System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting medical information from a user; means for obtaining relevant information from a medical database by a server; means for optimizing the obtained relevant information by a AI tool and suggesting the relevant information to the user by the server; means for confirming and confirming the suggested information by the user; and means for recording the confirmed information in the medical database by the server.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] As the super-aging society progresses, the shortage of medical staff is becoming even more serious. In particular, medical administrative tasks increase the burden on doctors and nurses, reducing the efficiency of medical services. In response to this, there is a need to introduce technology to support administrative tasks that do not require specialized knowledge in order to improve efficiency and alleviate the staff shortage. However, with existing systems, the standards of individual manufacturers vary, making it difficult to provide consistent support for work. Furthermore, the lack of a system that can provide information quickly and accurately means that the workload of medical staff remains a significant burden. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. First, a means is provided for users to input medical data, which is received by a server. Next, the server obtains related information such as the patient's medical history, doctor's schedule, and in-hospital resource information from a medical database based on the received medical data. The obtained related information is optimized using an AI tool and proposed to the user. The user checks the proposed information and confirms the optimal appointment. The confirmed information is recorded in the medical database by the server and notified to the doctor and patient as necessary. This reduces the burden on medical administration and creates an environment in which medical professionals can focus on their core duties.

[0006] A "user" is a person who performs medical administrative support work and is responsible for inputting medical data.

[0007] "Server" means a computer system that analyzes medical data received from users and retrieves, processes, and records relevant information from medical databases.

[0008] "Terminal" means a device that allows a user to input medical data, display information from the server, and confirm or select it.

[0009] "Medical data" refers to data related to medical care, such as basic information about the patient, desired examination date, and examination details.

[0010] A "medical database" is a system that stores various medical data, such as patient medical history, doctor schedules, and hospital resource information.

[0011] An "AI tool" is an artificial intelligence application program that optimizes relevant information obtained from medical databases and suggests it to users.

[0012] A "request packet" is a data format for transmitting medical data input by a user to a terminal to a server.

[0013] A "response packet" is a data format used by the server to send relevant information optimized by AI tools to the terminal.

[0014] "Suggestions" are information optimized by AI tools and provided to users as candidates for the user to review and select.

[0015] "Confirmation" refers to the action of the user making the optimal selection from the suggested information and completing the reservation or procedure. [Brief explanation of the drawings]

[0016] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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, a 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), and an APU (Accelerated Processing Unit).

[0020] 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.

[0021] 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.

[0022] 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), Bluetooth (registered trademark), etc.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] 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.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention is a system for supporting medical administrative support work, and operates through the interaction of users (medical administrative assistants), servers, and terminals. The basic configuration of the system and the roles of each component are described below.

[0038] Program Description

[0039] User-initiated medical data entry

[0040] The user is a medical administrative assistant. He logs in to a terminal and accesses the appointment management interface. Through this interface, the user inputs medical data such as the patient's basic information, desired appointment date, and details of the appointment based on a request from the doctor. The terminal compiles the input data into a form and generates a request packet to send to the server.

[0041] Server receives request and retrieves information

[0042] The server analyzes the request packet received from the terminal and extracts the necessary patient information and examination details.The server then retrieves the patient's past medical history, the current doctor's schedule, and hospital resource information (such as the availability of clinical tests and examination rooms) from the medical database.

[0043] Optimization process using AI tools

[0044] The server passes the acquired data to an AI tool that optimizes appointment scheduling, taking into account the following factors:

[0045] Important patient medical history

[0046] Doctor's schedule availability

[0047] Availability of examination rooms and necessary equipment

[0048] The AI ​​tool takes these conditions into account and generates the most efficient reservation options, which are returned to the server.

[0049] Server proposal and data transmission

[0050] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. The converted reservation candidates are stored in a response packet and sent to the device.

[0051] Display and confirm information on the terminal

[0052] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user. The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[0053] Server confirmation and recording

[0054] The server records the confirmed appointment information received from the terminal in the medical database, confirms that the appointment has been officially registered, and notifies the doctor and patient of the confirmed information as necessary.

[0055] Specific examples

[0056] Scenario: Managing patient appointments

[0057] 1. The user logs in and enters the necessary information into the appointment management interface on the terminal: patient name, ID, desired appointment date, appointment details, etc.

[0058] 2. The terminal generates and sends a request packet to send the input data to the server.

[0059] 3. The server receives the request and retrieves the patient's medical history, doctor schedule, and resource information from the medical database.

[0060] 4. The server passes the acquired information to the AI ​​tool, which then generates the best reservation candidates.

[0061] 5. The server organizes the reservation candidates received from the AI ​​tool and sends them to the device.

[0062] 6. The terminal displays reservation options to the user, and the user selects and confirms the most suitable reservation.

[0063] 7. The server records the confirmed information in the medical database and notifies the doctor and patient.

[0064] By combining this program with processing steps, a system can be created that streamlines the work of medical administrative assistants and reduces the burden on medical professionals.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The user (medical administrative assistant) logs in to the terminal and accesses the appointment management interface.

[0068] Step 2:

[0069] Based on a request from a doctor, the user enters medical data such as the patient's name, ID, desired date of examination, and details of the examination.

[0070] Step 3:

[0071] The terminal compiles the input data into a form and generates a request packet to be sent to the server.

[0072] Step 4:

[0073] The server analyzes the request packet received from the terminal and extracts the necessary patient information and examination details data.

[0074] Step 5:

[0075] The server retrieves the patient's past medical history, doctor's schedule, and hospital resource information (such as availability of clinical tests and examination rooms) from the medical database.

[0076] Step 6:

[0077] The server then passes the acquired data to an AI tool that optimizes appointment scheduling, taking into account the patient's important medical history, the doctor's available schedule, and the availability of examination rooms and necessary equipment.

[0078] Step 7:

[0079] The AI ​​tool generates optimal reservation options and returns them to the server.

[0080] Step 8:

[0081] The server organizes the reservation suggestions received from the AI ​​tool and converts them into a format that is easy for users to understand.

[0082] Step 9:

[0083] The server stores the converted reservation candidates in a response packet and transmits it to the terminal.

[0084] Step 10:

[0085] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user.

[0086] Step 11:

[0087] The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[0088] Step 12:

[0089] The terminal generates a request packet to transmit the user's final selection back to the server.

[0090] Step 13:

[0091] The server records the confirmed reservation information received from the terminal in the medical database and confirms that the reservation has been officially registered.

[0092] Step 14:

[0093] The server confirms that the reservation information has been registered and notifies the terminal of that information.

[0094] Step 15:

[0095] The terminal receives the notification from the server and displays to the user that the reservation has been confirmed.

[0096] Step 16:

[0097] If necessary, the server automatically sends a notification email to the doctor and patient confirming the appointment.

[0098] Example 1

[0099] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0100] In recent years, there has been a demand for greater efficiency and accuracy in medical administration, but manual medical data entry and appointment management is time-consuming and prone to errors. Furthermore, coordinating doctor schedules and managing examination room resources is complicated, making it difficult to secure optimal appointment times. This results in longer waiting times for patients and an increased burden on medical professionals.

[0101] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0102] In this invention, the server includes a means for a user to log in and input medical data, a means for a terminal to compile the medical data, generate a request, and send it to the server, a means for the server to receive and analyze the medical data and acquire related information from a medical database, a means for the server to pass the acquired related information to an AI tool for optimization and suggest candidate appointments to the user, a means for the user to select and confirm the optimal appointment from the suggested candidates, and a means for the server to record the confirmed information in the medical database and notify the user as necessary. This enables accurate and efficient management of medical data and securing optimal appointment times.

[0103] "User" is a medical administrative assistant who inputs medical data and operates the system.

[0104] A "terminal" is an information processing device used by a user, and is a device that has the function of sending input medical data collectively to a server.

[0105] A "server" is an information processing device that receives and analyzes medical data sent by users, and is responsible for obtaining the necessary information from the medical database and passing it on to the AI ​​tool.

[0106] "Medical data" refers to data that includes information necessary for scheduling an appointment, such as the patient's name, ID, desired date of appointment, and details of the appointment.

[0107] A "request packet" is a data packet created by a terminal to collect medical data input by a user and send it to a server.

[0108] A "medical database" is a database for storing and managing medical information such as patient medical history, doctor schedules, and the availability of examination rooms and necessary equipment.

[0109] The "AI tool" is software that uses artificial intelligence to generate optimal appointment options based on medical data obtained from the server.

[0110] "Suggested appointments" are suggestions for optimal dates and times for patient appointments generated by AI tools.

[0111] A "response packet" is a packet of data created by the server to organize the reservation candidates received from the AI ​​tool and send them to the terminal.

[0112] "Notification" refers to the transmission of information by the server to inform the doctor and patient that a reservation has been confirmed.

[0113] The present invention is a system for supporting medical administrative support work, and is a system that operates through interactions between a user (medical administrative assistant), a server, and a terminal. The following describes in detail the mode for carrying out the present invention.

[0114] First, the user logs in to a terminal. The terminal is an information processing device such as a PC or tablet, and must be connected to the Internet. Once the user logs in, they can access the appointment management interface. This interface consists of a form for entering basic patient information (name, ID), desired appointment date, details of the appointment, etc.

[0115] Next, the device compiles the medical data entered by the user into a request packet. The request packet is created in a data structure such as JSON format and sent to the server using a protocol such as an HTTP POST request. The server analyzes the received request packet and extracts patient information and examination details.

[0116] Based on this data, the server retrieves the necessary information from the medical database, which contains the patient's past medical history, current doctor schedules, and hospital resource information (such as the availability of examination rooms and necessary equipment).

[0117] The server passes the acquired data to the AI ​​tool, which uses an artificial intelligence algorithm to generate the optimal reservation date and time, taking into account the following factors:

[0118] Important patient medical history

[0119] Doctor's schedule availability

[0120] Availability of examination rooms and necessary equipment

[0121] The reservation suggestions generated by the AI ​​tool are returned to the server, which organizes them and converts them into a format that is easy for the user to understand. The organized reservation suggestions are stored in a response packet and sent to the device.

[0122] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user. The user selects the most suitable date and time from the displayed reservation candidates and clicks the Confirm button. The confirmed reservation information is then sent back to the server.

[0123] The server records the received confirmed reservation information in the medical database, thereby confirming that the reservation has been officially registered. The server also notifies the doctor and patient of the confirmed reservation information as necessary.

[0124] Specific examples

[0125] For example, when making an appointment for a patient to visit the hospital, the user inputs the patient's name, ID, desired date of the appointment, and the details of the appointment into the terminal. After that, the system goes through the above process and proposes the optimal appointment date and time, which the user confirms.

[0126] Prompt Sentence Examples

[0127] An example of a prompt to explain the system overview to a generative AI model is:

[0128] It would be something like, "Please explain an AI system that optimizes medical appointments."

[0129] This system will streamline the work of medical administrative assistants and reduce the burden on medical professionals.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Program processing steps

[0132] Step 1: User enters medical data

[0133] Specific description:

[0134] The user logs in to the terminal and accesses the appointment management interface, which displays fields for entering the patient's basic information (name, ID), the desired appointment date, and the details of the appointment.

[0135] Input: Basic patient information, desired appointment date, and appointment details entered by the user.

[0136] Specific behavior:

[0137] The user logs in by entering a user name and password into the terminal.

[0138] After logging in, the appointment management interface will be displayed.

[0139] The user enters data into fields such as "Name," "Patient ID," "Desired appointment date," and "Expectation details."

[0140] After completing the input, the user clicks the "Submit" button.

[0141] Step 2: Terminal sends data and generates request packet

[0142] Specific description:

[0143] The device collects the medical data entered by the user and assembles it into a request packet, which is then sent to the server as an HTTP POST request.

[0144] Input: Medical data entered by the user.

[0145] Output: The request packet sent to the server.

[0146] Specific behavior:

[0147] The terminal converts the input data into JSON format.

[0148] The converted data is sent to the server as an HTTP POST request.

[0149] When the sending process is complete, the terminal will display a "Sending Complete" message.

[0150] Step 3: The server receives the request and retrieves information from the medical database.

[0151] Specific description:

[0152] The server receives the request packet sent from the device, analyzes it, and extracts the necessary patient information and examination details. After extraction, it executes a query to obtain past medical history, current doctor schedules, and resource information from the medical database.

[0153] Input: The request packet received from the device.

[0154] Output: Parsed patient information and consultation details, plus additional information retrieved from medical databases.

[0155] Specific behavior:

[0156] The server receives the HTTP request and parses the JSON data to extract patient information and medical details.

[0157] The server generates an SQL query and sends it to the database.

[0158] Retrieve patient history, physician schedule, and resource information from the database.

[0159] Save the retrieved data in a temporary data store.

[0160] Step 4: Providing data from the server to the AI ​​tool and optimizing it

[0161] Specific description:

[0162] The server passes the acquired data to an AI tool, which then uses this data to generate optimal appointment candidates, taking into account past medical history, doctor availability, and examination room usage.

[0163] Input: Various information obtained from medical databases.

[0164] Output: Optimized booking candidates.

[0165] Specific behavior:

[0166] The server sends the acquired data to the AI ​​tool as an API request.

[0167] AI tools perform the calculations and generate the best booking options.

[0168] The reservation suggestions are sent back to the server.

[0169] Step 5: Data sorting by the server and sending to the device

[0170] Specific description:

[0171] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. The converted data is stored in a response packet and sent to the device as an HTTP response.

[0172] Input: Booking suggestions received from the AI ​​tool.

[0173] Output: Response packet to send to the device.

[0174] Specific behavior:

[0175] The server converts the received data into HTML or JSON format and includes it in the response.

[0176] Sends an HTTP response to the device.

[0177] Step 6: Terminal displays information and user selection

[0178] Specific description:

[0179] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user. The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[0180] Input: The response packet received from the server.

[0181] Output: Reservation information selected and confirmed by the user.

[0182] Specific behavior:

[0183] The terminal analyzes the response data and displays a list of reservation options on the screen.

[0184] The user selects the best reservation option from the list.

[0185] The user clicks the "Confirm Reservation" button.

[0186] Step 7: Server processes and notifies the reservation

[0187] Specific description:

[0188] The server records the confirmed reservation information received from the terminal in the medical database. This officially registers the reservation. The server also notifies the doctor and patient of the confirmed information as necessary.

[0189] Input: Reservation information confirmed by the user.

[0190] Output: Appointment information recorded in the medical database, and notifications to the doctor and patient.

[0191] Specific behavior:

[0192] The server receives the confirmed reservation information and sends an INSERT query to the database.

[0193] Verify that the reservation information was successfully recorded in the database.

[0194] The server sends a confirmation of the appointment to the doctor and patient via email or SMS.

[0195] (Application example 1)

[0196] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0197] In conventional systems, medical administrative support tasks and factory robot maintenance schedule management were performed manually, resulting in problems such as human error and time loss. Furthermore, it was difficult to optimize the schedule, resulting in reduced work efficiency and wasted resources. For these reasons, there was a demand for a system that could achieve highly accurate and efficient reservation management and maintenance schedule management.

[0198] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0199] In this invention, the server includes means for inputting data from a user, means for the server to receive the data and acquire related information from a database, means for the server to optimize the acquired related information using an AI tool and propose it to the user, means for the user to confirm and confirm the proposed information, means for the server to record the confirmed information in the database, and means for the server to perform optimization using a generative AI model, thereby enabling users to manage their reservations and schedules with high accuracy and efficiency.

[0200] A "user" is an entity that uses the system to input and manage data.

[0201] "Data" is a general term for information that users input into the system, including medical information, maintenance information, and the like.

[0202] A "server" is a device or system that receives data, retrieves relevant information, performs optimization processing, and provides the results to the user.

[0203] A "database" is a collection of information in which various types of information are systematically organized and stored, including medical information and machine operation history.

[0204] "Related information" refers to additional information that the server obtains based on the data entered by the user, and includes medical history, operation history, schedule, and resource information.

[0205] "AI tools" refers to artificial intelligence technology that performs optimization processing using related information acquired by the server.

[0206] "Optimization" is the process of deriving the most efficient and appropriate results based on relevant information, based on the user's requests and conditions.

[0207] A "generative AI model" is an artificial intelligence algorithm that generates useful information and predictions from data.

[0208] "Proposing" refers to the act of the server informing the user of the optimized results and prompting them to make a selection or confirm.

[0209] "Confirming" refers to the act of the user confirming the proposed information and making a final decision.

[0210] "Recording" refers to the act of the server storing the determined information in a database for future reference if necessary.

[0211] The present invention relates to a system for improving the efficiency of maintenance schedule management for factory robots. The specific system configuration and program processing will be described below.

[0212] Program Description

[0213] User data entry

[0214] Users use a smartphone app to enter maintenance information for factory robots, specifically, data such as the robot ID, desired maintenance date, and work content. The entered data is sent from the smartphone to the server.

[0215] Server receives request and retrieves information

[0216] The server receives the data sent by the user and retrieves the robot's operation history and past maintenance history from the database, as well as the current robot schedule and resource information.

[0217] Optimization process using AI tools

[0218] The server passes the acquired information to a generative AI model to generate an optimal maintenance schedule. The AI ​​tool considers the robot's important operating history, operating schedule, resource utilization, and other factors to create efficient maintenance candidates.

[0219] Server proposal and data transmission

[0220] The server organizes the maintenance suggestions received from the AI ​​tool and converts them into a format that is easy for users to understand. The converted maintenance suggestions are then sent to a smartphone app and displayed to the user.

[0221] User displays and confirms information

[0222] The user can check the proposed maintenance schedule through a smartphone app, select the optimal date and time, and confirm the schedule. The confirmed information is then sent from the smartphone to the server.

[0223] Server confirmation and recording

[0224] The server records the confirmed maintenance information received from the user in a database, and if necessary, notifies the maintenance personnel or relevant departments by email.

[0225] Hardware and Software Configuration

[0226] Hardware: Smartphones, servers

[0227] Software: Java programs, javax.mail library, database management systems (e.g., MySQL), AI tools (e.g., TensorFlow)

[0228] Specific examples

[0229] For example, consider a factory worker entering maintenance information for robot ID "R12345." The worker uses a smartphone app to enter the following:

[0230] Robot ID: R12345

[0231] Desired maintenance date: 2023-10-01

[0232] Work: Parts replacement

[0233] The server then receives this information, retrieves the robot's operating history and past maintenance history from a database, and uses AI tools to generate an optimal maintenance schedule. This optimal schedule is displayed on a smartphone app, and employees can select the most suitable date and time to confirm it. The confirmed information is recorded on the server and, if necessary, notified to relevant departments via email.

[0234] Prompt Sentence Examples

[0235] Please enter the maintenance information for robot ID "R12345". Example: Maintenance date "2023-10-01", work content "Part replacement".

[0236] The optimal maintenance schedule is displayed.

[0237] Your selected maintenance schedule has been confirmed and notifications have been sent.

[0238] In this way, users can manage the maintenance schedule of factory robots with high accuracy and efficiency.

[0239] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0240] Step 1:

[0241] The user uses a smartphone app to input maintenance information for the factory robot. Specific input information includes the robot ID, desired maintenance date, and work details. The smartphone app compiles this data into packets and sends them to the server.

[0242] Input: Robot ID, desired maintenance date, work content

[0243] Output: Request packet to the server

[0244] Step 2:

[0245] The server receives the request packet sent by the user. The server analyzes the packet and extracts data such as the robot ID, desired maintenance date, and work content. The server then retrieves the robot's operation history and past maintenance history from the database.

[0246] Input: Request packet

[0247] Output: Operation history, past maintenance history, resource information

[0248] Step 3:

[0249] The server passes the information obtained from the database to a generative AI model to generate an optimal maintenance schedule. The generative AI model considers the robot's important operating history, operating schedule, resource usage status, and other factors to create efficient maintenance candidates.

[0250] Input: Operation history, past maintenance history, resource information

[0251] Output: Optimal maintenance schedule candidates

[0252] Step 4:

[0253] The server organizes the maintenance schedule candidates obtained from the generative AI model and converts them into a format that is easy for users to understand. The converted maintenance candidates are packaged into a response packet and sent to the smartphone app.

