system

Abstract

The system according to the embodiment aims to quickly select an appropriate medical institution and provide emergency response according to the symptoms of the user. [Solution] A system according to an embodiment includes an information collection unit, a diagnosis unit, a collaboration unit, a reservation unit, and a travel arrangement unit. The information collection unit collects the user's symptoms. The diagnosis unit analyzes the information collected by the information collection unit and performs a diagnosis. The collaboration unit determines the level of urgency based on the diagnosis results obtained by the diagnosis unit, and connects to an emergency contact if the level of urgency is high. The reservation unit searches for availability information at nearby primary medical institutions based on the results determined by the collaboration unit and makes a reservation. The travel arrangement unit arranges transportation to the medical institution reserved by the reservation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not allow for prompt selection of an appropriate medical institution based on the user's symptoms or for emergency response, and there is room for improvement.

[0005] The system according to the embodiment aims to quickly select an appropriate medical institution and provide emergency response according to the symptoms of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a diagnosis unit, a collaboration unit, a reservation unit, and a travel arrangement unit. The information collection unit collects the user's symptoms. The diagnosis unit analyzes the information collected by the information collection unit and performs a diagnosis. The collaboration unit determines the level of urgency based on the diagnosis result obtained by the diagnosis unit, and connects to an emergency contact if the level of urgency is high. The reservation unit searches for availability information at nearby primary medical institutions based on the result determined by the collaboration unit and makes a reservation. The travel arrangement unit arranges transportation to the medical institution reserved by the reservation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly select an appropriate medical institution and provide emergency response according to the symptoms of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An emergency medical support system according to an embodiment of the present invention provides appropriate medical support in emergencies. When a user faces an emergency, the system accesses the system, collects the user's symptoms via chat or image submission, and AI performs a diagnosis. Based on the diagnosis result, if the urgency is high, the system directly connects the user to an emergency contact. On the other hand, if the symptoms are relatively mild, the system connects the user to availability information at nearby primary care medical institutions and helps the user secure a reservation and transportation. For example, when a user faces an emergency, the system can access the system through a smartphone app. The user inputs their symptoms or submits an image of their symptoms. The system collects the user's symptoms via chat or image submission. For example, if a user types "chest pain," the system collects that information. If the user submits an image of their chest pain, the image is also collected. Based on the collected information, AI performs a diagnosis. The AI ​​analyzes the collected text and image information and determines the level of urgency. For example, if the AI ​​analyzes an image of chest pain and determines a high possibility of myocardial infarction, the system determines the level of urgency. Based on the diagnosis result, if the urgency is high, the system directly connects the user to an emergency contact. For example, if the AI ​​determines that the patient is likely to have a myocardial infarction, the system automatically connects to emergency contacts and dispatches an ambulance. On the other hand, if the patient's symptoms are relatively mild, the system will link with availability information at nearby primary care medical institutions and help secure a reservation and transportation. For example, if the AI ​​determines that the patient has mild stomach pain, the system will search for availability information at nearby primary care medical institutions and make a reservation. It will also arrange transportation, helping the user to travel smoothly to the medical institution. This system provides appropriate medical support in emergencies and ensures the user's safety. For example, in the case of a highly urgent symptom such as a myocardial infarction, an ambulance will be dispatched quickly, allowing for early treatment. Even in mild cases, access to an appropriate medical institution is ensured, allowing the user to receive medical care with peace of mind. This enables the emergency medical support system to efficiently collect the user's symptoms, diagnose, determine the level of urgency, make a reservation, and arrange transportation.

[0029] The emergency medical support system according to the embodiment includes an information collection unit, a diagnosis unit, a linking unit, a reservation unit, and a transportation arrangement unit. The information collection unit collects a user's symptoms. The user's symptoms include, but are not limited to, physical and mental symptoms. The information collection unit collects information, for example, by the user inputting symptoms through a smartphone app or sending images of the symptoms. The information collection unit can also estimate the user's emotions and adjust the symptom collection method based on the estimated user emotions. For example, if the user is feeling anxious, the system provides a simple and intuitive interface to minimize input steps. The diagnosis unit analyzes the collected information and generates a diagnosis result. For example, the diagnosis unit analyzes the collected text information and image information to determine the level of urgency. For example, if AI analyzes an image of chest pain and determines a high possibility of myocardial infarction, the system determines the level of urgency as high. The linking unit determines the level of urgency based on the diagnosis result and connects to an emergency contact if the level of urgency is high. For example, if the AI ​​determines that there is a high possibility of myocardial infarction, the coordination unit will automatically connect to the emergency contact and arrange for an ambulance. The reservation unit will search for availability information at nearby primary medical institutions based on the results determined by the coordination unit and make a reservation. For example, if the AI ​​determines that the patient has mild stomach pain, the system will search for availability information at nearby primary medical institutions and make a reservation. The transportation arrangement unit will arrange transportation to the medical institution reserved by the reservation unit. The transportation arrangement unit will, for example, arrange for a taxi or public transportation, and support the user so that they can travel smoothly to the medical institution. This enables the emergency medical support system to efficiently collect the user's symptoms, make a diagnosis, determine the level of urgency, make a reservation, and arrange transportation.

[0030] The information collection unit can collect the user's symptoms as text or images. The information collection unit collects information, for example, by the user inputting their symptoms through a smartphone app or sending an image of their symptoms. For example, if the user inputs "my chest hurts," the information collection unit collects that information. Also, if the user sends an image of their chest pain, that image is also collected. This allows the user's symptoms to be collected in a variety of formats. Some or all of the above-described processing in the information collection unit may be performed using, or without, AI, for example. For example, the information collection unit can input image data sent by the user to a generation AI and have the generation AI generate text data from the image data.

[0031] The diagnostic unit can analyze the collected information and generate a diagnostic result. The diagnostic unit, for example, analyzes the collected text information and image information and determines the level of urgency. For example, if an AI analyzes an image of chest pain and determines that there is a high possibility of myocardial infarction, the level of urgency is determined to be high. The diagnostic unit can analyze information using techniques such as data mining and statistical analysis. For example, the diagnostic unit can analyze the collected text information using natural language processing technology and determine the severity of the symptoms. The diagnostic unit can also analyze the collected image information using image analysis technology and identify the symptoms. This allows for the generation of an accurate diagnostic result based on the collected information. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input the collected information to a generation AI and cause the generation AI to generate a diagnostic result.

[0032] The linking unit can determine the level of urgency based on the diagnosis results and connect to emergency contacts if the level of urgency is high. For example, if the AI ​​determines that there is a high possibility of myocardial infarction, the system automatically connects to emergency contacts and dispatches an ambulance. The linking unit needs to clarify the criteria and method for determining the level of urgency. For example, it can determine the level of urgency based on the severity and frequency of symptoms. The linking unit needs to clarify the specific type of emergency contact and the connection method. For example, it can connect to emergency services, family, medical institutions, etc. This enables appropriate response depending on the level of urgency. Some or all of the above-mentioned processing in the linking unit may be performed using AI, or may be performed without using AI. For example, the linking unit can input the diagnosis results into the generation AI and have the generation AI determine the level of urgency.

[0033] The reservation unit can search for availability information at nearby primary medical institutions and make a reservation. For example, if the AI ​​determines that the patient has mild stomach pain, the reservation unit will search for availability information at nearby primary medical institutions and make a reservation. The reservation unit needs to clarify the specific method and criteria for collecting availability information. For example, availability information can be collected using real-time data or a reservation system. This makes it possible to quickly make a reservation based on the availability information of nearby medical institutions. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input availability information into the generation AI and have the generation AI perform the reservation procedure.

