system

Abstract

The system according to this embodiment aims to enable ambulances to quickly search for the nearest available hospital bed and facilitate the early transport of patients. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a guidance unit, a telephone unit, and a display unit. The reception unit receives the patient's symptoms and requests via voice input. The analysis unit analyzes the voice input received by the reception unit. The search unit searches for the nearest available hospital bed based on the information analyzed by the analysis unit. The guidance unit provides map information and route guidance based on the hospital bed information found by the search unit. The telephone unit automatically makes a phone call to the hospital determined by the search unit. The display unit displays the number of available hospital beds in real time, as entered by the hospital.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003] <00,00016>

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for an ambulance to quickly grasp the availability of emergency beds in the nearest hospital, and there are problems in the early transfer of patients.

[0005] The system according to the embodiment aims to enable an ambulance to quickly search for the nearest available beds and realize the early transfer of patients.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a guidance unit, a telephone unit, and a display unit. The reception unit receives the patient's symptoms and requests via voice input. The analysis unit analyzes the voice input received by the reception unit. The search unit searches for the nearest available hospital bed based on the information analyzed by the analysis unit. The guidance unit provides map information and route guidance based on the hospital bed information found by the search unit. The telephone unit automatically makes a phone call to the hospital identified by the search unit. The display unit displays the number of available hospital beds in real time, as entered by the hospital. [Effects of the Invention]

[0007] The system according to this embodiment allows ambulances to quickly search for the nearest available hospital bed, enabling early transport of patients. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An emergency bed search system according to an embodiment of the present invention is a system for ambulances to search for available emergency beds at the nearest hospital. The emergency bed search system allows paramedics to voice input the patient's symptoms and requests using a smartphone or tablet. The system analyzes the voice input and automatically searches for the nearest available bed. Furthermore, it provides map information, route guidance, and traffic information to facilitate early patient transport. An automatic telephone calling function also notifies hospitals of the patient's condition in advance, prompting them to prepare for admission. This strengthens cooperation between paramedics and medical institutions, enabling a rapid response to save lives. For example, paramedics voice input the patient's symptoms and requests using a smartphone or tablet. For instance, they might voice-input symptoms such as "My chest hurts" or "I'm having trouble breathing." This voice input is analyzed by the system. Next, the system analyzes the voice input and automatically searches for the nearest available bed. The system displays the number of available beds in hospitals in real time and identifies the nearest hospital. For example, the system might display "The nearest hospital is XX Hospital." Furthermore, the system provides map information, route guidance, and traffic information to enable early patient transport. The system uses GPS to pinpoint the ambulance's current location and guides it along the shortest route. For example, it might instruct the ambulance to "Go straight down XX Street and turn right." It also features an automated telephone calling function to notify hospitals of the patient's condition in advance, prompting them to prepare for admission. The system automatically calls the designated hospital and relays the patient's condition, for example, "The patient is complaining of chest pain." Hospitals can also input the number of available beds into the system, which is displayed in real time. This allows paramedics to select a hospital based on accurate information. For example, it might display information such as, "XX Hospital currently has 3 available beds." This system shortens the time from ambulance arrival to patient admission, contributing to improved survival rates. It also reduces the workload of paramedics and ensures appropriate patient transport. Furthermore, strengthened collaboration with medical institutions enables rapid response to patients with high-priority emergencies. For example, it solves the problem of ambulances waiting for several hours to find a hospital bed during the COVID-19 pandemic.This allows the emergency bed search system to receive and analyze patient symptoms and requests via voice input, search for the nearest available bed, provide map information and route guidance, automatically call hospitals, and display the number of available beds in real time, thereby enabling early patient transport.

[0029] The emergency bed search system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a guidance unit, a telephone unit, and a display unit. The reception unit receives the patient's symptoms and requests via voice input. The reception unit can convert the voice into text using, for example, speech recognition technology. For example, the reception unit uses the microphone of a smartphone or tablet to input voice and converts it into text using speech recognition technology. The reception unit can also use noise cancellation technology to improve the accuracy of voice input. For example, the reception unit removes ambient noise to improve the accuracy of voice input. The analysis unit analyzes the voice input. The analysis unit can analyze the voice input using, for example, natural language processing technology. For example, the analysis unit converts the voice input into text data and analyzes it using natural language processing technology. The analysis unit can also understand the content of the voice input and identify the patient's symptoms and requests. For example, the analysis unit analyzes symptoms such as "chest pain" and "difficulty breathing" and takes appropriate action. The search unit searches for the nearest available hospital bed based on the information analyzed by the analysis unit. The search unit can, for example, display the number of available hospital beds in real time and identify the nearest hospital. For example, the search unit can refer to a hospital database and display the number of available beds in real time. The search unit can also identify the nearest hospital based on the hospital's location information. For example, the search unit can use GPS to determine the current location of an ambulance and search for the nearest hospital. The guidance unit provides map information and route guidance based on the hospital bed information retrieved by the search unit. For example, the guidance unit can use GPS to determine the current location of an ambulance and guide the user to the shortest route. For example, the guidance unit can provide instructions such as, "Go straight down XX Street and turn right." The guidance unit can also provide real-time traffic information. For example, the guidance unit can guide the user to the optimal route based on traffic congestion information. The telephone unit automatically calls the hospital determined by the search unit. For example, the telephone unit can notify the hospital in advance of the patient's condition and encourage them to prepare for admission. For example, the telephone unit can tell the hospital, "The patient is complaining of chest pain." Furthermore, the telephone unit can automatically obtain the hospital's phone number and make calls.The display unit receives input from the hospital regarding the number of available beds and displays it in real time. The display unit can, for example, refer to the hospital's database and display the number of available beds in real time. For example, the display unit may display information such as, "There are currently 3 available beds at XX Hospital." The display unit can also have a mechanism for the hospital to periodically update the data. For example, the display unit can provide the latest information by having the hospital periodically update the number of available beds. As a result, the emergency bed search system according to this embodiment can receive and analyze the patient's symptoms and requests via voice input, search for the nearest available bed, provide map information and route guidance, automatically call the hospital, and display the number of available beds in real time, thereby enabling early patient transport.

[0030] The reception desk receives patient symptoms and requests via voice input. The reception desk can convert speech to text using, for example, speech recognition technology. Specifically, it uses the microphone of a smartphone or tablet for voice input and converts it to text using speech recognition technology. This speech recognition technology employs an advanced algorithm using deep learning, enabling highly accurate analysis of speech characteristics. Furthermore, the reception desk can also use noise cancellation technology to improve the accuracy of voice input. For example, to remove ambient noise and improve the accuracy of voice input, a directional microphone is used to pick up only voice from a specific direction. The speech recognition engine is also designed to learn from multiple voice samples and handle different accents and speaking styles. As a result, the reception desk can convert speech to text with high accuracy regardless of the environment in which the patient performs voice input. Additionally, by allowing the reception desk to simultaneously input basic information such as the patient's age, gender, and medical history during voice input, more detailed information can be collected, streamlining the processing of subsequent analysis and search units.

[0031] The analysis unit analyzes voice input. For example, the analysis unit can analyze voice input using natural language processing (NLP) technology. Specifically, it converts voice input into text data and analyzes it using NLP. NLP performs detailed analysis of the text data through processes such as tokenization, part-of-speech tagging, and dependency analysis. The analysis unit can also understand the content of the voice input and identify the patient's symptoms and needs. For example, it can analyze symptoms such as "chest pain" or "difficulty breathing" and provide appropriate responses. Furthermore, to assess the severity of symptoms, the analysis unit can link with medical databases and refer to past cases and statistical data. This allows the analysis unit to quickly determine whether the patient's symptoms are urgent and provide appropriate responses. The analysis unit can also perform sentiment analysis from the patient's voice input to understand the patient's psychological state. For example, it can analyze the tone, speed, and volume of the voice to determine whether the patient is feeling anxiety or fear. This allows the analysis unit to respond according to the patient's psychological state and provide more appropriate medical services.

[0032] The search unit searches for the nearest available hospital bed based on information analyzed by the analysis unit. For example, the search unit can display the number of available hospital beds in real time and identify the nearest hospital. Specifically, it refers to a hospital database and displays the number of available beds in real time. The search unit can also identify the nearest hospital based on the hospital's location information. For example, it can use GPS to pinpoint the current location of an ambulance and search for the nearest hospital. Furthermore, the search unit can also consider hospital specialties and facilities when performing searches. For example, if a cardiology-specific bed is needed, it will prioritize searching for hospitals specializing in cardiology. The search unit can also select the most suitable hospital by considering hospital congestion and waiting times. This allows the search unit to quickly identify the hospital best suited to the patient's symptoms and needs, and provide appropriate medical services. Furthermore, the search unit can provide more accurate search results based on past search history and the patient's medical history. For example, if a patient has previously received treatment at a specific hospital, prioritizing that hospital in the search can increase the patient's sense of security.

[0033] The guidance unit provides map information and route guidance based on the hospital bed information retrieved by the search unit. For example, the guidance unit can use GPS to pinpoint the ambulance's current location and guide it along the shortest route. Specifically, it can provide guidance such as, "Go straight on XX Street and turn right." The guidance unit can also provide real-time traffic information. For example, it can guide the ambulance along the optimal route based on traffic congestion information. Furthermore, the guidance unit can provide voice and visual guidance to the ambulance driver to ensure safety while driving. For example, it can display map information on a screen so that the driver can visually confirm it, and provide route instructions via voice guidance. The guidance unit can also monitor the ambulance's operation status in real time and recalculate the route as needed. For example, if an unexpected event such as a traffic accident or road construction occurs, the guidance unit can immediately calculate a new route and guide the driver. In this way, the guidance unit can help the ambulance reach its destination in the shortest possible time, enabling rapid patient transport. Furthermore, the guidance unit can notify the hospital of the ambulance's estimated arrival time, allowing the hospital time to prepare for the arrival.

[0034] The telephone unit automatically calls hospitals identified by the search unit. For example, the telephone unit can notify hospitals in advance of a patient's condition and encourage them to prepare for admission. Specifically, it might tell the hospital, "The patient is complaining of chest pain." The telephone unit can also automatically obtain and call hospital phone numbers. Furthermore, it can automatically generate messages using speech synthesis technology and transmit them to hospitals. For example, it can automatically generate voice messages about the patient's symptoms and estimated arrival time and transmit them to the hospital. The telephone unit can also receive responses from hospitals and collect necessary information. For example, if a hospital is able to accept the patient, it can confirm this and verify whether they are ready. This allows the telephone unit to support smooth patient transport and strengthen collaboration with hospitals. Additionally, the telephone unit can call multiple hospitals simultaneously and select the hospital that responds first. This allows for rapid determination of the patient's destination and reduces transport time.

