System

Abstract

To provide a system for the rapid identification of an appropriate medical department and an available hospital based on symptoms.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, an identification unit, a search unit, and a provision unit. The reception unit receives input of symptoms. The analysis unit analyzes the symptoms received by the reception unit. The identification unit identifies a medical department based on the symptoms analyzed by the analysis unit. The search unit searches for an available hospital based on the medical department identified by the identification unit. The provision unit provides the information on the hospital found by the search unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 identify appropriate medical departments and hospitals that could accept patients.

[0005] The system according to the embodiment aims to quickly identify an appropriate medical department and hospital that can accept a patient based on the patient's symptoms. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an identification unit, a search unit, and a provision unit. The reception unit receives input of symptoms. The analysis unit analyzes the symptoms received by the reception unit. The identification unit identifies a medical department based on the symptoms analyzed by the analysis unit. The search unit searches for hospitals that can accept the patient based on the medical department identified by the identification unit. The provision unit provides information on the hospitals searched for by the search unit. Effect of the Invention

[0007] The system according to the embodiment can quickly identify the appropriate medical department and hospital that can accept the patient based on the symptoms. [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 the 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 medical referral system according to the embodiment of the present invention is a system that introduces an appropriate medical department or the nearest hospital that can accept emergency outpatients when it is not serious enough to call an ambulance but it seems better to receive emergency medical care. In this system, a user inputs symptoms in a chat format, and an AI analyzes the input symptoms, identifies the corresponding medical department, searches for the nearest hospital that can currently accept the patient, and introduces the patient to the user. For example, if a user inputs "I have a headache and feel nauseous," the AI ​​analyzes this and determines that neurology or neurosurgery is appropriate. The AI ​​then searches for the nearest hospital that can currently accept the patient and introduces the patient to the user. This mechanism allows the user to visit an appropriate medical institution quickly. This service has the following effects. First, the user does not need to make a phone call and can easily input symptoms in chat format, which solves the problem of difficulty in connecting. In addition, since the AI ​​analyzes the symptoms, it is possible to introduce an appropriate medical department or hospital without relying on the skills of the person who answers the phone. Furthermore, since the nearest hospital that can accept the patient is searched for, the patient can be examined quickly. As a result, the emergency medical referral system allows the user to visit an appropriate medical institution quickly.

[0029] The emergency medical referral system according to the embodiment includes a reception unit, an analysis unit, a specification unit, a search unit, and a provision unit. The reception unit accepts a user to input symptoms in a chat format. For example, the user can input "I have a headache and feel nauseous." The analysis unit uses AI to analyze the symptoms accepted by the reception unit. For example, the AI ​​uses natural language processing technology to analyze the input symptoms and identify the severity of the symptoms and the relevant medical department. The specification unit specifies an appropriate medical department based on the symptoms analyzed by the analysis unit. For example, the AI ​​determines that neurology or neurosurgery is appropriate. The search unit searches for a hospital that can accept the patient based on the medical department specified by the specification unit. For example, the AI ​​searches for the nearest hospital that can currently accept the patient. The provision unit provides the user with information on the hospital searched by the search unit. For example, information such as the name, address, contact information, and time period when the hospital can be accepted is provided. As a result, the emergency medical referral system according to the embodiment allows the user to quickly visit an appropriate medical institution. The elements of the reception unit, analysis unit, specification unit, search unit, and provision unit are sequentially related to each other. This allows the emergency medical referral system to allow the user to quickly visit an appropriate medical institution.

[0030] The reception unit accepts the user to input symptoms in a chat format. For example, the user can input "I have a headache and feel nauseous." The reception unit provides an interface that the user can intuitively operate, allowing the user to smoothly input symptoms. Specifically, when the user inputs symptoms, a predictive conversion function and a candidate list are displayed to reduce the effort required for input. In addition, the reception unit has a function of transmitting the symptoms input by the user to the analysis unit in real time, and analysis can be started before the user completes input. Furthermore, the reception unit can refer to the user's past medical history and account information and automatically complement information related to the input symptoms. For example, if the user has complained of similar symptoms in the past, additional questions can be asked based on that information to collect more detailed information. As a result, the reception unit provides an environment in which the user can quickly and accurately input symptoms, thereby improving the efficiency of the entire system.

[0031] The analysis unit uses AI to analyze the symptoms received by the reception unit. For example, the AI ​​uses natural language processing technology to analyze the input symptoms and identify the severity of the symptoms and the relevant medical department. Specifically, the AI ​​tokenizes the text input by the user and analyzes the meaning of each token. Then, it analyzes the relationship between the tokens and understands the context. For example, for the input "I have a headache and feel nauseous," the AI ​​extracts the symptoms "headache" and "nausea" and evaluates the severity of each. The AI ​​also refers to past medical data and medical literature to identify the medical department related to the input symptoms. For example, if "headache" and "nausea" occur simultaneously, it determines that neurology or neurosurgery is appropriate. Furthermore, the analysis unit can perform more accurate analysis by taking into account personal information such as the user's age, gender, and medical history. This allows the analysis unit to quickly and accurately analyze the symptoms input by the user and provide information to identify the appropriate medical department.

[0032] The identification unit identifies an appropriate medical department based on the symptoms analyzed by the analysis unit. For example, the AI ​​determines that neurology or neurosurgery is appropriate. The identification unit uses a rule base or machine learning model to identify the medical department based on the information provided by the analysis unit. Specifically, the identification unit refers to a database for evaluating the association between symptoms and medical departments, and identifies the medical department based on past medical data and medical literature. For example, if "headache" and "nausea" occur simultaneously, it determines that neurology or neurosurgery is appropriate. In addition, the identification unit can identify the medical department with higher accuracy by taking into account the user's personal information and past medical history. For example, if the user has visited a neurology department in the past for similar symptoms, the identification unit can recommend a neurology department again based on that information. This allows the identification unit to support the user in quickly visiting an appropriate medical department.

[0033] The search unit searches for hospitals that can accept patients based on the medical department identified by the identification unit. For example, the AI ​​searches for the nearest hospital that can currently accept patients. The search unit refers to a hospital database and performs a search based on information such as the acceptance status of each medical department, the location of the hospital, and the consultation hours. Specifically, the search unit identifies the nearest hospital based on the user's current location and checks the acceptance status of the medical department in real time. The search unit can also select the optimal hospital by taking into account the hospital's evaluation and user feedback. For example, if the user desires a highly rated hospital, the search unit can filter the search results based on that information. Furthermore, the search unit can prioritize displaying hospitals where the user can receive treatment quickly, taking into account the hospital's reservation status and waiting time. This allows the search unit to support the user in receiving treatment quickly and appropriately at a medical institution.

[0034] The providing unit provides the user with information on the hospitals searched for by the search unit. For example, the providing unit provides information such as the name, address, contact information, and available hours of the hospital. The providing unit provides a visually easy-to-understand interface so that the user can easily check the information. Specifically, the providing unit has a map display and navigation function for the hospital, so that the user can easily check the route to the hospital. The providing unit also provides additional information such as detailed information on the hospital, explanations of the medical department, and doctor profiles, to help the user to visit the hospital with peace of mind. Furthermore, the providing unit has a function that allows the user to directly contact the hospital, so that reservations and inquiries can be made smoothly. For example, the providing unit displays the telephone number and email address of the hospital, so that the user can contact the hospital with one click. In this way, the providing unit can help the user to visit an appropriate medical institution quickly and easily.

[0035] The analysis unit can refer to a medical database and analyze symptoms using natural language processing technology. The analysis unit, for example, refers to a medical database and analyzes symptoms using natural language processing technology. The medical database includes, but is not limited to, a medical history database, a drug database, and the like. The natural language processing technology can use, for example, morphological analysis, grammatical analysis, semantic analysis, and other technologies. As a result, by using the medical database and the natural language processing technology, the accuracy of symptom analysis is improved. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data acquired from the medical database to the generation AI and cause the generation AI to execute symptom analysis.