[0034] The travel arrangement unit can arrange transportation based on information from the reservation unit. The travel arrangement unit, for example, arranges taxis or public transportation to support the user in traveling to the medical institution smoothly. The travel arrangement unit needs to clarify the specific type of transportation and the arrangement method. For example, it can arrange taxis, public transportation, ambulances, etc. This allows for smooth arrangement of transportation to the reserved medical institution. Some or all of the above-mentioned processing in the travel arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel arrangement unit can input the transportation method into the generation AI and have the generation AI execute the travel arrangement procedures.

[0035] The information collection unit can analyze the user's past medical history and select an appropriate collection method. For example, the information collection unit can automatically display symptoms frequently reported by the user in the past as candidates. It can also prioritize questions related to specific symptoms based on the user's past medical history. Furthermore, it can analyze the user's past medical history and suggest the most efficient collection method. This enables efficient symptom collection based on the past medical history. The specific type of medical history and collection method must be clarified. For example, this includes past diagnosis results and treatment history. Some or all of the above-mentioned processing in the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input the user's past medical history data into the generation AI and have the generation AI select the optimal collection method.

[0036] When collecting symptoms, the information collection unit can perform filtering based on the user's current living situation and environment. For example, when the user inputs their current living situation, the information collection unit prioritizes collecting related symptoms based on that information. The information collection unit can also display appropriate questions taking into account the user's environmental information (e.g., workplace and home). Furthermore, it can omit unnecessary questions based on the user's living situation and environment, allowing for efficient collection. This enables appropriate symptom collection according to the user's living situation and environment. It is necessary to clarify the specific types of living situations and collection methods. Examples include lifestyle habits and living environment. Some or all of the above-described processing in the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input the user's living situation data into a generation AI and have the generation AI execute a filtering procedure.

[0037] When collecting symptoms, the information collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the information collection unit can prioritize collecting symptoms related to diseases that are prevalent in that area. The information collection unit can also collect related symptoms based on information about the medical institution closest to the user's current location. Furthermore, symptoms related to specific environmental factors can be collected based on the user's geographical location information. This allows highly relevant symptoms to be collected based on the geographical location information. Specific collection methods and standards for the geographical location information need to be clarified. Examples include GPS data, address information, etc. Some or all of the above-described processing by the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant information.

[0038] When collecting symptoms, the information collection unit can analyze the user's social media activity and collect related information. For example, the information collection unit can display related questions based on symptoms reported by the user on social media. It can also collect information related to specific symptoms from the user's social media activity. Furthermore, it can analyze the user's social media activity and collect the most relevant symptoms. This allows for the collection of highly relevant symptoms based on social media activity. Specific collection methods and criteria for social media activity need to be clarified. For example, this includes the content of posts and frequency of activity. Some or all of the above-described processing by the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input the user's social media data into the generation AI and have the generation AI collect related information.

[0039] The diagnostic unit can adjust the level of diagnostic detail based on the severity of the symptom during diagnosis. For example, the diagnostic unit can provide detailed diagnostic information for symptoms with high severity. It can also provide concise diagnostic information for symptoms with low severity. Furthermore, the level of diagnostic detail can be adjusted in stages depending on the severity of the symptom. This enables appropriate diagnosis according to the severity of the symptom. Specific criteria and methods for determining the severity must be clarified. For example, these criteria include the severity and frequency of the symptom. Some or all of the above-described processing in the diagnostic unit may be performed using, or without, AI. For example, the diagnostic unit can input symptom severity data into the generating AI and have the generating AI adjust the level of diagnostic detail.

[0040] The diagnostic unit can apply different diagnostic algorithms depending on the symptom category during diagnosis. For example, the diagnostic unit can apply a dedicated diagnostic algorithm to respiratory symptoms. It can also apply a dedicated diagnostic algorithm to digestive symptoms. It can also apply a dedicated diagnostic algorithm to nervous system symptoms. This enables appropriate diagnosis according to the symptom category. It is necessary to clarify the specific types of categories and classification methods. Examples include physical symptoms, mental symptoms, etc. Some or all of the above-described processing in the diagnostic unit may be performed using, or without, AI. For example, the diagnostic unit can input symptom category data into a generating AI and have the generating AI apply a diagnostic algorithm.

[0041] The diagnosis unit can determine the priority of diagnosis based on the time of symptom onset during diagnosis. For example, the diagnosis unit can prioritize diagnosis for recently occurring symptoms. It can also perform a detailed diagnosis for symptoms that have persisted for a long period of time. Furthermore, the priority of diagnosis can be adjusted in stages depending on the time of symptom onset. This enables appropriate diagnosis according to the time of symptom onset. It is necessary to clarify the specific collection method and criteria for the time of onset. For example, this includes the date of onset and the duration of symptoms. Some or all of the above-mentioned processing in the diagnosis unit may be performed using, or without, AI. For example, the diagnosis unit can input symptom onset data into the generation AI and have the generation AI determine the priority.

[0042] The diagnostic unit can adjust the order of diagnoses based on the relevance of symptoms during diagnosis. For example, the diagnostic unit can prioritize diagnosis of highly relevant symptoms. Also, the diagnostic unit can postpone diagnosis of less relevant symptoms. Furthermore, the order of diagnoses can be adjusted in stages depending on the relevance of symptoms. This enables an appropriate diagnostic order depending on the relevance of symptoms. Specific criteria and methods for determining the relevance must be clarified. For example, these include co-occurrence and causal relationships between symptoms. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, or without, AI. For example, the diagnostic unit can input symptom relevance data into a generation AI and have the generation AI adjust the order of diagnoses.

[0043] The linking unit can improve the accuracy of the determination of the urgency level by taking into account the interrelationships between symptoms. For example, when multiple symptoms occur simultaneously, the linking unit determines the urgency level by taking into account their interrelationships. The linking unit can also more accurately determine the urgency level based on the combination of symptoms. Furthermore, the interrelationships between symptoms can be analyzed to improve the accuracy of the urgency determination. By taking the interrelationships between symptoms into consideration, the accuracy of the urgency determination can be improved. Specific criteria and methods for determining the interrelationships must be clarified. For example, these include co-occurrence relationships and causal relationships between symptoms. Some or all of the above-mentioned processing in the linking unit may be performed using AI, or may be performed without AI. For example, the linking unit can input symptom interrelationship data into the generation AI and have the generation AI perform the urgency determination.

[0044] The collaboration unit can determine the level of urgency by taking into account the user's attribute information. The collaboration unit can determine the level of urgency by taking into account, for example, the user's age and gender. The collaboration unit can also determine the level of urgency by taking into account the user's medical history. Furthermore, the collaboration unit can determine the level of urgency by taking into account the user's lifestyle habits. This improves the accuracy of determining the level of urgency by taking into account the user's attribute information. The specific types of attribute information and the collection method must be clarified. For example, this includes age, gender, medical history, etc. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, or may be performed without using AI. For example, the collaboration unit can input the user's attribute information into the generation AI and have the generation AI perform the urgency determination.

