For example, it will select the optimal response method based on past response results. This will allow the system to learn from the past response history of counselors and propose the optimal response method.

[0039] The counselor support unit can share data on the user's symptoms in real time, allowing counselors to respond quickly. The counselor support unit, for example, builds a system that shares data on the user's symptoms in real time, allowing counselors to respond quickly. For example, it instantly displays the user's symptoms and past medical history. The counselor support unit also shares the user's symptom data in real time, allowing counselors to respond quickly. For example, it provides an interface that allows the details and urgency of symptoms to be immediately confirmed. The counselor support unit also develops an algorithm that shares data on the user's symptoms in real time, allowing counselors to respond quickly. For example, it introduces a system that instantly shares symptom analysis results. This allows the user's symptom data to be shared in real time, allowing counselors to respond quickly.

[0040] The counselor support department can use AI to automatically generate FAQs and provide them to users in order to reduce the workload on counselors. For example, the counselor support department builds a system in which AI automatically generates FAQs and provides them to users in order to reduce the workload on counselors. For example, it automatically generates answers to frequently asked questions. The counselor support department also uses AI to automatically generate FAQs based on user questions to reduce the workload on counselors. For example, it analyzes the content of the user's question and provides an appropriate answer. The counselor support department also develops an algorithm in which AI automatically generates FAQs and provides them to users in order to reduce the workload on counselors. For example, it generates FAQs based on past question data. This reduces the workload on counselors and enables FAQs to be automatically provided to users.

[0041] The Counselor Support Department will build a system where AI will provide real-time support to counselors while they are responding and will instantly provide them with the information they need. For example, it will instantly display information about the user's symptoms. The Counselor Support Department will also develop an algorithm where AI will provide real-time support to counselors while they are responding and will instantly provide them with the information they need. For example, it will instantly provide information about the user's past medical history and current symptoms. The Counselor Support Department will also develop an algorithm where AI will provide real-time support to counselors while they are responding and will instantly provide them with the information they need. For example, it will instantly provide appropriate answers to the user's questions. This will allow counselors to instantly provide the information they need while they are responding.

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

[0043] The symptom analysis unit analyzes changes in the user's voice tone and speaking style to detect symptoms of high urgency early on. For example, it determines the urgency when the user's breathing becomes heavy or the voice is trembling. The symptom analysis unit also uses voice analysis technology to detect changes in the speed and rhythm of the user's speech to identify symptoms of high urgency. For example, it issues a warning if the user's speaking speed suddenly slows down. The symptom analysis unit also analyzes the user's voice data to detect signs of stress or anxiety. For example, it determines the urgency when the user's voice tone becomes higher or the user stumbles over words. This enables early detection of symptoms of high urgency and enables a prompt response.

[0044] The advice providing unit can automatically recommend appropriate self-care videos and articles based on the user's symptoms. For example, it can recommend videos on how to deal with headaches. The advice providing unit also automatically provides self-care content appropriate for the user based on the results of symptom analysis. For example, it can display articles introducing stretching and massage techniques. The advice providing unit also develops an algorithm to recommend self-care videos and articles according to the user's symptoms. For example, it can automatically display videos on how to deal with stomach pain. This makes it possible to provide information for the user to perform appropriate self-care.

[0045] The hospital selection unit can analyze hospital congestion and waiting times in real time and select the most suitable hospital. For example, it can prioritize recommending hospitals with short waiting times. The hospital selection unit will also develop an algorithm to select the most suitable hospital based on the user's location information and hospital congestion. For example, it will recommend a nearby hospital with available seats. The hospital selection unit will also introduce a system that analyzes real-time hospital data and selects the most suitable hospital for the user. For example, it will recommend hospitals taking into account less crowded times. This will allow the system to select the most suitable hospital for the user and reduce waiting times.

[0046] The hospital selection unit can recommend the most accessible hospital, taking into account the user's means of transportation and travel time. For example, it can select hospitals based on public transportation usage. The hospital selection unit will also develop an algorithm to recommend the most accessible hospital based on the user's location information and means of transportation. For example, it can select hospitals taking into account travel time by car. The hospital selection unit will also introduce a system that analyzes the user's means of transportation and travel time in real time and recommends the most suitable hospital. For example, it can recommend hospitals that are easily accessible on foot. This allows the system to recommend the most accessible hospital to the user.

