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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technologies do not 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 historical times of past patient examinations and consultations. The design unit learns from 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. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of schedule management to reduce waiting times at hospitals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A hospital waiting time reduction system according to an embodiment of the present invention collects the historical times of past patient examinations and consultations, and a generation AI learns from this to design a daily hospital schedule and notify patients of the exact timing for their consultations and examinations. The hospital waiting time reduction system collects the historical times of past patient examinations and consultations, and the generation AI learns from this. The generation AI then designs a daily hospital schedule based on what it has learned. Finally, the system notifies patients of the exact timing for their consultations and examinations based on the schedule designed by the generation AI. For example, a hospital waiting time reduction system collects the historical times of past patient examinations and consultations. Detailed data, such as the start time, end time, and wait time for each patient's examination or consultation, is collected. For example, if patient A checks in at 9:00, begins his or her consultation at 9:30, and finishes at 10:00, this data is collected. This allows the historical times of past patient examinations and consultations to be understood. The generation AI then learns from the collected data. The generation AI analyzes the collected data and designs a daily hospital schedule. For example, if past data shows that consultations tend to be concentrated during certain times of the day, the system can design a schedule to avoid those times. This reduces waiting times. Furthermore, based on the schedule designed by the generative AI, the system notifies patients of the exact timing for their consultation or examination. For example, if Patient B is scheduled for a consultation at 10:00, the generative AI will notify them in advance so that they can arrive at the hospital at the appropriate time. This allows patients to receive their consultation or examination 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 receive their consultation or examination smoothly, reducing stress. Hospitals can also manage schedules more efficiently, improving overall operational efficiency. This allows the hospital's waiting time reduction system to reduce patient waiting times and improve patient satisfaction.

[0029] A hospital waiting time reduction system according to an embodiment includes a collection unit, a design unit, and a notification unit. The collection unit collects historical times of past patient examinations and consultations. The historical times of past patient examinations and consultations include, but are not limited to, the start and end times of consultations and the duration of each examination. The collection unit can also collect data such as the patient's age, gender, consultation details, and examination details. For example, the collection unit collects the patient's age and gender as input data and records the consultation details and examination details in detail. The design unit uses a generative AI to learn from the data collected by the collection unit and design a daily schedule for the hospital. The design unit uses an algorithm to predict the duration of consultations and examinations from past data and design a schedule based on the prediction. For example, the design unit predicts the duration of consultations and examinations using techniques such as regression analysis and time series analysis. The design unit can analyze past data and design an efficient schedule using the generative AI. The notification unit notifies the patient based on the schedule designed by the design unit. The notification unit provides notifications, for example, via a smartphone app, and also mentions the timing and content of the notifications. For example, the notification unit notifies patients of the exact timing for their consultation or examination, and provides information on waiting time predictions and preparations for the consultation or examination. The notification unit can provide information to patients at the appropriate time using generative AI. As a result, the hospital waiting time reduction system according to the embodiment can reduce patient waiting times and improve patient satisfaction. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, when notifying patients via a smartphone app, the notification unit can use AI to optimize the timing and content of the notifications.

[0030] The collection unit collects the historical time of past patient examinations and consultations. Examples of historical time of past patient examinations and consultations include, but are not limited to, the start time, end time, and duration of the examination. The collection unit can also collect data such as the patient's age, gender, examination details, and examination details. Specifically, the collection unit connects 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, and waiting time for test results. The collection unit also collects personal information about the patient, such as their age, gender, medical history, and current symptoms, and integrates and analyzes this data. For example, the collection unit collects the patient's age and gender as input data and records the details of the examination and examination details. This allows the collection unit to understand the treatment patterns and trends for each patient and provide basic data for more accurately predicting the duration of examinations and examinations. The collected data is stored in a central database and made accessible to the design unit and notification unit. This allows the collection unit to collect data efficiently and accurately, improving overall system performance.

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

[0032] The notification unit notifies patients based on the schedule designed by the design unit. The notification unit, for example, sends notifications via a smartphone app and determines the timing and content of the notifications. Specifically, the notification unit notifies patients of the exact timing of their consultations or examinations, predicts wait times, and provides information on preparation for the consultation or examination. The notification unit can use generative AI to provide information to patients at the appropriate time. For example, the notification unit notifies patients' smartphones of the scheduled time of their consultation or examination and sends reminders that take into account travel time to the examination or examination room. The notification unit can also monitor the progress of consultations or examinations in real time and immediately notify patients if the scheduled time changes. Furthermore, the notification unit can collect patient feedback and continuously improve the accuracy and effectiveness of the notification content. For example, the notification unit can optimize the timing and content of notifications based on patient feedback to provide more effective information. The notification unit can also reliably convey information using multiple communication methods. For example, it can use not only smartphone notifications but also voice calls, SMS, email, and other methods to ensure important information is delivered. This allows 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, gender, medical examination details, and test details. The collection unit, for example, collects data on the patient's age, gender, medical examination details, and test details. 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 details and test details 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 details include, for example, medical specialties such as internal medicine, surgery, and dermatology. The test details include, for example, blood tests, X-rays, and MRI scans. This allows the collection unit to collect detailed patient data, enabling more accurate schedule planning. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the patient's age and gender data into AI, which then analyzes and classifies the data.

