system

Abstract

The system according to this embodiment aims to avoid the inappropriate use of ambulances while arranging appropriate first aid and dispatch services. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a coordination unit, a guidance unit, and a voice response unit. The reception unit receives input of symptoms. The analysis unit analyzes the symptoms received by the reception unit and provides appropriate responses and first aid methods. The coordination unit arranges a vehicle dispatch service based on the first aid methods provided by the analysis unit. The guidance unit provides information on first aid methods and AED locations until the vehicle dispatch service arranged by the coordination unit arrives. The voice response unit provides voice responses based on the information obtained by the analysis unit.

Description

Technical Field

[0004] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that means for appropriately arranging emergency treatment and dispatching service while avoiding inappropriate use of ambulances are not sufficiently provided.

[0005] The system according to the embodiment aims to arrange appropriate emergency treatment and dispatching service while avoiding inappropriate use of ambulances.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a coordination unit, a guidance unit, and a voice response unit. The reception unit receives input of symptoms. The analysis unit analyzes the symptoms received by the reception unit and provides appropriate responses and first aid methods. The coordination unit arranges a vehicle dispatch service based on the first aid methods provided by the analysis unit. The guidance unit provides information on first aid methods and the locations of AEDs until the vehicle dispatch service arranged by the coordination unit arrives. The voice response unit provides voice responses based on the information obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can arrange for appropriate emergency medical treatment and dispatch services while avoiding the inappropriate use of ambulances. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An emergency consultation support system according to an embodiment of the present invention is a system that uses a generating AI to add emergency consultations to the chat function of a messaging app and assists the user in deciding whether or not to call an ambulance. The emergency consultation support system allows the user to input symptoms through the chat function of a messaging app. The generating AI analyzes the symptoms and provides appropriate responses and first aid methods. For example, if the user inputs "My chest hurts," the generating AI will suggest first aid methods based on the symptoms. In cases where the symptoms do not warrant calling an ambulance, the system can also call a private ambulance, taxi, or support cab in conjunction with a ride-hailing service. Furthermore, it provides guidance on first aid methods until arrival and the location of necessary items such as AEDs via maps and services. The generating AI can also provide voice responses to the local emergency information center. For example, if the user inputs "My head hurts," this information is sent to the generating AI. The generating AI analyzes the input symptoms and provides appropriate responses and first aid methods. For example, if "My head hurts" is input, the generating AI will provide a response such as "Drink some water and rest." Furthermore, for symptoms that do not warrant calling an ambulance, the generating AI can collaborate with ride-hailing services to call private ambulances, taxis, or support cabs. For example, if the user inputs "I sprained my ankle," the generating AI will suggest, "Shall I call a taxi?" and, if the user agrees, will arrange a taxi. It also provides information on first aid procedures until arrival and the locations of necessary items such as AEDs via maps and services. For example, it may provide information such as, "There is an AED at a nearby convenience store." The generating AI can also respond to the local emergency information center via voice. For example, if the user inputs "Please call an ambulance," the generating AI will respond with a voice message such as, "This is the generating AI. I will provide you with patient information." This system allows users to make appropriate decisions and avoid inappropriate use of ambulances. In addition, the generating AI's extensive medical knowledge allows users to confidently communicate their symptoms and follow instructions. Furthermore, the emotion generation engine helps users feel more comfortable communicating their symptoms. In this way, the emergency consultation support system helps users decide whether or not to call an ambulance and avoids inappropriate use of ambulances.

[0029] The emergency consultation support system according to this embodiment comprises a reception unit, an analysis unit, a coordination unit, a guidance unit, and a voice response unit. The reception unit receives the user's symptoms through the chat function of a messaging application. Examples of symptoms entered by the user include "I have a chest ache," "I have a headache," and "I sprained my ankle." The reception unit sends these symptoms to a generating AI. The analysis unit uses the generating AI to analyze the symptoms received by the reception unit and provides appropriate responses and first aid methods. For example, if the user enters "I have a chest ache," the analysis unit provides a response such as "Drink some water and rest." The analysis unit can also use the generating AI to suggest first aid methods based on the symptoms. For example, if the user enters "I sprained my ankle," the analysis unit provides first aid methods such as "Apply ice and rest." The coordination unit arranges transportation services based on the first aid methods provided by the analysis unit. For example, if the user asks "Shall I call a taxi?", and the user agrees, a taxi is arranged. The coordination unit can also arrange private ambulances or support cabs. The information unit provides guidance on first aid methods and the location of AEDs until the dispatch service arranged by the coordination unit arrives. For example, it provides information such as, "There is an AED at a nearby convenience store." The information unit can provide users with necessary information using maps and services. The voice response unit provides voice responses based on the information obtained by the analysis unit. For example, if the user inputs, "Please call an ambulance," the voice response unit will respond with a voice message such as, "This is the generating AI. We will provide you with patient information." In this way, the emergency consultation support system according to this embodiment can help users decide whether or not to call an ambulance and avoid inappropriate use of ambulances.

[0030] The reception desk receives user input of symptoms via the messaging app's chat function. Examples of symptoms include "chest pain," "headache," and "sprained ankle." The reception desk then sends these symptoms to a generating AI. Specifically, the messaging app's interface is designed to be intuitive and user-friendly, allowing users to easily input symptoms. The entered symptoms are sent to the reception desk in text format, which receives them in real time. The reception desk then performs preprocessing to convert the received text data into an appropriate format before sending it to the generating AI. This preprocessing includes text normalization and removal of unnecessary information. For example, if the input is "My chest hurts. What should I do?", only the main symptom, "chest pain," is extracted and sent to the generating AI. Furthermore, the reception desk can refer to the user's past consultation history and personal information to provide supplementary information for more accurate analysis. This allows the reception desk to quickly and accurately send the user's entered symptoms to the generating AI and smoothly transition to the next analysis step.

[0031] The analysis unit uses generative AI to analyze symptoms received by the reception unit and provide appropriate responses and first aid methods. For example, if a user inputs "My chest hurts," the analysis unit will provide a response such as "Drink some water and rest." The analysis unit can also use generative AI to suggest first aid methods based on the symptoms. Specifically, the generative AI utilizes natural language processing technology to analyze the input text and understand the meaning of the symptoms. The generative AI has learned from a large amount of medical data and past consultation history, and has the ability to generate the most appropriate response for the input symptoms. For example, if "My head hurts" is input, the generative AI will search for information on the causes and treatments of headaches and provide specific advice such as "Drink some water and rest in a dark place." The generative AI can also assess the severity of the symptoms and, if it is urgent, recommend immediate medical attention. Furthermore, the analysis unit can use visual content such as diagrams and videos to make the first aid methods provided by the generative AI easier for users to understand. This allows the analysis unit to help users quickly take appropriate first aid measures and prevent the worsening of symptoms.