[0045] The linking unit can take into account the geographical distribution of symptoms when determining the level of urgency. For example, the linking unit prioritizes determining symptoms related to a disease that is prevalent in a specific region. The linking unit can also determine the level of urgency based on the user's current location. Furthermore, the geographical distribution of symptoms can be analyzed to improve the accuracy of determining the level of urgency. By taking the geographical distribution of symptoms into consideration, the accuracy of determining the level of urgency can be improved. Specific collection methods and criteria for the geographical distribution must be clarified. Examples include the number of cases by region and the spread of infection. Some or all of the above-described processing in the linking unit may be performed using AI, or may be performed without AI. For example, the linking unit can input data on the geographical distribution of symptoms into the generation AI and have the generation AI determine the level of urgency.

[0046] The linking unit can improve the accuracy of the determination of the urgency level by referring to literature related to the symptom when determining the urgency level. For example, the linking unit determines the urgency level by referring to the latest medical literature related to the symptom. The linking unit can also determine the urgency level by referring to past case studies related to the symptom. Furthermore, the linking unit can determine the urgency level by referring to medical guidelines related to the symptom. In this way, by referring to literature related to the symptom, the accuracy of the urgency determination is improved. The specific types of related literature and the reference method must be clarified. Examples include medical papers, guidelines, etc. Some or all of the above-mentioned processing in the linking unit may be performed using AI, or may be performed without AI. For example, the linking unit can input literature data related to the symptom into the generation AI and have the generation AI perform the urgency determination.

[0047] When making a reservation, the reservation unit can select an appropriate reservation method by referring to the medical institution's past reservation data. For example, the reservation unit can suggest the optimal reservation time based on the medical institution's past reservation data. It can also analyze the medical institution's past reservation data and suggest a reservation method that avoids congestion. It can also suggest a method to increase the reservation success rate by referring to the medical institution's past reservation data. This makes it possible to provide the optimal reservation method based on past reservation data. The specific collection method and criteria for past reservation data must be clarified. For example, this includes reservation history and cancellation rate. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input past reservation data into the generation AI and have the generation AI select the reservation method.

[0048] The reservation unit can customize the reservation method based on the user's current living situation when making a reservation. For example, if the user is at work, the reservation unit can suggest a time slot during work breaks when the user can make a reservation. Also, if the user is at home, the reservation unit can suggest a reservation method that suits the user's schedule. Furthermore, the reservation unit can suggest the optimal reservation method (online, telephone, etc.) based on the user's living situation. This makes it possible to provide the optimal reservation method according to the user's living situation. The specific types of living situation and the method of collection need to be clarified. Examples include lifestyle habits and living environment. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's living situation data into a generation AI and have the generation AI customize the reservation method.

[0049] When making a reservation, the reservation unit can select the optimal reservation method taking into account the geographical location information of the medical institution. For example, the reservation unit can prioritize reserving the medical institution closest to the user's current location. The reservation unit can also suggest the optimal medical institution based on the user's means of transportation. Furthermore, the reservation unit can suggest the optimal reservation time based on the user's geographical location information. This makes it possible to provide the optimal reservation method based on the geographical location information. The specific collection method and criteria for the geographical location information must be clarified. For example, this may include GPS data, address information, etc. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, or may be performed without using AI. For example, the reservation unit can input the user's geographical location information into the generation AI and have the generation AI select the reservation method.

[0050] The reservation unit can improve reservation accuracy by referring to the medical institution's related literature when making a reservation. The reservation unit can, for example, suggest the optimal reservation method based on the medical institution's related literature. It can also suggest a method to increase the reservation success rate by referring to the medical institution's related literature. It can also analyze the medical institution's related literature and suggest the most efficient reservation method. This makes it possible to provide the optimal reservation method based on the related literature. The specific type of related literature and the reference method must be clarified. Examples include medical papers, guidelines, etc. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, or may be performed without AI. For example, the reservation unit can input the medical institution's related literature data into the generation AI and have the generation AI select the reservation method.

[0051] When making travel arrangements, the travel arrangement unit can select the optimal arrangement method by referring to the user's past travel history. The travel arrangement unit can, for example, suggest the optimal arrangement method based on the means of transportation used by the user in the past. It can also suggest arrangement methods that avoid congestion based on the user's past travel history. Furthermore, it can suggest methods to increase the success rate of travel arrangements by referring to the user's past travel history. This allows the optimal travel arrangement method to be provided based on the past travel history. It is necessary to clarify the specific collection method and criteria for the past travel history. For example, this includes the means of transportation and travel distance. Some or all of the above-described processing in the travel arrangement unit may be performed using, or without, AI. For example, the travel arrangement unit can input the user's past travel history data into a generation AI and have the generation AI select an arrangement method.

[0052] The travel arrangement unit can customize the means of arrangement based on the user's current living situation when making travel arrangements. For example, if the user is at work, the travel arrangement unit can suggest means of transportation that can be used during breaks at work. Also, if the user is at home, the travel arrangement unit can suggest means of transportation that fit the user's home schedule. Furthermore, the travel arrangement unit can suggest the optimal means of transportation (taxi, public transportation, etc.) based on the user's living situation. This makes it possible to provide the optimal means of travel arrangement according to the user's living situation. The specific types of living situation and the method of collection need to be clarified. Examples include lifestyle habits and living environment. Some or all of the above-mentioned processing in the travel arrangement unit may be performed using, or without, AI. For example, the travel arrangement unit can input the user's living situation data into a generation AI and have the generation AI customize the means of arrangement.

[0053] When making travel arrangements, the travel arrangement unit can select the optimal arrangement method by taking into account the user's geographical location information. For example, the travel arrangement unit prioritizes arrangement of the means of transportation closest to the user's current location. The travel arrangement unit can also suggest the optimal travel arrangement method depending on the user's means of transportation. Furthermore, the optimal travel arrangement method can be suggested based on the user's geographical location information. This makes it possible to provide the optimal travel arrangement method based on the geographical location information. The specific collection method and criteria for the geographical location information must be clarified. For example, this includes GPS data, address information, etc. Some or all of the above-mentioned processing in the travel arrangement unit may be performed using AI, or may be performed without AI. For example, the travel arrangement unit can input the user's geographical location information into a generation AI and have the generation AI select an arrangement method.

[0054] When making travel arrangements, the travel arrangement unit can analyze the user's social media activity and suggest arrangement methods. For example, the travel arrangement unit can suggest the optimal arrangement method based on the travel method reported by the user on social media. The travel arrangement unit can also collect information related to specific travel methods from the user's social media activity. Furthermore, the travel arrangement unit can analyze the user's social media activity and suggest the most relevant travel arrangement method. This allows the optimal travel arrangement method to be provided based on social media activity. The specific collection method and criteria for social media activity must be clarified. For example, this includes the content of posts and frequency of activity. Some or all of the above-mentioned processing in the travel arrangement unit may be performed using AI, for example, or without AI. For example, the travel arrangement unit can input the user's social media data into a generation AI and have the generation AI execute a travel arrangement method suggestion.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The emergency medical support system may further include a vital monitoring unit that monitors the user's vital signs in real time. The vital monitoring unit may collect data such as the user's heart rate, blood pressure, and body temperature, and determine the level of urgency based on this data. For example, if the user's heart rate increases suddenly, the level of urgency may be determined to be high. The vital monitoring unit may also provide the collected data to the diagnosis unit to improve the accuracy of the diagnosis results. Furthermore, the vital monitoring unit may provide the user's vital data to the connection unit to support rapid connection to emergency contacts.

[0057] The diagnostic unit can analyze the user's lifestyle habit data and reflect it in the diagnostic results. For example, if the user is a smoker, a diagnosis of respiratory symptoms is given priority. It can also diagnose digestive symptoms based on the user's dietary habit data. It can also analyze the user's exercise habit data and diagnose cardiovascular symptoms. This enables highly accurate diagnosis based on the user's lifestyle habits.

