System

Abstract

To provide a system for rapidly selecting an optimum hospital in emergency conveyance.SOLUTION: A system includes a collection section, an analysis section, a selection section, and a providing section. The collection section collects a patient's information. The analysis section analyzes information collected by the analysis section. The selection section selects an appropriate hospital on the basis of an analysis result obtained by the analysis section. The providing section provides information to a hospital selected by the selection section.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to quickly select the most suitable hospital when transporting a patient to an emergency room, and this took a lot of time.

[0005] The system according to the embodiment aims to quickly select the most suitable hospital in the event of emergency transport. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a selection unit, and a provision unit. The collection unit collects patient information. The analysis unit analyzes the information collected by the collection unit. The selection unit selects an appropriate hospital based on the analysis result obtained by the analysis unit. The provision unit provides information to the hospital selected by the selection unit. Effect of the Invention

[0007] The system according to the embodiment can quickly select the most suitable hospital in the event of emergency transport. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The emergency transport destination optimization system according to the embodiment of the present invention is a system that uses a generating AI to optimize the emergency transport destination of a patient. This system searches for the optimal hospital to transport to, taking into consideration information such as the patient's urgency, severity, underlying disease, specialty, and available hospital beds. For example, for a patient with a specific emergency medical condition, the generating AI searches for a hospital that specializes in that condition and has available hospital beds. This allows the patient to receive appropriate treatment quickly. As a result, the emergency transport destination optimization system can significantly improve the efficiency of emergency transport and increase the survival rate. In addition, it enables optimal allocation of medical resources, improving overall medical efficiency.

[0029] The emergency transport destination optimization system according to the embodiment includes a collection unit, an analysis unit, a selection unit, and a provision unit. The collection unit collects information on the patient. The patient information includes, for example, urgency, severity, underlying disease, and bed availability, but is not limited to such examples. The collection unit collects, for example, the severity of symptoms and the time since onset in order to evaluate the urgency of the patient. The collection unit can also collect the progression of the disease and the risk of life in order to evaluate the severity of the patient. Furthermore, the collection unit can collect the availability of beds in real time. For example, the collection unit accesses a hospital database and checks the current availability of beds. The analysis unit uses a generation AI to analyze the information collected by the collection unit. The analysis is performed, for example, to evaluate the urgency and severity of the patient, but is not limited to such examples. For example, the analysis unit uses a generation AI to analyze the severity of symptoms and the time since onset in order to evaluate the urgency of the patient. The analysis unit can also analyze the progression of the disease and the risk of life in order to evaluate the severity of the patient, using the generation AI. The selection unit selects the optimal hospital based on the analysis result obtained by the analysis unit. The selection is performed, for example, taking into consideration the specialty of the hospital and the availability of hospital beds, but is not limited to such an example. For example, the selection unit uses a generation AI to select the optimal hospital according to the urgency and severity of the patient. The selection unit can also use the generation AI to select the optimal hospital, taking into consideration the specialty of the hospital and the availability of hospital beds. The provision unit provides information to the hospital selected by the selection unit. The information provision is, for example, information on the urgency and severity of the patient, but is not limited to such an example. For example, the provision unit provides information on the urgency and severity of the patient to the selected hospital. The provision unit can also provide information on the patient's underlying disease and the availability of hospital beds to the selected hospital. This makes it possible for the emergency transport destination optimization system according to the embodiment to efficiently collect, analyze, select, and provide information on the patient. A part or all of the above-mentioned processes in the collection unit, analysis unit, selection unit, and provision unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection department can use generative AI to collect patient information and assess the urgency and severity of the patient.Furthermore, the analysis unit can use the generating AI to evaluate the urgency and severity of the patient in order to analyze the information collected by the collection unit. The selection unit can use the generating AI to select the most suitable hospital based on the analysis results obtained by the analysis unit, depending on the urgency and severity of the patient. The provision unit can use the generating AI to provide information regarding the urgency and severity of the patient in order to provide information to the hospital selected by the selection unit.

[0030] The collection unit collects information on the patient. The patient information includes, for example, urgency, severity, underlying disease, and bed availability, but is not limited to such examples. The collection unit collects, for example, the severity of symptoms and the time since onset in order to evaluate the urgency of the patient. Specifically, the collection unit monitors vital signs such as the patient's respiratory rate, heart rate, blood pressure, and body temperature, and collects these data in real time. The collection unit can also collect the progression of the disease and the risk to life in order to evaluate the severity of the patient. For example, the collection unit evaluates the patient's level of consciousness, the degree of pain, the presence or absence of bleeding, and collects these information. Furthermore, the collection unit can also collect the availability of beds in real time. For example, the collection unit accesses the hospital database and checks the current availability of beds. This allows the collection unit to comprehensively grasp the patient's condition and the hospital's resources. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and selection unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is performed, for example, to evaluate the urgency and severity of the patient, but is not limited to such an example. Specifically, the generation AI analyzes the vital signs and symptom data of the patient to evaluate the urgency. For example, the generation AI analyzes the fluctuations in the patient's respiratory rate and heart rate to detect abnormal patterns. The generation AI can also utilize past medical data and statistical information to evaluate the progression of the patient's condition and the risk to life. For example, the generation AI predicts the degree of risk indicated by a combination of specific symptoms and vital signs based on past patient data. Furthermore, the generation AI can also suggest the optimal transportation destination, taking into account the availability of hospital beds and the specialty of the hospital. This allows the analysis unit to quickly and accurately analyze the collected data and evaluate the patient's condition in real time. Furthermore, the analysis unit can also perform long-term risk assessment and trend analysis using past data and statistical information. For example, based on past transportation data, it predicts fluctuations in risk in a specific area or time period and plans future measures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The selection unit selects the optimal hospital based on the analysis results obtained by the analysis unit. The selection is performed, for example, taking into consideration the specialty of the hospital and the availability of hospital beds, but is not limited to such examples. Specifically, the selection unit uses the generation AI to select the optimal hospital according to the urgency and severity of the patient. For example, the generation AI analyzes the patient's symptoms and vital signs to identify the hospital with the most appropriate specialist. The generation AI also accesses the hospital database and checks the current availability of hospital beds to determine whether the destination hospital can accept the patient. Furthermore, the selection unit can propose the optimal transport route taking into consideration the geographic location and traffic conditions of the hospital. This allows the selection unit to comprehensively evaluate the patient's condition and the hospital's resources and quickly determine the optimal transport destination. The selection unit can centrally manage this information and cooperate with other systems and departments as necessary. For example, information on the selected hospital is sent to the provision unit to ensure that the patient is transported smoothly. In addition, the selection unit can continuously improve the accuracy of the selection algorithm by utilizing past transport data and statistical information. This enables the selection unit to efficiently and effectively select the most suitable hospital, thereby improving the performance of the entire system.

[0033] The providing unit provides information to the hospital selected by the selecting unit. The information provided includes, for example, information on the urgency and severity of the patient, but is not limited to such an example. Specifically, the providing unit provides the selected hospital with information on the patient's vital signs, details of symptoms, and underlying diseases. For example, the providing unit transmits data such as the patient's respiratory rate, heart rate, blood pressure, and body temperature in real time, so that the hospital can respond quickly. The providing unit also provides information on the progression of the patient's condition and the risk to life. Furthermore, the providing unit can also provide information on the planned transportation time and transportation means so that the hospital is prepared to accept the patient. This allows the selected hospital to quickly prepare to accept the patient. The providing unit can centrally manage this information and cooperate with other systems and departments as necessary. For example, the provided information is automatically registered in the hospital's electronic medical record system, so that medical staff can access it immediately. In addition, the providing unit can adjust the frequency and accuracy of information provided, allowing for flexible response according to specific situations and conditions. This allows the providing unit to provide information efficiently and effectively, improving the performance of the entire system.

[0034] The collection unit can collect the availability of hospital beds in real time. In order to collect the availability of hospital beds in real time, for example, it is necessary to access the hospital database and check the current availability of hospital beds. The collection unit, for example, accesses the hospital database in real time and collects the availability of hospital beds. The collection unit can also update the availability of hospital beds in real time by linking with the hospital information system. For example, the collection unit acquires the availability of hospital beds from the hospital information system and updates it in real time. This allows the collection unit to collect the availability of hospital beds in real time. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can access the hospital database and collect the availability of hospital beds using the generation AI.

