System

Abstract

An object of a system according to an embodiment is to enable an applicant for a hospital consultation to quickly find an appropriate hospital on the basis of voice information and confirm acceptability.SOLUTION: A system includes a reception part, an analysis part, a retrieval part, a guide part, a communication part, and a confirmation part. The reception unit receives voice information. The analysis unit analyzes the voice information received by the reception unit and converts the voice information into text data. The search unit searches for a hospital based on the information analyzed by the analysis unit. The presentation unit presents a result of the search by the search unit. The communication part communicates the information guided by the guide part to the hospital side. The confirmation unit confirms acceptability based on the information notified by the notification 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, it was difficult for people seeking medical treatment to quickly find an appropriate hospital, and it was also time-consuming to confirm whether the hospital would accept them.

[0005] The system according to the embodiment aims to enable a person wishing to visit a hospital to quickly find an appropriate hospital based on voice information and confirm whether the hospital will accept the patient. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, a guidance unit, a communication unit, and a confirmation unit. The reception unit receives voice information. The analysis unit analyzes the voice information received by the reception unit and converts it into text data. The search unit searches for a hospital based on the information analyzed by the analysis unit. The guidance unit provides the results of the search by the search unit. The communication unit communicates the information provided by the guidance unit to the hospital. The confirmation unit confirms whether or not the patient can be admitted based on the information communicated by the communication unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a person wishing to visit a hospital to quickly find an appropriate hospital based on voice information and check whether the hospital is open. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A hospital guidance system according to an embodiment of the present invention is a system in which a patient verbally describes their symptoms, age, anxiety level, etc., and based on that information, the system guides them to nearby hospitals that are open at that time and notifies the hospitals that the patient is on their way (including confirming whether they can be accommodated). When a patient verbally describes their symptoms, age, anxiety level, etc., the hospital guidance system guides them to nearby hospitals that are open at that time and notifies the hospitals that the patient is on their way. For example, the patient inputs voice information using a voice input device. For example, the patient inputs voice information such as "I have a headache, I'm 38 years old, and I'm very anxious." This voice information is analyzed by AI and converted into text data. Next, the hospital guidance system searches for nearby hospitals that are open based on the information analyzed by the AI. The AI ​​references a hospital database and selects the most appropriate hospital, taking into account factors such as current availability, distance, and medical specialties. For example, the system searches for hospitals with appropriate medical specialties that can treat headache symptoms and creates a list of available hospitals. The search results are sent to the patient, and the hospital is notified that the patient is on their way. The patient is provided with instructions on the screen of a voice input device or via voice. For example, the instructions may say, "A nearby hospital is available for consultation. The address is ____ and the clinic hours are ____." The hospital is notified that the patient is on their way and asked to confirm whether they can accept the patient. The hospital confirms whether they can accept the patient through the system and notifies the patient. For example, the notification may say, "The hospital is available for consultation. We look forward to seeing you." This allows the hospital guidance system to ensure that patients receive prompt and appropriate medical care. For example, even in the case of sudden illness or strong anxiety, patients can be referred to an appropriate hospital quickly, allowing them to visit with peace of mind. Furthermore, by knowing the patient's information in advance, hospitals can accept patients more smoothly.

[0029] A hospital guidance system according to an embodiment includes a reception unit, an analysis unit, a search unit, a guidance unit, a communication unit, and a confirmation unit. The reception unit receives audio information in which a patient describes their symptoms, age, anxiety level, etc., using voice input. For example, the patient uses a voice input device to input voice information. The analysis unit analyzes the audio information received by the reception unit and converts it into text data. For example, the analysis unit converts the audio information into text data using a voice recognition algorithm. The search unit searches for a hospital based on the information analyzed by the analysis unit. For example, the search unit references a hospital database and selects the most appropriate hospital based on the current availability of appointments, distance, medical specialty, etc. The guidance unit provides guidance based on the results searched by the search unit. For example, the guidance unit provides guidance to the patient via voice or on a screen. The communication unit communicates the information provided by the guidance unit to the hospital. For example, the communication unit notifies the hospital that the patient is on their way. The confirmation unit confirms whether the hospital can accept the patient based on the information communicated by the communication unit. For example, the confirmation unit allows the hospital to confirm whether the patient can be accepted and notify the patient. As a result, the hospital guidance system according to the embodiment can provide prompt and appropriate medical care by guiding the patient to an appropriate hospital based on the patient's voice information and contacting the hospital.

[0030] The analysis unit can convert speech information into text data. The analysis unit, for example, uses a speech recognition algorithm to convert speech information into text data. For example, the analysis unit uses speech recognition technology to convert speech information such as telephone speech, recorded speech, and real-time speech into text data. The analysis unit can also preprocess speech data to improve the accuracy of the speech recognition algorithm. For example, the analysis unit can remove noise and normalize speech to improve the accuracy of speech recognition. Furthermore, the analysis unit can update the learning data of the speech recognition algorithm to accommodate the latest speech data. For example, the analysis unit can add new speech data to improve the accuracy of the speech recognition algorithm. In this way, converting speech information into text data makes it easier to process information.

[0031] The search unit can refer to a hospital database and select an appropriate hospital based on the current availability, distance, medical specialty, etc. The search unit, for example, refers to a hospital database and selects the most appropriate hospital taking into consideration the current availability, distance, medical specialty, etc. For example, the search unit selects an appropriate hospital based on information such as the hospital's location, medical specialty, and availability. The search unit can also select the most appropriate hospital based on the patient's current location information. For example, the search unit selects the hospital closest to the patient's current location. Furthermore, the search unit can select an available hospital based on the hospital's consultation hours and reservation status. For example, the search unit selects a hospital that is open during consultation hours. By selecting the most appropriate hospital, it is possible to provide appropriate medical care to the patient.

[0032] The guidance unit can provide guidance to the patient by voice or on a screen. The guidance unit can, for example, provide guidance to the patient by voice or on a screen. For example, the guidance unit can provide guidance to the patient by voice using voice synthesis technology. The guidance unit can also provide guidance to the patient on a screen using screen display technology. For example, the guidance unit can display information about hospitals where the patient can be seen on the screen of a voice input device. Furthermore, the guidance unit can adjust the guidance method according to the patient's situation. For example, if the patient has an emergency, the guidance unit can provide quick guidance and omit detailed information. Also, the guidance unit can provide detailed information if the patient is relaxed. This makes it possible to provide appropriate guidance to the patient and help them seek medical attention.

[0033] The communication unit can notify the hospital that the patient is on his / her way. For example, the communication unit notifies the hospital that the patient is on his / her way. For example, the communication unit notifies the hospital by telephone, email, message, or other methods. The communication unit can also adjust the content of the communication. For example, the communication unit may include information such as the patient's symptoms, age, and anxiety level in the communication. Furthermore, the communication unit can also adjust the timing of the communication. For example, the communication unit may contact the hospital just before the patient is on his / her way to the hospital. This allows for a smooth admission by notifying the hospital that the patient is on his / her way.

[0034] The confirmation unit can confirm whether the hospital can accept the patient and notify the patient. For example, the confirmation unit confirms whether the hospital can accept the patient and notifies the patient. For example, the confirmation unit sets criteria for the hospital to confirm whether the patient can be accepted and confirms based on those criteria. The confirmation unit can also adjust the means of confirmation. For example, the confirmation unit confirms with the hospital by telephone, email, message sending, or other methods. Furthermore, the confirmation unit can also notify the patient of the confirmation result. For example, if the hospital can accept the patient, the confirmation unit notifies the patient, saying, "The hospital can accept the patient. We look forward to seeing you." In this way, by confirming whether the hospital can accept the patient and notifying the patient, it is possible to support appropriate medical examinations.

[0035] The reception unit can analyze the patient's past medical history and select an appropriate reception method. The reception unit, for example, analyzes the patient's past medical history and selects an appropriate reception method. For example, the reception unit preferentially suggests a voice input method that the patient has frequently used in the past. The reception unit can also customize an input method for specific symptoms based on the patient's past medical history. The reception unit can also predict and suggest an input method to be used during a specific time period based on the patient's past medical history. This makes it possible to select the optimal reception method based on the patient's past medical history and receive a reception that is appropriate for the patient.

[0036] The reception unit can filter the voice information based on the patient's current health condition and the level of urgency when receiving the voice information. For example, the reception unit filters the voice information based on the patient's current health condition and the level of urgency when receiving the voice information. For example, the reception unit prioritizes reception when a patient complains of a highly urgent symptom. The reception unit can also provide a reception method for requesting detailed information when the patient's health condition is stable. The reception unit can also simplify or detail the reception procedure depending on the patient's level of urgency. This makes it possible to respond appropriately by filtering based on the patient's health condition and the level of urgency.

