System

Abstract

An object of a system according to an embodiment is to quickly perform a process from reception of an emergency call to arrangement of an ambulance.SOLUTION: A system includes a notification reception part, an analysis part, an arrangement part, and an information provision part. The notification reception unit receives an emergency notification. The analysis part analyzes the notification contents received by the notification reception part. The arrangement unit arranges an optimal ambulance based on the information analyzed by the analysis unit. The information providing unit provides necessary information to the ambulance arranged by the arranging unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process from receiving an emergency call to dispatching an ambulance is not fast, and there is room for improvement.

[0005] The system according to the embodiment aims to speed up the process from receiving an emergency call to dispatching an ambulance. [Means for solving the problem]

[0006] The system according to the embodiment includes a call receiving unit, an analysis unit, a dispatch unit, and an information providing unit. The call receiving unit receives an emergency call. The analysis unit analyzes the content of the call received by the call receiving unit. The dispatch unit dispatches the most appropriate ambulance based on the information analyzed by the analysis unit. The information providing unit provides necessary information to the ambulance dispatched by the dispatch unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly carry out the process from receiving an emergency call to dispatching an ambulance. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The emergency transport system according to an embodiment of the present invention utilizes generative AI to achieve a fast and efficient response. This emergency transport system supports everything from receiving an emergency call to dispatching an ambulance and providing information. This allows the emergency transport system to quickly and efficiently handle everything from receiving an emergency call to dispatching an ambulance and providing information.

[0029] The emergency transport system according to the embodiment includes a call receiving unit, an analysis unit, a dispatch unit, and an information providing unit. The call receiving unit receives an emergency call. For example, the call receiving unit receives a telephone call. The call receiving unit can also receive an app call. The call receiving unit can also record the content of the emergency call. For example, the call receiving unit saves the content of the call as audio data. The call receiving unit can also save the content of the call as text data. The analysis unit analyzes the content of the call received by the call receiving unit. For example, the analysis unit analyzes the content of the call using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI analyzes the audio data of the call and analyzes the tone of the caller's voice and level of tension. The generation AI can also analyze the text data of the call to identify the location of the accident and the extent of injuries. The generation AI can also compare the text data with a database of similar past cases to propose optimal countermeasures. The dispatch unit dispatches the most appropriate ambulance based on the information analyzed by the analysis unit. For example, the dispatch unit identifies the ambulance closest to the accident site. The dispatch unit can also select the most appropriate ambulance by taking into account the ambulance's equipment status and the medical staff's expertise. The dispatch unit can also analyze traffic conditions and weather information in real time and propose the optimal route. The information provision unit provides necessary information to the ambulance dispatched by the dispatch unit. For example, the information provision unit can provide detailed location information of the accident site. The information provision unit can also provide the extent of injuries and necessary medical equipment. The information provision unit can also compare the information with a database of past cases and propose the optimal response to the ambulance team. As a result, the emergency transport system according to the embodiment can quickly and efficiently perform processes from receiving an emergency call to dispatching an ambulance and providing information. For example, the generation AI can analyze the content of the call, dispatch the most appropriate ambulance, and provide the necessary information to the ambulance team, enabling a rapid response. The generation AI can also compare the information with a database of past cases and propose the optimal response, enabling a more effective response.

[0030] The analysis unit compares the contents of the call with a database of similar past cases and can quickly propose the most appropriate response. For example, when analyzing the contents of a call, the generation AI compares it with a database of similar past cases. For example, it compares it with past traffic accident cases and proposes the most appropriate response. Furthermore, when analyzing the contents of a call, the generation AI searches for similar cases from a database of past cases and uses those cases as a reference for the response. For example, it compares it with past fire cases and proposes the most appropriate response. Furthermore, when analyzing the contents of a call, the generation AI compares it with a database of past cases in real time and proposes the most appropriate response. For example, it compares it with past cases of sudden illness and proposes the most appropriate response. In this way, by comparing it with a database of similar past cases, the most appropriate response can be quickly proposed.

[0031] The analysis unit visualizes the analysis results of the call content in real time, and can provide a dashboard that the operator can intuitively understand. For example, the generation AI provides a dashboard to visualize the analysis results of the call content in real time. For example, the location of the accident and the extent of the injuries are displayed on a map. When visualizing the analysis results of the call content, the generation AI uses graphs and charts. For example, calls with a high level of urgency are displayed in red. The analysis unit also updates the analysis results of the call content in real time, allowing the operator to intuitively understand. For example, the dashboard is automatically updated according to changes in the call content. In this way, the analysis results of the call content are visualized, allowing the operator to intuitively understand.

[0032] The analysis unit can improve the accuracy of the analysis by having multiple generation AIs cooperate to perform the analysis. For example, when analyzing the content of a report, the analysis unit has multiple generation AIs cooperate to perform the analysis. For example, a voice analysis AI and a text analysis AI work together to analyze the content of the report. The analysis unit also improves the accuracy of the analysis by having multiple generation AIs cooperate to perform the analysis. For example, AIs that use different analysis algorithms cooperate to analyze the content of the report. The analysis unit also improves the accuracy of the analysis by having multiple generation AIs cooperate to perform the analysis when analyzing the content of the report, and integrates the analysis results. For example, the voice analysis results and text analysis results are integrated to propose the optimal countermeasure. In this way, the analysis accuracy is improved by having multiple generation AIs cooperate to perform the analysis.

[0033] The dispatching unit can analyze traffic conditions and weather information in real time and propose the optimal route. In the dispatching unit, for example, the generation AI analyzes traffic conditions in real time and proposes the optimal route. For example, it calculates the shortest route based on congestion information. In addition, in the dispatching unit, the generation AI analyzes weather information in real time and proposes the optimal route. For example, it selects a safe route in bad weather. In addition, in the dispatching unit, the generation AI integrates and analyzes traffic conditions and weather information and proposes the optimal route. For example, it calculates a route that avoids traffic congestion and bad weather. In this way, the optimal route can be proposed by analyzing traffic conditions and weather information in real time.