[0035] The analysis unit can analyze the urgency and severity of the patient. The analysis unit, for example, analyzes the severity of symptoms and the time from onset in order to evaluate the urgency of the patient. For example, the analysis unit uses a generation AI to analyze the severity of symptoms and the time from onset in order to evaluate the urgency of the patient. The analysis unit can also analyze the progression of the disease and the risk to life in order to evaluate the severity of the patient. For example, the analysis unit uses a generation AI to analyze the progression of the disease and the risk to life in order to evaluate the severity of the patient. This allows the analysis unit to analyze the urgency and severity of the patient. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the severity of symptoms and the time from onset in order to evaluate the urgency and severity of the patient using a generation AI.

[0036] The selection unit can make a selection based on the specialty of the hospital. The selection unit selects the most suitable hospital, for example, taking into consideration the specialty of the hospital. For example, the selection unit uses a generation AI to evaluate the specialty of the hospital and select the most suitable hospital. The selection unit can also collect information on the specialty of the hospital and reflect it in the selection. For example, the selection unit collects information on the medical departments of the hospital and the specialty fields of the doctors and reflects it in the selection. This allows the selection unit to make a selection based on the specialty of the hospital. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the selection unit can use a generation AI to collect information on the medical departments of the hospital and the specialty fields of the doctors in order to evaluate the specialty of the hospital, and reflect it in the selection.

[0037] The providing unit can provide information to the selected hospital. The providing unit, for example, provides the selected hospital with information on the urgency and severity of the patient. For example, the providing unit uses the generation AI to provide the selected hospital with information on the urgency and severity of the patient. The providing unit can also provide the selected hospital with information on the patient's underlying disease and the availability of hospital beds. For example, the providing unit uses the generation AI to provide the selected hospital with information on the patient's underlying disease and the availability of hospital beds. This allows the providing unit to provide information to the selected hospital. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can provide the selected hospital with information on the patient's urgency and severity using the generation AI.

[0038] The collection unit can analyze the patient's past medical history and select the optimal information collection method. The collection unit, for example, preferentially collects related medical information based on the patient's past medical history. For example, the collection unit uses the generation AI to analyze the patient's medical records and prescription history and collect related medical information. The collection unit can also analyze the patient's past treatment history and collect information on effective treatment methods. For example, the collection unit uses the generation AI to analyze the patient's past treatment history and collect information on effective treatment methods. Furthermore, the collection unit can also collect information on necessary additional tests with reference to the patient's past test results. For example, the collection unit uses the generation AI to analyze the patient's past test results and collect information on necessary additional tests. This allows the collection unit to analyze the patient's past medical history and select the optimal information collection method. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can use the generation AI to analyze the patient's past medical history, and select the optimal information collection method.

[0039] The collection unit can perform filtering based on the patient's current living situation and environment when collecting information. For example, when the patient is undergoing treatment at home, the collection unit collects information on treatments that can be performed at home. For example, the collection unit uses the generation AI to analyze the patient's living environment and lifestyle habits and collects information on treatments that can be performed at home. In addition, when the patient is at work, the collection unit can also collect information on first aid at the workplace. For example, the collection unit uses the generation AI to analyze the patient's work environment and activities at the workplace and collect information on first aid at the workplace. In addition, when the patient is traveling, the collection unit can also collect information on medical institutions at the travel destination. For example, the collection unit uses the generation AI to analyze the environment and medical facilities at the patient's travel destination and collect information on medical institutions at the travel destination. This allows the collection unit to filter information based on the patient's living situation and environment. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, in order to analyze the patient's living situation and environment, the collection unit can analyze the living environment and lifestyle habits using the generation AI and filter information.

[0040] When collecting information, the collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the patient. For example, when the patient is in an urban area, the collection unit collects information on medical institutions in the urban area. For example, the collection unit uses the generation AI to analyze the GPS data and address information of the patient and collect information on medical institutions in the urban area. In addition, when the patient is in a rural area, the collection unit can also collect information on medical institutions in the rural area. For example, the collection unit uses the generation AI to analyze the movement history and address information of the patient and collect information on medical institutions in the rural area. In addition, when the patient is overseas, the collection unit can also collect information on local medical institutions. For example, the collection unit uses the generation AI to analyze the geographical location information of the patient and collect information on local medical institutions. In this way, the collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the patient. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can use the generation AI to analyze GPS data and address information in order to analyze the geographical location information of the patient, and preferentially collect highly relevant information.

[0041] The collection unit can analyze the social media activities of the patient at the time of information collection and collect related information. The collection unit collects related medical information based on, for example, health information shared by the patient on social media. For example, the collection unit uses the generation AI to analyze the content of posts on the patient's social media and collect related medical information. The collection unit can also collect information on medical professionals followed by the patient on social media. For example, the collection unit uses the generation AI to analyze the content of posts of medical professionals followed by the patient and collect related information. Furthermore, the collection unit can also collect information on health communities in which the patient participates on social media. For example, the collection unit uses the generation AI to analyze the content of posts in health communities in which the patient participates and collect related information. This allows the collection unit to analyze the social media activities of the patient and collect related information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can use the generation AI to analyze the content of posts and the number of followers in order to analyze the social media activities of the patient and collect related information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the patient during the analysis. For example, when the urgency of the patient is high, the analysis unit quickly presents the analysis result. For example, the analysis unit uses the generation AI to evaluate the urgency of the patient and quickly presents the analysis result. In addition, the analysis unit can also present a detailed analysis result when the severity of the patient is high. For example, the analysis unit uses the generation AI to evaluate the severity of the patient and presents a detailed analysis result. Furthermore, when the patient has many underlying diseases, the analysis unit can also analyze related information in detail. For example, the analysis unit uses the generation AI to evaluate the underlying diseases of the patient and analyze the related information in detail. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the patient. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can analyze the urgency and severity using the generation AI to evaluate the importance of the patient, and adjust the level of detail.

[0043] The analysis unit can apply different analysis algorithms depending on the patient category during analysis. For example, in the case of a pediatric patient, the analysis unit applies an analysis algorithm specialized in pediatrics. For example, the analysis unit uses a generation AI to analyze data specialized in pediatrics and provide an analysis result suitable for the pediatric patient. In addition, the analysis unit can also apply an analysis algorithm specialized in geriatrics in the case of an elderly patient. For example, the analysis unit uses a generation AI to analyze data specialized in geriatrics and provide an analysis result suitable for the elderly patient. In addition, the analysis unit can also apply an analysis algorithm specialized in obstetrics in the case of a pregnant woman. For example, the analysis unit uses a generation AI to analyze data specialized in obstetrics and provide an analysis result suitable for the pregnant woman. This allows the analysis unit to apply different analysis algorithms depending on the patient category. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can use a generation AI to analyze specialized data such as pediatrics, geriatrics, obstetrics, etc. depending on the patient category, and provide an appropriate analysis result.

[0044] The analysis unit can determine the priority of the analysis based on the submission time of the patient at the time of analysis. The analysis unit, for example, preferentially analyzes information of patients with high urgency. For example, the analysis unit uses the generation AI to evaluate the submission time of the patient and preferentially analyzes information of patients with high urgency. The analysis unit can also preferentially analyze information of patients with high severity. For example, the analysis unit uses the generation AI to evaluate the severity of the patient and preferentially analyze information of patients with high severity. Furthermore, the analysis unit can also preferentially analyze information of patients who have submitted early. For example, the analysis unit uses the generation AI to evaluate the submission time of the patient and preferentially analyze information of patients who have submitted early. This allows the analysis unit to determine the priority of the analysis based on the submission time of the patient. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to analyze the submission date and time and the elapsed time from the submission to evaluate the submission time of the patient and determine the priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the patient during analysis. For example, when the patient has many underlying diseases, the analysis unit prioritizes analysis of related information. For example, the analysis unit uses the generation AI to evaluate the patient's underlying diseases and prioritizes analysis of related information. In addition, the analysis unit can also prioritize analysis of related information when the patient's urgency is high. For example, the analysis unit uses the generation AI to evaluate the patient's urgency and prioritize analysis of related information. In addition, the analysis unit can also prioritize analysis of related information when the patient's severity is high. For example, the analysis unit uses the generation AI to evaluate the patient's severity and prioritize analysis of related information. This allows the analysis unit to adjust the order of analysis based on the relevance of the patient. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to analyze the underlying diseases, urgency, and severity to evaluate the relevance of the patient, and adjust the order.

