System

Abstract

To provide a system according to an embodiment, the system intending to appropriately assign a waiting space on the basis of symptoms of medical examinee and the day's situation, and realize waiting time reduction and resource optimization.SOLUTION: A system according to an embodiment includes: a reception part; an analysis part; an assignment part; a monitoring part; notification part; and a feedback part. The reception part inputs symptoms of medical examinee. The analysis part analyzes the symptom input by the reception part, the number of patients and progress of medical care on the day. The assignment part assigns a waiting space on the basis of the results analyzed by the analysis part. The monitoring part monitors a situation of the waiting space assigned by the assignment part in real-time, and if necessary, changes a priority. The notification part notifies the medical examinee on the basis of the information monitored by the monitoring part. The feedback part collects feedback from the medical examinee.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 is difficult to appropriately allocate waiting space based on patients' symptoms and the situation on that day, leaving room for improvement in reducing waiting times and optimizing resources.

[0005] The system of the embodiment aims to appropriately allocate waiting space based on the patient's symptoms and the situation on that day, thereby reducing waiting time and optimizing resources. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an allocation unit, a monitoring unit, a notification unit, and a feedback unit. The reception unit inputs the symptoms of the patient. The analysis unit analyzes the symptoms input by the reception unit, the number of patients on that day, and the progress of medical treatment. The allocation unit allocates waiting spaces based on the results of the analysis by the analysis unit. The monitoring unit monitors the status of the waiting spaces allocated by the allocation unit in real time, and changes the priority as necessary. The notification unit notifies the patient based on the information monitored by the monitoring unit. The feedback unit collects feedback from the patient. Effect of the Invention

[0007] The system according to the embodiment can appropriately allocate waiting space based on the patient's symptoms and the situation on that day, thereby reducing waiting times and optimizing resources. [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 waiting space optimization system according to the embodiment of the present invention is a system that segments waiting spaces based on the symptoms of patients and the situation of the day, and utilizes real-time priority changes, a notification system, and a feedback loop. In this system, when a patient arrives at a medical institution, the patient inputs symptoms at the reception desk. For example, symptoms such as fever, cough, and abdominal pain are input. This information is entered into the system. Next, the system analyzes the input symptoms and the situation of the day (for example, the congestion status of the waiting space and the availability of doctors) and assigns the optimal waiting space. For example, a patient with a fever is guided to a specific waiting space to be isolated from other patients. Furthermore, the system monitors the situation of the waiting space in real time and changes the priority as necessary. For example, if an emergency occurs, the priority of the patient is raised and the patient is dealt with promptly. In addition, the system utilizes a notification system to notify patients of changes in waiting time and waiting space in real time. For example, a shortened waiting time and a change in waiting space are notified through a smartphone app. Finally, the system utilizes a feedback loop to collect feedback from patients and use it to improve the system. For example, feedback on waiting time and comfort of the waiting space is collected and reflected in the next visit. This mechanism reduces waiting times and optimizes resources, improving patient satisfaction. As a result, the waiting space optimization system can optimize waiting spaces based on patients' symptoms and the situation on that day, reducing waiting times and optimizing resources.

[0029] The waiting space optimization system according to the embodiment includes a reception unit, an analysis unit, an allocation unit, a monitoring unit, a notification unit, and a feedback unit. The reception unit inputs the symptoms of the patient. The symptoms of the patient include, for example, fever, cough, abdominal pain, and the like, but are not limited to such examples. The reception unit can input the symptoms using, for example, a touch panel or voice input. The analysis unit analyzes the symptoms input by the reception unit and the situation on that day. The analysis unit analyzes the congestion status of the waiting space and the availability status of the doctor, for example, using statistical analysis or machine learning algorithms. The allocation unit allocates the waiting space based on the results of the analysis by the analysis unit. The allocation unit allocates an optimal waiting space, for example, based on the size of the waiting space and the number of available seats. The monitoring unit monitors the status of the waiting space allocated by the allocation unit in real time and changes the priority as necessary. The monitoring unit monitors the congestion status of the waiting space, for example, using camera monitoring or sensor monitoring. The notification unit notifies the patient based on the information monitored by the monitoring unit. The notification unit notifies the patient of changes to the waiting time and waiting space, for example, via a smartphone app. The feedback unit collects feedback from the patient. The feedback unit collects feedback from the patient, for example, through a questionnaire or an interview. As a result, the waiting space optimization system according to the embodiment can optimize the waiting space based on the patient's symptoms and the situation of the day, thereby reducing the waiting time and optimizing resources.

[0030] The reception unit inputs the symptoms of the examinee. The symptoms of the examinee include, but are not limited to, fever, cough, abdominal pain, etc. The reception unit can input the symptoms using, for example, a touch panel or voice input. Specifically, the touch panel is designed to allow intuitive operation and to allow the examinee to easily select the symptoms. For example, the touch panel displays common symptoms such as fever, cough, abdominal pain, etc. as icons or text, and the examinee can complete the input by simply tapping the corresponding symptom. Voice input is particularly useful for elderly people and visually impaired people, and the system can automatically recognize and input the symptoms simply by the examinee verbally describing his or her symptoms. Voice recognition technology uses natural language processing to analyze the examinee's speech and identify the appropriate symptoms. Furthermore, the reception unit can simultaneously input basic information of the examinee (such as name, age, and gender), which makes subsequent analysis and allocation more accurate. The reception unit immediately stores the input information in a database and makes it accessible to the analysis unit and other departments. This allows the reception unit to quickly and accurately collect the symptoms and basic information of the examinee, improving the efficiency of the entire system.

[0031] The analysis unit analyzes the symptoms input by the reception unit and the situation of the day. For the analysis, statistical analysis and machine learning algorithms are used to analyze the congestion status of the waiting area and the availability of doctors. Specifically, statistical analysis predicts the current congestion status based on past data and grasps the trend of peak hours and congestion. For example, by extracting congestion patterns on specific days of the week and time periods from past data and comparing them with the current situation, it is possible to predict future congestion. On the other hand, machine learning algorithms enable more advanced analysis and grasp and predict the situation in real time. For example, a model is constructed that uses the symptoms and basic information of patients, past medical history, etc. as input data to predict the optimal allocation of waiting spaces and the availability of doctors. This allows the analysis unit to comprehensively analyze the symptoms of patients and the situation of the day and provide information necessary for optimizing the waiting space. Furthermore, the analysis unit can continuously revise the analysis results based on data updated in real time to respond to the latest situation. For example, if the congestion status of the waiting area changes suddenly, the analysis unit immediately takes in new data and updates the analysis results. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The allocation unit allocates waiting spaces based on the results of the analysis by the analysis unit. The allocation unit allocates an optimal waiting space based on the size of the waiting space and the number of available seats. Specifically, the layout of the waiting space and the arrangement of the seats are taken into consideration so that patients can wait comfortably. For example, by allocating many patients to a large waiting space and a small number of patients to a small space, congestion can be avoided. The allocation unit can also set priorities according to the symptoms and urgency of the patients and change the allocation of waiting spaces as necessary. For example, an efficient waiting environment is provided by preferentially allocating a large waiting space to patients with serious symptoms and allocating other spaces to patients with mild symptoms. Furthermore, the allocation unit can continuously optimize the allocation of waiting spaces based on data updated in real time. For example, the allocation unit monitors the congestion status of the waiting space and the arrival status of patients, and adjusts the allocation as necessary. The allocation unit can also improve the arrangement and allocation method of the waiting space based on feedback from patients. As a result, the allocation unit can provide a comfortable and efficient waiting environment for patients and improve the performance of the entire system.

[0033] The monitoring unit monitors the status of the waiting space assigned by the allocation unit in real time, and changes the priority as necessary. The monitoring unit monitors the congestion status of the waiting space using camera monitoring and sensor monitoring. Specifically, in camera monitoring, the video of the waiting space is analyzed in real time to grasp the number and movements of patients. For example, the number of patients can be counted from the camera video and the congestion level can be evaluated. In addition, in sensor monitoring, various sensors (e.g., temperature sensors, humidity sensors, pressure sensors, etc.) installed in the waiting space are used to detect environmental changes and the movements of patients. This allows the monitoring unit to grasp the status of the waiting space in detail and change the priority as necessary. For example, if a specific waiting space is excessively crowded, the monitoring unit can instruct the patient to move to another waiting space. In addition, the monitoring unit can use an anomaly detection algorithm to detect unusual patterns or abnormal situations early and take appropriate measures. As a result, the monitoring unit can ensure the comfort and safety of patients by monitoring the status of the waiting space in real time and taking prompt and appropriate measures.

[0034] The notification unit notifies the patient based on the information monitored by the monitoring unit. The notification unit notifies the patient of changes in waiting time and waiting space through a smartphone app. Specifically, a push notification is sent to the patient's smartphone to notify the patient of updates to the waiting time and changes to the waiting space in real time. For example, if the waiting time is shortened or the waiting space is changed, the patient is immediately notified. The notification unit can reliably transmit information using multiple notification means such as voice guidance and vibration notification. This allows the patient to always grasp the latest information and take appropriate action. Furthermore, the notification unit can improve the content and method of notification based on the patient's feedback. For example, by analyzing feedback from the patient and optimizing the timing and content of the notification, more effective information transmission is achieved. The notification unit can also provide special notifications in the event of an emergency to encourage the patient to respond quickly. This allows the notification unit to quickly and reliably provide information to the patient, thereby helping to reduce waiting time and optimize resources.

