system

Abstract

We provide the system. [Solution] A means of receiving data on the user's health status and symptoms, A means for generating a list of diseases using natural language processing based on the aforementioned data, A means for selecting an appropriate medical professional from a database based on the aforementioned list of diseases, A means for obtaining the available dates and times of the aforementioned medical professional and notifying the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is difficult for ordinary users to select an appropriate medical expert based on their health status and symptoms, and it is also difficult to quickly secure a doctor's appointment in a busy life. Due to such problems, users cannot receive appropriate medical services promptly, and there is a possibility of worsening health problems sometimes. On the other hand, in medical institutions, the introduction of appropriate specialists to patients and the adjustment of appointments are also complicated, and efficient responses are required. [[ID=3%]]

Means for Solving the Problems

[0005] This invention receives health status and symptom data obtained from user input and generates a disease list using natural language processing technology based on that data. Based on the generated disease list, it selects an appropriate medical professional from a database, obtains available dates and times for consultation, and notifies the user, enabling the user to quickly book medical consultations and appointments. Furthermore, an algorithm that takes into account past treatment records and expert reviews can select the most appropriate medical professional. In addition, a means of sending appointment confirmation notices to both the user and the medical professional is provided to enhance the certainty of appointments. In this way, users can receive appropriate medical care in a short time, and medical institutions can efficiently handle patient care.

[0006] A "disease list" is a list of possible diseases and disorders generated based on the user's entered health status and symptoms.

[0007] "Natural language processing" is a technology that uses AI to analyze information entered by users in natural language, understand its meaning, and generate appropriate output.

[0008] A "medical professional" is a physician or medical professional who possesses specialized knowledge and experience in a particular medical field and provides medical services that address the user's symptoms.

[0009] A "database" is a storage system that accumulates data such as information on medical professionals, past treatment records, and reviews, and allows for searching and retrieval as needed.

[0010] An "algorithm" is a set of calculation procedures or processing steps that use information from a database to identify the medical professional best suited to a user's symptoms.

[0011] "Available dates and times" refers to the combination of dates and times when a medical professional can provide medical services to a user.

[0012] A "booking confirmation notice" is a notification sent to both the user and the medical professional to inform them that the booking has been officially confirmed. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention begins with a user inputting their health status and symptoms into a terminal. The information entered by the user is transmitted to a server via the network. The server receives this information and analyzes it using natural language processing technology. This analysis generates a list of potential diseases.

[0035] Based on the generated disease list, the server accesses a database and selects the most suitable medical professional based on past treatment records and reviews from other healthcare professionals. This selection process utilizes an algorithm to efficiently choose the professional best suited to the user's specific symptoms.

[0036] The server then retrieves the available dates and times of the selected medical professional from the database and notifies the terminal of this information. The user selects a desired date and time from the available dates and times provided on the terminal and tentatively confirms the reservation.

[0037] The server uses the provisionally confirmed reservation information to make a final confirmation with the medical institution and then confirms the reservation. A final reservation confirmation notice is sent to both the user and the medical professional. This allows the user to receive medical consultation and treatment quickly and smoothly.

[0038] For example, if a user enters symptoms such as "headache" and "eye pain," the server analyzes this information and estimates conditions such as "tension headache" or "eye strain." Then, based on past medical data, it matches the user with an appropriate specialist, such as an internist or ophthalmologist, and suggests available appointment times. In this way, users can quickly find the most suitable specialist and efficiently schedule appointments.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user enters their health status and symptoms into the device. The entered information includes specific symptoms and details of their physical ailments.

[0042] Step 2:

[0043] The terminal sends user input data to the server. A secure communication protocol is applied to this data transmission.

[0044] Step 3:

[0045] The server analyzes the received data. Natural language processing (NLP) techniques are used to extract potential disease indications from the entered symptoms.

[0046] Step 4:

[0047] The server accesses a database of medical professionals based on the generated disease list. It then selects the most suitable medical professional candidate based on their area of ​​expertise, past clinical experience, and reviews.

[0048] Step 5:

[0049] The server retrieves the available dates and times of the selected specialist and uses that information to generate candidate dates and times for appointment bookings.

[0050] Step 6:

[0051] The terminal displays the user with a list of available appointment dates and times received from the server. The user selects their preferred date and time and confirms the appointment request on the terminal.

[0052] Step 7:

[0053] A reservation request is sent from the terminal to the server. The server receives this request and initiates the final confirmation process with the medical institution.

[0054] Step 8:

[0055] After the server completes the final confirmation of the reservation, it sends a reservation confirmation notice to the user and the healthcare professional. This notice includes the reservation time, healthcare professional information, and instructions on how to access the consultation.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In modern society, users are required to quickly and accurately communicate their health status and symptoms to specialists and receive appropriate medical services. However, finding the right medical specialist and efficiently scheduling appointments is difficult. There is a need for a system that can solve these problems.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for receiving information on the user's health status and symptoms, means for generating a list of diseases using natural language processing with a generative AI model, and means for selecting an appropriate medical professional from a data collection device. This enables the user to quickly select an appropriate medical professional and efficiently book an appointment.

[0061] A "user" is someone who inputs their health status and symptoms into the system and receives medical services.

[0062] "Health status and symptom information" refers to data that users input into the system indicating their own physical and mental condition.

[0063] A "generative AI model" is an artificial intelligence technology that utilizes natural language processing in data analysis, understanding and analyzing the meaning of input text.

[0064] "Natural language processing" is a technology that understands text data as human language and extracts its meaning.

[0065] A "disease list" is a list of potential illnesses and physical problems based on the user's health information, obtained as a result of natural language processing.

[0066] A "medical professional" is a professional who has been trained to diagnose and treat specific diseases or health problems.

[0067] A "data collection device" is a database system that stores information about medical professionals and past medical records.

[0068] "Available consultation times" refers to the time period during which medical professionals can provide consultations to users.

[0069] "Communication" refers to the process of transmitting information to users and medical professionals, using the internet and other telecommunication methods.

[0070] A "recommendation algorithm" is a computational method that uses past medical records and evaluation information to select the most suitable medical professional for a user.

[0071] To implement this invention, the user first inputs their health status and symptoms using a terminal. Once the user inputs their symptoms through a dedicated application on their smartphone or computer, this information is transmitted to a server via the internet. After receiving this information, the server analyzes the data using natural language processing technology with a generative AI model. This analysis can utilize natural language processing services such as Google® Cloud Natural Language API and Amazon Comprehend.

[0072] Based on the analysis results, the server generates a list of potential diseases. Using this list, the server accesses a data aggregation device to select appropriate medical professionals. Here, a recommendation algorithm is used based on past medical records and evaluation information of medical professionals. The server retrieves the available appointment times of the selected medical professionals from the database and sends this information to the user's terminal. This allows the user to select a preferred appointment time from the notified available times and make a provisional booking.

[0073] For example, if a user reports "headache" and "eye pain," the server analyzes this information and estimates potential conditions such as "tension headache" or "eye strain." Based on this estimation, it then selects the appropriate specialist, such as an internist or ophthalmologist, and suggests an available appointment time. This process allows the user to receive appropriate and prompt medical assistance.