[0037] The reception unit can select an appropriate reception means depending on the patient's input method when receiving voice information. For example, when receiving voice information, the reception unit selects an appropriate reception means depending on the patient's input method. For example, if the patient selects voice input, the reception unit receives information using voice recognition technology. Also, if the patient selects text input, the reception unit can provide a text input form. Also, if the patient selects image input, the reception unit can receive information using image analysis technology. This allows for flexible response by selecting the optimal reception means depending on the patient's input method.

[0038] The reception unit can prioritize receiving highly relevant information in consideration of the geographical location information of the patient when receiving voice information. For example, the reception unit prioritizes receiving highly relevant information in consideration of the geographical location information of the patient when receiving voice information. For example, the reception unit prioritizes receiving information about hospitals that are close to the patient's current location. The reception unit can also provide information about the most appropriate hospital based on the geographical location information of the patient. The reception unit can also update the patient's location information in real time and provide the most appropriate reception method. This makes it possible to prioritize receiving highly relevant information by considering the geographical location information of the patient.

[0039] The reception unit can analyze the patient's social media activity and receive related information when receiving the voice information. For example, the reception unit analyzes the patient's social media activity and receives related information when receiving the voice information. For example, the reception unit receives the voice information based on health information shared by the patient on social media. The reception unit can also analyze the patient's social media activity and receive related information. The reception unit can also receive related information by referring to the activity of the patient's friends on social media. This makes it possible to receive related information by analyzing the patient's social media activity.

[0040] The reception unit can customize the reception method by reflecting the patient's past feedback when receiving voice information. The reception unit, for example, customizes the reception method by reflecting the patient's past feedback when receiving voice information. For example, the reception unit proposes an optimal reception method based on feedback provided by the patient in the past. The reception unit can also analyze the patient's past feedback and improve the reception procedure. The reception unit can also provide a customized reception method by reflecting the patient's feedback. This makes it possible to provide a customized reception method by reflecting the patient's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptom when analyzing the audio information. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the symptom when analyzing the audio information. For example, the analysis unit performs a detailed analysis for a symptom with a high level of importance. The analysis unit can also perform a simplified analysis for a symptom with a low level of importance. The analysis unit can also adjust the analysis procedure according to the importance of the symptom. In this way, by adjusting the level of detail of the analysis according to the importance of the symptom, appropriate analysis is possible.

[0042] The analysis unit can apply different analysis algorithms depending on the symptom category when analyzing the audio information. For example, when analyzing the audio information, the analysis unit applies different analysis algorithms depending on the symptom category. For example, in the case of a headache symptom, the analysis unit applies an analysis algorithm specialized for headache. In addition, in the case of a abdominal pain symptom, the analysis unit can also apply an analysis algorithm specialized for abdominal pain. In addition, in the case of a dyspnea symptom, the analysis unit can also apply an analysis algorithm specialized for dyspnea. In this way, by applying different analysis algorithms depending on the symptom category, appropriate analysis is possible.

[0043] When analyzing the voice information, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. For example, when analyzing the voice information, the analysis unit improves the accuracy of the analysis by referring to the patient's past analysis results. For example, the analysis unit performs the current analysis based on the patient's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the patient's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the patient's past analysis results. This makes it possible to improve the accuracy of the analysis by referring to the patient's past analysis results.

[0044] The analysis unit can determine the priority of analysis based on the time of symptom occurrence when analyzing the audio information. For example, the analysis unit determines the priority of analysis based on the time of symptom occurrence when analyzing the audio information. For example, the analysis unit prioritizes analysis of recently occurring symptoms. The analysis unit can also postpone analysis of symptoms that occurred in the past. The analysis unit can also adjust the priority of analysis based on the time of symptom occurrence. In this way, by determining the priority of analysis based on the time of symptom occurrence, appropriate analysis is possible.

[0045] The analysis unit can adjust the order of analysis based on the relevance of symptoms when analyzing the audio information. For example, the analysis unit adjusts the order of analysis based on the relevance of symptoms when analyzing the audio information. For example, the analysis unit prioritizes analysis of highly relevant symptoms. The analysis unit can also postpone analysis of less relevant symptoms. The analysis unit can also adjust the order of analysis based on the relevance of symptoms. In this way, adjusting the order of analysis based on the relevance of symptoms enables appropriate analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise when analyzing the voice information. For example, when analyzing the voice information, the analysis unit adjusts the use of technical terms in the analysis according to the patient's level of expertise. For example, if the patient has technical knowledge, the analysis unit provides the analysis results using technical terms. Also, if the patient does not have technical knowledge, the analysis unit can provide the analysis results in simple language. Also, the analysis unit can adjust the way the analysis results are expressed according to the patient's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the patient's level of expertise, it is possible to provide appropriate analysis results.

[0047] The search unit can improve the accuracy of the search by taking into account the interrelationships between symptoms when searching for a hospital. For example, the search unit improves the accuracy of the search by taking into account the interrelationships between symptoms when searching for a hospital. For example, if the symptoms of headache and fever are related, the search unit can search for hospitals that can treat both symptoms. Furthermore, if the symptoms of abdominal pain and diarrhea are related, the search unit can also search for hospitals that can treat both symptoms. Furthermore, if the symptoms of dyspnea and cough are related, the search unit can also search for hospitals that can treat both symptoms. In this way, by taking into account the interrelationships between symptoms, it is possible to improve the accuracy of the search.

[0048] The search unit can perform a search taking into account patient attribute information when searching for hospitals. For example, the search unit performs a search taking into account patient attribute information when searching for hospitals. For example, the search unit searches for hospitals with appropriate medical departments taking into account the patient's age. The search unit can also search for hospitals with appropriate medical departments taking into account the patient's gender. The search unit can also search for hospitals with appropriate medical departments taking into account the patient's medical history. This makes it possible to search for an appropriate hospital by taking into account the patient's attribute information.

[0049] The search unit can weight the search based on the frequency of occurrence of symptoms when searching for hospitals. For example, the search unit weights the search based on the frequency of occurrence of symptoms when searching for hospitals. For example, the search unit prioritizes searching for hospitals that can handle symptoms that occur frequently. The search unit can also search for hospitals that can handle symptoms that occur rarely at a later date. The search unit can also adjust the search weight according to the frequency of occurrence of symptoms. In this way, by weighting the search based on the frequency of occurrence of symptoms, it becomes possible to search for an appropriate hospital.

[0050] The search unit can perform a search taking into account the geographical distribution of hospitals when searching for hospitals. For example, the search unit performs a search taking into account the geographical distribution of hospitals when searching for hospitals. For example, the search unit preferentially searches for hospitals closest to the patient's current location. The search unit can also search for hospitals within a range that the patient can travel. The search unit can also search for the most suitable hospital taking into account the patient's geographical conditions. This makes it possible to search for an appropriate hospital by taking into account the geographical distribution of hospitals.

[0051] The search unit can improve the accuracy of the search by referring to related literature when searching for a hospital. For example, the search unit can improve the accuracy of the search by referring to related literature when searching for a hospital. For example, the search unit can search for the most suitable hospital by referring to the latest medical literature related to the symptoms. The search unit can also search for the most suitable hospital by referring to past medical literature related to the symptoms. The search unit can also improve the accuracy of the search results based on literature information related to the symptoms. This makes it possible to improve the accuracy of the search by referring to related literature.

[0052] When searching for a hospital, the search unit can perform the search taking into consideration the hospital's facilities and the doctor's expertise. When searching for a hospital, the search unit can perform the search taking into consideration the hospital's facilities and the doctor's expertise. For example, the search unit searches for a hospital that has facilities that can treat the patient's symptoms. The search unit can also search for a hospital that has a specialist who can treat the patient's symptoms. The search unit can also search for the most suitable hospital based on the hospital's facilities and the doctor's expertise. This makes it possible to search for an appropriate hospital by taking into consideration the hospital's facilities and the doctor's expertise.

[0053] The guidance unit can adjust the level of detail of the guidance based on the importance of the hospital when providing guidance. For example, the guidance unit adjusts the level of detail of the guidance based on the importance of the hospital when providing guidance. For example, the guidance unit provides detailed guidance for a hospital with a high level of importance. Furthermore, the guidance unit can also provide simplified guidance for a hospital with a low level of importance. Furthermore, the guidance unit can adjust the level of detail of the guidance depending on the importance of the hospital. In this way, by adjusting the level of detail of the guidance based on the importance of the hospital, it is possible to provide appropriate guidance.

[0054] The guidance unit can apply different guidance algorithms depending on the category of the hospital when providing guidance. For example, the guidance unit can provide quick guidance for an emergency hospital. The guidance unit can also provide detailed guidance for a specialized hospital. The guidance unit can also provide standard guidance for a general hospital. This makes it possible to provide appropriate guidance by applying different guidance algorithms depending on the category of the hospital.