[0036] The search unit can cooperate with the system of each hospital and acquire the admission status in real time. The search unit cooperates with the system of each hospital and acquires the admission status in real time, for example. The definition and criteria of real time include, but are not limited to, update frequency and delay time. As a result, the admission status can be acquired in real time, allowing the optimal hospital to be quickly searched for. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input data acquired from the system of each hospital to the generation AI and cause the generation AI to analyze the admission status.

[0037] The reception unit can provide a guideline for the user to input. The reception unit, for example, provides a guideline for the user to input. The content of the guideline includes, for example, input procedures and points to note, but is not limited to such examples. This provides a guideline so that the user can properly input symptoms. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the content of the user's input to a generation AI and cause the generation AI to provide the guideline.

[0038] The providing unit can provide information on the name of the medical department, the name of the hospital, the address, contact information, and the time period during which the patient can be accepted. The providing unit provides, for example, information on the name of the medical department, the name of the hospital, the address, contact information, and the time period during which the patient can be accepted. The content of the information provided includes, for example, the contact information of the hospital, the consultation hours, and details of the medical department, but is not limited to such examples. This allows the user to quickly obtain the information he or she needs. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input hospital information to the generating AI and cause the generating AI to provide the information.

[0039] The reception unit can analyze the user's past symptom input history and propose the optimal input method. The reception unit, for example, analyzes the user's past symptom input history and proposes the optimal input method. Criteria for the optimal input method include, for example, ease of input and accuracy, but are not limited to such examples. For example, symptoms frequently input by the user in the past can be automatically displayed as candidates. In addition, input methods (voice, text, etc.) used by the user in the past can be preferentially proposed. Furthermore, symptoms to be input in a specific time period can be predicted and proposed from the user's past input history. As a result, the optimal input method is proposed based on the past input history, improving the convenience of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI and cause the generation AI to execute the proposal of the optimal input method.

[0040] The reception unit can filter the input contents based on the user's current health condition and lifestyle habits when inputting symptoms. The reception unit, for example, filters the input contents based on the user's current health condition and lifestyle habits when inputting symptoms. Filtering criteria include, for example, filtering conditions based on health conditions and lifestyle habits, but are not limited to such examples. For example, the user can preferentially input related symptoms in consideration of the medicines currently being taken. In addition, related symptoms can be filtered based on the user's lifestyle habits (smoking, drinking, etc.). Furthermore, related symptoms can be preferentially input in consideration of the user's medical history. This makes it possible to input appropriate symptoms based on the user's health condition and lifestyle habits. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's health condition and lifestyle habit data to the generation AI and cause the generation AI to execute filtering of the input contents.

[0041] The reception unit can preferentially input highly relevant symptoms in consideration of the geographical location information of the user when inputting symptoms. For example, the reception unit preferentially inputs highly relevant symptoms in consideration of the geographical location information of the user when inputting symptoms. Methods for acquiring geographical location information include, but are not limited to, GPS data and location information services. For example, when the user is in a specific area, symptoms related to a disease prevalent in that area can be preferentially input. In addition, when the user is traveling, symptoms related to health risks at the travel destination can also be preferentially input. Furthermore, when the user is in a specific environment (factory, farm, etc.), symptoms related to that environment can also be preferentially input. In this way, symptoms specific to the area can be preferentially input by considering the geographical location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the priority order of highly relevant symptoms.

[0042] The reception unit can analyze the user's social media activity and input related symptoms when inputting symptoms. The reception unit can analyze the user's social media activity and input related symptoms when inputting symptoms, for example. Methods of analyzing social media activity include, for example, analysis of posted content and activity frequency, but are not limited to such examples. For example, related symptoms can be input based on health information shared by the user on social media. Also, related symptoms can be input based on information of health-related accounts that the user follows on social media. Furthermore, related symptoms can be input based on information of health-related groups that the user participates in on social media. As a result, by inputting related symptoms based on social media activity, a more accurate medical department can be identified. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to identify related symptoms.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the latest information in the medical database when analyzing symptoms. The analysis unit, for example, improves the accuracy of the analysis by referring to the latest information in the medical database when analyzing symptoms. The definition and acquisition method of the latest information include, for example, the update frequency of the database and the reliability of the information, but are not limited to such examples. For example, the analysis of symptoms can be performed by referring to the latest medical research data. The analysis of symptoms can also be performed by referring to the latest medical guidelines. Furthermore, the analysis of symptoms can also be performed by referring to the latest medical statistical data. As a result, the accuracy of the analysis is improved by referring to the latest medical database. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the latest information in the medical database to the generation AI and cause the generation AI to execute the symptom analysis.

[0044] The analysis unit can perform the analysis while considering the user's past medical history when analyzing symptoms. For example, the analysis unit performs the analysis while considering the user's past medical history when analyzing symptoms. Methods for acquiring the past medical history include, but are not limited to, for example, electronic medical records and medical records. For example, the analysis of symptoms can be performed by referring to the user's past medical records. In addition, the analysis of symptoms can be performed by referring to the user's past prescription history. Furthermore, the analysis of symptoms can be performed by referring to the user's past test results. As a result, more accurate symptom analysis is possible by considering the past medical history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past medical history data to the generation AI and cause the generation AI to execute the symptom analysis.

[0045] The analysis unit can perform the analysis while considering the user's living environment information when analyzing symptoms. The analysis unit performs the analysis while considering the user's living environment information, for example, while analyzing symptoms. The method of acquiring the living environment information includes, for example, the living environment and the work environment, but is not limited to such examples. For example, the analysis of symptoms can be performed by referring to the environmental information of the area where the user lives. The analysis of symptoms can also be performed by referring to the user's work environment information. Furthermore, the analysis of symptoms can also be performed by referring to the user's home environment information. As a result, by considering the living environment information, more accurate symptom analysis is possible. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's living environment information to the generation AI and cause the generation AI to execute the symptom analysis.

[0046] The analysis unit can improve the accuracy of the analysis by referring to related medical literature when analyzing symptoms. The analysis unit, for example, improves the accuracy of the analysis by referring to related medical literature when analyzing symptoms. Types of medical literature include, for example, academic papers and medical guidelines, but are not limited to such examples. For example, the latest medical literature can be referred to to analyze symptoms. Also, related medical research papers can be referred to to analyze symptoms. Furthermore, medical journal articles can be referred to to analyze symptoms. As a result, the accuracy of the analysis is improved by referring to related medical literature. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data of medical literature to the generation AI and cause the generation AI to execute symptom analysis.

[0047] The identification unit can improve the accuracy of identification by referring to past medical data when identifying a medical department. The identification unit, for example, improves the accuracy of identification by referring to past medical data when identifying a medical department. Methods for acquiring past medical data include, for example, electronic medical records and medical records, but are not limited to such examples. For example, a medical department corresponding to a symptom can be identified by referring to past medical data. In addition, the accuracy of identification can be improved by analyzing past medical data. Furthermore, an algorithm for identifying a medical department can be adjusted based on past medical data. As a result, the accuracy of identification is improved by referring to past medical data. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input past medical data to the generation AI and cause the generation AI to identify a medical department.

[0048] The identification unit can perform identification by considering the user's current health condition when identifying a medical department. For example, the identification unit performs identification by considering the user's current health condition when identifying a medical department. Methods for acquiring the current health condition include, but are not limited to, for example, health check results and self-reporting. For example, an appropriate medical department can be identified by referring to the user's current health condition. In addition, a medical department can be identified based on the user's current symptoms. Furthermore, a medical department can be identified by considering the user's current physical condition. As a result, an appropriate medical department can be identified by considering the current health condition. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's current health condition data to the generation AI and cause the generation AI to execute medical department identification.

[0049] The identification unit can perform identification in consideration of the geographical distribution of the user when identifying the medical department. For example, the identification unit performs identification in consideration of the geographical distribution of the user when identifying the medical department. Methods for acquiring the geographical distribution include, for example, regional patient data and a geographical information system, but are not limited to such examples. For example, the nearest medical department can be identified based on the user's current location. In addition, an appropriate medical department can be identified in consideration of the geographical distribution of the user. Furthermore, a medical department can be identified in consideration of the range in which the user can move. In this way, the optimal medical department can be identified by considering the geographical distribution. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the geographical distribution data of the user to the generation AI and cause the generation AI to execute the medical department identification.