[0074] An example of a prompt message is, "Please provide a specific example and detailed description of a system that analyzes a user's health information and recommends the most suitable medical services." This would enable the system to efficiently analyze the user's health status and provide the optimal treatment plan.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] Users input their health status and symptoms into the device. Specifically, this involves using a dedicated smartphone application to enter symptoms such as "headache" or "eye pain" into text boxes. The data entered is text-based information indicating the user's health status and symptoms.

[0078] Step 2:

[0079] The terminal sends the input data to the server. The data is transmitted over the internet and securely transferred using the HTTPS protocol. The input is text data, and the data is delivered to the server as output.

[0080] Step 3:

[0081] The server uses natural language processing to analyze the received data. It analyzes the input text using a generative AI model and extracts keywords and phrases. The input is text data submitted by the user, and the output is a list of key items and symptoms generated based on the analyzed information.

[0082] Step 4:

[0083] The server generates a list of potential diseases based on the analysis results. For example, natural language processing is used to list possible disease names such as "tension headache" and "eye strain." This process uses machine learning algorithms. The input is the analysis information obtained in the previous step, and the output is a list of estimated disease names.

[0084] Step 5:

[0085] The server selects appropriate medical professionals from the data aggregation device based on the disease list. A recommendation algorithm is used, utilizing past medical records and evaluation information of medical professionals. The input is the generated disease list, and the output is the selected medical professional information.

[0086] Step 6:

[0087] The server collects the available appointment times of selected medical professionals and sends this information to the user's terminal. The server retrieves the availability of medical professionals from the database in real time and sends emails or push notifications to the user. The input is the medical professional's data, and the output is the available appointment time presented to the user.

[0088] Step 7:

[0089] The user selects a desired time from the displayed available appointment times and makes a provisional reservation. The provisional reservation is completed when the user clicks the selection button on the terminal. The input is the available appointment times notified by the server, and the output is the user's selected preferred appointment time.

[0090] Step 8:

[0091] The server uses the provisional booking information to perform a final confirmation with the medical institution and finalize the reservation. Communication with the medical institution takes place for confirmation, and the reservation confirmation result is generated. The final result is notified to the user and the medical professional, completing the appointment. The input is the details of the provisional booking, and the output is the confirmed booking information.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] In the medical diagnostic process, there is a challenge in that it is difficult for users to quickly and easily find the appropriate specialist, make an appointment, and even handle the payment of medical fees in a unified manner. Currently, many medical systems handle appointment scheduling and payment separately, which increases the burden on users and reduces the efficiency of medical services.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for receiving data on the user's health status and symptoms, means for generating a list of diseases using natural language processing based on the data, means for selecting an appropriate medical professional from registered data based on the list of diseases, means for obtaining information on the medical professional's available hours and notifying the user, and electronic payment means for settling medical fees based on the information. This enables the user to book an appointment with an appropriate specialist and settle medical fees immediately.

[0097] A "user" is someone who uses the system to input their health status and symptoms and receive medical services.

[0098] "Health status and symptom data" refers to information that represents the user's physical or mental condition or specific symptoms.

[0099] "Natural language processing" is a technology that allows computers to understand and process human language, and it is used to analyze input data on health conditions and symptoms.

[0100] The "disease list" is a list of possible diseases based on the user's symptoms, generated using natural language processing technology.

[0101] A "medical professional" is a healthcare worker, such as a physician, who is capable of diagnosing and treating specific diseases or symptoms.

[0102] A "data registry" is a database that stores past medical history, expert evaluation information, and other data, and is used for selecting medical professionals.

[0103] "Time information" refers to information indicating the time periods during which medical professionals are available to provide medical care, and this information is provided to users.

[0104] "Electronic payment methods" refer to means of settling medical fees online, enabling users to easily pay for medical services.

[0105] To implement this invention, the server receives data about the user's health status and symptoms. The user inputs their health status and specific symptoms using a device such as a smartphone. The device transmits this input information to the server via the network. The server analyzes the received data using natural language processing technology and generates a list of potential diseases.

[0106] The server selects an appropriate medical professional based on the generated disease list. This selection uses an algorithm based on past treatment history and medical professional evaluations, efficiently selecting the most suitable professional from the database. The server retrieves the available appointment times of the selected medical professional and notifies the user's terminal. The user can then select a desired date and time from the provided time information on their terminal and tentatively confirm the appointment.

[0107] Subsequently, the server confirms the provisional reservation with the medical institution and finalizes the reservation. A final reservation confirmation notice is sent to both the user and the medical professional. The cost of the selected treatment is displayed on the terminal, and the user completes the payment using electronic payment methods. In this process, the server provides treatment recommendations and electronic payment in an integrated manner, enabling the user to receive the service smoothly and efficiently.

[0108] For example, if a user experiences headaches and eye strain after prolonged computer work, they will be recommended an ophthalmologist, can select an appointment time, and pay the associated medical fees electronically. To support this process, the server utilizes a generative AI model to generate prompts such as: "The user has entered their symptoms in a smartphone app. Natural language processing will analyze the information, suggest the most suitable doctor and appointment, and complete the electronic payment for the medical fees. Please create an application program to achieve this."

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] Users input their health status and symptoms using a terminal. This input data includes specific symptoms and changes in physical condition. This data is initially processed on the terminal and then sent to the server in a formatted state.

[0112] Step 2:

[0113] The server receives data from the user. Based on this received data, it analyzes the information using natural language processing techniques. As a result of the analysis, a list of potential diseases is generated based on the input symptoms. The output is a list of estimated diseases.

[0114] Step 3:

[0115] The server selects appropriate medical professionals based on the generated disease list. This process involves accessing a database, considering medical history and professional evaluation information, and using a selection algorithm to identify the most suitable professional. The output provides information on the recommended medical professionals.

[0116] Step 4:

[0117] The server collects information on the availability of the selected medical professionals. The collected time information is organized into options that the user can book and is notified to the user's device. The user selects their desired appointment date and time from this information.

[0118] Step 5:

[0119] The user selects their desired appointment date and time on their device and makes a provisional reservation. The provisional reservation information is sent to the server for preliminary confirmation. In this step, the reservation time and the selected doctor's information are confirmed.

[0120] Step 6:

[0121] The server communicates with the medical institution based on the provisional booking information to perform a final booking confirmation. The final confirmed booking information is notified to both the user and the medical professional, and the booking is confirmed.

[0122] Step 7:

[0123] The server sends the confirmed medical fee to the user's terminal and prompts them to complete the payment using an electronic payment method. The user confirms the medical fee on their terminal and completes the electronic payment. As a result, the medical appointment and payment are integrated into a single process.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention begins when a user inputs their health status and symptoms into a terminal. The information entered by the user is transmitted to a server via a secure communication protocol. The server has the function to analyze the received data, and first analyzes the symptoms using natural language processing (NLP) technology. This analysis generates a list of possible diseases.

[0126] A distinctive feature of this invention is that the server incorporates an emotion engine that recognizes emotions from user input data. This emotion engine identifies emotions such as joy, anxiety, and pain from the text and voice input by the user, and adjusts subsequent processes based on that emotional state. For example, if the user's emotions of anxiety or pain are high, it is possible to prioritize the selection of a medical professional who can respond quickly.

[0127] Next, the server queries the database based on the emotion recognition results from the emotion engine and the generated list of diseases. The process then continues by selecting the most suitable medical professional based on past clinical experience and expert reviews. This ensures that the most appropriate medical service for the user's condition is provided quickly.