[0032] The Coordination Department arranges ride-hailing services based on first aid methods provided by the Analysis Department. For example, if a user suggests, "Shall I call a taxi?", and the user agrees, a taxi will be arranged. The Coordination Department can also arrange private ambulances or support cabs. Specifically, the Coordination Department obtains the user's current location from GPS data and sends a request to the nearest ride-hailing service provider. The Coordination Department works with multiple ride-hailing service providers and can select the most suitable service according to the user's situation. For example, if a user is experiencing serious symptoms, a private ambulance will be prioritized for rapid transport to a medical facility. The Coordination Department also has a function to track the estimated arrival time of the ride-hailing service and the vehicle's location in real time and notify the user. This allows the user to wait with peace of mind. Furthermore, the Coordination Department not only arranges ride-hailing services but also has a function to automatically send notifications to the user's emergency contacts. For example, if a user wishes to contact family or friends, the Coordination Department can obtain that information and contact them quickly. This allows the Coordination Department to support users in receiving appropriate medical assistance and facilitate a smooth response in emergencies.

[0033] The Information Department provides guidance on first aid methods and AED locations until the dispatch service arranged by the Coordination Department arrives. For example, it provides information such as, "There is an AED at a nearby convenience store." The Information Department can provide users with necessary information using maps and services. Specifically, based on the user's current location, the Information Department displays the location of the nearest AED and medical facility on a map. Users can check this information in real time via their smartphones or tablets. In addition, the Information Department can use images and videos in addition to text to explain first aid methods in an easy-to-understand manner. For example, it provides a video demonstrating the procedure for cardiopulmonary resuscitation to help users perform first aid accurately. Furthermore, the Information Department is equipped with a voice guidance function so that users can quickly obtain the information they need in an emergency. This ensures that even users with visual impairments or those whose hands are full can reliably obtain the necessary information. The Information Department can select the most appropriate method of information delivery according to the user's situation and provide guidance quickly and accurately. In this way, the Information Department helps users perform appropriate first aid and wait for the arrival of the dispatch service with peace of mind.

[0034] The voice response unit provides voice responses based on information obtained by the analysis unit. For example, if a user inputs "Please call an ambulance," the voice response unit will respond with a voice message such as "This is the generating AI. I will provide you with patient information." Specifically, the voice response unit converts the text information provided by the generating AI into voice using speech synthesis technology. Speech synthesis technology can generate voices with natural pronunciation and intonation, making it easy for users to understand the information. The voice response unit generates appropriate voice responses based on the text input by the user. For example, if "I have a headache" is input, the voice response unit will provide specific advice in voice, such as "If you have a headache, drink water and rest in a dark place." The voice response unit can also adjust the tone and speed of the voice according to the urgency of the user's situation. For example, in cases of high urgency, it will give instructions in a quick and clear voice to help the user take action quickly. Furthermore, the voice response unit has multilingual capabilities and can accommodate users who speak different languages. This allows the voice response unit to provide appropriate information to users without experiencing language barriers. The voice response unit plays a crucial role in providing users with quick and accurate voice responses, supporting emergency situations.

[0035] The information unit can provide information on first aid procedures and AED locations using maps and services. For example, the information unit can display first aid procedures on a map to visually guide users. It can also display AED locations on a map to guide users to the nearest AED. For example, the information unit can use a map service to display the location of the nearest AED to the user. The information unit can also provide first aid procedures in text or audio. For example, the information unit can provide first aid instructions such as "Cool the person and keep them still" in text. This allows users to quickly understand first aid procedures and AED locations.

[0036] The voice response unit can provide voice responses to the local emergency information center. For example, if a user inputs "Please call an ambulance," the voice response unit will respond with a voice message such as, "This is a generating AI. I will provide you with patient information." The voice response unit can also convey the user's symptoms verbally. For example, it may convey information such as, "The patient is complaining of chest pain." This allows the user to quickly transmit information to the emergency information center.

[0037] The coordination unit can arrange private ambulances, taxis, and support cabs. For example, if a user asks, "Shall I call a taxi?", the coordination unit will arrange a taxi if the user agrees. The coordination unit can also arrange private ambulances and support cabs. For example, if a user inputs, "I sprained my ankle," the coordination unit can arrange a private ambulance. This allows the user to use an appropriate ride-hailing service. Some or all of the above processing in the coordination unit may be performed using AI or not.

[0038] The reception desk allows users to input symptoms through the chat function of a messaging app. For example, the user might input "My chest hurts." This information is sent to the generating AI. The reception desk provides an intuitive interface to make it easy for users to input symptoms. For example, the reception desk supports text input and voice input. This makes it easy for users to input symptoms. Some or all of the above processing in the reception desk may be performed using AI or not.

[0039] The reception desk can analyze the user's past symptom input history and provide an optimal input interface. For example, the reception desk can automatically display symptoms that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest symptoms that the user will use at a specific time of day based on their past input history. This allows for the provision of an optimal input interface based on the user's past history. Some or all of the above processing in the reception desk may be performed using AI or not.

[0040] The reception desk can filter the input content based on the user's current health status and past medical history when symptoms are entered. For example, the reception desk can monitor the user's current health status in real time and only allow the user to enter relevant symptoms. The reception desk can also refer to the user's past medical history and filter out symptoms that are not relevant. Furthermore, the reception desk can combine the user's current health status and past medical history to suggest the most appropriate input content. This allows the reception desk to provide appropriate input content based on the user's health status and medical history. Some or all of the above processing in the reception desk may be performed using AI or not.

[0041] The reception system can prioritize the input of highly relevant symptoms by considering the user's geographical location when they enter their symptoms. For example, if the user is in a specific region, the reception system can prioritize the input of symptoms related to diseases prevalent in that region. Furthermore, if the user is traveling, the reception system can also prompt them to enter symptoms based on the health risks of their travel destination. Additionally, if the user is in a specific facility, the reception system can prompt them to enter symptoms based on the health risks of that facility. This allows the system to provide optimal input based on the user's geographical location. Some or all of the above processing in the reception system may be performed using AI, or it may not.

[0042] The reception desk can analyze the user's social media activity when they input symptoms and prompt them to input relevant symptoms. For example, the reception desk can prompt them to input relevant symptoms based on health information the user has shared on social media. The reception desk can also identify recent health risks from the user's social media activity and prompt them to input relevant symptoms. Furthermore, the reception desk can prompt them to input relevant symptoms based on health information the user follows on social media. This allows the system to provide optimal input content based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not.

[0043] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms. For example, in the case of a serious symptom, the analysis unit performs a detailed analysis and provides specific first aid methods. In the case of a mild symptom, the analysis unit can also perform a concise analysis and provide basic first aid methods. Furthermore, the analysis unit determines the priority of the analysis according to the severity of the symptoms. This allows for the provision of optimal analysis results according to the severity of the symptoms. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0044] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. For example, in the case of respiratory system symptoms, the analysis unit applies a specialized analysis algorithm. Furthermore, in the case of digestive system symptoms, it can apply a different analysis algorithm. In addition, in the case of neurological system symptoms, it applies yet another different analysis algorithm. This allows for the provision of optimal analysis results according to the symptom category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not.