[0035] The feedback unit collects feedback from patients. The feedback unit collects feedback from patients through questionnaires and interviews. Specifically, the feedback unit provides a questionnaire that allows patients to express their opinions about their experience in the waiting area and the usability of the system. For example, questions are asked about the length of the waiting time, the comfort of the waiting area, the ease of use of the system, etc., and the satisfaction of the patients is evaluated. In addition, in the interview, detailed feedback can be collected by directly interacting with the patients. This allows the feedback unit to grasp the opinions and requests of the patients in detail and use them to improve the system. Furthermore, the feedback unit stores the collected feedback in a database and makes it accessible to the analysis unit and other departments. This allows the feedback unit to improve the entire system based on the opinions of the patients and increase the satisfaction of the patients. For example, the layout of the waiting area and the operation method of the system are reviewed based on the feedback of the patients, providing a more comfortable and easy-to-use environment. In addition, the feedback unit regularly collects and analyzes feedback to make continuous improvements. This allows the feedback unit to improve the system by reflecting the opinions of the patients and increase the satisfaction of the patients.

[0036] The monitoring unit may include a sensor unit that monitors the congestion status of the waiting space in real time. The sensor unit may include, but is not limited to, an infrared sensor or a camera sensor. The monitoring unit may install an additional sensor to monitor the congestion status of the waiting space in real time. The monitoring unit may also improve the accuracy of the sensor to grasp the congestion status more accurately. Furthermore, the monitoring unit may analyze the sensor data in real time and adjust the allocation of the waiting space according to the congestion status. This makes it possible to take appropriate measures by monitoring the congestion status of the waiting space in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit may input data acquired from the sensor to the generation AI and cause the generation AI to execute an analysis of the congestion status.

[0037] The feedback unit may include an interface unit for collecting feedback from patients. The interface unit may include, but is not limited to, a touch panel or voice input. The feedback unit may be provided with, for example, a touch panel in order to efficiently collect feedback from patients. The feedback unit may also collect feedback from patients using voice input. Furthermore, the feedback unit may also collect feedback from patients in real time through the interface unit. This allows the feedback from patients to be efficiently collected and used to improve the system. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input feedback data acquired from the interface unit to a generation AI and cause the generation AI to analyze the feedback.

[0038] The analysis unit can analyze the congestion of the waiting space and the availability of the doctor. For example, the analysis unit can use the number of people count and the length of stay as evaluation criteria to analyze the congestion of the waiting space. The analysis unit can also use the consultation time and the reservation status as evaluation criteria to analyze the availability of the doctor. Furthermore, the analysis unit can analyze the congestion of the waiting space and the availability of the doctor using statistical analysis and machine learning algorithms. This makes it possible to optimally allocate the waiting space by analyzing the congestion of the waiting space and the availability of the doctor. 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 on the congestion of the waiting space and the availability of the doctor to the generation AI and cause the generation AI to perform the analysis.

[0039] The notification unit can notify the patient of changes to the waiting time and waiting space through a smartphone app. The notification unit can notify the patient of changes to the waiting time and waiting space through, for example, a smartphone app. The notification unit can notify the patient in real time using a push notification function. The notification unit can also notify the patient of changes to the waiting time and waiting space using a real-time update function. Furthermore, the notification unit can also build a system for notifying the patient of changes to the waiting time and waiting space through a smartphone app. This improves the convenience of the patient by notifying the patient of changes to the waiting time and waiting space in real time. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input information on changes to the waiting time and waiting space into the generation AI and cause the generation AI to generate the notification content.

[0040] The feedback unit can collect feedback from examinees and use it to improve the system. The feedback unit can, for example, conduct a questionnaire to collect feedback from examinees. The feedback unit can also collect feedback from examinees through interviews. Furthermore, the feedback unit can build a system for collecting feedback from examinees and using it to improve the system. This allows feedback from examinees to be collected and used to improve the system, thereby improving the satisfaction of examinees. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the collected feedback data to a generation AI and have the generation AI perform an analysis of the feedback.

[0041] The reception unit can analyze the examinee's past medical history and select the optimal input method. For example, the reception unit can automatically display symptoms that the examinee has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the examinee has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be used in a specific time period based on the examinee's past medical history. This improves input efficiency by selecting the optimal input method based on the examinee's past medical history. 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 past medical history data to the generation AI and cause the generation AI to select the optimal input method.

[0042] The reception unit can perform filtering based on the examinee's current health condition and living situation when inputting symptoms. For example, when the examinee inputs his / her current health condition, the reception unit can preferentially display related symptoms. The reception unit can also filter related symptoms taking into account the examinee's living situation (for example, work stress and home situation). Furthermore, if the examinee has a specific health condition (for example, chronic disease), the reception unit can also preferentially display symptoms related to that condition. As a result, by filtering based on the examinee's current health condition and living situation, related symptoms can be preferentially displayed. 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 data on the examinee's health condition and living situation to the generation AI and cause the generation AI to execute filtering.

[0043] When inputting symptoms, the reception unit can preferentially input highly relevant symptoms in consideration of the examinee's geographical location information. For example, if the examinee lives in a specific area, the reception unit can preferentially display symptoms that are prevalent in that area. In addition, if the examinee is traveling, the reception unit can also preferentially display symptoms related to health risks at the travel destination. Furthermore, if the examinee works in a specific environment (for example, a factory or a farm), the reception unit can also preferentially display symptoms related to that environment. In this way, by considering the examinee's geographical location information, highly relevant symptoms can be preferentially input. 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 examinee's geographical location information to the generation AI and cause the generation AI to select highly relevant symptoms.

[0044] The reception unit can analyze the examinee's social media activity and input related symptoms when inputting symptoms. The reception unit can suggest related symptoms based on, for example, health information shared by the examinee on social media. The reception unit can also preferentially display symptoms related to a specific health risk from the examinee's social media activity. Furthermore, the reception unit can automatically input symptoms mentioned by the examinee on social media. In this way, related symptoms can be efficiently input by analyzing the examinee's social media activity. 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 examinee's social media activity data to the generation AI and cause the generation AI to select related symptoms.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptom during the analysis. The analysis unit can perform a detailed analysis on an important symptom, for example. The analysis unit can also perform a simplified analysis on a minor symptom. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the symptom. As a result, by adjusting the level of detail of the analysis based on the importance of the symptom, a detailed analysis can be performed on an important symptom. 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 symptom importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. For example, the analysis unit can apply a dedicated analysis algorithm to respiratory symptoms. The analysis unit can also apply a dedicated analysis algorithm to digestive symptoms. The analysis unit can also apply a dedicated analysis algorithm to nervous symptoms. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the symptom category. Some 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 symptom category data to the generation AI and cause the generation AI to select an analysis algorithm.

[0047] The analysis unit can determine the priority of the analysis based on the time of submission of the symptoms during the analysis. For example, the analysis unit can prioritize the analysis of recently submitted symptoms. Also, the analysis unit can postpone the analysis of symptoms submitted in the past. Furthermore, the analysis unit can dynamically adjust the priority of the analysis according to the time of submission. In this way, by determining the priority of the analysis based on the time of submission of the symptoms, it is possible to quickly respond to the latest symptoms. 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 the data on the time of submission of the symptoms to the generation AI and cause the generation AI to determine the priority of the analysis.

[0048] The analysis unit can adjust the order of analysis based on the relevance of symptoms during analysis. For example, the analysis unit can prioritize analysis of highly related symptoms. Also, the analysis unit can postpone analysis of less related symptoms. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of symptoms. As a result, by adjusting the order of analysis based on the relevance of symptoms, it is possible to prioritize analysis of highly related symptoms. 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 symptom relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0049] At the time of allocation, the allocation unit can select the optimal allocation method by analyzing the past medical examination behavior of the patient. For example, the allocation unit can allocate the optimal waiting space based on the waiting space used by the patient in the past. The allocation unit can also allocate a waiting space that avoids congestion based on the patient's past medical examination behavior. Furthermore, the allocation unit can analyze the patient's past medical examination behavior and allocate the most efficient waiting space. As a result, the patient's comfort can be improved by allocating the optimal waiting space based on the patient's past medical examination behavior. A part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI or may be performed without using AI. For example, the allocation unit can input past medical examination behavior data to the generation AI and cause the generation AI to select the optimal allocation method.

[0050] The allocation unit can customize the allocation of the waiting space based on the current health condition of the patient at the time of allocation. For example, if the patient has a fever, the allocation unit can allocate an isolated waiting space. In addition, if the patient has a cough, the allocation unit can allocate a waiting space that allows the patient to keep a distance from other patients. Furthermore, if the patient complains of abdominal pain, the allocation unit can allocate a waiting space close to a toilet. In this way, by customizing the waiting space based on the current health condition of the patient, an appropriate waiting space can be provided. Part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI or may be performed without using AI. For example, the allocation unit can input the health condition data of the patient to the generation AI and cause the generation AI to execute the allocation of the waiting space.

[0051] The allocation unit can select the optimal waiting space by considering the geographical location information of the patient at the time of allocation. For example, if the patient lives in a specific area, the allocation unit can allocate a waiting space related to the area. In addition, if the patient is traveling, the allocation unit can also allocate a waiting space related to the health risk at the travel destination. Furthermore, if the patient works in a specific environment (e.g., a factory or a farm), the allocation unit can also allocate a waiting space related to the environment. In this way, by considering the geographical location information of the patient, a highly relevant waiting space can be provided. Part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI or may be performed without using AI. For example, the allocation unit can input the geographical location information of the patient to the generation AI and cause the generation AI to select the optimal waiting space.