[0047] The Counselor Support Department can learn from the past response history of counselors and propose the most appropriate response method. For example, a system can be built in which AI learns from the past response history of counselors and proposes the most appropriate response method. For example, a response method can be proposed based on past successful cases. The Counselor Support Department can also analyze the past response history of counselors and have AI propose the most appropriate response method in real time. For example, it can refer to past responses to similar symptoms. The Counselor Support Department can also develop an algorithm in which AI learns from the response history of counselors and proposes the most appropriate response method. For example, it can select the most appropriate response method based on past response results. This makes it possible to learn from the past response history of counselors and propose the most appropriate response method.

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

[0049] Step 1: The symptom analysis unit receives a phone consultation from a user and analyzes the user's symptoms. For example, if the user complains of a headache, the symptom analysis unit analyzes the symptom and identifies possible causes and solutions. The symptom analysis unit uses voice recognition technology to convert the user's comments into text data and analyzes the symptoms using natural language processing technology. Step 2: The advice providing unit provides advice based on the symptoms analyzed by the symptom analysis unit. For example, the advice providing unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to provide the user with specific advice such as "If your headache persists, apply ice or take a painkiller." The generation AI receives as input a prompt containing information about the user's symptoms and generates appropriate advice based on the prompt. Step 3: The hospital selection unit selects a hospital based on the advice provided by the advice provision unit. For example, if a user complains of "stomach pain," the hospital selection unit selects a hospital appropriate for that symptom and provides information such as "The nearest internal medicine hospital is XX. Consultation hours are △△." The hospital selection unit inputs a prompt containing the user's symptoms and location information into the generation AI, and selects a hospital based on that prompt. Step 4: The voice guidance providing unit provides voice guidance about the hospitals selected by the hospital selection unit. For example, the voice guidance providing unit uses a generation AI to provide the user with voice guidance such as, "The nearest internal medicine hospital is XX. Consultation hours are △△."

[0050] (Example 2) The emergency consultation system according to the embodiment of the present invention is a system that provides appropriate advice according to the user's symptoms, selects hospitals, and provides voice guidance. As a result, the emergency consultation system provides appropriate advice and hospital selection according to the user's symptoms, enabling a quick response.

[0051] An emergency consultation system according to an embodiment includes a symptom analysis unit, an advice providing unit, a hospital selection unit, and a voice guidance providing unit. The symptom analysis unit receives a telephone consultation from a user and analyzes the user's symptoms. For example, when a user complains of a headache, the symptom analysis unit analyzes the symptoms and identifies possible causes and solutions. The symptom analysis unit converts the user's speech into text data using, for example, speech recognition technology and analyzes the symptoms using natural language processing technology. The advice providing unit provides advice based on the symptoms analyzed by the symptom analysis unit. For example, the advice providing unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to provide the user with specific advice such as "If your headache persists, apply ice or take a painkiller." The generation AI receives a prompt containing information about the user's symptoms as input and generates appropriate advice based on the prompt. The hospital selection unit selects a hospital based on the advice provided by the advice providing unit. For example, if a user complains of "stomach pain," the hospital selection unit selects a hospital appropriate for the user's symptoms and provides information such as, "The nearest internal medicine hospital is XX. Its office hours are △△." The hospital selection unit inputs a prompt including the user's symptoms and location information into the generation AI, and selects a hospital based on the prompt. The voice guidance providing unit provides voice guidance regarding the hospital selected by the hospital selection unit. For example, the voice guidance providing unit uses the generation AI to provide voice guidance to the user such as, "The nearest internal medicine hospital is XX. Its office hours are △△." This enables the emergency consultation system according to the embodiment to provide appropriate advice and hospital selection according to the user's symptoms, enabling a prompt response.