[0254] Input: Optimal maintenance schedule candidates

[0255] Output: User-visible response packet

[0256] Step 5:

[0257] The user checks the proposed maintenance schedule through the smartphone app, selects the most suitable date and time from the displayed maintenance schedule candidates, and clicks the confirm button. The selected data is sent from the smartphone app to the server.

[0258] Input: Maintenance schedule candidate

[0259] Output: Confirmed maintenance schedule

[0260] Step 6:

[0261] The server records the confirmed maintenance information received from the user in a database, and if necessary, sends an email notification to the maintenance staff or related departments.

[0262] Input: Confirmed maintenance schedule

[0263] Output: Database records, email notifications

[0264] In this way, we will build a system in which the data entered at each step is appropriately processed and calculated, and the necessary data is output for the next step, and ultimately notified to the user and related departments.

[0265] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0266] This invention is a system that operates through the interaction of a user (medical administrative assistant), a server, a terminal, and an emotion engine to support medical administrative support tasks. This system makes suggestions that take into account the user's emotions, thereby achieving more flexible and efficient business support than conventional systems. The system's programs and processing are described in detail below.

[0267] Program Description

[0268] User-initiated medical data entry and emotion recognition

[0269] The user is a medical administrative assistant. He logs in to the terminal and accesses the appointment management interface. Through the interface, the user inputs medical data such as the patient's basic information, desired appointment date, and details of the appointment based on a request from the doctor. The terminal compiles the input data into a form, and an emotion engine analyzes the user's input content, input speed, and facial expressions (if equipped with a camera) to recognize the user's emotions. The terminal generates a request packet containing the medical data and emotion data and sends it to the server.

[0270] Server receives request and retrieves information

[0271] The server analyzes the request packet received from the terminal and extracts the necessary patient information and examination details.The server then retrieves the patient's past medical history, doctor schedules, and hospital resource information (such as clinical testing and examination room availability) from the medical database.

[0272] Optimization processing using AI tools and use of emotional data

[0273] The server then passes the acquired data and sentiment data to an AI tool to optimize appointment scheduling, taking into account the following factors:

[0274] Important patient medical history

[0275] Doctor's schedule availability

[0276] Availability of examination rooms and necessary equipment

[0277] The user's emotional state (stress, anxiety, satisfaction, etc.)

[0278] Taking these conditions into consideration, the AI ​​tool generates the most efficient and least stressful reservation options for the user and returns them to the server.

[0279] Server proposal and data transmission

[0280] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. The converted reservation candidates are stored in a response packet and sent to the device.

[0281] Displaying information and reflecting emotions on devices

[0282] The terminal analyzes the response packet received from the server and displays suggested reservation options to the user. Priorities and explanations based on the user's feelings are added, allowing the user to easily review the suggested options. The user selects the most suitable date and time from the displayed reservation options and clicks the confirm button.

[0283] Server confirmation and recording

[0284] The server records the confirmed reservation information received from the device in the medical database, confirming that the reservation has been officially registered, and also records the emotion data recognized by the emotion engine for use in improving the accuracy of future suggestions.

[0285] Specific examples

[0286] Scenario: Managing patient appointments with emotions in mind

[0287] 1. The user logs in and enters the necessary information into the appointment management interface on their device: patient name, ID, desired appointment date, appointment details, etc. The emotion engine also recognizes the user's emotions from their facial expressions and input speed.

[0288] 2. The terminal generates and sends a request packet to send the input data and emotion data to the server.

[0289] 3. The server receives the request and retrieves the patient's medical history, doctor schedule, and resource information from the medical database.

[0290] 4. The server passes the acquired information and emotional data to the AI ​​tool, which then generates the best reservation candidates.

[0291] 5. The server organizes the reservation suggestions received from the AI ​​tool and sends them to the device. The suggestions are displayed in a way that takes the user's emotions into consideration.

[0292] 6. The terminal displays the proposed reservation options to the user, and the user selects and confirms the most suitable reservation.

[0293] 7. The server records the confirmed information in a medical database, notifies the doctor and patient, and records emotional data for future improvements to the entire system.

[0294] By combining this program with processing steps, a system is realized that not only streamlines the work of medical administrative assistants and reduces the burden on medical professionals, but also provides flexible support that takes the user's emotions into consideration.

[0295] The processing flow will be explained below.

[0296] Step 1:

[0297] The user (medical administrative assistant) logs in to the terminal and accesses the appointment management interface.

[0298] Step 2:

[0299] Based on a request from a doctor, the user enters medical data such as the patient's name, ID, desired date of examination, and details of the examination.

[0300] Step 3:

[0301] The device compiles the entered data into a form, and an emotion engine analyzes the input content, input speed, and in some cases the user's facial expressions to recognize the user's emotions.

[0302] Step 4:

[0303] The terminal generates a request packet including medical data and emotion data and transmits it to the server.

[0304] Step 5:

[0305] The server analyzes the request packet received from the terminal and extracts patient information and examination details.

[0306] Step 6:

[0307] The server retrieves patient past medical history, doctor schedules, and hospital resource information from a medical database.

[0308] Step 7:

[0309] The server then passes the acquired data and emotional data to an AI tool that optimizes appointments, taking into account the patient's important medical history, the doctor's available schedule, the availability of examination rooms and necessary equipment, and the user's emotional state.

[0310] Step 8:

[0311] The AI ​​tool generates optimal reservation options and returns them to the server.

[0312] Step 9:

[0313] The server organizes the reservation suggestions received from the AI ​​tool and converts them into a format that is easy for users to understand.

[0314] Step 10:

[0315] The server stores the converted reservation candidates in a response packet and transmits it to the terminal.

[0316] Step 11:

[0317] The terminal analyzes the response packet received from the server and displays a list of reservation options to the user. At this time, the suggestions are displayed in a way that takes the user's emotions into consideration.

[0318] Step 12:

[0319] The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[0320] Step 13:

[0321] The terminal generates a request packet to transmit the user's final selection back to the server.

[0322] Step 14:

[0323] The server records the confirmed reservation information received from the terminal in the medical database and confirms that the reservation has been officially registered.

[0324] Step 15:

[0325] The server confirms that the reservation information has been registered and notifies the terminal of that information.

[0326] Step 16:

[0327] The terminal receives the notification from the server and displays to the user that the reservation has been confirmed.

[0328] Step 17:

[0329] If necessary, the server automatically sends a notification email to the doctor and patient confirming the appointment.

[0330] Step 18:

[0331] The server records the emotion data recognized by the emotion engine and uses it to improve the accuracy of future suggestions.

[0332] Example 2

[0333] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0334] Conventional medical administrative support systems do not take user emotions into consideration when entering data or managing appointments, which reduces work efficiency and the user experience. They also lack the flexibility to integrate and optimize a wide range of information, such as medical data, patient medical history, and doctor schedules. There is a need to solve these problems, reduce user stress and anxiety, and provide more efficient and user-friendly work support.

[0335] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting medical data from a user, means for the server to receive the medical data and the user's emotional data and acquire related information from a medical database, means for the server to optimize the acquired related information and emotional data using an AI tool and propose it to the user, means for the user to confirm and confirm the proposed information, and means for the server to record the confirmed information in the medical database and record the emotional data. This enables flexible and efficient business support that takes user emotions into consideration.

[0336] "User" refers to the medical administrative assistant who operates the system and inputs medical data.

[0337] "Medical data" refers to information necessary for medical administrative support work, such as basic patient information, desired appointment date, and details of the appointment.

[0338] "Emotional data" refers to information about the user's psychological state analyzed from their input speed, facial expressions, etc.

[0339] "Server" refers to a central processing unit for receiving medical data and emotion data sent from terminals, obtaining related information, and optimizing it.

[0340] A "medical database" refers to data storage that stores medical-related information such as patient medical history, doctor schedules, and hospital resource information.

[0341] "Relevant information" refers to the patient's medical history, doctor's schedule, and hospital resource information.

[0342] "AI tools" refers to software and algorithms with artificial intelligence technology used on servers.

[0343] "Optimization" refers to the process of generating optimal appointment candidates based on the relevant information and sentiment data obtained.

[0344] "Suggestion" refers to presenting optimized information to the user using AI tools.

[0345] "Confirmation" refers to the act of the user selecting the most suitable appointment from the proposed candidates and making a final decision.

[0346] "Recording" refers to the act of storing established information in a medical database.

[0347] The present invention is a flexible and efficient business support system that optimizes medical administrative support tasks and takes into account the user's emotions. The system operates through the interaction of the user, server, terminal, and emotion engine.

[0348] System configuration and program description

[0349] User data entry and emotion recognition

[0350] The user logs in to the terminal as a medical administrative assistant and accesses the appointment management interface. The user enters basic patient information (name, ID), desired appointment date, details of the appointment, etc. through the terminal. At this time, the terminal compiles the entered data into an appropriate format.

[0351] Furthermore, the device uses an emotion engine to analyze the user's input speed and facial expressions. For example, if the device is equipped with a camera, it can capture and analyze the user's facial expressions to determine stress or anxiety levels. The emotion engine indicates that the user may be feeling stressed even if the input speed is slow. The emotion data and medical data generated in this way are sent to the server.

[0352] Server receives request and retrieves information

[0353] The server analyzes the medical and emotional data received from the device and extracts the necessary patient information and examination details.The server then accesses a medical database (e.g., MySQL) to obtain the patient's past medical history, doctor schedules, and hospital resource information.

[0354] Optimization with AI tools and use of sentiment data

[0355] The server passes the acquired data and emotional data to an AI tool (e.g., TensorFlow) to optimize appointment scheduling. The AI ​​tool generates optimal appointment candidates by taking into account the patient's past medical history, doctor's schedule, availability of examination rooms and necessary equipment, and the user's emotional state (stress, anxiety, satisfaction). The results are returned to the server.

[0356] Proposal and confirmation

[0357] The server organizes the reservation candidates received from the AI ​​tool, converts them into a format that is easy for the user to understand, and sends them to the device. The device displays the received reservation candidates on its interface, and the user selects the best date and time from the displayed reservation candidates. When the user clicks the confirm button, the confirmation information is sent back to the server.

[0358] Finalization and recording

[0359] The server records the confirmation information received from the device in the medical database and confirms that the appointment has been officially registered. The server also records the emotion data from the emotion engine and uses it to improve the accuracy of future recommendations. The server also notifies the doctor and patient that the appointment has been confirmed.

[0360] Specific examples

[0361] For example, suppose a user logs in to a terminal and inputs the desired consultation date for patient "Yamada Taro" as "2023 / 10 / 15" and the consultation content as "consultation for lower back pain." The prompt text in this case is as follows:

[0362] ---

[0363] Patient demographics:

[0364] Name: Taro Yamada

[0365] ID:12345

[0366] Desired appointment date: 2023 / 10 / 15

[0367] Examination content: Examination of lower back pain

[0368] ---

[0369] This system will streamline the work of medical administrative assistants and utilize structured medical and emotional data to make suggestions that reduce users' stress and anxiety. Theoretically, it is expected to create optimal medical appointments for both patients and medical professionals.

[0370] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0371] Step 1: User data entry and emotion recognition

[0372] A user logs in to a terminal as a medical administrative assistant and accesses the appointment management interface. The user enters the patient's basic information (name, ID), desired appointment date, and details of the appointment. At this time, the terminal organizes the entered data into an appropriate format. The terminal then uses an emotion engine to analyze the user's input speed and facial expressions. Specifically, the terminal's camera captures the user's facial expressions and analyzes them in real time. For example, if the input speed is slow, it is determined that the user is "feeling stressed." The medical data and emotional data generated in this way are sent to the server as a request packet.

[0373] Input: Patient's basic information, desired consultation date, consultation details

[0374] Data processing: Formatting input data, generating emotional data (input speed and facial expression analysis)

[0375] Output: A request packet containing medical and emotional data.

[0376] Step 2: Server receives request and retrieves information

[0377] The server receives the request packet from the terminal. It analyzes the received packet and extracts the patient information and consultation details. The server then accesses a medical database (e.g., MySQL) to retrieve the patient's past medical history, doctor schedule, and hospital resource information. This provides all the data necessary to process the appointment.

[0378] Input: A request packet containing medical and emotional data

[0379] Data processing: Analyzing request packets and extracting patient information and medical examination details

[0380] Output: Patient's medical history, doctor's schedule, hospital resource information

[0381] Step 3: Optimizing with AI tools and using sentiment data

[0382] The server passes the acquired data and emotional data to an AI tool (e.g., TensorFlow) to perform optimization analysis for appointment scheduling. The AI ​​tool generates optimal appointment candidates based on the patient's past medical history, doctor schedules, hospital resource information, and the user's emotional state (stress, anxiety, satisfaction). For example, it also takes into account cases where a specific examination room is required based on past medical history.

[0383] Input: Patient's medical history, doctor's schedule, hospital resource information, emotional data

[0384] Data processing: Data analysis and optimization using AI tools

[0385] Output: Best booking candidates

[0386] Step 4: Server Proposal and Data Transmission

[0387] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. For example, it summarizes the date, time, and location of each reservation candidate. The converted reservation candidates are then stored in a response packet and sent to the terminal.

[0388] Input: Best booking candidate

[0389] Data processing: Formatting reservation candidates and converting them into response packets

[0390] Output: Response packet

[0391] Step 5: Terminal displays information and user selection

[0392] The terminal analyzes the response packet received from the server and extracts its contents. Next, it displays suggested reservation options on the interface. The user selects the most suitable date and time from the displayed reservation options and clicks the Confirm button. Based on the user's selection, the terminal resends a request packet containing the confirmation information to the server.

[0393] Input: Response packet

[0394] Data processing: Analyzing response packets and displaying reservation candidates

[0395] Output: Request packet containing confirmation information

[0396] Step 6: Server confirmation and recording

[0397] The server analyzes the confirmation information received from the device and records it in a medical database. It then confirms that the appointment has been officially registered and notifies the doctor and patient that the appointment has been confirmed. Emotional data is also recorded and will be used to improve the system in the future.

[0398] Input: Request packet containing confirmation information

[0399] Data processing: Analysis of confirmed information and recording in a database

[0400] Output: Notification to doctor and patient, recorded emotion data

[0401] This concretely shows the processing steps of the present system and clarifies the interactions between the user, server, and terminal.

[0402] (Application example 2)

[0403] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0404] In modern manufacturing, efficient schedule and resource management is extremely important. However, current systems do not take into account user emotions, which can create a stressful environment for workers. Furthermore, there is no established method for optimally managing work history and resource information, which can lead to problems such as reduced productivity and inefficient operations.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting business data from a user, means for the server to receive the business data and acquire related information from a database, and means for optimizing the acquired related information and emotion data from an emotion engine using an AI tool and proposing the optimization to the user. This enables optimal schedule and resource management that takes user emotions into consideration.

[0406] A "user" is a person who uses the system and performs operations to input business data.

[0407] "Business data" refers to data such as work details, schedules, and resource usage status that users input into the system.

[0408] A "server" is a device that receives business data from users, retrieves necessary information from a database, and optimizes it using AI tools.

[0409] A "database" is an information storage device that stores information related to business operations.

[0410] "Related information" refers to information necessary for business execution, such as work history, schedule, and resource information.

[0411] An "emotion engine" is a system that recognizes emotions from user input and facial expressions, and processes them as data.

[0412] "Emotion data" is data that indicates the emotional state of the user as recognized by the emotion engine.

[0413] An "AI tool" is software or hardware equipped with artificial intelligence algorithms for analyzing data and performing optimization processing.

[0414] "Optimization" is the process of using AI tools to generate the most efficient and least stressful schedule and resource allocation for users based on relevant information and emotional data.

[0415] "Proposing" means that the server presents the results of the optimization process to the user.

[0416] "Confirming" means that the user selects the most appropriate information from the proposed information and confirms its execution.

[0417] "Recording" refers to the process in which the server saves the confirmed information back into the database.

[0418] This invention is a system that inputs, optimizes, proposes, confirms, and records business data through interactions between users, servers, databases, emotion engines, and AI tools.

[0419] System configuration

[0420] 1. User operations

[0421] Users input business data using a smartphone or head-mounted display. The business data includes the work content, schedule, required resources, etc. The device equipped with an emotion engine acquires emotional data from the user's facial expressions and input speed.

[0422] 2. Receipt and processing of data

[0423] The device assembles task data and emotion data into packets and sends them to the server. The server receives these packets and retrieves related information (past work history, current resource status, schedule, etc.) from a database.

[0424] 3. Optimization process

[0425] The server passes relevant information and emotional data to the AI ​​tool, which then performs an optimization process. The AI ​​tool then uses this information to generate the most efficient and least stressful schedule for the user and returns it to the server. This optimization process uses programming languages ​​such as Python and machine learning algorithms.

[0426] 4. Suggestions and Selections

[0427] The server organizes the schedule candidates received from the AI ​​tool and presents them to the user. The user selects the best one from the presented schedule candidates and confirms it in the system. The information is displayed using a web interface or application.

[0428] 5. Records and Notifications

[0429] The server records the confirmed information in a database and notifies relevant departments as necessary. For example, a notification function is implemented to inform workers and managers of confirmed schedules.

[0430] The specific hardware and software used

[0431] Hardware

[0432] User devices: smartphone, head-mounted display, camera

[0433] Server: A high-performance server for data processing and optimization.

[0434] Database server: a data storage device for storing business-related information

[0435] software

[0436] Emotion Engine: Software for Recognizing User Emotions

[0437] AI Tools: Artificial intelligence algorithms that optimize data processing

[0438] Database management software: e.g., MySQL or PostgreSQL

[0439] Web interface or application: a platform through which users can enter data and view suggestions

[0440] Specific examples

[0441] Scenario: Optimizing factory production schedules

[0442] 1. The user inputs the work content and schedule using a smartphone or head-mounted display.

[0443] 2. The emotion engine analyzes the user's facial expressions and obtains emotional data.

[0444] 3. The device sends business data and emotion data to the server.

[0445] 4. The server retrieves the relevant information from the database and passes it to the AI ​​tool.

[0446] 5. The AI ​​tool generates an optimal schedule and returns it to the server.

[0447] 6. The server proposes a schedule to the user.

[0448] 7. User checks the schedule, selects the best one and confirms.

[0449] 8. The server records the confirmed information in the database and notifies the relevant departments.

[0450] Example prompts for generative AI models

[0451] "To optimize the production schedule in your factory, please create a schedule taking into account the following data:

[0452] Worker name and data

[0453] Specific work content and schedule

[0454] Emotional data (stress, anxiety, satisfaction, etc.)

[0455] Generate the optimal schedule and present it to you."

[0456] In this way, a system is realized that can reduce user stress and improve work efficiency by proposing an optimized production schedule based on emotional data.

[0457] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0458] Step 1:

[0459] The user inputs business data using a smartphone or head-mounted display. The input business data includes the work content, schedule, required resources, etc. The emotion engine then analyzes the user's facial expressions and input speed to obtain emotional data. Input: Business data, emotional data. Output: Input business data, obtained emotional data.

[0460] Step 2:

[0461] The device assembles task data and emotion data into packets and sends them to the server. This ensures that all important information is delivered to the server in a single request packet. Input: task data, emotion data. Output: request packet.

[0462] Step 3:

[0463] The server receives the request packet and analyzes it. It extracts business data and emotion data from the analyzed data and obtains related information (work history, resource information, schedule, etc.) from the database. Input: Request packet. Output: Related information, business data, emotion data.

[0464] Step 4:

[0465] The server passes the acquired related information and emotional data to the AI ​​tool, which then performs optimization processing. The AI ​​tool performs calculations using multidimensional arrays based on the related information and emotional data to generate the most efficient and least stressful schedule. Input: Related information, emotional data. Output: Optimal schedule candidates.

[0466] Step 5:

[0467] The server organizes the optimal schedule candidates received from the AI ​​tool and presents them to the user. The user selects the optimal one from the presented schedule candidates. At this stage, the user confirms the decision. Input: Optimal schedule candidate. Output: Confirmed information by the user.