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

[0035] The notification unit may provide notifications through a smartphone app and may also specify the timing or content of the notifications. For example, the notification unit may provide notifications through a smartphone app and may also specify the timing and content of the notifications. For example, the notification unit may notify patients of the exact timing of their consultation or examination, and provide them with information on waiting times and preparations for the consultation or examination. The notification unit may use a generation AI to provide information to patients at the appropriate time. For example, when notifying patients through a smartphone app, the notification unit may use a generation AI to optimize the timing and content of the notifications. For example, the notification unit may notify patients the day before their consultation and provide information on preparations for the consultation. The notification unit may also notify patients just before their consultation and provide them with an estimated waiting time. This allows the notification unit to provide information to patients at the appropriate time by providing notifications through a smartphone app. Some or all of the above-described processing in the notification unit is performed using a generation AI. For example, the notification unit inputs the timing and content of the notifications into the generation AI, which then determines the optimal notification method.

[0036] The notification unit can provide patients with information regarding waiting time predictions or preparations for consultations and examinations. The notification unit can provide patients with information regarding waiting time predictions or preparations for consultations and examinations. For example, the notification unit can predict waiting times for consultations and examinations and provide the patients with that information. The notification unit can also provide patients with information regarding preparations for consultations and examinations. For example, the notification unit can notify patients the day before their consultation and provide information regarding preparations for the consultation. The notification unit can use a generation AI to provide information to patients at an appropriate time. For example, the notification unit inputs predicted waiting time data into the generation AI, which then analyzes the data and notifies the patient. This improves patient convenience by providing information regarding predicted waiting times and preparations for consultations and examinations. Some or all of the above-mentioned processing in the notification unit is performed using the generation AI. For example, the notification unit inputs predicted waiting time data into the generation AI, which then 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 details of medical examinations the patient has received in the past. Furthermore, if a specific test is required from the patient's past medical examination history, the collection unit can also select a data collection method appropriate for that test. Furthermore, the collection unit can analyze the patient's past medical examination history and suggest an efficient data collection method. In this way, the collection unit can select the optimal data collection method by analyzing the patient's past medical examination history. Some 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 the patient's past medical examination history data into AI, which 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. For example, the collection unit can perform 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 prioritize collection of highly relevant data based on the patient's living situation. Furthermore, the collection unit can adjust the scope of data collection according to the patient's health condition and living situation. This allows the collection unit to collect only necessary data depending on the patient's health condition and living situation. Some 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, which can analyze the data and perform filtering.

[0039] The collection unit can prioritize collecting highly relevant data based on the patient's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data based on the patient's geographical location information when collecting data. For example, if the patient lives in a specific area, the collection unit can prioritize collecting data related to that area. Also, if the patient is traveling, the collection unit can collect data related to the patient's current location. Furthermore, the collection unit can select an optimal data collection method based on the patient's geographical location information. In this way, the collection unit can prioritize collecting highly relevant data by taking the patient's geographical location information into consideration. Some or all of the above-described 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 the patient's geographical location information into AI, which can analyze the data and prioritize collecting highly relevant data.

[0040] The collection unit can analyze the patient's social media posts and collect relevant data when collecting data. For example, the collection unit can analyze the patient's social media posts and collect relevant data when collecting data. For example, the collection unit can analyze health-related posts from the patient's social media activities and collect relevant data. The collection unit can also collect data related to 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 relevant data by analyzing the patient's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the patient's social media posts into AI, which can analyze the data and collect relevant 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. For example, the design unit can design a detailed schedule for important examinations or examinations. The design unit can also design a simplified schedule for general examinations or examinations. Furthermore, the design unit can adjust the level of detail of the schedule depending on the importance of the examination or examination. This allows the design unit to adjust the level of detail of the schedule depending on the importance of the examination or examination, thereby enabling efficient schedule design. Some or all of the above-mentioned processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input importance data of examinations and examinations into AI, which 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 examination or examination when designing a schedule. For example, the design unit can apply different design algorithms depending on the category of 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. Also, 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 examination or examination. This allows the design unit to select an optimal schedule design algorithm depending on the category of examination or examination. Some or all of the above-mentioned processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input category data of examination or examination into AI, which can analyze the data and apply the optimal design algorithm.