[0046] The selection unit can improve the accuracy of the selection by considering the mutual relationship between the hospitals at the time of selection. The selection unit selects the optimal hospital by considering, for example, the cooperation situation between the hospitals. For example, the selection unit uses the generation AI to evaluate the cooperation situation between the hospitals and select the optimal hospital. The selection unit can also select the optimal hospital by considering the expertise and cooperation situation of the hospital. For example, the selection unit uses the generation AI to evaluate the expertise and cooperation situation of the hospital and select the optimal hospital. Furthermore, the selection unit can also select the optimal hospital based on the past cooperation record of the hospital. For example, the selection unit uses the generation AI to evaluate the past cooperation record of the hospital and select the optimal hospital. Thereby, the selection unit can improve the accuracy of the selection by considering the mutual relationship between the hospitals. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the selection unit can use the generation AI to analyze the cooperation system and the introduction record in order to evaluate the cooperation situation between the hospitals, and improve the accuracy of the selection.

[0047] The selection unit can make a selection by considering the attribute information of the hospital. The selection unit selects the optimal hospital by considering, for example, the specialty of the hospital. For example, the selection unit uses a generation AI to evaluate the specialty of the hospital and select the optimal hospital. The selection unit can also select the optimal hospital by considering the facility status of the hospital. For example, the selection unit uses a generation AI to evaluate the facility status of the hospital and select the optimal hospital. Furthermore, the selection unit can also select the optimal hospital by considering the expertise of the medical staff of the hospital. For example, the selection unit uses a generation AI to evaluate the expertise of the medical staff of the hospital and select the optimal hospital. This allows the selection unit to make a selection by considering the attribute information of the hospital. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the selection unit can use a generation AI to analyze the specialty, facility status, and expertise of the medical staff in order to evaluate the attribute information of the hospital, and make a selection.

[0048] The selection unit can select a hospital by considering the geographical distribution of the hospitals. For example, the selection unit preferentially selects a hospital that is closest to the patient's current location. For example, the selection unit uses the generation AI to analyze the patient's GPS data and address information and selects the nearest hospital. The selection unit can also select the optimal hospital within the patient's movable range. For example, the selection unit uses the generation AI to evaluate the patient's movable range and select the optimal hospital. Furthermore, the selection unit can also select the optimal hospital according to the urgency of the patient. For example, the selection unit uses the generation AI to evaluate the urgency of the patient and select the optimal hospital. This allows the selection unit to make a selection by considering the geographical distribution of the hospitals. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the selection unit can use the generation AI to analyze GPS data and address information in order to evaluate the geographical distribution of hospitals and make a selection.

[0049] The selection unit can improve the accuracy of the selection by referring to the related literature of the hospital at the time of selection. The selection unit selects the optimal hospital based on, for example, the past treatment record of the hospital. For example, the selection unit uses the generation AI to evaluate the past treatment record of the hospital and select the optimal hospital. The selection unit can also select the optimal hospital by referring to literature on the hospital's expertise. For example, the selection unit uses the generation AI to evaluate literature on the hospital's expertise and select the optimal hospital. Furthermore, the selection unit can also select the optimal hospital by referring to literature on the treatment effect of the hospital. For example, the selection unit uses the generation AI to evaluate literature on the treatment effect of the hospital and select the optimal hospital. Thereby, the selection unit can improve the accuracy of the selection by referring to the related literature of the hospital. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the selection unit can use the generation AI to analyze literature on past treatment record, expertise, and treatment effect in order to evaluate the related literature of the hospital, and improve the accuracy of the selection.

[0050] The providing unit can select the optimal information provision method by analyzing the patient's past medical behavior when providing information. The providing unit selects the optimal method based on the information provision method used by the patient in the past, for example. For example, the providing unit uses the generating AI to analyze the patient's medical records and prescription history, evaluates the information provision methods used in the past, and selects the optimal method. The providing unit can also analyze the patient's past medical behavior and select an effective information provision method. For example, the providing unit uses the generating AI to analyze the patient's past medical history and selects an effective information provision method. Furthermore, the providing unit can also select the optimal information provision method with reference to the patient's past medical history. For example, the providing unit uses the generating AI to analyze the patient's past medical history and selects the optimal information provision method. As a result, the providing unit can analyze the patient's past medical behavior and select the optimal information provision method. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can analyze the medical records and prescription history using the generating AI to analyze the patient's past medical behavior, and select the optimal information provision method.

[0051] The providing unit can customize the means of providing information based on the patient's current living situation when providing information. For example, when the patient is undergoing treatment at home, the providing unit selects an information providing means that can be used at home. For example, the providing unit uses the generating AI to analyze the patient's living environment and living habits and selects an information providing means that can be used at home. In addition, when the patient is at work, the providing unit can also select an information providing means that can be used at work. For example, the providing unit uses the generating AI to analyze the patient's work environment and activities at work and selects an information providing means that can be used at work. In addition, when the patient is traveling, the providing unit can also select an information providing means that can be used at the travel destination. For example, the providing unit uses the generating AI to analyze the environment and medical facilities at the patient's travel destination and selects an information providing means that can be used at the travel destination. This allows the providing unit to customize the means of providing information based on the patient's living situation. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the information providing department can use generative AI to analyze the patient's living situation, analyze the living environment and lifestyle habits, and customize the means of providing information.

[0052] When providing information, the providing unit can select the optimal information provision method in consideration of the geographical location information of the patient. For example, when the patient is in an urban area, the providing unit selects an information provision means that can be used in the urban area. For example, the providing unit uses the generating AI to analyze the GPS data and address information of the patient and selects an information provision means that can be used in the urban area. In addition, when the patient is in a rural area, the providing unit can also select an information provision means that can be used in the rural area. For example, the providing unit uses the generating AI to analyze the movement history and address information of the patient and selects an information provision means that can be used in the rural area. In addition, when the patient is overseas, the providing unit can also select an information provision means that can be used locally. For example, the providing unit uses the generating AI to analyze the geographical location information of the patient and selects an information provision means that can be used locally. As a result, the providing unit can select the optimal information provision method in consideration of the geographical location information of the patient. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the provision department can use generation AI to analyze GPS data and address information to analyze the patient's geographic location information and select the optimal information provision method.

[0053] The providing unit can analyze the social media activity of the patient at the time of providing information and propose a means of providing information. The providing unit proposes a relevant means of providing information based on, for example, health information shared by the patient on social media. For example, the providing unit uses a generating AI to analyze the content of posts on the patient's social media and proposes a relevant means of providing information. The providing unit can also propose a means of providing information based on information on medical professionals followed by the patient on social media. For example, the providing unit uses a generating AI to analyze the content of posts of medical professionals followed by the patient and proposes a relevant means of providing information. The providing unit can also propose a means of providing information based on information on health communities in which the patient participates on social media. For example, the providing unit uses a generating AI to analyze the content of posts of health communities in which the patient participates and proposes a relevant means of providing information. This allows the providing unit to analyze the social media activity of the patient and propose a means of providing information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the providing unit can use a generating AI to analyze the content of posts and the number of followers in order to analyze the social media activity of the patient and propose a means of providing information.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The collection unit can collect the patient's family history and genetic information and provide it to the analysis unit. For example, the collection unit can collect information for evaluating a genetic risk based on the patient's family history. The collection unit can also analyze the patient's genetic information and evaluate a disease risk associated with a specific gene mutation. Furthermore, the collection unit can collect information regarding preventive medical measures based on the patient's family history and genetic information. This allows the collection unit to provide more accurate information taking into account the patient's family history and genetic information.

[0056] The analysis unit can analyze the lifestyle habit data of the patient and evaluate the health risk. For example, the analysis unit can analyze the patient's diet and exercise habits and evaluate the risk of lifestyle-related diseases. The analysis unit can also analyze the patient's sleep patterns and evaluate the risk of sleep disorders. Furthermore, the analysis unit can analyze the patient's stress level and evaluate the risk of stress-related diseases. This allows the analysis unit to comprehensively evaluate the health risk based on the patient's lifestyle habit data.

[0057] The selection unit can select the most appropriate hospital by taking into consideration the socioeconomic background of the patient. For example, the selection unit evaluates the patient's income and insurance status and selects a hospital that is less of a financial burden. The selection unit can also evaluate the medical access situation in the patient's residential area and select a hospital that is easily accessible. Furthermore, the selection unit can evaluate the patient's social support network and select a hospital where support is easily available. In this way, the selection unit can select a more appropriate hospital by taking into consideration the patient's socioeconomic background.

[0058] The information delivery department can tailor the way it provides information based on the patient's language and cultural background. For example, the information delivery department can provide information in the patient's native language to eliminate language barriers. The information delivery department can also provide information that takes into account the patient's cultural background to avoid cultural misunderstandings. Furthermore, the information delivery department can provide information that takes into account the patient's religious beliefs to respect faith-based medical choices. This allows the information delivery department to provide more appropriate information based on the patient's language and cultural background.