[0035] The display unit allows hospitals to input and display the number of available beds in real time. For example, it can refer to the hospital's database to display the number of available beds in real time. Specifically, it can display information such as, "There are currently 3 available beds at XX Hospital." The display unit can also have a mechanism for hospitals to periodically update the data. For example, the hospital can periodically update the number of available beds to provide the latest information. Furthermore, the display unit can also display information about the hospital's specialties and facilities. For example, it can display the availability of cardiology beds or ICU beds, providing information to help patients select the hospital best suited to their condition. The display unit can also display hospital congestion levels and waiting times. This allows the display unit to provide patients and emergency medical personnel with real-time information on the most suitable hospital, supporting a rapid response. Additionally, the display unit can display statistical information and trend analysis based on past data. For example, it can predict bed availability during specific times or days of the week, providing information to plan future countermeasures. This allows the display unit to improve the operational efficiency of the hospital and support the rapid admission of patients.

[0036] The reception unit can convert speech to text using speech recognition technology. For example, the reception unit can input speech using the microphone of a smartphone or tablet and convert it to text using speech recognition technology. The reception unit can also use noise cancellation technology to improve the accuracy of speech input. For example, the reception unit can remove ambient noise to improve the accuracy of speech input. This allows for accurate conversion of speech to text using speech recognition technology. Speech recognition technologies include, for example, speech recognition using deep learning and speech recognition using HMM (Hidden Markov Model). Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input speech data into a generating AI and have the generating AI perform the conversion from speech data to text data.

[0037] The analysis unit can analyze speech input using natural language processing technology. For example, the analysis unit can analyze speech input using natural language processing technology. For example, the analysis unit can convert speech input into text data and analyze it using natural language processing technology. The analysis unit can also understand the content of the speech input and identify the patient's symptoms and requests. For example, the analysis unit can analyze symptoms such as "chest pain" and "difficulty breathing" and take appropriate action. In this way, speech input can be accurately analyzed by using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input speech data into a generating AI and have the generating AI perform the analysis of the speech data.

[0038] The search unit can display the number of available hospital beds in real time. For example, the search unit can display the number of available hospital beds in real time by referring to a hospital database. The search unit can also identify the nearest hospital based on the hospital's location information. For example, the search unit can use GPS to determine the current location of an ambulance and search for the nearest hospital. This allows for the rapid selection of the most suitable hospital by displaying the number of available hospital beds in real time. In order to display the information in real time, it is necessary to clarify, for example, the data update frequency and display format. Some or all of the above-described processes in the search unit may be performed using AI, or not using AI. For example, the search unit can input the hospital database into a generating AI and have the generating AI perform the display of the number of available hospital beds.

[0039] The guidance unit can use GPS to pinpoint the ambulance's current location and guide it along the shortest route. For example, the guidance unit can use GPS to pinpoint the ambulance's current location and guide it along the shortest route. For example, the guidance unit can give instructions such as, "Go straight down XX Street and turn right." The guidance unit can also provide real-time traffic information. For example, the guidance unit can guide the ambulance along the optimal route based on traffic congestion information. In this way, by using GPS, the current location of the ambulance can be accurately pinpointed and guided along the shortest route. Regarding the use of GPS, for example, it is necessary to clarify the method of acquiring location information and the standards for accuracy. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input GPS data into a generating AI and have the generating AI execute the shortest route guidance.

[0040] The telephone unit can automatically call a designated hospital and communicate the patient's condition. For example, the telephone unit can notify the hospital in advance of the patient's condition and prompt them to prepare for admission. For example, the telephone unit might tell the hospital, "The patient is complaining of chest pain." The telephone unit can also automatically obtain the hospital's phone number and make the call. This allows the hospital to quickly communicate the patient's condition and prompt them to prepare for admission by making an automated call. To make calls automatically, it is necessary to clarify, for example, how to obtain the phone number and how to automatically generate the call content. Some or all of the above processes in the telephone unit may be performed using AI, or not. For example, the telephone unit can input patient condition data into a generating AI and have the generating AI generate the call content.

[0041] The display unit can have a mechanism for the hospital to periodically update the data. For example, the display unit can refer to the hospital's database and display the number of available beds in real time. For example, the display unit can display information such as, "There are currently 3 available beds at XX Hospital." The display unit can also provide the latest information by having the hospital periodically update the data. For example, the display unit can provide the latest information by having the hospital periodically update the number of available beds. This ensures that the hospital can always provide the most up-to-date information on available beds by periodically updating the data. In order to periodically update the data, for example, it is necessary to clarify the update frequency and the method of data acquisition. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input hospital data into a generating AI and have the generating AI perform the data update.

[0042] The reception desk can select the most appropriate reception method by referring to the patient's past medical history during voice input. For example, if the patient has a history of heart disease, the reception desk can provide an interface that prioritizes the input of heart-related symptoms. The reception desk can also provide an interface that prioritizes the input of allergy-related information if the patient has a history of allergies. The reception desk can also provide an interface that prioritizes the input of mental symptoms if the patient has a history of mental illness. This allows for the selection of a more appropriate reception method by referring to the patient's past medical history. To refer to past medical history, it is necessary to clarify, for example, the use of electronic medical records or the referencing of past medical records. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the patient's past medical history data into a generating AI and have the AI ​​select the optimal reception method.

[0043] The reception desk can filter the reception content by considering the patient's current location information when voice input is received. For example, if the patient is in a specific area, the reception desk will prioritize displaying hospital information for that area. The reception desk can also suggest the most suitable hospital based on the patient's current location if the patient is on the move. The reception desk can also prioritize displaying hospital information for the nearest major city if the patient is in a remote location. This allows for the provision of more appropriate reception content by considering the patient's current location information. To consider the current location information, it is necessary to clarify, for example, the use of GPS data or location services. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the patient's location information data into a generating AI and have the generating AI perform the filtering of the reception content.

[0044] The reception desk can select the most appropriate reception method based on the patient's age and gender when voice input is received. For example, the reception desk can provide an interface that encourages slow voice input for elderly patients. The reception desk can also provide an interface that encourages simple and easy-to-understand voice input for children. The reception desk can also provide an interface that prioritizes inputting symptoms specific to women for female patients. This allows for more appropriate responses by selecting the most appropriate reception method based on the patient's age and gender. To select the most appropriate reception method based on age and gender, it is necessary to clearly define the response methods for each age group and the response methods according to gender. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the patient's age and gender data into a generating AI and have the generating AI select the most appropriate reception method.

[0045] The reception desk can analyze the patient's social media activity during voice input and receive relevant information. For example, if the patient has made health-related posts on social media, the reception desk can adjust the reception content based on that information. The reception desk can also prioritize receiving information related to a specific illness if the patient has mentioned it on social media. The reception desk can also suggest the most suitable hospital based on the location information the patient has shared on social media. This allows for the reception of more appropriate information by analyzing the patient's social media activity. To analyze social media activity, it is necessary to clearly analyze, for example, the content of posts and the frequency of activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input patient social media data into a generating AI and have the AI ​​perform an analysis of the relevant information.

[0046] The analysis unit can optimize its analysis algorithm by referring to the patient's past medical history during voice analysis. For example, if the patient has a history of heart disease, the analysis unit will prioritize voice analysis related to the heart. The analysis unit can also prioritize voice analysis related to allergies if the patient has a history of allergies. The analysis unit can also prioritize voice analysis related to mental symptoms if the patient has a history of mental illness. This allows for the selection of a more appropriate analysis algorithm by referring to the patient's past medical history. Referring to past medical history requires, for example, the use of electronic medical records or referencing past medical records. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's past medical history data into the generating AI and have the generating AI optimize the analysis algorithm.

[0047] The analysis unit can adjust the level of detail of the voice analysis based on the patient's current symptoms. For example, if the patient is in an emergency, the analysis unit can perform a concise and rapid voice analysis. For example, if the patient is in an emergency, the analysis unit can perform a concise and rapid voice analysis. The analysis unit can also perform a detailed voice analysis to improve accuracy if the patient is stable. For example, if the patient is experiencing mild symptoms, the analysis unit can prioritize other emergency inputs over those with mild symptoms. This allows for more appropriate analysis by adjusting the level of detail based on the patient's current symptoms. To adjust the level of detail based on current symptoms, it is necessary to clarify, for example, the severity and urgency of the symptoms. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input patient symptom data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0048] The analysis unit can determine the priority of analysis based on the patient's submission date during voice analysis. For example, if the patient is in an emergency, the analysis unit will prioritize voice analysis. The analysis unit can also perform analysis with the same priority as other inputs if the patient is stable. The analysis unit can also postpone analysis of patients with mild symptoms compared to other emergency inputs. This allows for more appropriate analysis by determining the priority of analysis based on the patient's submission date. To determine the priority of analysis based on the submission date, for example, it is necessary to clarify the submission date and time and the degree of urgency. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input patient submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0049] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient during speech analysis. For example, if the patient has heart disease, the analysis unit can improve the accuracy of its analysis by referring to literature related to heart disease. The analysis unit can also improve the accuracy of its analysis by referring to literature related to allergies if the patient has allergies. The analysis unit can also improve the accuracy of its analysis by referring to literature related to mental illness if the patient has a mental illness. In this way, the accuracy of the analysis can be improved by referring to relevant literature on the patient. In order to refer to relevant literature, it is necessary to clearly indicate, for example, the reference of medical papers or the use of past research results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient-related literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0050] The search unit can optimize its search algorithm by referring to a hospital's past admission history when searching for hospital beds. For example, the search unit can prioritize searching for hospitals that can accept patients based on a hospital's past admission history. The search unit can also prioritize searching for hospitals that avoid congestion based on a hospital's past admission history. The search unit can also analyze a hospital's past admission history and prioritize searching for the most efficient hospital. This allows for the selection of a more appropriate search algorithm by referring to a hospital's past admission history. In order to refer to past admission history, it is necessary to clarify, for example, the use of hospital admission performance data or the referencing of past patient data. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past admission history data of a hospital into a generating AI, and have the generating AI optimize the search algorithm.

[0051] The search unit can adjust the level of detail in a bed search based on the patient's current symptoms. For example, if the patient is in an emergency, the search unit can perform a concise and rapid bed search. For example, if the patient is in an emergency, the search unit can perform a concise and rapid bed search. For example, if the patient is stable, the search unit can perform a detailed bed search to improve accuracy. For example, if the patient is experiencing mild symptoms, the search unit can prioritize them over other emergency patients. For example, if the search unit is experiencing mild symptoms, the search unit can prioritize them over other emergency patients. This allows for more appropriate searches by adjusting the level of detail based on the patient's current symptoms. To adjust the level of detail based on current symptoms, it is necessary to clarify, for example, the severity and urgency of the symptoms. Some or all of the above processing in the search unit may be performed using AI, or not. For example, the search unit can input patient symptom data into a generating AI and have the generating AI perform the adjustment of the level of detail in the search.

[0052] The search unit can perform bed searches while considering the geographical distribution of hospitals. For example, if the patient is in an urban area, the search unit will prioritize searching for hospitals in urban areas. For example, if the patient is in an urban area, the search unit will prioritize searching for hospitals in urban areas. For example, if the patient is in a suburban area, the search unit will prioritize searching for hospitals in suburban areas. For example, if the patient is in a remote area, the search unit will prioritize searching for hospitals in the nearest major city. For example, if the patient is in a remote area, the search unit will prioritize searching for hospitals in the nearest major city. By considering the geographical distribution of hospitals, a more appropriate hospital can be selected. In order to consider geographical distribution, for example, it is necessary to clarify the location information of hospitals and the distribution of regional medical resources. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of hospitals into a generating AI and have the generating AI perform the search.