[0043] The design unit can determine the priority of schedules based on the timing of consultations and examinations when designing a schedule. The design unit, for example, determines the priority of schedules based on the timing of consultations and examinations when designing a schedule. For example, the design unit can design a schedule that prioritizes emergency consultations and examinations. The design unit can also design a regular schedule for general consultations and examinations. Furthermore, the design unit can determine the priority of schedules based on the timing of consultations and examinations. This enables the design unit to determine the priority of schedules based on the timing of consultations and examinations, thereby enabling efficient schedule design. Some or all of the above-mentioned processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input data on the timing of consultations and examinations to AI, which can analyze the data and determine the priority of schedules.

[0044] The design unit can adjust the order of the schedule based on the relevance of examinations and tests when designing a schedule. The design unit, for example, adjusts the order of the schedule based on the relevance of examinations and tests when designing a schedule. For example, the design unit can include highly related examinations and tests consecutively in the schedule. The design unit can also distribute less related examinations and tests in the schedule. Furthermore, the design unit can adjust the order of the schedule based on the relevance of examinations and tests. This enables the design unit to design an efficient schedule by adjusting the order of the schedule based on the relevance of examinations and tests. Some or all of the above-mentioned processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input relevance data of examinations and tests into AI, which can analyze the data and 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 test when issuing a notification. The notification unit, for example, adjusts the level of detail of the notification based on the importance of the examination or test when issuing a notification. For example, the notification unit can provide detailed notification in the case of an important examination or test. The notification unit can also provide simplified notification in the case of a general examination or test. Furthermore, the notification unit can adjust the level of detail of the notification according to the importance of the examination or test. This allows the notification unit to adjust the level of detail of the notification according to the importance of the examination or test, thereby enabling efficient notification. Some 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 importance data of the examination or test into AI, which 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 test when making a notification. The notification unit can, for example, apply different notification algorithms depending on the category of the examination or test when making a notification. For example, in the case of an examination, the notification unit can apply a notification algorithm suitable for the examination. Furthermore, in the case of an test, the notification unit can apply a notification algorithm suitable for the test. Furthermore, the notification unit can select an optimal notification algorithm depending on the category of the examination or test. This allows the notification unit to select an optimal notification algorithm depending on the category of the examination or test. Some 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 test into AI, which can analyze the data and apply the optimal notification algorithm.

[0047] The notification unit can determine the priority of notifications based on the timing of submission of consultations and tests at the time of notification. The notification unit, for example, determines the priority of notifications based on the timing of submission of consultations and tests at the time of notification. For example, the notification unit can prioritize notifications in the case of emergency consultations and tests. The notification unit can also provide regular notifications in the case of general consultations and tests. Furthermore, the notification unit can determine the priority of notifications based on the timing of submission of consultations and tests. This enables efficient notifications by the notification unit determining the priority of notifications based on the timing of submission of consultations and tests. Some 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 data on the timing of submission of consultations and tests into AI, which can analyze the data and determine the priority of notifications.

[0048] The notification unit can adjust the order of notifications based on the relevance of the examinations and tests when notifying. The notification unit, for example, adjusts the order of notifications based on the relevance of the examinations and tests when notifying. For example, the notification unit can prioritize notifications of highly relevant examinations and tests. The notification unit can also postpone notifications 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. This enables efficient notifications by adjusting the order of notifications based on the relevance of the examinations and tests. Some 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 relevance data of examinations and tests into 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 a 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 patient's lifestyle data and evaluate the impact of specific lifestyle habits on the results of examinations and tests. Furthermore, the collection unit can provide health management advice based on the patient's lifestyle data. This allows the collection unit to design an optimal schedule based on the patient's lifestyle.

[0051] The collection unit can suggest the optimal location for consultation or testing based on the patient's geographical location information. For example, the collection unit can collect information on medical facilities in the area where the patient lives and suggest the optimal location for consultation 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 suggest the optimal location for consultation or testing, taking into account transportation means and travel time, based on the patient's geographical location information. In this way, the collection unit can provide more convenient medical services by taking into account the patient's geographical location information.

[0052] The collection unit can collect patient 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 if 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 based on the patient's health condition.

[0053] The collection unit can analyze the content of a patient's social media posts and optimize the schedule of medical examinations and tests based on the results. For example, the collection unit can analyze health-related posts from a patient's social media activity and adjust the timing of medical examinations and tests based on the results. The collection unit can also suggest medical examinations and tests 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 more appropriate schedules by taking the patient's social media activity into consideration.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects the history of past patient examinations and consultations. Specifically, it collects data such as the start time of the consultation, the end time of the consultation, and the duration of the examination. It can also collect data such as the patient's age, gender, examination details, and test details. 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 required time 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 department notifies patients based on the schedule designed by the design department. 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 predicting waiting times and preparing for their consultation or examination.