[0034] The dispatching department can select the most suitable ambulance by taking into consideration the ambulance's equipment status and the medical staff's expertise. For example, the generation AI in the dispatching department analyzes the ambulance's equipment status and selects the most suitable ambulance. For example, it selects an ambulance that is equipped with the necessary medical equipment. The dispatching department also selects the most suitable ambulance by taking into consideration the medical staff's expertise. For example, it selects an ambulance that has a specialist doctor on board. The dispatching department also selects the most suitable ambulance by integrating and analyzing the ambulance's equipment status and the medical staff's expertise. For example, it selects an ambulance that has the necessary medical equipment and a specialist doctor on board. This makes it possible to select the most suitable ambulance by taking into consideration the ambulance's equipment status and the medical staff's expertise.

[0035] The dispatch unit can also work with other emergency services (fire department, police) to achieve a comprehensive response. For example, the generation AI in the dispatch unit works with other emergency services (fire department, police) to achieve a comprehensive response. For example, emergency response at a fire scene works in cooperation with the fire department. In addition, the generation AI in the dispatch unit shares information with other emergency services in real time to achieve a comprehensive response. For example, emergency response at a traffic accident scene works in cooperation with the police. In addition, the generation AI in the dispatch unit works with other emergency services to arrange for the most appropriate ambulance. For example, it coordinates emergency response at a scene where multiple emergency services are involved. In this way, by working with other emergency services, a comprehensive response can be achieved.

[0036] The dispatching department can select the optimal destination for transport by taking into account local medical resources (number of hospital beds, doctor standby status). For example, the generation AI in the dispatching department analyzes the number of beds in local hospitals and selects the optimal destination for transport. For example, it selects a hospital with available beds. The dispatching department also analyzes the availability of local doctors and selects the optimal destination for transport. For example, it selects a hospital with a specialist on standby. The dispatching department also integrates and analyzes local medical resources and selects the optimal destination for transport. For example, it selects a hospital with available beds and a specialist on standby. This makes it possible to select the optimal destination for transport by taking into account local medical resources.

[0037] The information provision unit can analyze the situation at the scene in real time and provide the latest information to the emergency team. For example, the generation AI in the information provision unit analyzes the situation at the scene in real time and provides the latest information to the emergency team. For example, it provides detailed location information of the accident site. The information provision unit also analyzes the situation at the scene using the generation AI and provides the emergency team with the medical equipment and response methods needed. For example, it provides a list of medical equipment according to the level of injury. The information provision unit also updates the situation at the scene in real time using the generation AI and provides the latest information to the emergency team. For example, it updates information immediately if the situation at the scene changes. This allows the latest information to be provided to the emergency team by analyzing the situation at the scene in real time.

[0038] The information provision unit can compare the information with a database of past cases and propose the optimal response. For example, the generation AI in the information provision unit compares the information with a database of past cases and proposes the optimal response. For example, it compares the information with past traffic accident cases and proposes the optimal first aid. The information provision unit also has the generation AI search for similar cases in the database of past cases and use those responses as reference. For example, it compares the information with past fire cases and proposes the optimal response. The information provision unit also has the generation AI compare the information with the database of past cases in real time and propose the optimal response. For example, it compares the information with past cases of sudden illness and proposes the optimal response. In this way, the optimal response can be proposed by comparing the information with the database of past cases.

[0039] The information provision unit can also work with other emergency services (fire department, police) to realize a comprehensive response. For example, the generation AI in the information provision unit works with other emergency services (fire department, police) to realize a comprehensive response. For example, emergency response at the scene of a fire works in cooperation with the fire department. In addition, the generation AI in the information provision unit shares information with other emergency services in real time to realize a comprehensive response. For example, emergency response at the scene of a traffic accident works in cooperation with the police. In addition, the generation AI in the information provision unit works with other emergency services to realize the optimal emergency response. For example, it coordinates emergency response at a scene where multiple emergency services are involved. In this way, by working with other emergency services, a comprehensive response can be realized.

[0040] The information provision unit can perform 3D mapping of the situation at the scene, allowing emergency responders to intuitively understand the situation. In the information provision unit, for example, the generation AI performs 3D mapping of the situation at the scene and provides it to the emergency responders. For example, detailed location information of the accident site is displayed on a 3D map. In addition, the information provision unit performs 3D mapping of the situation at the scene in real time and provides it to the emergency responders. For example, if the situation at the scene changes, the 3D map is updated immediately. In addition, the information provision unit performs 3D mapping of the situation at the scene by the generation AI, allowing emergency responders to intuitively understand the situation. For example, the extent of injuries and necessary medical equipment are displayed on a 3D map. In this way, the 3D mapping of the situation at the scene allows emergency responders to intuitively understand the situation.

[0041] The information provision unit can provide detailed information about the patient's condition and the medical equipment required when requesting a hospital to prepare to accept a patient. For example, when the generation AI requests a hospital to prepare to accept a patient, the information provision unit provides detailed information about the patient's condition. For example, it provides specific information about the extent of the injury and symptoms. The information provision unit also provides detailed information about the medical equipment required by the generation AI to the hospital. For example, it provides a list of the required medical equipment. The information provision unit also provides a comprehensive information about the patient's condition and the required medical equipment to the hospital. For example, it provides a list of medical equipment according to the extent of the injury. In this way, by providing detailed information about the patient's condition and the required medical equipment, the hospital can quickly prepare to accept the patient.

[0042] The information providing unit can notify the hospital of the patient's expected arrival time, encouraging a prompt response. In the information providing unit, for example, the generation AI notifies the hospital of the patient's expected arrival time. For example, it specifically notifies the ambulance's current location and expected arrival time. In addition, the information providing unit can have the generation AI update the hospital of the patient's expected arrival time in real time, encouraging a prompt response. For example, it updates the expected arrival time according to changes in traffic conditions. In addition, the information providing unit can have the generation AI notify the hospital of the patient's expected arrival time and request that necessary preparations be made. For example, it can request that medical staff be on standby at the expected arrival time. In this way, by notifying the hospital of the patient's expected arrival time, the hospital can respond promptly.

[0043] The information providing unit can report the patient's condition to the hospital in real time and instruct the necessary response. In the information providing unit, for example, the generating AI reports the patient's condition to the hospital in real time. For example, the patient's vital signs in the ambulance are conveyed in real time. The information providing unit also instructs the hospital on the necessary response. For example, it instructs first aid according to the patient's condition. The information providing unit also instructs the hospital on the necessary response. For example, it instructs the hospital on immediate response if the patient's condition changes. In this way, by reporting the patient's condition in real time and instructing the necessary response, the hospital can respond quickly.