[0045] The analysis unit can determine the priority of the analysis based on the timing of symptom onset. For example, the analysis unit prioritizes the analysis of recently occurring symptoms. The analysis unit can also perform a detailed analysis of symptoms that have persisted for a long period. Furthermore, the analysis unit adjusts the priority of the analysis according to the timing of symptom onset. This allows for the provision of optimal analysis results according to the timing of symptom onset. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0046] The analysis unit can adjust the order of analysis results based on the relationships between symptoms during the analysis. For example, the analysis unit may analyze important symptoms first and related symptoms later. The analysis unit can also adjust the order of analysis results based on the relationships between symptoms. Furthermore, the analysis unit groups the analysis results considering the relationships between symptoms. This allows for the provision of optimal analysis results according to the relationships between symptoms. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0047] The integration unit can analyze the user's past usage history to select the optimal arrangement method when arranging a ride-hailing service. For example, the integration unit may prioritize arranging ride-hailing services that the user has used in the past. The integration unit can also suggest the optimal ride-hailing option based on the user's past usage history. Furthermore, the integration unit selects the most efficient ride-hailing method based on the user's past usage history. This allows the integration unit to provide the optimal ride-hailing service arrangement method based on the user's past usage history. Some or all of the above processing in the integration unit may be performed using AI, or not.

[0048] The integration unit can customize the ride-hailing service based on the user's current situation. For example, if the user is in a hurry, the integration unit will arrange the fastest ride-hailing service. If the user is relaxed, the integration unit can also provide detailed ride-hailing options. Furthermore, the integration unit will suggest the optimal ride-hailing method according to the user's current situation. This allows the integration unit to provide the most suitable ride-hailing service arrangement method for the user's current situation. Some or all of the above processing in the integration unit may be performed using AI or not.

[0049] The collaboration unit can arrange the most suitable ride-hailing service by considering the user's geographical location when arranging a ride-hailing service. For example, if the user is in a specific region, the collaboration unit will arrange a ride-hailing service available in that region. Furthermore, if the user is traveling, the collaboration unit can arrange a ride-hailing service available at their travel destination. In addition, the collaboration unit will suggest the most suitable ride-hailing service based on the user's geographical location. This allows for the provision of the most suitable ride-hailing service based on the user's geographical location. Some or all of the above-described processes in the collaboration unit may be performed using AI, or they may not.

[0050] The integration unit can analyze a user's social media activity when arranging a ride-hailing service and propose the most suitable service. For example, the integration unit can propose the most suitable ride-hailing service based on information shared by the user on social media. Furthermore, the integration unit can identify recent travel patterns from the user's social media activity and propose the most suitable ride-hailing service. In addition, the integration unit can propose the most suitable service based on the ride-hailing services the user follows on social media. This enables the provision of optimal ride-hailing services based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI, or not.

[0051] The guidance unit can provide the optimal guidance method by referring to the user's past guidance history during guidance. For example, the guidance unit can suggest the optimal guidance method based on the guidance methods the user has received in the past. The guidance unit can also select the most effective guidance method from the user's past guidance history. Furthermore, the guidance unit analyzes the user's past guidance history and provides the optimal guidance method. This allows the guidance unit to provide the optimal guidance method based on the user's past guidance history. Some or all of the above processing in the guidance unit may be performed using AI or not.

[0052] The guidance system can customize the guidance content based on the user's current situation. For example, if the user is in a hurry, the guidance system will provide the quickest guidance method. If the user is relaxed, the guidance system can also provide a more detailed guidance method. Furthermore, the guidance system will suggest the most suitable guidance content according to the user's current situation. This ensures that the guidance system provides optimal guidance tailored to the user's current circumstances. Some or all of the above processing in the guidance system may be performed using AI, or it may not.

[0053] The guidance unit can provide the optimal guidance method by considering the user's geographical location information during guidance. For example, if the user is in a specific region, the guidance unit can provide guidance methods available in that region. Furthermore, if the user is traveling, the guidance unit can provide guidance methods available at the travel destination. In addition, the guidance unit proposes the optimal guidance method based on the user's geographical location information. This allows the guidance unit to provide the optimal guidance method based on the user's geographical location. Some or all of the above processing in the guidance unit may be performed using AI, or it may be performed without AI.

[0054] The guidance unit can analyze the user's social media activity to provide optimal guidance content. For example, the guidance unit can provide optimal guidance content based on information shared by the user on social media. Furthermore, the guidance unit can identify recent health risks from the user's social media activity and provide relevant guidance content. In addition, the guidance unit can provide optimal guidance content based on the health information the user follows on social media. This allows for the provision of optimal guidance content based on the user's social media activity. Some or all of the above processing in the guidance unit may be performed using AI, or not.

[0055] The voice response unit can provide the optimal response method by referring to the user's past voice response history when providing a voice response. For example, the voice response unit can suggest the optimal response method based on the voice responses the user has received in the past. The voice response unit can also select the most effective response method from the user's past voice response history. Furthermore, the voice response unit analyzes the user's past voice response history and provides the optimal response method. This allows the system to provide the optimal response method based on the user's past voice response history. Some or all of the above processing in the voice response unit may be performed using AI or not.

[0056] The voice response unit can customize its responses based on the user's current situation when providing voice responses. For example, if the user is in a hurry, the voice response unit can provide the quickest possible response. If the user is relaxed, the voice response unit can also provide a more detailed response. Furthermore, the voice response unit suggests the most appropriate response based on the user's current situation. This ensures that the system provides the most suitable response for the user's current circumstances. Some or all of the above processing in the voice response unit may be performed using AI, or it may be performed without AI.

[0057] The voice response unit can provide the optimal response method when providing a voice response, taking into account the user's geographical location information. For example, if the user is in a specific region, the voice response unit can provide a response method available in that region. Furthermore, if the user is traveling, the voice response unit can provide a response method available at their travel destination. In addition, the voice response unit proposes the optimal response method based on the user's geographical location information. This allows the system to provide the optimal response method based on the user's geographical location. Some or all of the above processing in the voice response unit may be performed using AI, or it may be performed without AI.

[0058] The voice response unit can analyze the user's social media activity during voice response to provide the most appropriate response. For example, the voice response unit can provide the most appropriate response based on information the user has shared on social media. Furthermore, the voice response unit can identify recent health risks from the user's social media activity and provide relevant responses. In addition, the voice response unit can provide the most appropriate response based on the health information the user follows on social media. This allows for the provision of optimal responses based on the user's social media activity. Some or all of the above processing in the voice response unit may be performed using AI, or it may not.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk can analyze the user's past symptom input history and provide the optimal input interface. For example, it can automatically display symptoms that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest symptoms that the user might use at specific times of day based on their past input history. This allows for the provision of an optimal input interface based on the user's past history.

[0061] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms. For example, for serious symptoms, a detailed analysis is performed to provide specific first-aid methods. For mild symptoms, a concise analysis is performed to provide basic first-aid methods. Furthermore, the analysis priority is determined according to the severity of the symptoms. This allows for the provision of optimal analysis results tailored to the severity of the symptoms.

[0062] The reception system can filter input based on the user's current health status and past medical history when they enter symptoms. For example, it can monitor the user's current health status in real time and only allow them to enter relevant symptoms. It can also refer to the user's past medical history and filter out symptoms with low relevance. Furthermore, it can combine the user's current health status and past medical history to suggest the most appropriate input content. This ensures that the system provides appropriate input content based on the user's health status and medical history.