[0052] At the time of allocation, the allocation unit can analyze the social media activity of the patient and allocate the waiting space. The allocation unit can allocate a relevant waiting space based on, for example, health information shared by the patient on social media. The allocation unit can also allocate a waiting space related to a specific health risk from the patient's social media activity. Furthermore, the allocation unit can allocate an optimal waiting space based on symptoms mentioned by the patient on social media. In this way, a highly relevant waiting space can be provided by analyzing the patient's social media activity. A part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI or may be performed without using AI. For example, the allocation unit can input the patient's social media activity data to the generation AI and cause the generation AI to select the optimal waiting space.

[0053] The monitoring unit can strengthen the sensor unit that monitors the congestion status of the waiting space in real time during monitoring. The monitoring unit can install additional sensors, for example, to monitor the congestion status of the waiting space in real time. The monitoring unit can also improve the accuracy of the sensors to grasp the congestion status more accurately. Furthermore, the monitoring unit can analyze the sensor data in real time and adjust the allocation of the waiting space according to the congestion status. This makes it possible to take appropriate measures by monitoring the congestion status of the waiting space in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input data acquired from the sensor to the generation AI and cause the generation AI to execute an analysis of the congestion status.

[0054] The monitoring unit can collect environmental data of the waiting space during monitoring to improve the accuracy of monitoring. The monitoring unit can, for example, monitor the temperature and humidity of the waiting space in real time and collect environmental data. The monitoring unit can also evaluate the comfort of the waiting space based on the environmental data and adjust it as necessary. Furthermore, the monitoring unit can analyze the environmental data and identify areas for improvement in the waiting space. As a result, the accuracy of monitoring is improved by collecting environmental data of the waiting space. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the environmental data to the generation AI and have the generation AI perform the analysis.

[0055] The monitoring unit can perform monitoring while taking into consideration the geographical distribution of the waiting spaces. The monitoring unit can perform efficient monitoring, for example, by taking into consideration the geographical distribution of the waiting spaces. The monitoring unit can also introduce a system for centrally monitoring geographically distributed waiting spaces. Furthermore, the monitoring unit can also optimize the placement of the surveillance cameras based on the geographical distribution. This enables efficient monitoring by taking into consideration the geographical distribution of the waiting spaces. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input geographical distribution data of the waiting spaces to the generation AI and cause the generation AI to execute optimization of monitoring.

[0056] The monitoring unit can improve the accuracy of monitoring by referring to literature related to waiting spaces during monitoring. The monitoring unit can, for example, refer to the latest research literature on monitoring waiting spaces to improve the monitoring method. The monitoring unit can also introduce new technologies for improving the accuracy of monitoring based on the related literature. Furthermore, the monitoring unit can also refer to related literature and adopt best practices for monitoring waiting spaces. As a result, the accuracy of monitoring is improved by referring to literature related to waiting spaces. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input data from related literature into the generation AI and cause the generation AI to execute improvements to the monitoring method.

[0057] The notification unit can adjust the level of detail of the notification based on the importance of the waiting time at the time of notification. For example, when the waiting time is long, the notification unit can provide a detailed notification. Also, when the waiting time is short, the notification unit can provide a concise notification. Furthermore, the notification unit can dynamically adjust the level of detail of the notification according to the importance of the waiting time. As a result, by adjusting the level of detail of the notification based on the importance of the waiting time, it is possible to provide an appropriate notification for the patient. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the importance data of the waiting time to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0058] The notification unit can apply different notification algorithms depending on the category of the waiting time at the time of notification. For example, the notification unit can apply a quick notification algorithm to an emergency waiting time. The notification unit can also apply a standard notification algorithm to a normal waiting time. Furthermore, the notification unit can select an optimal notification algorithm depending on the category of the waiting time. In this way, by applying an appropriate notification algorithm depending on the category of the waiting time, it is possible to provide an appropriate notification to the patient. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the category data of the waiting time to the generation AI and cause the generation AI to select the notification algorithm.

[0059] The notification unit can determine the priority of the notification based on the submission time of the waiting time at the time of notification. For example, the notification unit can give priority to the recently submitted waiting time. Also, the notification unit can postpone the notification of the waiting time submitted in the past. Furthermore, the notification unit can dynamically adjust the priority of the notification according to the submission time. In this way, by determining the priority of the notification based on the submission time of the waiting time, it is possible to quickly respond to the latest waiting time. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the submission time data of the waiting time to the generation AI and cause the generation AI to execute the determination of the priority of the notification.

[0060] The notification unit can adjust the order of notifications based on the relevance of the waiting times at the time of notification. For example, the notification unit can give priority to notifications for highly relevant waiting times. In addition, the notification unit can also give notifications later for waiting times with low relevance. Furthermore, the notification unit can dynamically adjust the order of notifications according to the relevance of the waiting times. As a result, by adjusting the order of notifications based on the relevance of the waiting times, notifications can be given priority to waiting times with high relevance. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input relevance data of the waiting times to the generation AI and cause the generation AI to adjust the order of notifications.

[0061] The feedback unit can select the optimal collection method by referring to the examinee's past feedback history when collecting feedback. The feedback unit can, for example, suggest the optimal collection method based on feedback provided by the examinee in the past. The feedback unit can also preferentially collect specific feedback items from the examinee's past feedback history. Furthermore, the feedback unit can analyze the examinee's past feedback history and select the most efficient collection method. This enables efficient feedback collection by selecting the optimal collection method based on the examinee's past feedback history. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input past feedback history data to the generation AI and cause the generation AI to select the optimal collection method.

[0062] The feedback unit can customize the collection of feedback based on the patient's current health condition when collecting feedback. For example, if the patient has a fever, the feedback unit can provide a brief feedback form. Also, if the patient is relaxed, the feedback unit can provide a detailed feedback form. Furthermore, if the patient is in a hurry, the feedback unit can provide a method for quickly collecting feedback. This allows appropriate feedback to be collected by customizing the collection of feedback based on the patient's current health condition. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input the patient's health condition data to the generation AI and cause the generation AI to customize the method of collecting feedback.

[0063] The feedback unit can select the optimal collection method in consideration of the examinee's geographical location information when collecting feedback. For example, if the examinee lives in a specific area, the feedback unit can preferentially collect feedback related to that area. In addition, if the examinee is traveling, the feedback unit can also preferentially collect feedback related to health risks at the travel destination. Furthermore, if the examinee works in a specific environment (e.g., a factory or a farm), the feedback unit can also preferentially collect feedback related to that environment. In this way, by considering the examinee's geographical location information, highly relevant feedback can be collected. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input the examinee's geographical location information to the generation AI and cause the generation AI to select the optimal collection method.

[0064] The feedback unit can collect feedback by analyzing the patient's social media activities when collecting feedback. The feedback unit can collect relevant feedback based on, for example, health information shared by the patient on social media. The feedback unit can also preferentially collect feedback related to a specific health risk from the patient's social media activities. Furthermore, the feedback unit can collect optimal feedback based on symptoms mentioned by the patient on social media. In this way, by analyzing the patient's social media activities, highly relevant feedback can be collected. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input the patient's social media activity data to the generation AI and cause the generation AI to collect feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The monitoring unit may further include an environmental monitoring unit. The environmental monitoring unit has the function of collecting and analyzing environmental data such as the temperature, humidity, and air quality of the waiting area in real time. For example, if the temperature of the waiting area is too high, the environmental monitoring unit can issue instructions to the air conditioning system to appropriately adjust the temperature. It can also operate a humidifier if the humidity is low. Furthermore, it can operate the ventilation system if the air quality deteriorates. This makes it possible to always maintain the environment of the waiting area in an optimal state, improving the comfort of patients.

[0067] The analysis unit can further include a predictive analysis unit. The predictive analysis unit has a function of predicting future congestion in the waiting area and doctor availability based on past data. For example, if congestion is predicted on a specific day of the week or time period based on past data, the predictive analysis unit can adjust the allocation of waiting areas in advance based on that information. It can also predict doctor availability and adjust doctor schedules as necessary. This makes it possible to prevent congestion in the waiting area in advance and further shorten patients' waiting times.

[0068] The notification unit can further include a multilingual support unit. When patients speak different languages, the multilingual support unit has a function of providing notifications corresponding to the languages. For example, notifications can be provided in English to patients who speak English, and in Spanish to patients who speak Spanish. The multilingual support unit can also automatically select an appropriate language based on the patient's language setting. This allows information to be provided smoothly to patients who speak different languages, improving convenience.

[0069] The reception unit may further include a vital sign measurement unit. The vital sign measurement unit has the function of measuring the patient's vital signs, such as body temperature, blood pressure, and heart rate, and inputting the data into the system. For example, the patient's body temperature can be measured at the reception desk, and the data can be automatically input into the system. Similarly, blood pressure and heart rate can also be measured and input into the system. This makes it possible to more accurately analyze symptoms and allocate waiting spaces based on the patient's vital sign data.

[0070] The process flow of the first embodiment will be briefly described below.

