System

Abstract

To provide a system according to an embodiment which increases the efficiency of management of schedules for reducing the waiting time in hospitals.SOLUTION: The system according to an embodiment includes a collection unit, a design unit, and a notification unit. The collection unit collects the recorded times of tests or examination of patients in the past. The design unit learns data collected by the collection unit and designs a schedule of a day in a hospital. The notification unit issues a notification on the basis of the schedule designed by the design unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately manage schedules to reduce waiting times at hospitals, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of schedule management in order to reduce waiting times at hospitals. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a design unit, and a notification unit. The collection unit collects the history times of past examinations and consultations of patients. The design unit learns the data collected by the collection unit and designs a daily schedule for the hospital. The notification unit notifies patients based on the schedule designed by the design unit. Effect of the Invention

[0007] The system according to the embodiment can improve the efficiency of schedule management to reduce waiting times at hospitals. [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 hospital waiting time reduction system according to the embodiment of the present invention is a system that collects the past history times of examinations and consultations of patients, and the generation AI learns from the past history times to design a daily schedule for the hospital, and notifies patients of the exact timing of their consultations and examinations. The hospital waiting time reduction system collects the past history times of examinations and consultations of patients, and the generation AI learns from the past history times. Next, the generation AI designs a daily schedule for the hospital based on the learning content. Finally, based on the schedule designed by the generation AI, the system notifies patients of the exact timing of their consultations and examinations. For example, the hospital waiting time reduction system collects the past history times of examinations and consultations of patients. At this time, detailed data such as the start time, end time, and waiting time of each patient's examination or consultation is collected. For example, if patient A checks in at 9:00, starts the consultation at 9:30, and ends the consultation at 10:00, the data is collected. This makes it possible to grasp the past history times of examinations and consultations of patients. Next, the generation AI learns from the collected data. The generation AI analyzes the collected data and designs a daily schedule for the hospital. For example, if past data shows that consultations tend to be concentrated during certain times of the day, the system designs a schedule to avoid those times. This reduces waiting times. Furthermore, based on the schedule designed by the generation AI, the system notifies patients of the exact timing of their consultations and examinations. For example, if patient B is scheduled to be examined at 10 o'clock, the generation AI notifies them beforehand so that the patient can arrive at the hospital at the appropriate time. This allows patients to be examined or examined smoothly without wasting time. This reduces waiting times at the hospital and improves patient satisfaction. Patients can arrive at the hospital at the appropriate time and be examined or examined smoothly, reducing stress. In addition, the hospital can efficiently manage schedules, improving overall work efficiency. This allows the hospital's waiting time reduction system to reduce waiting times for patients and improve patient satisfaction.

[0029] The hospital waiting time reduction system according to the embodiment includes a collection unit, a design unit, and a notification unit. The collection unit collects the history time of the past examinations and consultations of the patient. The history time of the past examinations and consultations of the patient includes, for example, the start time of the examination, the end time of the examination, the required time of the examination, etc., but is not limited to such examples. The collection unit can also collect data such as the patient's age, gender, examination contents, and examination contents. For example, the collection unit collects the patient's age and gender as input data and records the examination contents and examination contents in detail. The design unit uses a generation AI to learn the data collected by the collection unit and design a daily schedule for the hospital. The design unit uses an algorithm that predicts the required time of the examination or examination from past data and designs a schedule based on the prediction. For example, the design unit predicts the required time of the examination or examination using a method such as regression analysis or time series analysis. The design unit can use a generation AI to analyze past data and design an efficient schedule. The notification unit notifies the patient based on the schedule designed by the design unit. The notification unit notifies the patient, for example, through a smartphone app, and also mentions the timing and content of the notification. For example, the notification unit notifies the patient of the exact timing of the examination or test, and provides information on the prediction of the waiting time and preparation for the examination or test. The notification unit can provide information to the patient at an appropriate time using a generating AI. As a result, the hospital waiting time reduction system according to the embodiment can reduce the waiting time of the patient and improve the satisfaction of 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, when notifying the patient through a smartphone app, the notification unit can optimize the timing and content of the notification using AI.

[0030] The collection unit collects the historical time of the past examinations and consultations of the patient. The historical time of the past examinations and consultations of the patient includes, for example, the start time of the examination, the end time of the examination, the time required for the examination, and the like, but is not limited to such examples. The collection unit can also collect data such as the patient's age, gender, examination contents, and examination contents. Specifically, the collection unit cooperates with the hospital's electronic medical record system and medical record system to automatically obtain detailed medical history for each patient. This includes waiting time for the examination, time spent in the examination room, time spent in the examination room, waiting time for the test results, and the like. In addition, the collection unit also collects personal information such as the patient's age, gender, medical history, and current symptoms, and integrates and analyzes these data. For example, the collection unit collects the patient's age and gender as input data and records the examination contents and examination contents in detail. This allows the collection unit to grasp the medical treatment patterns and trends of each patient and provide basic data for more accurately predicting the time required for the examination and examination. The collected data is stored in a central database and made accessible to the design unit and the notification unit. This allows the collection unit to collect data efficiently and accurately, improving overall system performance.

[0031] The design department uses the generation AI to learn the data collected by the collection department and to design a daily schedule for the hospital. For example, the design department uses an algorithm to predict the time required for consultations and examinations from past data and to design a schedule based on the prediction. Specifically, the design department predicts the time required for consultations and examinations using methods such as regression analysis and time series analysis. The generation AI learns a large amount of past medical data and builds a model that predicts the time required for each consultation and examination with high accuracy. For example, the generation AI generates a model that outputs the time required for consultations and examinations by inputting features such as the patient's age, gender, consultation content, and examination content. This model is used to predict the time required for each consultation and examination based on past data and to design an efficient schedule. The design department can use the generation AI to analyze past data and design an efficient schedule. For example, the design department predicts the time required for consultations and examinations and optimizes the start and end times of each consultation and examination based on the prediction. The design department can also design schedules to optimize the order of consultations and examinations and minimize patient waiting times, allowing the design department to design efficient and effective schedules and improve the operational efficiency of the hospital.

[0032] The notification unit notifies the patient based on the schedule designed by the design unit. For example, the notification unit notifies the patient through a smartphone app, and also mentions the timing and content of the notification. Specifically, the notification unit notifies the patient of the exact timing of the examination or examination, and provides information on the predicted waiting time and preparation for the examination or examination. The notification unit can provide information to the patient at the appropriate time using generative AI. For example, the notification unit notifies the patient's smartphone of the scheduled time of the examination or examination, and sends a reminder that takes into account the travel time to the examination room or examination room. In addition, the notification unit can monitor the progress of the examination or examination in real time, and immediately notify the patient if there is a change in the scheduled time. In addition, the notification unit can collect patient feedback and continuously improve the accuracy and effectiveness of the notification content. For example, the notification unit optimizes the timing and content of the notification based on feedback from the patient, and provides more effective information. In addition, the notification unit can reliably transmit information using multiple communication means. For example, important information can be reliably delivered by using not only smartphone notifications, but also voice calls, SMS, emails, etc. This enables the notification unit to provide information to patients quickly and reliably, reducing waiting times and improving patient satisfaction.