[0063] The collaboration unit can analyze a user's past usage history to select the optimal dispatch method when arranging a ride-hailing service. For example, it can prioritize dispatching services that the user has used in the past. It can also suggest the most suitable dispatch option based on the user's past usage history. Furthermore, it selects the most efficient dispatch method based on the user's past usage history. This allows the system to provide the optimal ride-hailing service arrangement method based on the user's past usage history.

[0064] The guidance system can provide the most suitable guidance method by considering the user's geographical location. For example, if the user is in a specific region, it can provide guidance methods available in that region. If the user is traveling, it can also provide guidance methods available at their travel destination. Furthermore, it can suggest the most suitable guidance method based on the user's geographical location. This allows the system to provide the most appropriate guidance method based on the user's geographical location.

[0065] The voice response unit can analyze the user's social media activity during voice response to provide the most appropriate response. For example, it can provide the most appropriate response based on information the user has shared on social media. It can also identify recent health risks from the user's social media activity and provide relevant responses. Furthermore, it can provide the most appropriate response based on the health information the user follows on social media. In this way, it can provide the most appropriate response based on the user's social media activity.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives the user's symptoms via the chat function of a messaging app. For example, symptoms such as "chest pain," "headache," or "sprained ankle" are entered. The reception desk sends these symptoms to the AI ​​generator. Step 2: The analysis unit uses a generation AI to analyze the symptoms received by the reception unit and provides appropriate responses and first aid methods. For example, if "I have a chest ache" is entered, it will provide a response such as "Drink some water and rest," and if "I sprained my ankle" is entered, it will provide first aid methods such as "Apply ice and rest." Step 3: The Coordination Department arranges a ride-hailing service based on the emergency response methods provided by the Analysis Department. For example, it might ask, "Shall I call a taxi?" and, if the user agrees, arrange a taxi. It can also arrange a private ambulance or support cab. Step 4: The information department provides guidance on first aid methods and the location of AEDs until the dispatch service arranged by the coordination department arrives. For example, they provide information such as, "There is an AED at a nearby convenience store." The information department provides users with the necessary information using maps and services. Step 5: The voice response unit provides a voice response based on the information obtained by the analysis unit. For example, if the input is "Please call an ambulance," it will respond with a voice message such as, "This is the generating AI. I will provide you with patient information."

[0068] (Example of form 2) An emergency consultation support system according to an embodiment of the present invention is a system that uses a generating AI to add emergency consultations to the chat function of a messaging app and assists the user in deciding whether or not to call an ambulance. The emergency consultation support system allows the user to input symptoms through the chat function of a messaging app. The generating AI analyzes the symptoms and provides appropriate responses and first aid methods. For example, if the user inputs "My chest hurts," the generating AI will suggest first aid methods based on the symptoms. In cases where the symptoms do not warrant calling an ambulance, the system can also call a private ambulance, taxi, or support cab in conjunction with a ride-hailing service. Furthermore, it provides guidance on first aid methods until arrival and the location of necessary items such as AEDs via maps and services. The generating AI can also provide voice responses to the local emergency information center. For example, if the user inputs "My head hurts," this information is sent to the generating AI. The generating AI analyzes the input symptoms and provides appropriate responses and first aid methods. For example, if "My head hurts" is input, the generating AI will provide a response such as "Drink some water and rest." Furthermore, for symptoms that do not warrant calling an ambulance, the generating AI can collaborate with ride-hailing services to call private ambulances, taxis, or support cabs. For example, if the user inputs "I sprained my ankle," the generating AI will suggest, "Shall I call a taxi?" and, if the user agrees, will arrange a taxi. It also provides information on first aid procedures until arrival and the locations of necessary items such as AEDs via maps and services. For example, it may provide information such as, "There is an AED at a nearby convenience store." The generating AI can also respond to the local emergency information center via voice. For example, if the user inputs "Please call an ambulance," the generating AI will respond with a voice message such as, "This is the generating AI. I will provide you with patient information." This system allows users to make appropriate decisions and avoid inappropriate use of ambulances. In addition, the generating AI's extensive medical knowledge allows users to confidently communicate their symptoms and follow instructions. Furthermore, the emotion generation engine helps users feel more comfortable communicating their symptoms. In this way, the emergency consultation support system helps users decide whether or not to call an ambulance and avoids inappropriate use of ambulances.

[0069] The emergency consultation support system according to this embodiment comprises a reception unit, an analysis unit, a coordination unit, a guidance unit, and a voice response unit. The reception unit receives the user's symptoms through the chat function of a messaging application. Examples of symptoms entered by the user include "I have a chest ache," "I have a headache," and "I sprained my ankle." The reception unit sends these symptoms to a generating AI. The analysis unit uses the generating AI to analyze the symptoms received by the reception unit and provides appropriate responses and first aid methods. For example, if the user enters "I have a chest ache," the analysis unit provides a response such as "Drink some water and rest." The analysis unit can also use the generating AI to suggest first aid methods based on the symptoms. For example, if the user enters "I sprained my ankle," the analysis unit provides first aid methods such as "Apply ice and rest." The coordination unit arranges transportation services based on the first aid methods provided by the analysis unit. For example, if the user asks "Shall I call a taxi?", and the user agrees, a taxi is arranged. The coordination unit can also arrange private ambulances or support cabs. The information unit provides guidance on first aid methods and the location of AEDs until the dispatch service arranged by the coordination unit arrives. For example, it provides information such as, "There is an AED at a nearby convenience store." The information unit can provide users with necessary information using maps and services. The voice response unit provides voice responses based on the information obtained by the analysis unit. For example, if the user inputs, "Please call an ambulance," the voice response unit will respond with a voice message such as, "This is the generating AI. We will provide you with patient information." In this way, the emergency consultation support system according to this embodiment can help users decide whether or not to call an ambulance and avoid inappropriate use of ambulances.

[0070] The reception desk receives user input of symptoms via the messaging app's chat function. Examples of symptoms include "chest pain," "headache," and "sprained ankle." The reception desk then sends these symptoms to a generating AI. Specifically, the messaging app's interface is designed to be intuitive and user-friendly, allowing users to easily input symptoms. The entered symptoms are sent to the reception desk in text format, which receives them in real time. The reception desk then performs preprocessing to convert the received text data into an appropriate format before sending it to the generating AI. This preprocessing includes text normalization and removal of unnecessary information. For example, if the input is "My chest hurts. What should I do?", only the main symptom, "chest pain," is extracted and sent to the generating AI. Furthermore, the reception desk can refer to the user's past consultation history and personal information to provide supplementary information for more accurate analysis. This allows the reception desk to quickly and accurately send the user's entered symptoms to the generating AI and smoothly transition to the next analysis step.