[0050] The identification unit can improve the accuracy of identification by referring to the related medical guidelines when identifying a medical department. The identification unit improves the accuracy of identification by referring to the related medical guidelines when identifying a medical department, for example. Types of medical guidelines include, for example, clinical guidelines and treatment protocols, but are not limited to such examples. For example, the medical department can be identified by referring to the latest medical guidelines. In addition, the algorithm for identifying the medical department can be adjusted based on the related medical guidelines. Furthermore, the medical department can be identified based on the information of the medical guidelines. As a result, the accuracy of identification is improved by referring to the medical guidelines. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input data of the medical guidelines to the generation AI and cause the generation AI to execute the identification of the medical department.

[0051] The search unit can improve the accuracy of the search by acquiring the latest admission status of each hospital in real time during a hospital search. For example, the search unit can improve the accuracy of the search by acquiring the latest admission status of each hospital in real time during a hospital search. The method of acquiring the latest admission status includes, for example, cooperation with the hospital's system and acquisition of real-time data, but is not limited to such examples. For example, the admission status can be acquired in real time by cooperation with the system of each hospital. Also, the optimal hospital can be searched for based on the latest admission status of each hospital. Furthermore, the search result can be provided taking into account the real-time admission status. As a result, the optimal hospital can be quickly searched for by acquiring the real-time admission status. A part or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input hospital admission status data to the generation AI and cause the generation AI to execute a hospital search.

[0052] The search unit can perform a search while considering the user's past medical history when searching for a hospital. For example, the search unit performs a search while considering the user's past medical history when searching for a hospital. Methods for acquiring the past medical history include, but are not limited to, electronic medical records and medical records. For example, the optimal hospital can be searched for by referring to the user's past medical history. In addition, related hospitals can be preferentially searched based on the user's past medical history. Furthermore, the user's past medical history can be analyzed to provide a search result. As a result, related hospitals can be preferentially searched for by considering the past medical history. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the user's past medical history data to the generation AI and cause the generation AI to execute a hospital search.

[0053] The search unit can search for an optimal hospital by considering the geographical location information of the user when searching for a hospital. For example, the search unit searches for an optimal hospital by considering the geographical location information of the user when searching for a hospital. Methods for acquiring geographical location information include, but are not limited to, GPS data and location information services. For example, the nearest hospital can be searched for based on the user's current location. In addition, an appropriate hospital can be searched for by considering the geographical location information of the user. Furthermore, a hospital can be searched for by considering the range in which the user can move. As a result, the nearest hospital can be quickly searched for by considering the geographical location information. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute a hospital search.

[0054] The search unit can improve the accuracy of the search by referring to related medical network information when searching for a hospital. The search unit can improve the accuracy of the search by referring to related medical network information when searching for a hospital, for example. The types of medical network information include, for example, medical institution collaboration information and network configuration, but are not limited to such examples. For example, the optimal hospital can be searched for by referring to the medical network information. In addition, a search result can be provided based on the related medical network information. Furthermore, the accuracy of the search can be improved by taking the medical network information into consideration. As a result, the accuracy of the search is improved by referring to the medical network information. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the medical network information to the generation AI and cause the generation AI to execute the hospital search.

[0055] The providing unit can provide optimal information by referring to the user's past medical history when providing hospital information. The providing unit, for example, provides optimal information by referring to the user's past medical history when providing hospital information. Methods for acquiring the past medical history include, for example, electronic medical records and medical records, but are not limited to such examples. For example, related hospital information can be provided by referring to the user's past medical history. In addition, optimal hospital information can be provided based on the user's past medical history. Furthermore, the accuracy of information provision can be improved by analyzing the user's past medical history. As a result, related hospital information can be provided by referring to the past medical history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past medical history data to the generating AI and cause the generating AI to execute information provision.

[0056] The providing unit can customize information based on the user's current health condition when providing hospital information. The providing unit, for example, customizes information based on the user's current health condition when providing hospital information. Methods for acquiring the current health condition include, for example, health check results and self-reporting, but are not limited to such examples. For example, optimal hospital information can be provided by referring to the user's current health condition. Also, related hospital information can be provided based on the user's current symptoms. Furthermore, information can be customized taking into account the user's current physical condition. As a result, optimal hospital information can be provided by taking into account the current health condition. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's current health condition data to the generating AI and cause the generating AI to execute the customization of the information.

[0057] The providing unit can provide information based on the geographical location information of the user when providing hospital information. The providing unit, for example, provides information based on the geographical location information of the user when providing hospital information. Methods for acquiring geographical location information include, for example, GPS data and location information services, but are not limited to such examples. For example, the nearest hospital information can be provided based on the user's current location. In addition, appropriate hospital information can be provided taking into account the geographical location information of the user. Furthermore, hospital information can be provided taking into account the range in which the user can move. As a result, by taking into account the geographical location information, the nearest hospital information can be provided quickly. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to execute information provision.

[0058] The providing unit can improve the accuracy of information by referring to related medical guidelines when providing hospital information. The providing unit can improve the accuracy of information by referring to related medical guidelines when providing hospital information, for example. Types of medical guidelines include, but are not limited to, clinical guidelines and treatment protocols. For example, hospital information can be provided by referring to the latest medical guidelines. In addition, the accuracy of information provision can be improved based on related medical guidelines. Furthermore, hospital information can be provided based on information on medical guidelines. As a result, the accuracy of information provision is improved by referring to medical guidelines. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data of medical guidelines to a generating AI and cause the generating AI to execute information provision.

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

[0060] The reception unit can support voice input when the user inputs symptoms. For example, when the user says "I have a headache and feel nauseous," the reception unit can convert the speech into text using voice recognition technology and send it to the analysis unit. In addition, when the speech is input, the reception unit can analyze the user's speech rate and tone to estimate the urgency of the speech. Furthermore, in order to improve the accuracy of the speech input, the reception unit can learn the user's past speech data and respond to individual speech patterns. Thus, by supporting the speech input, the user can input symptoms more easily.

[0061] The analysis unit can take into account the user's age and gender when analyzing the user's symptoms. For example, the frequency and risk of occurrence of a particular symptom can be evaluated based on age and gender, and reflected in the analysis results. In addition, it can also refer to medical guidelines according to age and gender to identify a more appropriate medical department. Furthermore, it can evaluate the severity of symptoms by taking into account medical history and family history related to age and gender. This enables analysis that takes into account the individual characteristics of the user, and allows for more accurate identification of a medical department.

[0062] When acquiring the hospital acceptance status, the search unit can also take into consideration the availability of specialists at each hospital. For example, it is possible to preferentially search for hospitals that have specialists in a specific medical department and provide them to the user. It is also possible to evaluate the quality of medical care and recommend the most suitable hospital based on the availability of specialists. Furthermore, it is also possible to acquire the schedules of specialists in real time and provide the user with the time periods when they are available for consultation. In this way, by taking into consideration the availability of specialists, it is possible to quickly search for hospitals that provide higher quality medical care.

[0063] The reception unit can provide an interactive question format when the user inputs symptoms. For example, if the user inputs "I have a headache," additional questions such as "How intense is the pain?" and "Where is the pain?" can be presented to collect more detailed information. The content of the question can also dynamically change depending on the user's answer, allowing more specific information to be obtained. Furthermore, the question format can be not only text, but also images and options. Thus, by providing an interactive question format, the user can input more detailed symptom information.

[0064] The providing unit can include user reviews and ratings in the hospital information provided to the user. For example, reviews and ratings from users who have used the hospital in the past can be displayed to help users select a hospital. Reviews and ratings can also be displayed separately for each medical department. Furthermore, the content of reviews and ratings can be analyzed and filtered based on specific keywords (e.g., waiting time, doctor's response, etc.). In this way, by including user reviews and ratings, reference information for selecting a hospital can be provided.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit receives input of symptoms from a user in a chat format. For example, the user can input "I have a headache and feel nauseous." Step 2: The analysis unit uses AI to analyze the symptoms received by the reception unit. For example, the AI ​​uses natural language processing technology to analyze the input symptoms and identify the severity of the symptoms and the relevant medical departments. Step 3: The identification unit identifies the appropriate medical specialty based on the symptoms analyzed by the analysis unit. For example, the AI ​​may determine that neurology or neurosurgery is appropriate. Step 4: The search unit searches for hospitals that can accept the patient based on the medical department identified by the identification unit. For example, the AI ​​searches for the nearest hospital that can currently accept the patient. Step 5: The providing unit provides the user with information about the hospitals searched for by the searching unit, such as the hospital's name, address, contact information, and available hours.