[0468] Step 6:

[0469] The server records the confirmed schedule information in the database. This allows the confirmed schedule to be shared throughout the system and notified to other departments as necessary. Notifications are made via email or the internal notification system. Input: Confirmed information by the user. Output: Recorded confirmed schedule, notification.

[0470] 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.

[0471] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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 a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0472] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0473] [Second embodiment]

[0474] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0475] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0476] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0477] 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.

[0478] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0479] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0480] 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.

[0481] 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.

[0482] 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 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.

[0483] 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.

[0484] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0485] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0486] The present invention is a system for supporting medical administrative support work, and operates through the interaction of users (medical administrative assistants), servers, and terminals. The basic configuration of the system and the roles of each component are described below.

[0487] Program Description

[0488] User-initiated medical data entry

[0489] The user is a medical administrative assistant. He logs in to a terminal and accesses the appointment management interface. Through this interface, the user inputs medical data such as the patient's basic information, desired appointment date, and details of the appointment based on a request from the doctor. The terminal compiles the input data into a form and generates a request packet to send to the server.

[0490] Server receives request and retrieves information

[0491] The server analyzes the request packet received from the terminal and extracts the necessary patient information and examination details.The server then retrieves the patient's past medical history, the current doctor's schedule, and hospital resource information (such as the availability of clinical tests and examination rooms) from the medical database.

[0492] Optimization process using AI tools

[0493] The server passes the acquired data to an AI tool that optimizes appointment scheduling, taking into account the following factors:

[0494] Important patient medical history

[0495] Doctor's schedule availability

[0496] Availability of examination rooms and necessary equipment

[0497] The AI ​​tool takes these conditions into account and generates the most efficient reservation options, which are returned to the server.

[0498] Server proposal and data transmission

[0499] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. The converted reservation candidates are stored in a response packet and sent to the device.

[0500] Display and confirm information on the terminal

[0501] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user. The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[0502] Server confirmation and recording

[0503] The server records the confirmed appointment information received from the terminal in the medical database, confirms that the appointment has been officially registered, and notifies the doctor and patient of the confirmed information as necessary.

[0504] Specific examples

[0505] Scenario: Managing patient appointments

[0506] 1. The user logs in and enters the necessary information into the appointment management interface on the terminal: patient name, ID, desired appointment date, appointment details, etc.

[0507] 2. The terminal generates and sends a request packet to send the input data to the server.

[0508] 3. The server receives the request and retrieves the patient's medical history, doctor schedule, and resource information from the medical database.

[0509] 4. The server passes the acquired information to the AI ​​tool, which then generates the best reservation candidates.

[0510] 5. The server organizes the reservation candidates received from the AI ​​tool and sends them to the device.

[0511] 6. The terminal displays reservation options to the user, and the user selects and confirms the most suitable reservation.

[0512] 7. The server records the confirmed information in the medical database and notifies the doctor and patient.

[0513] By combining this program with processing steps, a system can be created that streamlines the work of medical administrative assistants and reduces the burden on medical professionals.

[0514] The processing flow will be explained below.

[0515] Step 1:

[0516] The user (medical administrative assistant) logs in to the terminal and accesses the appointment management interface.

[0517] Step 2:

[0518] Based on a request from a doctor, the user enters medical data such as the patient's name, ID, desired date of examination, and details of the examination.

[0519] Step 3:

[0520] The terminal compiles the input data into a form and generates a request packet to be sent to the server.

[0521] Step 4:

[0522] The server analyzes the request packet received from the terminal and extracts the necessary patient information and examination details data.

[0523] Step 5:

[0524] The server retrieves the patient's past medical history, doctor's schedule, and hospital resource information (such as availability of clinical tests and examination rooms) from the medical database.

[0525] Step 6:

[0526] The server then passes the acquired data to an AI tool that optimizes appointment scheduling, taking into account the patient's important medical history, the doctor's available schedule, and the availability of examination rooms and necessary equipment.

[0527] Step 7:

[0528] The AI ​​tool generates optimal reservation options and returns them to the server.

[0529] Step 8:

[0530] The server organizes the reservation suggestions received from the AI ​​tool and converts them into a format that is easy for users to understand.

[0531] Step 9:

[0532] The server stores the converted reservation candidates in a response packet and transmits it to the terminal.

[0533] Step 10:

[0534] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user.

[0535] Step 11:

[0536] The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[0537] Step 12:

[0538] The terminal generates a request packet to transmit the user's final selection back to the server.

[0539] Step 13:

[0540] The server records the confirmed reservation information received from the terminal in the medical database and confirms that the reservation has been officially registered.

[0541] Step 14:

[0542] The server confirms that the reservation information has been registered and notifies the terminal of that information.

[0543] Step 15:

[0544] The terminal receives the notification from the server and displays to the user that the reservation has been confirmed.

[0545] Step 16:

[0546] If necessary, the server automatically sends a notification email to the doctor and patient confirming the appointment.

[0547] Example 1

[0548] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0549] In recent years, there has been a demand for greater efficiency and accuracy in medical administration, but manual medical data entry and appointment management is time-consuming and prone to errors. Furthermore, coordinating doctor schedules and managing examination room resources is complicated, making it difficult to secure optimal appointment times. This results in longer waiting times for patients and an increased burden on medical professionals.

[0550] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0551] In this invention, the server includes a means for a user to log in and input medical data, a means for a terminal to compile the medical data, generate a request, and send it to the server, a means for the server to receive and analyze the medical data and acquire related information from a medical database, a means for the server to pass the acquired related information to an AI tool for optimization and suggest candidate appointments to the user, a means for the user to select and confirm the optimal appointment from the suggested candidates, and a means for the server to record the confirmed information in the medical database and notify the user as necessary. This enables accurate and efficient management of medical data and securing optimal appointment times.

[0552] "User" is a medical administrative assistant who inputs medical data and operates the system.

[0553] A "terminal" is an information processing device used by a user, and is a device that has the function of sending input medical data collectively to a server.

[0554] A "server" is an information processing device that receives and analyzes medical data sent by users, and is responsible for obtaining the necessary information from the medical database and passing it on to the AI ​​tool.

[0555] "Medical data" refers to data that includes information necessary for scheduling an appointment, such as the patient's name, ID, desired date of appointment, and details of the appointment.

[0556] A "request packet" is a data packet created by a terminal to collect medical data input by a user and send it to a server.

[0557] A "medical database" is a database for storing and managing medical information such as patient medical history, doctor schedules, and the availability of examination rooms and necessary equipment.

[0558] The "AI tool" is software that uses artificial intelligence to generate optimal appointment options based on medical data obtained from the server.

[0559] "Suggested appointments" are suggestions for optimal dates and times for patient appointments generated by AI tools.

[0560] A "response packet" is a packet of data created by the server to organize the reservation candidates received from the AI ​​tool and send them to the terminal.

[0561] "Notification" refers to the transmission of information by the server to inform the doctor and patient that a reservation has been confirmed.

[0562] The present invention is a system for supporting medical administrative support work, and is a system that operates through interactions between a user (medical administrative assistant), a server, and a terminal. The following describes in detail the mode for carrying out the present invention.

[0563] First, the user logs in to a terminal. The terminal is an information processing device such as a PC or tablet, and must be connected to the Internet. Once the user logs in, they can access the appointment management interface. This interface consists of a form for entering basic patient information (name, ID), desired appointment date, details of the appointment, etc.

[0564] Next, the device compiles the medical data entered by the user into a request packet. The request packet is created in a data structure such as JSON format and sent to the server using a protocol such as an HTTP POST request. The server analyzes the received request packet and extracts patient information and examination details.

[0565] Based on this data, the server retrieves the necessary information from the medical database, which contains the patient's past medical history, current doctor schedules, and hospital resource information (such as the availability of examination rooms and necessary equipment).

[0566] The server passes the acquired data to the AI ​​tool, which uses an artificial intelligence algorithm to generate the optimal reservation date and time, taking into account the following factors:

[0567] Important patient medical history

[0568] Doctor's schedule availability

[0569] Availability of examination rooms and necessary equipment

[0570] The reservation suggestions generated by the AI ​​tool are returned to the server, which organizes them and converts them into a format that is easy for the user to understand. The organized reservation suggestions are stored in a response packet and sent to the device.

[0571] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user. The user selects the most suitable date and time from the displayed reservation candidates and clicks the Confirm button. The confirmed reservation information is then sent back to the server.

[0572] The server records the received confirmed reservation information in the medical database, thereby confirming that the reservation has been officially registered. The server also notifies the doctor and patient of the confirmed reservation information as necessary.

[0573] Specific examples

[0574] For example, when making an appointment for a patient to visit the hospital, the user inputs the patient's name, ID, desired date of the appointment, and the details of the appointment into the terminal. After that, the system goes through the above process and proposes the optimal appointment date and time, which the user confirms.

[0575] Prompt Sentence Examples

[0576] An example of a prompt to explain the system overview to a generative AI model is:

[0577] It would be something like, "Please explain an AI system that optimizes medical appointments."

[0578] This system will streamline the work of medical administrative assistants and reduce the burden on medical professionals.

[0579] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0580] Program processing steps

[0581] Step 1: User enters medical data

[0582] Specific description:

[0583] The user logs in to the terminal and accesses the appointment management interface, which displays fields for entering the patient's basic information (name, ID), the desired appointment date, and the details of the appointment.

[0584] Input: Basic patient information, desired appointment date, and appointment details entered by the user.

[0585] Specific behavior:

[0586] The user logs in by entering a user name and password into the terminal.

[0587] After logging in, the appointment management interface will be displayed.

[0588] The user enters data into fields such as "Name," "Patient ID," "Desired appointment date," and "Expectation details."

[0589] After completing the input, the user clicks the "Submit" button.

[0590] Step 2: Terminal sends data and generates request packet

[0591] Specific description:

[0592] The device collects the medical data entered by the user and assembles it into a request packet, which is then sent to the server as an HTTP POST request.

[0593] Input: Medical data entered by the user.

[0594] Output: The request packet sent to the server.

[0595] Specific behavior:

[0596] The terminal converts the input data into JSON format.

[0597] The converted data is sent to the server as an HTTP POST request.

[0598] When the sending process is complete, the terminal will display a "Sending Complete" message.

[0599] Step 3: The server receives the request and retrieves information from the medical database.

[0600] Specific description:

[0601] The server receives the request packet sent from the device, analyzes it, and extracts the necessary patient information and examination details. After extraction, it executes a query to obtain past medical history, current doctor schedules, and resource information from the medical database.

[0602] Input: The request packet received from the device.

[0603] Output: Parsed patient information and consultation details, plus additional information retrieved from medical databases.

[0604] Specific behavior:

[0605] The server receives the HTTP request and parses the JSON data to extract patient information and medical details.

[0606] The server generates an SQL query and sends it to the database.

[0607] Retrieve patient history, physician schedule, and resource information from the database.

[0608] Save the retrieved data in a temporary data store.

[0609] Step 4: Providing data from the server to the AI ​​tool and optimizing it

[0610] Specific description:

[0611] The server passes the acquired data to an AI tool, which then uses this data to generate optimal appointment candidates, taking into account past medical history, doctor availability, and examination room usage.

[0612] Input: Various information obtained from medical databases.

[0613] Output: Optimized booking candidates.

[0614] Specific behavior:

[0615] The server sends the acquired data to the AI ​​tool as an API request.

[0616] AI tools perform the calculations and generate the best booking options.

[0617] The reservation suggestions are sent back to the server.

[0618] Step 5: Data sorting by the server and sending to the device

[0619] Specific description:

[0620] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. The converted data is stored in a response packet and sent to the device as an HTTP response.

[0621] Input: Booking suggestions received from the AI ​​tool.

[0622] Output: Response packet to send to the device.

[0623] Specific behavior:

[0624] The server converts the received data into HTML or JSON format and includes it in the response.

[0625] Sends an HTTP response to the device.

[0626] Step 6: Terminal displays information and user selection

[0627] Specific description:

[0628] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user. The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[0629] Input: The response packet received from the server.

[0630] Output: Reservation information selected and confirmed by the user.

[0631] Specific behavior:

[0632] The terminal analyzes the response data and displays a list of reservation options on the screen.

[0633] The user selects the best reservation option from the list.

[0634] The user clicks the "Confirm Reservation" button.

[0635] Step 7: Server processes and notifies the reservation

[0636] Specific description:

[0637] The server records the confirmed reservation information received from the terminal in the medical database. This officially registers the reservation. The server also notifies the doctor and patient of the confirmed information as necessary.

[0638] Input: Reservation information confirmed by the user.

[0639] Output: Appointment information recorded in the medical database, and notifications to the doctor and patient.

[0640] Specific behavior:

[0641] The server receives the confirmed reservation information and sends an INSERT query to the database.

[0642] Verify that the reservation information was successfully recorded in the database.

[0643] The server sends a confirmation of the appointment to the doctor and patient via email or SMS.

[0644] (Application example 1)

[0645] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0646] In conventional systems, medical administrative support tasks and factory robot maintenance schedule management were performed manually, resulting in problems such as human error and time loss. Furthermore, it was difficult to optimize the schedule, resulting in reduced work efficiency and wasted resources. For these reasons, there was a demand for a system that could achieve highly accurate and efficient reservation management and maintenance schedule management.

[0647] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0648] In this invention, the server includes means for inputting data from a user, means for the server to receive the data and acquire related information from a database, means for the server to optimize the acquired related information using an AI tool and propose it to the user, means for the user to confirm and confirm the proposed information, means for the server to record the confirmed information in the database, and means for the server to perform optimization using a generative AI model, thereby enabling users to manage their reservations and schedules with high accuracy and efficiency.

[0649] A "user" is an entity that uses the system to input and manage data.

[0650] "Data" is a general term for information that users input into the system, including medical information, maintenance information, and the like.

[0651] A "server" is a device or system that receives data, retrieves relevant information, performs optimization processing, and provides the results to the user.

[0652] A "database" is a collection of information in which various types of information are systematically organized and stored, including medical information and machine operation history.

[0653] "Related information" refers to additional information that the server obtains based on the data entered by the user, and includes medical history, operation history, schedule, and resource information.

[0654] "AI tools" refers to artificial intelligence technology that performs optimization processing using related information acquired by the server.

[0655] "Optimization" is the process of deriving the most efficient and appropriate results based on relevant information, based on the user's requests and conditions.

[0656] A "generative AI model" is an artificial intelligence algorithm that generates useful information and predictions from data.

[0657] "Proposing" refers to the act of the server informing the user of the optimized results and prompting them to make a selection or confirm.

[0658] "Confirming" refers to the act of the user confirming the proposed information and making a final decision.

[0659] "Recording" refers to the act of the server storing the determined information in a database for future reference if necessary.

[0660] The present invention relates to a system for improving the efficiency of maintenance schedule management for factory robots. The specific system configuration and program processing will be described below.

[0661] Program Description

[0662] User data entry

[0663] Users use a smartphone app to enter maintenance information for factory robots, specifically, data such as the robot ID, desired maintenance date, and work content. The entered data is sent from the smartphone to the server.

[0664] Server receives request and retrieves information

[0665] The server receives the data sent by the user and retrieves the robot's operation history and past maintenance history from the database, as well as the current robot schedule and resource information.

[0666] Optimization process using AI tools

[0667] The server passes the acquired information to a generative AI model to generate an optimal maintenance schedule. The AI ​​tool considers the robot's important operating history, operating schedule, resource utilization, and other factors to create efficient maintenance candidates.

[0668] Server proposal and data transmission

[0669] The server organizes the maintenance suggestions received from the AI ​​tool and converts them into a format that is easy for users to understand. The converted maintenance suggestions are then sent to a smartphone app and displayed to the user.

[0670] User displays and confirms information

[0671] The user can check the proposed maintenance schedule through a smartphone app, select the optimal date and time, and confirm the schedule. The confirmed information is then sent from the smartphone to the server.

[0672] Server confirmation and recording

[0673] The server records the confirmed maintenance information received from the user in a database, and if necessary, notifies the maintenance personnel or relevant departments by email.

[0674] Hardware and Software Configuration

[0675] Hardware: Smartphones, servers

[0676] Software: Java programs, javax.mail library, database management systems (e.g., MySQL), AI tools (e.g., TensorFlow)

[0677] Specific examples

[0678] For example, consider a factory worker entering maintenance information for robot ID "R12345." The worker uses a smartphone app to enter the following:

[0679] Robot ID: R12345

[0680] Desired maintenance date: 2023-10-01

[0681] Work: Parts replacement

[0682] The server then receives this information, retrieves the robot's operating history and past maintenance history from a database, and uses AI tools to generate an optimal maintenance schedule. This optimal schedule is displayed on a smartphone app, and employees can select the most suitable date and time to confirm it. The confirmed information is recorded on the server and, if necessary, notified to relevant departments via email.

[0683] Prompt Sentence Examples

[0684] Please enter the maintenance information for robot ID "R12345". Example: Maintenance date "2023-10-01", work content "Part replacement".

[0685] The optimal maintenance schedule is displayed.

[0686] Your selected maintenance schedule has been confirmed and notifications have been sent.

[0687] In this way, users can manage the maintenance schedule of factory robots with high accuracy and efficiency.

[0688] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0689] Step 1:

[0690] The user uses a smartphone app to input maintenance information for the factory robot. Specific input information includes the robot ID, desired maintenance date, and work details. The smartphone app compiles this data into packets and sends them to the server.

[0691] Input: Robot ID, desired maintenance date, work content

[0692] Output: Request packet to the server

[0693] Step 2:

[0694] The server receives the request packet sent by the user. The server analyzes the packet and extracts data such as the robot ID, desired maintenance date, and work content. The server then retrieves the robot's operation history and past maintenance history from the database.

[0695] Input: Request packet

[0696] Output: Operation history, past maintenance history, resource information

[0697] Step 3:

[0698] The server passes the information obtained from the database to a generative AI model to generate an optimal maintenance schedule. The generative AI model considers the robot's important operating history, operating schedule, resource usage status, and other factors to create efficient maintenance candidates.

[0699] Input: Operation history, past maintenance history, resource information

[0700] Output: Optimal maintenance schedule candidates

[0701] Step 4:

[0702] The server organizes the maintenance schedule candidates obtained from the generative AI model and converts them into a format that is easy for users to understand. The converted maintenance candidates are packaged into a response packet and sent to the smartphone app.

[0703] Input: Optimal maintenance schedule candidates

[0704] Output: User-visible response packet

[0705] Step 5:

[0706] The user checks the proposed maintenance schedule through the smartphone app, selects the most suitable date and time from the displayed maintenance schedule candidates, and clicks the confirm button. The selected data is sent from the smartphone app to the server.

[0707] Input: Maintenance schedule candidate

[0708] Output: Confirmed maintenance schedule

[0709] Step 6:

[0710] The server records the confirmed maintenance information received from the user in a database, and if necessary, sends an email notification to the maintenance staff or related departments.

[0711] Input: Confirmed maintenance schedule

[0712] Output: Database records, email notifications

[0713] In this way, we will build a system in which the data entered at each step is appropriately processed and calculated, and the necessary data is output for the next step, and ultimately notified to the user and related departments.

[0714] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0715] This invention is a system that operates through the interaction of a user (medical administrative assistant), a server, a terminal, and an emotion engine to support medical administrative support tasks. This system makes suggestions that take into account the user's emotions, thereby achieving more flexible and efficient business support than conventional systems. The system's programs and processing are described in detail below.

[0716] Program Description

[0717] User-initiated medical data entry and emotion recognition

[0718] The user is a medical administrative assistant. He logs in to the terminal and accesses the appointment management interface. Through the interface, the user inputs medical data such as the patient's basic information, desired appointment date, and details of the appointment based on a request from the doctor. The terminal compiles the input data into a form, and an emotion engine analyzes the user's input content, input speed, and facial expressions (if equipped with a camera) to recognize the user's emotions. The terminal generates a request packet containing the medical data and emotion data and sends it to the server.