[0044] The information providing unit can notify the hospital of the patient's expected arrival time and request that necessary medical staff be on standby. In the information providing unit, for example, the generation AI notifies the hospital of the patient's expected arrival time and requests that necessary medical staff be on standby. For example, it requests that a specialist be on standby. In addition, the information providing unit updates the patient's expected arrival time to the hospital in real time and requests that necessary medical staff be on standby. For example, it updates the expected arrival time according to changes in traffic conditions. In addition, the information providing unit notifies the hospital of the patient's expected arrival time and requests that necessary medical staff be on standby. For example, it adjusts medical staff shifts to match the expected arrival time. This allows the hospital to respond quickly by notifying the hospital of the patient's expected arrival time and requesting that necessary medical staff be on standby.

[0045] The information provision unit can analyze the patient's condition in real time during emergency transport and suggest necessary first aid. For example, the generation AI in the information provision unit analyzes the patient's vital signs in real time during emergency transport and suggests necessary first aid. For example, it suggests first aid based on changes in heart rate and blood pressure. The information provision unit also analyzes the patient's condition during emergency transport and suggests necessary first aid. For example, it suggests first aid for symptoms of difficulty breathing. The information provision unit also updates the patient's condition in real time during emergency transport and suggests necessary first aid. For example, it suggests first aid immediately if the patient's condition changes. This makes it possible to analyze the patient's condition in real time during emergency transport and suggest necessary first aid, enabling a rapid response.

[0046] The information provision unit can report the patient's condition during ambulance transport and instruct the hospital on the necessary response. For example, the information provision unit has the generating AI report the patient's condition during ambulance transport and instruct the hospital on the necessary response. For example, it conveys the patient's vital signs in real time. The information provision unit also has the generating AI analyze the patient's condition during ambulance transport and instruct the hospital on the necessary response. For example, it instructs first aid according to the patient's condition. The information provision unit also has the generating AI update the patient's condition in real time during ambulance transport and instruct the hospital on the necessary response. For example, it instructs the hospital on the necessary response immediately if the patient's condition changes. This makes it possible to report the patient's condition during ambulance transport and instruct the hospital on the necessary response, enabling a rapid response.

[0047] The information provision unit can monitor the patient's condition in real time during emergency transport and instruct the use of necessary medical equipment. For example, the information provision unit allows the generating AI to monitor the patient's vital signs in real time during emergency transport and instruct the use of necessary medical equipment. For example, it may instruct the use of a defibrillator based on changes in heart rate. The information provision unit also allows the generating AI to analyze the patient's condition during emergency transport and instruct the use of necessary medical equipment. For example, it may instruct the use of an oxygen mask for symptoms of difficulty breathing. The information provision unit also allows the generating AI to update the patient's condition in real time during emergency transport and instruct the use of necessary medical equipment. For example, it may immediately instruct the use of medical equipment if the patient's condition changes. This enables rapid response by monitoring the patient's condition in real time during emergency transport and instructing the use of necessary medical equipment.

[0048] The information provision unit can report the patient's condition during emergency transport and request the hospital to prepare the necessary medical equipment. For example, the generation AI reports the patient's condition during emergency transport and requests the hospital to prepare the necessary medical equipment. For example, the information provision unit specifies the necessary medical equipment based on the patient's vital signs. The information provision unit also analyzes the patient's condition during emergency transport and requests the hospital to prepare the necessary medical equipment. For example, it provides a list of medical equipment according to the patient's symptoms. The information provision unit also updates the patient's condition in real time during emergency transport and requests the hospital to prepare the necessary medical equipment. For example, if the patient's condition changes, the information provision unit immediately requests the hospital to prepare the necessary medical equipment. This enables a rapid response by reporting the patient's condition during emergency transport and requesting the hospital to prepare the necessary medical equipment.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The emergency transport system can further include a location information acquisition unit that automatically acquires the location information of the caller. For example, if the caller makes a call using a smartphone, the location information acquisition unit acquires GPS data and identifies the caller's exact location. The location information acquisition unit can also identify the caller's location using Wi-Fi or Bluetooth signals, even if the caller is inside a building. Furthermore, if the caller is moving, the location information acquisition unit can continue to update the location information in real time. This allows for accurate identification of the caller's location, enabling rapid emergency response.

[0051] When analyzing the contents of a call, the analysis unit can take into account the caller's health condition and medical history. For example, if the caller has a chronic illness, the urgency level can be determined based on that information. The analysis unit can also monitor the caller's current health condition in real time when analyzing the contents of a call. For example, if the caller is wearing a smartwatch, the data from that watch can be acquired and used for analysis. The analysis unit can also take into account the health conditions of the caller's family and neighbors when analyzing the contents of a call. For example, if the caller's family members are complaining of the same symptoms, the urgency level can be determined based on that information. This allows for more accurate analysis of the contents of a call and the most appropriate response measures to be proposed.

[0052] When visualizing the analysis results of the call content, the analysis unit can provide an operator with a customizable dashboard. For example, the operator can select and display the information they need. The analysis unit can also use 3D mapping technology to visualize the analysis results of the call content. For example, detailed location information of the accident site can be displayed on a 3D map. When visualizing the analysis results of the call content, the analysis unit can also use colors and icons to help the operator intuitively understand. For example, calls with a high level of urgency can be displayed in red, and the severity of the injury can be indicated with an icon. This visualization of the analysis results of the call content allows the operator to respond quickly and accurately.

[0053] When multiple generation AIs cooperate to perform analysis, the analysis unit can combine AIs with different specializations. For example, an AI specializing in medical care and an AI specializing in traffic analysis work together to analyze the contents of a call report. Furthermore, when multiple generation AIs cooperate to perform analysis, the analysis unit can integrate different data sources to perform the analysis. For example, it can integrate and analyze voice data and text data. Furthermore, when multiple generation AIs cooperate to perform analysis, the analysis unit can integrate the analysis results in real time and propose the optimal response. For example, it can integrate the voice analysis results and text analysis results to propose the optimal response. In this way, when multiple generation AIs cooperate to perform analysis, the accuracy of the analysis is improved, enabling more effective responses.

[0054] When analyzing traffic conditions and weather information, the dispatching department can make predictions based on past data. For example, it can predict future congestion based on past traffic congestion data. The dispatching department can also integrate and analyze multiple data sources to analyze traffic conditions and weather information. For example, it can integrate and analyze video data from traffic cameras and weather data. The dispatching department can also continuously update data in real time when analyzing traffic conditions and weather information. For example, it can recalculate the optimal route depending on changes in traffic congestion and weather. This allows for more accurate analysis of traffic conditions and weather information and suggests the optimal route.