[0052] The symptom analysis unit can refer to the user's past medical history and reflect it in the symptom analysis. For example, the symptom analysis unit retrieves the user's past medical history from a database and reflects it in the current symptom analysis. For example, if a user who was previously diagnosed with a headache complains of a headache again, advice is provided taking into account the past diagnosis results and prescribed medications. The symptom analysis unit also refers to the user's past medical history and allergy information and reflects it in the symptom analysis. For example, it may advise the user to avoid medications that have caused allergic reactions in the past. The symptom analysis unit also analyzes the progression of symptoms and the effectiveness of treatment based on the user's past medical records and provides optimal advice for the current symptoms. For example, if a past treatment was effective, it may recommend that treatment be repeated. This makes it possible to provide personalized advice that takes into account the user's past medical history.

[0053] The symptom analysis unit analyzes changes in the user's voice tone and speaking style, and can detect symptoms of high urgency early. The symptom analysis unit, for example, analyzes changes in the user's voice tone and speaking style in real time to detect symptoms of high urgency early. For example, it determines the level of urgency when the user's breathing becomes rough or the voice is trembling. The symptom analysis unit also uses voice analysis technology to detect changes in the speed and rhythm of the user's speech and identify symptoms of high urgency. For example, it issues a warning if the user's speaking speed suddenly slows down. The symptom analysis unit also analyzes the user's voice data to detect signs of stress or anxiety. For example, it determines the level of urgency when the user's voice tone becomes higher or the user stumbles over words. This enables early detection of symptoms of high urgency and enables prompt response.

[0054] The symptom analysis unit can use the emotion estimation function to analyze the user's emotional state and provide advice to reduce stress and anxiety. For example, the symptom analysis unit uses the emotion estimation function to analyze the user's tone of voice and facial expression to measure the level of stress and anxiety. For example, if the user's voice is trembling, the symptom analysis unit can suggest ways to relax. The symptom analysis unit can also analyze the user's emotional state in real time and provide specific advice to reduce stress and anxiety. For example, it can recommend deep breathing or relaxation techniques. The symptom analysis unit can also build a system that provides advice based on the user's emotional state based on the emotion estimation data. For example, if anxiety is high, a message that provides a sense of security can be displayed. This allows the system to provide advice based on the user's emotional state and reduce stress and anxiety.

[0055] The advice providing unit can notify the user of the symptom analysis results in real time to promote self-management. For example, the advice providing unit can notify the user of the symptom analysis results in real time to the smartphone app, allowing the user to self-manage. For example, the cause of a headache and how to deal with it can be displayed in the app. The advice providing unit can also notify the user of the symptom analysis results in the smartphone app, setting a reminder for self-management. For example, it can notify the user of the time to take medicine. The advice providing unit can also provide the user with the symptom analysis results through the smartphone app to promote self-management. For example, it can recommend a doctor's consultation if symptoms do not improve. This promotes self-management by the user, improving health management.

[0056] The advice providing unit can automatically recommend appropriate self-care videos and articles based on the user's symptoms. The advice providing unit, for example, builds a system that automatically recommends appropriate self-care videos and articles based on the user's symptoms. For example, it recommends videos on how to deal with headaches. The advice providing unit also automatically provides self-care content appropriate for the user based on the symptom analysis results. For example, it displays articles introducing stretching and massage techniques. The advice providing unit also develops an algorithm that recommends self-care videos and articles according to the user's symptoms. For example, it automatically displays videos on how to deal with stomach pain. This makes it possible to provide the user with information to perform appropriate self-care.

[0057] The advice providing unit can use the emotion estimation function to provide relaxation methods and mental care advice according to the user's emotional state. The advice providing unit, for example, uses the emotion estimation function to analyze the user's emotional state and provide relaxation methods and mental care advice. For example, it may recommend meditation methods when stress is high. The advice providing unit also builds a system that provides relaxation methods according to the user's emotional state. For example, it may suggest deep breathing or relaxation methods when anxiety is high. The advice providing unit also provides mental care advice according to the user's emotional state based on the emotion estimation data. For example, it may recommend music or videos for relaxation. This makes it possible to provide relaxation methods and mental care advice according to the user's emotional state.