[0055] The guidance unit can improve the accuracy of guidance by referring to the patient's past guidance results when providing guidance. For example, the guidance unit improves the accuracy of guidance by referring to the patient's past guidance results when providing guidance. For example, the guidance unit provides current guidance based on the patient's past guidance results. The guidance unit can also adjust the guidance algorithm by referring to the patient's past guidance results. The guidance unit can also improve the accuracy of guidance by using the patient's past guidance results. This makes it possible to improve the accuracy of guidance by referring to the patient's past guidance results.

[0056] The guidance unit can determine the priority of guidance based on the timing of hospital visits when providing guidance. The guidance unit, for example, determines the priority of guidance based on the timing of hospital visits when providing guidance. For example, the guidance unit gives priority to hospitals with upcoming visit dates. The guidance unit can also postpone providing guidance to hospitals with further away visit dates. The guidance unit can also adjust the priority of guidance based on the timing of hospital visits. In this way, by determining the priority of guidance based on the timing of hospital visits, it becomes possible to provide appropriate guidance.

[0057] The guidance unit can adjust the order of guidance based on the relevance of the hospitals when providing guidance. For example, the guidance unit adjusts the order of guidance based on the relevance of the hospitals when providing guidance. For example, the guidance unit gives priority to providing guidance to highly relevant hospitals. The guidance unit can also postpone providing guidance to less relevant hospitals. The guidance unit can also adjust the order of guidance according to the relevance of the hospitals. In this way, by adjusting the order of guidance based on the relevance of the hospitals, it is possible to provide appropriate guidance.

[0058] The guidance unit can adjust the use of technical terms in the guidance depending on the level of expertise of the patient when providing guidance. For example, the guidance unit adjusts the use of technical terms in the guidance depending on the level of expertise of the patient when providing guidance. For example, if the patient has technical knowledge, the guidance unit provides guidance using technical terms. Also, if the patient does not have technical knowledge, the guidance unit can provide guidance in simple language. Also, the guidance unit can adjust the way the guidance is expressed depending on the level of expertise of the patient. In this way, by adjusting the use of technical terms in the guidance depending on the level of expertise of the patient, it is possible to provide appropriate guidance.

[0059] The communication unit can adjust the level of detail of the communication based on the importance of the hospital when making a communication. For example, the communication unit adjusts the level of detail of the communication based on the importance of the hospital when making a communication. For example, the communication unit makes detailed communication in the case of a hospital with a high importance. The communication unit can also make simplified communication in the case of a hospital with a low importance. The communication unit can also adjust the level of detail of the communication depending on the importance of the hospital. As a result, by adjusting the level of detail of the communication based on the importance of the hospital, appropriate communication is possible.

[0060] The communication unit can apply different communication algorithms depending on the category of the hospital when making a contact. For example, the communication unit applies different communication algorithms depending on the category of the hospital when making a contact. For example, the communication unit makes a quick contact in the case of an emergency hospital. The communication unit can also make a detailed contact in the case of a specialized hospital. The communication unit can also make a standard contact in the case of a general hospital. In this way, by applying different communication algorithms depending on the category of the hospital, appropriate communication is possible.

[0061] The communication unit can improve the accuracy of communication when making contact by referring to the patient's past contact results. For example, when making contact, the communication unit improves the accuracy of communication by referring to the patient's past contact results. For example, the communication unit makes current contact based on the patient's past contact results. The communication unit can also adjust the contact algorithm by referring to the patient's past contact results. The communication unit can also improve the accuracy of communication by using the patient's past contact results. This makes it possible to improve the accuracy of communication by referring to the patient's past contact results.

[0062] The communication unit can determine the priority of contact based on the time of hospital visit when making a contact. The communication unit, for example, determines the priority of contact based on the time of hospital visit when making a contact. For example, the communication unit prioritizes contacting hospitals with upcoming visit dates. The communication unit can also postpone contacting hospitals with further away visit dates. The communication unit can also adjust the priority of contact based on the time of hospital visit. In this way, by determining the priority of contact based on the time of hospital visit, appropriate contact is possible.

[0063] The communication unit can adjust the order of contact based on the relevance of the hospital when making contact. For example, the communication unit adjusts the order of contact based on the relevance of the hospital when making contact. For example, the communication unit prioritizes contacting highly relevant hospitals. The communication unit can also contact less relevant hospitals later. The communication unit can also adjust the order of contact depending on the relevance of the hospital. In this way, adjusting the order of contact based on the relevance of the hospital enables appropriate contact.

[0064] The communication unit can adjust the use of technical terminology when making contact depending on the patient's level of expertise. For example, when making contact, the communication unit adjusts the use of technical terminology when making contact depending on the patient's level of expertise. For example, if the patient has technical knowledge, the communication unit uses technical terminology when making contact. Also, if the patient does not have technical knowledge, the communication unit can also make contact in simple language. The communication unit can also adjust the way the communication is expressed depending on the patient's level of expertise. In this way, by adjusting the use of technical terminology when making contact depending on the patient's level of expertise, appropriate communication is possible.

[0065] The confirmation unit can adjust the level of detail of the confirmation based on the importance of the hospital when confirming whether or not a patient can be admitted. For example, the confirmation unit adjusts the level of detail of the confirmation based on the importance of the hospital when confirming whether or not a patient can be admitted. For example, the confirmation unit performs detailed confirmation for hospitals with high importance. Furthermore, the confirmation unit can also perform simplified confirmation for hospitals with low importance. Furthermore, the confirmation unit can adjust the level of detail of the confirmation depending on the importance of the hospital. In this way, by adjusting the level of detail of the confirmation based on the importance of the hospital, appropriate confirmation is possible.

[0066] The verification unit can apply different verification algorithms depending on the category of the hospital when verifying whether or not a patient is accepted. For example, the verification unit can apply different verification algorithms depending on the category of the hospital when verifying whether or not a patient is accepted. For example, the verification unit can perform a quick verification in the case of an emergency hospital. The verification unit can also perform a detailed verification in the case of a specialized hospital. The verification unit can also perform a standard verification in the case of a general hospital. This allows for appropriate verification by applying different verification algorithms depending on the category of the hospital.

[0067] The confirmation unit can improve the accuracy of confirmation by referring to the patient's past confirmation results when confirming whether a patient is acceptable. For example, the confirmation unit improves the accuracy of confirmation by referring to the patient's past confirmation results when confirming whether a patient is acceptable. For example, the confirmation unit performs the current confirmation based on the patient's past confirmation results. The confirmation unit can also adjust the confirmation algorithm by referring to the patient's past confirmation results. The confirmation unit can also improve the accuracy of confirmation by using the patient's past confirmation results. This makes it possible to improve the accuracy of confirmation by referring to the patient's past confirmation results.

[0068] The confirmation unit can determine the confirmation priority based on the time of hospital visit when confirming whether a patient can be accepted. For example, the confirmation unit determines the confirmation priority based on the time of hospital visit when confirming whether a patient can be accepted. For example, the confirmation unit prioritizes checking hospitals with upcoming visit times. The confirmation unit can also postpone checking hospitals with more distant visit times. The confirmation unit can also adjust the confirmation priority according to the time of visit. As a result, by determining the confirmation priority based on the time of hospital visit, appropriate confirmation is possible.

[0069] The confirmation unit can adjust the order of confirmation based on the relevance of the hospitals when confirming whether a patient is acceptable. For example, the confirmation unit adjusts the order of confirmation based on the relevance of the hospitals when confirming whether a patient is acceptable. For example, the confirmation unit prioritizes checking hospitals with high relevance. The confirmation unit can also postpone checking hospitals with low relevance. The confirmation unit can also adjust the order of confirmation depending on the relevance of the hospitals. As a result, adjusting the order of confirmation based on the relevance of the hospitals enables appropriate confirmation.

[0070] The confirmation unit can adjust the use of technical terms for confirmation according to the patient's level of expertise when confirming acceptability. For example, the confirmation unit adjusts the use of technical terms for confirmation according to the patient's level of expertise when confirming acceptability. For example, if the patient has technical knowledge, the confirmation unit can use technical terms to confirm. Also, if the patient does not have technical knowledge, the confirmation unit can use simple language to confirm. Also, the confirmation unit can adjust the way the confirmation is expressed according to the patient's level of expertise. In this way, by adjusting the use of technical terms for confirmation according to the patient's level of expertise, appropriate confirmation is possible.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] When receiving the patient's voice information, the reception unit can refer to the patient's past medical history and preferentially recommend hospitals where the patient has previously visited for similar symptoms. For example, if the patient has previously visited a hospital for a headache, the reception unit can preferentially recommend that hospital. The reception unit can also identify the patient's preferred medical departments and doctors from the past medical history and provide guidance based on that information. Furthermore, if the patient tends to visit the hospital during a specific time period based on the past medical history, the reception unit can also recommend hospitals that are available during that time period. This makes it possible to provide more appropriate guidance by utilizing the patient's past medical history.