[0053] The search unit can improve the accuracy of its search by referring to relevant hospital literature when searching for hospital beds. For example, the search unit can refer to relevant hospital literature and prioritize searching for hospitals that can accept patients. The search unit can also refer to relevant hospital literature and prioritize searching for hospitals that are less crowded. The search unit can also analyze relevant hospital literature and prioritize searching for the most efficient hospitals. This improves the accuracy of the search by referring to relevant hospital literature. To refer to relevant literature, it is necessary to clearly indicate, for example, the use of medical papers and past research results. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input relevant hospital literature data into a generating AI and have the generating AI perform the search accuracy improvement.

[0054] The guidance unit can select the optimal route by referring to past traffic data when providing route guidance. For example, the guidance unit can suggest a route that avoids congestion based on past traffic congestion data. For example, the guidance unit can suggest a route that avoids congestion based on past traffic congestion data. The guidance unit can also suggest a safe route based on past traffic accident data. For example, the guidance unit can suggest a safe route based on past traffic accident data. The guidance unit can also suggest the most efficient route based on past traffic volume data. For example, the guidance unit can suggest the most efficient route based on past traffic volume data. In this way, a more appropriate route can be selected by referring to past traffic data. In order to refer to past traffic data, for example, traffic congestion information and past traffic accident data must be clearly defined. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input past traffic data into a generating AI and have the generating AI select the optimal route.

[0055] The guidance unit can adjust the level of detail of the directions based on the patient's current location information when providing route guidance. For example, if the patient is in an urban area, the guidance unit can provide detailed route guidance. For example, if the patient is in an urban area, the guidance unit can provide detailed route guidance. For example, if the patient is in a suburban area, the guidance unit can provide concise route guidance. For example, if the patient is in a remote area, the guidance unit can provide route guidance to the nearest major city. For example, if the patient is in a remote area, the guidance unit can provide route guidance to the nearest major city. By adjusting the level of detail of the directions based on the patient's current location information, more appropriate guidance becomes possible. In order to adjust the level of detail of the directions based on the current location information, it is necessary to clarify, for example, the use of GPS data or location information services. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the patient's location information data into a generating AI and have the generating AI perform the adjustment of the level of detail of the directions.

[0056] The guidance unit can adjust the order of directions based on changes in traffic conditions when providing route guidance. For example, if traffic congestion occurs, the guidance unit can suggest an alternative route. For example, if a traffic accident occurs, the guidance unit can suggest a safe route. For example, if traffic volume increases, the guidance unit can suggest the most efficient route. By adjusting the order of directions based on changes in traffic conditions, more appropriate guidance becomes possible. To adjust the order of directions based on changes in traffic conditions, it is necessary to clearly utilize real-time traffic information and predict traffic congestion, for example. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input traffic condition data into a generating AI and have the generating AI perform the adjustment of the order of directions.

[0057] The guidance unit can improve the accuracy of route guidance by referring to relevant geographic information. For example, the guidance unit can suggest the optimal route based on geographic information. The guidance unit can also suggest routes that avoid congestion based on geographic information. The guidance unit can also analyze geographic information and suggest the most efficient route. This allows for improved guidance accuracy by referring to relevant geographic information. To refer to relevant geographic information, it is necessary to clarify, for example, the use of map data or geographic information systems (GIS). Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input geographic information data into a generating AI and have the generating AI perform the task of improving the accuracy of the guidance.

[0058] The telephone unit can select the optimal call content by referring to the hospital's past admission history when making automated calls. For example, the telephone unit can call hospitals that can accept patients based on the hospital's past admission history. The telephone unit can also call hospitals that are less crowded based on the hospital's past admission history. For example, the telephone unit can call hospitals that are less crowded based on the hospital's past admission history. The telephone unit can also analyze the hospital's past admission history and call the most efficient hospital. For example, the telephone unit can analyze the hospital's past admission history and call the most efficient hospital. This allows for the selection of more appropriate call content by referring to the hospital's past admission history. In order to refer to past admission history, it is necessary to clarify, for example, the use of hospital admission performance data and the referencing of past patient data. Some or all of the above processing in the telephone unit may be performed using AI, for example, or not using AI. For example, the telephone unit can input the hospital's past admission history data into a generating AI and have the generating AI select the optimal call content.

[0059] The telephone unit can adjust the level of detail in an automated call based on the patient's current symptoms. For example, if the patient is in an emergency, the telephone unit can make a concise and quick call. For example, if the patient is in an emergency, the telephone unit can make a concise and quick call. The telephone unit can also make a call with more detailed information if the patient is stable. For example, if the patient is stable, the telephone unit can make a call with more detailed information. The telephone unit can also prioritize patients with mild symptoms over other emergency patients. For example, if the patient is experiencing mild symptoms, the telephone unit can prioritize patients with mild symptoms over other emergency patients. This allows for a more appropriate response by adjusting the level of detail in the call based on the patient's current symptoms. To adjust the level of detail in the call based on current symptoms, for example, it is necessary to clarify the severity and urgency of the symptoms. Some or all of the above processing in the telephone unit may be performed using AI, or not, for example. For example, the telephone unit can input patient symptom data into a generating AI and have the generating AI perform the adjustment of the level of detail in the call.

[0060] The telephone unit can make automated phone calls while considering the geographical distribution of hospitals. For example, if the patient is in an urban area, the telephone unit will call a hospital in an urban area. For example, if the patient is in an urban area, the telephone unit can call a hospital in an urban area. For example, if the patient is in a suburban area, the telephone unit can call a hospital in a suburban area. For example, if the patient is in a remote area, the telephone unit can call a hospital in the nearest major city. For example, if the patient is in a remote area, the telephone unit can call a hospital in the nearest major city. By considering the geographical distribution of hospitals, it is possible to call a more appropriate hospital. In order to consider geographical distribution, for example, it is necessary to clarify the location information of hospitals and the distribution of regional medical resources. Some or all of the above processing in the telephone unit may be performed using AI, for example, or not using AI. For example, the telephone unit can input geographical distribution data of hospitals into a generating AI and have the generating AI perform the phone call.

[0061] The telephone unit can improve the accuracy of automated phone calls by referring to relevant hospital literature. For example, the telephone unit can refer to relevant hospital literature and call hospitals that can accept patients. The telephone unit can also refer to relevant hospital literature and call hospitals that are less crowded. For example, the telephone unit can refer to relevant hospital literature and call hospitals that are less crowded. The telephone unit can also analyze relevant hospital literature and call the most efficient hospital. For example, the telephone unit can analyze relevant hospital literature and call the most efficient hospital. This improves the accuracy of phone calls by referring to relevant hospital literature. In order to refer to relevant literature, it is necessary to clearly indicate, for example, the use of medical papers and past research results. Some or all of the above processing in the telephone unit may be performed using AI, for example, or not using AI. For example, the telephone unit can input hospital relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of phone calls.

[0062] The display unit can optimize its display algorithm by referring to the hospital's past admission history when displaying information. For example, the display unit can prioritize displaying hospitals that can accept patients based on their past admission history. The display unit can also prioritize displaying hospitals that avoid congestion based on their past admission history. The display unit can also analyze the hospital's past admission history and prioritize displaying the most efficient hospital. This allows for the selection of a more appropriate display algorithm by referring to the hospital's past admission history. To refer to past admission history, it is necessary to clearly define the use of hospital admission performance data and the reference to past patient data. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input hospital's past admission history data into a generating AI and have the generating AI perform the optimization of the display algorithm.

[0063] The display unit can adjust the level of detail in its display based on the patient's current symptoms. For example, if the patient is in an emergency, the display unit will provide a concise and rapid display. The display unit can also provide a detailed display to improve accuracy if the patient is stable. The display unit can also prioritize patients with mild symptoms over other emergency patients. This allows for more appropriate displays by adjusting the level of detail based on the patient's current symptoms. To adjust the level of detail based on current symptoms, it is necessary to clarify, for example, the severity and urgency of the symptoms. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input patient symptom data into a generating AI and have the generating AI adjust the level of detail in the display.

[0064] The display unit can display information while considering the geographical distribution of hospitals. For example, if the patient is in an urban area, the display unit will prioritize displaying hospitals in urban areas. For example, if the patient is in an urban area, the display unit will prioritize displaying hospitals in urban areas. For example, if the patient is in a suburban area, the display unit will prioritize displaying hospitals in suburban areas. For example, if the patient is in a remote area, the display unit will prioritize displaying hospitals in the nearest major city. For example, if the patient is in a remote area, the display unit will prioritize displaying hospitals in the nearest major city. By considering the geographical distribution of hospitals, a more appropriate hospital can be displayed. To consider geographical distribution, for example, it is necessary to clarify the location information of hospitals and the distribution of regional medical resources. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input geographical distribution data of hospitals into a generating AI and have the generating AI perform the display.

[0065] The display unit can improve the accuracy of its display by referring to relevant literature on hospitals during the display process. For example, the display unit can refer to relevant literature on hospitals and prioritize displaying hospitals that can accept patients. For example, the display unit can refer to relevant literature on hospitals and prioritize displaying hospitals that can accept patients. The display unit can also prioritize displaying hospitals that avoid congestion based on relevant literature on hospitals. For example, the display unit can analyze relevant literature on hospitals and prioritize displaying the most efficient hospitals. For example, the display unit can analyze relevant literature on hospitals and prioritize displaying the most efficient hospitals. This improves the accuracy of the display by referring to relevant literature on hospitals. In order to refer to relevant literature, it is necessary to clearly indicate, for example, the reference to medical papers or the use of past research results. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input hospital relevant literature data into a generating AI and have the generating AI perform the display accuracy improvement.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The emergency hospital bed search system can also be equipped with a monitoring unit that monitors the patient's vital signs in real time. The monitoring unit continuously measures vital signs such as the patient's heart rate, blood pressure, and oxygen saturation, and transmits this data to an analysis unit. Based on this vital sign data, the analysis unit can more accurately assess the patient's condition and select an appropriate hospital. For example, if the heart rate is abnormally high, the system can prioritize searching for hospitals specializing in cardiology. Similarly, if the oxygen saturation is low, it can prioritize searching for hospitals specializing in respiratory medicine. This makes it possible to select a more appropriate hospital based on the patient's vital signs.

[0068] The emergency hospital bed search system may also include a medical record referencing unit that accesses the patient's past medical records. This unit, for example, may work in conjunction with an electronic medical record system to retrieve the patient's past medical records. The analysis unit then uses these medical records to compare the patient's current symptoms with their past medical history and select an appropriate hospital. For example, a patient with a history of heart disease would be prioritized for hospitals specializing in cardiology. Similarly, a patient with a history of allergic reactions could be prioritized for hospitals capable of handling allergies. This allows for the selection of a more appropriate hospital based on the patient's past medical records.