[0071] Step 1: The reception unit inputs the symptoms of the patient. The symptoms of the patient include, but are not limited to, fever, cough, abdominal pain, etc. The reception unit can input the symptoms using, for example, a touch panel or voice input. Step 2: The analysis unit analyzes the symptoms entered by the reception unit and the situation on that day. For example, the analysis unit uses statistical analysis or machine learning algorithms to analyze the congestion situation of the waiting area and the availability of doctors. Step 3: The allocation unit allocates the waiting space based on the results of the analysis by the analysis unit. The allocation unit allocates an optimal waiting space based on, for example, the size of the waiting space and the number of available seats. Step 4: The monitoring unit monitors the status of the waiting space allocated by the allocation unit in real time and changes the priority as necessary. The monitoring unit monitors the congestion status of the waiting space, for example, by using a camera or a sensor. Step 5: The notification unit notifies the patient based on the information monitored by the monitoring unit. For example, the notification unit notifies the patient of changes to the waiting time or waiting space via a smartphone app. Step 6: The feedback section collects feedback from the examinees. The feedback section collects feedback from the examinees, for example, through a questionnaire or interview.

[0072] (Example 2) The waiting space optimization system according to the embodiment of the present invention is a system that segments waiting spaces based on the symptoms of patients and the situation of the day, and utilizes real-time priority changes, a notification system, and a feedback loop. In this system, when a patient arrives at a medical institution, the patient inputs symptoms at the reception desk. For example, symptoms such as fever, cough, and abdominal pain are input. This information is entered into the system. Next, the system analyzes the input symptoms and the situation of the day (for example, the congestion status of the waiting space and the availability of doctors) and assigns the optimal waiting space. For example, a patient with a fever is guided to a specific waiting space to be isolated from other patients. Furthermore, the system monitors the situation of the waiting space in real time and changes the priority as necessary. For example, if an emergency occurs, the priority of the patient is raised and the patient is dealt with promptly. In addition, the system utilizes a notification system to notify patients of changes in waiting time and waiting space in real time. For example, a shortened waiting time and a change in waiting space are notified through a smartphone app. Finally, the system utilizes a feedback loop to collect feedback from patients and use it to improve the system. For example, feedback on waiting time and comfort of the waiting space is collected and reflected in the next visit. This mechanism reduces waiting times and optimizes resources, improving patient satisfaction. As a result, the waiting space optimization system can optimize waiting spaces based on patients' symptoms and the situation on that day, reducing waiting times and optimizing resources.

[0073] The waiting space optimization system according to the embodiment includes a reception unit, an analysis unit, an allocation unit, a monitoring unit, a notification unit, and a feedback unit. The reception unit inputs the symptoms of the patient. The symptoms of the patient include, for example, fever, cough, abdominal pain, and the like, but are not limited to such examples. The reception unit can input the symptoms using, for example, a touch panel or voice input. The analysis unit analyzes the symptoms input by the reception unit and the situation on that day. The analysis unit analyzes the congestion status of the waiting space and the availability status of the doctor, for example, using statistical analysis or machine learning algorithms. The allocation unit allocates the waiting space based on the results of the analysis by the analysis unit. The allocation unit allocates an optimal waiting space, for example, based on the size of the waiting space and the number of available seats. The monitoring unit monitors the status of the waiting space allocated by the allocation unit in real time and changes the priority as necessary. The monitoring unit monitors the congestion status of the waiting space, for example, using camera monitoring or sensor monitoring. The notification unit notifies the patient based on the information monitored by the monitoring unit. The notification unit notifies the patient of changes to the waiting time and waiting space, for example, via a smartphone app. The feedback unit collects feedback from the patient. The feedback unit collects feedback from the patient, for example, through a questionnaire or an interview. As a result, the waiting space optimization system according to the embodiment can optimize the waiting space based on the patient's symptoms and the situation of the day, thereby reducing the waiting time and optimizing resources.

[0074] The reception unit inputs the symptoms of the examinee. The symptoms of the examinee include, but are not limited to, fever, cough, abdominal pain, etc. The reception unit can input the symptoms using, for example, a touch panel or voice input. Specifically, the touch panel is designed to allow intuitive operation and to allow the examinee to easily select the symptoms. For example, the touch panel displays common symptoms such as fever, cough, abdominal pain, etc. as icons or text, and the examinee can complete the input by simply tapping the corresponding symptom. Voice input is particularly useful for elderly people and visually impaired people, and the system can automatically recognize and input the symptoms simply by the examinee verbally describing his or her symptoms. Voice recognition technology uses natural language processing to analyze the examinee's speech and identify the appropriate symptoms. Furthermore, the reception unit can simultaneously input basic information of the examinee (such as name, age, and gender), which makes subsequent analysis and allocation more accurate. The reception unit immediately stores the input information in a database and makes it accessible to the analysis unit and other departments. This allows the reception unit to quickly and accurately collect the symptoms and basic information of the examinee, improving the efficiency of the entire system.

[0075] The analysis unit analyzes the symptoms input by the reception unit and the situation of the day. For the analysis, statistical analysis and machine learning algorithms are used to analyze the congestion status of the waiting area and the availability of doctors. Specifically, statistical analysis predicts the current congestion status based on past data and grasps the trend of peak hours and congestion. For example, by extracting congestion patterns on specific days of the week and time periods from past data and comparing them with the current situation, it is possible to predict future congestion. On the other hand, machine learning algorithms enable more advanced analysis and grasp and predict the situation in real time. For example, a model is constructed that uses the symptoms and basic information of patients, past medical history, etc. as input data to predict the optimal allocation of waiting spaces and the availability of doctors. This allows the analysis unit to comprehensively analyze the symptoms of patients and the situation of the day and provide information necessary for optimizing the waiting space. Furthermore, the analysis unit can continuously revise the analysis results based on data updated in real time to respond to the latest situation. For example, if the congestion status of the waiting area changes suddenly, the analysis unit immediately takes in new data and updates the analysis results. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0076] The allocation unit allocates waiting spaces based on the results of the analysis by the analysis unit. The allocation unit allocates an optimal waiting space based on the size of the waiting space and the number of available seats. Specifically, the layout of the waiting space and the arrangement of the seats are taken into consideration so that patients can wait comfortably. For example, by allocating many patients to a large waiting space and a small number of patients to a small space, congestion can be avoided. The allocation unit can also set priorities according to the symptoms and urgency of the patients and change the allocation of waiting spaces as necessary. For example, an efficient waiting environment is provided by preferentially allocating a large waiting space to patients with serious symptoms and allocating other spaces to patients with mild symptoms. Furthermore, the allocation unit can continuously optimize the allocation of waiting spaces based on data updated in real time. For example, the allocation unit monitors the congestion status of the waiting space and the arrival status of patients, and adjusts the allocation as necessary. The allocation unit can also improve the arrangement and allocation method of the waiting space based on feedback from patients. As a result, the allocation unit can provide a comfortable and efficient waiting environment for patients and improve the performance of the entire system.

[0077] The monitoring unit monitors the status of the waiting space assigned by the allocation unit in real time, and changes the priority as necessary. The monitoring unit monitors the congestion status of the waiting space using camera monitoring and sensor monitoring. Specifically, in camera monitoring, the video of the waiting space is analyzed in real time to grasp the number and movements of patients. For example, the number of patients can be counted from the camera video and the congestion level can be evaluated. In addition, in sensor monitoring, various sensors (e.g., temperature sensors, humidity sensors, pressure sensors, etc.) installed in the waiting space are used to detect environmental changes and the movements of patients. This allows the monitoring unit to grasp the status of the waiting space in detail and change the priority as necessary. For example, if a specific waiting space is excessively crowded, the monitoring unit can instruct the patient to move to another waiting space. In addition, the monitoring unit can use an anomaly detection algorithm to detect unusual patterns or abnormal situations early and take appropriate measures. As a result, the monitoring unit can ensure the comfort and safety of patients by monitoring the status of the waiting space in real time and taking prompt and appropriate measures.

[0078] The notification unit notifies the patient based on the information monitored by the monitoring unit. The notification unit notifies the patient of changes in waiting time and waiting space through a smartphone app. Specifically, a push notification is sent to the patient's smartphone to notify the patient of updates to the waiting time and changes to the waiting space in real time. For example, if the waiting time is shortened or the waiting space is changed, the patient is notified immediately. The notification unit can reliably transmit information using multiple notification means such as voice guidance and vibration notification. This allows the patient to always grasp the latest information and take appropriate action. Furthermore, the notification unit can improve the content and method of notification based on the patient's feedback. For example, by analyzing feedback from the patient and optimizing the timing and content of the notification, more effective information transmission is achieved. The notification unit can also provide special notifications in the event of an emergency to encourage the patient to respond quickly. This allows the notification unit to quickly and reliably provide information to the patient, thereby helping to reduce waiting time and optimize resources.

[0079] The feedback unit collects feedback from patients. The feedback unit collects feedback from patients through questionnaires and interviews. Specifically, the feedback unit provides a questionnaire that allows patients to express their opinions about their experience in the waiting area and the usability of the system. For example, questions are asked about the length of the waiting time, the comfort of the waiting area, the ease of use of the system, etc., and the satisfaction of the patients is evaluated. In addition, in the interview, detailed feedback can be collected by directly interacting with the patients. This allows the feedback unit to grasp the opinions and requests of the patients in detail and use them to improve the system. Furthermore, the feedback unit stores the collected feedback in a database and makes it accessible to the analysis unit and other departments. This allows the feedback unit to improve the entire system based on the opinions of the patients and increase the satisfaction of the patients. For example, the layout of the waiting area and the operation method of the system are reviewed based on the feedback of the patients, providing a more comfortable and easy-to-use environment. In addition, the feedback unit regularly collects and analyzes feedback to make continuous improvements. This allows the feedback unit to improve the system by reflecting the opinions of the patients and increase the satisfaction of the patients.