[0073] When referencing the hospital database, the search unit can adjust search priorities based on the patient's current health condition and urgency. For example, if a patient complains of highly urgent symptoms, the search unit will prioritize searching for hospitals that can provide prompt treatment. Also, if the patient's health condition is stable, the search unit can search for hospitals that provide detailed information. Furthermore, the search procedure can be simplified or detailed depending on the patient's urgency. This makes it possible to search for an appropriate hospital by adjusting search priorities based on the patient's health condition and urgency.

[0074] When informing the hospital that a patient is on their way, the communication department can refer to the patient's past communication history and prioritize the use of methods that have worked well in the past. For example, if phone contact has worked well in the past, the department can make another phone call. Also, if email contact has been effective in the past, email can be used as a priority. Furthermore, the timing of contact can be adjusted based on the past communication history. This makes it possible to communicate smoothly by utilizing past communication history.

[0075] When receiving voice information from a patient, the reception unit can prioritize receiving highly relevant information by taking into account the patient's geographical location information. For example, the reception unit can prioritize receiving information about hospitals that are closest to the patient's current location. The reception unit can also provide information about the most suitable hospital based on the patient's geographical location information. Furthermore, the reception unit can update the patient's location information in real time and provide the most suitable reception method. This makes it possible to prioritize receiving highly relevant information by taking into account the patient's geographical location information.

[0076] When converting voice information into text data, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise. For example, if the patient has technical expertise, the analysis results can be provided using technical terms. Alternatively, if the patient does not have technical expertise, the analysis results can be provided in simple language. Furthermore, the way in which the analysis results are expressed can be adjusted according to the patient's level of expertise. This makes it possible to provide appropriate analysis results by adjusting the use of technical terms in the analysis according to the patient's level of expertise.

[0077] When providing guidance to a patient via voice or screen, the guidance unit can improve the accuracy of the guidance by referring to the patient's past guidance results. For example, the current guidance is provided based on the patient's past guidance results. The guidance unit can also adjust the guidance algorithm by referring to the patient's past guidance results. Furthermore, the guidance unit can also improve the accuracy of the guidance by using the patient's past guidance results. This makes it possible to improve the accuracy of the guidance by referring to the patient's past guidance results.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The reception unit receives voice information in which the patient verbally describes symptoms, age, anxiety level, etc. For example, the patient performs voice input using a voice input device. Step 2: The analysis unit analyzes the voice information received by the reception unit and converts it into text data. For example, the analysis unit converts the voice information into text data using a voice recognition algorithm. Step 3: The search unit searches for hospitals based on the information analyzed by the analysis unit. For example, the search unit refers to a hospital database and selects the most suitable hospital, taking into account the current availability of medical treatment, distance, medical specialty, etc. Step 4: The guidance unit guides the patient through the results searched by the search unit. For example, the guidance unit provides guidance to the patient by voice or on a screen. Step 5: The communication unit communicates the information provided by the guidance unit to the hospital side. For example, the communication unit notifies the hospital side that the patient is on his way. Step 6: The confirmation unit confirms whether the patient is accepted based on the information communicated by the communication unit. For example, the confirmation unit confirms whether the hospital is willing to accept the patient and notifies the patient.

[0080] (Example 2) A hospital guidance system according to an embodiment of the present invention is a system in which a patient verbally describes their symptoms, age, anxiety level, etc., and based on that information, the system guides them to nearby hospitals that are open at that time and notifies the hospitals that the patient is on their way (including confirming whether they can be accommodated). When a patient verbally describes their symptoms, age, anxiety level, etc., the hospital guidance system guides them to nearby hospitals that are open at that time and notifies the hospitals that the patient is on their way. For example, the patient inputs voice information using a voice input device. For example, the patient inputs voice information such as "I have a headache, I'm 38 years old, and I'm very anxious." This voice information is analyzed by AI and converted into text data. Next, the hospital guidance system searches for nearby hospitals that are open based on the information analyzed by the AI. The AI ​​references a hospital database and selects the most appropriate hospital, taking into account factors such as current availability, distance, and medical specialties. For example, the system searches for hospitals with appropriate medical specialties that can treat headache symptoms and creates a list of available hospitals. The search results are sent to the patient, and the hospital is notified that the patient is on their way. The patient is provided with instructions on the screen of a voice input device or via voice. For example, the instructions may say, "A nearby hospital is available for consultation. The address is ____ and the clinic hours are ____." The hospital is notified that the patient is on their way and asked to confirm whether they can accept the patient. The hospital confirms whether they can accept the patient through the system and notifies the patient. For example, the notification may say, "The hospital is available for consultation. We look forward to seeing you." This allows the hospital guidance system to ensure that patients receive prompt and appropriate medical care. For example, even in the case of sudden illness or strong anxiety, patients can be referred to an appropriate hospital quickly, allowing them to visit with peace of mind. Furthermore, by knowing the patient's information in advance, hospitals can accept patients more smoothly.

[0081] A hospital guidance system according to an embodiment includes a reception unit, an analysis unit, a search unit, a guidance unit, a communication unit, and a confirmation unit. The reception unit receives audio information in which a patient describes their symptoms, age, anxiety level, etc., using voice input. For example, the patient uses a voice input device to input voice information. The analysis unit analyzes the audio information received by the reception unit and converts it into text data. For example, the analysis unit converts the audio information into text data using a voice recognition algorithm. The search unit searches for a hospital based on the information analyzed by the analysis unit. For example, the search unit references a hospital database and selects the most appropriate hospital based on the current availability of appointments, distance, medical specialty, etc. The guidance unit provides guidance based on the results searched by the search unit. For example, the guidance unit provides guidance to the patient via voice or on a screen. The communication unit communicates the information provided by the guidance unit to the hospital. For example, the communication unit notifies the hospital that the patient is on their way. The confirmation unit confirms whether the hospital can accept the patient based on the information communicated by the communication unit. For example, the confirmation unit allows the hospital to confirm whether the patient can be accepted and notify the patient. As a result, the hospital guidance system according to the embodiment can provide prompt and appropriate medical care by guiding the patient to an appropriate hospital based on the patient's voice information and contacting the hospital.

[0082] The analysis unit can convert speech information into text data. The analysis unit, for example, uses a speech recognition algorithm to convert speech information into text data. For example, the analysis unit uses speech recognition technology to convert speech information such as telephone speech, recorded speech, and real-time speech into text data. The analysis unit can also preprocess speech data to improve the accuracy of the speech recognition algorithm. For example, the analysis unit can remove noise and normalize speech to improve the accuracy of speech recognition. Furthermore, the analysis unit can update the learning data of the speech recognition algorithm to accommodate the latest speech data. For example, the analysis unit can add new speech data to improve the accuracy of the speech recognition algorithm. In this way, converting speech information into text data makes it easier to process information.

[0083] The search unit can refer to a hospital database and select an appropriate hospital based on the current availability, distance, medical specialty, etc. The search unit, for example, refers to a hospital database and selects the most appropriate hospital taking into consideration the current availability, distance, medical specialty, etc. For example, the search unit selects an appropriate hospital based on information such as the hospital's location, medical specialty, and availability. The search unit can also select the most appropriate hospital based on the patient's current location information. For example, the search unit selects the hospital closest to the patient's current location. Furthermore, the search unit can select an available hospital based on the hospital's consultation hours and reservation status. For example, the search unit selects a hospital that is open during consultation hours. By selecting the most appropriate hospital, it is possible to provide appropriate medical care to the patient.

[0084] The guidance unit can provide guidance to the patient by voice or on a screen. The guidance unit can, for example, provide guidance to the patient by voice or on a screen. For example, the guidance unit can provide guidance to the patient by voice using voice synthesis technology. The guidance unit can also provide guidance to the patient on a screen using screen display technology. For example, the guidance unit can display information about hospitals where the patient can be seen on the screen of a voice input device. Furthermore, the guidance unit can adjust the guidance method according to the patient's situation. For example, if the patient has an emergency, the guidance unit can provide quick guidance and omit detailed information. Also, the guidance unit can provide detailed information if the patient is relaxed. This makes it possible to provide appropriate guidance to the patient and help them seek medical attention.

[0085] The communication unit can notify the hospital that the patient is on his / her way. For example, the communication unit notifies the hospital that the patient is on his / her way. For example, the communication unit notifies the hospital by telephone, email, message, or other methods. The communication unit can also adjust the content of the communication. For example, the communication unit may include information such as the patient's symptoms, age, and anxiety level in the communication. Furthermore, the communication unit can also adjust the timing of the communication. For example, the communication unit may contact the hospital just before the patient is on his / her way to the hospital. This allows for a smooth admission by notifying the hospital that the patient is on his / her way.