[0058] The hospital selection unit can analyze hospital congestion and waiting times in real time and select the most suitable hospital. The hospital selection unit, for example, analyzes hospital congestion and waiting times in real time and builds a system to select the most suitable hospital for the user. For example, it may preferentially recommend hospitals with short waiting times. The hospital selection unit also develops an algorithm to select the most suitable hospital based on the user's location information and hospital congestion. For example, it may recommend a nearby hospital with less availability. The hospital selection unit also introduces a system that analyzes real-time hospital data and selects the most suitable hospital for the user. For example, it may recommend a hospital taking into account times when congestion is less. This allows the most suitable hospital to be selected for the user and shortens waiting times.

[0059] The hospital selection unit can recommend the most accessible hospital by taking into account the user's means of transportation and travel time. The hospital selection unit, for example, builds a system that recommends the most accessible hospital by taking into account the user's means of transportation and travel time. For example, it selects hospitals based on the usage of public transportation. The hospital selection unit also develops an algorithm that recommends the most accessible hospital based on the user's location information and means of transportation. For example, it selects hospitals by taking into account travel time by car. The hospital selection unit also introduces a system that analyzes the user's means of transportation and travel time in real time and recommends the most suitable hospital. For example, it recommends hospitals that are easily accessible on foot. This makes it possible to recommend the most accessible hospital to the user.

[0060] The hospital selection unit can use the emotion estimation function to provide reassuring guidance to reduce the user's anxiety. The hospital selection unit, for example, uses the emotion estimation function to build a system that provides reassuring guidance to reduce the user's anxiety. For example, it provides gentle voice guidance and encouraging messages. The hospital selection unit also analyzes the user's emotional state in real time and provides specific guidance to reduce anxiety. For example, it provides detailed information about the hospital and doctor profiles. The hospital selection unit also develops a system that provides reassuring guidance to reduce the user's anxiety based on the emotion estimation data. For example, it provides detailed directions to the hospital and transportation information. This makes it possible to provide guidance that reduces the user's anxiety and gives them a sense of security.

[0061] The hospital selection unit can link the results of hospital selection with the user's calendar app to automatically set appointments and reminders. For example, the hospital selection unit can link the results of hospital selection with the user's calendar app to build a system that automatically sets appointments and reminders. For example, it can automatically add medical appointments to the calendar. The hospital selection unit can also link with the user's calendar app to set reminders based on the results of hospital selection. For example, it can send notifications before the appointment starts. The hospital selection unit can also reflect the results of hospital selection in the calendar app, allowing users to smoothly manage appointments and reminders. For example, it can allow users to check and change medical appointments from the calendar. This allows users to smoothly manage appointments and reminders.

[0062] The hospital selection unit can provide online medical consultation options based on the user's symptoms, enabling remote consultations. The hospital selection unit, for example, builds a system that provides online medical consultation options based on the user's symptoms. For example, it may recommend online medical consultations in cases where the symptoms are mild. The hospital selection unit also provides online medical consultation options to the user based on the results of symptom analysis. For example, it may suggest a consultation via video call. The hospital selection unit also develops an algorithm that provides online medical consultation options according to the user's symptoms. For example, it may recommend online medical consultations to users who live in remote locations. This allows the user to receive medical consultations remotely.

[0063] The hospital selection unit can use the emotion estimation function to provide detailed route guidance and traffic information so that the user can go to the hospital with peace of mind. The hospital selection unit, for example, uses the emotion estimation function to build a system that provides detailed route guidance and traffic information so that the user can go to the hospital with peace of mind. For example, it provides route guidance in a gentle voice and detailed transportation information. The hospital selection unit also analyzes the user's emotional state in real time and provides specific route guidance and traffic information to give a sense of security. For example, it provides information on the shortest route to the hospital and how to use transportation. The hospital selection unit also develops a system that provides detailed route guidance and traffic information based on the emotion estimation data so that the user can go to the hospital with peace of mind. For example, it provides traffic congestion information and parking availability information. This makes it possible to provide detailed route guidance and traffic information so that the user can go to the hospital with peace of mind.