[0080] The monitoring unit may include a sensor unit that monitors the congestion status of the waiting space in real time. The sensor unit may include, but is not limited to, an infrared sensor or a camera sensor. The monitoring unit may install an additional sensor to monitor the congestion status of the waiting space in real time. The monitoring unit may also improve the accuracy of the sensor to grasp the congestion status more accurately. Furthermore, the monitoring unit may analyze the sensor data in real time and adjust the allocation of the waiting space according to the congestion status. This makes it possible to take appropriate measures by monitoring the congestion status of the waiting space in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit may input data acquired from the sensor to the generation AI and cause the generation AI to execute an analysis of the congestion status.

[0081] The feedback unit may include an interface unit for collecting feedback from patients. The interface unit may include, but is not limited to, a touch panel or voice input. The feedback unit may be provided with, for example, a touch panel in order to efficiently collect feedback from patients. The feedback unit may also collect feedback from patients using voice input. Furthermore, the feedback unit may also collect feedback from patients in real time through the interface unit. This allows the feedback from patients to be efficiently collected and used to improve the system. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input feedback data acquired from the interface unit to a generation AI and cause the generation AI to analyze the feedback.

[0082] The analysis unit can analyze the congestion of the waiting space and the availability of the doctor. For example, the analysis unit can use the number of people count and the length of stay as evaluation criteria to analyze the congestion of the waiting space. The analysis unit can also use the consultation time and the reservation status as evaluation criteria to analyze the availability of the doctor. Furthermore, the analysis unit can analyze the congestion of the waiting space and the availability of the doctor using statistical analysis and machine learning algorithms. This makes it possible to optimally allocate the waiting space by analyzing the congestion of the waiting space and the availability of the doctor. 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 on the congestion of the waiting space and the availability of the doctor to the generation AI and cause the generation AI to perform the analysis.

[0083] The notification unit can notify the patient of changes to the waiting time and waiting space through a smartphone app. The notification unit can notify the patient of changes to the waiting time and waiting space through, for example, a smartphone app. The notification unit can notify the patient in real time using a push notification function. The notification unit can also notify the patient of changes to the waiting time and waiting space using a real-time update function. Furthermore, the notification unit can also build a system for notifying the patient of changes to the waiting time and waiting space through a smartphone app. This improves the convenience of the patient by notifying the patient of changes to the waiting time and waiting space in real time. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input information on changes to the waiting time and waiting space into the generation AI and cause the generation AI to generate the notification content.

[0084] The feedback unit can collect feedback from examinees and use it to improve the system. The feedback unit can, for example, conduct a questionnaire to collect feedback from examinees. The feedback unit can also collect feedback from examinees through interviews. Furthermore, the feedback unit can build a system for collecting feedback from examinees and using it to improve the system. This allows feedback from examinees to be collected and used to improve the system, thereby improving the satisfaction of examinees. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the collected feedback data to a generation AI and have the generation AI perform an analysis of the feedback.

[0085] The reception unit can estimate the emotion of the examinee and adjust the symptom input method based on the estimated emotion of the examinee. For example, when the examinee feels anxious, the reception unit can provide a simple and intuitive interface and minimize the input procedure. In addition, when the examinee is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the examinee is in a hurry, the reception unit can prioritize voice input and enable the examinee to input symptoms quickly. In this way, the stress of the examinee can be reduced by adjusting the symptom input method according to the emotion of the examinee. 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 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 facial expression data of the examinee to the generation AI and cause the generation AI to execute emotion estimation.

[0086] The reception unit can analyze the examinee's past medical history and select the optimal input method. For example, the reception unit can automatically display symptoms that the examinee has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the examinee has used in the past. Furthermore, the reception unit can predict and suggest symptoms to be used in a specific time period based on the examinee's past medical history. This improves input efficiency by selecting the optimal input method based on the examinee's past medical history. 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 past medical history data to the generation AI and cause the generation AI to select the optimal input method.

[0087] The reception unit can perform filtering based on the examinee's current health condition and living situation when inputting symptoms. For example, when the examinee inputs his / her current health condition, the reception unit can preferentially display related symptoms. The reception unit can also filter related symptoms taking into account the examinee's living situation (for example, work stress and home situation). Furthermore, if the examinee has a specific health condition (for example, chronic disease), the reception unit can also preferentially display symptoms related to that condition. As a result, by filtering based on the examinee's current health condition and living situation, related symptoms can be preferentially displayed. 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 data on the examinee's health condition and living situation to the generation AI and cause the generation AI to execute filtering.

[0088] The reception unit can estimate the emotion of the examinee and determine the priority of the symptoms to be input based on the estimated emotion of the examinee. For example, when the examinee feels anxious, the reception unit can urge the examinee to input important symptoms with priority. Also, when the examinee feels relaxed, the reception unit can urge the examinee to input detailed symptoms. Furthermore, when the examinee is in a hurry, the reception unit can enable the examinee to input major symptoms quickly. In this way, by determining the priority of symptoms according to the emotion of the examinee, important symptoms can be input quickly. 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 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 facial expression data of the examinee to the generation AI and cause the generation AI to execute emotion estimation.

[0089] When inputting symptoms, the reception unit can preferentially input highly relevant symptoms in consideration of the examinee's geographical location information. For example, if the examinee lives in a specific area, the reception unit can preferentially display symptoms that are prevalent in that area. In addition, if the examinee is traveling, the reception unit can also preferentially display symptoms related to health risks at the travel destination. Furthermore, if the examinee works in a specific environment (for example, a factory or a farm), the reception unit can also preferentially display symptoms related to that environment. In this way, by considering the examinee's geographical location information, highly relevant symptoms can be preferentially input. 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 examinee's geographical location information to the generation AI and cause the generation AI to select highly relevant symptoms.

[0090] The reception unit can analyze the examinee's social media activity and input related symptoms when inputting symptoms. The reception unit can suggest related symptoms based on, for example, health information shared by the examinee on social media. The reception unit can also preferentially display symptoms related to a specific health risk from the examinee's social media activity. Furthermore, the reception unit can automatically input symptoms mentioned by the examinee on social media. In this way, related symptoms can be efficiently input by analyzing the examinee's social media activity. 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 examinee's social media activity data to the generation AI and cause the generation AI to select related symptoms.

[0091] The analysis unit can estimate the emotion of the examinee and adjust the expression method of the analysis based on the estimated emotion of the examinee. For example, when the examinee feels anxious, the analysis unit can provide a simple and easy-to-understand analysis result. In addition, when the examinee is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, when the examinee is in a hurry, the analysis unit can also provide an analysis result that focuses on the main points. In this way, by adjusting the expression method of the analysis according to the emotion of the examinee, it is possible to provide an analysis result that is easy for the examinee to understand. 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 facial expression data of the examinee to the generation AI and cause the generation AI to execute emotion estimation.

[0092] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptom during the analysis. The analysis unit can perform a detailed analysis on an important symptom, for example. The analysis unit can also perform a simplified analysis on a minor symptom. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the symptom. As a result, by adjusting the level of detail of the analysis based on the importance of the symptom, a detailed analysis can be performed on an important symptom. 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 symptom importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0093] The analysis unit can apply different analysis algorithms depending on the symptom category during analysis. For example, the analysis unit can apply a dedicated analysis algorithm to respiratory symptoms. The analysis unit can also apply a dedicated analysis algorithm to digestive symptoms. The analysis unit can also apply a dedicated analysis algorithm to nervous symptoms. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the symptom category. Some 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 symptom category data to the generation AI and cause the generation AI to select an analysis algorithm.

[0094] The analysis unit can estimate the emotion of the examinee and adjust the length of the analysis based on the estimated emotion of the examinee. For example, when the examinee feels anxious, the analysis unit can provide a short and to the point analysis result. In addition, when the examinee is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, when the examinee is in a hurry, the analysis unit can quickly provide the analysis result. In this way, by adjusting the length of the analysis according to the emotion of the examinee, an analysis result of an appropriate length for the examinee 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 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 facial expression data of the examinee to the generation AI and cause the generation AI to execute emotion estimation.

[0095] The analysis unit can determine the priority of the analysis based on the time of submission of the symptoms during the analysis. For example, the analysis unit can prioritize the analysis of recently submitted symptoms. Also, the analysis unit can postpone the analysis of symptoms submitted in the past. Furthermore, the analysis unit can dynamically adjust the priority of the analysis according to the time of submission. In this way, by determining the priority of the analysis based on the time of submission of the symptoms, it is possible to quickly respond to the latest symptoms. 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 the data on the time of submission of the symptoms to the generation AI and cause the generation AI to determine the priority of the analysis.