[0058] The linking unit can pre-register the user's emergency contact information and automatically notify them in the event of an emergency. For example, when the user faces an emergency, a notification is automatically sent to pre-registered family and friends. The linking unit can also provide the emergency contacts with the user's current location and symptoms. Furthermore, if there are multiple emergency contacts, priorities can be set and notifications can be sent to the most appropriate contact. This enables a quick and appropriate response in the event of an emergency.

[0059] The reservation unit can register the user's insurance information in advance and automatically apply it at the time of reservation. For example, when the user makes a reservation at a medical institution, the applicable insurance is automatically selected based on the insurance information registered in advance. It can also suggest the most suitable medical institution within the scope of insurance coverage. Furthermore, it can calculate the cost at the time of reservation in advance based on the insurance information and present it to the user. This allows the user to make a reservation at a medical institution with peace of mind.

[0060] The travel arrangement unit can register the user's transportation preferences in advance and take them into consideration when making travel arrangements. For example, if the user prefers taxis, the unit will arrange taxis first. Also, if public transportation is used, the unit can suggest the optimal route. Furthermore, the method of travel arrangements can be customized based on the user's transportation preferences. This makes it possible to make optimal travel arrangements according to the user's transportation preferences.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The information collection unit collects the user's symptoms. The user's symptoms can include physical and mental symptoms. The information collection unit collects information by having the user input their symptoms through a smartphone app or by sending images of their symptoms. The information collection unit can also estimate the user's emotions and adjust the symptom collection method based on the estimated user emotions. For example, if the user is feeling anxious, the system provides a simple and intuitive interface and minimizes input steps. Step 2: The diagnostic unit analyzes the collected information and generates a diagnosis. The diagnostic unit analyzes the collected text and image information and determines the level of urgency. For example, if the AI ​​analyzes an image of chest pain and determines that there is a high possibility of myocardial infarction, the level of urgency is determined to be high. Step 3: The communication unit determines the level of urgency based on the diagnosis results, and if the level of urgency is high, it connects to emergency contacts. For example, if the AI ​​determines that there is a high possibility of a myocardial infarction, the system automatically connects to emergency contacts and arranges for an ambulance. Step 4: The reservation unit searches for availability at nearby primary medical institutions based on the results determined by the collaboration unit and makes a reservation. For example, if the AI ​​determines that the patient has mild stomach pain, the system searches for availability at nearby primary medical institutions and makes a reservation. Step 5: The transportation arrangement unit arranges transportation to the medical institution reserved by the reservation unit. For example, it arranges for a taxi or public transportation, and supports the user to travel smoothly to the medical institution.

[0063] (Example 2) An emergency medical support system according to an embodiment of the present invention provides appropriate medical support in emergencies. When a user faces an emergency, the system accesses the system, collects the user's symptoms via chat or image submission, and AI performs a diagnosis. Based on the diagnosis result, if the urgency is high, the system directly connects the user to an emergency contact. On the other hand, if the symptoms are relatively mild, the system connects the user to availability information at nearby primary care medical institutions and helps the user secure a reservation and transportation. For example, when a user faces an emergency, the system can access the system through a smartphone app. The user inputs their symptoms or submits an image of their symptoms. The system collects the user's symptoms via chat or image submission. For example, if a user types "chest pain," the system collects that information. If the user submits an image of their chest pain, the image is also collected. Based on the collected information, AI performs a diagnosis. The AI ​​analyzes the collected text and image information and determines the level of urgency. For example, if the AI ​​analyzes an image of chest pain and determines a high possibility of myocardial infarction, the system determines the level of urgency. Based on the diagnosis result, if the urgency is high, the system directly connects the user to an emergency contact. For example, if the AI ​​determines that the patient is likely to have a myocardial infarction, the system automatically connects to emergency contacts and dispatches an ambulance. On the other hand, if the patient's symptoms are relatively mild, the system will link with availability information at nearby primary care medical institutions and help secure a reservation and transportation. For example, if the AI ​​determines that the patient has mild stomach pain, the system will search for availability information at nearby primary care medical institutions and make a reservation. It will also arrange transportation, helping the user to travel smoothly to the medical institution. This system provides appropriate medical support in emergencies and ensures the user's safety. For example, in the case of a highly urgent symptom such as a myocardial infarction, an ambulance will be dispatched quickly, allowing for early treatment. Even in mild cases, access to an appropriate medical institution is ensured, allowing the user to receive medical care with peace of mind. This enables the emergency medical support system to efficiently collect the user's symptoms, diagnose, determine the level of urgency, make a reservation, and arrange transportation.

[0064] The emergency medical support system according to the embodiment includes an information collection unit, a diagnosis unit, a linking unit, a reservation unit, and a transportation arrangement unit. The information collection unit collects a user's symptoms. The user's symptoms include, but are not limited to, physical and mental symptoms. The information collection unit collects information, for example, by the user inputting symptoms through a smartphone app or sending images of the symptoms. The information collection unit can also estimate the user's emotions and adjust the symptom collection method based on the estimated user emotions. For example, if the user is feeling anxious, the system provides a simple and intuitive interface to minimize input steps. The diagnosis unit analyzes the collected information and generates a diagnosis result. For example, the diagnosis unit analyzes the collected text information and image information to determine the level of urgency. For example, if AI analyzes an image of chest pain and determines a high possibility of myocardial infarction, the system determines the level of urgency as high. The linking unit determines the level of urgency based on the diagnosis result and connects to an emergency contact if the level of urgency is high. For example, if the AI ​​determines that there is a high possibility of myocardial infarction, the coordination unit will automatically connect to the emergency contact and arrange for an ambulance. The reservation unit will search for availability information at nearby primary medical institutions based on the results determined by the coordination unit and make a reservation. For example, if the AI ​​determines that the patient has mild stomach pain, the system will search for availability information at nearby primary medical institutions and make a reservation. The transportation arrangement unit will arrange transportation to the medical institution reserved by the reservation unit. The transportation arrangement unit will, for example, arrange for a taxi or public transportation, and support the user so that they can travel smoothly to the medical institution. This enables the emergency medical support system to efficiently collect the user's symptoms, make a diagnosis, determine the level of urgency, make a reservation, and arrange transportation.

[0065] The information collection unit can collect the user's symptoms as text or images. The information collection unit collects information, for example, by the user inputting their symptoms through a smartphone app or sending an image of their symptoms. For example, if the user inputs "my chest hurts," the information collection unit collects that information. Also, if the user sends an image of their chest pain, that image is also collected. This allows the user's symptoms to be collected in a variety of formats. Some or all of the above-described processing in the information collection unit may be performed using, or without, AI, for example. For example, the information collection unit can input image data sent by the user to a generation AI and have the generation AI generate text data from the image data.

[0066] The diagnostic unit can analyze the collected information and generate a diagnostic result. The diagnostic unit, for example, analyzes the collected text information and image information and determines the level of urgency. For example, if an AI analyzes an image of chest pain and determines that there is a high possibility of myocardial infarction, the level of urgency is determined to be high. The diagnostic unit can analyze information using techniques such as data mining and statistical analysis. For example, the diagnostic unit can analyze the collected text information using natural language processing technology and determine the severity of the symptoms. The diagnostic unit can also analyze the collected image information using image analysis technology and identify the symptoms. This allows for the generation of an accurate diagnostic result based on the collected information. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input the collected information to a generation AI and cause the generation AI to generate a diagnostic result.