[0064] The Counselor Support Department can learn from the past response history of counselors and propose the optimal response method. For example, the Counselor Support Department will build a system in which AI learns from the past response history of counselors and proposes the optimal response method. For example, it will propose a response method based on past successful cases. The Counselor Support Department will also analyze the past response history of counselors and have AI propose the optimal response method in real time. For example, it will refer to past responses to similar symptoms. The Counselor Support Department will also develop an algorithm in which AI learns from the response history of counselors and proposes the optimal response method. For example, it will select the optimal response method based on past response results. This will allow the system to learn from the past response history of counselors and propose the optimal response method.

[0065] The counselor support unit can share data on the user's symptoms in real time, allowing counselors to respond quickly. The counselor support unit, for example, builds a system that shares data on the user's symptoms in real time, allowing counselors to respond quickly. For example, it instantly displays the user's symptoms and past medical history. The counselor support unit also shares the user's symptom data in real time, allowing counselors to respond quickly. For example, it provides an interface that allows the details and urgency of symptoms to be immediately confirmed. The counselor support unit also develops an algorithm that shares data on the user's symptoms in real time, allowing counselors to respond quickly. For example, it introduces a system that instantly shares symptom analysis results. This allows the user's symptom data to be shared in real time, allowing counselors to respond quickly.

[0066] The counselor support unit can use the emotion estimation function to grasp the user's emotional state in advance, allowing the counselor to respond appropriately. For example, the counselor support unit uses the emotion estimation function to build a system that grasps the user's emotional state in advance and enables the counselor to respond appropriately. For example, if the user is feeling anxious, the counselor support unit recommends a gentle response. The counselor support unit also analyzes the user's emotional state in real time and enables the counselor to respond appropriately. For example, if the user is angry, the counselor support unit recommends a calm response. The counselor support unit also develops an algorithm based on the emotion estimation data to grasp the user's emotional state in advance and enable the counselor to respond appropriately. For example, the counselor suggests a response method based on the user's emotion score. This allows the counselor to grasp the user's emotional state in advance and respond appropriately.

[0067] The counselor support department can use AI to automatically generate FAQs and provide them to users in order to reduce the workload on counselors. For example, the counselor support department builds a system in which AI automatically generates FAQs and provides them to users in order to reduce the workload on counselors. For example, it automatically generates answers to frequently asked questions. The counselor support department also uses AI to automatically generate FAQs based on user questions to reduce the workload on counselors. For example, it analyzes the content of the user's question and provides an appropriate answer. The counselor support department also develops an algorithm in which AI automatically generates FAQs and provides them to users in order to reduce the workload on counselors. For example, it generates FAQs based on past question data. This reduces the workload on counselors and enables FAQs to be automatically provided to users.

[0068] The Counselor Support Department will build a system where AI will provide real-time support to counselors while they are responding and will instantly provide them with the information they need. For example, it will instantly display information about the user's symptoms. The Counselor Support Department will also develop an algorithm where AI will provide real-time support to counselors while they are responding and will instantly provide them with the information they need. For example, it will instantly provide information about the user's past medical history and current symptoms. The Counselor Support Department will also develop an algorithm where AI will provide real-time support to counselors while they are responding and will instantly provide them with the information they need. For example, it will instantly provide appropriate answers to the user's questions. This will allow counselors to instantly provide the information they need while they are responding.

[0069] The counselor support unit can use the emotion estimation function to automatically generate a response manual according to the user's emotional state and provide it to the counselor. The counselor support unit, for example, uses the emotion estimation function to build a system that automatically generates a response manual according to the user's emotional state and provides it to the counselor. For example, it suggests an appropriate response method when the user is feeling anxious. The counselor support unit also analyzes the user's emotional state in real time and automatically generates a response manual. For example, it suggests a calm response method when the user is angry. The counselor support unit also develops an algorithm that automatically generates a response manual according to the user's emotional state based on the emotion estimation data and provides it to the counselor. For example, it suggests a response method based on the user's emotion score. This makes it possible to automatically generate a response manual according to the user's emotional state and provide it to the counselor.