[0056] (Example 2) A hospital waiting time reduction system according to an embodiment of the present invention collects the historical times of past patient examinations and consultations, and a generation AI learns from this to design a daily hospital schedule and notify patients of the exact timing for their consultations and examinations. The hospital waiting time reduction system collects the historical times of past patient examinations and consultations, and the generation AI learns from this. The generation AI then designs a daily hospital schedule based on what it has learned. Finally, the system notifies patients of the exact timing for their consultations and examinations based on the schedule designed by the generation AI. For example, a hospital waiting time reduction system collects the historical times of past patient examinations and consultations. Detailed data, such as the start time, end time, and wait time for each patient's examination or consultation, is collected. For example, if patient A checks in at 9:00, begins his or her consultation at 9:30, and finishes at 10:00, this data is collected. This allows the historical times of past patient examinations and consultations to be understood. The generation AI then learns from the collected data. The generation AI analyzes the collected data and designs a daily hospital schedule. For example, if past data shows that consultations tend to be concentrated during certain times of the day, the system can design a schedule to avoid those times. This reduces waiting times. Furthermore, based on the schedule designed by the generative AI, the system notifies patients of the exact timing for their consultation or examination. For example, if Patient B is scheduled for a consultation at 10:00, the generative AI will notify them in advance so that they can arrive at the hospital at the appropriate time. This allows patients to receive their consultation or examination 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 receive their consultation or examination smoothly, reducing stress. Hospitals can also manage schedules more efficiently, improving overall operational efficiency. This allows the hospital's waiting time reduction system to reduce patient waiting times and improve patient satisfaction.

[0057] A hospital waiting time reduction system according to an embodiment includes a collection unit, a design unit, and a notification unit. The collection unit collects historical times of past patient examinations and consultations. The historical times of past patient examinations and consultations include, but are not limited to, the start and end times of consultations and the duration of each examination. The collection unit can also collect data such as the patient's age, gender, consultation details, and examination details. For example, the collection unit collects the patient's age and gender as input data and records the consultation details and examination details in detail. The design unit uses a generative AI to learn from the data collected by the collection unit and design a daily schedule for the hospital. The design unit uses an algorithm to predict the duration of consultations and examinations from past data and design a schedule based on the prediction. For example, the design unit predicts the duration of consultations and examinations using techniques such as regression analysis and time series analysis. The design unit can analyze past data and design an efficient schedule using the generative AI. The notification unit notifies the patient based on the schedule designed by the design unit. The notification unit provides notifications, for example, via a smartphone app, and also mentions the timing and content of the notifications. For example, the notification unit notifies patients of the exact timing for their consultation or examination, and provides information on waiting time predictions and preparations for the consultation or examination. The notification unit can provide information to patients at the appropriate time using generative AI. As a result, the hospital waiting time reduction system according to the embodiment can reduce patient waiting times and improve patient satisfaction. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, when notifying patients via a smartphone app, the notification unit can use AI to optimize the timing and content of the notifications.

[0058] The collection unit collects the historical time of past patient examinations and consultations. Examples of historical time of past patient examinations and consultations include, but are not limited to, the start time, end time, and duration of the examination. The collection unit can also collect data such as the patient's age, gender, examination details, and examination details. Specifically, the collection unit connects 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, and waiting time for test results. The collection unit also collects personal information about the patient, such as their age, gender, medical history, and current symptoms, and integrates and analyzes this data. For example, the collection unit collects the patient's age and gender as input data and records the details of the examination and examination details. This allows the collection unit to understand the treatment patterns and trends for each patient and provide basic data for more accurately predicting the duration of examinations and examinations. The collected data is stored in a central database and made accessible to the design unit and notification unit. This allows the collection unit to collect data efficiently and accurately, improving overall system performance.