[0071] The analysis unit uses generative AI to analyze symptoms received by the reception unit and provide appropriate responses and first aid methods. For example, if a user inputs "My chest hurts," the analysis unit will provide a response such as "Drink some water and rest." The analysis unit can also use generative AI to suggest first aid methods based on the symptoms. Specifically, the generative AI utilizes natural language processing technology to analyze the input text and understand the meaning of the symptoms. The generative AI has learned from a large amount of medical data and past consultation history, and has the ability to generate the most appropriate response for the input symptoms. For example, if "My head hurts" is input, the generative AI will search for information on the causes and treatments of headaches and provide specific advice such as "Drink some water and rest in a dark place." The generative AI can also assess the severity of the symptoms and, if it is urgent, recommend immediate medical attention. Furthermore, the analysis unit can use visual content such as diagrams and videos to make the first aid methods provided by the generative AI easier for users to understand. This allows the analysis unit to help users quickly take appropriate first aid measures and prevent the worsening of symptoms.

[0072] The Coordination Department arranges ride-hailing services based on first aid methods provided by the Analysis Department. For example, if a user suggests, "Shall I call a taxi?", and the user agrees, a taxi will be arranged. The Coordination Department can also arrange private ambulances or support cabs. Specifically, the Coordination Department obtains the user's current location from GPS data and sends a request to the nearest ride-hailing service provider. The Coordination Department works with multiple ride-hailing service providers and can select the most suitable service according to the user's situation. For example, if a user is experiencing serious symptoms, a private ambulance will be prioritized for rapid transport to a medical facility. The Coordination Department also has a function to track the estimated arrival time of the ride-hailing service and the vehicle's location in real time and notify the user. This allows the user to wait with peace of mind. Furthermore, the Coordination Department not only arranges ride-hailing services but also has a function to automatically send notifications to the user's emergency contacts. For example, if a user wishes to contact family or friends, the Coordination Department can obtain that information and contact them quickly. This allows the Coordination Department to support users in receiving appropriate medical assistance and facilitate a smooth response in emergencies.

[0073] The Information Department provides guidance on first aid methods and AED locations until the dispatch service arranged by the Coordination Department arrives. For example, it provides information such as, "There is an AED at a nearby convenience store." The Information Department can provide users with necessary information using maps and services. Specifically, based on the user's current location, the Information Department displays the location of the nearest AED and medical facility on a map. Users can check this information in real time via their smartphones or tablets. In addition, the Information Department can use images and videos in addition to text to explain first aid methods in an easy-to-understand manner. For example, it provides a video demonstrating the procedure for cardiopulmonary resuscitation to help users perform first aid accurately. Furthermore, the Information Department is equipped with a voice guidance function so that users can quickly obtain the information they need in an emergency. This ensures that even users with visual impairments or those whose hands are full can reliably obtain the necessary information. The Information Department can select the most appropriate method of information delivery according to the user's situation and provide guidance quickly and accurately. In this way, the Information Department helps users perform appropriate first aid and wait for the arrival of the dispatch service with peace of mind.

[0074] The voice response unit provides voice responses based on information obtained by the analysis unit. For example, if a user inputs "Please call an ambulance," the voice response unit will respond with a voice message such as "This is the generating AI. I will provide you with patient information." Specifically, the voice response unit converts the text information provided by the generating AI into voice using speech synthesis technology. Speech synthesis technology can generate voices with natural pronunciation and intonation, making it easy for users to understand the information. The voice response unit generates appropriate voice responses based on the text input by the user. For example, if "I have a headache" is input, the voice response unit will provide specific advice in voice, such as "If you have a headache, drink water and rest in a dark place." The voice response unit can also adjust the tone and speed of the voice according to the urgency of the user's situation. For example, in cases of high urgency, it will give instructions in a quick and clear voice to help the user take action quickly. Furthermore, the voice response unit has multilingual capabilities and can accommodate users who speak different languages. This allows the voice response unit to provide appropriate information to users without experiencing language barriers. The voice response unit plays a crucial role in providing users with quick and accurate voice responses, supporting emergency situations.

[0075] The information unit can provide information on first aid procedures and AED locations using maps and services. For example, the information unit can display first aid procedures on a map to visually guide users. It can also display AED locations on a map to guide users to the nearest AED. For example, the information unit can use a map service to display the location of the nearest AED to the user. The information unit can also provide first aid procedures in text or audio. For example, the information unit can provide first aid instructions such as "Cool the person and keep them still" in text. This allows users to quickly understand first aid procedures and AED locations.

[0076] The voice response unit can provide voice responses to the local emergency information center. For example, if a user inputs "Please call an ambulance," the voice response unit will respond with a voice message such as, "This is a generating AI. I will provide you with patient information." The voice response unit can also convey the user's symptoms verbally. For example, it may convey information such as, "The patient is complaining of chest pain." This allows the user to quickly transmit information to the emergency information center.

[0077] The analysis unit can provide users with a sense of security using an emotion generation engine. For example, if the user is feeling anxious, the analysis unit can use the emotion generation engine to provide a reassuring response. For example, it might provide a response such as, "It's okay, please calm down." The analysis unit can also adjust the tone and content of the response according to the user's emotions. For example, if the user is relaxed, it can provide more detailed information. This allows the user to confidently communicate their symptoms. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The coordination unit can arrange private ambulances, taxis, and support cabs. For example, if a user asks, "Shall I call a taxi?", the coordination unit will arrange a taxi if the user agrees. The coordination unit can also arrange private ambulances and support cabs. For example, if a user inputs, "I sprained my ankle," the coordination unit can arrange a private ambulance. This allows the user to use an appropriate ride-hailing service. Some or all of the above processing in the coordination unit may be performed using AI or not.

[0079] The reception desk allows users to input symptoms through the chat function of a messaging app. For example, the user might input "My chest hurts." This information is sent to the generating AI. The reception desk provides an intuitive interface to make it easy for users to input symptoms. For example, the reception desk supports text input and voice input. This makes it easy for users to input symptoms. Some or all of the above processing in the reception desk may be performed using AI or not.

[0080] The reception system can estimate the user's emotions and adjust the symptom input method based on the estimated emotions. For example, if the user is feeling anxious, the reception system can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception system can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick symptom input. This allows the system to provide the optimal input method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception desk can analyze the user's past symptom input history and provide an optimal input interface. For example, the reception desk can automatically display symptoms that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest symptoms that the user will use at a specific time of day based on their past input history. This allows for the provision of an optimal input interface based on the user's past history. Some or all of the above processing in the reception desk may be performed using AI or not.

[0082] The reception desk can filter the input content based on the user's current health status and past medical history when symptoms are entered. For example, the reception desk can monitor the user's current health status in real time and only allow the user to enter relevant symptoms. The reception desk can also refer to the user's past medical history and filter out symptoms that are not relevant. Furthermore, the reception desk can combine the user's current health status and past medical history to suggest the most appropriate input content. This allows the reception desk to provide appropriate input content based on the user's health status and medical history. Some or all of the above processing in the reception desk may be performed using AI or not.