[0033] The collection unit can collect data on the patient's age, sex, medical examination content, and examination content. The collection unit, for example, collects data on the patient's age, sex, medical examination content, and examination content. For example, the collection unit collects the patient's age as input data and classifies the patient's gender. The collection unit can also record the medical examination content and examination content in detail. For example, the collection unit collects the patient's age as numerical data and classifies the patient's gender into male, female, and other categories. The medical examination content includes, for example, medical departments such as internal medicine, surgery, and dermatology. The examination content includes, for example, blood tests, X-ray tests, and MRI tests. This allows the collection unit to collect detailed data on the patient, thereby enabling more accurate schedule design. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's age and gender data into AI, which can analyze and classify the data.

[0034] The design unit can use an algorithm that predicts the time required for consultations and examinations from past data and designs a schedule based on the prediction. The design unit, for example, uses an algorithm that predicts the time required for consultations and examinations from past data and designs a schedule based on the prediction. For example, the design unit can predict the time required for consultations and examinations using regression analysis. The design unit can also analyze past data using time series analysis and predict the time required for consultations and examinations. For example, the design unit predicts the time required for consultations and examinations based on past data and designs a schedule based on the prediction result. The design unit can analyze past data and design an efficient schedule using a generation AI. For example, the design unit inputs past data into the generation AI, which analyzes the data and designs a schedule. As a result, the design unit can predict the time required for consultations and examinations, thereby enabling efficient schedule design. Part or all of the above-mentioned processing in the design unit is performed using a generation AI. For example, the design unit inputs past data into the generation AI, which analyzes the data and designs a schedule.

[0035] The notification unit can provide notification through a smartphone app and also mention the timing or content of the notification. The notification unit can provide notification through a smartphone app, for example, and also mention the timing and content of the notification. For example, the notification unit notifies the patient of the exact timing of the examination or examination, and provides information on the prediction of the waiting time and preparation for the examination or examination. The notification unit can provide information to the patient at an appropriate timing using the generation AI. For example, when notifying the patient through a smartphone app, the notification unit optimizes the timing and content of the notification using the generation AI. The notification unit can notify the patient, for example, the day before the examination and provide information on preparation for the examination. The notification unit can also notify the patient just before the examination and provide a prediction of the waiting time. As a result, the notification unit can provide information to the patient at an appropriate timing by notifying through the smartphone app. Part or all of the above-mentioned processing in the notification unit is performed using the generation AI. For example, the notification unit inputs the timing and content of the notification to the generation AI, and the generation AI determines the optimal notification method.

[0036] The notification unit can provide the patient with information regarding the prediction of the waiting time or the preparation for the examination / test. The notification unit, for example, provides the patient with information regarding the prediction of the waiting time or the preparation for the examination / test. For example, the notification unit can predict the waiting time for the examination or test and provide the patient with the information. The notification unit can also provide the patient with information regarding the preparation for the examination or test. For example, the notification unit notifies the patient on the day before the examination and provides information regarding the preparation for the examination. The notification unit can provide information to the patient at an appropriate time using the generation AI. For example, the notification unit inputs the predicted data of the waiting time into the generation AI, and the generation AI analyzes the data and notifies the patient. As a result, the notification unit improves the convenience of the patient by providing information regarding the prediction of the waiting time and the preparation for the examination / test. Part or all of the above-mentioned processing in the notification unit is performed using the generation AI. For example, the notification unit inputs the predicted data of the waiting time into the generation AI, and the generation AI analyzes the data and notifies the patient.

[0037] The collection unit can analyze the patient's past medical examination history and select the optimal data collection method. The collection unit, for example, analyzes the patient's past medical examination history and selects the optimal data collection method. For example, the collection unit can select the optimal data collection method based on the contents of the medical examination the patient has received in the past. In addition, when a specific examination is required from the patient's past medical examination history, the collection unit can also select a data collection method suitable for that examination. Furthermore, the collection unit can analyze the patient's past medical examination history and propose an efficient data collection method. As a result, the collection unit can select the optimal data collection method by analyzing the patient's past medical examination history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's past medical examination history data into AI, and the AI ​​can analyze the data and select the optimal data collection method.

[0038] The collection unit can perform filtering based on the patient's current health condition and living situation when collecting data. The collection unit, for example, performs filtering based on the patient's current health condition and living situation when collecting data. For example, the collection unit can collect only necessary data taking into account the patient's current health condition. The collection unit can also preferentially collect highly relevant data based on the patient's living situation. Furthermore, the collection unit can adjust the range of data collection according to the patient's health condition and living situation. This allows the collection unit to collect only necessary data according to the patient's health condition and living situation. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the patient's health condition and living situation into AI, and the AI ​​can analyze the data and perform filtering.

[0039] The collection unit can preferentially collect highly relevant data based on the geographical location information of the patient when collecting data. For example, the collection unit preferentially collects highly relevant data based on the geographical location information of the patient when collecting data. For example, if the patient lives in a specific area, the collection unit can preferentially collect data related to that area. In addition, if the patient is traveling, the collection unit can also collect data related to the current location. Furthermore, the collection unit can select an optimal data collection method based on the geographical location information of the patient. In this way, the collection unit can preferentially collect highly relevant data by taking into account the geographical location information of the patient. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the patient to AI, and the AI ​​can analyze the data and preferentially collect highly relevant data.

[0040] The collection unit can analyze the contents of the patient's social media posts at the time of data collection and collect related data. For example, the collection unit can analyze the contents of the patient's social media posts at the time of data collection and collect related data. For example, the collection unit can analyze health-related posts from the patient's social media activities and collect related data. The collection unit can also collect data on health conditions based on the patient's social media activities. Furthermore, the collection unit can analyze the patient's social media activities and efficiently collect necessary data. In this way, the collection unit can efficiently collect related data by analyzing the patient's social media activities. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the contents of the patient's social media posts into AI, and the AI ​​can analyze the data and collect related data.

[0041] The design unit can adjust the level of detail of the schedule based on the importance of the examination or examination when designing the schedule. The design unit, for example, adjusts the level of detail of the schedule based on the importance of the examination or examination when designing the schedule. For example, the design unit can design a detailed schedule for important examinations or examinations. In addition, the design unit can design a simplified schedule for general examinations or examinations. Furthermore, the design unit can adjust the level of detail of the schedule according to the importance of the examination or examination. As a result, the design unit can adjust the level of detail of the schedule according to the importance of the examination or examination, thereby enabling efficient schedule design. Part or all of the above-mentioned processing in the design unit may be performed, for example, using AI, or may be performed without using AI. For example, the design unit can input importance data of examinations and examinations to AI, and the AI ​​can analyze the data and adjust the level of detail of the schedule.