[0069] The emergency bed search system can further filter reception requests by considering the patient's current location. For example, if a patient is in a specific area, the reception system will prioritize displaying hospital information in that area. If the patient is on the move, it can suggest the most suitable hospital based on their current location. Furthermore, if the patient is in a remote location, it can prioritize displaying hospital information in the nearest major city. This allows for more appropriate reception services by considering the patient's current location.

[0070] The emergency bed search system can further select the most appropriate registration method based on the patient's age and gender. For example, the registration section can provide an interface that encourages slow voice input for elderly patients, a simple and easy-to-understand voice input interface for children, and an interface that prioritizes inputting symptoms specific to women for female patients. This allows for more appropriate care by selecting the most suitable registration method based on the patient's age and gender.

[0071] The emergency bed search system can further analyze patients' social media activity and receive relevant information. For example, if a patient has made health-related posts on social media, the system can adjust the request based on that information. If a patient mentions a specific illness, the system can prioritize receiving information related to that illness. Furthermore, if a patient shares their location on social media, the system can suggest the most suitable hospital based on that location. This allows for the reception of more appropriate information by analyzing patients' social media activity.

[0072] The emergency bed search system can further optimize its analysis algorithm by referencing the patient's past medical history. For example, if a patient has a history of heart disease, the analysis unit will prioritize voice analysis related to heart conditions. If a patient has a history of allergies, it can also prioritize voice analysis related to allergies. Furthermore, if a patient has a history of mental illness, it can prioritize voice analysis related to mental symptoms. This allows for the selection of a more appropriate analysis algorithm by referencing the patient's past medical history.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The reception desk receives patient symptoms and requests via voice input. For example, it uses the microphone on a smartphone or tablet to input voice, and converts it to text using speech recognition technology. It also uses noise cancellation technology to remove ambient noise and improve the accuracy of voice input. Step 2: The analysis unit analyzes the voice input received by the reception unit. For example, it uses natural language processing technology to convert the voice input into text data and identify the patient's symptoms and requests. Step 3: The search unit searches for the nearest available hospital bed based on the information analyzed by the analysis unit. For example, it refers to a hospital database, displays the number of available beds in real time, and identifies the nearest hospital based on the hospital's location information. Step 4: The guidance unit provides map information and route guidance based on the hospital bed information retrieved by the search unit. For example, it uses GPS to pinpoint the current location of an ambulance and guides the user along the shortest route. It also provides real-time traffic information and guides the user along the optimal route based on congestion information. Step 5: The telephone unit automatically calls the hospitals identified by the search unit. For example, it notifies the hospitals in advance of the patient's condition and prompts them to prepare for admission. Step 6: The display unit will display the number of available beds in real time, based on input from the hospital. For example, it will refer to the hospital's database and display the number of available beds in real time. The hospital will also periodically update the data to provide the latest information.

[0075] (Example of form 2) An emergency bed search system according to an embodiment of the present invention is a system for ambulances to search for available emergency beds at the nearest hospital. The emergency bed search system allows paramedics to voice input the patient's symptoms and requests using a smartphone or tablet. The system analyzes the voice input and automatically searches for the nearest available bed. Furthermore, it provides map information, route guidance, and traffic information to facilitate early patient transport. An automatic telephone calling function also notifies hospitals of the patient's condition in advance, prompting them to prepare for admission. This strengthens cooperation between paramedics and medical institutions, enabling a rapid response to save lives. For example, paramedics voice input the patient's symptoms and requests using a smartphone or tablet. For instance, they might voice-input symptoms such as "My chest hurts" or "I'm having trouble breathing." This voice input is analyzed by the system. Next, the system analyzes the voice input and automatically searches for the nearest available bed. The system displays the number of available beds in hospitals in real time and identifies the nearest hospital. For example, the system might display "The nearest hospital is XX Hospital." Furthermore, the system provides map information, route guidance, and traffic information to enable early patient transport. The system uses GPS to pinpoint the ambulance's current location and guides it along the shortest route. For example, it might instruct the ambulance to "Go straight down XX Street and turn right." It also features an automated telephone calling function to notify hospitals of the patient's condition in advance, prompting them to prepare for admission. The system automatically calls the designated hospital and relays the patient's condition, for example, "The patient is complaining of chest pain." Hospitals can also input the number of available beds into the system, which is displayed in real time. This allows paramedics to select a hospital based on accurate information. For example, it might display information such as, "XX Hospital currently has 3 available beds." This system shortens the time from ambulance arrival to patient admission, contributing to improved survival rates. It also reduces the workload of paramedics and ensures appropriate patient transport. Furthermore, strengthened collaboration with medical institutions enables rapid response to patients with high-priority emergencies. For example, it solves the problem of ambulances waiting for several hours to find a hospital bed during the COVID-19 pandemic.This allows the emergency bed search system to receive and analyze patient symptoms and requests via voice input, search for the nearest available bed, provide map information and route guidance, automatically call hospitals, and display the number of available beds in real time, thereby enabling early patient transport.

[0076] The emergency bed search system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a guidance unit, a telephone unit, and a display unit. The reception unit receives the patient's symptoms and requests via voice input. The reception unit can convert the voice into text using, for example, speech recognition technology. For example, the reception unit uses the microphone of a smartphone or tablet to input voice and converts it into text using speech recognition technology. The reception unit can also use noise cancellation technology to improve the accuracy of voice input. For example, the reception unit removes ambient noise to improve the accuracy of voice input. The analysis unit analyzes the voice input. The analysis unit can analyze the voice input using, for example, natural language processing technology. For example, the analysis unit converts the voice input into text data and analyzes it using natural language processing technology. The analysis unit can also understand the content of the voice input and identify the patient's symptoms and requests. For example, the analysis unit analyzes symptoms such as "chest pain" and "difficulty breathing" and takes appropriate action. The search unit searches for the nearest available hospital bed based on the information analyzed by the analysis unit. The search unit can, for example, display the number of available hospital beds in real time and identify the nearest hospital. For example, the search unit can refer to a hospital database and display the number of available beds in real time. The search unit can also identify the nearest hospital based on the hospital's location information. For example, the search unit can use GPS to determine the current location of an ambulance and search for the nearest hospital. The guidance unit provides map information and route guidance based on the hospital bed information retrieved by the search unit. For example, the guidance unit can use GPS to determine the current location of an ambulance and guide the user to the shortest route. For example, the guidance unit can provide instructions such as, "Go straight down XX Street and turn right." The guidance unit can also provide real-time traffic information. For example, the guidance unit can guide the user to the optimal route based on traffic congestion information. The telephone unit automatically calls the hospital determined by the search unit. For example, the telephone unit can notify the hospital in advance of the patient's condition and encourage them to prepare for admission. For example, the telephone unit can tell the hospital, "The patient is complaining of chest pain." Furthermore, the telephone unit can automatically obtain the hospital's phone number and make calls.The display unit receives input from the hospital regarding the number of available beds and displays it in real time. The display unit can, for example, refer to the hospital's database and display the number of available beds in real time. For example, the display unit may display information such as, "There are currently 3 available beds at XX Hospital." The display unit can also have a mechanism for the hospital to periodically update the data. For example, the display unit can provide the latest information by having the hospital periodically update the number of available beds. As a result, the emergency bed search system according to this embodiment can receive and analyze the patient's symptoms and requests via voice input, search for the nearest available bed, provide map information and route guidance, automatically call the hospital, and display the number of available beds in real time, thereby enabling early patient transport.

[0077] The reception desk receives patient symptoms and requests via voice input. The reception desk can convert speech to text using, for example, speech recognition technology. Specifically, it uses the microphone of a smartphone or tablet for voice input and converts it to text using speech recognition technology. This speech recognition technology employs an advanced algorithm using deep learning, enabling highly accurate analysis of speech characteristics. Furthermore, the reception desk can also use noise cancellation technology to improve the accuracy of voice input. For example, to remove ambient noise and improve the accuracy of voice input, a directional microphone is used to pick up only voice from a specific direction. The speech recognition engine is also designed to learn from multiple voice samples and handle different accents and speaking styles. As a result, the reception desk can convert speech to text with high accuracy regardless of the environment in which the patient performs voice input. Additionally, by allowing the reception desk to simultaneously input basic information such as the patient's age, gender, and medical history during voice input, more detailed information can be collected, streamlining the processing of subsequent analysis and search units.

[0078] The analysis unit analyzes voice input. For example, the analysis unit can analyze voice input using natural language processing (NLP) technology. Specifically, it converts voice input into text data and analyzes it using NLP. NLP performs detailed analysis of the text data through processes such as tokenization, part-of-speech tagging, and dependency analysis. The analysis unit can also understand the content of the voice input and identify the patient's symptoms and needs. For example, it can analyze symptoms such as "chest pain" or "difficulty breathing" and provide appropriate responses. Furthermore, to assess the severity of symptoms, the analysis unit can link with medical databases and refer to past cases and statistical data. This allows the analysis unit to quickly determine whether the patient's symptoms are urgent and provide appropriate responses. The analysis unit can also perform sentiment analysis from the patient's voice input to understand the patient's psychological state. For example, it can analyze the tone, speed, and volume of the voice to determine whether the patient is feeling anxiety or fear. This allows the analysis unit to respond according to the patient's psychological state and provide more appropriate medical services.

[0079] The search unit searches for the nearest available hospital bed based on information analyzed by the analysis unit. For example, the search unit can display the number of available hospital beds in real time and identify the nearest hospital. Specifically, it refers to a hospital database and displays the number of available beds in real time. The search unit can also identify the nearest hospital based on the hospital's location information. For example, it can use GPS to pinpoint the current location of an ambulance and search for the nearest hospital. Furthermore, the search unit can also consider hospital specialties and facilities when performing searches. For example, if a cardiology-specific bed is needed, it will prioritize searching for hospitals specializing in cardiology. The search unit can also select the most suitable hospital by considering hospital congestion and waiting times. This allows the search unit to quickly identify the hospital best suited to the patient's symptoms and needs, and provide appropriate medical services. Furthermore, the search unit can provide more accurate search results based on past search history and the patient's medical history. For example, if a patient has previously received treatment at a specific hospital, prioritizing that hospital in the search can increase the patient's sense of security.

[0080] The guidance unit provides map information and route guidance based on the hospital bed information retrieved by the search unit. For example, the guidance unit can use GPS to pinpoint the ambulance's current location and guide it along the shortest route. Specifically, it can provide guidance such as, "Go straight on XX Street and turn right." The guidance unit can also provide real-time traffic information. For example, it can guide the ambulance along the optimal route based on traffic congestion information. Furthermore, the guidance unit can provide voice and visual guidance to the ambulance driver to ensure safety while driving. For example, it can display map information on a screen so that the driver can visually confirm it, and provide route instructions via voice guidance. The guidance unit can also monitor the ambulance's operation status in real time and recalculate the route as needed. For example, if an unexpected event such as a traffic accident or road construction occurs, the guidance unit can immediately calculate a new route and guide the driver. In this way, the guidance unit can help the ambulance reach its destination in the shortest possible time, enabling rapid patient transport. Furthermore, the guidance unit can notify the hospital of the ambulance's estimated arrival time, allowing the hospital time to prepare for the arrival.