[0128] The server then extracts the available appointment times of the selected specialists and notifies the user's terminal. The user can select a preferred time slot from the appointment dates and times displayed on the terminal and tentatively confirm the reservation. Based on this tentative confirmation, the server confirms with the medical institution and makes the final reservation confirmation.

[0129] Once a reservation is confirmed, the server sends a notification to both the user and the medical professional based on the confirmed reservation information. This notification includes the date, time, and location of the appointment, as well as details about the medical professional, allowing the user to schedule their appointment with confidence.

[0130] For example, if a user enters "I've had a severe headache since this morning and I'm very anxious," the server uses NLP technology to add "headache" to the list of conditions and simultaneously recognizes the emotion of "anxiety" through its emotion engine. Based on this data, the system determines that the situation may be urgent and prioritizes suggesting specialists who can provide emergency assistance. In addition, appropriate advice is also provided to reassure the user.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] Users input their health status and specific symptoms into the device. This input may include text or voice, and the data is stored on the device.

[0134] Step 2:

[0135] The terminal sends user input data to the server. Encrypted protocols are used to ensure data security during transmission.

[0136] Step 3:

[0137] The server analyzes the user data it receives. First, it uses natural language processing (NLP) to analyze the symptoms based on the input information and generates a list of possible diseases.

[0138] Step 4:

[0139] The server uses an emotion engine to recognize the user's emotions. This analyzes the text content from the input data and, as a result, identifies emotional states such as "anxiety," "relief," and "pain."

[0140] Step 5:

[0141] The server considers the emotion recognition results and the disease list to select the appropriate medical professional from the database. This selection is algorithmically based on past clinical experience and patient reviews to determine the best professional.

[0142] Step 6:

[0143] The server retrieves the available dates and times of the selected medical professionals from the database. This information is organized to best suit the user's time requirements.

[0144] Step 7:

[0145] Based on the information received from the server, the terminal displays the doctor's name, specialty, and possible appointment dates and times to the user. The user then selects their preferred appointment date and time from the options.

[0146] Step 8:

[0147] The terminal sends the user's selected reservation date and time to the server, which then uses that information to finalize the reservation with the medical institution.

[0148] Step 9:

[0149] The server confirms that the reservation has been made and sends a notification to both the user and the medical professional via email or message. This notification includes detailed information about the consultation.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0152] In modern society, there is a challenge in that users with diverse health conditions and symptoms often have difficulty receiving appropriate and timely medical services. Furthermore, there is a need to consider the user's emotional state and provide the most appropriate support. Traditional medical systems often lack sufficient automation in symptom analysis and specialist selection, and fail to adequately address emotional issues, leading to a decline in service quality.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes means for recognizing emotions from user input information, means for adjusting the process based on the recognized emotions, means for generating disease candidates using natural language processing, and means for selecting an appropriate specialist. This makes it possible to quickly provide optimal medical services tailored to the user's symptoms and mental state.

[0155] A "user" refers to an individual who uses this system and is the entity that inputs information about their health status and symptoms.

[0156] "Health status" refers to the user's physical and mental condition, and includes information analyzed by the system.

[0157] A "symptom" refers to a specific physical or mental phenomenon or state that the user perceives, and is the subject of analysis by the system.

[0158] "Means of receiving information" refers to a mechanism for transferring user-entered information to a server and processing it appropriately.

[0159] "Natural language processing" is a technology used on servers, which involves analyzing input text data to understand its meaning.

[0160] A "candidate disease" is a list of potential medical problems generated by natural language processing, and is treated as foundational information for determining appropriate medical treatment.

[0161] "Experts" refer to individuals or organizations qualified to perform medical-related duties and whose role is to provide medical services to users.

[0162] An "information storage device" refers to a mechanism used to store data within a server, including expert information and user history.

[0163] "Emotions" refer to the internal psychological state recognized from the data entered by the user, and are detected in order to adjust the processes within the system.

[0164] "Means of adjusting the process" refers to functions that appropriately modify the system's operation or service delivery method based on recognized emotions and analysis results.

[0165] This invention is a system that begins with the user inputting their health status and symptoms into a terminal. The user inputs symptoms and emotions in text format using a smartphone or personal computer. The entered information is securely transmitted to the server via the HTTPS protocol.

[0166] The server analyzes the received information using advanced natural language processing techniques. Specifically, it uses "natural language processing libraries" and "cloud-based natural language APIs" to analyze symptoms and generate disease candidates. At the same time, data on emotions entered by the user is also analyzed. "Emotion recognition engines" and "voice analysis software" are used to recognize emotions. This allows the system to adjust the process while taking the user's emotional state into consideration.

[0167] Next, the server searches the database based on the generated disease candidates and emotional assessments to identify the most suitable specialist. This specialist selection process uses a "database management system" that takes into account past medical records and specialist evaluations.

[0168] Information about the selected specialist and their available appointment times are sent from the server to the terminal. The user can select a desired appointment date and time from the provided time slots and make a provisional reservation. The server then coordinates the appointment time with the medical institution and confirms the final reservation. The confirmed reservation information is sent back to the terminal as a notification to both the user and the specialist. The entire system aims to support the user's health management quickly and efficiently.

[0169] As a concrete example, consider a case where a user inputs into their device, "I've had a terrible headache since this morning. I'm filled with anxiety." In this case, the server uses natural language processing to add "headache" to the list of illnesses and recognizes the emotion of "anxiety." Based on this, it prioritizes selecting a specialist who can provide emergency assistance and also provides appropriate advice to reassure the user.

[0170] An example of a prompt message would be something like, "Regarding the procedure for analyzing the health symptoms entered by the user, identifying emotions, and adjusting the treatment accordingly."

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user enters their health status and symptoms into the device. This input is in text or voice format, and the device converts it into digital data. This data then serves as the input for processing.

[0174] Step 2:

[0175] The terminal sends the input digital data to the server via a secure communication protocol, such as HTTPS. This ensures the security of the data. Server reception is the output of this step.

[0176] Step 3:

[0177] The server analyzes the received digital data using a natural language processing engine. Specifically, it tokenizes the text data, extracts important keywords, performs grammatical analysis, and lists the symptoms. Through this process, the analyzed symptoms are output.

[0178] Step 4:

[0179] The server inputs the analyzed symptom data into the emotion recognition engine to analyze the user's emotions. Here, emotions such as "anxiety," "joy," and "pain" are identified through text analysis and voice analysis. This identification result becomes the output of the step.

[0180] Step 5:

[0181] The server searches the database for appropriate specialists based on the analyzed list of symptoms and emotional data. A selection algorithm, based on past clinical experience and evaluation information, generates a list of optimal specialists. This result is the output of this step.

[0182] Step 6:

[0183] The server retrieves the available appointment times of the selected specialist from the database and notifies the terminal. The user selects a convenient time from the times displayed on the terminal and generates a provisional booking. The provisional booking information is then sent to the server as input.

[0184] Step 7:

[0185] The server coordinates the appointment with the medical institution based on the received provisional booking information. Once the medical institution returns the final appointment time, the server confirms it. The final booking information is the output of this step.