[0055] When considering the equipment status of the ambulance and the expertise of the medical staff, the dispatching department can refer to the maintenance history of the ambulance and the training history of the medical staff. For example, it can prioritize the selection of ambulances that have recently undergone maintenance. The dispatching department can also continue to update data in real time to consider the equipment status of the ambulance and the expertise of the medical staff. For example, it can check the equipment status of the ambulance and the shift status of the medical staff in real time. The dispatching department can also refer to past response history when considering the equipment status of the ambulance and the expertise of the medical staff. For example, it can select ambulances and medical staff that have responded to similar emergencies in the past. This allows for a more accurate understanding of the equipment status of the ambulance and the expertise of the medical staff, enabling the most appropriate ambulance to be selected.

[0056] When coordinating with other emergency services (fire department, police), the dispatch department can share information by using a common database. For example, detailed information on an emergency situation can be shared in real time. The dispatch department can also use a common communication protocol to coordinate with other emergency services. For example, a dedicated communication protocol can be used to quickly transmit information on an emergency situation. The dispatch department can also use a common response manual when coordinating with other emergency services. For example, common procedures for emergency response at a fire scene can be used. This allows for more effective coordination with other emergency services and a comprehensive response.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The call reception unit receives an emergency call. For example, the call reception unit can receive a phone call or an app call. The call reception unit can also record the contents of the emergency call and save it as audio data or text data. Step 2: The analysis unit analyzes the report received by the report reception unit. For example, it uses generation AI to analyze the audio data of the report and analyze the caller's tone of voice and level of tension. It can also analyze text data to identify the location of the accident and the extent of injuries. It can also compare the data with a database of similar past cases to propose optimal countermeasures. Step 3: The dispatching department dispatches the most suitable ambulance based on the information analyzed by the analysis department. For example, it identifies the ambulance closest to the accident site and selects the most suitable ambulance taking into account the ambulance's equipment status and the expertise of the medical staff. It can also analyze traffic conditions and weather information in real time to suggest the best route. Step 4: The information provision department provides the necessary information to the ambulance dispatched by the dispatch department. For example, it provides detailed information on the location of the accident site, the extent of injuries, and information on the medical equipment required. It can also compare this information with a database of past cases and propose the best response measures.

[0059] (Example 2) The emergency transport system according to an embodiment of the present invention utilizes generative AI to achieve a fast and efficient response. This emergency transport system supports everything from receiving an emergency call to dispatching an ambulance and providing information. This allows the emergency transport system to quickly and efficiently handle everything from receiving an emergency call to dispatching an ambulance and providing information.

[0060] The emergency transport system according to the embodiment includes a call receiving unit, an analysis unit, a dispatch unit, and an information providing unit. The call receiving unit receives an emergency call. For example, the call receiving unit receives a telephone call. The call receiving unit can also receive an app call. The call receiving unit can also record the content of the emergency call. For example, the call receiving unit saves the content of the call as audio data. The call receiving unit can also save the content of the call as text data. The analysis unit analyzes the content of the call received by the call receiving unit. For example, the analysis unit analyzes the content of the call using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI analyzes the audio data of the call and analyzes the tone of the caller's voice and level of tension. The generation AI can also analyze the text data of the call to identify the location of the accident and the extent of injuries. The generation AI can also compare the text data with a database of similar past cases to propose optimal countermeasures. The dispatch unit dispatches the most appropriate ambulance based on the information analyzed by the analysis unit. For example, the dispatch unit identifies the ambulance closest to the accident site. The dispatch unit can also select the most appropriate ambulance by taking into account the ambulance's equipment status and the medical staff's expertise. The dispatch unit can also analyze traffic conditions and weather information in real time and propose the optimal route. The information provision unit provides necessary information to the ambulance dispatched by the dispatch unit. For example, the information provision unit can provide detailed location information of the accident site. The information provision unit can also provide the extent of injuries and necessary medical equipment. The information provision unit can also compare the information with a database of past cases and propose the optimal response to the ambulance team. As a result, the emergency transport system according to the embodiment can quickly and efficiently perform processes from receiving an emergency call to dispatching an ambulance and providing information. For example, the generation AI can analyze the content of the call, dispatch the most appropriate ambulance, and provide the necessary information to the ambulance team, enabling a rapid response. The generation AI can also compare the information with a database of past cases and propose the optimal response, enabling a more effective response.

[0061] The analysis unit analyzes the caller's tone of voice and level of tension, enabling it to determine the level of urgency with high accuracy. For example, the generation AI uses voice analysis technology to analyze the caller's tone of voice and level of tension. For example, it analyzes changes in the pitch, speed, and volume of the caller's voice to determine the level of urgency. When analyzing the caller's tone of voice and level of tension, the generation AI also compares it with past call data. For example, it determines a call with similar voice characteristics to past high-urgency calls as being of high urgency. The analysis unit also uses emotion analysis technology to analyze the caller's tone of voice and level of tension. For example, it infers the caller's emotional state from their voice and determines the level of urgency. This makes it possible to determine the level of urgency with high accuracy by analyzing the caller's tone of voice and level of tension.

[0062] The analysis unit compares the contents of the call with a database of similar past cases and can quickly propose the most appropriate response. For example, when analyzing the contents of a call, the generation AI compares it with a database of similar past cases. For example, it compares it with past traffic accident cases and proposes the most appropriate response. Furthermore, when analyzing the contents of a call, the generation AI searches for similar cases from a database of past cases and uses those cases as a reference for the response. For example, it compares it with past fire cases and proposes the most appropriate response. Furthermore, when analyzing the contents of a call, the generation AI compares it with a database of past cases in real time and proposes the most appropriate response. For example, it compares it with past cases of sudden illness and proposes the most appropriate response. In this way, by comparing it with a database of similar past cases, the most appropriate response can be quickly proposed.