[0059] The collection unit can customize the method of information collection based on the patient's living environment. For example, if the patient lives in an urban area, the collection unit can preferentially collect information about medical resources in urban areas. Also, if the patient lives in a rural area, the collection unit can preferentially collect information about medical resources in rural areas. Furthermore, if the patient is in a specific environmental condition, the collection unit can collect medical information appropriate for that environment. This allows the collection unit to customize the method of information collection based on the patient's living environment and provide appropriate information.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The collection unit collects patient information. Patient information includes urgency, severity, underlying diseases, and available hospital beds. For example, the collection unit collects the severity of symptoms and the time since onset to evaluate the urgency of the patient, and collects the progression of the disease and the risk to life to evaluate the severity. In addition, the collection unit accesses the hospital database to collect real-time availability of hospital beds. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is performed to evaluate the urgency and severity of the patient's condition. For example, the analysis unit analyzes the severity of symptoms and the time since onset, and analyzes the progression of the disease and the risk to life. Step 3: The selection unit selects the most suitable hospital based on the analysis results obtained by the analysis unit. The selection is made taking into consideration the hospital's expertise and available beds. For example, the selection unit uses a generative AI to select the most suitable hospital based on the patient's urgency and severity, taking into consideration the hospital's expertise and available beds. Step 4: The provider provides the information to the hospital selected by the selector. The information provider provides information on the urgency and severity of the patient. For example, the provider provides the selected hospital with information on the urgency and severity of the patient, underlying diseases, and available hospital beds.

[0062] (Example 2) The emergency transport destination optimization system according to the embodiment of the present invention is a system that uses a generating AI to optimize the emergency transport destination of a patient. This system searches for the optimal hospital to transport to, taking into consideration information such as the patient's urgency, severity, underlying disease, specialty, and available hospital beds. For example, for a patient with a specific emergency medical condition, the generating AI searches for a hospital that specializes in that condition and has available hospital beds. This allows the patient to receive appropriate treatment quickly. As a result, the emergency transport destination optimization system can significantly improve the efficiency of emergency transport and increase the survival rate. In addition, it enables optimal allocation of medical resources, improving overall medical efficiency.

[0063] The emergency transport destination optimization system according to the embodiment includes a collection unit, an analysis unit, a selection unit, and a provision unit. The collection unit collects information on the patient. The patient information includes, for example, urgency, severity, underlying disease, and bed availability, but is not limited to such examples. The collection unit collects, for example, the severity of symptoms and the time since onset in order to evaluate the urgency of the patient. The collection unit can also collect the progression of the disease and the risk of life in order to evaluate the severity of the patient. Furthermore, the collection unit can collect the availability of beds in real time. For example, the collection unit accesses a hospital database and checks the current availability of beds. The analysis unit uses a generation AI to analyze the information collected by the collection unit. The analysis is performed, for example, to evaluate the urgency and severity of the patient, but is not limited to such examples. For example, the analysis unit uses a generation AI to analyze the severity of symptoms and the time since onset in order to evaluate the urgency of the patient. The analysis unit can also analyze the progression of the disease and the risk of life in order to evaluate the severity of the patient, using the generation AI. The selection unit selects the optimal hospital based on the analysis result obtained by the analysis unit. The selection is performed, for example, taking into consideration the specialty of the hospital and the availability of hospital beds, but is not limited to such an example. For example, the selection unit uses a generation AI to select the optimal hospital according to the urgency and severity of the patient. The selection unit can also use the generation AI to select the optimal hospital, taking into consideration the specialty of the hospital and the availability of hospital beds. The provision unit provides information to the hospital selected by the selection unit. The information provision is, for example, information on the urgency and severity of the patient, but is not limited to such an example. For example, the provision unit provides information on the urgency and severity of the patient to the selected hospital. The provision unit can also provide information on the patient's underlying disease and the availability of hospital beds to the selected hospital. This makes it possible for the emergency transport destination optimization system according to the embodiment to efficiently collect, analyze, select, and provide information on the patient. A part or all of the above-mentioned processes in the collection unit, analysis unit, selection unit, and provision unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection department can use generative AI to collect patient information and assess the urgency and severity of the patient.Furthermore, the analysis unit can use the generating AI to evaluate the urgency and severity of the patient in order to analyze the information collected by the collection unit. The selection unit can use the generating AI to select the most suitable hospital based on the analysis results obtained by the analysis unit, depending on the urgency and severity of the patient. The provision unit can use the generating AI to provide information regarding the urgency and severity of the patient in order to provide information to the hospital selected by the selection unit.

[0064] The collection unit collects information on the patient. The patient information includes, for example, urgency, severity, underlying disease, and bed availability, but is not limited to such examples. The collection unit collects, for example, the severity of symptoms and the time since onset in order to evaluate the urgency of the patient. Specifically, the collection unit monitors vital signs such as the patient's respiratory rate, heart rate, blood pressure, and body temperature, and collects these data in real time. The collection unit can also collect the progression of the disease and the risk to life in order to evaluate the severity of the patient. For example, the collection unit evaluates the patient's level of consciousness, the degree of pain, the presence or absence of bleeding, and collects these information. Furthermore, the collection unit can also collect the availability of beds in real time. For example, the collection unit accesses the hospital database and checks the current availability of beds. This allows the collection unit to comprehensively grasp the patient's condition and the hospital's resources. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis unit and selection unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0065] The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is performed, for example, to evaluate the urgency and severity of the patient, but is not limited to such an example. Specifically, the generation AI analyzes the vital signs and symptom data of the patient to evaluate the urgency. For example, the generation AI analyzes the fluctuations in the patient's respiratory rate and heart rate to detect abnormal patterns. The generation AI can also utilize past medical data and statistical information to evaluate the progression of the patient's condition and the risk to life. For example, the generation AI predicts the degree of risk indicated by a combination of specific symptoms and vital signs based on past patient data. Furthermore, the generation AI can also suggest the optimal transportation destination, taking into account the availability of hospital beds and the specialty of the hospital. This allows the analysis unit to quickly and accurately analyze the collected data and evaluate the patient's condition in real time. Furthermore, the analysis unit can also perform long-term risk assessment and trend analysis using past data and statistical information. For example, based on past transportation data, it predicts fluctuations in risk in a specific area or time period and plans future measures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0066] The selection unit selects the optimal hospital based on the analysis results obtained by the analysis unit. The selection is performed, for example, taking into consideration the specialty of the hospital and the availability of hospital beds, but is not limited to such examples. Specifically, the selection unit uses the generation AI to select the optimal hospital according to the urgency and severity of the patient. For example, the generation AI analyzes the patient's symptoms and vital signs to identify the hospital with the most appropriate specialist. The generation AI also accesses the hospital database and checks the current availability of hospital beds to determine whether the destination hospital can accept the patient. Furthermore, the selection unit can propose the optimal transport route taking into consideration the geographic location and traffic conditions of the hospital. This allows the selection unit to comprehensively evaluate the patient's condition and the hospital's resources and quickly determine the optimal transport destination. The selection unit can centrally manage this information and cooperate with other systems and departments as necessary. For example, information on the selected hospital is sent to the provision unit to ensure that the patient is transported smoothly. In addition, the selection unit can continuously improve the accuracy of the selection algorithm by utilizing past transport data and statistical information. This enables the selection unit to efficiently and effectively select the most suitable hospital, thereby improving the performance of the entire system.

[0067] The providing unit provides information to the hospital selected by the selecting unit. The information provided includes, for example, information on the urgency and severity of the patient, but is not limited to such an example. Specifically, the providing unit provides the selected hospital with information on the patient's vital signs, details of symptoms, and underlying diseases. For example, the providing unit transmits data such as the patient's respiratory rate, heart rate, blood pressure, and body temperature in real time, so that the hospital can respond quickly. The providing unit also provides information on the progression of the patient's condition and the risk to life. Furthermore, the providing unit can also provide information on the planned transportation time and transportation means so that the hospital is prepared to accept the patient. This allows the selected hospital to quickly prepare to accept the patient. The providing unit can centrally manage this information and cooperate with other systems and departments as necessary. For example, the provided information is automatically registered in the hospital's electronic medical record system, so that medical staff can access it immediately. In addition, the providing unit can adjust the frequency and accuracy of information provided, allowing for flexible response according to specific situations and conditions. This allows the providing unit to provide information efficiently and effectively, improving the performance of the entire system.