[0186] Step 8:

[0187] The server resends the confirmed appointment information to the terminal, notifying both the user and the professional. The notification includes the appointment date and time, location, and professional details, allowing the user to prepare for the appointment. This transmission is the final output.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0190] In today's world, many people need quick and accurate access to appropriate medical professionals for their health conditions and symptoms. However, systems that do not consider the emotional state of the user may not adequately respond to the urgency and anxiety they feel. Furthermore, it is necessary to go beyond simply generating disease lists and adjust the booking process so that users can receive medical services with peace of mind.

[0191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0192] In this invention, the server includes a device for receiving information on the user's health status and symptoms, a device for generating a list of diseases using natural language processing technology based on the information, a device for selecting an appropriate medical professional from a data set based on the list of diseases, and a device for analyzing the user's emotions and adjusting the process based on the analysis results. This enables the rapid and accurate selection and booking of a medical professional that is in line with the user's health status and emotional state.

[0193] A "user" refers to an individual who inputs information about their health status and symptoms into the system.

[0194] "Health status and symptom information" refers to information in natural language or other formats that users input as data about their own physical condition.

[0195] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0196] A "list of diseases" is a list of possible illnesses and health problems generated using natural language processing technology.

[0197] A "medical professional" refers to a healthcare worker who is qualified to diagnose and treat specific diseases.

[0198] A "data set" refers to a database containing information about medical professionals.

[0199] A "device that analyzes emotions" refers to a device that recognizes emotions from information or voice input by the user and reflects the results in the process.

[0200] "Adjusting the process" means optimizing the procedures for consultations and appointments according to the user's emotions and circumstances.

[0201] "Rapid and accurate selection and booking of medical professionals" refers to quickly and appropriately selecting and booking medical professionals according to the user's condition.

[0202] The system that realizes this invention begins with the user inputting information about their health status and symptoms via a terminal. This data is transmitted to a server via the internet. The server analyzes the received information using natural language processing technology and generates a list of possible diseases. Possible natural language processing technologies used here include open-source NLP libraries such as "spaCy" and "NLTK". In addition, emotion recognition models using TENSORFLOW® or PyTorch can be utilized to analyze emotions.

[0203] For example, if a user enters "I've had a severe headache since this morning and I'm very anxious," the server analyzes the input, generates a list of diseases that include "headache," and identifies the emotion of anxiety through emotion analysis. Based on this emotion, the server determines that a high-priority response is needed and selects a medical professional from the database who can respond quickly.

[0204] The user's device is notified of the available dates and times of the selected medical professional, and the user selects a preferred time from the displayed options. The final booking information obtained as a result of the selection is also shared with the medical professional.

[0205] An example of a prompt message would be that the user inputs "My back hurts from working at a desk for long hours," to which the system would respond with "We suggest you make an appointment with an orthopedic specialist."

[0206] This system allows users to receive medical services that are tailored to their health condition and emotional state quickly and accurately, while also enabling them to proceed with treatment with peace of mind.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The user uses their device to input information about their health status and symptoms. This input is in text format and is sent to the server via a secure communication protocol on the device. The input data might take the form of, for example, "I've had a severe headache since this morning, and I'm very worried."

[0210] Step 2:

[0211] The server analyzes the user's input data using natural language processing techniques. It extracts disease names and related keywords from the received text data using a natural language processing engine (e.g., spaCy) and generates a list of diseases. As a result, diseases such as "headache" are added to the list.

[0212] Step 3:

[0213] The server uses a generative AI model to analyze emotions from user input data. Based on the input text, it applies an emotion recognition model (e.g., an emotion analysis model using TensorFlow) to determine the emotion. The output is an emotion label such as "anxiety" or "pain." This allows the server to identify the user's emotional state.

[0214] Step 4:

[0215] The server uses the analysis results—a list of diseases and emotion labels—to query the data set and execute a process to select appropriate medical professionals. The selection prioritizes diseases and emotions based on their urgency; that is, it selects medical professionals who possess specialized knowledge of the identified diseases and can respond quickly.

[0216] Step 5:

[0217] The server extracts the available time slots for the selected medical professional and notifies the terminal of this information. The user selects a preferred time slot from the displayed dates and times on the terminal and makes a provisional reservation. The selected time slot is then sent to the server via the terminal.

[0218] Step 6:

[0219] Ultimately, the server verifies the provisional booking information with a specialist and confirms the appointment. Based on the confirmed booking information, notifications are sent to both the user and the medical specialist. These notifications include the date, time, location, and details of the medical specialist, allowing the user to schedule their appointment with confidence.

[0220] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0221] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0222] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0226] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0227] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0228] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0229] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0230] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0231] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0232] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0233] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0234] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0235] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0236] This invention begins with a user inputting their health status and symptoms into a terminal. The information entered by the user is transmitted to a server via the network. The server receives this information and analyzes it using natural language processing technology. This analysis generates a list of potential diseases.

[0237] Based on the generated disease list, the server accesses a database and selects the most suitable medical professional based on past treatment records and reviews from other healthcare professionals. This selection process utilizes an algorithm to efficiently choose the professional best suited to the user's specific symptoms.

[0238] The server then retrieves the available dates and times of the selected medical professional from the database and notifies the terminal of this information. The user selects a desired date and time from the available dates and times provided on the terminal and tentatively confirms the reservation.

[0239] The server uses the provisionally confirmed reservation information to make a final confirmation with the medical institution and then confirms the reservation. A final reservation confirmation notice is sent to both the user and the medical professional. This allows the user to receive medical consultation and treatment quickly and smoothly.

[0240] For example, if a user enters symptoms such as "headache" and "eye pain," the server analyzes this information and estimates conditions such as "tension headache" or "eye strain." Then, based on past medical data, it matches the user with an appropriate specialist, such as an internist or ophthalmologist, and suggests available appointment times. In this way, users can quickly find the most suitable specialist and efficiently schedule appointments.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The user enters their health status and symptoms into the device. The entered information includes specific symptoms and details of their physical ailments.

[0244] Step 2:

[0245] The terminal sends user input data to the server. A secure communication protocol is applied to this data transmission.

[0246] Step 3:

[0247] The server analyzes the received data. Natural language processing (NLP) techniques are used to extract potential disease indications from the entered symptoms.

[0248] Step 4:

[0249] The server accesses a database of medical professionals based on the generated disease list. It then selects the most suitable medical professional candidate based on their area of ​​expertise, past clinical experience, and reviews.

[0250] Step 5:

[0251] The server retrieves the available dates and times of the selected specialist and uses that information to generate candidate dates and times for appointment bookings.

[0252] Step 6:

[0253] The terminal displays the user with a list of available appointment dates and times received from the server. The user selects their preferred date and time and confirms the appointment request on the terminal.

[0254] Step 7:

[0255] A reservation request is sent from the terminal to the server. The server receives this request and initiates the final confirmation process with the medical institution.

[0256] Step 8:

[0257] After the server completes the final confirmation of the reservation, it sends a reservation confirmation notice to the user and the healthcare professional. This notice includes the reservation time, healthcare professional information, and instructions on how to access the consultation.

[0258] (Example 1)

[0259] Next, we will describe Example 1. 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."

[0260] In modern society, users are required to quickly and accurately communicate their health status and symptoms to specialists and receive appropriate medical services. However, finding the right medical specialist and efficiently scheduling appointments is difficult. There is a need for a system that can solve these problems.