[0081] The telephone unit automatically calls hospitals identified by the search unit. For example, the telephone unit can notify hospitals in advance of a patient's condition and encourage them to prepare for admission. Specifically, it might tell the hospital, "The patient is complaining of chest pain." The telephone unit can also automatically obtain and call hospital phone numbers. Furthermore, it can automatically generate messages using speech synthesis technology and transmit them to hospitals. For example, it can automatically generate voice messages about the patient's symptoms and estimated arrival time and transmit them to the hospital. The telephone unit can also receive responses from hospitals and collect necessary information. For example, if a hospital is able to accept the patient, it can confirm this and verify whether they are ready. This allows the telephone unit to support smooth patient transport and strengthen collaboration with hospitals. Additionally, the telephone unit can call multiple hospitals simultaneously and select the hospital that responds first. This allows for rapid determination of the patient's destination and reduces transport time.

[0082] The display unit allows hospitals to input and display the number of available beds in real time. For example, it can refer to the hospital's database to display the number of available beds in real time. Specifically, it can display information such as, "There are currently 3 available beds at XX Hospital." The display unit can also have a mechanism for hospitals to periodically update the data. For example, the hospital can periodically update the number of available beds to provide the latest information. Furthermore, the display unit can also display information about the hospital's specialties and facilities. For example, it can display the availability of cardiology beds or ICU beds, providing information to help patients select the hospital best suited to their condition. The display unit can also display hospital congestion levels and waiting times. This allows the display unit to provide patients and emergency medical personnel with real-time information on the most suitable hospital, supporting a rapid response. Additionally, the display unit can display statistical information and trend analysis based on past data. For example, it can predict bed availability during specific times or days of the week, providing information to plan future countermeasures. This allows the display unit to improve the operational efficiency of the hospital and support the rapid admission of patients.

[0083] The reception unit can convert speech to text using speech recognition technology. For example, the reception unit can input speech using the microphone of a smartphone or tablet and convert it to text using speech recognition technology. The reception unit can also use noise cancellation technology to improve the accuracy of speech input. For example, the reception unit can remove ambient noise to improve the accuracy of speech input. This allows for accurate conversion of speech to text using speech recognition technology. Speech recognition technologies include, for example, speech recognition using deep learning and speech recognition using HMM (Hidden Markov Model). Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input speech data into a generating AI and have the generating AI perform the conversion from speech data to text data.

[0084] The analysis unit can analyze speech input using natural language processing technology. For example, the analysis unit can analyze speech input using natural language processing technology. For example, the analysis unit can convert speech input into text data and analyze it using natural language processing technology. The analysis unit can also understand the content of the speech input and identify the patient's symptoms and requests. For example, the analysis unit can analyze symptoms such as "chest pain" and "difficulty breathing" and take appropriate action. In this way, speech input can be accurately analyzed by using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input speech data into a generating AI and have the generating AI perform the analysis of the speech data.

[0085] The search unit can display the number of available hospital beds in real time. For example, the search unit can display the number of available hospital beds in real time by referring to a hospital database. The search unit can also identify the nearest hospital based on the hospital's location information. For example, the search unit can use GPS to determine the current location of an ambulance and search for the nearest hospital. This allows for the rapid selection of the most suitable hospital by displaying the number of available hospital beds in real time. In order to display the information in real time, it is necessary to clarify, for example, the data update frequency and display format. Some or all of the above-described processes in the search unit may be performed using AI, or not using AI. For example, the search unit can input the hospital database into a generating AI and have the generating AI perform the display of the number of available hospital beds.

[0086] The guidance unit can use GPS to pinpoint the ambulance's current location and guide it along the shortest route. For example, the guidance unit can use GPS to pinpoint the ambulance's current location and guide it along the shortest route. For example, the guidance unit can give instructions such as, "Go straight down XX Street and turn right." The guidance unit can also provide real-time traffic information. For example, the guidance unit can guide the ambulance along the optimal route based on traffic congestion information. In this way, by using GPS, the current location of the ambulance can be accurately pinpointed and guided along the shortest route. Regarding the use of GPS, for example, it is necessary to clarify the method of acquiring location information and the standards for accuracy. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input GPS data into a generating AI and have the generating AI execute the shortest route guidance.

[0087] The telephone unit can automatically call a designated hospital and communicate the patient's condition. For example, the telephone unit can notify the hospital in advance of the patient's condition and prompt them to prepare for admission. For example, the telephone unit might tell the hospital, "The patient is complaining of chest pain." The telephone unit can also automatically obtain the hospital's phone number and make the call. This allows the hospital to quickly communicate the patient's condition and prompt them to prepare for admission by making an automated call. To make calls automatically, it is necessary to clarify, for example, how to obtain the phone number and how to automatically generate the call content. Some or all of the above processes in the telephone unit may be performed using AI, or not. For example, the telephone unit can input patient condition data into a generating AI and have the generating AI generate the call content.

[0088] The display unit can have a mechanism for the hospital to periodically update the data. For example, the display unit can refer to the hospital's database and display the number of available beds in real time. For example, the display unit can display information such as, "There are currently 3 available beds at XX Hospital." The display unit can also provide the latest information by having the hospital periodically update the data. For example, the display unit can provide the latest information by having the hospital periodically update the number of available beds. This ensures that the hospital can always provide the most up-to-date information on available beds by periodically updating the data. In order to periodically update the data, for example, it is necessary to clarify the update frequency and the method of data acquisition. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input hospital data into a generating AI and have the generating AI perform the data update.

[0089] The reception desk can estimate the patient's emotions and adjust the voice input reception method based on the estimated emotions. For example, if the patient is nervous, the reception desk can provide an interface that prompts voice input in a calm voice. For example, if the patient is nervous, the reception desk can provide an interface that prompts voice input in a calm voice. The reception desk can also provide an interface that prioritizes concise and quick voice input if the patient is in a panic state. For example, if the patient is in a panic state, the reception desk can provide an interface that prioritizes concise and quick voice input. The reception desk can also provide an interface that allows for the input of detailed information if the patient is relaxed. For example, if the reception desk allows for the input of detailed information if the patient is relaxed. This allows for a more appropriate response by adjusting the voice input reception method according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input patient voice data into a generating AI and have the AI ​​perform emotion estimation.

[0090] The reception desk can select the most appropriate reception method by referring to the patient's past medical history during voice input. For example, if the patient has a history of heart disease, the reception desk can provide an interface that prioritizes the input of heart-related symptoms. The reception desk can also provide an interface that prioritizes the input of allergy-related information if the patient has a history of allergies. The reception desk can also provide an interface that prioritizes the input of mental symptoms if the patient has a history of mental illness. This allows for the selection of a more appropriate reception method by referring to the patient's past medical history. To refer to past medical history, it is necessary to clarify, for example, the use of electronic medical records or the referencing of past medical records. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the patient's past medical history data into a generating AI and have the AI ​​select the optimal reception method.

[0091] The reception desk can filter the reception content by considering the patient's current location information when voice input is received. For example, if the patient is in a specific area, the reception desk will prioritize displaying hospital information for that area. The reception desk can also suggest the most suitable hospital based on the patient's current location if the patient is on the move. The reception desk can also prioritize displaying hospital information for the nearest major city if the patient is in a remote location. This allows for the provision of more appropriate reception content by considering the patient's current location information. To consider the current location information, it is necessary to clarify, for example, the use of GPS data or location services. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the patient's location information data into a generating AI and have the generating AI perform the filtering of the reception content.

[0092] The reception desk can estimate the patient's emotions and determine the priority of voice input based on the estimated emotions. For example, if the patient is in an emergency, the reception desk will process the voice input with the highest priority. For example, if the patient is in an emergency, the reception desk will process the voice input with the highest priority. For example, if the patient is stable, the reception desk will process the voice input with the same priority as other inputs. For example, if the patient is reporting mild symptoms, the reception desk will process the voice input with the same priority as other inputs. For example, if the patient is reporting mild symptoms, the reception desk will process the voice input with the same priority as other emergency inputs. For example, if the patient is reporting mild symptoms, the reception desk will process the voice input with the same priority as other emergency inputs. This allows for a more appropriate response by determining the priority of voice input according to the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk may input patient voice data into a generating AI and have the generating AI perform emotion estimation.

[0093] The reception desk can select the most appropriate reception method based on the patient's age and gender when voice input is received. For example, the reception desk can provide an interface that encourages slow voice input for elderly patients. The reception desk can also provide an interface that encourages simple and easy-to-understand voice input for children. The reception desk can also provide an interface that prioritizes inputting symptoms specific to women for female patients. This allows for more appropriate responses by selecting the most appropriate reception method based on the patient's age and gender. To select the most appropriate reception method based on age and gender, it is necessary to clearly define the response methods for each age group and the response methods according to gender. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the patient's age and gender data into a generating AI and have the generating AI select the most appropriate reception method.

[0094] The reception desk can analyze the patient's social media activity during voice input and receive relevant information. For example, if the patient has made health-related posts on social media, the reception desk can adjust the reception content based on that information. The reception desk can also prioritize receiving information related to a specific illness if the patient has mentioned it on social media. The reception desk can also suggest the most suitable hospital based on the location information the patient has shared on social media. This allows for the reception of more appropriate information by analyzing the patient's social media activity. To analyze social media activity, it is necessary to clearly analyze, for example, the content of posts and the frequency of activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input patient social media data into a generating AI and have the AI ​​perform an analysis of the relevant information.

[0095] The analysis unit can estimate the patient's emotions and adjust the voice analysis method based on the estimated emotions. For example, if the patient is tense, the analysis unit can perform a rapid voice analysis and provide results quickly. For example, if the patient is tense, the analysis unit can perform a rapid voice analysis and provide results quickly. The analysis unit can also perform a detailed voice analysis to improve accuracy if the patient is relaxed. For example, if the patient is relaxed, the analysis unit can perform a detailed voice analysis to improve accuracy. The analysis unit can also perform a concise voice analysis and provide results quickly if the patient is in a panic state. For example, if the analysis unit is in a panic state, the analysis unit can perform a concise voice analysis and provide results quickly. This allows for more appropriate analysis by adjusting the voice analysis method according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient voice data into a generating AI and have the generating AI perform emotion estimation.

[0096] The analysis unit can optimize its analysis algorithm by referring to the patient's past medical history during voice analysis. For example, if the patient has a history of heart disease, the analysis unit will prioritize voice analysis related to the heart. The analysis unit can also prioritize voice analysis related to allergies if the patient has a history of allergies. The analysis unit can also prioritize voice analysis related to mental symptoms if the patient has a history of mental illness. This allows for the selection of a more appropriate analysis algorithm by referring to the patient's past medical history. Referring to past medical history requires, for example, the use of electronic medical records or referencing past medical records. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's past medical history data into the generating AI and have the generating AI optimize the analysis algorithm.