[0068] The collection unit can collect the availability of hospital beds in real time. In order to collect the availability of hospital beds in real time, for example, it is necessary to access the hospital database and check the current availability of hospital beds. The collection unit, for example, accesses the hospital database in real time and collects the availability of hospital beds. The collection unit can also update the availability of hospital beds in real time by linking with the hospital information system. For example, the collection unit acquires the availability of hospital beds from the hospital information system and updates it in real time. This allows the collection unit to collect the availability of hospital beds in real time. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can access the hospital database and collect the availability of hospital beds using the generation AI.

[0069] The analysis unit can analyze the urgency and severity of the patient. The analysis unit, for example, analyzes the severity of symptoms and the time from onset in order to evaluate the urgency of the patient. For example, the analysis unit uses a generation AI to analyze the severity of symptoms and the time from onset in order to evaluate the urgency of the patient. The analysis unit can also analyze the progression of the disease and the risk to life in order to evaluate the severity of the patient. For example, the analysis unit uses a generation AI to analyze the progression of the disease and the risk to life in order to evaluate the severity of the patient. This allows the analysis unit to analyze the urgency and severity of the patient. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the severity of symptoms and the time from onset in order to evaluate the urgency and severity of the patient using a generation AI.

[0070] The selection unit can make a selection based on the specialty of the hospital. The selection unit selects the most suitable hospital, for example, taking into consideration the specialty of the hospital. For example, the selection unit uses a generation AI to evaluate the specialty of the hospital and select the most suitable hospital. The selection unit can also collect information on the specialty of the hospital and reflect it in the selection. For example, the selection unit collects information on the medical departments of the hospital and the specialty fields of the doctors and reflects it in the selection. This allows the selection unit to make a selection based on the specialty of the hospital. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the selection unit can use a generation AI to collect information on the medical departments of the hospital and the specialty fields of the doctors in order to evaluate the specialty of the hospital, and reflect it in the selection.

[0071] The providing unit can provide information to the selected hospital. The providing unit, for example, provides the selected hospital with information on the urgency and severity of the patient. For example, the providing unit uses the generation AI to provide the selected hospital with information on the urgency and severity of the patient. The providing unit can also provide the selected hospital with information on the patient's underlying disease and the availability of hospital beds. For example, the providing unit uses the generation AI to provide the selected hospital with information on the patient's underlying disease and the availability of hospital beds. This allows the providing unit to provide information to the selected hospital. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can provide the selected hospital with information on the patient's urgency and severity using the generation AI.

[0072] The collection unit can estimate the patient's emotions and determine the priority of information to be collected based on the estimated patient's emotions. For example, when the patient feels anxious, the collection unit first prioritizes collecting information to reassure the patient. For example, the collection unit uses the generation AI to analyze the patient's facial expression and voice and estimate whether the patient feels anxious. In addition, when the patient complains of pain, the collection unit can also prioritize collecting information on pain relief. For example, the collection unit uses the generation AI to analyze the patient's self-report and biometric data and estimate the degree of pain. In addition, when the patient is calm, the collection unit can also prioritize collecting detailed medical information. For example, the collection unit uses the generation AI to analyze the patient's facial expression and voice and estimate whether the patient is calm. This allows the collection unit to prioritize information based on the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can use the generation AI to analyze the facial expression and voice of the patient to estimate the patient's emotion.

[0073] The collection unit can analyze the patient's past medical history and select the optimal information collection method. The collection unit, for example, preferentially collects related medical information based on the patient's past medical history. For example, the collection unit uses the generation AI to analyze the patient's medical records and prescription history and collect related medical information. The collection unit can also analyze the patient's past treatment history and collect information on effective treatment methods. For example, the collection unit uses the generation AI to analyze the patient's past treatment history and collect information on effective treatment methods. Furthermore, the collection unit can also collect information on necessary additional tests with reference to the patient's past test results. For example, the collection unit uses the generation AI to analyze the patient's past test results and collect information on necessary additional tests. This allows the collection unit to analyze the patient's past medical history and select the optimal information collection method. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can use the generation AI to analyze the patient's past medical history, and select the optimal information collection method.

[0074] The collection unit can perform filtering based on the patient's current living situation and environment when collecting information. For example, when the patient is undergoing treatment at home, the collection unit collects information on treatments that can be performed at home. For example, the collection unit uses the generation AI to analyze the patient's living environment and lifestyle habits and collects information on treatments that can be performed at home. In addition, when the patient is at work, the collection unit can also collect information on first aid at the workplace. For example, the collection unit uses the generation AI to analyze the patient's work environment and activities at the workplace and collect information on first aid at the workplace. In addition, when the patient is traveling, the collection unit can also collect information on medical institutions at the travel destination. For example, the collection unit uses the generation AI to analyze the environment and medical facilities at the patient's travel destination and collect information on medical institutions at the travel destination. This allows the collection unit to filter information based on the patient's living situation and environment. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, in order to analyze the patient's living situation and environment, the collection unit can analyze the living environment and lifestyle habits using the generation AI and filter information.

[0075] The collection unit can estimate the patient's emotion and adjust the level of detail of the information to be collected based on the estimated emotion of the patient. For example, when the patient is feeling anxious, the collection unit collects concise and easy-to-understand information. For example, the collection unit uses the generation AI to analyze the patient's facial expression and voice, estimate whether the patient is feeling anxious, and collect concise and easy-to-understand information. In addition, the collection unit can also collect detailed medical information when the patient is calm. For example, the collection unit uses the generation AI to analyze the patient's facial expression and voice, estimate whether the patient is calm, and collect detailed medical information. In addition, the collection unit can also collect visually easy-to-understand information when the patient is excited. For example, the collection unit uses the generation AI to analyze the patient's facial expression and voice, estimate whether the patient is excited, and collect visually easy-to-understand information. This allows the collection unit to adjust the level of detail of the information based on the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can use the generation AI to analyze the facial expression and voice of the patient to estimate the patient's emotion.

[0076] When collecting information, the collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the patient. For example, when the patient is in an urban area, the collection unit collects information on medical institutions in the urban area. For example, the collection unit uses the generation AI to analyze the GPS data and address information of the patient and collect information on medical institutions in the urban area. In addition, when the patient is in a rural area, the collection unit can also collect information on medical institutions in the rural area. For example, the collection unit uses the generation AI to analyze the movement history and address information of the patient and collect information on medical institutions in the rural area. In addition, when the patient is overseas, the collection unit can also collect information on local medical institutions. For example, the collection unit uses the generation AI to analyze the geographical location information of the patient and collect information on local medical institutions. In this way, the collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the patient. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can use the generation AI to analyze GPS data and address information in order to analyze the geographical location information of the patient, and preferentially collect highly relevant information.

[0077] The collection unit can analyze the social media activities of the patient at the time of information collection and collect related information. The collection unit collects related medical information based on, for example, health information shared by the patient on social media. For example, the collection unit uses the generation AI to analyze the content of posts on the patient's social media and collect related medical information. The collection unit can also collect information on medical professionals followed by the patient on social media. For example, the collection unit uses the generation AI to analyze the content of posts of medical professionals followed by the patient and collect related information. Furthermore, the collection unit can also collect information on health communities in which the patient participates on social media. For example, the collection unit uses the generation AI to analyze the content of posts in health communities in which the patient participates and collect related information. This allows the collection unit to analyze the social media activities of the patient and collect related information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can use the generation AI to analyze the content of posts and the number of followers in order to analyze the social media activities of the patient and collect related information.

[0078] The analysis unit can estimate the patient's emotions and adjust the expression method of the analysis based on the estimated patient's emotions. For example, if the patient feels anxious, the analysis unit presents the analysis result in an expression method that gives a sense of security. For example, the analysis unit uses a generation AI to analyze the patient's facial expression and voice, estimate whether the patient feels anxious, and present the analysis result in an expression method that gives a sense of security. In addition, the analysis unit can also present detailed analysis results when the patient is calm. For example, the analysis unit uses a generation AI to analyze the patient's facial expression and voice, estimate whether the patient is calm, and present detailed analysis results. In addition, the analysis unit can also present analysis results in a visually easy-to-understand expression method when the patient is excited. For example, the analysis unit uses a generation AI to analyze the patient's facial expression and voice, estimate whether the patient is excited, and present the analysis result in a visually easy-to-understand expression method. This allows the analysis unit to adjust the expression method of the analysis based on the patient's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using 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-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may use the generation AI to analyze the patient's facial expression and voice and estimate the patient's emotion.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the patient during the analysis. For example, when the urgency of the patient is high, the analysis unit quickly presents the analysis result. For example, the analysis unit uses the generation AI to evaluate the urgency of the patient and quickly presents the analysis result. In addition, the analysis unit can also present a detailed analysis result when the severity of the patient is high. For example, the analysis unit uses the generation AI to evaluate the severity of the patient and presents a detailed analysis result. Furthermore, when the patient has many underlying diseases, the analysis unit can also analyze related information in detail. For example, the analysis unit uses the generation AI to evaluate the underlying diseases of the patient and analyze the related information in detail. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the patient. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can analyze the urgency and severity using the generation AI to evaluate the importance of the patient, and adjust the level of detail.