[0719] Server receives request and retrieves information

[0720] The server analyzes the request packet received from the terminal and extracts the necessary patient information and examination details.The server then retrieves the patient's past medical history, doctor schedules, and hospital resource information (such as clinical testing and examination room availability) from the medical database.

[0721] Optimization processing using AI tools and use of emotional data

[0722] The server then passes the acquired data and sentiment data to an AI tool to optimize appointment scheduling, taking into account the following factors:

[0723] Important patient medical history

[0724] Doctor's schedule availability

[0725] Availability of examination rooms and necessary equipment

[0726] The user's emotional state (stress, anxiety, satisfaction, etc.)

[0727] Taking these conditions into consideration, the AI ​​tool generates the most efficient and least stressful reservation options for the user and returns them to the server.

[0728] Server proposal and data transmission

[0729] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. The converted reservation candidates are stored in a response packet and sent to the device.

[0730] Displaying information and reflecting emotions on devices

[0731] The terminal analyzes the response packet received from the server and displays suggested reservation options to the user. Priorities and explanations based on the user's feelings are added, allowing the user to easily review the suggested options. The user selects the most suitable date and time from the displayed reservation options and clicks the confirm button.

[0732] Server confirmation and recording

[0733] The server records the confirmed reservation information received from the device in the medical database, confirming that the reservation has been officially registered, and also records the emotion data recognized by the emotion engine for use in improving the accuracy of future suggestions.

[0734] Specific examples

[0735] Scenario: Managing patient appointments with emotions in mind

[0736] 1. The user logs in and enters the necessary information into the appointment management interface on their device: patient name, ID, desired appointment date, appointment details, etc. The emotion engine also recognizes the user's emotions from their facial expressions and input speed.

[0737] 2. The terminal generates and sends a request packet to send the input data and emotion data to the server.

[0738] 3. The server receives the request and retrieves the patient's medical history, doctor schedule, and resource information from the medical database.

[0739] 4. The server passes the acquired information and emotional data to the AI ​​tool, which then generates the best reservation candidates.

[0740] 5. The server organizes the reservation suggestions received from the AI ​​tool and sends them to the device. The suggestions are displayed in a way that takes the user's emotions into consideration.

[0741] 6. The terminal displays the proposed reservation options to the user, and the user selects and confirms the most suitable reservation.

[0742] 7. The server records the confirmed information in a medical database, notifies the doctor and patient, and records emotional data for future improvements to the entire system.

[0743] By combining this program with processing steps, a system is realized that not only streamlines the work of medical administrative assistants and reduces the burden on medical professionals, but also provides flexible support that takes the user's emotions into consideration.

[0744] The processing flow will be explained below.

[0745] Step 1:

[0746] The user (medical administrative assistant) logs in to the terminal and accesses the appointment management interface.

[0747] Step 2:

[0748] Based on a request from a doctor, the user enters medical data such as the patient's name, ID, desired date of examination, and details of the examination.

[0749] Step 3:

[0750] The device compiles the entered data into a form, and an emotion engine analyzes the input content, input speed, and in some cases the user's facial expressions to recognize the user's emotions.

[0751] Step 4:

[0752] The terminal generates a request packet including medical data and emotion data and transmits it to the server.

[0753] Step 5:

[0754] The server analyzes the request packet received from the terminal and extracts patient information and examination details.

[0755] Step 6:

[0756] The server retrieves patient past medical history, doctor schedules, and hospital resource information from a medical database.

[0757] Step 7:

[0758] The server then passes the acquired data and emotional data to an AI tool that optimizes appointments, taking into account the patient's important medical history, the doctor's available schedule, the availability of examination rooms and necessary equipment, and the user's emotional state.

[0759] Step 8:

[0760] The AI ​​tool generates optimal reservation options and returns them to the server.

[0761] Step 9:

[0762] The server organizes the reservation suggestions received from the AI ​​tool and converts them into a format that is easy for users to understand.

[0763] Step 10:

[0764] The server stores the converted reservation candidates in a response packet and transmits it to the terminal.

[0765] Step 11:

[0766] The terminal analyzes the response packet received from the server and displays a list of reservation options to the user. At this time, the suggestions are displayed in a way that takes the user's emotions into consideration.

[0767] Step 12:

[0768] The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[0769] Step 13:

[0770] The terminal generates a request packet to transmit the user's final selection back to the server.

[0771] Step 14:

[0772] The server records the confirmed reservation information received from the terminal in the medical database and confirms that the reservation has been officially registered.

[0773] Step 15:

[0774] The server confirms that the reservation information has been registered and notifies the terminal of that information.

[0775] Step 16:

[0776] The terminal receives the notification from the server and displays to the user that the reservation has been confirmed.

[0777] Step 17:

[0778] If necessary, the server automatically sends a notification email to the doctor and patient confirming the appointment.

[0779] Step 18:

[0780] The server records the emotion data recognized by the emotion engine and uses it to improve the accuracy of future suggestions.

[0781] Example 2

[0782] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0783] Conventional medical administrative support systems do not take user emotions into consideration when entering data or managing appointments, which reduces work efficiency and the user experience. They also lack the flexibility to integrate and optimize a wide range of information, such as medical data, patient medical history, and doctor schedules. There is a need to solve these problems, reduce user stress and anxiety, and provide more efficient and user-friendly work support.

[0784] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting medical data from a user, means for the server to receive the medical data and the user's emotional data and acquire related information from a medical database, means for the server to optimize the acquired related information and emotional data using an AI tool and propose it to the user, means for the user to confirm and confirm the proposed information, and means for the server to record the confirmed information in the medical database and record the emotional data. This enables flexible and efficient business support that takes user emotions into consideration.

[0785] "User" refers to the medical administrative assistant who operates the system and inputs medical data.

[0786] "Medical data" refers to information necessary for medical administrative support work, such as basic patient information, desired appointment date, and details of the appointment.

[0787] "Emotional data" refers to information about the user's psychological state analyzed from their input speed, facial expressions, etc.

[0788] "Server" refers to a central processing unit for receiving medical data and emotion data sent from terminals, obtaining related information, and optimizing it.

[0789] A "medical database" refers to data storage that stores medical-related information such as patient medical history, doctor schedules, and hospital resource information.

[0790] "Relevant information" refers to the patient's medical history, doctor's schedule, and hospital resource information.

[0791] "AI tools" refers to software and algorithms with artificial intelligence technology used on servers.

[0792] "Optimization" refers to the process of generating optimal appointment candidates based on the relevant information and sentiment data obtained.

[0793] "Suggestion" refers to presenting optimized information to the user using AI tools.

[0794] "Confirmation" refers to the act of the user selecting the most suitable appointment from the proposed candidates and making a final decision.

[0795] "Recording" refers to the act of storing established information in a medical database.

[0796] The present invention is a flexible and efficient business support system that optimizes medical administrative support tasks and takes into account the user's emotions. The system operates through the interaction of the user, server, terminal, and emotion engine.

[0797] System configuration and program description

[0798] User data entry and emotion recognition

[0799] The user logs in to the terminal as a medical administrative assistant and accesses the appointment management interface. The user enters basic patient information (name, ID), desired appointment date, details of the appointment, etc. through the terminal. At this time, the terminal compiles the entered data into an appropriate format.

[0800] Furthermore, the device uses an emotion engine to analyze the user's input speed and facial expressions. For example, if the device is equipped with a camera, it can capture and analyze the user's facial expressions to determine stress or anxiety levels. The emotion engine indicates that the user may be feeling stressed even if the input speed is slow. The emotion data and medical data generated in this way are sent to the server.

[0801] Server receives request and retrieves information

[0802] The server analyzes the medical and emotional data received from the device and extracts the necessary patient information and examination details.The server then accesses a medical database (e.g., MySQL) to obtain the patient's past medical history, doctor schedules, and hospital resource information.

[0803] Optimization with AI tools and use of sentiment data

[0804] The server passes the acquired data and emotional data to an AI tool (e.g., TensorFlow) to optimize appointment scheduling. The AI ​​tool generates optimal appointment candidates by taking into account the patient's past medical history, doctor's schedule, availability of examination rooms and necessary equipment, and the user's emotional state (stress, anxiety, satisfaction). The results are returned to the server.

[0805] Proposal and confirmation

[0806] The server organizes the reservation candidates received from the AI ​​tool, converts them into a format that is easy for the user to understand, and sends them to the device. The device displays the received reservation candidates on its interface, and the user selects the best date and time from the displayed reservation candidates. When the user clicks the confirm button, the confirmation information is sent back to the server.

[0807] Finalization and recording

[0808] The server records the confirmation information received from the device in the medical database and confirms that the appointment has been officially registered. The server also records the emotion data from the emotion engine and uses it to improve the accuracy of future recommendations. The server also notifies the doctor and patient that the appointment has been confirmed.

[0809] Specific examples

[0810] For example, suppose a user logs in to a terminal and inputs the desired consultation date for patient "Yamada Taro" as "2023 / 10 / 15" and the consultation content as "consultation for lower back pain." The prompt text in this case is as follows:

[0811] ---

[0812] Patient demographics:

[0813] Name: Taro Yamada

[0814] ID:12345

[0815] Desired appointment date: 2023 / 10 / 15

[0816] Examination content: Examination of lower back pain

[0817] ---

[0818] This system will streamline the work of medical administrative assistants and utilize structured medical and emotional data to make suggestions that reduce users' stress and anxiety. Theoretically, it is expected to create optimal medical appointments for both patients and medical professionals.

[0819] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0820] Step 1: User data entry and emotion recognition

[0821] A user logs in to a terminal as a medical administrative assistant and accesses the appointment management interface. The user enters the patient's basic information (name, ID), desired appointment date, and details of the appointment. At this time, the terminal organizes the entered data into an appropriate format. The terminal then uses an emotion engine to analyze the user's input speed and facial expressions. Specifically, the terminal's camera captures the user's facial expressions and analyzes them in real time. For example, if the input speed is slow, it is determined that the user is "feeling stressed." The medical data and emotional data generated in this way are sent to the server as a request packet.

[0822] Input: Patient's basic information, desired consultation date, consultation details

[0823] Data processing: Formatting input data, generating emotional data (input speed and facial expression analysis)

[0824] Output: A request packet containing medical and emotional data.

[0825] Step 2: Server receives request and retrieves information

[0826] The server receives the request packet from the terminal. It analyzes the received packet and extracts the patient information and consultation details. The server then accesses a medical database (e.g., MySQL) to retrieve the patient's past medical history, doctor schedule, and hospital resource information. This provides all the data necessary to process the appointment.

[0827] Input: A request packet containing medical and emotional data

[0828] Data processing: Analyzing request packets and extracting patient information and medical examination details

[0829] Output: Patient's medical history, doctor's schedule, hospital resource information

[0830] Step 3: Optimizing with AI tools and using sentiment data

[0831] The server passes the acquired data and emotional data to an AI tool (e.g., TensorFlow) to perform optimization analysis for appointment scheduling. The AI ​​tool generates optimal appointment candidates based on the patient's past medical history, doctor schedules, hospital resource information, and the user's emotional state (stress, anxiety, satisfaction). For example, it also takes into account cases where a specific examination room is required based on past medical history.

[0832] Input: Patient's medical history, doctor's schedule, hospital resource information, emotional data

[0833] Data processing: Data analysis and optimization using AI tools

[0834] Output: Best booking candidates

[0835] Step 4: Server Proposal and Data Transmission

[0836] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. For example, it summarizes the date, time, and location of each reservation candidate. The converted reservation candidates are then stored in a response packet and sent to the terminal.

[0837] Input: Best booking candidate

[0838] Data processing: Formatting reservation candidates and converting them into response packets

[0839] Output: Response packet

[0840] Step 5: Terminal displays information and user selection

[0841] The terminal analyzes the response packet received from the server and extracts its contents. Next, it displays suggested reservation options on the interface. The user selects the most suitable date and time from the displayed reservation options and clicks the Confirm button. Based on the user's selection, the terminal resends a request packet containing the confirmation information to the server.

[0842] Input: Response packet

[0843] Data processing: Analyzing response packets and displaying reservation candidates

[0844] Output: Request packet containing confirmation information

[0845] Step 6: Server confirmation and recording

[0846] The server analyzes the confirmation information received from the device and records it in a medical database. It then confirms that the appointment has been officially registered and notifies the doctor and patient that the appointment has been confirmed. Emotional data is also recorded and will be used to improve the system in the future.

[0847] Input: Request packet containing confirmation information

[0848] Data processing: Analysis of confirmed information and recording in a database

[0849] Output: Notification to doctor and patient, recorded emotion data

[0850] This concretely shows the processing steps of the present system and clarifies the interactions between the user, server, and terminal.

[0851] (Application example 2)

[0852] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0853] In modern manufacturing, efficient schedule and resource management is extremely important. However, current systems do not take into account user emotions, which can create a stressful environment for workers. Furthermore, there is no established method for optimally managing work history and resource information, which can lead to problems such as reduced productivity and inefficient operations.

[0854] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting business data from a user, means for the server to receive the business data and acquire related information from a database, and means for optimizing the acquired related information and emotion data from an emotion engine using an AI tool and proposing the optimization to the user. This enables optimal schedule and resource management that takes user emotions into consideration.

[0855] A "user" is a person who uses the system and performs operations to input business data.

[0856] "Business data" refers to data such as work details, schedules, and resource usage status that users input into the system.

[0857] A "server" is a device that receives business data from users, retrieves necessary information from a database, and optimizes it using AI tools.

[0858] A "database" is an information storage device that stores information related to business operations.

[0859] "Related information" refers to information necessary for business execution, such as work history, schedule, and resource information.

[0860] An "emotion engine" is a system that recognizes emotions from user input and facial expressions, and processes them as data.

[0861] "Emotion data" is data that indicates the emotional state of the user as recognized by the emotion engine.

[0862] An "AI tool" is software or hardware equipped with artificial intelligence algorithms for analyzing data and performing optimization processing.

[0863] "Optimization" is the process of using AI tools to generate the most efficient and least stressful schedule and resource allocation for users based on relevant information and emotional data.

[0864] "Proposing" means that the server presents the results of the optimization process to the user.

[0865] "Confirming" means that the user selects the most appropriate information from the proposed information and confirms its execution.

[0866] "Recording" refers to the process in which the server saves the confirmed information back into the database.

[0867] This invention is a system that inputs, optimizes, proposes, confirms, and records business data through interactions between users, servers, databases, emotion engines, and AI tools.

[0868] System configuration

[0869] 1. User operations

[0870] Users input business data using a smartphone or head-mounted display. The business data includes the work content, schedule, required resources, etc. The device equipped with an emotion engine acquires emotional data from the user's facial expressions and input speed.

[0871] 2. Receipt and processing of data

[0872] The device assembles task data and emotion data into packets and sends them to the server. The server receives these packets and retrieves related information (past work history, current resource status, schedule, etc.) from a database.

[0873] 3. Optimization process

[0874] The server passes relevant information and emotional data to the AI ​​tool, which then performs an optimization process. The AI ​​tool then uses this information to generate the most efficient and least stressful schedule for the user and returns it to the server. This optimization process uses programming languages ​​such as Python and machine learning algorithms.

[0875] 4. Suggestions and Selections

[0876] The server organizes the schedule candidates received from the AI ​​tool and presents them to the user. The user selects the best one from the presented schedule candidates and confirms it in the system. The information is displayed using a web interface or application.

[0877] 5. Records and Notifications

[0878] The server records the confirmed information in a database and notifies relevant departments as necessary. For example, a notification function is implemented to inform workers and managers of confirmed schedules.

[0879] The specific hardware and software used

[0880] Hardware

[0881] User devices: smartphone, head-mounted display, camera

[0882] Server: A high-performance server for data processing and optimization.

[0883] Database server: a data storage device for storing business-related information

[0884] software

[0885] Emotion Engine: Software for Recognizing User Emotions

[0886] AI Tools: Artificial intelligence algorithms that optimize data processing

[0887] Database management software: e.g., MySQL or PostgreSQL

[0888] Web interface or application: a platform through which users can enter data and view suggestions

[0889] Specific examples

[0890] Scenario: Optimizing factory production schedules

[0891] 1. The user inputs the work content and schedule using a smartphone or head-mounted display.

[0892] 2. The emotion engine analyzes the user's facial expressions and obtains emotional data.

[0893] 3. The device sends business data and emotion data to the server.

[0894] 4. The server retrieves the relevant information from the database and passes it to the AI ​​tool.

[0895] 5. The AI ​​tool generates an optimal schedule and returns it to the server.

[0896] 6. The server proposes a schedule to the user.

[0897] 7. User checks the schedule, selects the best one and confirms.

[0898] 8. The server records the confirmed information in the database and notifies the relevant departments.

[0899] Example prompts for generative AI models

[0900] "To optimize the production schedule in your factory, please create a schedule taking into account the following data:

[0901] Worker name and data

[0902] Specific work content and schedule

[0903] Emotional data (stress, anxiety, satisfaction, etc.)

[0904] Generate the optimal schedule and present it to you."

[0905] In this way, a system is realized that can reduce user stress and improve work efficiency by proposing an optimized production schedule based on emotional data.

[0906] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0907] Step 1:

[0908] The user inputs business data using a smartphone or head-mounted display. The input business data includes the work content, schedule, required resources, etc. The emotion engine then analyzes the user's facial expressions and input speed to obtain emotional data. Input: Business data, emotional data. Output: Input business data, obtained emotional data.

[0909] Step 2:

[0910] The device assembles task data and emotion data into packets and sends them to the server. This ensures that all important information is delivered to the server in a single request packet. Input: task data, emotion data. Output: request packet.

[0911] Step 3:

[0912] The server receives the request packet and analyzes it. It extracts business data and emotion data from the analyzed data and obtains related information (work history, resource information, schedule, etc.) from the database. Input: Request packet. Output: Related information, business data, emotion data.

[0913] Step 4:

[0914] The server passes the acquired related information and emotional data to the AI ​​tool, which then performs optimization processing. The AI ​​tool performs calculations using multidimensional arrays based on the related information and emotional data to generate the most efficient and least stressful schedule. Input: Related information, emotional data. Output: Optimal schedule candidates.

[0915] Step 5:

[0916] The server organizes the optimal schedule candidates received from the AI ​​tool and presents them to the user. The user selects the optimal one from the presented schedule candidates. At this stage, the user confirms the decision. Input: Optimal schedule candidate. Output: Confirmed information by the user.

[0917] Step 6:

[0918] The server records the confirmed schedule information in the database. This allows the confirmed schedule to be shared throughout the system and notified to other departments as necessary. Notifications are made via email or the internal notification system. Input: Confirmed information by the user. Output: Recorded confirmed schedule, notification.

[0919] 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.

[0920] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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 a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0921] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0922] [Third embodiment]

[0923] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0924] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0925] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0926] 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.

[0927] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0928] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0929] 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.

[0930] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0931] 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 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.

[0932] 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.

[0933] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0934] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0935] The present invention is a system for supporting medical administrative support work, and operates through the interaction of users (medical administrative assistants), servers, and terminals. The basic configuration of the system and the roles of each component are described below.

[0936] Program Description

[0937] User-initiated medical data entry

[0938] The user is a medical administrative assistant. He logs in to a terminal and accesses the appointment management interface. Through this interface, the user inputs medical data such as the patient's basic information, desired appointment date, and details of the appointment based on a request from the doctor. The terminal compiles the input data into a form and generates a request packet to send to the server.

[0939] Server receives request and retrieves information

[0940] The server analyzes the request packet received from the terminal and extracts the necessary patient information and examination details.The server then retrieves the patient's past medical history, the current doctor's schedule, and hospital resource information (such as the availability of clinical tests and examination rooms) from the medical database.

[0941] Optimization process using AI tools

[0942] The server passes the acquired data to an AI tool that optimizes appointment scheduling, taking into account the following factors:

[0943] Important patient medical history

[0944] Doctor's schedule availability

[0945] Availability of examination rooms and necessary equipment

[0946] The AI ​​tool takes these conditions into account and generates the most efficient reservation options, which are returned to the server.