[0086] The confirmation unit can confirm whether the hospital can accept the patient and notify the patient. For example, the confirmation unit confirms whether the hospital can accept the patient and notifies the patient. For example, the confirmation unit sets criteria for the hospital to confirm whether the patient can be accepted and confirms based on those criteria. The confirmation unit can also adjust the means of confirmation. For example, the confirmation unit confirms with the hospital by telephone, email, message sending, or other methods. Furthermore, the confirmation unit can also notify the patient of the confirmation result. For example, if the hospital can accept the patient, the confirmation unit notifies the patient, saying, "The hospital can accept the patient. We look forward to seeing you." In this way, by confirming whether the hospital can accept the patient and notifying the patient, it is possible to support appropriate medical examinations.

[0087] The reception unit can estimate the patient's emotions and adjust the method for receiving voice information based on the estimated emotions. For example, the reception unit estimates the patient's emotions and adjusts the method for receiving voice information based on the estimated emotions. For example, if the patient is nervous, the reception unit provides guidance in a calm voice and simplifies the voice input procedure. Furthermore, if the patient is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the patient is in a hurry, the reception unit can prioritize voice input and enable the patient to quickly enter symptoms, age, and anxiety level. This allows for more appropriate reception by adjusting the method for receiving voice information according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The reception unit can analyze the patient's past medical history and select an appropriate reception method. The reception unit, for example, analyzes the patient's past medical history and selects an appropriate reception method. For example, the reception unit preferentially suggests a voice input method that the patient has frequently used in the past. The reception unit can also customize an input method for specific symptoms based on the patient's past medical history. The reception unit can also predict and suggest an input method to be used during a specific time period based on the patient's past medical history. This makes it possible to select the optimal reception method based on the patient's past medical history and receive a reception that is appropriate for the patient.

[0089] The reception unit can filter the voice information based on the patient's current health condition and the level of urgency when receiving the voice information. For example, the reception unit filters the voice information based on the patient's current health condition and the level of urgency when receiving the voice information. For example, the reception unit prioritizes reception when a patient complains of a highly urgent symptom. The reception unit can also provide a reception method for requesting detailed information when the patient's health condition is stable. The reception unit can also simplify or detail the reception procedure depending on the patient's level of urgency. This makes it possible to respond appropriately by filtering based on the patient's health condition and the level of urgency.

[0090] The reception unit can select an appropriate reception means depending on the patient's input method when receiving voice information. For example, when receiving voice information, the reception unit selects an appropriate reception means depending on the patient's input method. For example, if the patient selects voice input, the reception unit receives information using voice recognition technology. Also, if the patient selects text input, the reception unit can provide a text input form. Also, if the patient selects image input, the reception unit can receive information using image analysis technology. This allows for flexible response by selecting the optimal reception means depending on the patient's input method.

[0091] The reception unit can estimate the patient's emotion and determine the priority of the voice information to be received based on the estimated emotion. The reception unit, for example, estimates the patient's emotion and determines the priority of the voice information to be received based on the estimated emotion. For example, if the patient is feeling anxious, the reception unit will prioritize reception and respond quickly. The reception unit can also apply a normal reception procedure if the patient is relaxed. The reception unit can also increase the priority of reception if the patient is nervous. This enables a quick response by determining the priority of the voice information according to the patient's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The reception unit can prioritize receiving highly relevant information in consideration of the geographical location information of the patient when receiving voice information. For example, the reception unit prioritizes receiving highly relevant information in consideration of the geographical location information of the patient when receiving voice information. For example, the reception unit prioritizes receiving information about hospitals that are close to the patient's current location. The reception unit can also provide information about the most appropriate hospital based on the geographical location information of the patient. The reception unit can also update the patient's location information in real time and provide the most appropriate reception method. This makes it possible to prioritize receiving highly relevant information by considering the geographical location information of the patient.

[0093] The reception unit can analyze the patient's social media activity and receive related information when receiving the voice information. For example, the reception unit analyzes the patient's social media activity and receives related information when receiving the voice information. For example, the reception unit receives the voice information based on health information shared by the patient on social media. The reception unit can also analyze the patient's social media activity and receive related information. The reception unit can also receive related information by referring to the activity of the patient's friends on social media. This makes it possible to receive related information by analyzing the patient's social media activity.

[0094] The reception unit can customize the reception method by reflecting the patient's past feedback when receiving voice information. The reception unit, for example, customizes the reception method by reflecting the patient's past feedback when receiving voice information. For example, the reception unit proposes an optimal reception method based on feedback provided by the patient in the past. The reception unit can also analyze the patient's past feedback and improve the reception procedure. The reception unit can also provide a customized reception method by reflecting the patient's feedback. This makes it possible to provide a customized reception method by reflecting the patient's past feedback.

[0095] The analysis unit can estimate the patient's emotion and adjust the analysis method of the voice information based on the estimated emotion. The analysis unit, for example, estimates the patient's emotion and adjusts the analysis method of the voice information based on the estimated emotion. For example, the analysis unit applies a simplified analysis method when the patient is nervous. The analysis unit can also apply a detailed analysis method when the patient is relaxed. The analysis unit can also apply a quick analysis method when the patient is in a hurry. This enables more appropriate analysis by adjusting the analysis method of the voice information according to the patient's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptom when analyzing the audio information. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the symptom when analyzing the audio information. For example, the analysis unit performs a detailed analysis for a symptom with a high level of importance. The analysis unit can also perform a simplified analysis for a symptom with a low level of importance. The analysis unit can also adjust the analysis procedure according to the importance of the symptom. In this way, by adjusting the level of detail of the analysis according to the importance of the symptom, appropriate analysis is possible.

[0097] The analysis unit can apply different analysis algorithms depending on the symptom category when analyzing the audio information. For example, when analyzing the audio information, the analysis unit applies different analysis algorithms depending on the symptom category. For example, in the case of a headache symptom, the analysis unit applies an analysis algorithm specialized for headache. In addition, in the case of a abdominal pain symptom, the analysis unit can also apply an analysis algorithm specialized for abdominal pain. In addition, in the case of a dyspnea symptom, the analysis unit can also apply an analysis algorithm specialized for dyspnea. In this way, by applying different analysis algorithms depending on the symptom category, appropriate analysis is possible.

[0098] When analyzing the voice information, the analysis unit can improve the accuracy of the analysis by referring to the patient's past analysis results. For example, when analyzing the voice information, the analysis unit improves the accuracy of the analysis by referring to the patient's past analysis results. For example, the analysis unit performs the current analysis based on the patient's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the patient's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the patient's past analysis results. This makes it possible to improve the accuracy of the analysis by referring to the patient's past analysis results.

[0099] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit, for example, estimates the patient's emotions and adjusts the length of the analysis based on the estimated emotions. For example, the analysis unit performs the analysis in a short time if the patient is in a hurry. The analysis unit can also perform a detailed analysis if the patient is relaxed. The analysis unit can also perform a simplified analysis if the patient is nervous. This allows for appropriate analysis by adjusting the length of the analysis according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] The analysis unit can determine the priority of analysis based on the time of symptom occurrence when analyzing the audio information. For example, the analysis unit determines the priority of analysis based on the time of symptom occurrence when analyzing the audio information. For example, the analysis unit prioritizes analysis of recently occurring symptoms. The analysis unit can also postpone analysis of symptoms that occurred in the past. The analysis unit can also adjust the priority of analysis based on the time of symptom occurrence. In this way, by determining the priority of analysis based on the time of symptom occurrence, appropriate analysis is possible.

[0101] The analysis unit can adjust the order of analysis based on the relevance of symptoms when analyzing the audio information. For example, the analysis unit adjusts the order of analysis based on the relevance of symptoms when analyzing the audio information. For example, the analysis unit prioritizes analysis of highly relevant symptoms. The analysis unit can also postpone analysis of less relevant symptoms. The analysis unit can also adjust the order of analysis based on the relevance of symptoms. In this way, adjusting the order of analysis based on the relevance of symptoms enables appropriate analysis.

[0102] The analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise when analyzing the voice information. For example, when analyzing the voice information, the analysis unit adjusts the use of technical terms in the analysis according to the patient's level of expertise. For example, if the patient has technical knowledge, the analysis unit provides the analysis results using technical terms. Also, if the patient does not have technical knowledge, the analysis unit can provide the analysis results in simple language. Also, the analysis unit can adjust the way the analysis results are expressed according to the patient's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the patient's level of expertise, it is possible to provide appropriate analysis results.