[0063] The analysis unit analyzes the caller's emotional state, and if the caller is in a panic, can provide appropriate advice to calm them down. The analysis unit, for example, uses an emotion estimation function to analyze the caller's emotional state. For example, it analyzes the tone and speed of the caller's voice to detect a panicked state. Furthermore, if the caller is in a panicked state, the analysis unit provides the generation AI with appropriate advice to calm them down. For example, it provides advice to encourage deep breathing. Furthermore, the analysis unit uses the emotion estimation function to analyze the caller's emotional state in real time and provides appropriate advice. For example, it provides specific instructions to stay calm. In this way, if the caller is in a panicked state, appropriate advice can be provided to calm them down.

[0064] The analysis unit visualizes the analysis results of the call content in real time, and can provide a dashboard that the operator can intuitively understand. For example, the generation AI provides a dashboard to visualize the analysis results of the call content in real time. For example, the location of the accident and the extent of the injuries are displayed on a map. When visualizing the analysis results of the call content, the generation AI uses graphs and charts. For example, calls with a high level of urgency are displayed in red. The analysis unit also updates the analysis results of the call content in real time, allowing the operator to intuitively understand. For example, the dashboard is automatically updated according to changes in the call content. In this way, the analysis results of the call content are visualized, allowing the operator to intuitively understand.

[0065] The analysis unit can improve the accuracy of the analysis by having multiple generation AIs cooperate to perform the analysis. For example, when analyzing the content of a report, the analysis unit has multiple generation AIs cooperate to perform the analysis. For example, a voice analysis AI and a text analysis AI work together to analyze the content of the report. The analysis unit also improves the accuracy of the analysis by having multiple generation AIs cooperate to perform the analysis. For example, AIs that use different analysis algorithms cooperate to analyze the content of the report. The analysis unit also improves the accuracy of the analysis by having multiple generation AIs cooperate to perform the analysis when analyzing the content of the report, and integrates the analysis results. For example, the voice analysis results and text analysis results are integrated to propose the optimal countermeasure. In this way, the analysis accuracy is improved by having multiple generation AIs cooperate to perform the analysis.

[0066] The analysis unit generates a customized response according to the caller's emotional state, thereby increasing the caller's sense of security. The analysis unit, for example, uses an emotion estimation function to generate a customized response according to the caller's emotional state. For example, if the caller is anxious, a reassuring response is provided. In addition, the analysis unit uses emotion analysis technology through the generation AI to generate a customized response according to the caller's emotional state. For example, the analysis unit analyzes the caller's tone and speed of voice and provides an appropriate response. In addition, the analysis unit uses the emotion estimation function to analyze the caller's emotional state in real time and generate a customized response. For example, the analysis unit provides specific instructions to help the caller stay calm. In this way, the generation of a customized response according to the caller's emotional state can increase the caller's sense of security.

[0067] The dispatching unit can analyze traffic conditions and weather information in real time and propose the optimal route. In the dispatching unit, for example, the generation AI analyzes traffic conditions in real time and proposes the optimal route. For example, it calculates the shortest route based on congestion information. In addition, in the dispatching unit, the generation AI analyzes weather information in real time and proposes the optimal route. For example, it selects a safe route in bad weather. In addition, in the dispatching unit, the generation AI integrates and analyzes traffic conditions and weather information and proposes the optimal route. For example, it calculates a route that avoids traffic congestion and bad weather. In this way, the optimal route can be proposed by analyzing traffic conditions and weather information in real time.

[0068] The dispatching department can select the most suitable ambulance by taking into consideration the ambulance's equipment status and the medical staff's expertise. For example, the generation AI in the dispatching department analyzes the ambulance's equipment status and selects the most suitable ambulance. For example, it selects an ambulance that is equipped with the necessary medical equipment. The dispatching department also selects the most suitable ambulance by taking into consideration the medical staff's expertise. For example, it selects an ambulance that has a specialist doctor on board. The dispatching department also selects the most suitable ambulance by integrating and analyzing the ambulance's equipment status and the medical staff's expertise. For example, it selects an ambulance that has the necessary medical equipment and a specialist doctor on board. This makes it possible to select the most suitable ambulance by taking into consideration the ambulance's equipment status and the medical staff's expertise.

[0069] The dispatch unit can also work with other emergency services (fire department, police) to achieve a comprehensive response. For example, the generation AI in the dispatch unit works with other emergency services (fire department, police) to achieve a comprehensive response. For example, emergency response at a fire scene works in cooperation with the fire department. In addition, the generation AI in the dispatch unit shares information with other emergency services in real time to achieve a comprehensive response. For example, emergency response at a traffic accident scene works in cooperation with the police. In addition, the generation AI in the dispatch unit works with other emergency services to arrange for the most appropriate ambulance. For example, it coordinates emergency response at a scene where multiple emergency services are involved. In this way, by working with other emergency services, a comprehensive response can be achieved.

[0070] The dispatching department can select the optimal destination for transport by taking into account local medical resources (number of hospital beds, doctor standby status). For example, the generation AI in the dispatching department analyzes the number of beds in local hospitals and selects the optimal destination for transport. For example, it selects a hospital with available beds. The dispatching department also analyzes the availability of local doctors and selects the optimal destination for transport. For example, it selects a hospital with a specialist on standby. The dispatching department also integrates and analyzes local medical resources and selects the optimal destination for transport. For example, it selects a hospital with available beds and a specialist on standby. This makes it possible to select the optimal destination for transport by taking into account local medical resources.

[0071] The dispatch unit uses the emotion estimation function to customize the method of notifying the caller of the estimated arrival time according to the caller's emotional state, thereby increasing the caller's sense of security. The dispatch unit, for example, uses the emotion estimation function to customize the method of notifying the caller of the estimated arrival time according to the caller's emotional state. For example, if the caller is anxious, a detailed explanation may be provided. The dispatch unit also analyzes the caller's emotional state and notifies the caller of the estimated arrival time using appropriate words to increase the caller's sense of security. For example, the dispatch unit notifies the caller using words that will reassure the caller. The dispatch unit also uses the emotion estimation function to analyze the caller's emotional state in real time and customize the method of notifying the caller of the estimated arrival time. For example, specific instructions may be provided to reduce the caller's anxiety. In this way, the caller's sense of security can be increased by customizing the method of notifying the caller of the estimated arrival time according to the caller's emotional state.

[0072] The information provision unit can analyze the situation at the scene in real time and provide the latest information to the emergency team. For example, the generation AI in the information provision unit analyzes the situation at the scene in real time and provides the latest information to the emergency team. For example, it provides detailed location information of the accident site. The information provision unit also analyzes the situation at the scene using the generation AI and provides the emergency team with the medical equipment and response methods needed. For example, it provides a list of medical equipment according to the level of injury. The information provision unit also updates the situation at the scene in real time using the generation AI and provides the latest information to the emergency team. For example, it updates information immediately if the situation at the scene changes. This allows the latest information to be provided to the emergency team by analyzing the situation at the scene in real time.