[0080] The analysis unit can apply different analysis algorithms depending on the patient category during analysis. For example, in the case of a pediatric patient, the analysis unit applies an analysis algorithm specialized in pediatrics. For example, the analysis unit uses a generation AI to analyze data specialized in pediatrics and provide an analysis result suitable for the pediatric patient. In addition, the analysis unit can also apply an analysis algorithm specialized in geriatrics in the case of an elderly patient. For example, the analysis unit uses a generation AI to analyze data specialized in geriatrics and provide an analysis result suitable for the elderly patient. In addition, the analysis unit can also apply an analysis algorithm specialized in obstetrics in the case of a pregnant woman. For example, the analysis unit uses a generation AI to analyze data specialized in obstetrics and provide an analysis result suitable for the pregnant woman. This allows the analysis unit to apply different analysis algorithms depending on the patient category. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can use a generation AI to analyze specialized data such as pediatrics, geriatrics, obstetrics, etc. depending on the patient category, and provide an appropriate analysis result.

[0081] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. For example, if the patient is feeling anxious, the analysis unit presents a concise and to the point analysis result. For example, the analysis unit uses a generation AI to analyze the patient's facial expressions and voice, estimate whether the patient is feeling anxious, and present a concise and to the point analysis result. In addition, the analysis unit can also present a detailed analysis result if the patient is calm. For example, the analysis unit uses a generation AI to analyze the patient's facial expressions and voice, estimate whether the patient is calm, and present a detailed analysis result. In addition, the analysis unit can also present a visually easy-to-understand analysis result if the patient is excited. For example, the analysis unit uses a generation AI to analyze the patient's facial expressions and voice, estimate whether the patient is excited, and present a visually easy-to-understand analysis result. This allows the analysis unit to adjust the length of the analysis based on the patient's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using 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-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may use the generation AI to analyze the patient's facial expression and voice and estimate the patient's emotion.

[0082] The analysis unit can determine the priority of the analysis based on the submission time of the patient at the time of analysis. The analysis unit, for example, preferentially analyzes information of patients with high urgency. For example, the analysis unit uses the generation AI to evaluate the submission time of the patient and preferentially analyzes information of patients with high urgency. The analysis unit can also preferentially analyze information of patients with high severity. For example, the analysis unit uses the generation AI to evaluate the severity of the patient and preferentially analyze information of patients with high severity. Furthermore, the analysis unit can also preferentially analyze information of patients who have submitted early. For example, the analysis unit uses the generation AI to evaluate the submission time of the patient and preferentially analyze information of patients who have submitted early. This allows the analysis unit to determine the priority of the analysis based on the submission time of the patient. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to analyze the submission date and time and the elapsed time from the submission to evaluate the submission time of the patient and determine the priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the patient during analysis. For example, when the patient has many underlying diseases, the analysis unit prioritizes analysis of related information. For example, the analysis unit uses the generation AI to evaluate the patient's underlying diseases and prioritizes analysis of related information. In addition, the analysis unit can also prioritize analysis of related information when the patient's urgency is high. For example, the analysis unit uses the generation AI to evaluate the patient's urgency and prioritize analysis of related information. In addition, the analysis unit can also prioritize analysis of related information when the patient's severity is high. For example, the analysis unit uses the generation AI to evaluate the patient's severity and prioritize analysis of related information. This allows the analysis unit to adjust the order of analysis based on the relevance of the patient. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to analyze the underlying diseases, urgency, and severity to evaluate the relevance of the patient, and adjust the order.

[0084] The selection unit can estimate the patient's emotions and adjust the selection criteria based on the estimated patient's emotions. For example, when the patient is feeling anxious, the selection unit preferentially selects a hospital that gives a sense of security. For example, the selection unit uses the generation AI to analyze the patient's facial expressions and voice, estimates whether the patient is feeling anxious, and preferentially selects a hospital that gives a sense of security. In addition, when the patient is calm, the selection unit can also preferentially select a hospital with high expertise. For example, the selection unit uses the generation AI to analyze the patient's facial expressions and voice, estimates whether the patient is calm, and preferentially selects a hospital with high expertise. In addition, when the patient is excited, the selection unit can also preferentially select a hospital that can respond quickly. For example, the selection unit uses the generation AI to analyze the patient's facial expressions and voice, estimates whether the patient is excited, and preferentially selects a hospital that can respond quickly. This allows the selection unit to adjust the selection criteria based on the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, 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-mentioned processing in the selection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, in order to estimate the patient's emotions, the selection unit may use the generation AI to analyze the patient's facial expressions and voice and estimate the emotions.

[0085] The selection unit can improve the accuracy of the selection by considering the mutual relationship between the hospitals at the time of selection. The selection unit selects the optimal hospital by considering, for example, the cooperation situation between the hospitals. For example, the selection unit uses the generation AI to evaluate the cooperation situation between the hospitals and select the optimal hospital. The selection unit can also select the optimal hospital by considering the expertise and cooperation situation of the hospital. For example, the selection unit uses the generation AI to evaluate the expertise and cooperation situation of the hospital and select the optimal hospital. Furthermore, the selection unit can also select the optimal hospital based on the past cooperation record of the hospital. For example, the selection unit uses the generation AI to evaluate the past cooperation record of the hospital and select the optimal hospital. Thereby, the selection unit can improve the accuracy of the selection by considering the mutual relationship between the hospitals. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the selection unit can use the generation AI to analyze the cooperation system and the introduction record in order to evaluate the cooperation situation between the hospitals, and improve the accuracy of the selection.

[0086] The selection unit can make a selection by considering the attribute information of the hospital. The selection unit selects the optimal hospital by considering, for example, the specialty of the hospital. For example, the selection unit uses a generation AI to evaluate the specialty of the hospital and select the optimal hospital. The selection unit can also select the optimal hospital by considering the facility status of the hospital. For example, the selection unit uses a generation AI to evaluate the facility status of the hospital and select the optimal hospital. Furthermore, the selection unit can also select the optimal hospital by considering the expertise of the medical staff of the hospital. For example, the selection unit uses a generation AI to evaluate the expertise of the medical staff of the hospital and select the optimal hospital. This allows the selection unit to make a selection by considering the attribute information of the hospital. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the selection unit can use a generation AI to analyze the specialty, facility status, and expertise of the medical staff in order to evaluate the attribute information of the hospital, and make a selection.

[0087] The selection unit can estimate the patient's emotion and adjust the order of displaying the selection results based on the estimated patient's emotion. For example, if the patient is feeling anxious, the selection unit first displays a hospital that gives a sense of security. For example, the selection unit uses the generation AI to analyze the patient's facial expression and voice, estimate whether the patient is feeling anxious, and first displays a hospital that gives a sense of security. In addition, the selection unit can also display a hospital with high expertise first if the patient is calm. For example, the selection unit uses the generation AI to analyze the patient's facial expression and voice, estimate whether the patient is calm, and first displays a hospital with high expertise. In addition, the selection unit can also display a hospital that can respond quickly if the patient is excited. For example, the selection unit uses the generation AI to analyze the patient's facial expression and voice, estimate whether the patient is excited, and first displays a hospital that can respond quickly. This allows the selection unit to adjust the order of displaying the selection results based on the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the selection unit can use the generation AI to analyze the facial expression and voice of the patient to estimate the emotion of the patient.

[0088] The selection unit can select a hospital by considering the geographical distribution of the hospitals. For example, the selection unit preferentially selects a hospital that is closest to the patient's current location. For example, the selection unit uses the generation AI to analyze the patient's GPS data and address information and selects the nearest hospital. The selection unit can also select the optimal hospital within the patient's movable range. For example, the selection unit uses the generation AI to evaluate the patient's movable range and select the optimal hospital. Furthermore, the selection unit can also select the optimal hospital according to the urgency of the patient. For example, the selection unit uses the generation AI to evaluate the urgency of the patient and select the optimal hospital. This allows the selection unit to make a selection by considering the geographical distribution of the hospitals. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the selection unit can use the generation AI to analyze GPS data and address information in order to evaluate the geographical distribution of hospitals and make a selection.