[0103] The search unit can estimate the patient's emotions and adjust the hospital search criteria based on the estimated emotions. For example, the search unit estimates the patient's emotions and adjusts the hospital search criteria based on the estimated emotions. For example, if the patient is feeling anxious, the search unit prioritizes searching for hospitals that can provide quick consultation. If the patient is relaxed, the search unit can also search for hospitals that provide detailed information. If the patient is nervous, the search unit can also search for hospitals that can provide consultation with simple procedures. This makes it possible to search for an appropriate hospital by adjusting the hospital search criteria according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The search unit can improve the accuracy of the search by taking into account the interrelationships between symptoms when searching for a hospital. For example, the search unit improves the accuracy of the search by taking into account the interrelationships between symptoms when searching for a hospital. For example, if the symptoms of headache and fever are related, the search unit can search for hospitals that can treat both symptoms. Furthermore, if the symptoms of abdominal pain and diarrhea are related, the search unit can also search for hospitals that can treat both symptoms. Furthermore, if the symptoms of dyspnea and cough are related, the search unit can also search for hospitals that can treat both symptoms. In this way, by taking into account the interrelationships between symptoms, it is possible to improve the accuracy of the search.

[0105] The search unit can perform a search taking into account patient attribute information when searching for hospitals. For example, the search unit performs a search taking into account patient attribute information when searching for hospitals. For example, the search unit searches for hospitals with appropriate medical departments taking into account the patient's age. The search unit can also search for hospitals with appropriate medical departments taking into account the patient's gender. The search unit can also search for hospitals with appropriate medical departments taking into account the patient's medical history. This makes it possible to search for an appropriate hospital by taking into account the patient's attribute information.

[0106] The search unit can weight the search based on the frequency of occurrence of symptoms when searching for hospitals. For example, the search unit weights the search based on the frequency of occurrence of symptoms when searching for hospitals. For example, the search unit prioritizes searching for hospitals that can handle symptoms that occur frequently. The search unit can also search for hospitals that can handle symptoms that occur rarely at a later date. The search unit can also adjust the search weight according to the frequency of occurrence of symptoms. In this way, by weighting the search based on the frequency of occurrence of symptoms, it becomes possible to search for an appropriate hospital.

[0107] The search unit can estimate the patient's emotions and adjust the display order of search results based on the estimated emotions. The search unit, for example, estimates the patient's emotions and adjusts the display order of search results based on the estimated emotions. For example, if the patient is feeling anxious, the search unit can display hospitals that provide quick consultation at the top. Furthermore, if the patient is relaxed, the search unit can also display hospitals that provide detailed information at the top. Furthermore, if the patient is nervous, the search unit can also display hospitals that can be consulted with simple procedures at the top. This makes it possible to display appropriate hospitals by adjusting the display order of search results according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] The search unit can perform a search taking into account the geographical distribution of hospitals when searching for hospitals. For example, the search unit performs a search taking into account the geographical distribution of hospitals when searching for hospitals. For example, the search unit preferentially searches for hospitals closest to the patient's current location. The search unit can also search for hospitals within a range that the patient can travel. The search unit can also search for the most suitable hospital taking into account the patient's geographical conditions. This makes it possible to search for an appropriate hospital by taking into account the geographical distribution of hospitals.

[0109] The search unit can improve the accuracy of the search by referring to related literature when searching for a hospital. For example, the search unit can improve the accuracy of the search by referring to related literature when searching for a hospital. For example, the search unit can search for the most suitable hospital by referring to the latest medical literature related to the symptoms. The search unit can also search for the most suitable hospital by referring to past medical literature related to the symptoms. The search unit can also improve the accuracy of the search results based on literature information related to the symptoms. This makes it possible to improve the accuracy of the search by referring to related literature.

[0110] When searching for a hospital, the search unit can perform the search taking into consideration the hospital's facilities and the doctor's expertise. When searching for a hospital, the search unit can perform the search taking into consideration the hospital's facilities and the doctor's expertise. For example, the search unit searches for a hospital that has facilities that can treat the patient's symptoms. The search unit can also search for a hospital that has a specialist who can treat the patient's symptoms. The search unit can also search for the most suitable hospital based on the hospital's facilities and the doctor's expertise. This makes it possible to search for an appropriate hospital by taking into consideration the hospital's facilities and the doctor's expertise.

[0111] The guidance unit can estimate the patient's emotions and adjust the guidance display method based on the estimated emotions. For example, the guidance unit estimates the patient's emotions and adjusts the guidance display method based on the estimated emotions. For example, if the patient is nervous, the guidance unit provides a simple, highly visible display method. If the patient is relaxed, the guidance unit can provide a display method including detailed information. If the patient is in a hurry, the guidance unit can provide a display method that focuses on the main points. This makes it possible to provide appropriate guidance by adjusting the guidance display method according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0112] The guidance unit can adjust the level of detail of the guidance based on the importance of the hospital when providing guidance. For example, the guidance unit adjusts the level of detail of the guidance based on the importance of the hospital when providing guidance. For example, the guidance unit provides detailed guidance for a hospital with a high level of importance. Furthermore, the guidance unit can also provide simplified guidance for a hospital with a low level of importance. Furthermore, the guidance unit can adjust the level of detail of the guidance depending on the importance of the hospital. In this way, by adjusting the level of detail of the guidance based on the importance of the hospital, it is possible to provide appropriate guidance.

[0113] The guidance unit can apply different guidance algorithms depending on the category of the hospital when providing guidance. For example, the guidance unit can provide quick guidance for an emergency hospital. The guidance unit can also provide detailed guidance for a specialized hospital. The guidance unit can also provide standard guidance for a general hospital. This makes it possible to provide appropriate guidance by applying different guidance algorithms depending on the category of the hospital.

[0114] The guidance unit can improve the accuracy of guidance by referring to the patient's past guidance results when providing guidance. For example, the guidance unit improves the accuracy of guidance by referring to the patient's past guidance results when providing guidance. For example, the guidance unit provides current guidance based on the patient's past guidance results. The guidance unit can also adjust the guidance algorithm by referring to the patient's past guidance results. The guidance unit can also improve the accuracy of guidance by using the patient's past guidance results. This makes it possible to improve the accuracy of guidance by referring to the patient's past guidance results.

[0115] The guidance unit can estimate the patient's emotions and adjust the length of the guidance based on the estimated emotions. The guidance unit, for example, estimates the patient's emotions and adjusts the length of the guidance based on the estimated emotions. For example, if the patient is in a hurry, the guidance unit can provide short, to-the-point guidance. If the patient is relaxed, the guidance unit can provide longer guidance with detailed explanations. If the patient is nervous, the guidance unit can provide simplified guidance. This makes it possible to provide appropriate guidance by adjusting the length of the guidance according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] The guidance unit can determine the priority of guidance based on the timing of hospital visits when providing guidance. The guidance unit, for example, determines the priority of guidance based on the timing of hospital visits when providing guidance. For example, the guidance unit gives priority to hospitals with upcoming visit dates. The guidance unit can also postpone providing guidance to hospitals with further away visit dates. The guidance unit can also adjust the priority of guidance based on the timing of hospital visits. In this way, by determining the priority of guidance based on the timing of hospital visits, it becomes possible to provide appropriate guidance.

[0117] The guidance unit can adjust the order of guidance based on the relevance of the hospitals when providing guidance. For example, the guidance unit adjusts the order of guidance based on the relevance of the hospitals when providing guidance. For example, the guidance unit gives priority to providing guidance to highly relevant hospitals. The guidance unit can also postpone providing guidance to less relevant hospitals. The guidance unit can also adjust the order of guidance according to the relevance of the hospitals. In this way, by adjusting the order of guidance based on the relevance of the hospitals, it is possible to provide appropriate guidance.

[0118] The guidance unit can adjust the use of technical terms in the guidance depending on the level of expertise of the patient when providing guidance. For example, the guidance unit adjusts the use of technical terms in the guidance depending on the level of expertise of the patient when providing guidance. For example, if the patient has technical knowledge, the guidance unit provides guidance using technical terms. Also, if the patient does not have technical knowledge, the guidance unit can provide guidance in simple language. Also, the guidance unit can adjust the way the guidance is expressed depending on the level of expertise of the patient. In this way, by adjusting the use of technical terms in the guidance depending on the level of expertise of the patient, it is possible to provide appropriate guidance.