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

[0060] The notification unit notifies patients based on the schedule designed by the design unit. The notification unit, for example, sends notifications via a smartphone app and determines the timing and content of the notifications. Specifically, the notification unit notifies patients of the exact timing of their consultations or examinations, predicts wait times, and provides information on preparation for the consultation or examination. The notification unit can use generative AI to provide information to patients at the appropriate time. For example, the notification unit notifies patients' smartphones of the scheduled time of their consultation or examination and sends reminders that take into account travel time to the examination or examination room. The notification unit can also monitor the progress of consultations or examinations in real time and immediately notify patients if the scheduled time changes. Furthermore, the notification unit can collect patient feedback and continuously improve the accuracy and effectiveness of the notification content. For example, the notification unit can optimize the timing and content of notifications based on patient feedback to provide more effective information. The notification unit can also reliably convey information using multiple communication methods. For example, it can use not only smartphone notifications but also voice calls, SMS, email, and other methods to ensure important information is delivered. This allows 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, gender, medical examination details, and test details. The collection unit, for example, collects data on the patient's age, gender, medical examination details, and test details. 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 details and test details 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 details include, for example, medical specialties such as internal medicine, surgery, and dermatology. The test details include, for example, blood tests, X-rays, and MRI scans. This allows the collection unit to collect detailed patient data, enabling more accurate schedule planning. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the patient's age and gender data into AI, which then analyzes and classifies the data.

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

[0063] The notification unit may provide notifications through a smartphone app and may also specify the timing or content of the notifications. For example, the notification unit may provide notifications through a smartphone app and may also specify the timing and content of the notifications. For example, the notification unit may notify patients of the exact timing of their consultation or examination, and provide them with information on waiting times and preparations for the consultation or examination. The notification unit may use a generation AI to provide information to patients at the appropriate time. For example, when notifying patients through a smartphone app, the notification unit may use a generation AI to optimize the timing and content of the notifications. For example, the notification unit may notify patients the day before their consultation and provide information on preparations for the consultation. The notification unit may also notify patients just before their consultation and provide them with an estimated waiting time. This allows the notification unit to provide information to patients at the appropriate time by providing notifications through a smartphone app. Some or all of the above-described processing in the notification unit is performed using a generation AI. For example, the notification unit inputs the timing and content of the notifications into the generation AI, which then determines the optimal notification method.

[0064] The notification unit can provide patients with information regarding waiting time predictions or preparations for consultations and examinations. The notification unit can provide patients with information regarding waiting time predictions or preparations for consultations and examinations. For example, the notification unit can predict waiting times for consultations and examinations and provide the patients with that information. The notification unit can also provide patients with information regarding preparations for consultations and examinations. For example, the notification unit can notify patients the day before their consultation and provide information regarding preparations for the consultation. The notification unit can use a generation AI to provide information to patients at an appropriate time. For example, the notification unit inputs predicted waiting time data into the generation AI, which then analyzes the data and notifies the patient. This improves patient convenience by providing information regarding predicted waiting times and preparations for consultations and examinations. Some or all of the above-mentioned processing in the notification unit is performed using the generation AI. For example, the notification unit inputs predicted waiting time data into the generation AI, which then analyzes the data and notifies the patient.

[0065] The collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated patient's emotions. For example, the collection unit estimates the patient's emotions and adjusts the timing of data collection based on the estimated patient's emotions. For example, if the patient is feeling stressed, the collection unit adjusts the collection timing to collect data when the patient is in a relaxed state. The collection unit can also collect data immediately and efficiently acquire information when the patient is relaxed. Furthermore, if the patient is in a hurry, the collection unit can quickly collect data and minimize waiting time. This allows the collection unit to adjust the timing of data collection according to the patient's emotions, enabling more appropriate data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input patient emotion data into a generation AI, which then estimates the emotion and adjusts 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 details of medical examinations the patient has received in the past. Furthermore, if a specific test is required from the patient's past medical examination history, the collection unit can also select a data collection method appropriate for that test. Furthermore, the collection unit can analyze the patient's past medical examination history and suggest an efficient data collection method. In this way, the collection unit can select the optimal data collection method by analyzing the patient's past medical examination history. Some 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 the patient's past medical examination history data into AI, which 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. For example, the collection unit can perform 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 prioritize collection of highly relevant data based on the patient's living situation. Furthermore, the collection unit can adjust the scope of data collection according to the patient's health condition and living situation. This allows the collection unit to collect only necessary data depending on the patient's health condition and living situation. Some 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, which can analyze the data and perform filtering.

[0068] The collection unit can estimate the patient's emotions and determine the priority of data to be collected based on the estimated patient's emotions. For example, the collection unit can estimate the patient's emotions and determine the priority of data to be collected based on the estimated patient's emotions. For example, the collection unit can prioritize collecting important data 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 efficient data collection by determining the priority of data to be collected based on the patient's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can input patient emotion data into a generation AI, which then estimates the emotion and determines the priority of data based on the result.