[0261] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0262] In this invention, the server includes means for receiving information on the user's health status and symptoms, means for generating a list of diseases using natural language processing with a generative AI model, and means for selecting an appropriate medical professional from a data collection device. This enables the user to quickly select an appropriate medical professional and efficiently book an appointment.

[0263] A "user" is someone who inputs their health status and symptoms into the system and receives medical services.

[0264] "Health status and symptom information" refers to data that users input into the system indicating their own physical and mental condition.

[0265] A "generative AI model" is an artificial intelligence technology that utilizes natural language processing in data analysis, understanding and analyzing the meaning of input text.

[0266] "Natural language processing" is a technology that understands text data as human language and extracts its meaning.

[0267] A "disease list" is a list of potential illnesses and physical problems based on the user's health information, obtained as a result of natural language processing.

[0268] A "medical professional" is a professional who has been trained to diagnose and treat specific diseases or health problems.

[0269] A "data collection device" is a database system that stores information about medical professionals and past medical records.

[0270] "Available consultation times" refers to the time period during which medical professionals can provide consultations to users.

[0271] "Communication" refers to the process of transmitting information to users and medical professionals, using the internet and other telecommunication methods.

[0272] A "recommendation algorithm" is a computational method that uses past medical records and evaluation information to select the most suitable medical professional for a user.

[0273] To implement this invention, the user first inputs their health status and symptoms using a terminal. Once the user inputs their symptoms via a dedicated application on their smartphone or computer, this information is transmitted to a server via the internet. After receiving this information, the server analyzes the data using natural language processing technology with a generative AI model. This analysis can utilize natural language processing services such as Google Cloud Natural Language API or Amazon Comprehend.

[0274] Based on the analysis results, the server generates a list of potential diseases. Using this list, the server accesses a data aggregation device to select appropriate medical professionals. Here, a recommendation algorithm is used based on past medical records and evaluation information of medical professionals. The server retrieves the available appointment times of the selected medical professionals from the database and sends this information to the user's terminal. This allows the user to select a preferred appointment time from the notified available times and make a provisional booking.

[0275] For example, if a user reports "headache" and "eye pain," the server analyzes this information and estimates potential conditions such as "tension headache" or "eye strain." Based on this estimation, it then selects the appropriate specialist, such as an internist or ophthalmologist, and suggests an available appointment time. This process allows the user to receive appropriate and prompt medical assistance.

[0276] An example of a prompt message is, "Please provide a specific example and detailed description of a system that analyzes a user's health information and recommends the most suitable medical services." This would enable the system to efficiently analyze the user's health status and provide the optimal treatment plan.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] Users input their health status and symptoms into the device. Specifically, this involves using a dedicated smartphone application to enter symptoms such as "headache" or "eye pain" into text boxes. The data entered is text-based information indicating the user's health status and symptoms.

[0280] Step 2:

[0281] The terminal sends the input data to the server. The data is transmitted over the internet and securely transferred using the HTTPS protocol. The input is text data, and the data is delivered to the server as output.

[0282] Step 3:

[0283] The server uses natural language processing to analyze the received data. It analyzes the input text using a generative AI model and extracts keywords and phrases. The input is text data submitted by the user, and the output is a list of key items and symptoms generated based on the analyzed information.

[0284] Step 4:

[0285] Based on the analysis results, the server generates a list of potential diseases. For example, through natural language analysis, disease names such as "tension headache" and "eye strain" that may be present are listed. In this process, machine learning algorithms are used. The input is the analysis information obtained in the previous step, and the output is a list of estimated disease names.

[0286] Step 5:

[0287] Based on the disease list, the server selects appropriate medical experts from the data integration device. Here, past medical records and evaluation information of medical experts are utilized, and a recommendation algorithm is used. The input is the generated disease list, and the output is the selected medical expert information.

[0288] Step 6:

[0289] The server collects the available appointment times of the selected medical experts and transmits that information to the user's terminal. The server obtains the availability of medical experts from the database in real time and sends email or push notifications to the user. The input is the data of medical experts, and the output is the available appointment time slots presented to the user.

[0290] Step 7:

[0291] The user selects a desired time from the presented available appointment times and makes a provisional reservation. By clicking the selection button on the terminal, the selection of the provisional reservation is completed. The input is the available appointment date and time notified by the server, and the output is the desired reservation time selected by the user.

[0292] Step 8:

[0293] The server uses the provisional booking information to perform a final confirmation with the medical institution and finalize the reservation. Communication with the medical institution takes place for confirmation, and the reservation confirmation result is generated. The final result is notified to the user and the medical professional, completing the appointment. The input is the details of the provisional booking, and the output is the confirmed booking information.

[0294] (Application Example 1)

[0295] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0296] In the medical diagnostic process, there is a challenge in that it is difficult for users to quickly and easily find the appropriate specialist, make an appointment, and even handle the payment of medical fees in a unified manner. Currently, many medical systems handle appointment scheduling and payment separately, which increases the burden on users and reduces the efficiency of medical services.

[0297] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0298] In this invention, the server includes means for receiving data on the user's health status and symptoms, means for generating a list of diseases using natural language processing based on the data, means for selecting an appropriate medical professional from registered data based on the list of diseases, means for obtaining information on the medical professional's available hours and notifying the user, and electronic payment means for settling medical fees based on the information. This enables the user to book an appointment with an appropriate specialist and settle medical fees immediately.

[0299] A "user" is someone who uses the system to input their health status and symptoms and receive medical services.

[0300] "Health status and symptom data" refers to information that represents the user's physical or mental condition or specific symptoms.

[0301] "Natural language processing" is a technology that enables a computer to understand and process human language and is used to analyze the input data on health conditions and symptoms.

[0302] The "disease list" is a list of potential diseases based on the user's symptoms generated by natural language processing technology.

[0303] A "medical expert" is a medical professional such as a doctor who can diagnose and treat specific diseases and symptoms.

[0304] A "data registry" is a database that stores past medical records and expert evaluation information and is used for selecting medical experts.

[0305] "Time information" is information indicating the time slots during which a medical expert can provide medical services and is provided to the user.

[0306] "Electronic payment means" is a means for online settlement of medical fees and enables the user to easily pay the fees for medical services.

[0307] To implement this invention, the server receives data on the user's health condition and symptoms. The user uses a terminal such as a smartphone to input the health condition and specific symptoms. The terminal transmits this input information to the server via a network. The server analyzes the received data using natural language processing technology and generates a list of potential diseases.

[0308] The server selects an appropriate medical expert based on the generated disease list. An algorithm based on past medical records and evaluations of medical experts is used for this selection, and the optimal expert is efficiently selected from the database. The server obtains the time information during which the selected medical expert can provide medical services and notifies the user's terminal. The user can select a desired date and time from the time information provided on the terminal and tentatively confirm the medical treatment.

[0309] Subsequently, the server confirms the provisional reservation with the medical institution and finalizes the reservation. A final reservation confirmation notice is sent to both the user and the medical professional. The cost of the selected treatment is displayed on the terminal, and the user completes the payment using electronic payment methods. In this process, the server provides treatment recommendations and electronic payment in an integrated manner, enabling the user to receive the service smoothly and efficiently.