[0067] (Example 2) The emergency medical referral system according to the embodiment of the present invention is a system that introduces an appropriate medical department or the nearest hospital that can accept emergency outpatients when it is not serious enough to call an ambulance but it seems better to receive emergency medical care. In this system, a user inputs symptoms in a chat format, and an AI analyzes the input symptoms, identifies the corresponding medical department, searches for the nearest hospital that can currently accept the patient, and introduces the patient to the user. For example, if a user inputs "I have a headache and feel nauseous," the AI ​​analyzes this and determines that neurology or neurosurgery is appropriate. The AI ​​then searches for the nearest hospital that can currently accept the patient and introduces the patient to the user. This mechanism allows the user to visit an appropriate medical institution quickly. This service has the following effects. First, the user does not need to make a phone call and can easily input symptoms in chat format, which solves the problem of difficulty in connecting. In addition, since the AI ​​analyzes the symptoms, it is possible to introduce an appropriate medical department or hospital without relying on the skills of the person who answers the phone. Furthermore, since the nearest hospital that can accept the patient is searched for, the patient can be examined quickly. As a result, the emergency medical referral system allows the user to visit an appropriate medical institution quickly.

[0068] The emergency medical referral system according to the embodiment includes a reception unit, an analysis unit, a specification unit, a search unit, and a provision unit. The reception unit accepts a user to input symptoms in a chat format. For example, the user can input "I have a headache and feel nauseous." The analysis unit uses AI to analyze the symptoms accepted by the reception unit. For example, the AI ​​uses natural language processing technology to analyze the input symptoms and identify the severity of the symptoms and the relevant medical department. The specification unit specifies an appropriate medical department based on the symptoms analyzed by the analysis unit. For example, the AI ​​determines that neurology or neurosurgery is appropriate. The search unit searches for a hospital that can accept the patient based on the medical department specified by the specification unit. For example, the AI ​​searches for the nearest hospital that can currently accept the patient. The provision unit provides the user with information on the hospital searched by the search unit. For example, information such as the name, address, contact information, and time period when the hospital can be accepted is provided. As a result, the emergency medical referral system according to the embodiment allows the user to quickly visit an appropriate medical institution. The elements of the reception unit, analysis unit, specification unit, search unit, and provision unit are sequentially related to each other. This allows the emergency medical referral system to allow the user to quickly visit an appropriate medical institution.

[0069] The reception unit accepts the user to input symptoms in a chat format. For example, the user can input "I have a headache and feel nauseous." The reception unit provides an interface that the user can intuitively operate, allowing the user to smoothly input symptoms. Specifically, when the user inputs symptoms, a predictive conversion function and a candidate list are displayed to reduce the effort required for input. In addition, the reception unit has a function of transmitting the symptoms input by the user to the analysis unit in real time, and analysis can be started before the user completes input. Furthermore, the reception unit can refer to the user's past medical history and account information and automatically complement information related to the input symptoms. For example, if the user has complained of similar symptoms in the past, additional questions can be asked based on that information to collect more detailed information. As a result, the reception unit provides an environment in which the user can quickly and accurately input symptoms, thereby improving the efficiency of the entire system.

[0070] The analysis unit uses AI to analyze the symptoms received by the reception unit. For example, the AI ​​uses natural language processing technology to analyze the input symptoms and identify the severity of the symptoms and the relevant medical department. Specifically, the AI ​​tokenizes the text input by the user and analyzes the meaning of each token. Then, it analyzes the relationship between the tokens and understands the context. For example, for the input "I have a headache and feel nauseous," the AI ​​extracts the symptoms "headache" and "nausea" and evaluates the severity of each. The AI ​​also refers to past medical data and medical literature to identify the medical department related to the input symptoms. For example, if "headache" and "nausea" occur simultaneously, it determines that neurology or neurosurgery is appropriate. Furthermore, the analysis unit can perform more accurate analysis by taking into account personal information such as the user's age, gender, and medical history. This allows the analysis unit to quickly and accurately analyze the symptoms input by the user and provide information to identify the appropriate medical department.

[0071] The identification unit identifies an appropriate medical department based on the symptoms analyzed by the analysis unit. For example, the AI ​​determines that neurology or neurosurgery is appropriate. The identification unit uses a rule base or machine learning model to identify the medical department based on the information provided by the analysis unit. Specifically, the identification unit refers to a database for evaluating the association between symptoms and medical departments, and identifies the medical department based on past medical data and medical literature. For example, if "headache" and "nausea" occur simultaneously, it determines that neurology or neurosurgery is appropriate. In addition, the identification unit can identify the medical department with higher accuracy by taking into account the user's personal information and past medical history. For example, if the user has visited a neurology department in the past for similar symptoms, the identification unit can recommend a neurology department again based on that information. This allows the identification unit to support the user in quickly visiting an appropriate medical department.

[0072] The search unit searches for hospitals that can accept patients based on the medical department identified by the identification unit. For example, the AI ​​searches for the nearest hospital that can currently accept patients. The search unit refers to a hospital database and performs a search based on information such as the acceptance status of each medical department, the location of the hospital, and the consultation hours. Specifically, the search unit identifies the nearest hospital based on the user's current location and checks the acceptance status of the medical department in real time. The search unit can also select the optimal hospital by taking into account the hospital's evaluation and user feedback. For example, if the user desires a highly rated hospital, the search unit can filter the search results based on that information. Furthermore, the search unit can prioritize displaying hospitals where the user can receive treatment quickly, taking into account the hospital's reservation status and waiting time. This allows the search unit to support the user in receiving treatment quickly and appropriately at a medical institution.

[0073] The providing unit provides the user with information on the hospitals searched for by the search unit. For example, the providing unit provides information such as the name, address, contact information, and available hours of the hospital. The providing unit provides a visually easy-to-understand interface so that the user can easily check the information. Specifically, the providing unit has a map display and navigation function for the hospital, so that the user can easily check the route to the hospital. The providing unit also provides additional information such as detailed information on the hospital, explanations of the medical department, and doctor profiles, to help the user to visit the hospital with peace of mind. Furthermore, the providing unit has a function that allows the user to directly contact the hospital, so that reservations and inquiries can be made smoothly. For example, the providing unit displays the telephone number and email address of the hospital, so that the user can contact the hospital with one click. In this way, the providing unit can help the user to visit an appropriate medical institution quickly and easily.

[0074] The analysis unit can refer to a medical database and analyze symptoms using natural language processing technology. The analysis unit, for example, refers to a medical database and analyzes symptoms using natural language processing technology. The medical database includes, but is not limited to, a medical history database, a drug database, and the like. The natural language processing technology can use, for example, morphological analysis, grammatical analysis, semantic analysis, and other technologies. As a result, by using the medical database and the natural language processing technology, the accuracy of symptom analysis is improved. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data acquired from the medical database to the generation AI and cause the generation AI to execute symptom analysis.

[0075] The search unit can cooperate with the system of each hospital and acquire the admission status in real time. The search unit cooperates with the system of each hospital and acquires the admission status in real time, for example. The definition and criteria of real time include, but are not limited to, update frequency and delay time. As a result, the admission status can be acquired in real time, allowing the optimal hospital to be quickly searched for. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input data acquired from the system of each hospital to the generation AI and cause the generation AI to analyze the admission status.

[0076] The reception unit can provide a guideline for the user to input. The reception unit, for example, provides a guideline for the user to input. The content of the guideline includes, for example, input procedures and points to note, but is not limited to such examples. This provides a guideline so that the user can properly input symptoms. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the content of the user's input to a generation AI and cause the generation AI to provide the guideline.