[0089] The selection unit can improve the accuracy of the selection by referring to the related literature of the hospital at the time of selection. The selection unit selects the optimal hospital based on, for example, the past treatment record of the hospital. For example, the selection unit uses the generation AI to evaluate the past treatment record of the hospital and select the optimal hospital. The selection unit can also select the optimal hospital by referring to literature on the hospital's expertise. For example, the selection unit uses the generation AI to evaluate literature on the hospital's expertise and select the optimal hospital. Furthermore, the selection unit can also select the optimal hospital by referring to literature on the treatment effect of the hospital. For example, the selection unit uses the generation AI to evaluate literature on the treatment effect of the hospital and select the optimal hospital. Thereby, the selection unit can improve the accuracy of the selection by referring to the related literature of the hospital. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the selection unit can use the generation AI to analyze literature on past treatment record, expertise, and treatment effect in order to evaluate the related literature of the hospital, and improve the accuracy of the selection.

[0090] The providing unit can estimate the patient's emotion and adjust the method of providing information based on the estimated emotion of the patient. For example, when the patient feels anxious, the providing unit provides information in a manner that gives a sense of security. For example, the providing unit uses the generation AI to analyze the facial expression and voice of the patient, estimate whether the patient feels anxious, and provide information in a manner that gives a sense of security. In addition, the providing unit can also provide detailed information when the patient is calm. For example, the providing unit uses the generation AI to analyze the facial expression and voice of the patient, estimate whether the patient is calm, and provide detailed information. In addition, the providing unit can also provide information in a visually easy-to-understand manner when the patient is excited. For example, the providing unit uses the generation AI to analyze the facial expression and voice of the patient, estimate whether the patient is excited, and provide information in a visually easy-to-understand manner. This allows the providing unit to adjust the method of providing information based on the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can use the generation AI to analyze the facial expression and voice of the patient to estimate the emotion of the patient.

[0091] The providing unit can select the optimal information provision method by analyzing the patient's past medical behavior when providing information. The providing unit selects the optimal method based on the information provision method used by the patient in the past, for example. For example, the providing unit uses the generating AI to analyze the patient's medical records and prescription history, evaluates the information provision methods used in the past, and selects the optimal method. The providing unit can also analyze the patient's past medical behavior and select an effective information provision method. For example, the providing unit uses the generating AI to analyze the patient's past medical history and selects an effective information provision method. Furthermore, the providing unit can also select the optimal information provision method with reference to the patient's past medical history. For example, the providing unit uses the generating AI to analyze the patient's past medical history and selects the optimal information provision method. As a result, the providing unit can analyze the patient's past medical behavior and select the optimal information provision method. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit can analyze the medical records and prescription history using the generating AI to analyze the patient's past medical behavior, and select the optimal information provision method.

[0092] The providing unit can customize the means of providing information based on the patient's current living situation when providing information. For example, when the patient is undergoing treatment at home, the providing unit selects an information providing means that can be used at home. For example, the providing unit uses the generating AI to analyze the patient's living environment and living habits and selects an information providing means that can be used at home. In addition, when the patient is at work, the providing unit can also select an information providing means that can be used at work. For example, the providing unit uses the generating AI to analyze the patient's work environment and activities at work and selects an information providing means that can be used at work. In addition, when the patient is traveling, the providing unit can also select an information providing means that can be used at the travel destination. For example, the providing unit uses the generating AI to analyze the environment and medical facilities at the patient's travel destination and selects an information providing means that can be used at the travel destination. This allows the providing unit to customize the means of providing information based on the patient's living situation. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the information providing department can use generative AI to analyze the patient's living situation, analyze the living environment and lifestyle habits, and customize the means of providing information.

[0093] The providing unit can estimate the patient's emotion and determine the priority of information provision based on the estimated emotion of the patient. For example, when the patient feels anxious, the providing unit preferentially provides information that gives a sense of security. For example, the providing unit uses the generation AI to analyze the facial expression and voice of the patient, estimate whether the patient feels anxious, and preferentially provide information that gives a sense of security. In addition, the providing unit can also preferentially provide detailed information when the patient is calm. For example, the providing unit uses the generation AI to analyze the facial expression and voice of the patient, estimate whether the patient is calm, and preferentially provide detailed information. In addition, when the patient is excited, the providing unit can also preferentially provide information that is visually easy to understand. For example, the providing unit uses the generation AI to analyze the facial expression and voice of the patient, estimate whether the patient is excited, and preferentially provide information that is visually easy to understand. This allows the providing unit to determine the priority of information provision based on the patient's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can use the generation AI to analyze the facial expression and voice of the patient to estimate the emotion of the patient.

[0094] When providing information, the providing unit can select the optimal information provision method in consideration of the geographical location information of the patient. For example, when the patient is in an urban area, the providing unit selects an information provision means that can be used in the urban area. For example, the providing unit uses the generating AI to analyze the GPS data and address information of the patient and selects an information provision means that can be used in the urban area. In addition, when the patient is in a rural area, the providing unit can also select an information provision means that can be used in the rural area. For example, the providing unit uses the generating AI to analyze the movement history and address information of the patient and selects an information provision means that can be used in the rural area. In addition, when the patient is overseas, the providing unit can also select an information provision means that can be used locally. For example, the providing unit uses the generating AI to analyze the geographical location information of the patient and selects an information provision means that can be used locally. As a result, the providing unit can select the optimal information provision method in consideration of the geographical location information of the patient. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the provision department can use generation AI to analyze GPS data and address information to analyze the patient's geographic location information and select the optimal information provision method.

[0095] The providing unit can analyze the social media activity of the patient at the time of providing information and propose a means of providing information. The providing unit proposes a relevant means of providing information based on, for example, health information shared by the patient on social media. For example, the providing unit uses a generating AI to analyze the content of posts on the patient's social media and proposes a relevant means of providing information. The providing unit can also propose a means of providing information based on information on medical professionals followed by the patient on social media. For example, the providing unit uses a generating AI to analyze the content of posts of medical professionals followed by the patient and proposes a relevant means of providing information. The providing unit can also propose a means of providing information based on information on health communities in which the patient participates on social media. For example, the providing unit uses a generating AI to analyze the content of posts of health communities in which the patient participates and proposes a relevant means of providing information. This allows the providing unit to analyze the social media activity of the patient and propose a means of providing information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the providing unit can use a generating AI to analyze the content of posts and the number of followers in order to analyze the social media activity of the patient and propose a means of providing information.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The collection unit can collect the patient's family history and genetic information and provide it to the analysis unit. For example, the collection unit can collect information for evaluating a genetic risk based on the patient's family history. The collection unit can also analyze the patient's genetic information and evaluate a disease risk associated with a specific gene mutation. Furthermore, the collection unit can collect information regarding preventive medical measures based on the patient's family history and genetic information. This allows the collection unit to provide more accurate information taking into account the patient's family history and genetic information.

[0098] The analysis unit can analyze the lifestyle habit data of the patient and evaluate the health risk. For example, the analysis unit can analyze the patient's diet and exercise habits and evaluate the risk of lifestyle-related diseases. The analysis unit can also analyze the patient's sleep patterns and evaluate the risk of sleep disorders. Furthermore, the analysis unit can analyze the patient's stress level and evaluate the risk of stress-related diseases. This allows the analysis unit to comprehensively evaluate the health risk based on the patient's lifestyle habit data.

[0099] The selection unit can select the most appropriate hospital by taking into consideration the socioeconomic background of the patient. For example, the selection unit evaluates the patient's income and insurance status and selects a hospital that is less of a financial burden. The selection unit can also evaluate the medical access situation in the patient's residential area and select a hospital that is easily accessible. Furthermore, the selection unit can evaluate the patient's social support network and select a hospital where support is easily available. In this way, the selection unit can select a more appropriate hospital by taking into consideration the patient's socioeconomic background.

[0100] The information delivery department can tailor the way it provides information based on the patient's language and cultural background. For example, the information delivery department can provide information in the patient's native language to eliminate language barriers. The information delivery department can also provide information that takes into account the patient's cultural background to avoid cultural misunderstandings. Furthermore, the information delivery department can provide information that takes into account the patient's religious beliefs to respect faith-based medical choices. This allows the information delivery department to provide more appropriate information based on the patient's language and cultural background.