[0119] The communication unit can estimate the patient's emotions and adjust the method of communication based on the estimated emotions. The communication unit, for example, estimates the patient's emotions and adjusts the method of communication based on the estimated emotions. For example, if the patient is nervous, the communication unit can contact the patient in a calm voice. If the patient is relaxed, the communication unit can also contact the patient in a cheerful voice. If the patient is in a hurry, the communication unit can also contact the patient quickly and concisely. This allows appropriate communication by adjusting the method of communication according to the patient's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0120] The communication unit can adjust the level of detail of the communication based on the importance of the hospital when making a communication. For example, the communication unit adjusts the level of detail of the communication based on the importance of the hospital when making a communication. For example, the communication unit makes detailed communication in the case of a hospital with a high importance. The communication unit can also make simplified communication in the case of a hospital with a low importance. The communication unit can also adjust the level of detail of the communication depending on the importance of the hospital. As a result, by adjusting the level of detail of the communication based on the importance of the hospital, appropriate communication is possible.

[0121] The communication unit can apply different communication algorithms depending on the category of the hospital when making a contact. For example, the communication unit applies different communication algorithms depending on the category of the hospital when making a contact. For example, the communication unit makes a quick contact in the case of an emergency hospital. The communication unit can also make a detailed contact in the case of a specialized hospital. The communication unit can also make a standard contact in the case of a general hospital. In this way, by applying different communication algorithms depending on the category of the hospital, appropriate communication is possible.

[0122] The communication unit can improve the accuracy of communication when making contact by referring to the patient's past contact results. For example, when making contact, the communication unit improves the accuracy of communication by referring to the patient's past contact results. For example, the communication unit makes current contact based on the patient's past contact results. The communication unit can also adjust the contact algorithm by referring to the patient's past contact results. The communication unit can also improve the accuracy of communication by using the patient's past contact results. This makes it possible to improve the accuracy of communication by referring to the patient's past contact results.

[0123] The communication unit can estimate the patient's emotions and adjust the length of the communication based on the estimated emotions. The communication unit, for example, estimates the patient's emotions and adjusts the length of the communication based on the estimated emotions. For example, if the patient is in a hurry, the communication unit can send a short, to-the-point message. If the patient is relaxed, the communication unit can also send a longer message including a detailed explanation. If the patient is nervous, the communication unit can also send a simplified message. This allows for appropriate communication by adjusting the length of the message according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0124] The communication unit can determine the priority of contact based on the time of hospital visit when making a contact. The communication unit, for example, determines the priority of contact based on the time of hospital visit when making a contact. For example, the communication unit prioritizes contacting hospitals with upcoming visit dates. The communication unit can also postpone contacting hospitals with further away visit dates. The communication unit can also adjust the priority of contact based on the time of hospital visit. In this way, by determining the priority of contact based on the time of hospital visit, appropriate contact is possible.

[0125] The communication unit can adjust the order of contact based on the relevance of the hospital when making contact. For example, the communication unit adjusts the order of contact based on the relevance of the hospital when making contact. For example, the communication unit prioritizes contacting highly relevant hospitals. The communication unit can also contact less relevant hospitals later. The communication unit can also adjust the order of contact depending on the relevance of the hospital. In this way, adjusting the order of contact based on the relevance of the hospital enables appropriate contact.

[0126] The communication unit can adjust the use of technical terminology when making contact depending on the patient's level of expertise. For example, when making contact, the communication unit adjusts the use of technical terminology when making contact depending on the patient's level of expertise. For example, if the patient has technical knowledge, the communication unit uses technical terminology when making contact. Also, if the patient does not have technical knowledge, the communication unit can also make contact in simple language. The communication unit can also adjust the way the communication is expressed depending on the patient's level of expertise. In this way, by adjusting the use of technical terminology when making contact depending on the patient's level of expertise, appropriate communication is possible.

[0127] The confirmation unit can estimate the patient's emotions and adjust the method of confirming whether or not the patient is acceptable based on the estimated emotions. The confirmation unit, for example, estimates the patient's emotions and adjusts the method of confirming whether or not the patient is acceptable based on the estimated emotions. For example, the confirmation unit provides a simplified confirmation method when the patient is nervous. The confirmation unit can also provide a detailed confirmation method when the patient is relaxed. The confirmation unit can also provide a quick confirmation method when the patient is in a hurry. This allows for appropriate confirmation by adjusting the method of confirming whether or not the patient is acceptable based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0128] The confirmation unit can adjust the level of detail of the confirmation based on the importance of the hospital when confirming whether or not a patient can be admitted. For example, the confirmation unit adjusts the level of detail of the confirmation based on the importance of the hospital when confirming whether or not a patient can be admitted. For example, the confirmation unit performs detailed confirmation for hospitals with high importance. Furthermore, the confirmation unit can also perform simplified confirmation for hospitals with low importance. Furthermore, the confirmation unit can adjust the level of detail of the confirmation depending on the importance of the hospital. In this way, by adjusting the level of detail of the confirmation based on the importance of the hospital, appropriate confirmation is possible.

[0129] The verification unit can apply different verification algorithms depending on the category of the hospital when verifying whether or not a patient is accepted. For example, the verification unit can apply different verification algorithms depending on the category of the hospital when verifying whether or not a patient is accepted. For example, the verification unit can perform a quick verification in the case of an emergency hospital. The verification unit can also perform a detailed verification in the case of a specialized hospital. The verification unit can also perform a standard verification in the case of a general hospital. This allows for appropriate verification by applying different verification algorithms depending on the category of the hospital.

[0130] The confirmation unit can improve the accuracy of confirmation by referring to the patient's past confirmation results when confirming whether a patient is acceptable. For example, the confirmation unit improves the accuracy of confirmation by referring to the patient's past confirmation results when confirming whether a patient is acceptable. For example, the confirmation unit performs the current confirmation based on the patient's past confirmation results. The confirmation unit can also adjust the confirmation algorithm by referring to the patient's past confirmation results. The confirmation unit can also improve the accuracy of confirmation by using the patient's past confirmation results. This makes it possible to improve the accuracy of confirmation by referring to the patient's past confirmation results.

[0131] The confirmation unit can estimate the patient's emotions and adjust the length of the confirmation based on the estimated emotions. The confirmation unit, for example, estimates the patient's emotions and adjusts the length of the confirmation based on the estimated emotions. For example, if the patient is in a hurry, the confirmation unit can provide a short, to-the-point confirmation. If the patient is relaxed, the confirmation unit can also provide a longer confirmation including detailed explanations. If the patient is nervous, the confirmation unit can also provide a simplified confirmation. This allows for appropriate confirmation by adjusting the length of the confirmation according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0132] The confirmation unit can determine the confirmation priority based on the time of hospital visit when confirming whether a patient can be accepted. For example, the confirmation unit determines the confirmation priority based on the time of hospital visit when confirming whether a patient can be accepted. For example, the confirmation unit prioritizes checking hospitals with upcoming visit times. The confirmation unit can also postpone checking hospitals with more distant visit times. The confirmation unit can also adjust the confirmation priority according to the time of visit. As a result, by determining the confirmation priority based on the time of hospital visit, appropriate confirmation is possible.

[0133] The confirmation unit can adjust the order of confirmation based on the relevance of the hospitals when confirming whether a patient is acceptable. For example, the confirmation unit adjusts the order of confirmation based on the relevance of the hospitals when confirming whether a patient is acceptable. For example, the confirmation unit prioritizes checking hospitals with high relevance. The confirmation unit can also postpone checking hospitals with low relevance. The confirmation unit can also adjust the order of confirmation depending on the relevance of the hospitals. As a result, adjusting the order of confirmation based on the relevance of the hospitals enables appropriate confirmation.