[0077] The providing unit can provide information on the name of the medical department, the name of the hospital, the address, contact information, and the time period during which the patient can be accepted. The providing unit provides, for example, information on the name of the medical department, the name of the hospital, the address, contact information, and the time period during which the patient can be accepted. The content of the information provided includes, for example, the contact information of the hospital, the consultation hours, and details of the medical department, but is not limited to such examples. This allows the user to quickly obtain the information he or she needs. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input hospital information to the generating AI and cause the generating AI to provide the information.

[0078] The reception unit can estimate the user's emotion and adjust the symptom input guideline based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the symptom input guideline based on the estimated user's emotion. For example, a technique such as facial expression recognition or voice analysis can be used to estimate the emotion. The adjustment of the guideline includes, for example, changing advice according to the emotion, but is not limited to such an example. For example, when the user feels anxious, a concise guideline that gives a sense of security can be provided. In addition, when the user is in a hurry, the steps can be minimized so that the input can be completed quickly. Furthermore, when the user is relaxed, detailed input options can be provided to collect more accurate information. This allows appropriate symptom input by providing a guideline according to the user'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, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0079] The reception unit can analyze the user's past symptom input history and propose the optimal input method. The reception unit, for example, analyzes the user's past symptom input history and proposes the optimal input method. Criteria for the optimal input method include, for example, ease of input and accuracy, but are not limited to such examples. For example, symptoms frequently input by the user in the past can be automatically displayed as candidates. In addition, input methods (voice, text, etc.) used by the user in the past can be preferentially proposed. Furthermore, symptoms to be input in a specific time period can be predicted and proposed from the user's past input history. As a result, the optimal input method is proposed based on the past input history, improving the convenience of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI and cause the generation AI to execute the proposal of the optimal input method.

[0080] The reception unit can filter the input contents based on the user's current health condition and lifestyle habits when inputting symptoms. The reception unit, for example, filters the input contents based on the user's current health condition and lifestyle habits when inputting symptoms. Filtering criteria include, for example, filtering conditions based on health conditions and lifestyle habits, but are not limited to such examples. For example, the user can preferentially input related symptoms in consideration of the medicines currently being taken. In addition, related symptoms can be filtered based on the user's lifestyle habits (smoking, drinking, etc.). Furthermore, related symptoms can be preferentially input in consideration of the user's medical history. This makes it possible to input appropriate symptoms based on the user's health condition and lifestyle habits. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's health condition and lifestyle habit data to the generation AI and cause the generation AI to execute filtering of the input contents.

[0081] The reception unit can estimate the user's emotion and determine the priority of the symptoms to be input based on the estimated user's emotion. The reception unit can estimate the user's emotion and determine the priority of the symptoms to be input based on the estimated user's emotion. For example, the emotion can be estimated using a technology such as facial expression recognition or voice analysis. The priority order can be determined based on, for example, the severity and urgency of the symptoms, but is not limited to such an example. For example, when the user feels anxious, the user can be guided to input serious symptoms with priority. In addition, when the user is relaxed, the user can be guided to input detailed symptoms. Furthermore, when the user is impatient, the main symptoms can be input with priority so that the symptoms can be input quickly. In this way, the symptoms can be input with priority according to the user's emotion, so that the appropriate medical department can be quickly identified. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0082] The reception unit can preferentially input highly relevant symptoms in consideration of the geographical location information of the user when inputting symptoms. For example, the reception unit preferentially inputs highly relevant symptoms in consideration of the geographical location information of the user when inputting symptoms. Methods for acquiring geographical location information include, but are not limited to, GPS data and location information services. For example, when the user is in a specific area, symptoms related to a disease prevalent in that area can be preferentially input. In addition, when the user is traveling, symptoms related to health risks at the travel destination can also be preferentially input. Furthermore, when the user is in a specific environment (factory, farm, etc.), symptoms related to that environment can also be preferentially input. In this way, symptoms specific to the area can be preferentially input by considering the geographical location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the priority order of highly relevant symptoms.

[0083] The reception unit can analyze the user's social media activity and input related symptoms when inputting symptoms. The reception unit can analyze the user's social media activity and input related symptoms when inputting symptoms, for example. Methods of analyzing social media activity include, for example, analysis of posted content and activity frequency, but are not limited to such examples. For example, related symptoms can be input based on health information shared by the user on social media. Also, related symptoms can be input based on information of health-related accounts that the user follows on social media. Furthermore, related symptoms can be input based on information of health-related groups that the user participates in on social media. As a result, by inputting related symptoms based on social media activity, a more accurate medical department can be identified. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to identify related symptoms.

[0084] The analysis unit can estimate the user's emotion and adjust the symptom analysis algorithm based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the symptom analysis algorithm based on the estimated user's emotion. For example, a technique such as facial expression recognition or voice analysis can be used to estimate the emotion. For example, the adjustment of the algorithm includes, but is not limited to, changing parameters according to the emotion. For example, when the user feels anxious, an analysis can be performed quickly and a result can be provided. In addition, when the user is relaxed, a detailed analysis can be performed and a result can be provided. Furthermore, when the user is impatient, an analysis can be performed by focusing on the main symptoms. This enables appropriate symptom analysis by adjusting the algorithm according to the user'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, for example, 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the algorithm.

[0085] The analysis unit can improve the accuracy of the analysis by referring to the latest information in the medical database when analyzing symptoms. The analysis unit, for example, improves the accuracy of the analysis by referring to the latest information in the medical database when analyzing symptoms. The definition and acquisition method of the latest information include, for example, the update frequency of the database and the reliability of the information, but are not limited to such examples. For example, the analysis of symptoms can be performed by referring to the latest medical research data. The analysis of symptoms can also be performed by referring to the latest medical guidelines. Furthermore, the analysis of symptoms can also be performed by referring to the latest medical statistical data. As a result, the accuracy of the analysis is improved by referring to the latest medical database. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the latest information in the medical database to the generation AI and cause the generation AI to execute the symptom analysis.

[0086] The analysis unit can perform the analysis while considering the user's past medical history when analyzing symptoms. For example, the analysis unit performs the analysis while considering the user's past medical history when analyzing symptoms. Methods for acquiring the past medical history include, but are not limited to, for example, electronic medical records and medical records. For example, the analysis of symptoms can be performed by referring to the user's past medical records. In addition, the analysis of symptoms can be performed by referring to the user's past prescription history. Furthermore, the analysis of symptoms can be performed by referring to the user's past test results. As a result, more accurate symptom analysis is possible by considering the past medical history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past medical history data to the generation AI and cause the generation AI to execute the symptom analysis.

[0087] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, a technique such as facial expression recognition or voice analysis can be used to estimate the emotion. For example, the adjustment of the display method includes, but is not limited to, changing the display format according to the emotion. For example, when the user feels anxious, a display method that is concise and gives a sense of security can be provided. Furthermore, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is impatient, a display method that can be quickly understood can be provided. As a result, by providing a display method according to the user's emotion, appropriate information can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the display method.

[0088] The analysis unit can perform the analysis while considering the user's living environment information when analyzing symptoms. The analysis unit performs the analysis while considering the user's living environment information, for example, while analyzing symptoms. The method of acquiring the living environment information includes, for example, the living environment and the work environment, but is not limited to such examples. For example, the analysis of symptoms can be performed by referring to the environmental information of the area where the user lives. The analysis of symptoms can also be performed by referring to the user's work environment information. Furthermore, the analysis of symptoms can also be performed by referring to the user's home environment information. As a result, by considering the living environment information, more accurate symptom analysis is possible. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's living environment information to the generation AI and cause the generation AI to execute the symptom analysis.

[0089] The analysis unit can improve the accuracy of the analysis by referring to related medical literature when analyzing symptoms. The analysis unit, for example, improves the accuracy of the analysis by referring to related medical literature when analyzing symptoms. Types of medical literature include, for example, academic papers and medical guidelines, but are not limited to such examples. For example, the latest medical literature can be referred to to analyze symptoms. Also, related medical research papers can be referred to to analyze symptoms. Furthermore, medical journal articles can be referred to to analyze symptoms. As a result, the accuracy of the analysis is improved by referring to related medical literature. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input data of medical literature to the generation AI and cause the generation AI to execute symptom analysis.