[0042] The design unit can apply different design algorithms depending on the category of the examination or examination when designing a schedule. The design unit can apply different design algorithms depending on the category of the examination or examination when designing a schedule. For example, in the case of an examination, the design unit can apply a schedule design algorithm suitable for the examination. In addition, in the case of an examination, the design unit can apply a schedule design algorithm suitable for the examination. Furthermore, the design unit can select an optimal schedule design algorithm depending on the category of the examination or examination. In this way, the design unit can select an optimal schedule design algorithm depending on the category of the examination or examination. Part or all of the above-mentioned processing in the design unit may be performed, for example, using AI, or may be performed without using AI. For example, the design unit can input category data of the examination or examination to AI, and the AI ​​can analyze the data and apply the optimal design algorithm.

[0043] The design unit can determine the priority of the schedule based on the submission timing of the medical examination or examination when designing the schedule. The design unit, for example, determines the priority of the schedule based on the submission timing of the medical examination or examination when designing the schedule. For example, the design unit can design a schedule with priority in the case of an emergency medical examination or examination. In addition, the design unit can also design a normal schedule in the case of a general medical examination or examination. Furthermore, the design unit can determine the priority of the schedule based on the submission timing of the medical examination or examination. As a result, the design unit can determine the priority of the schedule based on the submission timing of the medical examination or examination, thereby enabling efficient schedule design. Part or all of the above-mentioned processing in the design unit may be performed, for example, using AI, or may be performed without using AI. For example, the design unit can input the submission timing data of the medical examination or examination into AI, and the AI ​​can analyze the data to determine the priority of the schedule.

[0044] The design unit can adjust the order of the schedule based on the relevance of the examinations and examinations when designing the schedule. The design unit, for example, adjusts the order of the schedule based on the relevance of the examinations and examinations when designing the schedule. For example, the design unit can incorporate highly related examinations and examinations into the schedule consecutively. The design unit can also distribute and incorporate less related examinations and examinations into the schedule. Furthermore, the design unit can adjust the order of the schedule based on the relevance of the examinations and examinations. As a result, the design unit can adjust the order of the schedule based on the relevance of the examinations and examinations, thereby enabling efficient schedule design. Part or all of the above-mentioned processing in the design unit may be performed, for example, using AI, or may be performed without using AI. For example, the design unit can input relevance data of the examinations and examinations into AI, and the AI ​​can analyze the data to adjust the order of the schedule.

[0045] The notification unit can adjust the level of detail of the notification based on the importance of the examination or examination at the time of notification. The notification unit, for example, adjusts the level of detail of the notification based on the importance of the examination or examination at the time of notification. For example, the notification unit can provide detailed notification in the case of an important examination or examination. In addition, the notification unit can provide simplified notification in the case of a general examination or examination. Furthermore, the notification unit can adjust the level of detail of the notification according to the importance of the examination or examination. As a result, the notification unit can adjust the level of detail of the notification according to the importance of the examination or examination, thereby enabling efficient notification. 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 importance data of the examination or examination to AI, and the AI ​​can analyze the data and adjust the level of detail of the notification.

[0046] The notification unit can apply different notification algorithms depending on the category of the examination or examination at the time of notification. The notification unit, for example, applies different notification algorithms depending on the category of the examination or examination at the time of notification. For example, in the case of an examination, the notification unit can apply a notification algorithm suitable for the examination. In addition, in the case of an examination, the notification unit can also apply a notification algorithm suitable for the examination. Furthermore, the notification unit can select an optimal notification algorithm depending on the category of the examination or examination. Thereby, the notification unit can select an optimal notification algorithm depending on the category of the examination or examination. Part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input category data of the examination or examination to AI, and the AI ​​can analyze the data and apply the optimal notification algorithm.

[0047] The notification unit can determine the priority of the notification based on the time of submission of the examination or test at the time of notification. The notification unit, for example, determines the priority of the notification based on the time of submission of the examination or test at the time of notification. For example, the notification unit can give priority to notification in the case of emergency examination or test. In addition, the notification unit can also give normal notification in the case of general examination or test. Furthermore, the notification unit can determine the priority of the notification based on the time of submission of the examination or test. As a result, the notification unit can determine the priority of the notification based on the time of submission of the examination or test, thereby enabling efficient notification. 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 data on the time of submission of the examination or test into AI, and the AI ​​can analyze the data to determine the priority of the notification.

[0048] The notification unit can adjust the order of notifications based on the relevance of the examinations and tests at the time of notification. The notification unit, for example, adjusts the order of notifications based on the relevance of the examinations and tests at the time of notification. For example, the notification unit can give priority to highly relevant examinations and tests. The notification unit can also postpone the notification of less relevant examinations and tests. Furthermore, the notification unit can adjust the order of notifications based on the relevance of the examinations and tests. As a result, the notification unit can adjust the order of notifications based on the relevance of the examinations and tests, thereby enabling efficient notification. 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 examinations and tests to AI, and the AI ​​can analyze the data and adjust the order of notifications.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The collection unit can collect lifestyle data of the patient and optimize the schedule of examinations and tests based on the collected data. For example, the collection unit can collect data such as the patient's diet, exercise, and sleep patterns, and adjust the timing of examinations and tests based on the collected data. The collection unit can also analyze the lifestyle data of the patient and evaluate the impact of a particular lifestyle on the results of the examination or test. Furthermore, the collection unit can provide health management advice based on the lifestyle data of the patient. This allows the collection unit to design an optimal schedule according to the patient's lifestyle.

[0051] The collection unit can suggest the optimal location for examination or testing based on the geographical location information of the patient. For example, the collection unit can collect information on medical facilities in the area where the patient lives and suggest the optimal location for examination or testing. In addition, if the patient is traveling, the collection unit can also suggest a medical facility close to the patient's current location. Furthermore, the collection unit can also suggest the optimal location for examination or testing, taking into account transportation means and travel time, based on the geographical location information of the patient. In this way, the collection unit can provide more convenient medical services by taking into account the geographical location information of the patient.

[0052] The collection unit can collect the patient's health data in real time and dynamically adjust the schedule of examinations and tests based on the collected data. For example, the collection unit can collect data such as the patient's heart rate, blood pressure, and body temperature in real time and adjust the timing of examinations and tests based on the collected data. The collection unit can also analyze the patient's health data and immediately schedule examinations and tests when an abnormality is detected. Furthermore, the collection unit can suggest preventive examinations and tests based on the patient's health data. This allows the collection unit to design an optimal schedule according to the patient's health condition.

[0053] The collection unit can analyze the content of the patient's social media posts and optimize the schedule of consultations and examinations based on the analysis. For example, the collection unit can analyze health-related posts from the patient's social media activity and adjust the timing of consultations and examinations based on the analysis. The collection unit can also suggest consultations and examinations for specific health issues based on the patient's social media activity. Furthermore, the collection unit can analyze the patient's social media activity and provide health management advice. This allows the collection unit to design a more appropriate schedule by taking into account the patient's social media activity.