[0096] The analysis unit can adjust the order of analysis based on the relevance of symptoms during analysis. For example, the analysis unit can prioritize analysis of highly related symptoms. Also, the analysis unit can postpone analysis of less related symptoms. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of symptoms. As a result, by adjusting the order of analysis based on the relevance of symptoms, it is possible to prioritize analysis of highly related symptoms. 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 symptom relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0097] The allocation unit can estimate the emotion of the patient and adjust the waiting space allocation method based on the estimated emotion of the patient. For example, when the patient feels anxious, the allocation unit can preferentially allocate a quiet waiting space. In addition, when the patient is relaxed, the allocation unit can also allocate a general waiting space. Furthermore, when the patient is in a hurry, the allocation unit can also allocate a waiting space that can be quickly responded to. This can improve the comfort of the patient by adjusting the waiting space allocation method according to the emotion of the patient. 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 allocation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the allocation unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0098] At the time of allocation, the allocation unit can select the optimal allocation method by analyzing the past medical examination behavior of the patient. For example, the allocation unit can allocate the optimal waiting space based on the waiting space used by the patient in the past. The allocation unit can also allocate a waiting space that avoids congestion based on the patient's past medical examination behavior. Furthermore, the allocation unit can analyze the patient's past medical examination behavior and allocate the most efficient waiting space. As a result, the patient's comfort can be improved by allocating the optimal waiting space based on the patient's past medical examination behavior. A part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI or may be performed without using AI. For example, the allocation unit can input past medical examination behavior data to the generation AI and cause the generation AI to select the optimal allocation method.

[0099] The allocation unit can customize the allocation of the waiting space based on the current health condition of the patient at the time of allocation. For example, if the patient has a fever, the allocation unit can allocate an isolated waiting space. In addition, if the patient has a cough, the allocation unit can allocate a waiting space that allows the patient to keep a distance from other patients. Furthermore, if the patient complains of abdominal pain, the allocation unit can allocate a waiting space close to a toilet. In this way, by customizing the waiting space based on the current health condition of the patient, an appropriate waiting space can be provided. Part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI or may be performed without using AI. For example, the allocation unit can input the health condition data of the patient to the generation AI and cause the generation AI to execute the allocation of the waiting space.

[0100] The allocation unit can estimate the emotion of the patient and determine the priority of waiting spaces based on the estimated emotion of the patient. For example, when the patient feels anxious, the allocation unit can preferentially allocate a quiet waiting space. Furthermore, when the patient is relaxed, the allocation unit can also allocate a general waiting space. Furthermore, when the patient is in a hurry, the allocation unit can also preferentially allocate a waiting space that can be quickly responded to. This can improve the comfort of the patient by determining the priority of waiting spaces according to the emotion of the patient. 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 allocation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the allocation unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0101] The allocation unit can select the optimal waiting space by considering the geographical location information of the patient at the time of allocation. For example, if the patient lives in a specific area, the allocation unit can allocate a waiting space related to the area. In addition, if the patient is traveling, the allocation unit can also allocate a waiting space related to the health risk at the travel destination. Furthermore, if the patient works in a specific environment (e.g., a factory or a farm), the allocation unit can also allocate a waiting space related to the environment. In this way, by considering the geographical location information of the patient, a highly relevant waiting space can be provided. Part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI or may be performed without using AI. For example, the allocation unit can input the geographical location information of the patient to the generation AI and cause the generation AI to select the optimal waiting space.

[0102] At the time of allocation, the allocation unit can analyze the social media activity of the patient and allocate the waiting space. The allocation unit can allocate a relevant waiting space based on, for example, health information shared by the patient on social media. The allocation unit can also allocate a waiting space related to a specific health risk from the patient's social media activity. Furthermore, the allocation unit can allocate an optimal waiting space based on symptoms mentioned by the patient on social media. In this way, a highly relevant waiting space can be provided by analyzing the patient's social media activity. A part or all of the above-mentioned processing in the allocation unit may be performed, for example, using AI or may be performed without using AI. For example, the allocation unit can input the patient's social media activity data to the generation AI and cause the generation AI to select the optimal waiting space.

[0103] The monitoring unit can estimate the emotion of the patient and adjust the monitoring method based on the estimated emotion of the patient. For example, when the patient feels anxious, the monitoring unit can reduce the visual presence of the monitoring camera. Also, when the patient is relaxed, the monitoring unit can maintain the normal monitoring method. Furthermore, when the patient is in a hurry, the monitoring unit can expand the monitoring range so as to respond quickly. This can improve the comfort of the patient by adjusting the monitoring method according to the emotion of the patient. 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 monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0104] The monitoring unit can strengthen the sensor unit that monitors the congestion status of the waiting space in real time during monitoring. The monitoring unit can install additional sensors, for example, to monitor the congestion status of the waiting space in real time. The monitoring unit can also improve the accuracy of the sensors to grasp the congestion status more accurately. Furthermore, the monitoring unit can analyze the sensor data in real time and adjust the allocation of the waiting space according to the congestion status. This makes it possible to take appropriate measures by monitoring the congestion status of the waiting space in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input data acquired from the sensor to the generation AI and cause the generation AI to execute an analysis of the congestion status.

[0105] The monitoring unit can collect environmental data of the waiting space during monitoring to improve the accuracy of monitoring. The monitoring unit can, for example, monitor the temperature and humidity of the waiting space in real time and collect environmental data. The monitoring unit can also evaluate the comfort of the waiting space based on the environmental data and adjust it as necessary. Furthermore, the monitoring unit can analyze the environmental data and identify areas for improvement in the waiting space. As a result, the accuracy of monitoring is improved by collecting environmental data of the waiting space. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the environmental data to the generation AI and have the generation AI perform the analysis.

[0106] The monitoring unit can estimate the emotion of the patient and determine the priority of monitoring based on the estimated emotion of the patient. For example, if the patient feels anxious, the monitoring unit can prioritize monitoring of the patient. In addition, the monitoring unit can perform normal monitoring when the patient is relaxed. Furthermore, the monitoring unit can quickly monitor the patient when the patient is in a hurry. This can improve the comfort of the patient by determining the priority of monitoring according to the emotion of the patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0107] The monitoring unit can perform monitoring while taking into consideration the geographical distribution of the waiting spaces. The monitoring unit can perform efficient monitoring, for example, by taking into consideration the geographical distribution of the waiting spaces. The monitoring unit can also introduce a system for centrally monitoring geographically distributed waiting spaces. Furthermore, the monitoring unit can also optimize the placement of the surveillance cameras based on the geographical distribution. This enables efficient monitoring by taking into consideration the geographical distribution of the waiting spaces. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input geographical distribution data of the waiting spaces to the generation AI and cause the generation AI to execute optimization of monitoring.

[0108] The monitoring unit can improve the accuracy of monitoring by referring to literature related to waiting spaces during monitoring. The monitoring unit can, for example, refer to the latest research literature on monitoring waiting spaces to improve the monitoring method. The monitoring unit can also introduce new technologies for improving the accuracy of monitoring based on the related literature. Furthermore, the monitoring unit can also refer to related literature and adopt best practices for monitoring waiting spaces. As a result, the accuracy of monitoring is improved by referring to literature related to waiting spaces. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input data from related literature into the generation AI and cause the generation AI to execute improvements to the monitoring method.

[0109] The notification unit can estimate the emotion of the patient and adjust the expression method of the notification based on the estimated emotion of the patient. For example, if the patient feels anxious, the notification unit can notify the patient using a calm expression. Furthermore, if the patient is relaxed, the notification unit can also notify the patient using a bright expression. Furthermore, if the patient is in a hurry, the notification unit can also notify the patient using a quick and concise expression. In this way, by adjusting the expression method of the notification according to the emotion of the patient, it is possible to provide a notification that is easy for the patient to understand. 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 notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0110] The notification unit can adjust the level of detail of the notification based on the importance of the waiting time at the time of notification. For example, when the waiting time is long, the notification unit can provide a detailed notification. Also, when the waiting time is short, the notification unit can provide a concise notification. Furthermore, the notification unit can dynamically adjust the level of detail of the notification according to the importance of the waiting time. As a result, by adjusting the level of detail of the notification based on the importance of the waiting time, it is possible to provide an appropriate notification for the patient. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the importance data of the waiting time to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0111] The notification unit can apply different notification algorithms depending on the category of the waiting time at the time of notification. For example, the notification unit can apply a quick notification algorithm to an emergency waiting time. The notification unit can also apply a standard notification algorithm to a normal waiting time. Furthermore, the notification unit can select an optimal notification algorithm depending on the category of the waiting time. In this way, by applying an appropriate notification algorithm depending on the category of the waiting time, it is possible to provide an appropriate notification to the patient. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the category data of the waiting time to the generation AI and cause the generation AI to select the notification algorithm.

[0112] The notification unit can estimate the emotion of the patient and adjust the length of the notification based on the estimated emotion of the patient. For example, when the patient feels anxious, the notification unit can provide a short and to the point notification. In addition, when the patient is relaxed, the notification unit can provide a detailed notification. Furthermore, when the patient is in a hurry, the notification unit can provide a quick notification. In this way, by adjusting the length of the notification according to the emotion of the patient, a notification of an appropriate length for the patient 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 examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0113] The notification unit can determine the priority of the notification based on the submission time of the waiting time at the time of notification. For example, the notification unit can give priority to the recently submitted waiting time. Also, the notification unit can postpone the notification of the waiting time submitted in the past. Furthermore, the notification unit can dynamically adjust the priority of the notification according to the submission time. In this way, by determining the priority of the notification based on the submission time of the waiting time, it is possible to quickly respond to the latest waiting time. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the submission time data of the waiting time to the generation AI and cause the generation AI to execute the determination of the priority of the notification.