[0069] The collection unit can prioritize collecting highly relevant data based on the patient's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data based on the patient's geographical location information when collecting data. For example, if the patient lives in a specific area, the collection unit can prioritize collecting data related to that area. Also, if the patient is traveling, the collection unit can collect data related to the patient's current location. Furthermore, the collection unit can select an optimal data collection method based on the patient's geographical location information. In this way, the collection unit can prioritize collecting highly relevant data by taking the patient's geographical location information into consideration. Some or all of the above-described 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 the patient's geographical location information into AI, which can analyze the data and prioritize collecting highly relevant data.

[0070] The collection unit can analyze the patient's social media posts and collect relevant data when collecting data. For example, the collection unit can analyze the patient's social media posts and collect relevant data when collecting data. For example, the collection unit can analyze health-related posts from the patient's social media activities and collect relevant data. The collection unit can also collect data related to 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 relevant data by analyzing the patient's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the patient's social media posts into AI, which can analyze the data and collect relevant data.

[0071] The design unit can estimate the patient's emotions and adjust the schedule design algorithm based on the estimated patient emotions. For example, the design unit can estimate the patient's emotions and adjust the schedule design algorithm based on the estimated patient emotions. For example, if the patient is feeling stressed, the design unit can design a schedule that allows the patient to relax. Furthermore, if the patient is relaxed, the design unit can design an efficient schedule. Furthermore, if the patient is in a hurry, the design unit can design a schedule that allows the patient to receive medical examinations and tests quickly. This allows the design unit to adjust the schedule design algorithm according to the patient's emotions, enabling more appropriate schedule design. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the design unit is performed using the generative AI. For example, the design unit can input patient emotion data into the generative AI, which can estimate the emotion and adjust the schedule design algorithm based on the results.

[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. For example, the design unit can design a detailed schedule for important examinations or examinations. The design unit can also design a simplified schedule for general examinations or examinations. Furthermore, the design unit can adjust the level of detail of the schedule depending on the importance of the examination or examination. This allows the design unit to adjust the level of detail of the schedule depending on the importance of the examination or examination, thereby enabling efficient schedule design. Some or all of the above-mentioned processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input importance data of examinations and examinations into AI, which 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 examination or examination when designing a schedule. For example, the design unit can apply different design algorithms depending on the category of 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. Also, 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 examination or examination. This allows the design unit to select an optimal schedule design algorithm depending on the category of examination or examination. Some or all of the above-mentioned processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input category data of examination or examination into AI, which can analyze the data and apply the optimal design algorithm.

[0074] The design unit can estimate a patient's emotions and prioritize schedules based on the estimated patient emotions. For example, the design unit can estimate a patient's emotions and prioritize schedules based on the estimated patient emotions. For example, if a patient is stressed, the design unit can prioritize important consultations and examinations. Furthermore, if a patient is relaxed, the design unit can prioritize efficient schedules. Furthermore, if a patient is in a hurry, the design unit can prioritize schedules that allow for quick consultations and examinations. This enables the design unit to prioritize schedules based on the patient's emotions, thereby enabling efficient schedule design. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the design unit is performed using the generative AI. For example, the design unit can input patient emotion data into the generative AI, which can estimate the patient's emotions and prioritize schedules based on the results.

[0075] The design unit can determine the priority of schedules based on the timing of consultations and examinations when designing a schedule. The design unit, for example, determines the priority of schedules based on the timing of consultations and examinations when designing a schedule. For example, the design unit can design a schedule that prioritizes emergency consultations and examinations. The design unit can also design a regular schedule for general consultations and examinations. Furthermore, the design unit can determine the priority of schedules based on the timing of consultations and examinations. This enables the design unit to determine the priority of schedules based on the timing of consultations and examinations, thereby enabling efficient schedule design. Some or all of the above-mentioned processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input data on the timing of consultations and examinations to AI, which can analyze the data and determine the priority of schedules.

[0076] The design unit can adjust the order of the schedule based on the relevance of examinations and tests when designing a schedule. The design unit, for example, adjusts the order of the schedule based on the relevance of examinations and tests when designing a schedule. For example, the design unit can include highly related examinations and tests consecutively in the schedule. The design unit can also distribute less related examinations and tests in the schedule. Furthermore, the design unit can adjust the order of the schedule based on the relevance of examinations and tests. This enables the design unit to design an efficient schedule by adjusting the order of the schedule based on the relevance of examinations and tests. Some or all of the above-mentioned processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input relevance data of examinations and tests into AI, which can analyze the data and adjust the order of the schedule.