[0054] The process flow of the first embodiment will be briefly described below.

[0055] Step 1: The collection unit collects the history of past examinations and consultations of patients. Specifically, data such as the start time of the consultation, the end time of the consultation, and the time required for the examination are collected. In addition, data such as the patient's age, gender, details of the examination, and details of the examination can also be collected. Step 2: The design department studies the data collected by the collection department and designs the hospital's daily schedule. Specifically, it uses generative AI to predict the time required for consultations and tests from past data, and designs the schedule based on that prediction. Methods such as regression analysis and time series analysis can be used. Step 3: The notification unit notifies patients based on the schedule designed by the design unit. Specifically, notifications are sent via a smartphone app, and the timing and content of notifications are optimized. This allows patients to be notified of the exact timing of their consultation or examination, and provides them with information on predicted wait times and preparations for consultations and examinations.

[0056] (Example 2) The hospital waiting time reduction system according to the embodiment of the present invention is a system that collects the past history times of examinations and consultations of patients, and the generation AI learns from the past history times to design a daily schedule for the hospital, and notifies patients of the exact timing of their consultations and examinations. The hospital waiting time reduction system collects the past history times of examinations and consultations of patients, and the generation AI learns from the past history times. Next, the generation AI designs a daily schedule for the hospital based on the learning content. Finally, based on the schedule designed by the generation AI, the system notifies patients of the exact timing of their consultations and examinations. For example, the hospital waiting time reduction system collects the past history times of examinations and consultations of patients. At this time, detailed data such as the start time, end time, and waiting time of each patient's examination or consultation is collected. For example, if patient A checks in at 9:00, starts the consultation at 9:30, and ends the consultation at 10:00, the data is collected. This makes it possible to grasp the past history times of examinations and consultations of patients. Next, the generation AI learns from the collected data. The generation AI analyzes the collected data and designs a daily schedule for the hospital. For example, if past data shows that consultations tend to be concentrated during certain times of the day, the system designs a schedule to avoid those times. This reduces waiting times. Furthermore, based on the schedule designed by the generation AI, the system notifies patients of the exact timing of their consultations and examinations. For example, if patient B is scheduled to be examined at 10 o'clock, the generation AI notifies them beforehand so that the patient can arrive at the hospital at the appropriate time. This allows patients to be examined or examined smoothly without wasting time. This reduces waiting times at the hospital and improves patient satisfaction. Patients can arrive at the hospital at the appropriate time and be examined or examined smoothly, reducing stress. In addition, the hospital can efficiently manage schedules, improving overall work efficiency. This allows the hospital's waiting time reduction system to reduce waiting times for patients and improve patient satisfaction.

[0057] The hospital waiting time reduction system according to the embodiment includes a collection unit, a design unit, and a notification unit. The collection unit collects the history time of the past examinations and consultations of the patient. The history time of the past examinations and consultations of the patient includes, for example, the start time of the examination, the end time of the examination, the required time of the examination, etc., but is not limited to such examples. The collection unit can also collect data such as the patient's age, gender, examination contents, and examination contents. For example, the collection unit collects the patient's age and gender as input data and records the examination contents and examination contents in detail. The design unit uses a generation AI to learn the data collected by the collection unit and design a daily schedule for the hospital. The design unit uses an algorithm that predicts the required time of the examination or examination from past data and designs a schedule based on the prediction. For example, the design unit predicts the required time of the examination or examination using a method such as regression analysis or time series analysis. The design unit can use a generation AI to analyze past data and design an efficient schedule. The notification unit notifies the patient based on the schedule designed by the design unit. The notification unit notifies the patient, for example, through a smartphone app, and also mentions the timing and content of the notification. For example, the notification unit notifies the patient of the exact timing of the examination or test, and provides information on the prediction of the waiting time and preparation for the examination or test. The notification unit can provide information to the patient at an appropriate time using a generating AI. As a result, the hospital waiting time reduction system according to the embodiment can reduce the waiting time of the patient and improve the satisfaction of 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, when notifying the patient through a smartphone app, the notification unit can optimize the timing and content of the notification using AI.

[0058] The collection unit collects the historical time of the past examinations and consultations of the patient. The historical time of the past examinations and consultations of the patient includes, for example, the start time of the examination, the end time of the examination, the time required for the examination, and the like, but is not limited to such examples. The collection unit can also collect data such as the patient's age, gender, examination contents, and examination contents. Specifically, the collection unit cooperates with the hospital's electronic medical record system and medical record system to automatically obtain detailed medical history for each patient. This includes waiting time for the examination, time spent in the examination room, time spent in the examination room, waiting time for the test results, and the like. In addition, the collection unit also collects personal information such as the patient's age, gender, medical history, and current symptoms, and integrates and analyzes these data. For example, the collection unit collects the patient's age and gender as input data and records the examination contents and examination contents in detail. This allows the collection unit to grasp the medical treatment patterns and trends of each patient and provide basic data for more accurately predicting the time required for the examination and examination. The collected data is stored in a central database and made accessible to the design unit and the notification unit. This allows the collection unit to collect data efficiently and accurately, improving overall system performance.

[0059] The design department uses the generation AI to learn the data collected by the collection department and to design a daily schedule for the hospital. For example, the design department uses an algorithm to predict the time required for consultations and examinations from past data and to design a schedule based on the prediction. Specifically, the design department predicts the time required for consultations and examinations using methods such as regression analysis and time series analysis. The generation AI learns a large amount of past medical data and builds a model that predicts the time required for each consultation and examination with high accuracy. For example, the generation AI generates a model that outputs the time required for consultations and examinations by inputting features such as the patient's age, gender, consultation content, and examination content. This model is used to predict the time required for each consultation and examination based on past data and to design an efficient schedule. The design department can use the generation AI to analyze past data and design an efficient schedule. For example, the design department predicts the time required for consultations and examinations and optimizes the start and end times of each consultation and examination based on the prediction. The design department can also design schedules to optimize the order of consultations and examinations and minimize patient waiting times, allowing the design department to design efficient and effective schedules and improve the operational efficiency of the hospital.

[0060] The notification unit notifies the patient based on the schedule designed by the design unit. For example, the notification unit notifies the patient through a smartphone app, and also mentions the timing and content of the notification. Specifically, the notification unit notifies the patient of the exact timing of the examination or examination, and provides information on the predicted waiting time and preparation for the examination or examination. The notification unit can provide information to the patient at the appropriate time using generative AI. For example, the notification unit notifies the patient's smartphone of the scheduled time of the examination or examination, and sends a reminder that takes into account the travel time to the examination room or examination room. In addition, the notification unit can monitor the progress of the examination or examination in real time, and immediately notify the patient if there is a change in the scheduled time. In addition, the notification unit can collect patient feedback and continuously improve the accuracy and effectiveness of the notification content. For example, the notification unit optimizes the timing and content of the notification based on feedback from the patient, and provides more effective information. In addition, the notification unit can reliably transmit information using multiple communication means. For example, important information can be reliably delivered by using not only smartphone notifications, but also voice calls, SMS, emails, etc. This enables the notification unit to provide information to patients quickly and reliably, reducing waiting times and improving patient satisfaction.