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

[0071] The symptom analysis unit analyzes changes in the user's voice tone and speaking style to detect symptoms of high urgency early on. For example, it determines the urgency when the user's breathing becomes heavy or the voice is trembling. The symptom analysis unit also uses voice analysis technology to detect changes in the speed and rhythm of the user's speech to identify symptoms of high urgency. For example, it issues a warning if the user's speaking speed suddenly slows down. The symptom analysis unit also analyzes the user's voice data to detect signs of stress or anxiety. For example, it determines the urgency when the user's voice tone becomes higher or the user stumbles over words. This enables early detection of symptoms of high urgency and enables a prompt response.

[0072] The advice providing unit can automatically recommend appropriate self-care videos and articles based on the user's symptoms. For example, it can recommend videos on how to deal with headaches. The advice providing unit also automatically provides self-care content appropriate for the user based on the results of symptom analysis. For example, it can display articles introducing stretching and massage techniques. The advice providing unit also develops an algorithm to recommend self-care videos and articles according to the user's symptoms. For example, it can automatically display videos on how to deal with stomach pain. This makes it possible to provide information for the user to perform appropriate self-care.

[0073] The hospital selection unit can analyze hospital congestion and waiting times in real time and select the most suitable hospital. For example, it can prioritize recommending hospitals with short waiting times. The hospital selection unit will also develop an algorithm to select the most suitable hospital based on the user's location information and hospital congestion. For example, it will recommend a nearby hospital with available seats. The hospital selection unit will also introduce a system that analyzes real-time hospital data and selects the most suitable hospital for the user. For example, it will recommend hospitals taking into account less crowded times. This will allow the system to select the most suitable hospital for the user and reduce waiting times.

[0074] The hospital selection unit can recommend the most accessible hospital, taking into account the user's means of transportation and travel time. For example, it can select hospitals based on public transportation usage. The hospital selection unit will also develop an algorithm to recommend the most accessible hospital based on the user's location information and means of transportation. For example, it can select hospitals taking into account travel time by car. The hospital selection unit will also introduce a system that analyzes the user's means of transportation and travel time in real time and recommends the most suitable hospital. For example, it can recommend hospitals that are easily accessible on foot. This allows the system to recommend the most accessible hospital to the user.

[0075] The Counselor Support Department can learn from the past response history of counselors and propose the most appropriate response method. For example, a system can be built in which AI learns from the past response history of counselors and proposes the most appropriate response method. For example, a response method can be proposed based on past successful cases. The Counselor Support Department can also analyze the past response history of counselors and have AI propose the most appropriate response method in real time. For example, it can refer to past responses to similar symptoms. The Counselor Support Department can also develop an algorithm in which AI learns from the response history of counselors and proposes the most appropriate response method. For example, it can select the most appropriate response method based on past response results. This makes it possible to learn from the past response history of counselors and propose the most appropriate response method.

[0076] The symptom analysis unit can use the emotion estimation function to analyze the user's emotional state and provide advice to reduce stress and anxiety. For example, the emotion estimation function can be used to analyze the user's tone of voice and facial expression to measure the level of stress and anxiety. For example, if the user's voice is trembling, the symptom analysis unit can suggest ways to relax. The symptom analysis unit can also analyze the user's emotional state in real time and provide specific advice to reduce stress and anxiety. For example, it can recommend deep breathing or relaxation techniques. The symptom analysis unit can also build a system that provides advice based on the user's emotional state based on the emotion estimation data. For example, if anxiety is high, a message that provides reassurance can be displayed. This allows the system to provide advice based on the user's emotional state and reduce stress and anxiety.

[0077] The advice providing unit can use the emotion estimation function to provide relaxation methods and mental care advice according to the user's emotional state. For example, the emotion estimation function is used to analyze the user's emotional state and provide relaxation methods and mental care advice. For example, meditation methods may be recommended when stress is high. The advice providing unit also builds a system that provides relaxation methods according to the user's emotional state. For example, deep breathing and relaxation methods may be suggested when anxiety is high. The advice providing unit also provides mental care advice according to the user's emotional state based on the emotion estimation data. For example, music or videos for relaxation may be recommended. This makes it possible to provide relaxation methods and mental care advice according to the user's emotional state.