[0067] The linking unit can determine the level of urgency based on the diagnosis results and connect to emergency contacts if the level of urgency is high. For example, if the AI ​​determines that there is a high possibility of myocardial infarction, the system automatically connects to emergency contacts and dispatches an ambulance. The linking unit needs to clarify the criteria and method for determining the level of urgency. For example, it can determine the level of urgency based on the severity and frequency of symptoms. The linking unit needs to clarify the specific type of emergency contact and the connection method. For example, it can connect to emergency services, family, medical institutions, etc. This enables appropriate response depending on the level of urgency. Some or all of the above-mentioned processing in the linking unit may be performed using AI, or may be performed without using AI. For example, the linking unit can input the diagnosis results into the generation AI and have the generation AI determine the level of urgency.

[0068] The reservation unit can search for availability information at nearby primary medical institutions and make a reservation. For example, if the AI ​​determines that the patient has mild stomach pain, the reservation unit will search for availability information at nearby primary medical institutions and make a reservation. The reservation unit needs to clarify the specific method and criteria for collecting availability information. For example, availability information can be collected using real-time data or a reservation system. This makes it possible to quickly make a reservation based on the availability information of nearby medical institutions. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or may be performed without using AI. For example, the reservation unit can input availability information into the generation AI and have the generation AI perform the reservation procedure.

[0069] The travel arrangement unit can arrange transportation based on information from the reservation unit. The travel arrangement unit, for example, arranges taxis or public transportation to support the user in traveling to the medical institution smoothly. The travel arrangement unit needs to clarify the specific type of transportation and the arrangement method. For example, it can arrange taxis, public transportation, ambulances, etc. This allows for smooth arrangement of transportation to the reserved medical institution. Some or all of the above-mentioned processing in the travel arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the travel arrangement unit can input the transportation method into the generation AI and have the generation AI execute the travel arrangement procedures.

[0070] The information collection unit can estimate the user's emotions and adjust the symptom collection method based on the estimated user emotions. For example, if the user is feeling anxious, the information collection unit can provide a simple and intuitive interface and minimize input steps. Alternatively, if the user is relaxed, the information collection unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the information collection unit can prioritize voice input to enable quick symptom input. This allows for an optimal symptom collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information collection unit can be performed using, for example, AI, or without AI. For example, the information collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0071] The information collection unit can analyze the user's past medical history and select an appropriate collection method. For example, the information collection unit can automatically display symptoms frequently reported by the user in the past as candidates. It can also prioritize questions related to specific symptoms based on the user's past medical history. Furthermore, it can analyze the user's past medical history and suggest the most efficient collection method. This enables efficient symptom collection based on the past medical history. The specific type of medical history and collection method must be clarified. For example, this includes past diagnosis results and treatment history. Some or all of the above-mentioned processing in the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input the user's past medical history data into the generation AI and have the generation AI select the optimal collection method.

[0072] When collecting symptoms, the information collection unit can perform filtering based on the user's current living situation and environment. For example, when the user inputs their current living situation, the information collection unit prioritizes collecting related symptoms based on that information. The information collection unit can also display appropriate questions taking into account the user's environmental information (e.g., workplace and home). Furthermore, it can omit unnecessary questions based on the user's living situation and environment, allowing for efficient collection. This enables appropriate symptom collection according to the user's living situation and environment. It is necessary to clarify the specific types of living situations and collection methods. Examples include lifestyle habits and living environment. Some or all of the above-described processing in the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input the user's living situation data into a generation AI and have the generation AI execute a filtering procedure.

[0073] The information collection unit can estimate the user's emotions and determine the priority of symptoms to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the information collection unit can prioritize collecting symptoms with high urgency. Also, if the user is relaxed, the information collection unit can collect detailed symptoms. Furthermore, if the user is in a hurry, the information collection unit can prioritize collecting major symptoms. This allows symptoms to be collected in a priority order according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority order.

[0074] When collecting symptoms, the information collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the information collection unit can prioritize collecting symptoms related to diseases that are prevalent in that area. The information collection unit can also collect related symptoms based on information about the medical institution closest to the user's current location. Furthermore, symptoms related to specific environmental factors can be collected based on the user's geographical location information. This allows highly relevant symptoms to be collected based on the geographical location information. Specific collection methods and standards for the geographical location information need to be clarified. Examples include GPS data, address information, etc. Some or all of the above-described processing by the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant information.

[0075] When collecting symptoms, the information collection unit can analyze the user's social media activity and collect related information. For example, the information collection unit can display related questions based on symptoms reported by the user on social media. It can also collect information related to specific symptoms from the user's social media activity. Furthermore, it can analyze the user's social media activity and collect the most relevant symptoms. This allows for the collection of highly relevant symptoms based on social media activity. Specific collection methods and criteria for social media activity need to be clarified. For example, this includes the content of posts and frequency of activity. Some or all of the above-described processing by the information collection unit may be performed using AI, or may be performed without AI. For example, the information collection unit can input the user's social media data into the generation AI and have the generation AI collect related information.

[0076] The diagnostic unit can estimate the user's emotions and adjust the way the diagnosis is presented based on the estimated user's emotions. For example, if the user is feeling anxious, the diagnostic unit can provide a simple and reassuring presentation. If the user is relaxed, the diagnostic unit can provide detailed diagnostic information. Furthermore, if the user is in a hurry, the diagnostic unit can provide concise diagnostic information that focuses on the key points. This allows the optimal diagnostic presentation to be provided according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the diagnostic unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the diagnostic unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the diagnosis is presented.

[0077] The diagnostic unit can adjust the level of diagnostic detail based on the severity of the symptom during diagnosis. For example, the diagnostic unit can provide detailed diagnostic information for symptoms with high severity. It can also provide concise diagnostic information for symptoms with low severity. Furthermore, the level of diagnostic detail can be adjusted in stages depending on the severity of the symptom. This enables appropriate diagnosis according to the severity of the symptom. Specific criteria and methods for determining the severity must be clarified. For example, these criteria include the severity and frequency of the symptom. Some or all of the above-described processing in the diagnostic unit may be performed using, or without, AI. For example, the diagnostic unit can input symptom severity data into the generating AI and have the generating AI adjust the level of diagnostic detail.

[0078] The diagnostic unit can apply different diagnostic algorithms depending on the symptom category during diagnosis. For example, the diagnostic unit can apply a dedicated diagnostic algorithm to respiratory symptoms. It can also apply a dedicated diagnostic algorithm to digestive symptoms. It can also apply a dedicated diagnostic algorithm to nervous system symptoms. This enables appropriate diagnosis according to the symptom category. It is necessary to clarify the specific types of categories and classification methods. Examples include physical symptoms, mental symptoms, etc. Some or all of the above-described processing in the diagnostic unit may be performed using, or without, AI. For example, the diagnostic unit can input symptom category data into a generating AI and have the generating AI apply a diagnostic algorithm.

[0079] The diagnosis unit can estimate the user's emotions and adjust the length of the diagnosis based on the estimated user emotions. For example, if the user is feeling anxious, the diagnosis unit can provide a short and concise diagnosis. If the user is relaxed, the diagnosis unit can also provide a detailed diagnosis. Furthermore, if the user is in a hurry, the diagnosis unit can also provide a quick and concise diagnosis. This allows the diagnosis length to be optimized according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the diagnosis unit can be performed using, for example, AI, or without AI. For example, the diagnosis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the diagnosis.