[0061] The collection unit can collect data on the patient's age, sex, medical examination content, and examination content. The collection unit, for example, collects data on the patient's age, sex, medical examination content, and examination content. For example, the collection unit collects the patient's age as input data and classifies the patient's gender. The collection unit can also record the medical examination content and examination content in detail. For example, the collection unit collects the patient's age as numerical data and classifies the patient's gender into male, female, and other categories. The medical examination content includes, for example, medical departments such as internal medicine, surgery, and dermatology. The examination content includes, for example, blood tests, X-ray tests, and MRI tests. This allows the collection unit to collect detailed data on the patient, thereby enabling more accurate schedule design. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's age and gender data into AI, which can analyze and classify the data.

[0062] The design unit can use an algorithm that predicts the time required for consultations and examinations from past data and designs a schedule based on the prediction. The design unit, for example, uses an algorithm that predicts the time required for consultations and examinations from past data and designs a schedule based on the prediction. For example, the design unit can predict the time required for consultations and examinations using regression analysis. The design unit can also analyze past data using time series analysis and predict the time required for consultations and examinations. For example, the design unit predicts the time required for consultations and examinations based on past data and designs a schedule based on the prediction result. The design unit can analyze past data and design an efficient schedule using a generation AI. For example, the design unit inputs past data into the generation AI, which analyzes the data and designs a schedule. As a result, the design unit can predict the time required for consultations and examinations, thereby enabling efficient schedule design. Part or all of the above-mentioned processing in the design unit is performed using a generation AI. For example, the design unit inputs past data into the generation AI, which analyzes the data and designs a schedule.

[0063] The notification unit can provide notification through a smartphone app and also mention the timing or content of the notification. The notification unit can provide notification through a smartphone app, for example, and also mention the timing and content of the notification. For example, the notification unit notifies the patient of the exact timing of the examination or examination, and provides information on the prediction of the waiting time and preparation for the examination or examination. The notification unit can provide information to the patient at an appropriate timing using the generation AI. For example, when notifying the patient through a smartphone app, the notification unit optimizes the timing and content of the notification using the generation AI. The notification unit can notify the patient, for example, the day before the examination and provide information on preparation for the examination. The notification unit can also notify the patient just before the examination and provide a prediction of the waiting time. As a result, the notification unit can provide information to the patient at an appropriate timing by notifying through the smartphone app. Part or all of the above-mentioned processing in the notification unit is performed using the generation AI. For example, the notification unit inputs the timing and content of the notification to the generation AI, and the generation AI determines the optimal notification method.

[0064] The notification unit can provide the patient with information regarding the prediction of the waiting time or the preparation for the examination / test. The notification unit, for example, provides the patient with information regarding the prediction of the waiting time or the preparation for the examination / test. For example, the notification unit can predict the waiting time for the examination or test and provide the patient with the information. The notification unit can also provide the patient with information regarding the preparation for the examination or test. For example, the notification unit notifies the patient on the day before the examination and provides information regarding the preparation for the examination. The notification unit can provide information to the patient at an appropriate time using the generation AI. For example, the notification unit inputs the predicted data of the waiting time into the generation AI, and the generation AI analyzes the data and notifies the patient. As a result, the notification unit improves the convenience of the patient by providing information regarding the prediction of the waiting time and the preparation for the examination / test. Part or all of the above-mentioned processing in the notification unit is performed using the generation AI. For example, the notification unit inputs the predicted data of the waiting time into the generation AI, and the generation AI analyzes the data and notifies the patient.

[0065] The collection unit can estimate the patient's emotion and adjust the timing of data collection based on the estimated patient's emotion. The collection unit, for example, estimates the patient's emotion and adjusts the timing of data collection based on the estimated patient's emotion. For example, when the patient is stressed, the collection unit adjusts the collection timing to collect data in a relaxed state. In addition, when the patient is relaxed, the collection unit can also collect data immediately and efficiently acquire information. Furthermore, when the patient is in a hurry, the collection unit can also quickly collect data and minimize waiting time. This allows the collection unit to adjust the timing of data collection according to the patient's emotion, thereby enabling more appropriate data collection. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the patient's emotion data to the generation AI, which estimates the emotion, and adjust the timing of data collection based on the result.

[0066] The collection unit can analyze the patient's past medical examination history and select the optimal data collection method. The collection unit, for example, analyzes the patient's past medical examination history and selects the optimal data collection method. For example, the collection unit can select the optimal data collection method based on the contents of the medical examination the patient has received in the past. In addition, when a specific examination is required from the patient's past medical examination history, the collection unit can also select a data collection method suitable for that examination. Furthermore, the collection unit can analyze the patient's past medical examination history and propose an efficient data collection method. As a result, the collection unit can select the optimal data collection method by analyzing the patient's past medical examination history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the patient's past medical examination history data into AI, and the AI ​​can analyze the data and select the optimal data collection method.

[0067] The collection unit can perform filtering based on the patient's current health condition and living situation when collecting data. The collection unit, for example, performs filtering based on the patient's current health condition and living situation when collecting data. For example, the collection unit can collect only necessary data taking into account the patient's current health condition. The collection unit can also preferentially collect highly relevant data based on the patient's living situation. Furthermore, the collection unit can adjust the range of data collection according to the patient's health condition and living situation. This allows the collection unit to collect only necessary data according to the patient's health condition and living situation. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the patient's health condition and living situation into AI, and the AI ​​can analyze the data and perform filtering.

[0068] The collection unit can estimate the patient's emotion and determine the priority of data to be collected based on the estimated patient's emotion. The collection unit, for example, estimates the patient's emotion and determines the priority of data to be collected based on the estimated patient's emotion. For example, the collection unit can collect important data preferentially when the patient is stressed. The collection unit can also collect detailed data when the patient is relaxed. Furthermore, the collection unit can quickly collect the minimum necessary data when the patient is in a hurry. This enables the collection unit to determine the priority of data to be collected according to the patient's emotion, thereby enabling efficient data collection. 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the patient's emotion data to the generation AI, which can estimate the emotion, and determine the priority of the data based on the result.

[0069] The collection unit can preferentially collect highly relevant data based on the geographical location information of the patient when collecting data. For example, the collection unit preferentially collects highly relevant data based on the geographical location information of the patient when collecting data. For example, if the patient lives in a specific area, the collection unit can preferentially collect data related to that area. In addition, if the patient is traveling, the collection unit can also collect data related to the current location. Furthermore, the collection unit can select an optimal data collection method based on the geographical location information of the patient. In this way, the collection unit can preferentially collect highly relevant data by taking into account the geographical location information of the patient. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the patient to AI, and the AI ​​can analyze the data and preferentially collect highly relevant data.