[0090] The identification unit can estimate the user's emotion and adjust the criteria for identifying the medical department based on the estimated user's emotion. The identification unit, for example, estimates the user's emotion and adjusts the criteria for identifying the medical department based on the estimated user's emotion. For example, a technology such as facial expression recognition or voice analysis can be used to estimate the emotion. The adjustment of the criteria includes, for example, changing the criteria for selecting the medical department according to the emotion, but is not limited to such an example. For example, when the user feels anxious, the criteria for identifying the medical department can be set quickly. In addition, when the user is relaxed, the criteria for identifying the medical department can be set based on detailed information. Furthermore, when the user is impatient, the criteria for identifying the medical department can be set based on the main symptoms. In this way, the appropriate medical department can be quickly identified by identifying the medical department based on the criteria according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input user emotion data to the generation AI and cause the generation AI to adjust the criteria.

[0091] The identification unit can improve the accuracy of identification by referring to past medical data when identifying a medical department. The identification unit, for example, improves the accuracy of identification by referring to past medical data when identifying a medical department. Methods for acquiring past medical data include, for example, electronic medical records and medical records, but are not limited to such examples. For example, a medical department corresponding to a symptom can be identified by referring to past medical data. In addition, the accuracy of identification can be improved by analyzing past medical data. Furthermore, an algorithm for identifying a medical department can be adjusted based on past medical data. As a result, the accuracy of identification is improved by referring to past medical data. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input past medical data to the generation AI and cause the generation AI to identify a medical department.

[0092] The identification unit can perform identification by considering the user's current health condition when identifying a medical department. For example, the identification unit performs identification by considering the user's current health condition when identifying a medical department. Methods for acquiring the current health condition include, but are not limited to, for example, health check results and self-reporting. For example, an appropriate medical department can be identified by referring to the user's current health condition. In addition, a medical department can be identified based on the user's current symptoms. Furthermore, a medical department can be identified by considering the user's current physical condition. As a result, an appropriate medical department can be identified by considering the current health condition. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the user's current health condition data to the generation AI and cause the generation AI to execute medical department identification.

[0093] The identification unit can estimate the user's emotion and adjust the order in which the results of the medical department identification are displayed based on the estimated user's emotion. The identification unit, for example, estimates the user's emotion and adjusts the order in which the results of the medical department identification are displayed based on the estimated user's emotion. For example, a technique such as facial expression recognition or voice analysis can be used to estimate the emotion. The adjustment of the order includes, for example, changing the display order according to the emotion, but is not limited to such an example. For example, when the user feels anxious, the most relevant medical department can be displayed first. In addition, when the user is relaxed, medical departments including detailed information can be displayed in an orderly manner. Furthermore, when the user is impatient, the main medical department can be displayed first so that the user can quickly understand. In this way, by displaying the results of the medical department identification in an order according to the user's emotion, appropriate information can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input user emotion data to the generation AI and cause the generation AI to adjust the display order.

[0094] The identification unit can perform identification in consideration of the geographical distribution of the user when identifying the medical department. For example, the identification unit performs identification in consideration of the geographical distribution of the user when identifying the medical department. Methods for acquiring the geographical distribution include, for example, regional patient data and a geographical information system, but are not limited to such examples. For example, the nearest medical department can be identified based on the user's current location. In addition, an appropriate medical department can be identified in consideration of the geographical distribution of the user. Furthermore, a medical department can be identified in consideration of the range in which the user can move. In this way, the optimal medical department can be identified by considering the geographical distribution. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input the geographical distribution data of the user to the generation AI and cause the generation AI to execute the medical department identification.

[0095] The identification unit can improve the accuracy of identification by referring to the related medical guidelines when identifying a medical department. The identification unit improves the accuracy of identification by referring to the related medical guidelines when identifying a medical department, for example. Types of medical guidelines include, for example, clinical guidelines and treatment protocols, but are not limited to such examples. For example, the medical department can be identified by referring to the latest medical guidelines. In addition, the algorithm for identifying the medical department can be adjusted based on the related medical guidelines. Furthermore, the medical department can be identified based on the information of the medical guidelines. As a result, the accuracy of identification is improved by referring to the medical guidelines. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the identification unit can input data of the medical guidelines to the generation AI and cause the generation AI to execute the identification of the medical department.

[0096] The search unit can estimate the user's emotion and adjust the hospital search algorithm based on the estimated user's emotion. The search unit can estimate the user's emotion and adjust the hospital search algorithm based on the estimated user's emotion. For example, the emotion can be estimated using a technology such as facial expression recognition or voice analysis. The algorithm adjustment includes, for example, changing parameters according to the emotion, but is not limited to such an example. For example, when the user feels anxious, an algorithm that quickly provides search results can be set. In addition, when the user is relaxed, an algorithm that provides search results including detailed information can be set. Furthermore, when the user is impatient, an algorithm that preferentially displays major hospitals can be set. This enables an appropriate hospital search by adjusting the algorithm according to the user'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, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input user emotion data into the generation AI and have the generation AI adjust the algorithm.

[0097] The search unit can improve the accuracy of the search by acquiring the latest admission status of each hospital in real time during a hospital search. For example, the search unit can improve the accuracy of the search by acquiring the latest admission status of each hospital in real time during a hospital search. The method of acquiring the latest admission status includes, for example, cooperation with the hospital's system and acquisition of real-time data, but is not limited to such examples. For example, the admission status can be acquired in real time by cooperation with the system of each hospital. Also, the optimal hospital can be searched for based on the latest admission status of each hospital. Furthermore, the search result can be provided taking into account the real-time admission status. As a result, the optimal hospital can be quickly searched for by acquiring the real-time admission status. A part or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input hospital admission status data to the generation AI and cause the generation AI to execute a hospital search.

[0098] The search unit can perform a search while considering the user's past medical history when searching for a hospital. For example, the search unit performs a search while considering the user's past medical history when searching for a hospital. Methods for acquiring the past medical history include, but are not limited to, electronic medical records and medical records. For example, the optimal hospital can be searched for by referring to the user's past medical history. In addition, related hospitals can be preferentially searched based on the user's past medical history. Furthermore, the user's past medical history can be analyzed to provide a search result. As a result, related hospitals can be preferentially searched for by considering the past medical history. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the user's past medical history data to the generation AI and cause the generation AI to execute a hospital search.

[0099] The search unit can estimate the user's emotion and adjust the display method of the search results based on the estimated user's emotion. The search unit can estimate the user's emotion and adjust the display method of the search results based on the estimated user's emotion. For example, the emotion can be estimated using a technology such as facial expression recognition or voice analysis. The display method adjustment includes, for example, changing the display format according to the emotion, but is not limited to such an example. For example, when the user feels anxious, a display method that is concise and gives a sense of security can be provided. Furthermore, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is impatient, a display method that can be quickly understood can be provided. As a result, by providing a display method according to the user's emotion, appropriate information can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input a user's emotion data into the generation AI and have the generation AI adjust the display method.

[0100] The search unit can search for an optimal hospital by considering the geographical location information of the user when searching for a hospital. For example, the search unit searches for an optimal hospital by considering the geographical location information of the user when searching for a hospital. Methods for acquiring geographical location information include, but are not limited to, GPS data and location information services. For example, the nearest hospital can be searched for based on the user's current location. In addition, an appropriate hospital can be searched for by considering the geographical location information of the user. Furthermore, a hospital can be searched for by considering the range in which the user can move. As a result, the nearest hospital can be quickly searched for by considering the geographical location information. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute a hospital search.

[0101] The search unit can improve the accuracy of the search by referring to related medical network information when searching for a hospital. The search unit can improve the accuracy of the search by referring to related medical network information when searching for a hospital, for example. The types of medical network information include, for example, medical institution collaboration information and network configuration, but are not limited to such examples. For example, the optimal hospital can be searched for by referring to the medical network information. In addition, a search result can be provided based on the related medical network information. Furthermore, the accuracy of the search can be improved by taking the medical network information into consideration. As a result, the accuracy of the search is improved by referring to the medical network information. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the medical network information to the generation AI and cause the generation AI to execute the hospital search.