[0947] Server proposal and data transmission

[0948] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. The converted reservation candidates are stored in a response packet and sent to the device.

[0949] Display and confirm information on the terminal

[0950] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user. The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[0951] Server confirmation and recording

[0952] The server records the confirmed appointment information received from the terminal in the medical database, confirms that the appointment has been officially registered, and notifies the doctor and patient of the confirmed information as necessary.

[0953] Specific examples

[0954] Scenario: Managing patient appointments

[0955] 1. The user logs in and enters the necessary information into the appointment management interface on the terminal: patient name, ID, desired appointment date, appointment details, etc.

[0956] 2. The terminal generates and sends a request packet to send the input data to the server.

[0957] 3. The server receives the request and retrieves the patient's medical history, doctor schedule, and resource information from the medical database.

[0958] 4. The server passes the acquired information to the AI ​​tool, which then generates the best reservation candidates.

[0959] 5. The server organizes the reservation candidates received from the AI ​​tool and sends them to the device.

[0960] 6. The terminal displays reservation options to the user, and the user selects and confirms the most suitable reservation.

[0961] 7. The server records the confirmed information in the medical database and notifies the doctor and patient.

[0962] By combining this program with processing steps, a system can be created that streamlines the work of medical administrative assistants and reduces the burden on medical professionals.

[0963] The processing flow will be explained below.

[0964] Step 1:

[0965] The user (medical administrative assistant) logs in to the terminal and accesses the appointment management interface.

[0966] Step 2:

[0967] Based on a request from a doctor, the user enters medical data such as the patient's name, ID, desired date of examination, and details of the examination.

[0968] Step 3:

[0969] The terminal compiles the input data into a form and generates a request packet to be sent to the server.

[0970] Step 4:

[0971] The server analyzes the request packet received from the terminal and extracts the necessary patient information and examination details data.

[0972] Step 5:

[0973] The server retrieves the patient's past medical history, doctor's schedule, and hospital resource information (such as availability of clinical tests and examination rooms) from the medical database.

[0974] Step 6:

[0975] The server then passes the acquired data to an AI tool that optimizes appointment scheduling, taking into account the patient's important medical history, the doctor's available schedule, and the availability of examination rooms and necessary equipment.

[0976] Step 7:

[0977] The AI ​​tool generates optimal reservation options and returns them to the server.

[0978] Step 8:

[0979] The server organizes the reservation suggestions received from the AI ​​tool and converts them into a format that is easy for users to understand.

[0980] Step 9:

[0981] The server stores the converted reservation candidates in a response packet and transmits it to the terminal.

[0982] Step 10:

[0983] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user.

[0984] Step 11:

[0985] The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[0986] Step 12:

[0987] The terminal generates a request packet to transmit the user's final selection back to the server.

[0988] Step 13:

[0989] The server records the confirmed reservation information received from the terminal in the medical database and confirms that the reservation has been officially registered.

[0990] Step 14:

[0991] The server confirms that the reservation information has been registered and notifies the terminal of that information.

[0992] Step 15:

[0993] The terminal receives the notification from the server and displays to the user that the reservation has been confirmed.

[0994] Step 16:

[0995] If necessary, the server automatically sends a notification email to the doctor and patient confirming the appointment.

[0996] Example 1

[0997] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0998] In recent years, there has been a demand for greater efficiency and accuracy in medical administration, but manual medical data entry and appointment management is time-consuming and prone to errors. Furthermore, coordinating doctor schedules and managing examination room resources is complicated, making it difficult to secure optimal appointment times. This results in longer waiting times for patients and an increased burden on medical professionals.

[0999] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1000] In this invention, the server includes a means for a user to log in and input medical data, a means for a terminal to compile the medical data, generate a request, and send it to the server, a means for the server to receive and analyze the medical data and acquire related information from a medical database, a means for the server to pass the acquired related information to an AI tool for optimization and suggest candidate appointments to the user, a means for the user to select and confirm the optimal appointment from the suggested candidates, and a means for the server to record the confirmed information in the medical database and notify the user as necessary. This enables accurate and efficient management of medical data and securing optimal appointment times.

[1001] "User" is a medical administrative assistant who inputs medical data and operates the system.

[1002] A "terminal" is an information processing device used by a user, and is a device that has the function of sending input medical data collectively to a server.

[1003] A "server" is an information processing device that receives and analyzes medical data sent by users, and is responsible for obtaining the necessary information from the medical database and passing it on to the AI ​​tool.

[1004] "Medical data" refers to data that includes information necessary for scheduling an appointment, such as the patient's name, ID, desired date of appointment, and details of the appointment.

[1005] A "request packet" is a data packet created by a terminal to collect medical data input by a user and send it to a server.

[1006] A "medical database" is a database for storing and managing medical information such as patient medical history, doctor schedules, and the availability of examination rooms and necessary equipment.

[1007] The "AI tool" is software that uses artificial intelligence to generate optimal appointment options based on medical data obtained from the server.

[1008] "Suggested appointments" are suggestions for optimal dates and times for patient appointments generated by AI tools.

[1009] A "response packet" is a packet of data created by the server to organize the reservation candidates received from the AI ​​tool and send them to the terminal.

[1010] "Notification" refers to the transmission of information by the server to inform the doctor and patient that a reservation has been confirmed.

[1011] The present invention is a system for supporting medical administrative support work, and is a system that operates through interactions between a user (medical administrative assistant), a server, and a terminal. The following describes in detail the mode for carrying out the present invention.

[1012] First, the user logs in to a terminal. The terminal is an information processing device such as a PC or tablet, and must be connected to the Internet. Once the user logs in, they can access the appointment management interface. This interface consists of a form for entering basic patient information (name, ID), desired appointment date, details of the appointment, etc.

[1013] Next, the device compiles the medical data entered by the user into a request packet. The request packet is created in a data structure such as JSON format and sent to the server using a protocol such as an HTTP POST request. The server analyzes the received request packet and extracts patient information and examination details.

[1014] Based on this data, the server retrieves the necessary information from the medical database, which contains the patient's past medical history, current doctor schedules, and hospital resource information (such as the availability of examination rooms and necessary equipment).

[1015] The server passes the acquired data to the AI ​​tool, which uses an artificial intelligence algorithm to generate the optimal reservation date and time, taking into account the following factors:

[1016] Important patient medical history

[1017] Doctor's schedule availability

[1018] Availability of examination rooms and necessary equipment

[1019] The reservation suggestions generated by the AI ​​tool are returned to the server, which organizes them and converts them into a format that is easy for the user to understand. The organized reservation suggestions are stored in a response packet and sent to the device.

[1020] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user. The user selects the most suitable date and time from the displayed reservation candidates and clicks the Confirm button. The confirmed reservation information is then sent back to the server.

[1021] The server records the received confirmed reservation information in the medical database, thereby confirming that the reservation has been officially registered. The server also notifies the doctor and patient of the confirmed reservation information as necessary.

[1022] Specific examples

[1023] For example, when making an appointment for a patient to visit the hospital, the user inputs the patient's name, ID, desired date of the appointment, and the details of the appointment into the terminal. After that, the system goes through the above process and proposes the optimal appointment date and time, which the user confirms.

[1024] Prompt Sentence Examples

[1025] An example of a prompt to explain the system overview to a generative AI model is:

[1026] It would be something like, "Please explain an AI system that optimizes medical appointments."

[1027] This system will streamline the work of medical administrative assistants and reduce the burden on medical professionals.

[1028] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1029] Program processing steps

[1030] Step 1: User enters medical data

[1031] Specific description:

[1032] The user logs in to the terminal and accesses the appointment management interface, which displays fields for entering the patient's basic information (name, ID), the desired appointment date, and the details of the appointment.

[1033] Input: Basic patient information, desired appointment date, and appointment details entered by the user.

[1034] Specific behavior:

[1035] The user logs in by entering a user name and password into the terminal.

[1036] After logging in, the appointment management interface will be displayed.

[1037] The user enters data into fields such as "Name," "Patient ID," "Desired appointment date," and "Expectation details."

[1038] After completing the input, the user clicks the "Submit" button.

[1039] Step 2: Terminal sends data and generates request packet

[1040] Specific description:

[1041] The device collects the medical data entered by the user and assembles it into a request packet, which is then sent to the server as an HTTP POST request.

[1042] Input: Medical data entered by the user.

[1043] Output: The request packet sent to the server.

[1044] Specific behavior:

[1045] The terminal converts the input data into JSON format.

[1046] The converted data is sent to the server as an HTTP POST request.

[1047] When the sending process is complete, the terminal will display a "Sending Complete" message.

[1048] Step 3: The server receives the request and retrieves information from the medical database.

[1049] Specific description:

[1050] The server receives the request packet sent from the device, analyzes it, and extracts the necessary patient information and examination details. After extraction, it executes a query to obtain past medical history, current doctor schedules, and resource information from the medical database.

[1051] Input: The request packet received from the device.

[1052] Output: Parsed patient information and consultation details, plus additional information retrieved from medical databases.

[1053] Specific behavior:

[1054] The server receives the HTTP request and parses the JSON data to extract patient information and medical details.

[1055] The server generates an SQL query and sends it to the database.

[1056] Retrieve patient history, physician schedule, and resource information from the database.

[1057] Save the retrieved data in a temporary data store.

[1058] Step 4: Providing data from the server to the AI ​​tool and optimizing it

[1059] Specific description:

[1060] The server passes the acquired data to an AI tool, which then uses this data to generate optimal appointment candidates, taking into account past medical history, doctor availability, and examination room usage.

[1061] Input: Various information obtained from medical databases.

[1062] Output: Optimized booking candidates.

[1063] Specific behavior:

[1064] The server sends the acquired data to the AI ​​tool as an API request.

[1065] AI tools perform the calculations and generate the best booking options.

[1066] The reservation suggestions are sent back to the server.

[1067] Step 5: Data sorting by the server and sending to the device

[1068] Specific description:

[1069] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. The converted data is stored in a response packet and sent to the device as an HTTP response.

[1070] Input: Booking suggestions received from the AI ​​tool.

[1071] Output: Response packet to send to the device.

[1072] Specific behavior:

[1073] The server converts the received data into HTML or JSON format and includes it in the response.

[1074] Sends an HTTP response to the device.

[1075] Step 6: Terminal displays information and user selection

[1076] Specific description:

[1077] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user. The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[1078] Input: The response packet received from the server.

[1079] Output: Reservation information selected and confirmed by the user.

[1080] Specific behavior:

[1081] The terminal analyzes the response data and displays a list of reservation options on the screen.

[1082] The user selects the best reservation option from the list.

[1083] The user clicks the "Confirm Reservation" button.

[1084] Step 7: Server processes and notifies the reservation

[1085] Specific description:

[1086] The server records the confirmed reservation information received from the terminal in the medical database. This officially registers the reservation. The server also notifies the doctor and patient of the confirmed information as necessary.

[1087] Input: Reservation information confirmed by the user.

[1088] Output: Appointment information recorded in the medical database, and notifications to the doctor and patient.

[1089] Specific behavior:

[1090] The server receives the confirmed reservation information and sends an INSERT query to the database.

[1091] Verify that the reservation information was successfully recorded in the database.

[1092] The server sends a confirmation of the appointment to the doctor and patient via email or SMS.

[1093] (Application example 1)

[1094] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1095] In conventional systems, medical administrative support tasks and factory robot maintenance schedule management were performed manually, resulting in problems such as human error and time loss. Furthermore, it was difficult to optimize the schedule, resulting in reduced work efficiency and wasted resources. For these reasons, there was a demand for a system that could achieve highly accurate and efficient reservation management and maintenance schedule management.

[1096] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1097] In this invention, the server includes means for inputting data from a user, means for the server to receive the data and acquire related information from a database, means for the server to optimize the acquired related information using an AI tool and propose it to the user, means for the user to confirm and confirm the proposed information, means for the server to record the confirmed information in the database, and means for the server to perform optimization using a generative AI model, thereby enabling users to manage their reservations and schedules with high accuracy and efficiency.

[1098] A "user" is an entity that uses the system to input and manage data.

[1099] "Data" is a general term for information that users input into the system, including medical information, maintenance information, and the like.

[1100] A "server" is a device or system that receives data, retrieves relevant information, performs optimization processing, and provides the results to the user.

[1101] A "database" is a collection of information in which various types of information are systematically organized and stored, including medical information and machine operation history.

[1102] "Related information" refers to additional information that the server obtains based on the data entered by the user, and includes medical history, operation history, schedule, and resource information.

[1103] "AI tools" refers to artificial intelligence technology that performs optimization processing using related information acquired by the server.

[1104] "Optimization" is the process of deriving the most efficient and appropriate results based on relevant information, based on the user's requests and conditions.

[1105] A "generative AI model" is an artificial intelligence algorithm that generates useful information and predictions from data.

[1106] "Proposing" refers to the act of the server informing the user of the optimized results and prompting them to make a selection or confirm.

[1107] "Confirming" refers to the act of the user confirming the proposed information and making a final decision.

[1108] "Recording" refers to the act of the server storing the determined information in a database for future reference if necessary.

[1109] The present invention relates to a system for improving the efficiency of maintenance schedule management for factory robots. The specific system configuration and program processing will be described below.

[1110] Program Description

[1111] User data entry

[1112] Users use a smartphone app to enter maintenance information for factory robots, specifically, data such as the robot ID, desired maintenance date, and work content. The entered data is sent from the smartphone to the server.

[1113] Server receives request and retrieves information

[1114] The server receives the data sent by the user and retrieves the robot's operation history and past maintenance history from the database, as well as the current robot schedule and resource information.

[1115] Optimization process using AI tools

[1116] The server passes the acquired information to a generative AI model to generate an optimal maintenance schedule. The AI ​​tool considers the robot's important operating history, operating schedule, resource utilization, and other factors to create efficient maintenance candidates.

[1117] Server proposal and data transmission

[1118] The server organizes the maintenance suggestions received from the AI ​​tool and converts them into a format that is easy for users to understand. The converted maintenance suggestions are then sent to a smartphone app and displayed to the user.

[1119] User displays and confirms information

[1120] The user can check the proposed maintenance schedule through a smartphone app, select the optimal date and time, and confirm the schedule. The confirmed information is then sent from the smartphone to the server.

[1121] Server confirmation and recording

[1122] The server records the confirmed maintenance information received from the user in a database, and if necessary, notifies the maintenance personnel or relevant departments by email.

[1123] Hardware and Software Configuration

[1124] Hardware: Smartphones, servers

[1125] Software: Java programs, javax.mail library, database management systems (e.g., MySQL), AI tools (e.g., TensorFlow)

[1126] Specific examples

[1127] For example, consider a factory worker entering maintenance information for robot ID "R12345." The worker uses a smartphone app to enter the following:

[1128] Robot ID: R12345

[1129] Desired maintenance date: 2023-10-01

[1130] Work: Parts replacement

[1131] The server then receives this information, retrieves the robot's operating history and past maintenance history from a database, and uses AI tools to generate an optimal maintenance schedule. This optimal schedule is displayed on a smartphone app, and employees can select the most suitable date and time to confirm it. The confirmed information is recorded on the server and, if necessary, notified to relevant departments via email.

[1132] Prompt Sentence Examples

[1133] Please enter the maintenance information for robot ID "R12345". Example: Maintenance date "2023-10-01", work content "Part replacement".

[1134] The optimal maintenance schedule is displayed.

[1135] Your selected maintenance schedule has been confirmed and notifications have been sent.

[1136] In this way, users can manage the maintenance schedule of factory robots with high accuracy and efficiency.

[1137] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1138] Step 1:

[1139] The user uses a smartphone app to input maintenance information for the factory robot. Specific input information includes the robot ID, desired maintenance date, and work details. The smartphone app compiles this data into packets and sends them to the server.

[1140] Input: Robot ID, desired maintenance date, work content

[1141] Output: Request packet to the server

[1142] Step 2:

[1143] The server receives the request packet sent by the user. The server analyzes the packet and extracts data such as the robot ID, desired maintenance date, and work content. The server then retrieves the robot's operation history and past maintenance history from the database.

[1144] Input: Request packet

[1145] Output: Operation history, past maintenance history, resource information

[1146] Step 3:

[1147] The server passes the information obtained from the database to a generative AI model to generate an optimal maintenance schedule. The generative AI model considers the robot's important operating history, operating schedule, resource usage status, and other factors to create efficient maintenance candidates.

[1148] Input: Operation history, past maintenance history, resource information

[1149] Output: Optimal maintenance schedule candidates

[1150] Step 4:

[1151] The server organizes the maintenance schedule candidates obtained from the generative AI model and converts them into a format that is easy for users to understand. The converted maintenance candidates are packaged into a response packet and sent to the smartphone app.

[1152] Input: Optimal maintenance schedule candidates

[1153] Output: User-visible response packet

[1154] Step 5:

[1155] The user checks the proposed maintenance schedule through the smartphone app, selects the most suitable date and time from the displayed maintenance schedule candidates, and clicks the confirm button. The selected data is sent from the smartphone app to the server.

[1156] Input: Maintenance schedule candidate

[1157] Output: Confirmed maintenance schedule

[1158] Step 6:

[1159] The server records the confirmed maintenance information received from the user in a database, and if necessary, sends an email notification to the maintenance staff or related departments.

[1160] Input: Confirmed maintenance schedule

[1161] Output: Database records, email notifications

[1162] In this way, we will build a system in which the data entered at each step is appropriately processed and calculated, and the necessary data is output for the next step, and ultimately notified to the user and related departments.

[1163] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1164] This invention is a system that operates through the interaction of a user (medical administrative assistant), a server, a terminal, and an emotion engine to support medical administrative support tasks. This system makes suggestions that take into account the user's emotions, thereby achieving more flexible and efficient business support than conventional systems. The system's programs and processing are described in detail below.

[1165] Program Description

[1166] User-initiated medical data entry and emotion recognition

[1167] The user is a medical administrative assistant. He logs in to the terminal and accesses the appointment management interface. Through the interface, the user inputs medical data such as the patient's basic information, desired appointment date, and details of the appointment based on a request from the doctor. The terminal compiles the input data into a form, and an emotion engine analyzes the user's input content, input speed, and facial expressions (if equipped with a camera) to recognize the user's emotions. The terminal generates a request packet containing the medical data and emotion data and sends it to the server.

[1168] Server receives request and retrieves information

[1169] The server analyzes the request packet received from the terminal and extracts the necessary patient information and examination details.The server then retrieves the patient's past medical history, doctor schedules, and hospital resource information (such as clinical testing and examination room availability) from the medical database.

[1170] Optimization processing using AI tools and use of emotional data

[1171] The server then passes the acquired data and sentiment data to an AI tool to optimize appointment scheduling, taking into account the following factors:

[1172] Important patient medical history

[1173] Doctor's schedule availability

[1174] Availability of examination rooms and necessary equipment

[1175] The user's emotional state (stress, anxiety, satisfaction, etc.)

[1176] Taking these conditions into consideration, the AI ​​tool generates the most efficient and least stressful reservation options for the user and returns them to the server.

[1177] Server proposal and data transmission

[1178] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. The converted reservation candidates are stored in a response packet and sent to the device.

[1179] Displaying information and reflecting emotions on devices

[1180] The terminal analyzes the response packet received from the server and displays suggested reservation options to the user. Priorities and explanations based on the user's feelings are added, allowing the user to easily review the suggested options. The user selects the most suitable date and time from the displayed reservation options and clicks the confirm button.

[1181] Server confirmation and recording

[1182] The server records the confirmed reservation information received from the device in the medical database, confirming that the reservation has been officially registered, and also records the emotion data recognized by the emotion engine for use in improving the accuracy of future suggestions.

[1183] Specific examples

[1184] Scenario: Managing patient appointments with emotions in mind

[1185] 1. The user logs in and enters the necessary information into the appointment management interface on their device: patient name, ID, desired appointment date, appointment details, etc. The emotion engine also recognizes the user's emotions from their facial expressions and input speed.