[0077] The notification unit can estimate the patient's emotions and adjust the notification expression method based on the estimated patient's emotions. For example, the notification unit can estimate the patient's emotions and adjust the notification expression method based on the estimated patient's emotions. For example, if the patient is stressed, the notification unit can provide a notification in a calm manner. If the patient is relaxed, the notification unit can provide a notification in a cheerful manner. If the patient is in a hurry, the notification unit can provide a notification in a quick and concise manner. This allows the notification unit to adjust the notification expression method according to the patient's emotions, enabling more appropriate notification. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit is performed using the generation AI. For example, the notification unit can input patient emotion data into the generation AI, which can estimate the emotion and adjust the notification expression method 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 test when issuing a notification. The notification unit, for example, adjusts the level of detail of the notification based on the importance of the examination or test when issuing a notification. For example, the notification unit can provide detailed notification in the case of an important examination or test. The notification unit can also provide simplified notification in the case of a general examination or test. Furthermore, the notification unit can adjust the level of detail of the notification according to the importance of the examination or test. This allows the notification unit to adjust the level of detail of the notification according to the importance of the examination or test, thereby enabling efficient notification. Some 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 importance data of the examination or test into AI, which 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 test when making a notification. The notification unit can, for example, apply different notification algorithms depending on the category of the examination or test when making a notification. For example, in the case of an examination, the notification unit can apply a notification algorithm suitable for the examination. Furthermore, in the case of an test, the notification unit can apply a notification algorithm suitable for the test. Furthermore, the notification unit can select an optimal notification algorithm depending on the category of the examination or test. This allows the notification unit to select an optimal notification algorithm depending on the category of the examination or test. Some 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 test into AI, which can analyze the data and apply the optimal notification algorithm.

[0080] The notification unit can estimate the patient's emotions and adjust the timing of the notification based on the estimated patient's emotions. For example, the notification unit can estimate the patient's emotions and adjust the timing of the notification based on the estimated patient's emotions. For example, if the patient is feeling stressed, the notification unit can notify the patient when they are relaxed. Furthermore, if the patient is relaxed, the notification unit can notify the patient immediately. Furthermore, if the patient is in a hurry, the notification unit can notify the patient quickly. This allows the notification unit to adjust the timing of the notification according to the patient's emotions, enabling more appropriate notification. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit is performed using the generation AI. For example, the notification unit can input patient emotion data into 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 notifications based on the timing of submission of consultations and tests at the time of notification. The notification unit, for example, determines the priority of notifications based on the timing of submission of consultations and tests at the time of notification. For example, the notification unit can prioritize notifications in the case of emergency consultations and tests. The notification unit can also provide regular notifications in the case of general consultations and tests. Furthermore, the notification unit can determine the priority of notifications based on the timing of submission of consultations and tests. This enables efficient notifications by the notification unit determining the priority of notifications based on the timing of submission of consultations and tests. Some 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 data on the timing of submission of consultations and tests into AI, which can analyze the data and determine the priority of notifications.

[0082] The notification unit can adjust the order of notifications based on the relevance of the examinations and tests when notifying. The notification unit, for example, adjusts the order of notifications based on the relevance of the examinations and tests when notifying. For example, the notification unit can prioritize notifications of highly relevant examinations and tests. The notification unit can also postpone notifications 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. This enables efficient notifications by adjusting the order of notifications based on the relevance of the examinations and tests. Some 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 relevance data of examinations and tests into 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 a 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 patient's lifestyle data and evaluate the impact of specific lifestyle habits on the results of examinations and tests. Furthermore, the collection unit can provide health management advice based on the patient's lifestyle data. This allows the collection unit to design an optimal schedule based on 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 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, enabling more appropriate schedule design.

[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. If the patient is relaxed, the notification unit can also 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 customize the content of the notification according to the patient's emotions, making it possible to provide more appropriate information.

[0087] The collection unit can suggest the optimal location for consultation or testing based on the patient's geographical location information. For example, the collection unit can collect information on medical facilities in the area where the patient lives and suggest the optimal location for consultation 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 suggest the optimal location for consultation or testing, taking into account transportation means and travel time, based on the patient's geographical location information. In this way, the collection unit can provide more convenient medical services by taking into account the patient's geographical location information.

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

[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. The notification unit can also provide a voice notification if the patient is relaxed. Furthermore, the notification unit can also provide a push notification if the patient is in a hurry. This allows the notification unit to select a notification method according to the patient's emotions, making it possible to provide more appropriate information.

[0090] The collection unit can collect patient 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 if 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 based on the patient's health condition.

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

[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 adjust the frequency of notifications according to the patient's emotions, making it possible to provide more appropriate information.

[0093] The collection unit can analyze the content of a patient's social media posts and optimize the schedule of medical examinations and tests based on the results. For example, the collection unit can analyze health-related posts from a patient's social media activity and adjust the timing of medical examinations and tests based on the results. The collection unit can also suggest medical examinations and tests 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 more appropriate schedules by taking the patient's social media activity into consideration.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The collection unit collects the history of past patient examinations and consultations. Specifically, it collects data such as the start time of the consultation, the end time of the consultation, and the duration of the examination. It can also collect data such as the patient's age, gender, examination details, and test details. 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 required time 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 department notifies patients based on the schedule designed by the design department. 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 predicting waiting times and preparing for their consultation or examination.