[0102] The providing unit can estimate the user's emotion and adjust the hospital information providing method based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the hospital information providing method based on the estimated user's emotion. For example, a technology such as facial expression recognition or voice analysis can be used for estimating the emotion. For example, the adjustment of the providing method includes changing the information providing format according to the emotion, but is not limited to such an example. For example, when the user feels anxious, a concise information providing method that gives a sense of security can be set. In addition, when the user is relaxed, an information providing method that includes detailed information can be set. Furthermore, when the user is impatient, an information providing method that can be quickly understood can be set. In this way, appropriate hospital information can be provided by an information providing method according to the user'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, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to adjust the information providing method.

[0103] The providing unit can provide optimal information by referring to the user's past medical history when providing hospital information. The providing unit, for example, provides optimal information by referring to the user's past medical history when providing hospital information. Methods for acquiring the past medical history include, for example, electronic medical records and medical records, but are not limited to such examples. For example, related hospital information can be provided by referring to the user's past medical history. In addition, optimal hospital information can be provided based on the user's past medical history. Furthermore, the accuracy of information provision can be improved by analyzing the user's past medical history. As a result, related hospital information can be provided by referring to the past medical history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past medical history data to the generating AI and cause the generating AI to execute information provision.

[0104] The providing unit can customize information based on the user's current health condition when providing hospital information. The providing unit, for example, customizes information based on the user's current health condition when providing hospital information. Methods for acquiring the current health condition include, for example, health check results and self-reporting, but are not limited to such examples. For example, optimal hospital information can be provided by referring to the user's current health condition. Also, related hospital information can be provided based on the user's current symptoms. Furthermore, information can be customized taking into account the user's current physical condition. As a result, optimal hospital information can be provided by taking into account the current health condition. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's current health condition data to the generating AI and cause the generating AI to execute the customization of the information.

[0105] The providing unit can estimate the user's emotion and determine the priority of the hospital information based on the estimated user's emotion. The providing unit can estimate the user's emotion and determine the priority of the hospital information based on the estimated user's emotion. For example, the emotion can be estimated using a technology such as facial expression recognition or voice analysis. The priority determination can include, for example, changing the priority of information according to the emotion, but is not limited to such an example. For example, when the user feels anxious, the most relevant hospital information can be provided first. In addition, when the user is relaxed, hospital information including detailed information can be provided in an orderly manner. Furthermore, when the user is impatient, the main hospital information can be provided first so that the user can understand quickly. In this way, by providing hospital information in a priority order according to the user's emotion, appropriate information can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and have the generation AI determine the priorities.

[0106] The providing unit can provide information based on the geographical location information of the user when providing hospital information. The providing unit, for example, provides information based on the geographical location information of the user when providing hospital information. Methods for acquiring geographical location information include, for example, GPS data and location information services, but are not limited to such examples. For example, the nearest hospital information can be provided based on the user's current location. In addition, appropriate hospital information can be provided taking into account the geographical location information of the user. Furthermore, hospital information can be provided taking into account the range in which the user can move. As a result, by taking into account the geographical location information, the nearest hospital information can be provided quickly. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to execute information provision.

[0107] The providing unit can improve the accuracy of information by referring to related medical guidelines when providing hospital information. The providing unit can improve the accuracy of information by referring to related medical guidelines when providing hospital information, for example. Types of medical guidelines include, but are not limited to, clinical guidelines and treatment protocols. For example, hospital information can be provided by referring to the latest medical guidelines. In addition, the accuracy of information provision can be improved based on related medical guidelines. Furthermore, hospital information can be provided based on information on medical guidelines. As a result, the accuracy of information provision is improved by referring to medical guidelines. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data of medical guidelines to a generating AI and cause the generating AI to execute information provision.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The reception unit can support voice input when the user inputs symptoms. For example, when the user says "I have a headache and feel nauseous," the reception unit can convert the speech into text using voice recognition technology and send it to the analysis unit. In addition, when the speech is input, the reception unit can analyze the user's speech rate and tone to estimate the urgency of the speech. Furthermore, in order to improve the accuracy of the speech input, the reception unit can learn the user's past speech data and respond to individual speech patterns. Thus, by supporting the speech input, the user can input symptoms more easily.

[0110] The analysis unit can take into account the user's age and gender when analyzing the user's symptoms. For example, the frequency and risk of occurrence of a particular symptom can be evaluated based on age and gender, and reflected in the analysis results. In addition, it can also refer to medical guidelines according to age and gender to identify a more appropriate medical department. Furthermore, it can evaluate the severity of symptoms by taking into account medical history and family history related to age and gender. This enables analysis that takes into account the individual characteristics of the user, and allows for more accurate identification of a medical department.

[0111] When acquiring the hospital acceptance status, the search unit can also take into consideration the availability of specialists at each hospital. For example, it is possible to preferentially search for hospitals that have specialists in a specific medical department and provide them to the user. It is also possible to evaluate the quality of medical care and recommend the most suitable hospital based on the availability of specialists. Furthermore, it is also possible to acquire the schedules of specialists in real time and provide the user with the time periods when they are available for consultation. In this way, by taking into consideration the availability of specialists, it is possible to quickly search for hospitals that provide higher quality medical care.

[0112] The reception unit can provide an interactive question format when the user inputs symptoms. For example, if the user inputs "I have a headache," additional questions such as "How intense is the pain?" and "Where is the pain?" can be presented to collect more detailed information. The content of the question can also dynamically change depending on the user's answer, allowing more specific information to be obtained. Furthermore, the question format can be not only text, but also images and options. Thus, by providing an interactive question format, the user can input more detailed symptom information.

[0113] The providing unit can include user reviews and ratings in the hospital information provided to the user. For example, reviews and ratings from users who have used the hospital in the past can be displayed to help users select a hospital. Reviews and ratings can also be displayed separately for each medical department. Furthermore, the content of reviews and ratings can be analyzed and filtered based on specific keywords (e.g., waiting time, doctor's response, etc.). In this way, by including user reviews and ratings, reference information for selecting a hospital can be provided.

[0114] The reception unit can estimate the user's emotions and customize the symptom input interface based on the estimated user's emotions. For example, if the user feels anxious, the interface design can be changed to a simple, calm color scheme to provide a sense of security. If the user feels impatient, the input steps can be minimized to allow the user to complete the input quickly. Furthermore, if the user feels relaxed, detailed input options can be provided to collect more accurate information. This allows the user to input symptoms appropriately by providing an interface according to the user's emotions.

[0115] The analysis unit can estimate the user's emotions and adjust the method of feedback of the analysis results based on the estimated user's emotions. For example, if the user feels anxious, the analysis results can be fed back in concise, reassuring words. If the user feels relaxed, detailed analysis results can be provided to deepen understanding. Furthermore, if the user feels impatient, key points can be emphasized and feedback can be provided quickly. This makes it possible to provide appropriate information by providing a feedback method according to the user's emotions.

[0116] The identification unit can estimate the user's emotion and adjust the process of identifying a medical department based on the estimated user's emotion. For example, if the user feels anxious, a process of quickly identifying a medical department can be set. Also, if the user feels relaxed, a process of identifying a medical department based on detailed information can be set. Furthermore, if the user feels anxious, a process of identifying a medical department based on major symptoms can be set. In this way, by identifying a medical department through a process according to the user's emotion, an appropriate medical department can be quickly identified.

[0117] The search unit can estimate the user's emotions and customize the hospital search results based on the estimated user's emotions. For example, if the user is feeling anxious, the most highly rated hospitals can be displayed preferentially. If the user is relaxed, search results including detailed hospital information can be provided. Furthermore, if the user is anxious, hospitals that can be accessed quickly can be displayed preferentially. This allows the user to select an appropriate hospital by providing search results according to the user's emotions.