[1186] 2. The terminal generates and sends a request packet to send the input data and emotion data to the server.

[1187] 3. The server receives the request and retrieves the patient's medical history, doctor schedule, and resource information from the medical database.

[1188] 4. The server passes the acquired information and emotional data to the AI ​​tool, which then generates the best reservation candidates.

[1189] 5. The server organizes the reservation suggestions received from the AI ​​tool and sends them to the device. The suggestions are displayed in a way that takes the user's emotions into consideration.

[1190] 6. The terminal displays the proposed reservation options to the user, and the user selects and confirms the most suitable reservation.

[1191] 7. The server records the confirmed information in a medical database, notifies the doctor and patient, and records emotional data for future improvements to the entire system.

[1192] By combining this program with processing steps, a system is realized that not only streamlines the work of medical administrative assistants and reduces the burden on medical professionals, but also provides flexible support that takes the user's emotions into consideration.

[1193] The processing flow will be explained below.

[1194] Step 1:

[1195] The user (medical administrative assistant) logs in to the terminal and accesses the appointment management interface.

[1196] Step 2:

[1197] Based on a request from a doctor, the user enters medical data such as the patient's name, ID, desired date of examination, and details of the examination.

[1198] Step 3:

[1199] The device compiles the entered data into a form, and an emotion engine analyzes the input content, input speed, and in some cases the user's facial expressions to recognize the user's emotions.

[1200] Step 4:

[1201] The terminal generates a request packet including medical data and emotion data and transmits it to the server.

[1202] Step 5:

[1203] The server analyzes the request packet received from the terminal and extracts patient information and examination details.

[1204] Step 6:

[1205] The server retrieves patient past medical history, doctor schedules, and hospital resource information from a medical database.

[1206] Step 7:

[1207] The server then passes the acquired data and emotional data to an AI tool that optimizes appointments, taking into account the patient's important medical history, the doctor's available schedule, the availability of examination rooms and necessary equipment, and the user's emotional state.

[1208] Step 8:

[1209] The AI ​​tool generates optimal reservation options and returns them to the server.

[1210] Step 9:

[1211] The server organizes the reservation suggestions received from the AI ​​tool and converts them into a format that is easy for users to understand.

[1212] Step 10:

[1213] The server stores the converted reservation candidates in a response packet and transmits it to the terminal.

[1214] Step 11:

[1215] The terminal analyzes the response packet received from the server and displays a list of reservation options to the user. At this time, the suggestions are displayed in a way that takes the user's emotions into consideration.

[1216] Step 12:

[1217] The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[1218] Step 13:

[1219] The terminal generates a request packet to transmit the user's final selection back to the server.

[1220] Step 14:

[1221] The server records the confirmed reservation information received from the terminal in the medical database and confirms that the reservation has been officially registered.

[1222] Step 15:

[1223] The server confirms that the reservation information has been registered and notifies the terminal of that information.

[1224] Step 16:

[1225] The terminal receives the notification from the server and displays to the user that the reservation has been confirmed.

[1226] Step 17:

[1227] If necessary, the server automatically sends a notification email to the doctor and patient confirming the appointment.

[1228] Step 18:

[1229] The server records the emotion data recognized by the emotion engine and uses it to improve the accuracy of future suggestions.

[1230] Example 2

[1231] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1232] Conventional medical administrative support systems do not take user emotions into consideration when entering data or managing appointments, which reduces work efficiency and the user experience. They also lack the flexibility to integrate and optimize a wide range of information, such as medical data, patient medical history, and doctor schedules. There is a need to solve these problems, reduce user stress and anxiety, and provide more efficient and user-friendly work support.

[1233] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting medical data from a user, means for the server to receive the medical data and the user's emotional data and acquire related information from a medical database, means for the server to optimize the acquired related information and emotional data using an AI tool and propose it to the user, means for the user to confirm and confirm the proposed information, and means for the server to record the confirmed information in the medical database and record the emotional data. This enables flexible and efficient business support that takes user emotions into consideration.

[1234] "User" refers to the medical administrative assistant who operates the system and inputs medical data.

[1235] "Medical data" refers to information necessary for medical administrative support work, such as basic patient information, desired appointment date, and details of the appointment.

[1236] "Emotional data" refers to information about the user's psychological state analyzed from their input speed, facial expressions, etc.

[1237] "Server" refers to a central processing unit for receiving medical data and emotion data sent from terminals, obtaining related information, and optimizing it.

[1238] A "medical database" refers to data storage that stores medical-related information such as patient medical history, doctor schedules, and hospital resource information.

[1239] "Relevant information" refers to the patient's medical history, doctor's schedule, and hospital resource information.

[1240] "AI tools" refers to software and algorithms with artificial intelligence technology used on servers.

[1241] "Optimization" refers to the process of generating optimal appointment candidates based on the relevant information and sentiment data obtained.

[1242] "Suggestion" refers to presenting optimized information to the user using AI tools.

[1243] "Confirmation" refers to the act of the user selecting the most suitable appointment from the proposed candidates and making a final decision.

[1244] "Recording" refers to the act of storing established information in a medical database.

[1245] The present invention is a flexible and efficient business support system that optimizes medical administrative support tasks and takes into account the user's emotions. The system operates through the interaction of the user, server, terminal, and emotion engine.

[1246] System configuration and program description

[1247] User data entry and emotion recognition

[1248] The user logs in to the terminal as a medical administrative assistant and accesses the appointment management interface. The user enters basic patient information (name, ID), desired appointment date, details of the appointment, etc. through the terminal. At this time, the terminal compiles the entered data into an appropriate format.

[1249] Furthermore, the device uses an emotion engine to analyze the user's input speed and facial expressions. For example, if the device is equipped with a camera, it can capture and analyze the user's facial expressions to determine stress or anxiety levels. The emotion engine indicates that the user may be feeling stressed even if the input speed is slow. The emotion data and medical data generated in this way are sent to the server.

[1250] Server receives request and retrieves information

[1251] The server analyzes the medical and emotional data received from the device and extracts the necessary patient information and examination details.The server then accesses a medical database (e.g., MySQL) to obtain the patient's past medical history, doctor schedules, and hospital resource information.

[1252] Optimization with AI tools and use of sentiment data

[1253] The server passes the acquired data and emotional data to an AI tool (e.g., TensorFlow) to optimize appointment scheduling. The AI ​​tool generates optimal appointment candidates by taking into account the patient's past medical history, doctor's schedule, availability of examination rooms and necessary equipment, and the user's emotional state (stress, anxiety, satisfaction). The results are returned to the server.

[1254] Proposal and confirmation

[1255] The server organizes the reservation candidates received from the AI ​​tool, converts them into a format that is easy for the user to understand, and sends them to the device. The device displays the received reservation candidates on its interface, and the user selects the best date and time from the displayed reservation candidates. When the user clicks the confirm button, the confirmation information is sent back to the server.

[1256] Finalization and recording

[1257] The server records the confirmation information received from the device in the medical database and confirms that the appointment has been officially registered. The server also records the emotion data from the emotion engine and uses it to improve the accuracy of future recommendations. The server also notifies the doctor and patient that the appointment has been confirmed.

[1258] Specific examples

[1259] For example, suppose a user logs in to a terminal and inputs the desired consultation date for patient "Yamada Taro" as "2023 / 10 / 15" and the consultation content as "consultation for lower back pain." The prompt text in this case is as follows:

[1260] ---

[1261] Patient demographics:

[1262] Name: Taro Yamada

[1263] ID:12345

[1264] Desired appointment date: 2023 / 10 / 15

[1265] Examination content: Examination of lower back pain

[1266] ---

[1267] This system will streamline the work of medical administrative assistants and utilize structured medical and emotional data to make suggestions that reduce users' stress and anxiety. Theoretically, it is expected to create optimal medical appointments for both patients and medical professionals.

[1268] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1269] Step 1: User data entry and emotion recognition

[1270] A user logs in to a terminal as a medical administrative assistant and accesses the appointment management interface. The user enters the patient's basic information (name, ID), desired appointment date, and details of the appointment. At this time, the terminal organizes the entered data into an appropriate format. The terminal then uses an emotion engine to analyze the user's input speed and facial expressions. Specifically, the terminal's camera captures the user's facial expressions and analyzes them in real time. For example, if the input speed is slow, it is determined that the user is "feeling stressed." The medical data and emotional data generated in this way are sent to the server as a request packet.

[1271] Input: Patient's basic information, desired consultation date, consultation details

[1272] Data processing: Formatting input data, generating emotional data (input speed and facial expression analysis)

[1273] Output: A request packet containing medical and emotional data.

[1274] Step 2: Server receives request and retrieves information

[1275] The server receives the request packet from the terminal. It analyzes the received packet and extracts the patient information and consultation details. The server then accesses a medical database (e.g., MySQL) to retrieve the patient's past medical history, doctor schedule, and hospital resource information. This provides all the data necessary to process the appointment.

[1276] Input: A request packet containing medical and emotional data

[1277] Data processing: Analyzing request packets and extracting patient information and medical examination details

[1278] Output: Patient's medical history, doctor's schedule, hospital resource information

[1279] Step 3: Optimizing with AI tools and using sentiment data

[1280] The server passes the acquired data and emotional data to an AI tool (e.g., TensorFlow) to perform optimization analysis for appointment scheduling. The AI ​​tool generates optimal appointment candidates based on the patient's past medical history, doctor schedules, hospital resource information, and the user's emotional state (stress, anxiety, satisfaction). For example, it also takes into account cases where a specific examination room is required based on past medical history.

[1281] Input: Patient's medical history, doctor's schedule, hospital resource information, emotional data

[1282] Data processing: Data analysis and optimization using AI tools

[1283] Output: Best booking candidates

[1284] Step 4: Server Proposal and Data Transmission

[1285] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. For example, it summarizes the date, time, and location of each reservation candidate. The converted reservation candidates are then stored in a response packet and sent to the terminal.

[1286] Input: Best booking candidate

[1287] Data processing: Formatting reservation candidates and converting them into response packets

[1288] Output: Response packet

[1289] Step 5: Terminal displays information and user selection

[1290] The terminal analyzes the response packet received from the server and extracts its contents. Next, it displays suggested reservation options on the interface. The user selects the most suitable date and time from the displayed reservation options and clicks the Confirm button. Based on the user's selection, the terminal resends a request packet containing the confirmation information to the server.

[1291] Input: Response packet

[1292] Data processing: Analyzing response packets and displaying reservation candidates

[1293] Output: Request packet containing confirmation information

[1294] Step 6: Server confirmation and recording

[1295] The server analyzes the confirmation information received from the device and records it in a medical database. It then confirms that the appointment has been officially registered and notifies the doctor and patient that the appointment has been confirmed. Emotional data is also recorded and will be used to improve the system in the future.

[1296] Input: Request packet containing confirmation information

[1297] Data processing: Analysis of confirmed information and recording in a database

[1298] Output: Notification to doctor and patient, recorded emotion data

[1299] This concretely shows the processing steps of the present system and clarifies the interactions between the user, server, and terminal.

[1300] (Application example 2)

[1301] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1302] In modern manufacturing, efficient schedule and resource management is extremely important. However, current systems do not take into account user emotions, which can create a stressful environment for workers. Furthermore, there is no established method for optimally managing work history and resource information, which can lead to problems such as reduced productivity and inefficient operations.

[1303] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting business data from a user, means for the server to receive the business data and acquire related information from a database, and means for optimizing the acquired related information and emotion data from an emotion engine using an AI tool and proposing the optimization to the user. This enables optimal schedule and resource management that takes user emotions into consideration.

[1304] A "user" is a person who uses the system and performs operations to input business data.

[1305] "Business data" refers to data such as work details, schedules, and resource usage status that users input into the system.

[1306] A "server" is a device that receives business data from users, retrieves necessary information from a database, and optimizes it using AI tools.

[1307] A "database" is an information storage device that stores information related to business operations.

[1308] "Related information" refers to information necessary for business execution, such as work history, schedule, and resource information.

[1309] An "emotion engine" is a system that recognizes emotions from user input and facial expressions, and processes them as data.

[1310] "Emotion data" is data that indicates the emotional state of the user as recognized by the emotion engine.

[1311] An "AI tool" is software or hardware equipped with artificial intelligence algorithms for analyzing data and performing optimization processing.

[1312] "Optimization" is the process of using AI tools to generate the most efficient and least stressful schedule and resource allocation for users based on relevant information and emotional data.

[1313] "Proposing" means that the server presents the results of the optimization process to the user.

[1314] "Confirming" means that the user selects the most appropriate information from the proposed information and confirms its execution.

[1315] "Recording" refers to the process in which the server saves the confirmed information back into the database.

[1316] This invention is a system that inputs, optimizes, proposes, confirms, and records business data through interactions between users, servers, databases, emotion engines, and AI tools.

[1317] System configuration

[1318] 1. User operations

[1319] Users input business data using a smartphone or head-mounted display. The business data includes the work content, schedule, required resources, etc. The device equipped with an emotion engine acquires emotional data from the user's facial expressions and input speed.

[1320] 2. Receipt and processing of data

[1321] The device assembles task data and emotion data into packets and sends them to the server. The server receives these packets and retrieves related information (past work history, current resource status, schedule, etc.) from a database.

[1322] 3. Optimization process

[1323] The server passes relevant information and emotional data to the AI ​​tool, which then performs an optimization process. The AI ​​tool then uses this information to generate the most efficient and least stressful schedule for the user and returns it to the server. This optimization process uses programming languages ​​such as Python and machine learning algorithms.

[1324] 4. Suggestions and Selections

[1325] The server organizes the schedule candidates received from the AI ​​tool and presents them to the user. The user selects the best one from the presented schedule candidates and confirms it in the system. The information is displayed using a web interface or application.

[1326] 5. Records and Notifications

[1327] The server records the confirmed information in a database and notifies relevant departments as necessary. For example, a notification function is implemented to inform workers and managers of confirmed schedules.

[1328] The specific hardware and software used

[1329] Hardware

[1330] User devices: smartphone, head-mounted display, camera

[1331] Server: A high-performance server for data processing and optimization.

[1332] Database server: a data storage device for storing business-related information

[1333] software

[1334] Emotion Engine: Software for Recognizing User Emotions

[1335] AI Tools: Artificial intelligence algorithms that optimize data processing

[1336] Database management software: e.g., MySQL or PostgreSQL

[1337] Web interface or application: a platform through which users can enter data and view suggestions

[1338] Specific examples

[1339] Scenario: Optimizing factory production schedules

[1340] 1. The user inputs the work content and schedule using a smartphone or head-mounted display.

[1341] 2. The emotion engine analyzes the user's facial expressions and obtains emotional data.

[1342] 3. The device sends business data and emotion data to the server.

[1343] 4. The server retrieves the relevant information from the database and passes it to the AI ​​tool.

[1344] 5. The AI ​​tool generates an optimal schedule and returns it to the server.

[1345] 6. The server proposes a schedule to the user.

[1346] 7. User checks the schedule, selects the best one and confirms.

[1347] 8. The server records the confirmed information in the database and notifies the relevant departments.

[1348] Example prompts for generative AI models

[1349] "To optimize the production schedule in your factory, please create a schedule taking into account the following data:

[1350] Worker name and data

[1351] Specific work content and schedule

[1352] Emotional data (stress, anxiety, satisfaction, etc.)

[1353] Generate the optimal schedule and present it to you."

[1354] In this way, a system is realized that can reduce user stress and improve work efficiency by proposing an optimized production schedule based on emotional data.

[1355] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1356] Step 1:

[1357] The user inputs business data using a smartphone or head-mounted display. The input business data includes the work content, schedule, required resources, etc. The emotion engine then analyzes the user's facial expressions and input speed to obtain emotional data. Input: Business data, emotional data. Output: Input business data, obtained emotional data.

[1358] Step 2:

[1359] The device assembles task data and emotion data into packets and sends them to the server. This ensures that all important information is delivered to the server in a single request packet. Input: task data, emotion data. Output: request packet.

[1360] Step 3:

[1361] The server receives the request packet and analyzes it. It extracts business data and emotion data from the analyzed data and obtains related information (work history, resource information, schedule, etc.) from the database. Input: Request packet. Output: Related information, business data, emotion data.

[1362] Step 4:

[1363] The server passes the acquired related information and emotional data to the AI ​​tool, which then performs optimization processing. The AI ​​tool performs calculations using multidimensional arrays based on the related information and emotional data to generate the most efficient and least stressful schedule. Input: Related information, emotional data. Output: Optimal schedule candidates.

[1364] Step 5:

[1365] The server organizes the optimal schedule candidates received from the AI ​​tool and presents them to the user. The user selects the optimal one from the presented schedule candidates. At this stage, the user confirms the decision. Input: Optimal schedule candidate. Output: Confirmed information by the user.

[1366] Step 6:

[1367] The server records the confirmed schedule information in the database. This allows the confirmed schedule to be shared throughout the system and notified to other departments as necessary. Notifications are made via email or the internal notification system. Input: Confirmed information by the user. Output: Recorded confirmed schedule, notification.

[1368] 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.

[1369] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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 a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1370] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1371] [Fourth embodiment]

[1372] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1373] 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.

[1374] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1375] 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.

[1376] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1377] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1378] 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.

[1379] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1380] 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.

[1381] 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 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.

[1382] 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.

[1383] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1384] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1385] The present invention is a system for supporting medical administrative support work, and operates through the interaction of users (medical administrative assistants), servers, and terminals. The basic configuration of the system and the roles of each component are described below.

[1386] Program Description

[1387] User-initiated medical data entry

[1388] The user is a medical administrative assistant. He logs in to a terminal and accesses the appointment management interface. Through this interface, the user inputs medical data such as the patient's basic information, desired appointment date, and details of the appointment based on a request from the doctor. The terminal compiles the input data into a form and generates a request packet to send to the server.

[1389] Server receives request and retrieves information

[1390] The server analyzes the request packet received from the terminal and extracts the necessary patient information and examination details.The server then retrieves the patient's past medical history, the current doctor's schedule, and hospital resource information (such as the availability of clinical tests and examination rooms) from the medical database.

[1391] Optimization process using AI tools

[1392] The server passes the acquired data to an AI tool that optimizes appointment scheduling, taking into account the following factors:

[1393] Important patient medical history

[1394] Doctor's schedule availability

[1395] Availability of examination rooms and necessary equipment

[1396] The AI ​​tool takes these conditions into account and generates the most efficient reservation options, which are returned to the server.

[1397] Server proposal and data transmission

[1398] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. The converted reservation candidates are stored in a response packet and sent to the device.

[1399] Display and confirm information on the terminal

[1400] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user. The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[1401] Server confirmation and recording

[1402] The server records the confirmed appointment information received from the terminal in the medical database, confirms that the appointment has been officially registered, and notifies the doctor and patient of the confirmed information as necessary.

[1403] Specific examples

[1404] Scenario: Managing patient appointments

[1405] 1. The user logs in and enters the necessary information into the appointment management interface on the terminal: patient name, ID, desired appointment date, appointment details, etc.

[1406] 2. The terminal generates and sends a request packet to send the input data to the server.

[1407] 3. The server receives the request and retrieves the patient's medical history, doctor schedule, and resource information from the medical database.

[1408] 4. The server passes the acquired information to the AI ​​tool, which then generates the best reservation candidates.

[1409] 5. The server organizes the reservation candidates received from the AI ​​tool and sends them to the device.

[1410] 6. The terminal displays reservation options to the user, and the user selects and confirms the most suitable reservation.

[1411] 7. The server records the confirmed information in the medical database and notifies the doctor and patient.

[1412] By combining this program with processing steps, a system can be created that streamlines the work of medical administrative assistants and reduces the burden on medical professionals.

[1413] The processing flow will be explained below.

[1414] Step 1:

[1415] The user (medical administrative assistant) logs in to the terminal and accesses the appointment management interface.

[1416] Step 2:

[1417] Based on a request from a doctor, the user enters medical data such as the patient's name, ID, desired date of examination, and details of the examination.