[0310] For example, if a user experiences headaches and eye strain after prolonged computer work, they will be recommended an ophthalmologist, can select an appointment time, and pay the associated medical fees electronically. To support this process, the server utilizes a generative AI model to generate prompts such as: "The user has entered their symptoms in a smartphone app. Natural language processing will analyze the information, suggest the most suitable doctor and appointment, and complete the electronic payment for the medical fees. Please create an application program to achieve this."

[0311] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0312] Step 1:

[0313] Users input their health status and symptoms using a terminal. This input data includes specific symptoms and changes in physical condition. This data is initially processed on the terminal and then sent to the server in a formatted state.

[0314] Step 2:

[0315] The server receives data from the user. Based on this received data, it analyzes the information using natural language processing techniques. As a result of the analysis, a list of potential diseases is generated based on the input symptoms. The output is a list of estimated diseases.

[0316] Step 3:

[0317] The server selects appropriate medical professionals based on the generated disease list. This process involves accessing a database, considering medical history and professional evaluation information, and using a selection algorithm to identify the most suitable professional. The output provides information on the recommended medical professionals.

[0318] Step 4:

[0319] The server collects information on the availability of the selected medical professionals. The collected time information is organized into options that the user can book and is notified to the user's device. The user selects their desired appointment date and time from this information.

[0320] Step 5:

[0321] The user selects their desired appointment date and time on their device and makes a provisional reservation. The provisional reservation information is sent to the server for preliminary confirmation. In this step, the reservation time and the selected doctor's information are confirmed.

[0322] Step 6:

[0323] The server communicates with the medical institution based on the provisional booking information to perform a final booking confirmation. The final confirmed booking information is notified to both the user and the medical professional, and the booking is confirmed.

[0324] Step 7:

[0325] The server sends the confirmed medical fee to the user's terminal and prompts them to complete the payment using an electronic payment method. The user confirms the medical fee on their terminal and completes the electronic payment. As a result, the medical appointment and payment are integrated into a single process.

[0326] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0327] This invention begins when a user inputs their health status and symptoms into a terminal. The information entered by the user is transmitted to a server via a secure communication protocol. The server has the function to analyze the received data, and first analyzes the symptoms using natural language processing (NLP) technology. This analysis generates a list of possible diseases.

[0328] A distinctive feature of this invention is that the server incorporates an emotion engine that recognizes emotions from user input data. This emotion engine identifies emotions such as joy, anxiety, and pain from the text and voice input by the user, and adjusts subsequent processes based on that emotional state. For example, if the user's emotions of anxiety or pain are high, it is possible to prioritize the selection of a medical professional who can respond quickly.

[0329] Next, the server queries the database based on the emotion recognition results from the emotion engine and the generated list of diseases. The process then continues by selecting the most suitable medical professional based on past clinical experience and expert reviews. This ensures that the most appropriate medical service for the user's condition is provided quickly.

[0330] The server then extracts the available appointment times of the selected specialists and notifies the user's terminal. The user can select a preferred time slot from the appointment dates and times displayed on the terminal and tentatively confirm the reservation. Based on this tentative confirmation, the server confirms with the medical institution and makes the final reservation confirmation.

[0331] Once a reservation is confirmed, the server sends a notification to both the user and the medical professional based on the confirmed reservation information. This notification includes the date, time, and location of the appointment, as well as details about the medical professional, allowing the user to schedule their appointment with confidence.

[0332] For example, if a user enters "I've had a severe headache since this morning and I'm very anxious," the server uses NLP technology to add "headache" to the list of conditions and simultaneously recognizes the emotion of "anxiety" through its emotion engine. Based on this data, the system determines that the situation may be urgent and prioritizes suggesting specialists who can provide emergency assistance. In addition, appropriate advice is also provided to reassure the user.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] Users input their health status and specific symptoms into the device. This input may include text or voice, and the data is stored on the device.

[0336] Step 2:

[0337] The terminal sends user input data to the server. Encrypted protocols are used to ensure data security during transmission.

[0338] Step 3:

[0339] The server analyzes the user data it receives. First, it uses natural language processing (NLP) to analyze the symptoms based on the input information and generates a list of possible diseases.

[0340] Step 4:

[0341] The server uses an emotion engine to recognize the user's emotions. This analyzes the text content from the input data and, as a result, identifies emotional states such as "anxiety," "relief," and "pain."

[0342] Step 5:

[0343] The server considers the emotion recognition results and the disease list to select the appropriate medical professional from the database. This selection is algorithmically based on past clinical experience and patient reviews to determine the best professional.

[0344] Step 6:

[0345] The server retrieves the available dates and times of the selected medical professionals from the database. This information is organized to best suit the user's time requirements.

[0346] Step 7:

[0347] Based on the information received from the server, the terminal displays the doctor's name, specialty, and possible appointment dates and times to the user. The user then selects their preferred appointment date and time from the options.

[0348] Step 8:

[0349] The terminal sends the user's selected reservation date and time to the server, which then uses that information to finalize the reservation with the medical institution.

[0350] Step 9:

[0351] The server confirms that the reservation has been made and sends a notification to both the user and the medical professional via email or message. This notification includes detailed information about the consultation.

[0352] (Example 2)

[0353] Next, we will describe Example 2. 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".

[0354] In modern society, there is a challenge in that users with diverse health conditions and symptoms often have difficulty receiving appropriate and timely medical services. Furthermore, there is a need to consider the user's emotional state and provide the most appropriate support. Traditional medical systems often lack sufficient automation in symptom analysis and specialist selection, and fail to adequately address emotional issues, leading to a decline in service quality.

[0355] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0356] In this invention, the server includes means for recognizing emotions from user input information, means for adjusting the process based on the recognized emotions, means for generating disease candidates using natural language processing, and means for selecting an appropriate specialist. This makes it possible to quickly provide optimal medical services tailored to the user's symptoms and mental state.

[0357] A "user" refers to an individual who uses this system and is the entity that inputs information about their health status and symptoms.

[0358] "Health status" refers to the user's physical and mental condition, and includes information analyzed by the system.

[0359] A "symptom" refers to a specific physical or mental phenomenon or state that the user perceives, and is the subject of analysis by the system.

[0360] "Means of receiving information" refers to a mechanism for transferring user-entered information to a server and processing it appropriately.

[0361] "Natural language processing" is a technology used on servers, which involves analyzing input text data to understand its meaning.

[0362] A "candidate disease" is a list of potential medical problems generated by natural language processing, and is treated as foundational information for determining appropriate medical treatment.

[0363] "Experts" refer to individuals or organizations qualified to perform medical-related duties and whose role is to provide medical services to users.

[0364] An "information storage device" refers to a mechanism used to store data within a server, including expert information and user history.

[0365] "Emotions" refer to the internal psychological state recognized from the data entered by the user, and are detected in order to adjust the processes within the system.

[0366] "Means of adjusting the process" refers to functions that appropriately modify the system's operation or service delivery method based on recognized emotions and analysis results.

[0367] This invention is a system that begins with the user inputting their health status and symptoms into a terminal. The user inputs symptoms and emotions in text format using a smartphone or personal computer. The entered information is securely transmitted to the server via the HTTPS protocol.

[0368] The server analyzes the received information using advanced natural language processing techniques. Specifically, it uses "natural language processing libraries" and "cloud-based natural language APIs" to analyze symptoms and generate disease candidates. At the same time, data on emotions entered by the user is also analyzed. "Emotion recognition engines" and "voice analysis software" are used to recognize emotions. This allows the system to adjust the process while taking the user's emotional state into consideration.