[0080] The diagnosis unit can determine the priority of diagnosis based on the time of symptom onset during diagnosis. For example, the diagnosis unit can prioritize diagnosis for recently occurring symptoms. It can also perform a detailed diagnosis for symptoms that have persisted for a long period of time. Furthermore, the priority of diagnosis can be adjusted in stages depending on the time of symptom onset. This enables appropriate diagnosis according to the time of symptom onset. It is necessary to clarify the specific collection method and criteria for the time of onset. For example, this includes the date of onset and the duration of symptoms. Some or all of the above-mentioned processing in the diagnosis unit may be performed using, or without, AI. For example, the diagnosis unit can input symptom onset data into the generation AI and have the generation AI determine the priority.

[0081] The diagnostic unit can adjust the order of diagnoses based on the relevance of symptoms during diagnosis. For example, the diagnostic unit can prioritize diagnosis of highly relevant symptoms. Also, the diagnostic unit can postpone diagnosis of less relevant symptoms. Furthermore, the order of diagnoses can be adjusted in stages depending on the relevance of symptoms. This enables an appropriate diagnostic order depending on the relevance of symptoms. Specific criteria and methods for determining the relevance must be clarified. For example, these include co-occurrence and causal relationships between symptoms. Some or all of the above-mentioned processing in the diagnostic unit may be performed using, or without, AI. For example, the diagnostic unit can input symptom relevance data into a generation AI and have the generation AI adjust the order of diagnoses.

[0082] The collaboration unit can estimate the user's emotions and adjust the urgency criteria based on the estimated user emotions. For example, the collaboration unit can tighten the urgency criteria when the user is feeling anxious. Alternatively, the collaboration unit can loosen the urgency criteria when the user is relaxed. Furthermore, the collaboration unit can quickly determine the urgency when the user is in a hurry. This allows for providing optimal urgency criteria according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collaboration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collaboration unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the urgency criteria.

[0083] The linking unit can improve the accuracy of the determination of the urgency level by taking into account the interrelationships between symptoms. For example, when multiple symptoms occur simultaneously, the linking unit determines the urgency level by taking into account their interrelationships. The linking unit can also more accurately determine the urgency level based on the combination of symptoms. Furthermore, the interrelationships between symptoms can be analyzed to improve the accuracy of the urgency determination. By taking the interrelationships between symptoms into consideration, the accuracy of the urgency determination can be improved. Specific criteria and methods for determining the interrelationships must be clarified. For example, these include co-occurrence relationships and causal relationships between symptoms. Some or all of the above-mentioned processing in the linking unit may be performed using AI, or may be performed without AI. For example, the linking unit can input symptom interrelationship data into the generation AI and have the generation AI perform the urgency determination.

[0084] The collaboration unit can determine the level of urgency by taking into account the user's attribute information. The collaboration unit can determine the level of urgency by taking into account, for example, the user's age and gender. The collaboration unit can also determine the level of urgency by taking into account the user's medical history. Furthermore, the collaboration unit can determine the level of urgency by taking into account the user's lifestyle habits. This improves the accuracy of determining the level of urgency by taking into account the user's attribute information. The specific types of attribute information and the collection method must be clarified. For example, this includes age, gender, medical history, etc. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, or may be performed without using AI. For example, the collaboration unit can input the user's attribute information into the generation AI and have the generation AI perform the urgency determination.

[0085] The linking unit can estimate the user's emotions and adjust the display order of the urgency assessment results based on the estimated user emotions. For example, if the user is feeling anxious, the linking unit can display results with a high level of urgency first. Alternatively, if the user is relaxed, the linking unit can sequentially display detailed results. Furthermore, if the user is in a hurry, the linking unit can quickly display results that focus on the main points. This allows for an optimal display order of the urgency assessment results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the linking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the linking unit can input the user's emotion data into the generation AI and have the generation AI adjust the display order.

[0086] The linking unit can take into account the geographical distribution of symptoms when determining the level of urgency. For example, the linking unit prioritizes determining symptoms related to a disease that is prevalent in a specific region. The linking unit can also determine the level of urgency based on the user's current location. Furthermore, the geographical distribution of symptoms can be analyzed to improve the accuracy of determining the level of urgency. By taking the geographical distribution of symptoms into consideration, the accuracy of determining the level of urgency can be improved. Specific collection methods and criteria for the geographical distribution must be clarified. Examples include the number of cases by region and the spread of infection. Some or all of the above-described processing in the linking unit may be performed using AI, or may be performed without AI. For example, the linking unit can input data on the geographical distribution of symptoms into the generation AI and have the generation AI determine the level of urgency.

[0087] The linking unit can improve the accuracy of the determination of the urgency level by referring to literature related to the symptom when determining the urgency level. For example, the linking unit determines the urgency level by referring to the latest medical literature related to the symptom. The linking unit can also determine the urgency level by referring to past case studies related to the symptom. Furthermore, the linking unit can determine the urgency level by referring to medical guidelines related to the symptom. In this way, by referring to literature related to the symptom, the accuracy of the urgency determination is improved. The specific types of related literature and the reference method must be clarified. Examples include medical papers, guidelines, etc. Some or all of the above-mentioned processing in the linking unit may be performed using AI, or may be performed without AI. For example, the linking unit can input literature data related to the symptom into the generation AI and have the generation AI perform the urgency determination.

[0088] The reservation unit can estimate the user's emotions and adjust the reservation method based on the estimated user emotions. For example, if the user is feeling anxious, the reservation unit can provide a simple and intuitive reservation method. If the user is relaxed, the reservation unit can also provide detailed reservation options. Furthermore, if the user is in a hurry, the reservation unit can also provide a method for quickly completing the reservation. This allows the optimal reservation method to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reservation unit may be performed using an AI, for example, or without an AI. For example, the reservation unit can input the user's emotion data into the generation AI and have the generation AI adjust the reservation method.

[0089] When making a reservation, the reservation unit can select an appropriate reservation method by referring to the medical institution's past reservation data. For example, the reservation unit can suggest the optimal reservation time based on the medical institution's past reservation data. It can also analyze the medical institution's past reservation data and suggest a reservation method that avoids congestion. It can also suggest a method to increase the reservation success rate by referring to the medical institution's past reservation data. This makes it possible to provide the optimal reservation method based on past reservation data. The specific collection method and criteria for past reservation data must be clarified. For example, this includes reservation history and cancellation rate. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input past reservation data into the generation AI and have the generation AI select the reservation method.

[0090] The reservation unit can customize the reservation method based on the user's current living situation when making a reservation. For example, if the user is at work, the reservation unit can suggest a time slot during work breaks when the user can make a reservation. Also, if the user is at home, the reservation unit can suggest a reservation method that suits the user's schedule. Furthermore, the reservation unit can suggest the optimal reservation method (online, telephone, etc.) based on the user's living situation. This makes it possible to provide the optimal reservation method according to the user's living situation. The specific types of living situation and the method of collection need to be clarified. Examples include lifestyle habits and living environment. Some or all of the above-mentioned processing in the reservation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reservation unit can input the user's living situation data into a generation AI and have the generation AI customize the reservation method.

[0091] The reservation unit can estimate a user's emotions and prioritize reservations based on the estimated user emotions. For example, if a user feels anxious, the reservation unit can prioritize reservations with high urgency. If a user feels relaxed, the reservation unit can provide detailed reservation options. Furthermore, if a user is in a hurry, the reservation unit can provide a method for quickly completing reservations. This allows optimal reservation priorities to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reservation unit can be performed using, for example, AI, or without AI. For example, the reservation unit can input user emotion data into the generation AI and have the generation AI determine the reservation priorities.