[1418] Step 3:

[1419] The terminal compiles the input data into a form and generates a request packet to be sent to the server.

[1420] Step 4:

[1421] The server analyzes the request packet received from the terminal and extracts the necessary patient information and examination details data.

[1422] Step 5:

[1423] The server retrieves the patient's past medical history, doctor's schedule, and hospital resource information (such as availability of clinical tests and examination rooms) from the medical database.

[1424] Step 6:

[1425] The server then passes the acquired data to an AI tool that optimizes appointment scheduling, taking into account the patient's important medical history, the doctor's available schedule, and the availability of examination rooms and necessary equipment.

[1426] Step 7:

[1427] The AI ​​tool generates optimal reservation options and returns them to the server.

[1428] Step 8:

[1429] The server organizes the reservation suggestions received from the AI ​​tool and converts them into a format that is easy for users to understand.

[1430] Step 9:

[1431] The server stores the converted reservation candidates in a response packet and transmits it to the terminal.

[1432] Step 10:

[1433] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user.

[1434] Step 11:

[1435] The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[1436] Step 12:

[1437] The terminal generates a request packet to transmit the user's final selection back to the server.

[1438] Step 13:

[1439] The server records the confirmed reservation information received from the terminal in the medical database and confirms that the reservation has been officially registered.

[1440] Step 14:

[1441] The server confirms that the reservation information has been registered and notifies the terminal of that information.

[1442] Step 15:

[1443] The terminal receives the notification from the server and displays to the user that the reservation has been confirmed.

[1444] Step 16:

[1445] If necessary, the server automatically sends a notification email to the doctor and patient confirming the appointment.

[1446] Example 1

[1447] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1448] In recent years, there has been a demand for greater efficiency and accuracy in medical administration, but manual medical data entry and appointment management is time-consuming and prone to errors. Furthermore, coordinating doctor schedules and managing examination room resources is complicated, making it difficult to secure optimal appointment times. This results in longer waiting times for patients and an increased burden on medical professionals.

[1449] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1450] In this invention, the server includes a means for a user to log in and input medical data, a means for a terminal to compile the medical data, generate a request, and send it to the server, a means for the server to receive and analyze the medical data and acquire related information from a medical database, a means for the server to pass the acquired related information to an AI tool for optimization and suggest candidate appointments to the user, a means for the user to select and confirm the optimal appointment from the suggested candidates, and a means for the server to record the confirmed information in the medical database and notify the user as necessary. This enables accurate and efficient management of medical data and securing optimal appointment times.

[1451] "User" is a medical administrative assistant who inputs medical data and operates the system.

[1452] A "terminal" is an information processing device used by a user, and is a device that has the function of sending input medical data collectively to a server.

[1453] A "server" is an information processing device that receives and analyzes medical data sent by users, and is responsible for obtaining the necessary information from the medical database and passing it on to the AI ​​tool.

[1454] "Medical data" refers to data that includes information necessary for scheduling an appointment, such as the patient's name, ID, desired date of appointment, and details of the appointment.

[1455] A "request packet" is a data packet created by a terminal to collect medical data input by a user and send it to a server.

[1456] A "medical database" is a database for storing and managing medical information such as patient medical history, doctor schedules, and the availability of examination rooms and necessary equipment.

[1457] The "AI tool" is software that uses artificial intelligence to generate optimal appointment options based on medical data obtained from the server.

[1458] "Suggested appointments" are suggestions for optimal dates and times for patient appointments generated by AI tools.

[1459] A "response packet" is a packet of data created by the server to organize the reservation candidates received from the AI ​​tool and send them to the terminal.

[1460] "Notification" refers to the transmission of information by the server to inform the doctor and patient that a reservation has been confirmed.

[1461] The present invention is a system for supporting medical administrative support work, and is a system that operates through interactions between a user (medical administrative assistant), a server, and a terminal. The following describes in detail the mode for carrying out the present invention.

[1462] First, the user logs in to a terminal. The terminal is an information processing device such as a PC or tablet, and must be connected to the Internet. Once the user logs in, they can access the appointment management interface. This interface consists of a form for entering basic patient information (name, ID), desired appointment date, details of the appointment, etc.

[1463] Next, the device compiles the medical data entered by the user into a request packet. The request packet is created in a data structure such as JSON format and sent to the server using a protocol such as an HTTP POST request. The server analyzes the received request packet and extracts patient information and examination details.

[1464] Based on this data, the server retrieves the necessary information from the medical database, which contains the patient's past medical history, current doctor schedules, and hospital resource information (such as the availability of examination rooms and necessary equipment).

[1465] The server passes the acquired data to the AI ​​tool, which uses an artificial intelligence algorithm to generate the optimal reservation date and time, taking into account the following factors:

[1466] Important patient medical history

[1467] Doctor's schedule availability

[1468] Availability of examination rooms and necessary equipment

[1469] The reservation suggestions generated by the AI ​​tool are returned to the server, which organizes them and converts them into a format that is easy for the user to understand. The organized reservation suggestions are stored in a response packet and sent to the device.

[1470] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user. The user selects the most suitable date and time from the displayed reservation candidates and clicks the Confirm button. The confirmed reservation information is then sent back to the server.

[1471] The server records the received confirmed reservation information in the medical database, thereby confirming that the reservation has been officially registered. The server also notifies the doctor and patient of the confirmed reservation information as necessary.

[1472] Specific examples

[1473] For example, when making an appointment for a patient to visit the hospital, the user inputs the patient's name, ID, desired date of the appointment, and the details of the appointment into the terminal. After that, the system goes through the above process and proposes the optimal appointment date and time, which the user confirms.

[1474] Prompt Sentence Examples

[1475] An example of a prompt to explain the system overview to a generative AI model is:

[1476] It would be something like, "Please explain an AI system that optimizes medical appointments."

[1477] This system will streamline the work of medical administrative assistants and reduce the burden on medical professionals.

[1478] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1479] Program processing steps

[1480] Step 1: User enters medical data

[1481] Specific description:

[1482] The user logs in to the terminal and accesses the appointment management interface, which displays fields for entering the patient's basic information (name, ID), the desired appointment date, and the details of the appointment.

[1483] Input: Basic patient information, desired appointment date, and appointment details entered by the user.

[1484] Specific behavior:

[1485] The user logs in by entering a user name and password into the terminal.

[1486] After logging in, the appointment management interface will be displayed.

[1487] The user enters data into fields such as "Name," "Patient ID," "Desired appointment date," and "Expectation details."

[1488] After completing the input, the user clicks the "Submit" button.

[1489] Step 2: Terminal sends data and generates request packet

[1490] Specific description:

[1491] The device collects the medical data entered by the user and assembles it into a request packet, which is then sent to the server as an HTTP POST request.

[1492] Input: Medical data entered by the user.

[1493] Output: The request packet sent to the server.

[1494] Specific behavior:

[1495] The terminal converts the input data into JSON format.

[1496] The converted data is sent to the server as an HTTP POST request.

[1497] When the sending process is complete, the terminal will display a "Sending Complete" message.

[1498] Step 3: The server receives the request and retrieves information from the medical database.

[1499] Specific description:

[1500] The server receives the request packet sent from the device, analyzes it, and extracts the necessary patient information and examination details. After extraction, it executes a query to obtain past medical history, current doctor schedules, and resource information from the medical database.

[1501] Input: The request packet received from the device.

[1502] Output: Parsed patient information and consultation details, plus additional information retrieved from medical databases.

[1503] Specific behavior:

[1504] The server receives the HTTP request and parses the JSON data to extract patient information and medical details.

[1505] The server generates an SQL query and sends it to the database.

[1506] Retrieve patient history, physician schedule, and resource information from the database.

[1507] Save the retrieved data in a temporary data store.

[1508] Step 4: Providing data from the server to the AI ​​tool and optimizing it

[1509] Specific description:

[1510] The server passes the acquired data to an AI tool, which then uses this data to generate optimal appointment candidates, taking into account past medical history, doctor availability, and examination room usage.

[1511] Input: Various information obtained from medical databases.

[1512] Output: Optimized booking candidates.

[1513] Specific behavior:

[1514] The server sends the acquired data to the AI ​​tool as an API request.

[1515] AI tools perform the calculations and generate the best booking options.

[1516] The reservation suggestions are sent back to the server.

[1517] Step 5: Data sorting by the server and sending to the device

[1518] Specific description:

[1519] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. The converted data is stored in a response packet and sent to the device as an HTTP response.

[1520] Input: Booking suggestions received from the AI ​​tool.

[1521] Output: Response packet to send to the device.

[1522] Specific behavior:

[1523] The server converts the received data into HTML or JSON format and includes it in the response.

[1524] Sends an HTTP response to the device.

[1525] Step 6: Terminal displays information and user selection

[1526] Specific description:

[1527] The terminal analyzes the response packet received from the server and displays a list of reservation candidates to the user. The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[1528] Input: The response packet received from the server.

[1529] Output: Reservation information selected and confirmed by the user.

[1530] Specific behavior:

[1531] The terminal analyzes the response data and displays a list of reservation options on the screen.

[1532] The user selects the best reservation option from the list.

[1533] The user clicks the "Confirm Reservation" button.

[1534] Step 7: Server processes and notifies the reservation

[1535] Specific description:

[1536] The server records the confirmed reservation information received from the terminal in the medical database. This officially registers the reservation. The server also notifies the doctor and patient of the confirmed information as necessary.

[1537] Input: Reservation information confirmed by the user.

[1538] Output: Appointment information recorded in the medical database, and notifications to the doctor and patient.

[1539] Specific behavior:

[1540] The server receives the confirmed reservation information and sends an INSERT query to the database.

[1541] Verify that the reservation information was successfully recorded in the database.

[1542] The server sends a confirmation of the appointment to the doctor and patient via email or SMS.

[1543] (Application example 1)

[1544] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1545] In conventional systems, medical administrative support tasks and factory robot maintenance schedule management were performed manually, resulting in problems such as human error and time loss. Furthermore, it was difficult to optimize the schedule, resulting in reduced work efficiency and wasted resources. For these reasons, there was a demand for a system that could achieve highly accurate and efficient reservation management and maintenance schedule management.

[1546] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1547] In this invention, the server includes means for inputting data from a user, means for the server to receive the data and acquire related information from a database, means for the server to optimize the acquired related information using an AI tool and propose it to the user, means for the user to confirm and confirm the proposed information, means for the server to record the confirmed information in the database, and means for the server to perform optimization using a generative AI model, thereby enabling users to manage their reservations and schedules with high accuracy and efficiency.

[1548] A "user" is an entity that uses the system to input and manage data.

[1549] "Data" is a general term for information that users input into the system, including medical information, maintenance information, and the like.

[1550] A "server" is a device or system that receives data, retrieves relevant information, performs optimization processing, and provides the results to the user.

[1551] A "database" is a collection of information in which various types of information are systematically organized and stored, including medical information and machine operation history.

[1552] "Related information" refers to additional information that the server obtains based on the data entered by the user, and includes medical history, operation history, schedule, and resource information.

[1553] "AI tools" refers to artificial intelligence technology that performs optimization processing using related information acquired by the server.

[1554] "Optimization" is the process of deriving the most efficient and appropriate results based on relevant information, based on the user's requests and conditions.

[1555] A "generative AI model" is an artificial intelligence algorithm that generates useful information and predictions from data.

[1556] "Proposing" refers to the act of the server informing the user of the optimized results and prompting them to make a selection or confirm.

[1557] "Confirming" refers to the act of the user confirming the proposed information and making a final decision.

[1558] "Recording" refers to the act of the server storing the determined information in a database for future reference if necessary.

[1559] The present invention relates to a system for improving the efficiency of maintenance schedule management for factory robots. The specific system configuration and program processing will be described below.

[1560] Program Description

[1561] User data entry

[1562] Users use a smartphone app to enter maintenance information for factory robots, specifically, data such as the robot ID, desired maintenance date, and work content. The entered data is sent from the smartphone to the server.

[1563] Server receives request and retrieves information

[1564] The server receives the data sent by the user and retrieves the robot's operation history and past maintenance history from the database, as well as the current robot schedule and resource information.

[1565] Optimization process using AI tools

[1566] The server passes the acquired information to a generative AI model to generate an optimal maintenance schedule. The AI ​​tool considers the robot's important operating history, operating schedule, resource utilization, and other factors to create efficient maintenance candidates.

[1567] Server proposal and data transmission

[1568] The server organizes the maintenance suggestions received from the AI ​​tool and converts them into a format that is easy for users to understand. The converted maintenance suggestions are then sent to a smartphone app and displayed to the user.

[1569] User displays and confirms information

[1570] The user can check the proposed maintenance schedule through a smartphone app, select the optimal date and time, and confirm the schedule. The confirmed information is then sent from the smartphone to the server.

[1571] Server confirmation and recording

[1572] The server records the confirmed maintenance information received from the user in a database, and if necessary, notifies the maintenance personnel or relevant departments by email.

[1573] Hardware and Software Configuration

[1574] Hardware: Smartphones, servers

[1575] Software: Java programs, javax.mail library, database management systems (e.g., MySQL), AI tools (e.g., TensorFlow)

[1576] Specific examples

[1577] For example, consider a factory worker entering maintenance information for robot ID "R12345." The worker uses a smartphone app to enter the following:

[1578] Robot ID: R12345

[1579] Desired maintenance date: 2023-10-01

[1580] Work: Parts replacement

[1581] The server then receives this information, retrieves the robot's operating history and past maintenance history from a database, and uses AI tools to generate an optimal maintenance schedule. This optimal schedule is displayed on a smartphone app, and employees can select the most suitable date and time to confirm it. The confirmed information is recorded on the server and, if necessary, notified to relevant departments via email.

[1582] Prompt Sentence Examples

[1583] Please enter the maintenance information for robot ID "R12345". Example: Maintenance date "2023-10-01", work content "Part replacement".

[1584] The optimal maintenance schedule is displayed.

[1585] Your selected maintenance schedule has been confirmed and notifications have been sent.

[1586] In this way, users can manage the maintenance schedule of factory robots with high accuracy and efficiency.

[1587] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1588] Step 1:

[1589] The user uses a smartphone app to input maintenance information for the factory robot. Specific input information includes the robot ID, desired maintenance date, and work details. The smartphone app compiles this data into packets and sends them to the server.

[1590] Input: Robot ID, desired maintenance date, work content

[1591] Output: Request packet to the server

[1592] Step 2:

[1593] The server receives the request packet sent by the user. The server analyzes the packet and extracts data such as the robot ID, desired maintenance date, and work content. The server then retrieves the robot's operation history and past maintenance history from the database.

[1594] Input: Request packet

[1595] Output: Operation history, past maintenance history, resource information

[1596] Step 3:

[1597] The server passes the information obtained from the database to a generative AI model to generate an optimal maintenance schedule. The generative AI model considers the robot's important operating history, operating schedule, resource usage status, and other factors to create efficient maintenance candidates.

[1598] Input: Operation history, past maintenance history, resource information

[1599] Output: Optimal maintenance schedule candidates

[1600] Step 4:

[1601] The server organizes the maintenance schedule candidates obtained from the generative AI model and converts them into a format that is easy for users to understand. The converted maintenance candidates are packaged into a response packet and sent to the smartphone app.

[1602] Input: Optimal maintenance schedule candidates

[1603] Output: User-visible response packet

[1604] Step 5:

[1605] The user checks the proposed maintenance schedule through the smartphone app, selects the most suitable date and time from the displayed maintenance schedule candidates, and clicks the confirm button. The selected data is sent from the smartphone app to the server.

[1606] Input: Maintenance schedule candidate

[1607] Output: Confirmed maintenance schedule

[1608] Step 6:

[1609] The server records the confirmed maintenance information received from the user in a database, and if necessary, sends an email notification to the maintenance staff or related departments.

[1610] Input: Confirmed maintenance schedule

[1611] Output: Database records, email notifications

[1612] In this way, we will build a system in which the data entered at each step is appropriately processed and calculated, and the necessary data is output for the next step, and ultimately notified to the user and related departments.

[1613] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1614] This invention is a system that operates through the interaction of a user (medical administrative assistant), a server, a terminal, and an emotion engine to support medical administrative support tasks. This system makes suggestions that take into account the user's emotions, thereby achieving more flexible and efficient business support than conventional systems. The system's programs and processing are described in detail below.

[1615] Program Description

[1616] User-initiated medical data entry and emotion recognition

[1617] The user is a medical administrative assistant. He logs in to the terminal and accesses the appointment management interface. Through the interface, the user inputs medical data such as the patient's basic information, desired appointment date, and details of the appointment based on a request from the doctor. The terminal compiles the input data into a form, and an emotion engine analyzes the user's input content, input speed, and facial expressions (if equipped with a camera) to recognize the user's emotions. The terminal generates a request packet containing the medical data and emotion data and sends it to the server.

[1618] Server receives request and retrieves information

[1619] The server analyzes the request packet received from the terminal and extracts the necessary patient information and examination details.The server then retrieves the patient's past medical history, doctor schedules, and hospital resource information (such as clinical testing and examination room availability) from the medical database.

[1620] Optimization processing using AI tools and use of emotional data

[1621] The server then passes the acquired data and sentiment data to an AI tool to optimize appointment scheduling, taking into account the following factors:

[1622] Important patient medical history

[1623] Doctor's schedule availability

[1624] Availability of examination rooms and necessary equipment

[1625] The user's emotional state (stress, anxiety, satisfaction, etc.)

[1626] Taking these conditions into consideration, the AI ​​tool generates the most efficient and least stressful reservation options for the user and returns them to the server.

[1627] Server proposal and data transmission

[1628] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. The converted reservation candidates are stored in a response packet and sent to the device.

[1629] Displaying information and reflecting emotions on devices

[1630] The terminal analyzes the response packet received from the server and displays suggested reservation options to the user. Priorities and explanations based on the user's feelings are added, allowing the user to easily review the suggested options. The user selects the most suitable date and time from the displayed reservation options and clicks the confirm button.

[1631] Server confirmation and recording

[1632] The server records the confirmed reservation information received from the device in the medical database, confirming that the reservation has been officially registered, and also records the emotion data recognized by the emotion engine for use in improving the accuracy of future suggestions.

[1633] Specific examples

[1634] Scenario: Managing patient appointments with emotions in mind

[1635] 1. The user logs in and enters the necessary information into the appointment management interface on their device: patient name, ID, desired appointment date, appointment details, etc. The emotion engine also recognizes the user's emotions from their facial expressions and input speed.

[1636] 2. The terminal generates and sends a request packet to send the input data and emotion data to the server.

[1637] 3. The server receives the request and retrieves the patient's medical history, doctor schedule, and resource information from the medical database.

[1638] 4. The server passes the acquired information and emotional data to the AI ​​tool, which then generates the best reservation candidates.

[1639] 5. The server organizes the reservation suggestions received from the AI ​​tool and sends them to the device. The suggestions are displayed in a way that takes the user's emotions into consideration.

[1640] 6. The terminal displays the proposed reservation options to the user, and the user selects and confirms the most suitable reservation.

[1641] 7. The server records the confirmed information in a medical database, notifies the doctor and patient, and records emotional data for future improvements to the entire system.

[1642] By combining this program with processing steps, a system is realized that not only streamlines the work of medical administrative assistants and reduces the burden on medical professionals, but also provides flexible support that takes the user's emotions into consideration.

[1643] The processing flow will be explained below.

[1644] Step 1:

[1645] The user (medical administrative assistant) logs in to the terminal and accesses the appointment management interface.

[1646] Step 2:

[1647] Based on a request from a doctor, the user enters medical data such as the patient's name, ID, desired date of examination, and details of the examination.

[1648] Step 3:

[1649] The device compiles the entered data into a form, and an emotion engine analyzes the input content, input speed, and in some cases the user's facial expressions to recognize the user's emotions.

[1650] Step 4:

[1651] The terminal generates a request packet including medical data and emotion data and transmits it to the server.

[1652] Step 5:

[1653] The server analyzes the request packet received from the terminal and extracts patient information and examination details.