[0070] The collection unit can analyze the contents of the patient's social media posts at the time of data collection and collect related data. For example, the collection unit can analyze the contents of the patient's social media posts at the time of data collection and collect related data. For example, the collection unit can analyze health-related posts from the patient's social media activities and collect related data. The collection unit can also collect data on health conditions based on the patient's social media activities. Furthermore, the collection unit can analyze the patient's social media activities and efficiently collect necessary data. In this way, the collection unit can efficiently collect related data by analyzing the patient's social media activities. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the contents of the patient's social media posts into AI, and the AI ​​can analyze the data and collect related data.

[0071] The design unit can estimate the patient's emotions and adjust the schedule design algorithm based on the estimated patient's emotions. The design unit, for example, estimates the patient's emotions and adjusts the schedule design algorithm based on the estimated patient's emotions. For example, the design unit can design a schedule that allows the patient to relax when the patient is stressed. The design unit can also design an efficient schedule when the patient is relaxed. Furthermore, the design unit can design a schedule that allows the patient to receive a medical examination or test quickly when the patient is in a hurry. This allows the design unit to adjust the schedule design algorithm according to the patient's emotions, thereby enabling a more appropriate schedule design. 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 design unit is performed using the generation AI. For example, the design unit can input the patient's emotion data to the generation AI, which can estimate the emotion, and adjust the schedule design algorithm based on the result.

[0072] The design unit can adjust the level of detail of the schedule based on the importance of the examination or examination when designing the schedule. The design unit, for example, adjusts the level of detail of the schedule based on the importance of the examination or examination when designing the schedule. For example, the design unit can design a detailed schedule for important examinations or examinations. In addition, the design unit can design a simplified schedule for general examinations or examinations. Furthermore, the design unit can adjust the level of detail of the schedule according to the importance of the examination or examination. As a result, the design unit can adjust the level of detail of the schedule according to the importance of the examination or examination, thereby enabling efficient schedule design. Part or all of the above-mentioned processing in the design unit may be performed, for example, using AI, or may be performed without using AI. For example, the design unit can input importance data of examinations and examinations to AI, and the AI ​​can analyze the data and adjust the level of detail of the schedule.

[0073] The design unit can apply different design algorithms depending on the category of the examination or examination when designing a schedule. The design unit can apply different design algorithms depending on the category of the examination or examination when designing a schedule. For example, in the case of an examination, the design unit can apply a schedule design algorithm suitable for the examination. In addition, in the case of an examination, the design unit can apply a schedule design algorithm suitable for the examination. Furthermore, the design unit can select an optimal schedule design algorithm depending on the category of the examination or examination. In this way, the design unit can select an optimal schedule design algorithm depending on the category of the examination or examination. Part or all of the above-mentioned processing in the design unit may be performed, for example, using AI, or may be performed without using AI. For example, the design unit can input category data of the examination or examination to AI, and the AI ​​can analyze the data and apply the optimal design algorithm.

[0074] The design unit can estimate the patient's emotions and determine the priority of the schedule based on the estimated patient's emotions. The design unit, for example, estimates the patient's emotions and determines the priority of the schedule based on the estimated patient's emotions. For example, the design unit can prioritize important medical examinations and examinations when the patient is stressed. The design unit can also prioritize efficient schedules when the patient is relaxed. Furthermore, the design unit can also prioritize schedules that allow the patient to receive medical examinations and examinations quickly when the patient is in a hurry. This enables the design unit to determine the priority of the schedule according to the patient's emotions, thereby enabling efficient schedule design. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, 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 design unit is performed using the generation AI. For example, the design unit can input patient emotion data to the generation AI, which can estimate the emotion, and determine the priority of the schedule based on the result.

[0075] The design unit can determine the priority of the schedule based on the submission timing of the medical examination or examination when designing the schedule. The design unit, for example, determines the priority of the schedule based on the submission timing of the medical examination or examination when designing the schedule. For example, the design unit can design a schedule with priority in the case of an emergency medical examination or examination. In addition, the design unit can also design a normal schedule in the case of a general medical examination or examination. Furthermore, the design unit can determine the priority of the schedule based on the submission timing of the medical examination or examination. As a result, the design unit can determine the priority of the schedule based on the submission timing of the medical examination or examination, thereby enabling efficient schedule design. Part or all of the above-mentioned processing in the design unit may be performed, for example, using AI, or may be performed without using AI. For example, the design unit can input the submission timing data of the medical examination or examination into AI, and the AI ​​can analyze the data to determine the priority of the schedule.

[0076] The design unit can adjust the order of the schedule based on the relevance of the examinations and examinations when designing the schedule. The design unit, for example, adjusts the order of the schedule based on the relevance of the examinations and examinations when designing the schedule. For example, the design unit can incorporate highly related examinations and examinations into the schedule consecutively. The design unit can also distribute and incorporate less related examinations and examinations into the schedule. Furthermore, the design unit can adjust the order of the schedule based on the relevance of the examinations and examinations. As a result, the design unit can adjust the order of the schedule based on the relevance of the examinations and examinations, thereby enabling efficient schedule design. Part or all of the above-mentioned processing in the design unit may be performed, for example, using AI, or may be performed without using AI. For example, the design unit can input relevance data of the examinations and examinations into AI, and the AI ​​can analyze the data to adjust the order of the schedule.

[0077] The notification unit can estimate the patient's emotion and adjust the expression method of the notification based on the estimated patient's emotion. The notification unit, for example, estimates the patient's emotion and adjusts the expression method of the notification based on the estimated patient's emotion. For example, the notification unit can notify the patient in a calm expression method when the patient is stressed. Furthermore, the notification unit can also notify the patient in a bright expression method when the patient is relaxed. Furthermore, the notification unit can notify the patient in a quick and concise expression method when the patient is in a hurry. This allows the notification unit to adjust the expression method of the notification according to the patient's emotion, thereby enabling more appropriate notification. 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 is performed using the generation AI. For example, the notification unit can input the patient's emotion data to the generation AI, which can estimate the emotion, and adjust the expression method of the notification based on the result.

[0078] The notification unit can adjust the level of detail of the notification based on the importance of the examination or examination at the time of notification. The notification unit, for example, adjusts the level of detail of the notification based on the importance of the examination or examination at the time of notification. For example, the notification unit can provide detailed notification in the case of an important examination or examination. In addition, the notification unit can provide simplified notification in the case of a general examination or examination. Furthermore, the notification unit can adjust the level of detail of the notification according to the importance of the examination or examination. As a result, the notification unit can adjust the level of detail of the notification according to the importance of the examination or examination, thereby enabling efficient notification. 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 importance data of the examination or examination to AI, and the AI ​​can analyze the data and adjust the level of detail of the notification.