[0083] The reception system can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is nervous, the reception system will prioritize inputting important symptoms. If the user is relaxed, the reception system may also prompt for detailed symptoms. Furthermore, if the user is in a hurry, the reception system will prompt for inputting only the most important symptoms. This allows the system to provide optimal input prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The reception system can prioritize the input of highly relevant symptoms by considering the user's geographical location when they enter their symptoms. For example, if the user is in a specific region, the reception system can prioritize the input of symptoms related to diseases prevalent in that region. Furthermore, if the user is traveling, the reception system can also prompt them to enter symptoms based on the health risks of their travel destination. Additionally, if the user is in a specific facility, the reception system can prompt them to enter symptoms based on the health risks of that facility. This allows the system to provide optimal input based on the user's geographical location. Some or all of the above processing in the reception system may be performed using AI, or it may not.

[0085] The reception desk can analyze the user's social media activity when they input symptoms and prompt them to input relevant symptoms. For example, the reception desk can prompt them to input relevant symptoms based on health information the user has shared on social media. The reception desk can also identify recent health risks from the user's social media activity and prompt them to input relevant symptoms. Furthermore, the reception desk can prompt them to input relevant symptoms based on health information the user follows on social media. This allows the system to provide optimal input content based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not.

[0086] The analysis unit can estimate the user's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple and reassuring presentation. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. This allows for the provision of the most appropriate presentation of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms. For example, in the case of a serious symptom, the analysis unit performs a detailed analysis and provides specific first aid methods. In the case of a mild symptom, the analysis unit can also perform a concise analysis and provide basic first aid methods. Furthermore, the analysis unit determines the priority of the analysis according to the severity of the symptoms. This allows for the provision of optimal analysis results according to the severity of the symptoms. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0088] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. For example, in the case of respiratory system symptoms, the analysis unit applies a specialized analysis algorithm. Furthermore, in the case of digestive system symptoms, it can apply a different analysis algorithm. In addition, in the case of neurological system symptoms, it applies yet another different analysis algorithm. This allows for the provision of optimal analysis results according to the symptom category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit will provide a concise and rapid analysis result. This allows for the provision of an optimal analysis result length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The analysis unit can determine the priority of the analysis based on the timing of symptom onset. For example, the analysis unit prioritizes the analysis of recently occurring symptoms. The analysis unit can also perform a detailed analysis of symptoms that have persisted for a long period. Furthermore, the analysis unit adjusts the priority of the analysis according to the timing of symptom onset. This allows for the provision of optimal analysis results according to the timing of symptom onset. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0091] The analysis unit can adjust the order of analysis results based on the relationships between symptoms during the analysis. For example, the analysis unit may analyze important symptoms first and related symptoms later. The analysis unit can also adjust the order of analysis results based on the relationships between symptoms. Furthermore, the analysis unit groups the analysis results considering the relationships between symptoms. This allows for the provision of optimal analysis results according to the relationships between symptoms. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0092] The integration unit can estimate the user's emotions and adjust the ride-hailing service arrangement method based on the estimated emotions. For example, if the user is feeling anxious, the integration unit will quickly arrange a ride-hailing service. If the user is relaxed, the integration unit can also provide detailed ride-hailing options. Furthermore, if the user is in a hurry, the integration unit will arrange the fastest possible ride-hailing service. This allows for the provision of the optimal ride-hailing service arrangement method tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The integration unit can analyze the user's past usage history to select the optimal arrangement method when arranging a ride-hailing service. For example, the integration unit may prioritize arranging ride-hailing services that the user has used in the past. The integration unit can also suggest the optimal ride-hailing option based on the user's past usage history. Furthermore, the integration unit selects the most efficient ride-hailing method based on the user's past usage history. This allows the integration unit to provide the optimal ride-hailing service arrangement method based on the user's past usage history. Some or all of the above processing in the integration unit may be performed using AI, or not.

[0094] The integration unit can customize the ride-hailing service based on the user's current situation. For example, if the user is in a hurry, the integration unit will arrange the fastest ride-hailing service. If the user is relaxed, the integration unit can also provide detailed ride-hailing options. Furthermore, the integration unit will suggest the optimal ride-hailing method according to the user's current situation. This allows the integration unit to provide the most suitable ride-hailing service arrangement method for the user's current situation. Some or all of the above processing in the integration unit may be performed using AI or not.

[0095] The integration unit can estimate the user's emotions and prioritize ride-hailing services based on those emotions. For example, if the user is feeling anxious, the integration unit will prioritize arranging the fastest ride-hailing service. If the user is relaxed, the integration unit can also provide detailed ride-hailing options. Furthermore, if the user is in a hurry, the integration unit will prioritize arranging the fastest ride-hailing service. This allows for the provision of optimal ride-hailing service prioritization based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The collaboration unit can arrange the most suitable ride-hailing service by considering the user's geographical location when arranging a ride-hailing service. For example, if the user is in a specific region, the collaboration unit will arrange a ride-hailing service available in that region. Furthermore, if the user is traveling, the collaboration unit can arrange a ride-hailing service available at their travel destination. In addition, the collaboration unit will suggest the most suitable ride-hailing service based on the user's geographical location. This allows for the provision of the most suitable ride-hailing service based on the user's geographical location. Some or all of the above-described processes in the collaboration unit may be performed using AI, or they may not.

[0097] The integration unit can analyze a user's social media activity when arranging a ride-hailing service and propose the most suitable service. For example, the integration unit can propose the most suitable ride-hailing service based on information shared by the user on social media. Furthermore, the integration unit can identify recent travel patterns from the user's social media activity and propose the most suitable ride-hailing service. In addition, the integration unit can propose the most suitable service based on the ride-hailing services the user follows on social media. This enables the provision of optimal ride-hailing services based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI, or not.

[0098] The guidance system can estimate the user's emotions and adjust its guidance method based on those emotions. For example, if the user is feeling anxious, the guidance system can provide a simple and reassuring guidance method. If the user is relaxed, it can also provide a detailed guidance method. Furthermore, if the user is in a hurry, it can provide a concise and to-the-point guidance method. This allows the system to provide the most appropriate guidance method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The guidance unit can provide the optimal guidance method by referring to the user's past guidance history during guidance. For example, the guidance unit can suggest the optimal guidance method based on the guidance methods the user has received in the past. The guidance unit can also select the most effective guidance method from the user's past guidance history. Furthermore, the guidance unit analyzes the user's past guidance history and provides the optimal guidance method. This allows the guidance unit to provide the optimal guidance method based on the user's past guidance history. Some or all of the above processing in the guidance unit may be performed using AI or not.

[0100] The guidance system can customize the guidance content based on the user's current situation. For example, if the user is in a hurry, the guidance system will provide the quickest guidance method. If the user is relaxed, the guidance system can also provide a more detailed guidance method. Furthermore, the guidance system will suggest the most suitable guidance content according to the user's current situation. This ensures that the guidance system provides optimal guidance tailored to the user's current circumstances. Some or all of the above processing in the guidance system may be performed using AI, or it may not.