[1654] Step 6:

[1655] The server retrieves patient past medical history, doctor schedules, and hospital resource information from a medical database.

[1656] Step 7:

[1657] The server then passes the acquired data and emotional data to an AI tool that optimizes appointments, taking into account the patient's important medical history, the doctor's available schedule, the availability of examination rooms and necessary equipment, and the user's emotional state.

[1658] Step 8:

[1659] The AI ​​tool generates optimal reservation options and returns them to the server.

[1660] Step 9:

[1661] The server organizes the reservation suggestions received from the AI ​​tool and converts them into a format that is easy for users to understand.

[1662] Step 10:

[1663] The server stores the converted reservation candidates in a response packet and transmits it to the terminal.

[1664] Step 11:

[1665] The terminal analyzes the response packet received from the server and displays a list of reservation options to the user. At this time, the suggestions are displayed in a way that takes the user's emotions into consideration.

[1666] Step 12:

[1667] The user selects the most suitable date and time from the displayed reservation candidates and clicks the confirm button.

[1668] Step 13:

[1669] The terminal generates a request packet to transmit the user's final selection back to the server.

[1670] Step 14:

[1671] The server records the confirmed reservation information received from the terminal in the medical database and confirms that the reservation has been officially registered.

[1672] Step 15:

[1673] The server confirms that the reservation information has been registered and notifies the terminal of that information.

[1674] Step 16:

[1675] The terminal receives the notification from the server and displays to the user that the reservation has been confirmed.

[1676] Step 17:

[1677] If necessary, the server automatically sends a notification email to the doctor and patient confirming the appointment.

[1678] Step 18:

[1679] The server records the emotion data recognized by the emotion engine and uses it to improve the accuracy of future suggestions.

[1680] Example 2

[1681] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1682] Conventional medical administrative support systems do not take user emotions into consideration when entering data or managing appointments, which reduces work efficiency and the user experience. They also lack the flexibility to integrate and optimize a wide range of information, such as medical data, patient medical history, and doctor schedules. There is a need to solve these problems, reduce user stress and anxiety, and provide more efficient and user-friendly work support.

[1683] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting medical data from a user, means for the server to receive the medical data and the user's emotional data and acquire related information from a medical database, means for the server to optimize the acquired related information and emotional data using an AI tool and propose it to the user, means for the user to confirm and confirm the proposed information, and means for the server to record the confirmed information in the medical database and record the emotional data. This enables flexible and efficient business support that takes user emotions into consideration.

[1684] "User" refers to the medical administrative assistant who operates the system and inputs medical data.

[1685] "Medical data" refers to information necessary for medical administrative support work, such as basic patient information, desired appointment date, and details of the appointment.

[1686] "Emotional data" refers to information about the user's psychological state analyzed from their input speed, facial expressions, etc.

[1687] "Server" refers to a central processing unit for receiving medical data and emotion data sent from terminals, obtaining related information, and optimizing it.

[1688] A "medical database" refers to data storage that stores medical-related information such as patient medical history, doctor schedules, and hospital resource information.

[1689] "Relevant information" refers to the patient's medical history, doctor's schedule, and hospital resource information.

[1690] "AI tools" refers to software and algorithms with artificial intelligence technology used on servers.

[1691] "Optimization" refers to the process of generating optimal appointment candidates based on the relevant information and sentiment data obtained.

[1692] "Suggestion" refers to presenting optimized information to the user using AI tools.

[1693] "Confirmation" refers to the act of the user selecting the most suitable appointment from the proposed candidates and making a final decision.

[1694] "Recording" refers to the act of storing established information in a medical database.

[1695] The present invention is a flexible and efficient business support system that optimizes medical administrative support tasks and takes into account the user's emotions. The system operates through the interaction of the user, server, terminal, and emotion engine.

[1696] System configuration and program description

[1697] User data entry and emotion recognition

[1698] The user logs in to the terminal as a medical administrative assistant and accesses the appointment management interface. The user enters basic patient information (name, ID), desired appointment date, details of the appointment, etc. through the terminal. At this time, the terminal compiles the entered data into an appropriate format.

[1699] Furthermore, the device uses an emotion engine to analyze the user's input speed and facial expressions. For example, if the device is equipped with a camera, it can capture and analyze the user's facial expressions to determine stress or anxiety levels. The emotion engine indicates that the user may be feeling stressed even if the input speed is slow. The emotion data and medical data generated in this way are sent to the server.

[1700] Server receives request and retrieves information

[1701] The server analyzes the medical and emotional data received from the device and extracts the necessary patient information and examination details.The server then accesses a medical database (e.g., MySQL) to obtain the patient's past medical history, doctor schedules, and hospital resource information.

[1702] Optimization with AI tools and use of sentiment data

[1703] The server passes the acquired data and emotional data to an AI tool (e.g., TensorFlow) to optimize appointment scheduling. The AI ​​tool generates optimal appointment candidates by taking into account the patient's past medical history, doctor's schedule, availability of examination rooms and necessary equipment, and the user's emotional state (stress, anxiety, satisfaction). The results are returned to the server.

[1704] Proposal and confirmation

[1705] The server organizes the reservation candidates received from the AI ​​tool, converts them into a format that is easy for the user to understand, and sends them to the device. The device displays the received reservation candidates on its interface, and the user selects the best date and time from the displayed reservation candidates. When the user clicks the confirm button, the confirmation information is sent back to the server.

[1706] Finalization and recording

[1707] The server records the confirmation information received from the device in the medical database and confirms that the appointment has been officially registered. The server also records the emotion data from the emotion engine and uses it to improve the accuracy of future recommendations. The server also notifies the doctor and patient that the appointment has been confirmed.

[1708] Specific examples

[1709] For example, suppose a user logs in to a terminal and inputs the desired consultation date for patient "Yamada Taro" as "2023 / 10 / 15" and the consultation content as "consultation for lower back pain." The prompt text in this case is as follows:

[1710] ---

[1711] Patient demographics:

[1712] Name: Taro Yamada

[1713] ID:12345

[1714] Desired appointment date: 2023 / 10 / 15

[1715] Examination content: Examination of lower back pain

[1716] ---

[1717] This system will streamline the work of medical administrative assistants and utilize structured medical and emotional data to make suggestions that reduce users' stress and anxiety. Theoretically, it is expected to create optimal medical appointments for both patients and medical professionals.

[1718] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1719] Step 1: User data entry and emotion recognition

[1720] A user logs in to a terminal as a medical administrative assistant and accesses the appointment management interface. The user enters the patient's basic information (name, ID), desired appointment date, and details of the appointment. At this time, the terminal organizes the entered data into an appropriate format. The terminal then uses an emotion engine to analyze the user's input speed and facial expressions. Specifically, the terminal's camera captures the user's facial expressions and analyzes them in real time. For example, if the input speed is slow, it is determined that the user is "feeling stressed." The medical data and emotional data generated in this way are sent to the server as a request packet.

[1721] Input: Patient's basic information, desired consultation date, consultation details

[1722] Data processing: Formatting input data, generating emotional data (input speed and facial expression analysis)

[1723] Output: A request packet containing medical and emotional data.

[1724] Step 2: Server receives request and retrieves information

[1725] The server receives the request packet from the terminal. It analyzes the received packet and extracts the patient information and consultation details. The server then accesses a medical database (e.g., MySQL) to retrieve the patient's past medical history, doctor schedule, and hospital resource information. This provides all the data necessary to process the appointment.

[1726] Input: A request packet containing medical and emotional data

[1727] Data processing: Analyzing request packets and extracting patient information and medical examination details

[1728] Output: Patient's medical history, doctor's schedule, hospital resource information

[1729] Step 3: Optimizing with AI tools and using sentiment data

[1730] The server passes the acquired data and emotional data to an AI tool (e.g., TensorFlow) to perform optimization analysis for appointment scheduling. The AI ​​tool generates optimal appointment candidates based on the patient's past medical history, doctor schedules, hospital resource information, and the user's emotional state (stress, anxiety, satisfaction). For example, it also takes into account cases where a specific examination room is required based on past medical history.

[1731] Input: Patient's medical history, doctor's schedule, hospital resource information, emotional data

[1732] Data processing: Data analysis and optimization using AI tools

[1733] Output: Best booking candidates

[1734] Step 4: Server Proposal and Data Transmission

[1735] The server organizes the reservation candidates received from the AI ​​tool and converts them into a format that is easy for the user to understand. For example, it summarizes the date, time, and location of each reservation candidate. The converted reservation candidates are then stored in a response packet and sent to the terminal.

[1736] Input: Best booking candidate

[1737] Data processing: Formatting reservation candidates and converting them into response packets

[1738] Output: Response packet

[1739] Step 5: Terminal displays information and user selection

[1740] The terminal analyzes the response packet received from the server and extracts its contents. Next, it displays suggested reservation options on the interface. The user selects the most suitable date and time from the displayed reservation options and clicks the Confirm button. Based on the user's selection, the terminal resends a request packet containing the confirmation information to the server.

[1741] Input: Response packet

[1742] Data processing: Analyzing response packets and displaying reservation candidates

[1743] Output: Request packet containing confirmation information

[1744] Step 6: Server confirmation and recording

[1745] The server analyzes the confirmation information received from the device and records it in a medical database. It then confirms that the appointment has been officially registered and notifies the doctor and patient that the appointment has been confirmed. Emotional data is also recorded and will be used to improve the system in the future.

[1746] Input: Request packet containing confirmation information

[1747] Data processing: Analysis of confirmed information and recording in a database

[1748] Output: Notification to doctor and patient, recorded emotion data

[1749] This concretely shows the processing steps of the present system and clarifies the interactions between the user, server, and terminal.

[1750] (Application example 2)

[1751] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1752] In modern manufacturing, efficient schedule and resource management is extremely important. However, current systems do not take into account user emotions, which can create a stressful environment for workers. Furthermore, there is no established method for optimally managing work history and resource information, which can lead to problems such as reduced productivity and inefficient operations.

[1753] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting business data from a user, means for the server to receive the business data and acquire related information from a database, and means for optimizing the acquired related information and emotion data from an emotion engine using an AI tool and proposing the optimization to the user. This enables optimal schedule and resource management that takes user emotions into consideration.

[1754] A "user" is a person who uses the system and performs operations to input business data.

[1755] "Business data" refers to data such as work details, schedules, and resource usage status that users input into the system.

[1756] A "server" is a device that receives business data from users, retrieves necessary information from a database, and optimizes it using AI tools.

[1757] A "database" is an information storage device that stores information related to business operations.

[1758] "Related information" refers to information necessary for business execution, such as work history, schedule, and resource information.

[1759] An "emotion engine" is a system that recognizes emotions from user input and facial expressions, and processes them as data.

[1760] "Emotion data" is data that indicates the emotional state of the user as recognized by the emotion engine.

[1761] An "AI tool" is software or hardware equipped with artificial intelligence algorithms for analyzing data and performing optimization processing.

[1762] "Optimization" is the process of using AI tools to generate the most efficient and least stressful schedule and resource allocation for users based on relevant information and emotional data.

[1763] "Proposing" means that the server presents the results of the optimization process to the user.

[1764] "Confirming" means that the user selects the most appropriate information from the proposed information and confirms its execution.

[1765] "Recording" refers to the process in which the server saves the confirmed information back into the database.

[1766] This invention is a system that inputs, optimizes, proposes, confirms, and records business data through interactions between users, servers, databases, emotion engines, and AI tools.

[1767] System configuration

[1768] 1. User operations

[1769] Users input business data using a smartphone or head-mounted display. The business data includes the work content, schedule, required resources, etc. The device equipped with an emotion engine acquires emotional data from the user's facial expressions and input speed.

[1770] 2. Receipt and processing of data

[1771] The device assembles task data and emotion data into packets and sends them to the server. The server receives these packets and retrieves related information (past work history, current resource status, schedule, etc.) from a database.

[1772] 3. Optimization process

[1773] The server passes relevant information and emotional data to the AI ​​tool, which then performs an optimization process. The AI ​​tool then uses this information to generate the most efficient and least stressful schedule for the user and returns it to the server. This optimization process uses programming languages ​​such as Python and machine learning algorithms.

[1774] 4. Suggestions and Selections

[1775] The server organizes the schedule candidates received from the AI ​​tool and presents them to the user. The user selects the best one from the presented schedule candidates and confirms it in the system. The information is displayed using a web interface or application.

[1776] 5. Records and Notifications

[1777] The server records the confirmed information in a database and notifies relevant departments as necessary. For example, a notification function is implemented to inform workers and managers of confirmed schedules.

[1778] The specific hardware and software used

[1779] Hardware

[1780] User devices: smartphone, head-mounted display, camera

[1781] Server: A high-performance server for data processing and optimization.

[1782] Database server: a data storage device for storing business-related information

[1783] software

[1784] Emotion Engine: Software for Recognizing User Emotions

[1785] AI Tools: Artificial intelligence algorithms that optimize data processing

[1786] Database management software: e.g., MySQL or PostgreSQL

[1787] Web interface or application: a platform through which users can enter data and view suggestions

[1788] Specific examples

[1789] Scenario: Optimizing factory production schedules

[1790] 1. The user inputs the work content and schedule using a smartphone or head-mounted display.

[1791] 2. The emotion engine analyzes the user's facial expressions and obtains emotional data.

[1792] 3. The device sends business data and emotion data to the server.

[1793] 4. The server retrieves the relevant information from the database and passes it to the AI ​​tool.

[1794] 5. The AI ​​tool generates an optimal schedule and returns it to the server.

[1795] 6. The server proposes a schedule to the user.

[1796] 7. User checks the schedule, selects the best one and confirms.

[1797] 8. The server records the confirmed information in the database and notifies the relevant departments.

[1798] Example prompts for generative AI models

[1799] "To optimize the production schedule in your factory, please create a schedule taking into account the following data:

[1800] Worker name and data

[1801] Specific work content and schedule

[1802] Emotional data (stress, anxiety, satisfaction, etc.)

[1803] Generate the optimal schedule and present it to you."

[1804] In this way, a system is realized that can reduce user stress and improve work efficiency by proposing an optimized production schedule based on emotional data.

[1805] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1806] Step 1:

[1807] The user inputs business data using a smartphone or head-mounted display. The input business data includes the work content, schedule, required resources, etc. The emotion engine then analyzes the user's facial expressions and input speed to obtain emotional data. Input: Business data, emotional data. Output: Input business data, obtained emotional data.

[1808] Step 2:

[1809] The device assembles task data and emotion data into packets and sends them to the server. This ensures that all important information is delivered to the server in a single request packet. Input: task data, emotion data. Output: request packet.

[1810] Step 3:

[1811] The server receives the request packet and analyzes it. It extracts business data and emotion data from the analyzed data and obtains related information (work history, resource information, schedule, etc.) from the database. Input: Request packet. Output: Related information, business data, emotion data.

[1812] Step 4:

[1813] The server passes the acquired related information and emotional data to the AI ​​tool, which then performs optimization processing. The AI ​​tool performs calculations using multidimensional arrays based on the related information and emotional data to generate the most efficient and least stressful schedule. Input: Related information, emotional data. Output: Optimal schedule candidates.

[1814] Step 5:

[1815] The server organizes the optimal schedule candidates received from the AI ​​tool and presents them to the user. The user selects the optimal one from the presented schedule candidates. At this stage, the user confirms the decision. Input: Optimal schedule candidate. Output: Confirmed information by the user.

[1816] Step 6:

[1817] The server records the confirmed schedule information in the database. This allows the confirmed schedule to be shared throughout the system and notified to other departments as necessary. Notifications are made via email or the internal notification system. Input: Confirmed information by the user. Output: Recorded confirmed schedule, notification.

[1818] 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.

[1819] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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 a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1820] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1821] 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.

[1822] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1823] 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.

[1824] 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).

[1825] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1826] 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."

[1827] 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.

[1828] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1829] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1830] 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.

[1831] 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.

[1832] 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.

[1833] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1834] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1835] 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.

[1836] 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.

[1837] 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.

[1838] 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.

[1839] The following is further disclosed regarding the above embodiment.

[1840] (Claim 1)

[1841] a means for inputting medical data from a user;

[1842] a server receiving the medical data and retrieving related information from a medical database;

[1843] The server uses AI tools to optimize the relevant information it has acquired and provide suggestions to users.

[1844] a means for the user to review and confirm the proposed information;

[1845] a means for the server to record the determined information in a medical database;

[1846] A system including:

[1847] (Claim 2)

[1848] 10. The system of claim 1, wherein the relevant information obtained includes patient medical history, doctor schedules, and in-hospital resource information.

[1849] (Claim 3)

[1850] 2. The system of claim 1, further comprising: means for the server to notify the determined information.

[1851]

[1852] "Example 1"

[1853] (Claim 1)

[1854] a means for users to log in and enter medical data;

[1855] a means for the terminal to collect the medical data, generate a request, and transmit the request to the server;

[1856] a server that receives and analyzes the medical data and obtains related information from a medical database;

[1857] The server passes the acquired information to an AI tool to optimize it and suggest reservation options to the user.

[1858] A means for the user to select and confirm the best reservation from the proposed options;

[1859] a means for the server to record the determined information in a medical database and notify the same as necessary;

[1860] A system including:

[1861] (Claim 2)

[1862] 10. The system of claim 1, wherein the relevant information obtained includes patient medical history, doctor schedules, and in-hospital resource information.

[1863] (Claim 3)

[1864] The system of claim 1, wherein the server further comprises means for notifying the physician and the patient of the determined information.

[1865] "Application Example 1"

[1866] (Claim 1)

[1867] a means for inputting data from a user;

[1868] a server receiving the data and retrieving related information from a database;

[1869] The server uses AI tools to optimize the relevant information it has acquired and provide suggestions to users.

[1870] a means for the user to review and confirm the proposed information;

[1871] a means for the server to record the determined information in a database;

[1872] A means for the server to perform optimization using the generative AI model;

[1873] A system including:

[1874] (Claim 2)

[1875] 2. The system of claim 1, wherein the relevant information obtained includes medical history or operation history, schedule, and resource information.

[1876] (Claim 3)

[1877] 2. The system of claim 1, further comprising: means for the server to notify the determined information.

[1878] "Example 2: Combining Emotion Engines"

[1879] (Claim 1)

[1880] a means for inputting medical data from a user;

[1881] a server receiving the medical data and the user's emotion data and acquiring related information from a medical database;

[1882] The server optimizes the related information and emotion data it acquires using AI tools and proposes them to users.

[1883] a means for the user to review and confirm the proposed information;

[1884] a server for recording the determined information in a medical database and recording emotion data;

[1885] A system including:

[1886] (Claim 2)

[1887] 10. The system of claim 1, wherein the relevant information obtained includes patient medical history, physician schedules, and facility resource information.

[1888] (Claim 3)

[1889] 2. The system of claim 1, further comprising: means for the server to notify the determined information.

[1890] "Application example 2 when combining emotion engines"

[1891] (Claim 1)

[1892] A means for inputting business data from a user;

[1893] A server receives the business data and acquires related information from a database;

[1894] A means for optimizing related information acquired by the server and emotion data from the emotion engine using AI tools and proposing it to the user;

[1895] a means for the user to review and confirm the proposed information;

[1896] a means for the server to record the determined information in a database;

[1897] A system including:

[1898] (Claim 2)

[1899] 10. The system of claim 1, wherein the relevant information obtained includes work history, schedule, and resource information.

[1900] (Claim 3)

[1901] 2. The system of claim 1, further comprising: means for the server to notify the determined information. [Explanation of symbols]

[1902] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting medical data from a user; a server receiving the medical data and retrieving related information from a medical database; The server uses AI tools to optimize the relevant information it has acquired and provide suggestions to users. a means for the user to review and confirm the proposed information; a means for the server to record the determined information in a medical database; A system including:

2. The system of claim 1 , wherein the relevant information obtained includes patient medical history, doctor schedules, and in-hospital resource information.

3. 2. The system of claim 1, further comprising: means for the server to notify the determined information.

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