[0369] Next, the server searches the database based on the generated disease candidates and emotional assessments to identify the most suitable specialist. This specialist selection process uses a "database management system" that takes into account past medical records and specialist evaluations.

[0370] Information about the selected specialist and their available appointment times are sent from the server to the terminal. The user can select a desired appointment date and time from the provided time slots and make a provisional reservation. The server then coordinates the appointment time with the medical institution and confirms the final reservation. The confirmed reservation information is sent back to the terminal as a notification to both the user and the specialist. The entire system aims to support the user's health management quickly and efficiently.

[0371] As a concrete example, consider a case where a user inputs into their device, "I've had a terrible headache since this morning. I'm filled with anxiety." In this case, the server uses natural language processing to add "headache" to the list of illnesses and recognizes the emotion of "anxiety." Based on this, it prioritizes selecting a specialist who can provide emergency assistance and also provides appropriate advice to reassure the user.

[0372] An example of a prompt message would be something like, "Regarding the procedure for analyzing the health symptoms entered by the user, identifying emotions, and adjusting the treatment accordingly."

[0373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0374] Step 1:

[0375] The user enters their health status and symptoms into the device. This input is in text or voice format, and the device converts it into digital data. This data then serves as the input for processing.

[0376] Step 2:

[0377] The terminal sends the input digital data to the server via a secure communication protocol, such as HTTPS. This ensures the security of the data. Server reception is the output of this step.

[0378] Step 3:

[0379] The server analyzes the received digital data using a natural language processing engine. Specifically, it tokenizes the text data, extracts important keywords, performs grammatical analysis, and lists the symptoms. Through this process, the analyzed symptoms are output.

[0380] Step 4:

[0381] The server inputs the analyzed symptom data into the emotion recognition engine to analyze the user's emotions. Here, emotions such as "anxiety," "joy," and "pain" are identified through text analysis and voice analysis. This identification result becomes the output of the step.

[0382] Step 5:

[0383] The server searches the database for appropriate specialists based on the analyzed list of symptoms and emotional data. A selection algorithm, based on past clinical experience and evaluation information, generates a list of optimal specialists. This result is the output of this step.

[0384] Step 6:

[0385] The server retrieves the available appointment times of the selected specialist from the database and notifies the terminal. The user selects a convenient time from the times displayed on the terminal and generates a provisional booking. The provisional booking information is then sent to the server as input.

[0386] Step 7:

[0387] The server coordinates the appointment with the medical institution based on the received provisional booking information. Once the medical institution returns the final appointment time, the server confirms it. The final booking information is the output of this step.

[0388] Step 8:

[0389] The server resends the confirmed appointment information to the terminal, notifying both the user and the professional. The notification includes the appointment date and time, location, and professional details, allowing the user to prepare for the appointment. This transmission is the final output.

[0390] (Application Example 2)

[0391] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0392] In today's world, many people need quick and accurate access to appropriate medical professionals for their health conditions and symptoms. However, systems that do not consider the emotional state of the user may not adequately respond to the urgency and anxiety they feel. Furthermore, it is necessary to go beyond simply generating disease lists and adjust the booking process so that users can receive medical services with peace of mind.

[0393] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0394] In this invention, the server includes a device for receiving information on the user's health status and symptoms, a device for generating a list of diseases using natural language processing technology based on the information, a device for selecting an appropriate medical professional from a data set based on the list of diseases, and a device for analyzing the user's emotions and adjusting the process based on the analysis results. This enables the rapid and accurate selection and booking of a medical professional that is in line with the user's health status and emotional state.

[0395] A "user" refers to an individual who inputs information about their health status and symptoms into the system.

[0396] "Health status and symptom information" refers to information in natural language or other formats that users input as data about their own physical condition.

[0397] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0398] A "list of diseases" is a list of possible illnesses and health problems generated using natural language processing technology.

[0399] A "medical professional" refers to a healthcare worker who is qualified to diagnose and treat specific diseases.

[0400] A "data set" refers to a database containing information about medical professionals.

[0401] A "device that analyzes emotions" refers to a device that recognizes emotions from information or voice input by the user and reflects the results in the process.

[0402] "Adjusting the process" means optimizing the procedures for consultations and appointments according to the user's emotions and circumstances.

[0403] "Rapid and accurate selection and booking of medical professionals" refers to quickly and appropriately selecting and booking medical professionals according to the user's condition.

[0404] The system that realizes this invention begins with the user inputting information about their health status and symptoms via a terminal. This data is transmitted to a server via the internet. The server analyzes the received information using natural language processing technology and generates a list of possible diseases. Possible natural language processing technologies used here include open-source NLP libraries such as "spaCy" and "NLTK". In addition, emotion recognition models using TensorFlow or PyTorch can be utilized to analyze emotions.

[0405] For example, if a user enters "I've had a severe headache since this morning and I'm very anxious," the server analyzes the input, generates a list of diseases that include "headache," and identifies the emotion of anxiety through emotion analysis. Based on this emotion, the server determines that a high-priority response is needed and selects a medical professional from the database who can respond quickly.

[0406] The user's device is notified of the available dates and times of the selected medical professional, and the user selects a preferred time from the displayed options. The final booking information obtained as a result of the selection is also shared with the medical professional.

[0407] An example of a prompt message would be that the user inputs "My back hurts from working at a desk for long hours," to which the system would respond with "We suggest you make an appointment with an orthopedic specialist."

[0408] This system allows users to receive medical services that are tailored to their health condition and emotional state quickly and accurately, while also enabling them to proceed with treatment with peace of mind.

[0409] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0410] Step 1:

[0411] The user uses their device to input information about their health status and symptoms. This input is in text format and is sent to the server via a secure communication protocol on the device. The input data might take the form of, for example, "I've had a severe headache since this morning, and I'm very worried."

[0412] Step 2:

[0413] The server analyzes the user's input data using natural language processing techniques. It extracts disease names and related keywords from the received text data using a natural language processing engine (e.g., spaCy) and generates a list of diseases. As a result, diseases such as "headache" are added to the list.

[0414] Step 3:

[0415] The server uses a generative AI model to analyze emotions from user input data. Based on the input text, it applies an emotion recognition model (e.g., an emotion analysis model using TensorFlow) to determine the emotion. The output is an emotion label such as "anxiety" or "pain." This allows the server to identify the user's emotional state.

[0416] Step 4:

[0417] The server uses the analysis results—a list of diseases and emotion labels—to query the data set and execute a process to select appropriate medical professionals. The selection prioritizes diseases and emotions based on their urgency; that is, it selects medical professionals who possess specialized knowledge of the identified diseases and can respond quickly.

[0418] Step 5:

[0419] The server extracts the available time slots for the selected medical professional and notifies the terminal of this information. The user selects a preferred time slot from the displayed dates and times on the terminal and makes a provisional reservation. The selected time slot is then sent to the server via the terminal.

[0420] Step 6:

[0421] Ultimately, the server verifies the provisional booking information with a specialist and confirms the appointment. Based on the confirmed booking information, notifications are sent to both the user and the medical specialist. These notifications include the date, time, location, and details of the medical specialist, allowing the user to schedule their appointment with confidence.