[0097] The analysis unit can adjust the level of detail of the voice analysis based on the patient's current symptoms. For example, if the patient is in an emergency, the analysis unit can perform a concise and rapid voice analysis. For example, if the patient is in an emergency, the analysis unit can perform a concise and rapid voice analysis. The analysis unit can also perform a detailed voice analysis to improve accuracy if the patient is stable. For example, if the patient is experiencing mild symptoms, the analysis unit can prioritize other emergency inputs over those with mild symptoms. This allows for more appropriate analysis by adjusting the level of detail based on the patient's current symptoms. To adjust the level of detail based on current symptoms, it is necessary to clarify, for example, the severity and urgency of the symptoms. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input patient symptom data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0098] The analysis unit can estimate the patient's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the patient is tense, the analysis unit can provide a simple and easy-to-read display method. For example, if the patient is tense, the analysis unit can provide a simple and easy-to-read display method. The analysis unit can also provide a display method that includes detailed information if the patient is relaxed. For example, if the patient is relaxed, the analysis unit can provide a display method that includes detailed information. The analysis unit can also provide a display method that gets to the point if the patient is in a hurry. For example, if the patient is in a hurry, the analysis unit can provide a display method that gets to the point. By adjusting the display method of the analysis results according to the patient's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient voice data into a generating AI and have the generating AI perform emotion estimation.

[0099] The analysis unit can determine the priority of analysis based on the patient's submission date during voice analysis. For example, if the patient is in an emergency, the analysis unit will prioritize voice analysis. The analysis unit can also perform analysis with the same priority as other inputs if the patient is stable. The analysis unit can also postpone analysis of patients with mild symptoms compared to other emergency inputs. This allows for more appropriate analysis by determining the priority of analysis based on the patient's submission date. To determine the priority of analysis based on the submission date, for example, it is necessary to clarify the submission date and time and the degree of urgency. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input patient submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0100] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the patient during speech analysis. For example, if the patient has heart disease, the analysis unit can improve the accuracy of its analysis by referring to literature related to heart disease. The analysis unit can also improve the accuracy of its analysis by referring to literature related to allergies if the patient has allergies. The analysis unit can also improve the accuracy of its analysis by referring to literature related to mental illness if the patient has a mental illness. In this way, the accuracy of the analysis can be improved by referring to relevant literature on the patient. In order to refer to relevant literature, it is necessary to clearly indicate, for example, the reference of medical papers or the use of past research results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient-related literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0101] The search unit can estimate the patient's emotions and adjust the criteria for hospital bed searches based on the estimated emotions. For example, if the patient is in an emergency, the search unit will prioritize searching for the nearest hospital. For example, if the patient is in an emergency, the search unit will prioritize searching for the nearest hospital. For example, if the patient is stable, the search unit will prioritize searching for hospitals with good facilities. For example, if the patient is experiencing mild symptoms, the search unit will prioritize searching for hospitals with good facilities. For example, if the patient is experiencing mild symptoms, the search unit will prioritize searching for patients with mild symptoms. For example, if the patient is experiencing mild symptoms, the search unit will prioritize searching for patients with mild symptoms. This allows for more appropriate searches by adjusting the criteria for hospital bed searches 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patient emotion data into a generating AI and have the generating AI perform adjustments to the criteria for hospital bed searches.

[0102] The search unit can optimize its search algorithm by referring to a hospital's past admission history when searching for hospital beds. For example, the search unit can prioritize searching for hospitals that can accept patients based on a hospital's past admission history. The search unit can also prioritize searching for hospitals that avoid congestion based on a hospital's past admission history. The search unit can also analyze a hospital's past admission history and prioritize searching for the most efficient hospital. This allows for the selection of a more appropriate search algorithm by referring to a hospital's past admission history. In order to refer to past admission history, it is necessary to clarify, for example, the use of hospital admission performance data or the referencing of past patient data. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past admission history data of a hospital into a generating AI, and have the generating AI optimize the search algorithm.

[0103] The search unit can adjust the level of detail in a bed search based on the patient's current symptoms. For example, if the patient is in an emergency, the search unit can perform a concise and rapid bed search. For example, if the patient is in an emergency, the search unit can perform a concise and rapid bed search. For example, if the patient is stable, the search unit can perform a detailed bed search to improve accuracy. For example, if the patient is experiencing mild symptoms, the search unit can prioritize them over other emergency patients. For example, if the search unit is experiencing mild symptoms, the search unit can prioritize them over other emergency patients. This allows for more appropriate searches by adjusting the level of detail based on the patient's current symptoms. To adjust the level of detail based on current symptoms, it is necessary to clarify, for example, the severity and urgency of the symptoms. Some or all of the above processing in the search unit may be performed using AI, or not. For example, the search unit can input patient symptom data into a generating AI and have the generating AI perform the adjustment of the level of detail in the search.

[0104] The search unit can estimate the patient's emotions and adjust the display method of search results based on the estimated emotions. For example, if the patient is nervous, the search unit can provide a simple and highly visible display method. For example, if the patient is nervous, the search unit can provide a simple and highly visible display method. The search unit can also provide a display method that includes detailed information if the patient is relaxed. For example, if the patient is relaxed, the search unit can provide a display method that includes detailed information. The search unit can also provide a concise display method if the patient is in a hurry. For example, if the patient is in a hurry, the search unit can provide a concise display method. By adjusting the display method of search results according to the patient's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patient emotion data into a generating AI and have the generating AI adjust how the search results are displayed.

[0105] The search unit can perform bed searches while considering the geographical distribution of hospitals. For example, if the patient is in an urban area, the search unit will prioritize searching for hospitals in urban areas. For example, if the patient is in an urban area, the search unit will prioritize searching for hospitals in urban areas. For example, if the patient is in a suburban area, the search unit will prioritize searching for hospitals in suburban areas. For example, if the patient is in a remote area, the search unit will prioritize searching for hospitals in the nearest major city. For example, if the patient is in a remote area, the search unit will prioritize searching for hospitals in the nearest major city. By considering the geographical distribution of hospitals, a more appropriate hospital can be selected. In order to consider geographical distribution, for example, it is necessary to clarify the location information of hospitals and the distribution of regional medical resources. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of hospitals into a generating AI and have the generating AI perform the search.

[0106] The search unit can improve the accuracy of its search by referring to relevant hospital literature when searching for hospital beds. For example, the search unit can refer to relevant hospital literature and prioritize searching for hospitals that can accept patients. The search unit can also refer to relevant hospital literature and prioritize searching for hospitals that are less crowded. The search unit can also analyze relevant hospital literature and prioritize searching for the most efficient hospitals. This improves the accuracy of the search by referring to relevant hospital literature. To refer to relevant literature, it is necessary to clearly indicate, for example, the use of medical papers and past research results. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input relevant hospital literature data into a generating AI and have the generating AI perform the search accuracy improvement.

[0107] The guidance unit can estimate the patient's emotions and adjust the route guidance method based on the estimated emotions. For example, if the patient is nervous, the guidance unit can provide route guidance in a calm voice. For example, if the patient is nervous, the guidance unit can provide route guidance in a calm voice. The guidance unit can also provide detailed route guidance if the patient is relaxed. For example, if the patient is relaxed, the guidance unit can provide detailed route guidance. The guidance unit can also provide concise and quick route guidance if the patient is in a hurry. For example, if the guidance unit is in a hurry, the guidance unit can provide concise and quick route guidance. By adjusting the route guidance method according to the patient's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input patient emotion data into a generating AI and have the AI ​​adjust the route guidance method.

[0108] The guidance unit can select the optimal route by referring to past traffic data when providing route guidance. For example, the guidance unit can suggest a route that avoids congestion based on past traffic congestion data. For example, the guidance unit can suggest a route that avoids congestion based on past traffic congestion data. The guidance unit can also suggest a safe route based on past traffic accident data. For example, the guidance unit can suggest a safe route based on past traffic accident data. The guidance unit can also suggest the most efficient route based on past traffic volume data. For example, the guidance unit can suggest the most efficient route based on past traffic volume data. In this way, a more appropriate route can be selected by referring to past traffic data. In order to refer to past traffic data, for example, traffic congestion information and past traffic accident data must be clearly defined. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input past traffic data into a generating AI and have the generating AI select the optimal route.

[0109] The guidance unit can adjust the level of detail of the directions based on the patient's current location information when providing route guidance. For example, if the patient is in an urban area, the guidance unit can provide detailed route guidance. For example, if the patient is in an urban area, the guidance unit can provide detailed route guidance. For example, if the patient is in a suburban area, the guidance unit can provide concise route guidance. For example, if the patient is in a remote area, the guidance unit can provide route guidance to the nearest major city. For example, if the patient is in a remote area, the guidance unit can provide route guidance to the nearest major city. By adjusting the level of detail of the directions based on the patient's current location information, more appropriate guidance becomes possible. In order to adjust the level of detail of the directions based on the current location information, it is necessary to clarify, for example, the use of GPS data or location information services. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the patient's location information data into a generating AI and have the generating AI perform the adjustment of the level of detail of the directions.

[0110] The guidance unit can estimate the patient's emotions and determine the priority of guidance based on the estimated emotions. For example, if the patient is in an emergency, the guidance unit will give the highest priority to guidance. For example, if the patient is in an emergency, the guidance unit will give the highest priority to guidance. The guidance unit can also give the same priority to guidance as other guidance if the patient is stable. For example, if the guidance unit is stable, the guidance unit will give the same priority to guidance as other guidance. The guidance unit can also postpone guidance from patients with mild symptoms over other emergency guidance. For example, if the guidance unit is experiencing mild symptoms, it will postpone guidance from patients with mild symptoms over other emergency guidance. This allows for more appropriate guidance by determining the priority of guidance according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input patient emotional data into a generating AI and have the AI ​​determine the priority of guidance.

[0111] The guidance unit can adjust the order of directions based on changes in traffic conditions when providing route guidance. For example, if traffic congestion occurs, the guidance unit can suggest an alternative route. For example, if a traffic accident occurs, the guidance unit can suggest a safe route. For example, if traffic volume increases, the guidance unit can suggest the most efficient route. By adjusting the order of directions based on changes in traffic conditions, more appropriate guidance becomes possible. To adjust the order of directions based on changes in traffic conditions, it is necessary to clearly utilize real-time traffic information and predict traffic congestion, for example. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input traffic condition data into a generating AI and have the generating AI perform the adjustment of the order of directions.

[0112] The guidance unit can improve the accuracy of route guidance by referring to relevant geographic information. For example, the guidance unit can suggest the optimal route based on geographic information. The guidance unit can also suggest routes that avoid congestion based on geographic information. The guidance unit can also analyze geographic information and suggest the most efficient route. This allows for improved guidance accuracy by referring to relevant geographic information. To refer to relevant geographic information, it is necessary to clarify, for example, the use of map data or geographic information systems (GIS). Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input geographic information data into a generating AI and have the generating AI perform the task of improving the accuracy of the guidance.