[0073] The information provision unit can compare the information with a database of past cases and propose the optimal response. For example, the generation AI in the information provision unit compares the information with a database of past cases and proposes the optimal response. For example, it compares the information with past traffic accident cases and proposes the optimal first aid. The information provision unit also has the generation AI search for similar cases in the database of past cases and use those responses as reference. For example, it compares the information with past fire cases and proposes the optimal response. The information provision unit also has the generation AI compare the information with the database of past cases in real time and propose the optimal response. For example, it compares the information with past cases of sudden illness and proposes the optimal response. In this way, the optimal response can be proposed by comparing the information with the database of past cases.

[0074] The information providing unit can use the emotion estimation function to analyze the stress level of the paramedic and provide appropriate advice to reduce stress. The information providing unit, for example, uses the emotion estimation function to analyze the stress level of the paramedic. For example, the information providing unit analyzes the tone and speed of the paramedic's voice to determine the stress level. The information providing unit also analyzes the stress level of the paramedic and provides appropriate advice to reduce stress. For example, advice is given to encourage deep breathing. The information providing unit also uses the emotion estimation function to analyze the stress level of the paramedic in real time and provide appropriate advice. For example, specific instructions are given to reduce stress. In this way, stress can be reduced by analyzing the stress level of the paramedic and providing appropriate advice.

[0075] The information provision unit can also work with other emergency services (fire department, police) to realize a comprehensive response. For example, the generation AI in the information provision unit works with other emergency services (fire department, police) to realize a comprehensive response. For example, emergency response at the scene of a fire works in cooperation with the fire department. In addition, the generation AI in the information provision unit shares information with other emergency services in real time to realize a comprehensive response. For example, emergency response at the scene of a traffic accident works in cooperation with the police. In addition, the generation AI in the information provision unit works with other emergency services to realize the optimal emergency response. For example, it coordinates emergency response at a scene where multiple emergency services are involved. In this way, by working with other emergency services, a comprehensive response can be realized.

[0076] The information provision unit can perform 3D mapping of the situation at the scene, allowing emergency responders to intuitively understand the situation. In the information provision unit, for example, the generation AI performs 3D mapping of the situation at the scene and provides it to the emergency responders. For example, detailed location information of the accident site is displayed on a 3D map. In addition, the information provision unit performs 3D mapping of the situation at the scene in real time and provides it to the emergency responders. For example, if the situation at the scene changes, the 3D map is updated immediately. In addition, the information provision unit performs 3D mapping of the situation at the scene by the generation AI, allowing emergency responders to intuitively understand the situation. For example, the extent of injuries and necessary medical equipment are displayed on a 3D map. In this way, the 3D mapping of the situation at the scene allows emergency responders to intuitively understand the situation.

[0077] The information provision unit uses the emotion estimation function to customize the information provision method according to the emotional state of the paramedic, thereby optimizing the paramedic's performance. The information provision unit, for example, uses the emotion estimation function to customize the information provision method according to the paramedic's emotional state. For example, if the paramedic is feeling stressed, concise information is provided. The information provision unit also analyzes the paramedic's emotional state and selects an appropriate information provision method to optimize performance. For example, if the paramedic is calm, detailed information is provided. The information provision unit also uses the emotion estimation function to analyze the paramedic's emotional state in real time and customize the information provision method. For example, the amount and format of information are adjusted according to the paramedic's emotional state. In this way, the performance of the paramedic can be optimized by customizing the information provision method according to the paramedic's emotional state.

[0078] The information provision unit can provide detailed information about the patient's condition and the medical equipment required when requesting a hospital to prepare to accept a patient. For example, when the generation AI requests a hospital to prepare to accept a patient, the information provision unit provides detailed information about the patient's condition. For example, it provides specific information about the extent of the injury and symptoms. The information provision unit also provides detailed information about the medical equipment required by the generation AI to the hospital. For example, it provides a list of the required medical equipment. The information provision unit also provides a comprehensive information about the patient's condition and the required medical equipment to the hospital. For example, it provides a list of medical equipment according to the extent of the injury. In this way, by providing detailed information about the patient's condition and the required medical equipment, the hospital can quickly prepare to accept the patient.

[0079] The information providing unit can notify the hospital of the patient's expected arrival time, encouraging a prompt response. In the information providing unit, for example, the generation AI notifies the hospital of the patient's expected arrival time. For example, it specifically notifies the ambulance's current location and expected arrival time. In addition, the information providing unit can have the generation AI update the hospital of the patient's expected arrival time in real time, encouraging a prompt response. For example, it updates the expected arrival time according to changes in traffic conditions. In addition, the information providing unit can have the generation AI notify the hospital of the patient's expected arrival time and request that necessary preparations be made. For example, it can request that medical staff be on standby at the expected arrival time. In this way, by notifying the hospital of the patient's expected arrival time, the hospital can respond promptly.

[0080] The information providing unit can report the patient's condition to the hospital in real time and instruct the necessary response. In the information providing unit, for example, the generating AI reports the patient's condition to the hospital in real time. For example, the patient's vital signs in the ambulance are conveyed in real time. The information providing unit also instructs the hospital on the necessary response. For example, it instructs first aid according to the patient's condition. The information providing unit also instructs the hospital on the necessary response. For example, it instructs the hospital on immediate response if the patient's condition changes. In this way, by reporting the patient's condition in real time and instructing the necessary response, the hospital can respond quickly.

[0081] The information providing unit can notify the hospital of the patient's expected arrival time and request that necessary medical staff be on standby. In the information providing unit, for example, the generation AI notifies the hospital of the patient's expected arrival time and requests that necessary medical staff be on standby. For example, it requests that a specialist be on standby. In addition, the information providing unit updates the patient's expected arrival time to the hospital in real time and requests that necessary medical staff be on standby. For example, it updates the expected arrival time according to changes in traffic conditions. In addition, the information providing unit notifies the hospital of the patient's expected arrival time and requests that necessary medical staff be on standby. For example, it adjusts medical staff shifts to match the expected arrival time. This allows the hospital to respond quickly by notifying the hospital of the patient's expected arrival time and requesting that necessary medical staff be on standby.