[0101] The guidance system can estimate the user's emotions and prioritize guidance based on those emotions. For example, if the user is feeling anxious, the guidance system will prioritize providing the most important guidance. If the user is relaxed, the guidance system can also provide detailed guidance. Furthermore, if the user is in a hurry, the guidance system will prioritize providing concise guidance. This allows for the provision of optimal guidance priorities tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The guidance unit can provide the optimal guidance method by considering the user's geographical location information during guidance. For example, if the user is in a specific region, the guidance unit can provide guidance methods available in that region. Furthermore, if the user is traveling, the guidance unit can provide guidance methods available at the travel destination. In addition, the guidance unit proposes the optimal guidance method based on the user's geographical location information. This allows the guidance unit to provide the optimal guidance method based on the user's geographical location. Some or all of the above processing in the guidance unit may be performed using AI, or it may be performed without AI.

[0103] The guidance unit can analyze the user's social media activity to provide optimal guidance content. For example, the guidance unit can provide optimal guidance content based on information shared by the user on social media. Furthermore, the guidance unit can identify recent health risks from the user's social media activity and provide relevant guidance content. In addition, the guidance unit can provide optimal guidance content based on the health information the user follows on social media. This allows for the provision of optimal guidance content based on the user's social media activity. Some or all of the above processing in the guidance unit may be performed using AI, or not.

[0104] The voice response unit can estimate the user's emotions and adjust the way it expresses its voice response based on those emotions. For example, if the user is feeling anxious, the voice response unit will provide a calm voice response. Conversely, if the user is relaxed, it can provide a cheerful voice response. Furthermore, if the user is in a hurry, the voice response unit will provide a quick and concise voice response. This allows for the provision of the most appropriate voice response expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The voice response unit can provide the optimal response method by referring to the user's past voice response history when providing a voice response. For example, the voice response unit can suggest the optimal response method based on the voice responses the user has received in the past. The voice response unit can also select the most effective response method from the user's past voice response history. Furthermore, the voice response unit analyzes the user's past voice response history and provides the optimal response method. This allows the system to provide the optimal response method based on the user's past voice response history. Some or all of the above processing in the voice response unit may be performed using AI or not.

[0106] The voice response unit can customize its responses based on the user's current situation when providing voice responses. For example, if the user is in a hurry, the voice response unit can provide the quickest possible response. If the user is relaxed, the voice response unit can also provide a more detailed response. Furthermore, the voice response unit suggests the most appropriate response based on the user's current situation. This ensures that the system provides the most suitable response for the user's current circumstances. Some or all of the above processing in the voice response unit may be performed using AI, or it may be performed without AI.

[0107] The voice response unit can estimate the user's emotions and prioritize voice responses based on those emotions. For example, if the user is feeling anxious, the voice response unit will prioritize providing the most important answers. If the user is relaxed, the voice response unit can also provide detailed answers. Furthermore, if the user is in a hurry, the voice response unit will prioritize providing concise answers. This allows for the provision of optimal voice response priorities tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The voice response unit can provide the optimal response method when providing a voice response, taking into account the user's geographical location information. For example, if the user is in a specific region, the voice response unit can provide a response method available in that region. Furthermore, if the user is traveling, the voice response unit can provide a response method available at their travel destination. In addition, the voice response unit proposes the optimal response method based on the user's geographical location information. This allows the system to provide the optimal response method based on the user's geographical location. Some or all of the above processing in the voice response unit may be performed using AI, or it may be performed without AI.

[0109] The voice response unit can analyze the user's social media activity during voice response to provide the most appropriate response. For example, the voice response unit can provide the most appropriate response based on information the user has shared on social media. Furthermore, the voice response unit can identify recent health risks from the user's social media activity and provide relevant responses. In addition, the voice response unit can provide the most appropriate response based on the health information the user follows on social media. This allows for the provision of optimal responses based on the user's social media activity. Some or all of the above processing in the voice response unit may be performed using AI, or it may not.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The analysis unit can estimate the user's emotions and adjust the suggested first aid methods based on those emotions. For example, if the user is feeling anxious, the analysis unit will provide a reassuring response such as, "It's okay, please calm down." If the user is relaxed, it can also provide detailed first aid instructions. Furthermore, if the user is in a hurry, it will suggest concise and quick first aid instructions. This allows the system to provide the most appropriate first aid method according to the user's emotions.

[0112] The reception desk can analyze the user's past symptom input history and provide the optimal input interface. For example, it can automatically display symptoms that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest symptoms that the user might use at specific times of day based on their past input history. This allows for the provision of an optimal input interface based on the user's past history.

[0113] The integration unit can estimate the user's emotions and adjust the ride-hailing service arrangement based on those emotions. For example, if the user is feeling anxious, it can quickly arrange a ride-hailing service. If the user is relaxed, it can offer detailed ride-hailing options. Furthermore, if the user is in a hurry, it will arrange the fastest possible ride-hailing service. This allows the system to provide the optimal ride-hailing service arrangement tailored to the user's emotions.

[0114] The guidance system can estimate the user's emotions and adjust the guidance method based on those estimates. For example, if the user is feeling anxious, it can provide simple and reassuring guidance. If the user is relaxed, it can provide detailed guidance. Furthermore, if the user is in a hurry, it can provide concise guidance that gets straight to the point. This allows the system to provide the most appropriate guidance method according to the user's emotions.

[0115] The voice response unit can estimate the user's emotions and adjust the way it delivers its voice response based on those emotions. For example, if the user is feeling anxious, it can deliver a voice response in a calm voice. If the user is relaxed, it can deliver a voice response in a cheerful voice. Furthermore, if the user is in a hurry, it can deliver a quick and concise voice response. This allows the system to provide the most appropriate voice response style for each user's emotional state.

[0116] The analysis unit can adjust the level of detail of the analysis based on the severity of the symptoms. For example, for serious symptoms, a detailed analysis is performed to provide specific first-aid methods. For mild symptoms, a concise analysis is performed to provide basic first-aid methods. Furthermore, the analysis priority is determined according to the severity of the symptoms. This allows for the provision of optimal analysis results tailored to the severity of the symptoms.

[0117] The reception system can filter input based on the user's current health status and past medical history when they enter symptoms. For example, it can monitor the user's current health status in real time and only allow them to enter relevant symptoms. It can also refer to the user's past medical history and filter out symptoms with low relevance. Furthermore, it can combine the user's current health status and past medical history to suggest the most appropriate input content. This ensures that the system provides appropriate input content based on the user's health status and medical history.

[0118] The collaboration unit can analyze a user's past usage history to select the optimal dispatch method when arranging a ride-hailing service. For example, it can prioritize dispatching services that the user has used in the past. It can also suggest the most suitable dispatch option based on the user's past usage history. Furthermore, it selects the most efficient dispatch method based on the user's past usage history. This allows the system to provide the optimal ride-hailing service arrangement method based on the user's past usage history.

[0119] The guidance system can provide the most suitable guidance method by considering the user's geographical location. For example, if the user is in a specific region, it can provide guidance methods available in that region. If the user is traveling, it can also provide guidance methods available at their travel destination. Furthermore, it can suggest the most suitable guidance method based on the user's geographical location. This allows the system to provide the most appropriate guidance method based on the user's geographical location.