[0114] The notification unit can adjust the order of notifications based on the relevance of the waiting times at the time of notification. For example, the notification unit can give priority to notifications for highly relevant waiting times. In addition, the notification unit can also give notifications later for waiting times with low relevance. Furthermore, the notification unit can dynamically adjust the order of notifications according to the relevance of the waiting times. As a result, by adjusting the order of notifications based on the relevance of the waiting times, notifications can be given priority to waiting times with high relevance. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input relevance data of the waiting times to the generation AI and cause the generation AI to adjust the order of notifications.

[0115] The feedback unit can estimate the emotion of the patient and adjust the feedback collection method based on the estimated emotion of the patient. For example, when the patient feels anxious, the feedback unit can provide a simple and intuitive feedback form. Also, when the patient is relaxed, the feedback unit can provide a detailed feedback form. Furthermore, when the patient is in a hurry, the feedback unit can provide a method for quickly collecting feedback. This allows the feedback collection method to be adjusted according to the emotion of the patient, thereby providing a feedback collection method appropriate for the patient. 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 (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0116] The feedback unit can select the optimal collection method by referring to the examinee's past feedback history when collecting feedback. The feedback unit can, for example, suggest the optimal collection method based on feedback provided by the examinee in the past. The feedback unit can also preferentially collect specific feedback items from the examinee's past feedback history. Furthermore, the feedback unit can analyze the examinee's past feedback history and select the most efficient collection method. This enables efficient feedback collection by selecting the optimal collection method based on the examinee's past feedback history. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input past feedback history data to the generation AI and cause the generation AI to select the optimal collection method.

[0117] The feedback unit can customize the collection of feedback based on the patient's current health condition when collecting feedback. For example, if the patient has a fever, the feedback unit can provide a brief feedback form. Also, if the patient is relaxed, the feedback unit can provide a detailed feedback form. Furthermore, if the patient is in a hurry, the feedback unit can provide a method for quickly collecting feedback. This allows appropriate feedback to be collected by customizing the collection of feedback based on the patient's current health condition. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input the patient's health condition data to the generation AI and cause the generation AI to customize the method of collecting feedback.

[0118] The feedback unit can estimate the emotion of the patient and determine the priority of the feedback based on the estimated emotion of the patient. For example, when the patient feels anxious, the feedback unit can collect the feedback preferentially. In addition, when the patient is relaxed, the feedback unit can also perform normal feedback collection. Furthermore, when the patient is in a hurry, the feedback unit can also quickly collect the feedback. In this way, important feedback can be quickly collected by determining the priority of the feedback according to the emotion of the patient. 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 feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input facial expression data of the patient to the generation AI and cause the generation AI to execute emotion estimation.

[0119] The feedback unit can select the optimal collection method in consideration of the examinee's geographical location information when collecting feedback. For example, if the examinee lives in a specific area, the feedback unit can preferentially collect feedback related to that area. In addition, if the examinee is traveling, the feedback unit can also preferentially collect feedback related to health risks at the travel destination. Furthermore, if the examinee works in a specific environment (e.g., a factory or a farm), the feedback unit can also preferentially collect feedback related to that environment. In this way, by considering the examinee's geographical location information, highly relevant feedback can be collected. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input the examinee's geographical location information to the generation AI and cause the generation AI to select the optimal collection method.

[0120] The feedback unit can collect feedback by analyzing the patient's social media activities when collecting feedback. The feedback unit can collect relevant feedback based on, for example, health information shared by the patient on social media. The feedback unit can also preferentially collect feedback related to a specific health risk from the patient's social media activities. Furthermore, the feedback unit can collect optimal feedback based on symptoms mentioned by the patient on social media. In this way, by analyzing the patient's social media activities, highly relevant feedback can be collected. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input the patient's social media activity data to the generation AI and cause the generation AI to collect feedback.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The waiting space optimization system can further include a voice recognition unit. The voice recognition unit has a function of converting voice into text when a patient inputs symptoms by voice. For example, if a patient says, "I have a fever," the voice can be analyzed and input into the system as text. The voice recognition unit can also estimate emotions from the patient's voice and adjust the input method based on the estimated emotions. For example, if a patient feels anxious, the voice recognition unit can provide a simple and intuitive interface and minimize the input procedure. This reduces the patient's stress and enables smooth symptom input.

[0123] The monitoring unit may further include an environmental monitoring unit. The environmental monitoring unit has the function of collecting and analyzing environmental data such as the temperature, humidity, and air quality of the waiting area in real time. For example, if the temperature of the waiting area is too high, the environmental monitoring unit can issue instructions to the air conditioning system to appropriately adjust the temperature. It can also operate a humidifier if the humidity is low. Furthermore, it can operate the ventilation system if the air quality deteriorates. This makes it possible to always maintain the environment of the waiting area in an optimal state, improving the comfort of patients.

[0124] The feedback unit may further include an emotion analysis unit. The emotion analysis unit has a function of analyzing facial expressions and voice when the examinee provides feedback and estimating the emotion. For example, when the examinee fills in the feedback form, the facial expression can be captured by a camera, and the emotion analysis unit can estimate the emotion from the facial expression. In addition, in the case of voice input, the emotion can also be estimated from the tone and speed of the voice. This makes it possible to more accurately grasp the content of the feedback based on the examinee's emotion and to use the information to improve the system.

[0125] The analysis unit can further include a predictive analysis unit. The predictive analysis unit has a function of predicting future congestion in the waiting area and doctor availability based on past data. For example, if congestion is predicted on a specific day of the week or time period based on past data, the predictive analysis unit can adjust the allocation of waiting areas in advance based on that information. It can also predict doctor availability and adjust doctor schedules as necessary. This makes it possible to prevent congestion in the waiting area in advance and further shorten patients' waiting times.

[0126] The notification unit can further include a multilingual support unit. When patients speak different languages, the multilingual support unit has a function of providing notifications corresponding to the languages. For example, notifications can be provided in English to patients who speak English, and in Spanish to patients who speak Spanish. The multilingual support unit can also automatically select an appropriate language based on the patient's language setting. This allows information to be provided smoothly to patients who speak different languages, improving convenience.

[0127] The allocation unit can further use the emotion estimation function to adjust the allocation of waiting spaces based on the emotion of the patient. For example, if the patient feels anxious, a quiet and calm waiting space can be preferentially allocated. If the patient feels relaxed, a general waiting space can be allocated. Furthermore, if the patient is in a hurry, a waiting space that allows for quick response can be allocated. In this way, an optimal waiting space can be provided according to the emotion of the patient, improving the comfort of the patient.

[0128] The reception unit may further include a vital sign measurement unit. The vital sign measurement unit has the function of measuring the patient's vital signs, such as body temperature, blood pressure, and heart rate, and inputting the data into the system. For example, the patient's body temperature can be measured at the reception desk, and the data can be automatically input into the system. Similarly, blood pressure and heart rate can also be measured and input into the system. This makes it possible to more accurately analyze symptoms and allocate waiting spaces based on the patient's vital sign data.

[0129] The analysis unit can further use the emotion estimation function to adjust the way in which the analysis results are presented based on the patient's emotions. For example, if the patient is feeling anxious, it can provide simple, easy-to-understand analysis results. If the patient is relaxed, it can provide detailed analysis results. If the patient is in a hurry, it can provide analysis results that focus on the main points. In this way, by adjusting the way the analysis results are presented according to the patient's emotions, it is possible to provide information that is easy for the patient to understand.

[0130] The notification unit can further use the emotion estimation function to adjust the way the notification is expressed based on the emotion of the patient. For example, if the patient is feeling anxious, the notification can be made in a calm manner. If the patient is relaxed, the notification can be made in a cheerful manner. If the patient is in a hurry, the notification can be made in a quick and concise manner. In this way, by adjusting the way the notification is expressed according to the emotion of the patient, it is possible to provide a notification that is easy for the patient to understand.

[0131] The feedback unit can further use the emotion estimation function to adjust the feedback collection method based on the emotion of the patient. For example, if the patient feels anxious, a simple and intuitive feedback form can be provided. If the patient feels relaxed, a detailed feedback form can be provided. Furthermore, if the patient is in a hurry, a method for quickly collecting feedback can be provided. In this way, by adjusting the feedback collection method according to the emotion of the patient, a feedback collection method appropriate for the patient can be provided.

[0132] The process flow of the second embodiment will be briefly described below.