[0082] The information providing unit uses the emotion estimation function to customize the information provision method according to the emotional state of the hospital staff, thereby encouraging a prompt response. The information providing unit, for example, uses the emotion estimation function to customize the information provision method according to the emotional state of the hospital staff. For example, if the hospital staff is feeling stressed, concise information is provided. The information providing unit also analyzes the emotional state of the hospital staff and selects an appropriate information provision method to encourage a prompt response. For example, if the hospital staff is calm, detailed information is provided. The information providing unit also uses the emotion estimation function to analyze the emotional state of the hospital staff in real time and customize the information provision method. For example, the amount and format of information are adjusted according to the emotional state of the hospital staff. In this way, by customizing the information provision method according to the emotional state of the hospital staff, a prompt response can be encouraged.

[0083] The information provision unit can analyze the patient's condition in real time during emergency transport and suggest necessary first aid. For example, the generation AI in the information provision unit analyzes the patient's vital signs in real time during emergency transport and suggests necessary first aid. For example, it suggests first aid based on changes in heart rate and blood pressure. The information provision unit also analyzes the patient's condition during emergency transport and suggests necessary first aid. For example, it suggests first aid for symptoms of difficulty breathing. The information provision unit also updates the patient's condition in real time during emergency transport and suggests necessary first aid. For example, it suggests first aid immediately if the patient's condition changes. This makes it possible to analyze the patient's condition in real time during emergency transport and suggest necessary first aid, enabling a rapid response.

[0084] The information provision unit can report the patient's condition during ambulance transport and instruct the hospital on the necessary response. For example, the information provision unit has the generating AI report the patient's condition during ambulance transport and instruct the hospital on the necessary response. For example, it conveys the patient's vital signs in real time. The information provision unit also has the generating AI analyze the patient's condition during ambulance transport and instruct the hospital on the necessary response. For example, it instructs first aid according to the patient's condition. The information provision unit also has the generating AI update the patient's condition in real time during ambulance transport and instruct the hospital on the necessary response. For example, it instructs the hospital on the necessary response immediately if the patient's condition changes. This makes it possible to report the patient's condition during ambulance transport and instruct the hospital on the necessary response, enabling a rapid response.

[0085] The information provision unit can monitor the patient's condition in real time during emergency transport and instruct the use of necessary medical equipment. For example, the information provision unit allows the generating AI to monitor the patient's vital signs in real time during emergency transport and instruct the use of necessary medical equipment. For example, it may instruct the use of a defibrillator based on changes in heart rate. The information provision unit also allows the generating AI to analyze the patient's condition during emergency transport and instruct the use of necessary medical equipment. For example, it may instruct the use of an oxygen mask for symptoms of difficulty breathing. The information provision unit also allows the generating AI to update the patient's condition in real time during emergency transport and instruct the use of necessary medical equipment. For example, it may immediately instruct the use of medical equipment if the patient's condition changes. This enables rapid response by monitoring the patient's condition in real time during emergency transport and instructing the use of necessary medical equipment.

[0086] The information provision unit can report the patient's condition during emergency transport and request the hospital to prepare the necessary medical equipment. For example, the generation AI reports the patient's condition during emergency transport and requests the hospital to prepare the necessary medical equipment. For example, the information provision unit specifies the necessary medical equipment based on the patient's vital signs. The information provision unit also analyzes the patient's condition during emergency transport and requests the hospital to prepare the necessary medical equipment. For example, it provides a list of medical equipment according to the patient's symptoms. The information provision unit also updates the patient's condition in real time during emergency transport and requests the hospital to prepare the necessary medical equipment. For example, if the patient's condition changes, the information provision unit immediately requests the hospital to prepare the necessary medical equipment. This enables a rapid response by reporting the patient's condition during emergency transport and requesting the hospital to prepare the necessary medical equipment.

[0087] The information providing unit uses the emotion estimation function to customize first aid procedures according to the emotional state of the paramedic, thereby optimizing the paramedic's performance. The information providing unit, for example, uses the emotion estimation function to customize first aid procedures according to the emotional state of the paramedic. For example, if the paramedic is feeling stressed, it provides a concise procedure. The information providing unit also analyzes the emotional state of the paramedic and selects appropriate first aid procedures to optimize performance. For example, if the paramedic is calm, it provides a detailed procedure. The information providing unit also uses the emotion estimation function to analyze the emotional state of the paramedic in real time and customize the first aid procedures. For example, it adjusts the order and content of the procedures according to the paramedic's emotional state. In this way, the performance of the paramedic can be optimized by customizing first aid procedures according to the emotional state of the paramedic.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The emergency transport system can further include a location information acquisition unit that automatically acquires the location information of the caller. For example, if the caller makes a call using a smartphone, the location information acquisition unit acquires GPS data and identifies the caller's exact location. The location information acquisition unit can also identify the caller's location using Wi-Fi or Bluetooth signals, even if the caller is inside a building. Furthermore, if the caller is moving, the location information acquisition unit can continue to update the location information in real time. This allows for accurate identification of the caller's location, enabling rapid emergency response.

[0090] When analyzing the caller's tone of voice and level of tension, the analysis unit can refer to the caller's past call history. For example, if the same caller has made a call in the past, the analysis unit can refer to the content and urgency of that call. The analysis unit can also take into account the caller's personal information, such as their age and gender, to analyze the caller's tone of voice and level of tension. For example, the analysis unit can adjust the criteria for determining the level of urgency for older callers. The analysis unit can also take into account the caller's cultural background and language differences when analyzing the caller's tone of voice and level of tension. For example, it can apply criteria for determining the level of urgency based on different languages ​​and cultures. This allows for a more accurate analysis of the caller's tone of voice and level of tension.

[0091] When analyzing the contents of a call, the analysis unit can take into account the caller's health condition and medical history. For example, if the caller has a chronic illness, the urgency level can be determined based on that information. The analysis unit can also monitor the caller's current health condition in real time when analyzing the contents of a call. For example, if the caller is wearing a smartwatch, the data from that watch can be acquired and used for analysis. The analysis unit can also take into account the health conditions of the caller's family and neighbors when analyzing the contents of a call. For example, if the caller's family members are complaining of the same symptoms, the urgency level can be determined based on that information. This allows for more accurate analysis of the contents of a call and the most appropriate response measures to be proposed.