[0078] The hospital selection unit can use the emotion estimation function to provide reassuring guidance to reduce the user's anxiety. For example, a system can be constructed using the emotion estimation function to provide reassuring guidance to reduce the user's anxiety. For example, gentle voice guidance or encouraging messages can be provided. The hospital selection unit can also analyze the user's emotional state in real time and provide specific guidance to reduce anxiety. For example, detailed hospital information and doctor profiles can be provided. The hospital selection unit can also develop a system based on the emotion estimation data to provide reassuring guidance to reduce the user's anxiety. For example, detailed directions to the hospital and transportation information can be provided. This can reduce the user's anxiety and provide reassuring guidance.

[0079] The counselor support unit can use the emotion estimation function to grasp the user's emotional state in advance, allowing the counselor to respond appropriately. For example, a system can be constructed using the emotion estimation function to grasp the user's emotional state in advance, allowing the counselor to respond appropriately. For example, a gentle response can be recommended if the user is feeling anxious. The counselor support unit can also analyze the user's emotional state in real time, allowing the counselor to respond appropriately. For example, a calm response can be recommended if the user is angry. The counselor support unit can also develop an algorithm based on the emotion estimation data to grasp the user's emotional state in advance, allowing the counselor to respond appropriately. For example, a response method can be suggested based on the user's emotion score. This allows the counselor to grasp the user's emotional state in advance and respond appropriately.

[0080] The counselor support unit can use the emotion estimation function to automatically generate a response manual according to the user's emotional state and provide it to the counselor. For example, a system is constructed that uses the emotion estimation function to automatically generate a response manual according to the user's emotional state and provide it to the counselor. For example, an appropriate response method is suggested when the user is feeling anxious. The counselor support unit also analyzes the user's emotional state in real time and automatically generates a response manual. For example, a calm response method is suggested when the user is angry. The counselor support unit also develops an algorithm that automatically generates a response manual according to the user's emotional state based on the emotion estimation data and provides it to the counselor. For example, a response method is suggested based on the user's emotion score. This makes it possible to automatically generate a response manual according to the user's emotional state and provide it to the counselor.

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

[0082] Step 1: The symptom analysis unit receives a phone consultation from a user and analyzes the user's symptoms. For example, if the user complains of a headache, the symptom analysis unit analyzes the symptom and identifies possible causes and solutions. The symptom analysis unit uses voice recognition technology to convert the user's comments into text data and analyzes the symptoms using natural language processing technology. Step 2: The advice providing unit provides advice based on the symptoms analyzed by the symptom analysis unit. For example, the advice providing unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to provide the user with specific advice such as "If your headache persists, apply ice or take a painkiller." The generation AI receives as input a prompt containing information about the user's symptoms and generates appropriate advice based on the prompt. Step 3: The hospital selection unit selects a hospital based on the advice provided by the advice provision unit. For example, if a user complains of "stomach pain," the hospital selection unit selects a hospital appropriate for that symptom and provides information such as "The nearest internal medicine hospital is XX. Consultation hours are △△." The hospital selection unit inputs a prompt containing the user's symptoms and location information into the generation AI, and selects a hospital based on that prompt. Step 4: The voice guidance providing unit provides voice guidance about the hospitals selected by the hospital selection unit. For example, the voice guidance providing unit uses a generation AI to provide the user with voice guidance such as, "The nearest internal medicine hospital is XX. Consultation hours are △△."

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a symptom analysis unit that receives telephone consultations from users and analyzes the symptoms of the users; an advice providing unit that provides advice based on the symptoms analyzed by the symptom analysis unit; a hospital selection unit that selects a hospital based on the advice provided by the advice providing unit; a voice guidance providing unit that provides voice guidance regarding the hospitals selected by the hospital selecting unit. A system characterized by:

2. The symptom analysis unit Refer to the user's past medical history and reflect it in symptom analysis 2. The system of claim 1.

3. The symptom analysis unit Analyzing changes in the user's voice tone and speaking style to detect symptoms of high urgency early on 2. The system of claim 1.

4. The symptom analysis unit Analyzing the user's emotional state and providing advice to reduce stress and anxiety 2. The system of claim 1.

5. The advice providing unit Symptom analysis results are sent to the user's smartphone app in real time to promote self-management.

2. The system of claim 1.

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