[0134] The confirmation unit can adjust the use of technical terms for confirmation according to the patient's level of expertise when confirming acceptability. For example, the confirmation unit adjusts the use of technical terms for confirmation according to the patient's level of expertise when confirming acceptability. For example, if the patient has technical knowledge, the confirmation unit can use technical terms to confirm. Also, if the patient does not have technical knowledge, the confirmation unit can use simple language to confirm. Also, the confirmation unit can adjust the way the confirmation is expressed according to the patient's level of expertise. In this way, by adjusting the use of technical terms for confirmation according to the patient's level of expertise, appropriate confirmation is possible. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, search unit, guidance unit, contact unit, and confirmation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, and the patient uses a voice input device to input voice information. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, and converts voice information into text data. For example, the search unit is implemented by the specific processing unit 290 of the data processing device 12, and selects the most appropriate hospital by referring to the hospital database 24. For example, the guidance unit is implemented by the output device 40 of the smart device 14, and provides guidance to the patient via voice or screen. For example, the contact unit is implemented by the communication I / F 26 of the data processing device 12, and notifies the hospital that the patient is on their way. For example, the confirmation unit is implemented by the specific processing unit 290 of the data processing device 12, and the hospital confirms whether or not to accept the patient and notifies the patient. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, search unit, guidance unit, contact unit, and confirmation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and the patient uses a voice input device to input voice information. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and converts voice information into text data. For example, the search unit is realized by the specific processing unit 290 of the data processing device 12, and selects the most appropriate hospital by referring to the hospital database 24. For example, the guidance unit is realized by the speaker 240 of the smart glasses 214, and provides audio guidance to the patient. For example, the contact unit is realized by the communication I / F 26 of the data processing device 12, and notifies the hospital that the patient is on their way. For example, the confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and the hospital confirms whether or not to accept the patient and notifies the patient. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, search unit, guidance unit, communication unit, and confirmation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, and the patient uses a voice input device to input voice information. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, and converts voice information into text data. For example, the search unit is implemented by the specific processing unit 290 of the data processing device 12, and selects the most appropriate hospital by referring to the hospital database 24. For example, the guidance unit is implemented by the speaker 240 of the headset terminal 314, and provides audio guidance to the patient. For example, the communication unit is implemented by the communication I / F 26 of the data processing device 12, and notifies the hospital that the patient is on their way. For example, the confirmation unit is implemented by the specific processing unit 290 of the data processing device 12, and the hospital confirms whether the patient can be admitted and notifies the patient. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, search unit, guidance unit, communication unit, and confirmation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the patient uses a voice input device to input voice information. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and converts voice information into text data. For example, the search unit is realized by the specific processing unit 290 of the data processing device 12, and selects the most suitable hospital by referring to the hospital database 24. For example, the guidance unit is realized by the speaker 240 of the robot 414, and provides audio guidance to the patient. For example, the communication unit is realized by the communication I / F 26 of the data processing device 12, and notifies the hospital that the patient is on their way. For example, the confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and the hospital confirms whether the patient can be admitted and notifies the patient.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] When receiving the patient's voice information, the reception unit can refer to the patient's past medical history and preferentially recommend hospitals where the patient has previously visited for similar symptoms. For example, if the patient has previously visited a hospital for a headache, the reception unit can preferentially recommend that hospital. The reception unit can also identify the patient's preferred medical departments and doctors from the past medical history and provide guidance based on that information. Furthermore, if the patient tends to visit the hospital during a specific time period based on the past medical history, the reception unit can also recommend hospitals that are available during that time period. This makes it possible to provide more appropriate guidance by utilizing the patient's past medical history.

[0137] When converting voice information into text data, the analysis unit can estimate the patient's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the patient is nervous, the analysis unit can apply a simplified analysis method to quickly produce results. Alternatively, if the patient is relaxed, the analysis unit can apply a detailed analysis method to provide more accurate results. Furthermore, if the patient is in a hurry, the analysis unit can apply a quick analysis method to quickly produce results. This allows for more appropriate analysis by adjusting the accuracy of the analysis according to the patient's emotions.

[0138] When referencing the hospital database, the search unit can adjust search priorities based on the patient's current health condition and urgency. For example, if a patient complains of highly urgent symptoms, the search unit will prioritize searching for hospitals that can provide prompt treatment. Also, if the patient's health condition is stable, the search unit can search for hospitals that provide detailed information. Furthermore, the search procedure can be simplified or detailed depending on the patient's urgency. This makes it possible to search for an appropriate hospital by adjusting search priorities based on the patient's health condition and urgency.

[0139] When providing guidance to a patient via voice or screen, the guidance unit can estimate the patient's emotions and adjust the guidance display method based on the estimated emotions. For example, if the patient is nervous, a simple, highly visible display method can be provided. Alternatively, if the patient is relaxed, a display method including detailed information can be provided. Furthermore, if the patient is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide appropriate guidance by adjusting the guidance display method according to the patient's emotions.

[0140] When informing the hospital that a patient is on their way, the communication department can refer to the patient's past communication history and prioritize the use of methods that have worked well in the past. For example, if phone contact has worked well in the past, the department can make another phone call. Also, if email contact has been effective in the past, email can be used as a priority. Furthermore, the timing of contact can be adjusted based on the past communication history. This makes it possible to communicate smoothly by utilizing past communication history.

[0141] The confirmation unit can estimate the patient's emotions when the hospital confirms whether or not to admit the patient, and adjust the confirmation method based on the estimated emotions. For example, if the patient is nervous, a simplified confirmation method can be provided. If the patient is relaxed, a detailed confirmation method can be provided. Furthermore, if the patient is in a hurry, a quick confirmation method can be provided. This allows for appropriate confirmation by adjusting the confirmation method according to the patient's emotions.

[0142] When receiving voice information from a patient, the reception unit can prioritize receiving highly relevant information by taking into account the patient's geographical location information. For example, the reception unit can prioritize receiving information about hospitals that are closest to the patient's current location. The reception unit can also provide information about the most suitable hospital based on the patient's geographical location information. Furthermore, the reception unit can update the patient's location information in real time and provide the most suitable reception method. This makes it possible to prioritize receiving highly relevant information by taking into account the patient's geographical location information.

[0143] When converting voice information into text data, the analysis unit can adjust the use of technical terms in the analysis according to the patient's level of expertise. For example, if the patient has technical expertise, the analysis results can be provided using technical terms. Alternatively, if the patient does not have technical expertise, the analysis results can be provided in simple language. Furthermore, the way in which the analysis results are expressed can be adjusted according to the patient's level of expertise. This makes it possible to provide appropriate analysis results by adjusting the use of technical terms in the analysis according to the patient's level of expertise.

[0144] When referencing the hospital database, the search unit can estimate the patient's emotions and adjust the hospital search criteria based on the estimated emotions. For example, if the patient is feeling anxious, it can prioritize searching for hospitals that can be seen quickly. If the patient is relaxed, it can also search for hospitals that provide detailed information. Furthermore, if the patient is nervous, it can search for hospitals that can be seen with simple procedures. This makes it possible to search for an appropriate hospital by adjusting the hospital search criteria according to the patient's emotions.

[0145] When providing guidance to a patient via voice or screen, the guidance unit can improve the accuracy of the guidance by referring to the patient's past guidance results. For example, the current guidance is provided based on the patient's past guidance results. The guidance unit can also adjust the guidance algorithm by referring to the patient's past guidance results. Furthermore, the guidance unit can also improve the accuracy of the guidance by using the patient's past guidance results. This makes it possible to improve the accuracy of the guidance by referring to the patient's past guidance results.

[0146] The processing flow of the second embodiment will be briefly explained below.

[0147] Step 1: The reception unit receives voice information in which the patient verbally describes symptoms, age, anxiety level, etc. For example, the patient performs voice input using a voice input device. Step 2: The analysis unit analyzes the voice information received by the reception unit and converts it into text data. For example, the analysis unit converts the voice information into text data using a voice recognition algorithm. Step 3: The search unit searches for hospitals based on the information analyzed by the analysis unit. For example, the search unit refers to a hospital database and selects the most suitable hospital, taking into account the current availability of medical treatment, distance, medical specialty, etc. Step 4: The guidance unit guides the patient through the results searched by the search unit. For example, the guidance unit provides guidance to the patient by voice or on a screen. Step 5: The communication unit communicates the information provided by the guidance unit to the hospital side. For example, the communication unit notifies the hospital side that the patient is on his way. Step 6: The confirmation unit confirms whether the patient is accepted based on the information communicated by the communication unit. For example, the confirmation unit confirms whether the hospital is willing to accept the patient and notifies the patient.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 AI 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0179] 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.

[0180] 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.

[0181] 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 AI 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0196] 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.

[0197] 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.

[0198] 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 AI 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.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0206] 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."

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Explanation of symbols]

[0220] 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 reception unit that receives voice information; an analysis unit that analyzes the voice information received by the reception unit and converts it into text data; a search unit that searches for hospitals based on the information analyzed by the analysis unit; a guidance unit that provides guidance on the results of the search by the search unit; a communication unit that communicates the information provided by the guidance unit to the hospital; a confirmation unit that confirms whether or not the product is acceptable based on the information communicated by the communication unit; A system characterized by:

2. The analysis unit Converting audio information into text data 2. The system of claim 1.

3. The search unit Refer to the hospital database and select the appropriate hospital based on current availability, distance, medical specialty, etc.

2. The system of claim 1.

4. The guide unit is Providing audio or visual guidance to patients 2. The system of claim 1.

5. The communication unit Notify the hospital that the patient is on their way 2. The system of claim 1.

6. The confirmation unit The hospital will confirm whether or not the patient can be admitted and notify the patient.

2. The system of claim 1.

7. The reception unit Estimate the patient's emotions and adjust the method of receiving voice information based on the estimated emotions 2. The system of claim 1.

8. The reception unit Analyze the patient's past medical history and select the appropriate reception method 2. The system of claim 1.

9. The reception unit When receiving voice information, filtering is performed based on the patient's current health status and urgency.

2. The system of claim 1.

10. The reception unit When receiving voice information, select the appropriate reception method depending on the patient's input method.

2. The system of claim 1.

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