[0101] The collection unit can estimate the patient's emotions and evaluate the reliability of the information to be collected based on the estimated patient's emotions. For example, when the patient feels anxious, the collection unit preferentially collects information from highly reliable information sources. When the patient is calm, the collection unit can also collect information from multiple information sources and check the reliability. Furthermore, when the patient is excited, the collection unit can also quickly collect highly reliable information. This allows the collection unit to evaluate the reliability of information based on the patient's emotions and provide appropriate information.

[0102] The analysis unit can estimate the patient's emotions and adjust the accuracy of the analysis based on the estimated patient's emotions. For example, if the patient feels anxious, the analysis unit can quickly perform an analysis and provide the results. If the patient is calm, the analysis unit can also perform a detailed analysis and provide highly accurate results. Furthermore, if the patient is excited, the analysis unit can provide analysis results that are visually easy to understand. This allows the analysis unit to adjust the accuracy of the analysis based on the patient's emotions and provide appropriate results.

[0103] The selection unit can estimate the patient's emotions and adjust the selection process based on the estimated patient's emotions. For example, if the patient feels anxious, the selection unit preferentially selects a process that provides a sense of security. If the patient is calm, the selection unit can also make a selection while providing detailed information. Furthermore, if the patient is excited, the selection unit can quickly make a selection and provide the result. This allows the selection unit to adjust the selection process based on the patient's emotions and make an appropriate selection.

[0104] The providing unit can estimate the patient's emotions and adjust the timing of providing information based on the estimated patient's emotions. For example, when the patient feels anxious, the providing unit quickly provides information to give a sense of security. The providing unit can also adjust the timing of providing detailed information when the patient is calm. Furthermore, when the patient is excited, the providing unit can adjust the timing of providing visually easy-to-understand information. In this way, the providing unit can adjust the timing of providing information based on the patient's emotions and provide appropriate information.

[0105] The providing unit can estimate the patient's emotions and adjust the format of the information provided based on the estimated patient's emotions. For example, if the patient is feeling anxious, the providing unit can provide information in a concise and to the point format. Also, if the patient is calm, the providing unit can provide information in a detailed format. Furthermore, if the patient is excited, the providing unit can provide information in a visually easy-to-understand format. In this way, the providing unit can adjust the format of the information provided based on the patient's emotions and provide appropriate information.

[0106] The collection unit can customize the method of information collection based on the patient's living environment. For example, if the patient lives in an urban area, the collection unit can preferentially collect information about medical resources in urban areas. Also, if the patient lives in a rural area, the collection unit can preferentially collect information about medical resources in rural areas. Furthermore, if the patient is in a specific environmental condition, the collection unit can collect medical information appropriate for that environment. This allows the collection unit to customize the method of information collection based on the patient's living environment and provide appropriate information.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The collection unit collects patient information. Patient information includes urgency, severity, underlying diseases, and available hospital beds. For example, the collection unit collects the severity of symptoms and the time since onset to evaluate the urgency of the patient, and collects the progression of the disease and the risk to life to evaluate the severity. In addition, the collection unit accesses the hospital database to collect real-time availability of hospital beds. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is performed to evaluate the urgency and severity of the patient's condition. For example, the analysis unit analyzes the severity of symptoms and the time since onset, and analyzes the progression of the disease and the risk to life. Step 3: The selection unit selects the most suitable hospital based on the analysis results obtained by the analysis unit. The selection is made taking into consideration the hospital's expertise and available beds. For example, the selection unit uses a generative AI to select the most suitable hospital based on the patient's urgency and severity, taking into consideration the hospital's expertise and available beds. Step 4: The provider provides the information to the hospital selected by the selector. The information provider provides information on the urgency and severity of the patient. For example, the provider provides the selected hospital with information on the urgency and severity of the patient, underlying diseases, and available hospital beds.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned collection unit, analysis unit, selection unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects patient information using the camera 42 and microphone 38B of the smart device 14, and evaluates the urgency and severity of the patient by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects an optimal hospital based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides patient information to the selected hospital. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned collection unit, analysis unit, selection unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects patient information using the camera 42 and microphone 238 of the smart glasses 214, and evaluates the urgency and severity of the patient by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects an optimal hospital based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides patient information to the selected hospital. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned collection unit, analysis unit, selection unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects patient information using the camera 42 and microphone 238 of the headset type terminal 314, and evaluates the urgency and severity of the patient by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects the optimal hospital based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the patient's information to the selected hospital. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0161] Each of the multiple elements including the above-mentioned collection unit, analysis unit, selection unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects patient information using the camera 42 and microphone 238 of the robot 414, and evaluates the urgency and severity of the patient by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects an optimal hospital based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the patient information to the selected hospital. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) A collection unit for collecting patient information; an analysis unit that analyzes the information collected by the collection unit; A selection unit that selects an appropriate hospital based on the analysis result obtained by the analysis unit; A providing unit that provides information to the hospital selected by the selecting unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect real-time information on available hospital beds 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the urgency and severity of patients 2. The system of claim 1, (Appendix 4) The selection unit is Select based on hospital specialty 2. The system of claim 1, (Appendix 5) The providing unit is Provide information to selected hospitals 2. The system of claim 1, (Appendix 6) The collecting unit includes: Estimate the patient's emotions and prioritize the information to be collected based on the estimated patient's emotions. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Analyze the patient's past medical history and choose the appropriate method of collecting information 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting information, filter it based on the patient's current living situation and environment. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Estimate the patient's emotions and adjust the level of detail of information to be collected based on the estimated patient emotions. 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting information, prioritize collection of relevant information by taking into account the geographic location of the patient. 2. The system of claim 1, (Appendix 11) The collecting unit includes: At the time of collection, analyze the patient's social media activity and collect relevant information. 2. The system of claim 1, (Appendix 12) The analysis unit is Infer patient emotion and adjust analysis presentation based on the inferred emotion 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the level of detail based on the importance of the patient 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, different analysis algorithms are applied depending on the patient category. 2. The system of claim 1, (Appendix 15) The analysis unit is Inferring patient sentiment and adjusting the length of the analysis based on the estimated patient sentiment 2. The system of claim 1, (Appendix 16) The analysis unit is At the time of analysis, prioritize analysis based on when patients were submitted 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analyses based on patient relevance 2. The system of claim 1, (Appendix 18) The selection unit is Inferring the patient's emotions and adjusting the selection criteria based on the estimated patient's emotions 2. The system of claim 1, (Appendix 19) The selection unit is When selecting hospitals, improve the accuracy of the selection process by taking into account the interrelationships between hospitals. 2. The system of claim 1, (Appendix 20) The selection unit is When selecting hospitals, the hospital's attribute information is taken into consideration. 2. The system of claim 1, (Appendix 21) The selection unit is Inferring patient sentiment and adjusting the order in which selection results are displayed based on the inferred patient sentiment 2. The system of claim 1, (Appendix 22) The selection unit is At the time of selection, the geographical distribution of hospitals will be taken into consideration. 2. The system of claim 1, (Appendix 23) The selection unit is Improve the accuracy of selection based on the hospital's related literature during selection 2. The system of claim 1, (Appendix 24) The providing unit is Infer the patient's feelings and adjust the way information is provided based on the inferred feelings 2. The system of claim 1, (Appendix 25) The providing unit is When providing information, the patient's past medical behavior is analyzed to select the most appropriate method of providing information. 2. The system of claim 1, (Appendix 26) The providing unit is When providing information, tailor the information delivery method based on the patient's current life situation. 2. The system of claim 1, (Appendix 27) The providing unit is Estimate the patient's feelings and determine the priority of providing information based on the estimated patient's feelings 2. The system of claim 1, (Appendix 28) The providing unit is When providing information, the optimal method of providing information is selected taking into account the patient's geographic location information. 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, analyze the patient's social media activity to suggest a means of providing the information. 2. The system of claim 1, [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit for collecting patient information; an analysis unit that analyzes the information collected by the collection unit; A selection unit that selects an appropriate hospital based on the analysis result obtained by the analysis unit; A providing unit that provides information to the hospital selected by the selecting unit. A system characterized by:

2. The collecting unit includes: Collect real-time information on available hospital beds 2. The system of claim 1 .

3. The analysis unit is Analyze the urgency and severity of patients 2. The system of claim 1 .

4. The selection unit is Select based on hospital specialty 2. The system of claim 1 .

5. The collecting unit includes: Estimate the patient's emotions and prioritize the information to be collected based on the estimated patient's emotions.

2. The system of claim 1 .

6. The collecting unit includes: Analyze the patient's past medical history and choose the appropriate method of collecting information 2. The system of claim 1 .

7. The collecting unit includes: When collecting information, filter it based on the patient's current living situation and environment.

2. The system of claim 1 .

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