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

[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 engine).<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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats including 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. The AIs other than the generation AI are, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but are not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or in whole by AI, but are not limited to these 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 processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[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 historical times of patient examinations and consultations using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 via the control unit 46A. The design unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI designs a daily schedule for the hospital based on the collected data. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and notifies patients of the exact timing for their consultations and examinations based on the designed schedule. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

[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 a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[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 carried out in a secure state.

[0107] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[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. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0115] 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 glasses 214 and the data processing device 12. For example, the collection unit collects the historical times of patient examinations and consultations using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 via the control unit 46A. The design unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI designs a daily schedule for the hospital 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 for consultations and examinations 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 modifications 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 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[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 carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[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. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] 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 headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the historical times of patient examinations and consultations using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the collected data to the data processing device 12 via the control unit 46A. The design unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI designs a daily schedule for the hospital 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 patients of the exact timing for their consultations and examinations based on the designed schedule. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[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 carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] 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 robot 414 and the data processing device 12. For example, the collection unit collects the historical times of patient examinations and consultations using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 via the control unit 46A. The design unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI designs a daily schedule for the hospital based on the collected data. The notification unit is realized, for example, by the control unit 46A of the robot 414, and notifies patients of the exact timing for their consultations and examinations based on the designed schedule. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[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 USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[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 Collect data on the patient's age, gender, medical examination details, and test details 2. The system of claim 1. (Appendix 3) The design unit Use algorithms that use past data to predict the duration of consultations and tests, and then design schedules based on those predictions. 2. The system of claim 1. (Appendix 4) The notification unit Notification will be sent via a smartphone app, and the timing and content of the notification will also be mentioned. 2. The system of claim 1. (Appendix 5) The notification unit Providing patients with information about waiting times or preparation for appointments and tests 2. The system of claim 1. (Appendix 6) The collecting unit Estimate the patient's emotions and adjust the timing of data collection based on the estimated patient emotions 2. The system of claim 1. (Appendix 7) The collecting unit Analyze the patient's past medical history and select the most appropriate data collection method 2. The system of claim 1. (Appendix 8) The collecting unit Filtering data based on the patient's current health and living situation 2. The system of claim 1. (Appendix 9) The collecting unit Estimate patient emotions and prioritize data collection based on the estimated emotions 2. The system of claim 1. (Appendix 10) The collecting unit Prioritize the collection of relevant data based on the patient's geographic location during data collection 2. The system of claim 1. (Appendix 11) The collecting unit During data collection, analyze patients' social media posts and collect relevant data. 2. The system of claim 1. (Appendix 12) The design unit Estimate patient emotions and adjust the scheduling algorithm 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 the consultations and tests. 2. The system of claim 1. (Appendix 14) The design unit Apply different design algorithms to different categories of consultations and tests 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 appointments and tests based on their relevance. 2. The system of claim 1. (Appendix 18) The notification unit Inferring patient emotion and adjusting notification language based on the inferred emotion 2. The system of claim 1. (Appendix 19) The notification unit Adjust notification detail based on the importance of the consultation or test 2. The system of claim 1. (Appendix 20) The notification unit At the time of notification, different notification algorithms are applied depending on the category of visit or test. 2. The system of claim 1. (Appendix 21) The notification unit 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 When notifying, prioritize notifications based on when consultations or tests were submitted 2. The system of claim 1. (Appendix 23) The notification unit At notification time, adjust notification order based on 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 computer-implemented method for implementing a computer-implemented system, comprising: a processor; and a memory for storing a program executed by the processor; The processor executes the program, a collection unit that collects the history of the patient's past examinations and consultations and estimates the patient's emotions; a design unit that uses the data collected by the collection unit to learn a model that predicts the time required for consultations or examinations for each patient, and designs a daily schedule for the hospital by adjusting the order of consultations or examinations based on the time required predicted using the model and the emotions estimated by the collection unit; and a notification unit that adjusts a notification expression method in accordance with the emotion estimated by the collection unit based on the schedule designed by the design unit and notifies the patient. A system characterized by:

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

3. The notification unit If the emotion estimated by the collecting unit indicates that the patient is in a stressed state, the notification is given in a calm manner.

2. The system of claim 1.

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

2. The system of claim 1.

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

6. The collecting unit Estimate the patient's emotions and adjust the timing of data collection based on the estimated patient emotions 2. The system of claim 1.

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

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

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