[0079] The notification unit can apply different notification algorithms depending on the category of the examination or examination at the time of notification. The notification unit, for example, applies different notification algorithms depending on the category of the examination or examination at the time of notification. For example, in the case of an examination, the notification unit can apply a notification algorithm suitable for the examination. In addition, in the case of an examination, the notification unit can also apply a notification algorithm suitable for the examination. Furthermore, the notification unit can select an optimal notification algorithm depending on the category of the examination or examination. Thereby, the notification unit can select an optimal notification algorithm depending on the category of the examination or examination. Part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input category data of the examination or examination to AI, and the AI ​​can analyze the data and apply the optimal notification algorithm.

[0080] The notification unit can estimate the patient's emotion and adjust the timing of the notification based on the estimated emotion of the patient. The notification unit, for example, estimates the patient's emotion and adjusts the timing of the notification based on the estimated emotion of the patient. For example, if the patient is feeling stressed, the notification unit can notify at a timing when the patient is relaxed. Also, if the patient is relaxed, the notification unit can notify immediately. Furthermore, if the patient is in a hurry, the notification unit can notify quickly. This allows the notification unit to adjust the timing of the notification according to the emotion of the patient, thereby enabling more appropriate notification. 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 is performed using the generation AI. For example, the notification unit can input emotion data of the patient to the generation AI, which can estimate the emotion, and adjust the timing of the notification based on the result.

[0081] The notification unit can determine the priority of the notification based on the time of submission of the examination or test at the time of notification. The notification unit, for example, determines the priority of the notification based on the time of submission of the examination or test at the time of notification. For example, the notification unit can give priority to notification in the case of emergency examination or test. In addition, the notification unit can also give normal notification in the case of general examination or test. Furthermore, the notification unit can determine the priority of the notification based on the time of submission of the examination or test. As a result, the notification unit can determine the priority of the notification based on the time of submission of the examination or test, thereby enabling efficient notification. 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 data on the time of submission of the examination or test into AI, and the AI ​​can analyze the data to determine the priority of the notification.

[0082] The notification unit can adjust the order of notifications based on the relevance of the examinations and tests at the time of notification. The notification unit, for example, adjusts the order of notifications based on the relevance of the examinations and tests at the time of notification. For example, the notification unit can give priority to highly relevant examinations and tests. The notification unit can also postpone the notification of less relevant examinations and tests. Furthermore, the notification unit can adjust the order of notifications based on the relevance of the examinations and tests. As a result, the notification unit can adjust the order of notifications based on the relevance of the examinations and tests, thereby enabling efficient notification. 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 examinations and tests to AI, and the AI ​​can analyze the data and adjust the order of notifications.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The collection unit can collect lifestyle data of the patient and optimize the schedule of examinations and tests based on the collected data. For example, the collection unit can collect data such as the patient's diet, exercise, and sleep patterns, and adjust the timing of examinations and tests based on the collected data. The collection unit can also analyze the lifestyle data of the patient and evaluate the impact of a particular lifestyle on the results of the examination or test. Furthermore, the collection unit can provide health management advice based on the lifestyle data of the patient. This allows the collection unit to design an optimal schedule according to the patient's lifestyle.

[0085] The design department can estimate the patient's emotions and adjust the order of examinations and tests based on the estimated patient's emotions. For example, if the patient is feeling stressed, the design department can prioritize examinations and tests that will allow the patient to relax. Also, if the patient is relaxed, the design department can prioritize examinations and tests that are efficient. Furthermore, if the patient is in a hurry, the design department can set an order that allows the patient to receive examinations and tests quickly. This allows the design department to adjust the order of examinations and tests according to the patient's emotions, making it possible to design a more appropriate schedule.

[0086] The notification unit can estimate the patient's emotions and customize the content of the notification based on the estimated patient's emotions. For example, if the patient is feeling stressed, the notification unit can provide a notification including advice to help the patient relax. Also, if the patient is relaxed, the notification unit can provide a notification including detailed information. Furthermore, if the patient is in a hurry, the notification unit can provide a concise and quick notification. This allows the notification unit to provide more appropriate information by customizing the content of the notification according to the patient's emotions.

[0087] The collection unit can suggest the optimal location for examination or testing based on the geographical location information of the patient. For example, the collection unit can collect information on medical facilities in the area where the patient lives and suggest the optimal location for examination or testing. In addition, if the patient is traveling, the collection unit can also suggest a medical facility close to the patient's current location. Furthermore, the collection unit can also suggest the optimal location for examination or testing, taking into account transportation means and travel time, based on the geographical location information of the patient. In this way, the collection unit can provide more convenient medical services by taking into account the geographical location information of the patient.

[0088] The design department can estimate the patient's emotions and adjust the frequency of examinations and tests based on the estimated patient's emotions. For example, the design department can reduce the frequency of examinations and tests if the patient is feeling stressed. The design department can also increase the frequency of examinations and tests if the patient is relaxed. Furthermore, the design department can prioritize the minimum number of examinations and tests necessary if the patient is in a hurry. This allows the design department to design a more appropriate schedule by adjusting the frequency of examinations and tests according to the patient's emotions.

[0089] The notification unit can estimate the patient's emotions and select a notification method based on the estimated patient's emotions. For example, if the patient is feeling stressed, the notification unit can provide a text notification instead of a voice notification. Also, if the patient is relaxed, the notification unit can provide a voice notification. Furthermore, if the patient is in a hurry, the notification unit can provide a push notification. This allows the notification unit to provide more appropriate information by selecting a notification method according to the patient's emotions.

[0090] The collection unit can collect the patient's health data in real time and dynamically adjust the schedule of examinations and tests based on the collected data. For example, the collection unit can collect data such as the patient's heart rate, blood pressure, and body temperature in real time and adjust the timing of examinations and tests based on the collected data. The collection unit can also analyze the patient's health data and immediately schedule examinations and tests when an abnormality is detected. Furthermore, the collection unit can suggest preventive examinations and tests based on the patient's health data. This allows the collection unit to design an optimal schedule according to the patient's health condition.

[0091] The design department can estimate the patient's emotions and adjust the location of the examination or examination based on the estimated patient's emotions. For example, if the patient is feeling stressed, the design department can select an examination or examination location with a relaxing environment. Also, if the patient is relaxed, the design department can select an efficient examination or examination location. Furthermore, if the patient is in a hurry, the design department can select a location where the patient can receive the examination or examination quickly. This allows the design department to design a more appropriate schedule by adjusting the location of the examination or examination according to the patient's emotions.

[0092] The notification unit can estimate the patient's emotions and adjust the frequency of notifications based on the estimated patient's emotions. For example, the notification unit can reduce the frequency of notifications when the patient is stressed. The notification unit can also increase the frequency of notifications when the patient is relaxed. Furthermore, the notification unit can only provide important notifications when the patient is in a hurry. This allows the notification unit to provide more appropriate information by adjusting the frequency of notifications according to the patient's emotions.