[0113] The telephone unit can estimate the patient's emotions and adjust the content of the call based on the estimated emotions. For example, if the patient is nervous, the telephone unit can make a call in a calm voice. For example, if the patient is nervous, the telephone unit can make a call in a calm voice. For example, if the patient is relaxed, the telephone unit can make a call that includes detailed information. For example, if the patient is relaxed, the telephone unit can make a call that includes detailed information. For example, if the patient is in a hurry, the telephone unit can make a call that is concise and quick. For example, if the patient is in a hurry, the telephone unit can make a call that is concise and quick. This allows for a more appropriate response by adjusting the content of the call according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the telephone unit may be performed using AI, for example, or not using AI. For example, the telephone unit can input patient emotion data into a generative AI and have the generative AI adjust the content of the call.

[0114] The telephone unit can select the optimal call content by referring to the hospital's past admission history when making automated calls. For example, the telephone unit can call hospitals that can accept patients based on the hospital's past admission history. The telephone unit can also call hospitals that are less crowded based on the hospital's past admission history. For example, the telephone unit can call hospitals that are less crowded based on the hospital's past admission history. The telephone unit can also analyze the hospital's past admission history and call the most efficient hospital. For example, the telephone unit can analyze the hospital's past admission history and call the most efficient hospital. This allows for the selection of more appropriate call content by referring to the hospital's past admission history. In order to refer to past admission history, it is necessary to clarify, for example, the use of hospital admission performance data and the referencing of past patient data. Some or all of the above processing in the telephone unit may be performed using AI, for example, or not using AI. For example, the telephone unit can input the hospital's past admission history data into a generating AI and have the generating AI select the optimal call content.

[0115] The telephone unit can adjust the level of detail in an automated call based on the patient's current symptoms. For example, if the patient is in an emergency, the telephone unit can make a concise and quick call. For example, if the patient is in an emergency, the telephone unit can make a concise and quick call. The telephone unit can also make a call with more detailed information if the patient is stable. For example, if the patient is stable, the telephone unit can make a call with more detailed information. The telephone unit can also prioritize patients with mild symptoms over other emergency patients. For example, if the patient is experiencing mild symptoms, the telephone unit can prioritize patients with mild symptoms over other emergency patients. This allows for a more appropriate response by adjusting the level of detail in the call based on the patient's current symptoms. To adjust the level of detail in the call based on current symptoms, for example, it is necessary to clarify the severity and urgency of the symptoms. Some or all of the above processing in the telephone unit may be performed using AI, or not, for example. For example, the telephone unit can input patient symptom data into a generating AI and have the generating AI perform the adjustment of the level of detail in the call.

[0116] The telephone unit can estimate the patient's emotions and determine the priority of calls based on the estimated emotions. For example, if the patient is in an emergency, the telephone unit will prioritize the call. For example, if the patient is in an emergency, the telephone unit will prioritize the call. The telephone unit can also prioritize calls with the same priority as other calls if the patient is stable. For example, if the telephone unit is stable, the telephone unit will prioritize calls with the same priority as other calls. The telephone unit can also prioritize calls from patients with mild symptoms over other emergency calls. For example, if the telephone unit is experiencing mild symptoms, the telephone unit will prioritize calls from patients with mild symptoms over other emergency calls. This allows for more appropriate responses by prioritizing calls according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the telephone unit may be performed using AI, for example, or without AI. For example, the telephone unit can input patient emotional data into a generating AI and have the AI ​​determine the priority of phone calls.

[0117] The telephone unit can make automated phone calls while considering the geographical distribution of hospitals. For example, if the patient is in an urban area, the telephone unit will call a hospital in an urban area. For example, if the patient is in an urban area, the telephone unit can call a hospital in an urban area. For example, if the patient is in a suburban area, the telephone unit can call a hospital in a suburban area. For example, if the patient is in a remote area, the telephone unit can call a hospital in the nearest major city. For example, if the patient is in a remote area, the telephone unit can call a hospital in the nearest major city. By considering the geographical distribution of hospitals, it is possible to call a more appropriate hospital. In order to consider geographical distribution, for example, it is necessary to clarify the location information of hospitals and the distribution of regional medical resources. Some or all of the above processing in the telephone unit may be performed using AI, for example, or not using AI. For example, the telephone unit can input geographical distribution data of hospitals into a generating AI and have the generating AI perform the phone call.

[0118] The telephone unit can improve the accuracy of automated phone calls by referring to relevant hospital literature. For example, the telephone unit can refer to relevant hospital literature and call hospitals that can accept patients. The telephone unit can also refer to relevant hospital literature and call hospitals that are less crowded. For example, the telephone unit can refer to relevant hospital literature and call hospitals that are less crowded. The telephone unit can also analyze relevant hospital literature and call the most efficient hospital. For example, the telephone unit can analyze relevant hospital literature and call the most efficient hospital. This improves the accuracy of phone calls by referring to relevant hospital literature. In order to refer to relevant literature, it is necessary to clearly indicate, for example, the use of medical papers and past research results. Some or all of the above processing in the telephone unit may be performed using AI, for example, or not using AI. For example, the telephone unit can input hospital relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of phone calls.

[0119] The display unit can estimate the patient's emotions and adjust the displayed content based on the estimated emotions. For example, if the patient is tense, the display unit can provide a simple and highly visible display method. For example, if the patient is tense, the display unit can provide a simple and highly visible display method. The display unit can also provide a display method that includes detailed information if the patient is relaxed. For example, if the patient is relaxed, the display unit can provide a display method that includes detailed information. The display unit can also provide a display method that gets to the point if the patient is in a hurry. For example, if the patient is in a hurry, the display unit can provide a display method that gets to the point. By adjusting the displayed content according to the patient's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input patient emotion data into a generating AI and have the generating AI adjust the displayed content.

[0120] The display unit can optimize its display algorithm by referring to the hospital's past admission history when displaying information. For example, the display unit can prioritize displaying hospitals that can accept patients based on their past admission history. The display unit can also prioritize displaying hospitals that avoid congestion based on their past admission history. The display unit can also analyze the hospital's past admission history and prioritize displaying the most efficient hospital. This allows for the selection of a more appropriate display algorithm by referring to the hospital's past admission history. To refer to past admission history, it is necessary to clearly define the use of hospital admission performance data and the reference to past patient data. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input hospital's past admission history data into a generating AI and have the generating AI perform the optimization of the display algorithm.

[0121] The display unit can adjust the level of detail in its display based on the patient's current symptoms. For example, if the patient is in an emergency, the display unit will provide a concise and rapid display. The display unit can also provide a detailed display to improve accuracy if the patient is stable. The display unit can also prioritize patients with mild symptoms over other emergency patients. This allows for more appropriate displays by adjusting the level of detail based on the patient's current symptoms. To adjust the level of detail based on current symptoms, it is necessary to clarify, for example, the severity and urgency of the symptoms. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input patient symptom data into a generating AI and have the generating AI adjust the level of detail in the display.

[0122] The display unit can estimate the patient's emotions and determine the priority of displays based on the estimated emotions. For example, if the patient is in an emergency, the display unit will give the highest priority to display the patient. For example, if the patient is in an emergency, the display unit will give the highest priority to display the patient. The display unit can also give the same priority to displays as other displays if the patient is stable. For example, if the patient is stable, the display unit will give the same priority to displays as other displays. The display unit can also postpone displays of mild symptoms over other emergency displays. For example, if the patient is experiencing mild symptoms, the display unit will postpone displays of mild symptoms over other emergency displays. This allows for more appropriate displays by determining the priority of displays according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input patient emotion data into a generating AI and have the generating AI determine the priority of the displayed information.

[0123] The display unit can display information while considering the geographical distribution of hospitals. For example, if the patient is in an urban area, the display unit will prioritize displaying hospitals in urban areas. For example, if the patient is in an urban area, the display unit will prioritize displaying hospitals in urban areas. For example, if the patient is in a suburban area, the display unit will prioritize displaying hospitals in suburban areas. For example, if the patient is in a remote area, the display unit will prioritize displaying hospitals in the nearest major city. For example, if the patient is in a remote area, the display unit will prioritize displaying hospitals in the nearest major city. By considering the geographical distribution of hospitals, a more appropriate hospital can be displayed. To consider geographical distribution, for example, it is necessary to clarify the location information of hospitals and the distribution of regional medical resources. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input geographical distribution data of hospitals into a generating AI and have the generating AI perform the display.

[0124] The display unit can improve the accuracy of its display by referring to relevant literature on hospitals during the display process. For example, the display unit can refer to relevant literature on hospitals and prioritize displaying hospitals that can accept patients. For example, the display unit can refer to relevant literature on hospitals and prioritize displaying hospitals that can accept patients. The display unit can also prioritize displaying hospitals that avoid congestion based on relevant literature on hospitals. For example, the display unit can analyze relevant literature on hospitals and prioritize displaying the most efficient hospitals. For example, the display unit can analyze relevant literature on hospitals and prioritize displaying the most efficient hospitals. This improves the accuracy of the display by referring to relevant literature on hospitals. In order to refer to relevant literature, it is necessary to clearly indicate, for example, the reference to medical papers or the use of past research results. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input hospital relevant literature data into a generating AI and have the generating AI perform the display accuracy improvement.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The emergency hospital bed search system can also be equipped with a monitoring unit that monitors the patient's vital signs in real time. The monitoring unit continuously measures vital signs such as the patient's heart rate, blood pressure, and oxygen saturation, and transmits this data to an analysis unit. Based on this vital sign data, the analysis unit can more accurately assess the patient's condition and select an appropriate hospital. For example, if the heart rate is abnormally high, the system can prioritize searching for hospitals specializing in cardiology. Similarly, if the oxygen saturation is low, it can prioritize searching for hospitals specializing in respiratory medicine. This makes it possible to select a more appropriate hospital based on the patient's vital signs.

[0127] The emergency hospital bed search system may also include a medical record referencing unit that accesses the patient's past medical records. This unit, for example, may work in conjunction with an electronic medical record system to retrieve the patient's past medical records. The analysis unit then uses these medical records to compare the patient's current symptoms with their past medical history and select an appropriate hospital. For example, a patient with a history of heart disease would be prioritized for hospitals specializing in cardiology. Similarly, a patient with a history of allergic reactions could be prioritized for hospitals capable of handling allergies. This allows for the selection of a more appropriate hospital based on the patient's past medical records.

[0128] The emergency bed search system can further estimate the patient's emotions and adjust the voice input reception method based on the estimated emotions. For example, if the patient is anxious, the reception section can provide an interface that prompts voice input in a calm voice. If the patient is in a panic state, it can also provide an interface that prioritizes concise and quick voice input. If the patient is relaxed, it can also provide an interface that allows for the input of detailed information. This allows for more appropriate responses by adjusting the voice input reception method according to the patient's emotions.

[0129] The emergency bed search system can further filter reception requests by considering the patient's current location. For example, if a patient is in a specific area, the reception system will prioritize displaying hospital information in that area. If the patient is on the move, it can suggest the most suitable hospital based on their current location. Furthermore, if the patient is in a remote location, it can prioritize displaying hospital information in the nearest major city. This allows for more appropriate reception services by considering the patient's current location.