[0092] When making a reservation, the reservation unit can select the optimal reservation method taking into account the geographical location information of the medical institution. For example, the reservation unit can prioritize reserving the medical institution closest to the user's current location. The reservation unit can also suggest the optimal medical institution based on the user's means of transportation. Furthermore, the reservation unit can suggest the optimal reservation time based on the user's geographical location information. This makes it possible to provide the optimal reservation method based on the geographical location information. The specific collection method and criteria for the geographical location information must be clarified. For example, this may include GPS data, address information, etc. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, or may be performed without using AI. For example, the reservation unit can input the user's geographical location information into the generation AI and have the generation AI select the reservation method.

[0093] The reservation unit can improve reservation accuracy by referring to the medical institution's related literature when making a reservation. The reservation unit can, for example, suggest the optimal reservation method based on the medical institution's related literature. It can also suggest a method to increase the reservation success rate by referring to the medical institution's related literature. It can also analyze the medical institution's related literature and suggest the most efficient reservation method. This makes it possible to provide the optimal reservation method based on the related literature. The specific type of related literature and the reference method must be clarified. Examples include medical papers, guidelines, etc. Some or all of the above-mentioned processing in the reservation unit may be performed using AI, or may be performed without AI. For example, the reservation unit can input the medical institution's related literature data into the generation AI and have the generation AI select the reservation method.

[0094] The travel arrangement unit can estimate the user's emotions and adjust the travel arrangement method based on the estimated user emotions. For example, if the user is feeling anxious, the travel arrangement unit can provide a simple and intuitive travel arrangement method. Furthermore, if the user is relaxed, the travel arrangement unit can provide detailed travel options. Furthermore, if the user is in a hurry, the travel arrangement unit can provide a method for quickly completing travel arrangements. This allows the optimal travel arrangement method to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the travel arrangement unit can be performed using, for example, AI, or without AI. For example, the travel arrangement unit can input the user's emotion data into the generation AI and have the generation AI adjust the travel arrangement method.

[0095] When making travel arrangements, the travel arrangement unit can select the optimal arrangement method by referring to the user's past travel history. The travel arrangement unit can, for example, suggest the optimal arrangement method based on the means of transportation used by the user in the past. It can also suggest arrangement methods that avoid congestion based on the user's past travel history. Furthermore, it can suggest methods to increase the success rate of travel arrangements by referring to the user's past travel history. This allows the optimal travel arrangement method to be provided based on the past travel history. It is necessary to clarify the specific collection method and criteria for the past travel history. For example, this includes the means of transportation and travel distance. Some or all of the above-described processing in the travel arrangement unit may be performed using, or without, AI. For example, the travel arrangement unit can input the user's past travel history data into a generation AI and have the generation AI select an arrangement method.

[0096] The travel arrangement unit can customize the means of arrangement based on the user's current living situation when making travel arrangements. For example, if the user is at work, the travel arrangement unit can suggest means of transportation that can be used during breaks at work. Also, if the user is at home, the travel arrangement unit can suggest means of transportation that fit the user's home schedule. Furthermore, the travel arrangement unit can suggest the optimal means of transportation (taxi, public transportation, etc.) based on the user's living situation. This makes it possible to provide the optimal means of travel arrangement according to the user's living situation. The specific types of living situation and the method of collection need to be clarified. Examples include lifestyle habits and living environment. Some or all of the above-mentioned processing in the travel arrangement unit may be performed using, or without, AI. For example, the travel arrangement unit can input the user's living situation data into a generation AI and have the generation AI customize the means of arrangement.

[0097] The travel arrangement unit can estimate the user's emotions and prioritize travel arrangements based on the estimated user emotions. For example, if the user is feeling anxious, the travel arrangement unit can prioritize travel arrangements with high urgency. If the user is relaxed, the travel arrangement unit can also provide detailed travel options. Furthermore, if the user is in a hurry, the travel arrangement unit can provide a method for quickly completing travel arrangements. This allows optimal travel arrangement priorities to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the travel arrangement unit can be performed using, for example, AI, or without AI. For example, the travel arrangement unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of travel arrangements.

[0098] When making travel arrangements, the travel arrangement unit can select the optimal arrangement method by taking into account the user's geographical location information. For example, the travel arrangement unit prioritizes arrangement of the means of transportation closest to the user's current location. The travel arrangement unit can also suggest the optimal travel arrangement method depending on the user's means of transportation. Furthermore, the optimal travel arrangement method can be suggested based on the user's geographical location information. This makes it possible to provide the optimal travel arrangement method based on the geographical location information. The specific collection method and criteria for the geographical location information must be clarified. For example, this includes GPS data, address information, etc. Some or all of the above-mentioned processing in the travel arrangement unit may be performed using AI, or may be performed without AI. For example, the travel arrangement unit can input the user's geographical location information into a generation AI and have the generation AI select an arrangement method.

[0099] When making travel arrangements, the travel arrangement unit can analyze the user's social media activity and suggest arrangement methods. For example, the travel arrangement unit can suggest the optimal arrangement method based on the travel method reported by the user on social media. The travel arrangement unit can also collect information related to specific travel methods from the user's social media activity. Furthermore, the travel arrangement unit can analyze the user's social media activity and suggest the most relevant travel arrangement method. This allows the optimal travel arrangement method to be provided based on social media activity. The specific collection method and criteria for social media activity must be clarified. For example, this includes the content of posts and frequency of activity. Some or all of the above-mentioned processing in the travel arrangement unit may be performed using AI, for example, or without AI. For example, the travel arrangement unit can input the user's social media data into a generation AI and have the generation AI execute a travel arrangement method suggestion. === Hard Collateral 1-1 === Each of the multiple elements, including the information collection unit, diagnosis unit, linking unit, reservation unit, and travel arrangement unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information collection unit collects the user's symptoms using the reception device 38 of the smart device 14. The diagnosis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and generates a diagnosis. The linking unit, realized, for example, by the specific processing unit 290 of the data processing device 12, connects to an emergency contact when the emergency level is high. The reservation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, searches for availability information of nearby primary medical institutions and makes a reservation. The travel arrangement unit, realized, for example, by the control unit 46A of the smart device 14, arranges transportation to the reserved medical institution. === Hard Collateral 1-2 === Each of the multiple elements, including the information collection unit, diagnosis unit, collaboration unit, reservation unit, and travel arrangement unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information collection unit collects the user's symptoms using the microphone 238 of the smart glasses 214. The diagnosis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and generates a diagnosis. The collaboration unit, realized, for example, by the specific processing unit 290 of the data processing device 12, connects to an emergency contact when the emergency level is high. The reservation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, searches for availability information of nearby primary medical institutions and makes a reservation. The travel arrangement unit, realized, for example, by the control unit 46A of the smart glasses 214, arranges transportation to the reserved medical institution. === Hard Collateral 1-3 === Each of the multiple elements, including the information collection unit, diagnosis unit, linking unit, reservation unit, and travel arrangement unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the information collection unit collects the user's symptoms using the microphone 238 of the headset terminal 314. The diagnosis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and generates a diagnosis. The linking unit, realized, for example, by the specific processing unit 290 of the data processing device 12, connects to an emergency contact when the emergency level is high. The reservation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, searches for availability information of nearby primary medical institutions and makes a reservation. The travel arrangement unit, realized, for example, by the control unit 46A of the headset terminal 314, arranges transportation to the reserved medical institution. === Hard Collateral 1-4 === Each of the multiple elements, including the information collection unit, diagnosis unit, collaboration unit, reservation unit, and travel arrangement unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information collection unit collects the user's symptoms using the microphone 238 of the robot 414. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to generate a diagnosis. The collaboration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and connects to an emergency contact when the emergency is high. The reservation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and searches for availability information at nearby primary medical institutions and makes a reservation. The travel arrangement unit is realized, for example, by the control unit 46A of the robot 414, and arranges transportation to the reserved medical institution.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The emergency medical support system may further include a vital monitoring unit that monitors the user's vital signs in real time. The vital monitoring unit may collect data such as the user's heart rate, blood pressure, and body temperature, and determine the level of urgency based on this data. For example, if the user's heart rate increases suddenly, the level of urgency may be determined to be high. The vital monitoring unit may also provide the collected data to the diagnosis unit to improve the accuracy of the diagnosis results. Furthermore, the vital monitoring unit may provide the user's vital data to the connection unit to support rapid connection to emergency contacts.