[0093] The collection unit can analyze the content of the patient's social media posts and optimize the schedule of consultations and examinations based on the analysis. For example, the collection unit can analyze health-related posts from the patient's social media activity and adjust the timing of consultations and examinations based on the analysis. The collection unit can also suggest consultations and examinations for specific health issues based on the patient's social media activity. Furthermore, the collection unit can analyze the patient's social media activity and provide health management advice. This allows the collection unit to design a more appropriate schedule by taking into account the patient's social media activity.

[0094] The process flow of the second embodiment will be briefly described below.

[0095] Step 1: The collection unit collects the history of past examinations and consultations of patients. Specifically, data such as the start time of the consultation, the end time of the consultation, and the time required for the examination are collected. In addition, data such as the patient's age, gender, details of the examination, and details of the examination can also be collected. Step 2: The design department studies the data collected by the collection department and designs the hospital's daily schedule. Specifically, it uses generative AI to predict the time required for consultations and tests from past data, and designs the schedule based on that prediction. Methods such as regression analysis and time series analysis can be used. Step 3: The notification unit notifies patients based on the schedule designed by the design unit. Specifically, notifications are sent via a smartphone app, and the timing and content of notifications are optimized. This allows patients to be notified of the exact timing of their consultation or examination, and provides them with information on predicted wait times and preparations for consultations and examinations.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements including the collection unit, design unit, and notification 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 collection unit collects the history time of the patient's examination and consultation using the camera 42 and microphone 38B of the smart device 14, and transmits it to the data processing device 12 by the control unit 46A. The design unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI designs the hospital's daily schedule based on the collected data. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and notifies the patient of the exact timing of the consultation or examination based on the designed schedule. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements including the above-mentioned collection unit, design unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the history time of the patient's examination and consultation using the camera 42 and microphone 238 of the smart glasses 214, and transmits it to the data processing device 12 by the control unit 46A. The design unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI designs the hospital's daily schedule based on the collected data. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and notifies the patient of the exact timing of the consultation or examination based on the designed schedule. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements including the above-mentioned collection unit, design unit, and notification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the history time of the patient's examination and consultation using the camera 42 and microphone 238 of the headset type terminal 314, and transmits it to the data processing device 12 by the control unit 46A. The design unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI designs the hospital's daily schedule based on the collected data. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the patient of the exact timing of the consultation or examination based on the designed schedule. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

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

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0143] 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.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0147] 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.

[0148] Each of the multiple elements including the above-mentioned collection unit, design unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the history time of the patient's examination and consultation using the camera 42 and microphone 238 of the robot 414, and transmits it to the data processing device 12 by the control unit 46A. The design unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI designs the hospital's daily schedule based on the collected data. The notification unit is realized, for example, by the control unit 46A of the robot 414, and notifies the patient of the exact timing of the consultation or examination based on the designed schedule. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] 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.

[0150] 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.

[0151] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Appendix 1) A collection unit that collects the history times of past patient examinations and consultations; A design unit that learns the data collected by the collection unit and designs a daily schedule for the hospital; A notification unit that notifies the patient based on the schedule designed by the design unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect data on the patient's age, sex, medical examination, and examination details 2. The system of claim 1, (Appendix 3) The design unit Use algorithms that use past data to predict how long consultations and tests will take, and then design schedules based on those predictions. 2. The system of claim 1, (Appendix 4) The notification unit is Notification will be given via a smartphone app, and the timing or content of the notification will also be mentioned. 2. The system of claim 1, (Appendix 5) The notification unit is Providing patients with information about expected wait times or preparation for appointments and tests 2. The system of claim 1, (Appendix 6) The collecting unit includes: Inferring patient emotions and adjusting the timing of data collection based on the inferred patient emotions 2. The system of claim 1, (Appendix 7) The collecting unit includes: Analyze the patient's past medical history and choose the most appropriate data collection method 2. The system of claim 1, (Appendix 8) The collecting unit includes: Filter data collection based on the patient's current health and living situation 2. The system of claim 1, (Appendix 9) The collecting unit includes: Estimate patient emotions and prioritize data collection based on the estimated patient emotions 2. The system of claim 1, (Appendix 10) The collecting unit includes: During data collection, prioritize the collection of relevant data based on the patient's geographic location 2. The system of claim 1, (Appendix 11) The collecting unit includes: During data collection, analyze patients' social media posts to collect relevant data 2. The system of claim 1, (Appendix 12) The design unit Estimate patient emotions and adjust schedule design algorithms based on the estimated patient emotions 2. The system of claim 1, (Appendix 13) The design unit When designing a schedule, adjust the level of detail based on the importance of each consultation or examination. 2. The system of claim 1, (Appendix 14) The design unit Apply different design algorithms to different categories of consultations and exams when designing schedules 2. The system of claim 1, (Appendix 15) The design unit Estimate the patient's emotions and prioritize schedules based on the estimated patient emotions 2. The system of claim 1, (Appendix 16) The design unit When designing a schedule, prioritize the schedule based on the timing of consultations and examinations. 2. The system of claim 1, (Appendix 17) The design unit When designing a schedule, adjust the order of the schedule based on the relevance of consultations and tests. 2. The system of claim 1, (Appendix 18) The notification unit is Inferring patient emotion and adjusting notification language based on the inferred patient emotion 2. The system of claim 1, (Appendix 19) The notification unit is When notifying, adjust the level of detail of notifications based on the importance of the consultation or examination 2. The system of claim 1, (Appendix 20) The notification unit is At the time of notification, apply different notification algorithms depending on the category of visit or test 2. The system of claim 1, (Appendix 21) The notification unit is Estimate the patient's emotions and adjust the timing of notifications based on the estimated patient emotions 2. The system of claim 1, (Appendix 22) The notification unit is When notifying, prioritize notifications based on when consultations or tests were submitted 2. The system of claim 1, (Appendix 23) The notification unit is During notifications, adjust the order of notifications based on the relevance of visits and tests 2. The system of claim 1, [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects the history times of past patient examinations and consultations; A design unit that learns the data collected by the collection unit and designs a daily schedule for the hospital; A notification unit that notifies the patient based on the schedule designed by the design unit. A system characterized by:

2. The collecting unit includes: Collect data on the patient's age, sex, medical examination, and examination details 2. The system of claim 1 .

3. The design unit includes: Use algorithms that use past data to predict how long consultations and tests will take, and then design schedules based on those predictions.

2. The system of claim 1 .

4. The notification unit is Notification will be given via a smartphone app, and the timing or content of the notification will also be mentioned.

2. The system of claim 1 .

5. The notification unit is Providing patients with information on waiting times or preparation for appointments 2. The system of claim 1 .

6. The collecting unit includes: Inferring patient emotions and adjusting the timing of data collection based on the inferred patient emotions 2. The system of claim 1 .

7. The collecting unit includes: Analyze the patient's past medical history and choose the most appropriate data collection method 2. The system of claim 1 .

8. The collecting unit includes: Filter data collection based on the patient's current health and living situation 2. The system of claim 1 .

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