[0118] The providing unit can estimate the user's emotions and customize the method of providing hospital information based on the estimated user's emotions. For example, if the user is feeling anxious, a concise information providing method that gives a sense of security can be set. Also, if the user is relaxed, an information providing method that includes detailed information can be set. Furthermore, if the user is anxious, an information providing method that allows the user to understand quickly can be set. In this way, appropriate hospital information can be provided by an information providing method that corresponds to the user's emotions.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The reception unit receives input of symptoms from a user in a chat format. For example, the user can input "I have a headache and feel nauseous." Step 2: The analysis unit uses AI to analyze the symptoms received by the reception unit. For example, the AI ​​uses natural language processing technology to analyze the input symptoms and identify the severity of the symptoms and the relevant medical departments. Step 3: The identification unit identifies the appropriate medical specialty based on the symptoms analyzed by the analysis unit. For example, the AI ​​may determine that neurology or neurosurgery is appropriate. Step 4: The search unit searches for hospitals that can accept the patient based on the medical department identified by the identification unit. For example, the AI ​​searches for the nearest hospital that can currently accept the patient. Step 5: The providing unit provides the user with information about the hospitals searched for by the searching unit, such as the hospital's name, address, contact information, and available hours.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, search 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 reception unit is realized by the control unit 46A of the smart device 14, and accepts the user's input of symptoms in a chat format. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the input symptoms using AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies an appropriate medical department based on the analyzed symptoms. The search unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and searches for a hospital that can accept the patient based on the identified medical department. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the user with information on the searched 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.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, search 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 reception unit is realized by the control unit 46A of the smart glasses 214, and accepts the user's input of symptoms in a chat format. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the input symptoms using AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies an appropriate medical department based on the analyzed symptoms. The search unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and searches for a hospital that can accept the patient based on the identified medical department. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the user with information on the searched 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.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, search 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 reception unit is realized by the control unit 46A of the headset type terminal 314, and accepts the user's input of symptoms in a chat format. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the input symptoms using AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies an appropriate medical department based on the analyzed symptoms. The search unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and searches for a hospital that can accept the patient based on the identified medical department. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the user with information on the searched 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.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, search 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 reception unit is realized by the control unit 46A of the robot 414, and accepts the user's input of symptoms in a chat format. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the input symptoms using AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies an appropriate medical department based on the analyzed symptoms. The search unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and searches for a hospital that can accept the patient based on the identified medical department. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the user with information on the searched 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.

[0174] 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.

[0175] 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.

[0176] These emotions are distributed in the 3 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.

[0177] 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 you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] 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.

[0179] 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 experiences positive feelings such as "I want more" or "I want to know more."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Appendix 1) A reception unit that receives input of symptoms; an analysis unit that analyzes the symptoms received by the reception unit; An identification unit that identifies a medical department based on the symptoms analyzed by the analysis unit; A search unit that searches for hospitals that can accept patients based on the medical department identified by the identification unit; a providing unit that provides information on the hospitals searched for by the searching unit; A system characterized by: (Appendix 2) The analysis unit is Refer to medical databases and analyze symptoms using natural language processing technology 2. The system of claim 1, (Appendix 3) The search unit is Link with each hospital's system to obtain admission status in real time 2. The system of claim 1, (Appendix 4) The reception unit is Provide guidelines for users to follow when typing 2. The system of claim 1, (Appendix 5) The providing unit is Provide information on the name of the medical department, the name of the hospital, address, contact information, and available hours 2. The system of claim 1, (Appendix 6) The reception unit is Inferring user emotions and adjusting symptom input guidelines based on the estimated user emotions 2. The system of claim 1, (Appendix 7) The reception unit is Analyzes the user's symptom input history and suggests the optimal input method 2. The system of claim 1, (Appendix 8) The reception unit is When entering symptoms, filter the input based on the user's current health condition and lifestyle habits 2. The system of claim 1, (Appendix 9) The reception unit is Estimate the user's emotions and prioritize the symptoms to be input based on the estimated user's emotions. 2. The system of claim 1, (Appendix 10) The reception unit is When entering symptoms, the system takes into account the user's geographic location information and prioritizes relevant symptoms. 2. The system of claim 1, (Appendix 11) The reception unit is When a symptom is entered, the system analyzes the user's social media activity and enters related symptoms. 2. The system of claim 1, (Appendix 12) The analysis unit is Inferring a user's emotions and adjusting a symptom analysis algorithm based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is When analyzing symptoms, the accuracy of the analysis is improved by referring to the latest information in the medical database. 2. The system of claim 1, (Appendix 14) The analysis unit is When analyzing symptoms, the analysis is based on the user's past medical history. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is When analyzing symptoms, analysis is performed based on the user's living environment information. 2. The system of claim 1, (Appendix 17) The analysis unit is When analyzing symptoms, refer to relevant medical literature to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 18) The identification unit is Inferring user emotion and adjusting criteria for identifying medical departments based on the estimated user emotion 2. The system of claim 1, (Appendix 19) The identification unit is When identifying a medical specialty, improve the accuracy of identification by referring to past medical data 2. The system of claim 1, (Appendix 20) The identification unit is When identifying a medical department, the identification is based on the user's current health condition. 2. The system of claim 1, (Appendix 21) The identification unit is Infer user sentiment and adjust the order in which medical department-specific results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 22) The identification unit is When identifying medical departments, the identification is based on the geographical distribution of users. 2. The system of claim 1, (Appendix 23) The identification unit is When identifying medical specialties, refer to relevant medical guidelines to improve the accuracy of identification. 2. The system of claim 1, (Appendix 24) The search unit is Infer user sentiment and adjust hospital search algorithms based on the inferred user sentiment 2. The system of claim 1, (Appendix 25) The search unit is When searching for hospitals, the latest admission status of each hospital is obtained in real time to improve search accuracy. 2. The system of claim 1, (Appendix 26) The search unit is When searching for hospitals, the search is based on the user's past medical history. 2. The system of claim 1, (Appendix 27) The search unit is Infer user sentiment and adjust how search results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 28) The search unit is When searching for a hospital, the system considers the user's geographic location to find the best hospital. 2. The system of claim 1, (Appendix 29) The search unit is When searching for hospitals, refer to related medical network information to improve search accuracy 2. The system of claim 1, (Appendix 30) The providing unit is To estimate a user's emotion and adjust a method of providing hospital information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 31) The providing unit is When providing hospital information, the system provides optimal information by referring to the user's past medical history. 2. The system of claim 1, (Appendix 32) The providing unit is When providing hospital information, customize the information based on the user's current health condition 2. The system of claim 1, (Appendix 33) The providing unit is Estimating user emotions and prioritizing hospital information based on the estimated user emotions 2. The system of claim 1, (Appendix 34) The providing unit is When providing hospital information, information is provided based on the user's geographic location information 2. The system of claim 1, (Appendix 35) The providing unit is When providing hospital information, improve the accuracy of the information by referring to relevant medical guidelines 2. The system of claim 1, [Explanation of symbols]

[0193] 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 reception unit that receives input of symptoms; an analysis unit that analyzes the symptoms received by the reception unit; An identification unit that identifies a medical department based on the symptoms analyzed by the analysis unit; A search unit that searches for hospitals that can accept patients based on the medical department identified by the identification unit; a providing unit that provides information on the hospitals searched for by the searching unit; A system characterized by:

2. The analysis unit is Refer to medical databases and analyze symptoms using natural language processing technology 2. The system of claim 1 .

3. The search unit is Link with each hospital's system to obtain admission status in real time 2. The system of claim 1 .

4. The reception unit is Provide guidelines for users to follow when typing 2. The system of claim 1 .

5. The providing unit is Provide information on the name of the medical department, the name of the hospital, address, contact information, and available hours 2. The system of claim 1 .

6. The reception unit is Inferring user emotions and adjusting symptom input guidelines based on the estimated user emotions 2. The system of claim 1 .

7. The reception unit is Analyzes the user's symptom input history and suggests the optimal input method 2. The system of claim 1 .

8. The reception unit is When entering symptoms, filter the input based on the user's current health condition and lifestyle habits 2. The system of claim 1 .

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