[0102] The information collection unit can collect the user's voice data and identify symptoms using voice analysis technology. For example, if the user says, "I'm having trouble breathing," the voice data can be analyzed to identify the possibility of breathing difficulties. It can also estimate the user's emotions from the voice data and adjust the collection method based on the emotions. For example, if the user is feeling impatient, it can prioritize displaying simple questions. Furthermore, the voice data can be provided to the diagnosis unit to improve the accuracy of the diagnosis results.

[0103] The diagnostic unit can analyze the user's lifestyle habit data and reflect it in the diagnostic results. For example, if the user is a smoker, a diagnosis of respiratory symptoms is given priority. It can also diagnose digestive symptoms based on the user's dietary habit data. It can also analyze the user's exercise habit data and diagnose cardiovascular symptoms. This enables highly accurate diagnosis based on the user's lifestyle habits.

[0104] The linking unit can pre-register the user's emergency contact information and automatically notify them in the event of an emergency. For example, when the user faces an emergency, a notification is automatically sent to pre-registered family and friends. The linking unit can also provide the emergency contacts with the user's current location and symptoms. Furthermore, if there are multiple emergency contacts, priorities can be set and notifications can be sent to the most appropriate contact. This enables a quick and appropriate response in the event of an emergency.

[0105] The reservation unit can register the user's insurance information in advance and automatically apply it at the time of reservation. For example, when the user makes a reservation at a medical institution, the applicable insurance is automatically selected based on the insurance information registered in advance. It can also suggest the most suitable medical institution within the scope of insurance coverage. Furthermore, it can calculate the cost at the time of reservation in advance based on the insurance information and present it to the user. This allows the user to make a reservation at a medical institution with peace of mind.

[0106] The travel arrangement unit can register the user's transportation preferences in advance and take them into consideration when making travel arrangements. For example, if the user prefers taxis, the unit will arrange taxis first. Also, if public transportation is used, the unit can suggest the optimal route. Furthermore, the method of travel arrangements can be customized based on the user's transportation preferences. This makes it possible to make optimal travel arrangements according to the user's transportation preferences.

[0107] The information collection unit can estimate the user's emotions and adjust the level of detail of the information to be collected based on the estimated user's emotions. For example, if the user is feeling anxious, it can prioritize displaying simple questions that get to the point. Also, if the user is relaxed, it can display detailed questions. Furthermore, if the user is in a hurry, it can also be made possible to collect information quickly. This makes it possible to collect optimal information according to the user's emotions.

[0108] The diagnostic unit can estimate the user's emotions and adjust the method of notifying the diagnostic result based on the estimated user emotions. For example, if the user is feeling anxious, the diagnostic result can be notified using expressions that give a sense of security. If the user is relaxed, detailed diagnostic results can be provided. Furthermore, if the user is in a hurry, concise diagnostic results that focus on the main points can be quickly notified. This makes it possible to notify the user of the optimal diagnostic result according to their emotions.

[0109] The linking unit can estimate the user's emotions and adjust the content of notifications sent to emergency contacts based on the estimated user emotions. For example, if the user is feeling anxious, detailed symptom information can be provided to the emergency contacts. If the user is relaxed, concise symptom information can be provided. Furthermore, if the user is in a hurry, a notification can be sent quickly. This makes it possible to provide optimal notification content according to the user's emotions.

[0110] The reservation unit can estimate the user's emotions and adjust the reservation confirmation method based on the estimated user emotions. For example, if the user is feeling anxious, detailed reservation confirmation information can be provided. If the user is feeling relaxed, concise reservation confirmation information can be provided. Furthermore, if the user is in a hurry, reservation confirmation can be performed quickly. This allows optimal reservation confirmation according to the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The information collection unit collects the user's symptoms. The user's symptoms can include physical and mental symptoms. The information collection unit collects information by having the user input their symptoms through a smartphone app or by sending images of their symptoms. The information collection unit can also estimate the user's emotions and adjust the symptom collection method based on the estimated user emotions. For example, if the user is feeling anxious, the system provides a simple and intuitive interface and minimizes input steps. Step 2: The diagnostic unit analyzes the collected information and generates a diagnosis. The diagnostic unit analyzes the collected text and image information and determines the level of urgency. For example, if the AI ​​analyzes an image of chest pain and determines that there is a high possibility of myocardial infarction, the level of urgency is determined to be high. Step 3: The communication unit determines the level of urgency based on the diagnosis results, and if the level of urgency is high, it connects to emergency contacts. For example, if the AI ​​determines that there is a high possibility of a myocardial infarction, the system automatically connects to emergency contacts and arranges for an ambulance. Step 4: The reservation unit searches for availability at nearby primary medical institutions based on the results determined by the collaboration unit and makes a reservation. For example, if the AI ​​determines that the patient has mild stomach pain, the system searches for availability at nearby primary medical institutions and makes a reservation. Step 5: The transportation arrangement unit arranges transportation to the medical institution reserved by the reservation unit. For example, it arranges for a taxi or public transportation, and supports the user to travel smoothly to the medical institution.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information collection unit that collects symptoms of a user; a diagnosis unit that analyzes the information collected by the information collection unit and performs a diagnosis; a linking unit that determines the level of urgency based on the diagnosis result obtained by the diagnosis unit, and connects to an emergency contact if the level of urgency is high; a reservation unit that searches for availability information of nearby primary medical institutions based on the results determined by the collaboration unit and makes a reservation; a transportation arrangement unit that arranges transportation to the medical institution reserved by the reservation unit; A system characterized by:

2. The information collecting unit Collect user symptoms as text or images 2. The system of claim 1.

3. The diagnostic unit Analyze the collected information and generate diagnostic results 2. The system of claim 1.

4. The linking unit is Determines the level of urgency based on the diagnosis results, and connects to emergency contacts if the level of urgency is high 2. The system of claim 1.

5. The reservation unit Search for availability at nearby primary care medical institutions and make a reservation 2. The system of claim 1.

6. The movement arrangement unit Arrange transportation based on information from the reservation department 2. The system of claim 1.

7. The information collecting unit Inferring user emotions and adjusting symptom collection methods based on the estimated user emotions 2. The system of claim 1.

8. The information collecting unit Analyze the user's past medical history and select the appropriate collection method 2. The system of claim 1.

Images