[0130] The emergency bed search system can also estimate the patient's emotions and prioritize voice inputs based on those emotions. For example, the reception desk will process voice inputs with the highest priority if the patient is in an emergency. If the patient is stable, it can be processed with the same priority as other inputs. Furthermore, if the patient is reporting mild symptoms, it can be prioritized lower than other emergency inputs. This allows for more appropriate responses by prioritizing voice inputs according to the patient's emotions.

[0131] The emergency bed search system can further select the most appropriate registration method based on the patient's age and gender. For example, the registration section can provide an interface that encourages slow voice input for elderly patients, a simple and easy-to-understand voice input interface for children, and an interface that prioritizes inputting symptoms specific to women for female patients. This allows for more appropriate care by selecting the most suitable registration method based on the patient's age and gender.

[0132] The emergency bed search system can further analyze patients' social media activity and receive relevant information. For example, if a patient has made health-related posts on social media, the system can adjust the request based on that information. If a patient mentions a specific illness, the system can prioritize receiving information related to that illness. Furthermore, if a patient shares their location on social media, the system can suggest the most suitable hospital based on that location. This allows for the reception of more appropriate information by analyzing patients' social media activity.

[0133] The emergency bed search system can further estimate the patient's emotions and adjust the voice analysis method based on the estimated emotions. For example, if the patient is anxious, the analysis unit can perform rapid voice analysis and provide results quickly. If the patient is relaxed, it can perform detailed voice analysis to improve accuracy. Furthermore, if the patient is in a panic state, it can perform concise voice analysis and provide results quickly. This allows for more appropriate analysis by adjusting the voice analysis method according to the patient's emotions.

[0134] The emergency bed search system can further optimize its analysis algorithm by referencing the patient's past medical history. For example, if a patient has a history of heart disease, the analysis unit will prioritize voice analysis related to heart conditions. If a patient has a history of allergies, it can also prioritize voice analysis related to allergies. Furthermore, if a patient has a history of mental illness, it can prioritize voice analysis related to mental symptoms. This allows for the selection of a more appropriate analysis algorithm by referencing the patient's past medical history.

[0135] The emergency bed search system can further estimate the patient's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the patient is anxious, the analysis unit can provide a simple and highly visible display method. If the patient is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the patient is in a hurry, it can provide a display method that focuses on the essentials. By adjusting the display method of the analysis results according to the patient's emotions, a more appropriate display becomes possible.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The reception desk receives patient symptoms and requests via voice input. For example, it uses the microphone on a smartphone or tablet to input voice, and converts it to text using speech recognition technology. It also uses noise cancellation technology to remove ambient noise and improve the accuracy of voice input. Step 2: The analysis unit analyzes the voice input received by the reception unit. For example, it uses natural language processing technology to convert the voice input into text data and identify the patient's symptoms and requests. Step 3: The search unit searches for the nearest available hospital bed based on the information analyzed by the analysis unit. For example, it refers to a hospital database, displays the number of available beds in real time, and identifies the nearest hospital based on the hospital's location information. Step 4: The guidance unit provides map information and route guidance based on the hospital bed information retrieved by the search unit. For example, it uses GPS to pinpoint the current location of an ambulance and guides the user along the shortest route. It also provides real-time traffic information and guides the user along the optimal route based on congestion information. Step 5: The telephone unit automatically calls the hospitals identified by the search unit. For example, it notifies the hospitals in advance of the patient's condition and prompts them to prepare for admission. Step 6: The display unit will display the number of available beds in real time, based on input from the hospital. For example, it will refer to the hospital's database and display the number of available beds in real time. The hospital will also periodically update the data to provide the latest information.

[0138] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0139] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0140] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0141] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, guidance unit, telephone unit, and display unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit inputs voice using the microphone 38B of the smart device 14 and converts it into text using speech recognition technology by the control unit 46A. The analysis unit analyzes the voice input using natural language processing technology by the specific processing unit 290 of the data processing unit 12. The search unit, for example, refers to the hospital database 24 by the specific processing unit 290 of the data processing unit 12 and displays the number of available hospital beds in real time. The guidance unit, for example, uses the GPS function of the smart device 14 to determine the current location of the ambulance and guides the user along the shortest route. The telephone unit, for example, automatically makes a call to the hospital determined by the specific processing unit 290 of the data processing unit 12. The display unit, for example, displays the number of available hospital beds in real time by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, guidance unit, telephone unit, and display unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit inputs voice using the microphone 238 of the smart glasses 214 and converts it into text using speech recognition technology by the control unit 46A. The analysis unit analyzes the voice input using natural language processing technology by the identification processing unit 290 of the data processing unit 12. The search unit, for example, refers to the hospital database 24 by the identification processing unit 290 of the data processing unit 12 and displays the number of available hospital beds in real time. The guidance unit, for example, uses the GPS function of the smart glasses 214 to determine the current location of the ambulance and guides the user along the shortest route. The telephone unit, for example, automatically calls the hospital determined by the identification processing unit 290 of the data processing unit 12. The display unit, for example, displays the number of available hospital beds in real time by the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, guidance unit, telephone unit, and display unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit inputs voice using the microphone 238 of the headset terminal 314 and converts it into text using speech recognition technology by the control unit 46A. The analysis unit analyzes the voice input using natural language processing technology by the identification processing unit 290 of the data processing unit 12. The search unit, for example, refers to the hospital database 24 by the identification processing unit 290 of the data processing unit 12 and displays the number of available hospital beds in real time. The guidance unit, for example, uses the GPS function of the headset terminal 314 to determine the current location of the ambulance and guides the user along the shortest route. The telephone unit, for example, automatically makes a call to the hospital determined by the identification processing unit 290 of the data processing unit 12. The display unit, for example, displays the number of available hospital beds in real time by the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0176] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0177] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0178] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0180] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0181] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0182] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0183] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, guidance unit, telephone unit, and display unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit inputs voice using the microphone 238 of the robot 414 and converts it into text using speech recognition technology by the control unit 46A. The analysis unit analyzes the voice input using natural language processing technology by the specific processing unit 290 of the data processing unit 12. The search unit, for example, refers to the hospital database 24 by the specific processing unit 290 of the data processing unit 12 and displays the number of available hospital beds in real time. The guidance unit, for example, uses the GPS function of the robot 414 to determine the current location of the ambulance and guides it along the shortest route. The telephone unit, for example, automatically makes a call to the hospital determined by the specific processing unit 290 of the data processing unit 12. The display unit, for example, displays the number of available hospital beds in real time by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0191] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0193] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0194] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0195] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0198] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0199] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0200] 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.

[0201] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0202] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0203] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0204] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0206] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0207] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0208] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0209] (Note 1) A reception desk that takes patient symptoms and requests via voice input, An analysis unit analyzes the voice input received by the reception unit, A search unit searches for the nearest available hospital bed based on the information analyzed by the aforementioned analysis unit, A guidance unit provides map information and route guidance based on the hospital bed information retrieved by the aforementioned search unit. A telephone unit that automatically makes a phone call to the hospital identified by the search unit, It includes a display unit that allows the hospital to input and display the number of available beds in real time. A system characterized by the following features. (Note 2) The aforementioned reception unit is Converting speech to text using speech recognition technology The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze speech input using natural language processing techniques. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned search unit, Displays the number of available hospital beds in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned guide section is The system uses GPS to pinpoint the ambulance's current location and guides it along the shortest route. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned telephone unit, The system automatically calls the designated hospital and relays the patient's condition. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned display unit is The hospital has a system in place to update the data regularly. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the patient's emotions and adjusts the voice input acceptance method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During voice input, the system references the patient's past medical history to select the most appropriate registration method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When using voice input, the system filters the reception information by taking into account the patient's current location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates the patient's emotions and prioritizes voice input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When voice input is used, the system selects the most appropriate registration method based on the patient's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is During voice input, the system analyzes the patient's social media activity and receives relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the voice analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During voice analysis, the analysis algorithm is optimized by referring to the patient's past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During voice analysis, the level of detail of the analysis is adjusted based on the patient's current symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During voice analysis, the analysis priority is determined based on when the patient submitted their data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During voice analysis, we refer to relevant patient literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, The system estimates the patient's emotions and adjusts the criteria for hospital bed search based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, When searching for hospital beds, the search algorithm is optimized by referring to the hospital's past admission history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, When searching for hospital beds, adjust the level of detail in the search based on the patient's current symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned search unit, The system estimates the patient's emotions and adjusts how search results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned search unit, When searching for hospital beds, the search should take into account the geographical distribution of hospitals. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned search unit, When searching for hospital beds, referencing relevant hospital literature improves the accuracy of the search. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide section is The system estimates the patient's emotions and adjusts the route guidance method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide section is When providing route guidance, the system selects the optimal route by referring to past traffic data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide section is When providing route guidance, the level of detail in the directions is adjusted based on the patient's current location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned guide section is The system estimates the patient's emotions and determines the priority of guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned guide section is When providing route guidance, the order of directions will be adjusted based on changes in traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned guide section is When providing route guidance, the system references relevant geographical information to improve the accuracy of the guidance. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned telephone unit, The system estimates the patient's emotions and adjusts the content of the phone call based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned telephone unit, When making automated phone calls, the system selects the most appropriate call content by referring to the hospital's past admission history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned telephone unit, When making an automated phone call, the level of detail in the call is adjusted based on the patient's current symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned telephone unit, The system estimates the patient's emotions and prioritizes phone calls based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned telephone unit, When making automated phone calls, the system takes into account the geographical distribution of hospitals. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned telephone unit, When making automated phone calls, we improve the accuracy of the calls by referring to relevant hospital literature. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned display unit is The system estimates the patient's emotions and adjusts the displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned display unit is When displaying data, the display algorithm is optimized by referring to the hospital's past admission history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned display unit is When displaying information, adjust the level of detail based on the patient's current symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned display unit is The system estimates the patient's emotions and determines the display priority based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned display unit is When displaying information, the geographical distribution of hospitals should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned display unit is When displaying information, we refer to relevant hospital literature to improve the accuracy of the display. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that takes patient symptoms and requests via voice input, An analysis unit analyzes the voice input received by the reception unit, A search unit searches for the nearest available hospital bed based on the information analyzed by the aforementioned analysis unit, A guidance unit provides map information and route guidance based on the hospital bed information retrieved by the aforementioned search unit. A telephone unit that automatically makes a phone call to the hospital identified by the search unit, It includes a display unit that allows the hospital to input and display the number of available beds in real time. A system characterized by the following features.

2. The aforementioned reception unit is Converting speech to text using speech recognition technology The system according to feature 1.

3. The aforementioned analysis unit, Analyze speech input using natural language processing techniques. The system according to feature 1.

4. The aforementioned search unit, Displays the number of available hospital beds in real time. The system according to feature 1.

5. The aforementioned guide section is The system uses GPS to pinpoint the ambulance's current location and guides it along the shortest route. The system according to feature 1.

6. The aforementioned telephone unit, The system automatically calls the designated hospital and relays the patient's condition. The system according to feature 1.

7. The aforementioned display unit is The hospital has a system in place to update the data regularly. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the patient's emotions and adjusts the voice input acceptance method based on the estimated emotions. The system according to feature 1.

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