[0120] The voice response unit can analyze the user's social media activity during voice response to provide the most appropriate response. For example, it can provide the most appropriate response based on information the user has shared on social media. It can also identify recent health risks from the user's social media activity and provide relevant responses. Furthermore, it can provide the most appropriate response based on the health information the user follows on social media. In this way, it can provide the most appropriate response based on the user's social media activity.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives the user's symptoms via the chat function of a messaging app. For example, symptoms such as "chest pain," "headache," or "sprained ankle" are entered. The reception desk sends these symptoms to the AI ​​generator. Step 2: The analysis unit uses a generation AI to analyze the symptoms received by the reception unit and provides appropriate responses and first aid methods. For example, if "I have a chest ache" is entered, it will provide a response such as "Drink some water and rest," and if "I sprained my ankle" is entered, it will provide first aid methods such as "Apply ice and rest." Step 3: The Coordination Department arranges a ride-hailing service based on the emergency response methods provided by the Analysis Department. For example, it might ask, "Shall I call a taxi?" and, if the user agrees, arrange a taxi. It can also arrange a private ambulance or support cab. Step 4: The information department provides guidance on first aid methods and the location of AEDs until the dispatch service arranged by the coordination department arrives. For example, they provide information such as, "There is an AED at a nearby convenience store." The information department provides users with the necessary information using maps and services. Step 5: The voice response unit provides a voice response based on the information obtained by the analysis unit. For example, if the input is "Please call an ambulance," it will respond with a voice message such as, "This is the generating AI. I will provide you with patient information."

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, coordination unit, guidance unit, and voice response unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs symptoms through the chat function of a messaging application. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the symptoms using generating AI and provides appropriate responses and first aid methods. The coordination unit is implemented by the identification processing unit 290 of the data processing unit 12, which arranges a ride-hailing service. The guidance unit is implemented by the output device 40 of the smart device 14, which provides guidance on first aid methods until the ride-hailing service arrives and the location of AEDs. The voice response unit is implemented by the control unit 46A of the smart device 14, which provides voice responses based on the information obtained by the analysis unit. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, coordination unit, guidance unit, and voice response unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user inputs symptoms through the chat function of a messaging app. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the symptoms using generating AI and provides appropriate responses and first aid methods. The coordination unit is implemented by the identification processing unit 290 of the data processing unit 12, which arranges a ride-hailing service. The guidance unit is implemented by the speaker 240 of the smart glasses 214, which provides guidance on first aid methods until the ride-hailing service arrives and the location of AEDs. The voice response unit is implemented by the control unit 46A of the smart glasses 214, which provides voice responses based on the information obtained by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, coordination unit, guidance unit, and voice response unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs symptoms through the chat function of a messaging application. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the symptoms using generating AI and provides appropriate responses and first aid methods. The coordination unit is implemented by the identification processing unit 290 of the data processing unit 12, which arranges the dispatch service. The guidance unit is implemented by the display 343 of the headset terminal 314, which provides guidance on first aid methods until the dispatch service arrives and the location of AEDs. The voice response unit is implemented by the control unit 46A of the headset terminal 314, which provides voice responses based on the information obtained by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, analysis unit, coordination unit, guidance unit, and voice response unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user inputs symptoms through the chat function of a messaging application. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the symptoms using generating AI and provides appropriate responses and first aid methods. The coordination unit is implemented by the identification processing unit 290 of the data processing unit 12, which arranges the dispatch service. The guidance unit is implemented by the speaker 240 of the robot 414, which provides guidance on first aid methods until the dispatch service arrives and the location of AEDs. The voice response unit is implemented by the control unit 46A of the robot 414, which provides voice responses based on the information obtained by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A reception area where symptoms are entered, An analysis unit analyzes the symptoms received by the reception unit and provides appropriate responses and first aid methods, A coordination unit arranges a vehicle dispatch service based on the emergency response method provided by the analysis unit, The aforementioned coordination unit provides information on first aid procedures and the location of AEDs until the dispatch service arrives, and The system includes a voice response unit that provides voice responses based on the information obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned guide section is Provide information on first aid procedures and AED locations through maps and services. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned voice response unit is The local emergency information center will respond via voice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Using an emotion generation engine to provide users with a sense of security. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, Arrange private ambulances, taxis, support cabs, etc. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Enter your symptoms via the chat function of the messaging app. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the symptom input method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past symptom input history and provides the optimal input interface. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter symptoms, the system filters the input based on their current health status and past medical history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter symptoms, the system prioritizes the input of symptoms that are most relevant to their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter their symptoms, the system analyzes their social media activity and prompts them to enter related symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the symptom category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the timing of symptom onset. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of the analysis results is adjusted based on the relevance of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned linkage unit is, The system estimates the user's emotions and adjusts the ride-hailing service arrangements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned linkage unit is, When arranging a ride-hailing service, the system analyzes the user's past usage history to select the most suitable arrangement method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned linkage unit is, When arranging a ride-hailing service, the arrangements are customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned linkage unit is, The system estimates user sentiment and prioritizes ride-hailing services based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned linkage unit is, When arranging a ride-hailing service, the system takes the user's geographical location into consideration to arrange the most suitable service. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, When arranging a ride-hailing service, we analyze the user's social media activity to suggest the most suitable service. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide section is It estimates the user's emotions and adjusts the guidance method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide section is When providing guidance, the system refers to the user's past guidance history to provide the most suitable guidance method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide section is When providing guidance, customize the guidance content based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide section is The system estimates the user's emotions and determines the priority of guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned guide section is When providing directions, the system will consider the user's geographical location to provide the most suitable guidance method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned guide section is When providing guidance, we analyze the user's social media activity to deliver the most appropriate guidance content. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned voice response unit is The system estimates the user's emotions and adjusts the way voice responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned voice response unit is When providing voice responses, the system refers to the user's past voice response history to provide the most suitable response method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned voice response unit is When responding via voice, the response is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned voice response unit is The system estimates the user's emotions and prioritizes voice responses based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned voice response unit is When providing voice responses, the system takes the user's geographical location into consideration to provide the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned voice response unit is When users provide voice responses, the system analyzes their social media activity to provide the most appropriate answers. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where symptoms are entered, An analysis unit analyzes the symptoms received by the reception unit and provides appropriate responses and first aid methods, A coordination unit arranges a vehicle dispatch service based on the emergency response method provided by the analysis unit, The aforementioned coordination unit provides information on first aid procedures and the location of AEDs until the dispatch service arrives, and The system includes a voice response unit that provides voice responses based on the information obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned guide section is Provide information on first aid procedures and AED locations through maps and services. The system according to feature 1.

3. The aforementioned voice response unit is The local emergency information center will respond via voice. The system according to feature 1.

4. The aforementioned analysis unit, Using an emotion generation engine to provide users with a sense of security. The system according to feature 1.

5. The aforementioned linkage unit is, Arrange private ambulances, taxis, support cabs, etc. The system according to feature 1.

6. The aforementioned reception unit is Enter your symptoms via the chat function of the messaging app. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the symptom input method based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past symptom input history and provides the optimal input interface. The system according to feature 1.

9. The aforementioned reception unit is When entering symptoms, the system filters the input based on the user's current health status and past medical history. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

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