[0133] Step 1: The reception unit inputs the symptoms of the patient. The symptoms of the patient include, but are not limited to, fever, cough, abdominal pain, etc. The reception unit can input the symptoms using, for example, a touch panel or voice input. Step 2: The analysis unit analyzes the symptoms entered by the reception unit and the situation on that day. For example, the analysis unit uses statistical analysis or machine learning algorithms to analyze the congestion situation of the waiting area and the availability of doctors. Step 3: The allocation unit allocates the waiting space based on the results of the analysis by the analysis unit. The allocation unit allocates an optimal waiting space based on, for example, the size of the waiting space and the number of available seats. Step 4: The monitoring unit monitors the status of the waiting space allocated by the allocation unit in real time and changes the priority as necessary. The monitoring unit monitors the congestion status of the waiting space, for example, by using a camera or a sensor. Step 5: The notification unit notifies the patient based on the information monitored by the monitoring unit. For example, the notification unit notifies the patient of changes to the waiting time or waiting space via a smartphone app. Step 6: The feedback section collects feedback from the examinees. The feedback section collects feedback from the examinees, for example, through a questionnaire or interview.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements including the reception unit, analysis unit, allocation unit, monitoring unit, notification unit, and feedback unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit inputs the symptoms of the examinee using the touch panel 38A or microphone 38B of the smart device 14. The analysis unit analyzes the input symptoms and the situation of the day by the specific processing unit 290 of the data processing device 12. The allocation unit allocates an optimal waiting space by the specific processing unit 290 of the data processing device 12 based on the analysis result. The monitoring unit monitors the situation of the waiting space in real time using the camera 42 or sensor of the smart device 14, and changes the priority as necessary. The notification unit notifies the examinee of changes in waiting time and waiting space through the app of the smart device 14. The feedback unit collects feedback from the examinee using the questionnaire function of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements including the reception unit, analysis unit, allocation unit, monitoring unit, notification unit, and feedback unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit inputs the symptoms of the examinee using the microphone 238 of the smart glasses 214. The analysis unit analyzes the input symptoms and the situation of the day by the specific processing unit 290 of the data processing device 12. The allocation unit allocates an optimal waiting space by the specific processing unit 290 of the data processing device 12 based on the analysis result. The monitoring unit monitors the situation of the waiting space in real time using the camera 42 and sensors of the smart glasses 214, and changes the priority as necessary. The notification unit notifies the examinee of changes in waiting time and waiting space through the app of the smart glasses 214. The feedback unit collects feedback from the examinee using a questionnaire function of the smart glasses 214. The correspondence relationship between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements including the reception unit, analysis unit, allocation unit, monitoring unit, notification unit, and feedback unit described above 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 inputs the symptoms of the examinee using the microphone 238 of the headset type terminal 314. The analysis unit analyzes the input symptoms and the situation of the day by the specific processing unit 290 of the data processing device 12. The allocation unit allocates the optimal waiting space by the specific processing unit 290 of the data processing device 12 based on the analysis result. The monitoring unit monitors the situation of the waiting space in real time using the camera 42 and sensors of the headset type terminal 314, and changes the priority as necessary. The notification unit notifies the examinee of changes in waiting time and waiting space through the app of the headset type terminal 314. The feedback unit collects feedback from the examinee using a questionnaire function of the headset type terminal 314. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] Each of the multiple elements including the reception unit, analysis unit, allocation unit, monitoring unit, notification unit, and feedback unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit inputs the symptoms of the examinee using the microphone 238 of the robot 414. The analysis unit analyzes the input symptoms and the situation of the day by the specific processing unit 290 of the data processing device 12. The allocation unit allocates an optimal waiting space by the specific processing unit 290 of the data processing device 12 based on the analysis result. The monitoring unit monitors the situation of the waiting space in real time using the camera 42 and sensors of the robot 414, and changes the priority as necessary. The notification unit notifies the examinee of changes in waiting time and waiting space through the display and speaker of the robot 414. The feedback unit collects feedback from the examinee using the questionnaire function of the robot 414. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] (Appendix 1) A reception section for inputting the symptoms of patients; an analysis unit that analyzes the symptoms input by the reception unit, the number of patients on that day, and the progress of medical treatment; an allocation unit that allocates waiting spaces based on the results of the analysis by the analysis unit; a monitoring unit that monitors in real time the status of the waiting spaces allocated by the allocation unit and changes the priority as necessary; a notification unit that notifies a patient based on the information monitored by the monitoring unit; A feedback unit that collects feedback from patients. A system characterized by: (Appendix 2) The monitoring unit is Equipped with a sensor unit that monitors the congestion status of the waiting area in real time 2. The system of claim 1, (Appendix 3) The feedback unit is Equipped with an interface section for collecting feedback from patients 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze waiting area congestion and doctor availability 2. The system of claim 1, (Appendix 5) The notification unit is Notify patients of changes to waiting times and waiting areas via a smartphone app 2. The system of claim 1, (Appendix 6) The feedback unit is Collect feedback from patients to help improve the system 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the patient's emotions and adjust the symptom input method based on the estimated emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the patient's past medical history and select the appropriate input method 2. The system of claim 1, (Appendix 9) The reception unit is When entering symptoms, filtering is performed based on the patient's current health and living conditions. 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the patient's emotions and prioritize the symptoms to be entered based on the estimated emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When entering symptoms, the system takes into account the patient's geographic location information and prioritizes the most relevant symptoms. 2. The system of claim 1, (Appendix 12) The reception unit is When symptoms are entered, the patient's social media activity is analyzed and related symptoms are entered. 2. The system of claim 1, (Appendix 13) The analysis unit is Estimate the patient's emotions and adjust the way the analysis is presented based on the estimated emotions. 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the severity of symptoms 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the symptom category. 2. The system of claim 1, (Appendix 16) The analysis unit is Estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, analysis prioritization will be based on when symptoms were submitted. 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on symptom relevance 2. The system of claim 1, (Appendix 19) The allocation unit: Estimate the emotions of patients and adjust the allocation of waiting spaces based on the estimated emotions of patients. 2. The system of claim 1, (Appendix 20) The allocation unit: When allocating, the appropriate allocation method is selected by analyzing the patient's past medical history. 2. The system of claim 1, (Appendix 21) The allocation unit: Customize waiting space assignments based on patient's current health status at the time of assignment 2. The system of claim 1, (Appendix 22) The allocation unit: Estimate the emotions of patients and prioritize waiting areas based on the estimated emotions of patients. 2. The system of claim 1, (Appendix 23) The allocation unit: During allocation, the geographic location of the patient is taken into account to select the appropriate waiting space. 2. The system of claim 1, (Appendix 24) The allocation unit: At the time of allocation, the social media activity of patients is analyzed to allocate waiting spaces. 2. The system of claim 1, (Appendix 25) The monitoring unit is Estimating the patient's emotions and adjusting the monitoring method based on the estimated patient's emotions 2. The system of claim 1, (Appendix 26) The monitoring unit is Strengthen the sensor section that monitors the congestion status of the waiting area in real time during monitoring. 3. The system of claim 2, (Appendix 27) The monitoring unit is During monitoring, environmental data of the waiting area is collected to improve monitoring accuracy. 2. The system of claim 1, (Appendix 28) The monitoring unit is Estimating the patient's emotions and determining monitoring priorities based on the estimated patient's emotions 2. The system of claim 1, (Appendix 29) The monitoring unit is When monitoring, the geographical distribution of waiting areas is taken into account. 2. The system of claim 1, (Appendix 30) The monitoring unit is When monitoring, refer to relevant literature on waiting areas to improve the accuracy of monitoring. 2. The system of claim 1, (Appendix 31) The notification unit is Infer the patient's emotions and adjust the way notifications are presented based on the estimated emotions of the patient 2. The system of claim 1, (Appendix 32) The notification unit is When notifying, adjust notification detail based on the importance of the wait time 2. The system of claim 1, (Appendix 33) The notification unit is During notification, apply different notification algorithms depending on the latency category 2. The system of claim 1, (Appendix 34) The notification unit is Estimate the patient's emotions and adjust the length of the notification based on the estimated patient's emotions 2. The system of claim 1, (Appendix 35) The notification unit is At the time of notification, prioritize notifications based on when the waiting time was submitted 2. The system of claim 1, (Appendix 36) The notification unit is During notifications, adjust the order of notifications based on latency relevance 2. The system of claim 1, (Appendix 37) The feedback unit is Infer patient emotions and adjust feedback collection methods based on the inferred emotions 2. The system of claim 1, (Appendix 38) The feedback unit is When collecting feedback, select the most appropriate collection method by referring to the patient's past feedback history. 2. The system of claim 1, (Appendix 39) The feedback unit is When collecting feedback, customize the feedback collection based on the patient's current health status 2. The system of claim 1, (Appendix 40) The feedback unit is Estimate the patient's emotions and prioritize feedback based on the estimated emotions 2. The system of claim 1, (Appendix 41) The feedback unit is When collecting feedback, choose the most appropriate collection method based on the patient's geographic location. 2. The system of claim 1, (Appendix 42) The feedback unit is When collecting feedback, analyze the social media activity of patients to collect feedback. 2. The system of claim 1, [Explanation of symbols]

[0206] 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 section for inputting the symptoms of patients; an analysis unit that analyzes the symptoms input by the reception unit, the number of patients on that day, and the progress of medical treatment; an allocation unit that allocates waiting spaces based on the results of the analysis by the analysis unit; a monitoring unit that monitors in real time the status of the waiting spaces allocated by the allocation unit and changes the priority as necessary; a notification unit that notifies a patient based on the information monitored by the monitoring unit; A feedback unit that collects feedback from patients. A system characterized by:

2. The monitoring unit is Equipped with a sensor unit that monitors the congestion status of the waiting area in real time 2. The system of claim 1 .

3. The feedback unit is Equipped with an interface section for collecting feedback from patients 2. The system of claim 1 .

4. The analysis unit is Analyze waiting area congestion and doctor availability 2. The system of claim 1 .

5. The notification unit is Notify patients of changes to waiting times and waiting areas via a smartphone app 2. The system of claim 1 .

6. The feedback unit is Collect feedback from patients to help improve the system 2. The system of claim 1 .

7. The reception unit is Estimate the patient's emotions and adjust the symptom input method based on the estimated emotions 2. The system of claim 1 .

8. The reception unit is Analyze the patient's past medical history and select the appropriate input method 2. The system of claim 1 .

9. The reception unit is When entering symptoms, filtering is performed based on the patient's current health and living conditions.

2. The system of claim 1 .

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