[0092] When analyzing the caller's emotional state, the analysis unit can take into account the caller's psychological background and stress level. For example, if the caller has experienced trauma in the past, the analysis unit can determine the caller's emotional state based on that information. The analysis unit can also take into account the caller's living environment and social background to analyze the caller's emotional state. For example, if the caller is isolated, the analysis unit can determine the caller's emotional state based on that information. The analysis unit can also monitor the caller's current living situation and stress factors in real time when analyzing the caller's emotional state. For example, if the caller is experiencing stress at work or at home, the analysis unit can determine the caller's emotional state based on that information. This allows the caller's emotional state to be analyzed more accurately and appropriate advice to be provided.

[0093] When visualizing the analysis results of the call content, the analysis unit can provide an operator with a customizable dashboard. For example, the operator can select and display the information they need. The analysis unit can also use 3D mapping technology to visualize the analysis results of the call content. For example, detailed location information of the accident site can be displayed on a 3D map. When visualizing the analysis results of the call content, the analysis unit can also use colors and icons to help the operator intuitively understand. For example, calls with a high level of urgency can be displayed in red, and the severity of the injury can be indicated with an icon. This visualization of the analysis results of the call content allows the operator to respond quickly and accurately.

[0094] When multiple generation AIs cooperate to perform analysis, the analysis unit can combine AIs with different specializations. For example, an AI specializing in medical care and an AI specializing in traffic analysis work together to analyze the contents of a call report. Furthermore, when multiple generation AIs cooperate to perform analysis, the analysis unit can integrate different data sources to perform the analysis. For example, it can integrate and analyze voice data and text data. Furthermore, when multiple generation AIs cooperate to perform analysis, the analysis unit can integrate the analysis results in real time and propose the optimal response. For example, it can integrate the voice analysis results and text analysis results to propose the optimal response. In this way, when multiple generation AIs cooperate to perform analysis, the accuracy of the analysis is improved, enabling more effective responses.

[0095] When generating a customized response according to the caller's emotional state, the analysis unit can refer to the caller's past call history. For example, if the same caller has made a call in the past, the analysis unit can refer to the content of that call and the caller's emotional state. The analysis unit can also take into account the caller's personal information and living environment to generate a customized response according to the caller's emotional state. For example, if the caller is elderly, the analysis unit can provide a reassuring response. The analysis unit can also monitor the caller's current living situation and stressors in real time when generating a customized response according to the caller's emotional state. For example, if the caller is experiencing stress at work or at home, the analysis unit can provide an appropriate response based on that information. This allows the caller to feel more at ease by generating a customized response according to the caller's emotional state.

[0096] When analyzing traffic conditions and weather information, the dispatching department can make predictions based on past data. For example, it can predict future congestion based on past traffic congestion data. The dispatching department can also integrate and analyze multiple data sources to analyze traffic conditions and weather information. For example, it can integrate and analyze video data from traffic cameras and weather data. The dispatching department can also continuously update data in real time when analyzing traffic conditions and weather information. For example, it can recalculate the optimal route depending on changes in traffic congestion and weather. This allows for more accurate analysis of traffic conditions and weather information and suggests the optimal route.

[0097] When considering the equipment status of the ambulance and the expertise of the medical staff, the dispatching department can refer to the maintenance history of the ambulance and the training history of the medical staff. For example, it can prioritize the selection of ambulances that have recently undergone maintenance. The dispatching department can also continue to update data in real time to consider the equipment status of the ambulance and the expertise of the medical staff. For example, it can check the equipment status of the ambulance and the shift status of the medical staff in real time. The dispatching department can also refer to past response history when considering the equipment status of the ambulance and the expertise of the medical staff. For example, it can select ambulances and medical staff that have responded to similar emergencies in the past. This allows for a more accurate understanding of the equipment status of the ambulance and the expertise of the medical staff, enabling the most appropriate ambulance to be selected.

[0098] When coordinating with other emergency services (fire department, police), the dispatch department can share information by using a common database. For example, detailed information on an emergency situation can be shared in real time. The dispatch department can also use a common communication protocol to coordinate with other emergency services. For example, a dedicated communication protocol can be used to quickly transmit information on an emergency situation. The dispatch department can also use a common response manual when coordinating with other emergency services. For example, common procedures for emergency response at a fire scene can be used. This allows for more effective coordination with other emergency services and a comprehensive response.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The call reception unit receives an emergency call. For example, the call reception unit can receive a phone call or an app call. The call reception unit can also record the contents of the emergency call and save it as audio data or text data. Step 2: The analysis unit analyzes the report received by the report reception unit. For example, it uses generation AI to analyze the audio data of the report and analyze the caller's tone of voice and level of tension. It can also analyze text data to identify the location of the accident and the extent of injuries. It can also compare the data with a database of similar past cases to propose optimal countermeasures. Step 3: The dispatching department dispatches the most suitable ambulance based on the information analyzed by the analysis department. For example, it identifies the ambulance closest to the accident site and selects the most suitable ambulance taking into account the ambulance's equipment status and the expertise of the medical staff. It can also analyze traffic conditions and weather information in real time to suggest the best route. Step 4: The information provision department provides the necessary information to the ambulance dispatched by the dispatch department. For example, it provides detailed information on the location of the accident site, the extent of injuries, and information on the medical equipment required. It can also compare this information with a database of past cases and propose the best response measures.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] 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.

[0160] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, a call reception unit that receives emergency calls; an analysis unit that analyzes the content of the message received by the message receiving unit; a dispatch unit that dispatches an optimal ambulance based on the information analyzed by the analysis unit; an information providing unit that provides necessary information to the ambulance dispatched by the dispatch unit; A system characterized by:

2. The analysis unit Analyzes the caller's tone of voice and level of tension to accurately determine the level of urgency 2. The system of claim 1.

3. The analysis unit The contents of the report are compared with a database of similar cases from the past, and the most appropriate response measures are quickly proposed.

2. The system of claim 1.

4. The analysis unit Analyze the caller's emotional state and, if the caller is in a panic, provide appropriate advice to calm them down.

2. The system of claim 1.

5. The analysis unit The analysis results of the report content are visualized in real time, providing a dashboard that operators can intuitively understand.

2. The system of claim 1.

6. The analysis unit Multiple generation AIs work together to analyze and improve analysis accuracy 2. The system of claim 1.

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