[0422] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0423] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0424] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0425] [Third Embodiment]

[0426] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0427] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0428] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0429] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0430] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0431] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0432] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0433] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0434] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0435] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0436] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0437] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0438] This invention begins with a user inputting their health status and symptoms into a terminal. The information entered by the user is transmitted to a server via the network. The server receives this information and analyzes it using natural language processing technology. This analysis generates a list of potential diseases.

[0439] Based on the generated disease list, the server accesses a database and selects the most suitable medical professional based on past treatment records and reviews from other healthcare professionals. This selection process utilizes an algorithm to efficiently choose the professional best suited to the user's specific symptoms.

[0440] The server then retrieves the available dates and times of the selected medical professional from the database and notifies the terminal of this information. The user selects a desired date and time from the available dates and times provided on the terminal and tentatively confirms the reservation.

[0441] The server uses the provisionally confirmed reservation information to make a final confirmation with the medical institution and then confirms the reservation. A final reservation confirmation notice is sent to both the user and the medical professional. This allows the user to receive medical consultation and treatment quickly and smoothly.

[0442] For example, if a user enters symptoms such as "headache" and "eye pain," the server analyzes this information and estimates conditions such as "tension headache" or "eye strain." Then, based on past medical data, it matches the user with an appropriate specialist, such as an internist or ophthalmologist, and suggests available appointment times. In this way, users can quickly find the most suitable specialist and efficiently schedule appointments.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The user enters their health status and symptoms into the device. The entered information includes specific symptoms and details of their physical ailments.

[0446] Step 2:

[0447] The terminal sends user input data to the server. A secure communication protocol is applied to this data transmission.

[0448] Step 3:

[0449] The server analyzes the received data. Natural language processing (NLP) techniques are used to extract potential disease indications from the entered symptoms.

[0450] Step 4:

[0451] The server accesses a database of medical professionals based on the generated disease list. It then selects the most suitable medical professional candidate based on their area of ​​expertise, past clinical experience, and reviews.

[0452] Step 5:

[0453] The server retrieves the available dates and times of the selected specialist and uses that information to generate candidate dates and times for appointment bookings.

[0454] Step 6:

[0455] The terminal displays the user with a list of available appointment dates and times received from the server. The user selects their preferred date and time and confirms the appointment request on the terminal.

[0456] Step 7:

[0457] A reservation request is sent from the terminal to the server. The server receives this request and initiates the final confirmation process with the medical institution.

[0458] Step 8:

[0459] After the server completes the final confirmation of the reservation, it sends a reservation confirmation notice to the user and the healthcare professional. This notice includes the reservation time, healthcare professional information, and instructions on how to access the consultation.

[0460] (Example 1)

[0461] Next, we will describe Example 1. 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."

[0462] In modern society, users are required to quickly and accurately communicate their health status and symptoms to specialists and receive appropriate medical services. However, finding the right medical specialist and efficiently scheduling appointments is difficult. There is a need for a system that can solve these problems.

[0463] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0464] In this invention, the server includes means for receiving information on the user's health status and symptoms, means for generating a list of diseases using natural language processing with a generative AI model, and means for selecting an appropriate medical professional from a data collection device. This enables the user to quickly select an appropriate medical professional and efficiently book an appointment.

[0465] A "user" is someone who inputs their health status and symptoms into the system and receives medical services.

[0466] "Health status and symptom information" refers to data that users input into the system indicating their own physical and mental condition.

[0467] A "generative AI model" is an artificial intelligence technology that utilizes natural language processing in data analysis, understanding and analyzing the meaning of input text.

[0468] "Natural language processing" is a technology that understands text data as human language and extracts its meaning.

[0469] A "disease list" is a list of potential illnesses and physical problems based on the user's health information, obtained as a result of natural language processing.

[0470] A "medical professional" is a professional who has been trained to diagnose and treat specific diseases or health problems.

[0471] A "data collection device" is a database system that stores information about medical professionals and past medical records.

[0472] "Available consultation times" refers to the time period during which medical professionals can provide consultations to users.

[0473] "Communication" refers to the process of transmitting information to users and medical professionals, using the internet and other telecommunication methods.

[0474] A "recommendation algorithm" is a computational method that uses past medical records and evaluation information to select the most suitable medical professional for a user.

[0475] To implement this invention, the user first inputs their health status and symptoms using a terminal. Once the user inputs their symptoms via a dedicated application on their smartphone or computer, this information is transmitted to a server via the internet. After receiving this information, the server analyzes the data using natural language processing technology with a generative AI model. This analysis can utilize natural language processing services such as Google Cloud Natural Language API or Amazon Comprehend.

[0476] Based on the analysis results, the server generates a list of potential diseases. Using this list, the server accesses a data aggregation device to select appropriate medical professionals. Here, a recommendation algorithm is used based on past medical records and evaluation information of medical professionals. The server retrieves the available appointment times of the selected medical professionals from the database and sends this information to the user's terminal. This allows the user to select a preferred appointment time from the notified available times and make a provisional booking.

[0477] For example, if a user reports "headache" and "eye pain," the server analyzes this information and estimates potential conditions such as "tension headache" or "eye strain." Based on this estimation, it then selects the appropriate specialist, such as an internist or ophthalmologist, and suggests an available appointment time. This process allows the user to receive appropriate and prompt medical assistance.

[0478] An example of a prompt message is, "Please provide a specific example and detailed description of a system that analyzes a user's health information and recommends the most suitable medical services." This would enable the system to efficiently analyze the user's health status and provide the optimal treatment plan.

[0479] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0480] Step 1:

[0481] Users input their health status and symptoms into the device. Specifically, this involves using a dedicated smartphone application to enter symptoms such as "headache" or "eye pain" into text boxes. The data entered is text-based information indicating the user's health status and symptoms.

[0482] Step 2:

[0483] The terminal sends the input data to the server. The data is transmitted over the internet and securely transferred using the HTTPS protocol. The input is text data, and the data is delivered to the server as output.

[0484] Step 3:

[0485] The server uses natural language processing to analyze the received data. It analyzes the input text using a generative AI model and extracts keywords and phrases. The input is text data submitted by the user, and the output is a list of key items and symptoms generated based on the analyzed information.

[0486] Step 4:

[0487] The server generates a list of potential diseases based on the analysis results. For example, natural language processing is used to list possible disease names such as "tension headache" and "eye strain." This process uses machine learning algorithms. The input is the analysis information obtained in the previous step, and the output is a list of estimated disease names.

[0488] Step 5:

[0489] The server selects appropriate medical professionals from the data aggregation device based on the disease list. A recommendation algorithm is used, utilizing past medical records and evaluation information of medical professionals. The input is the generated disease list, and the output is the selected medical professional information.

[0490] Step 6:

[0491] The server collects the available appointment times of selected medical professionals and sends this information to the user's terminal. The server retrieves the availability of medical professionals from the database in real time and sends emails or push notifications to the user. The input is the medical professional's data, and the output is the available appointment time presented to the user.

[0492] Step 7:

[0493] The user selects a desired time from the displayed available appointment times and makes a provisional reservation. The provisional reservation is completed when the user clicks the selection button on the terminal. The input is the available appointment times notified by the server, and the output is the user's selected preferred appointment time.

[0494] Step 8:

[0495] The server uses the provisional booking information to perform a final confirmation with the medical institution and finalize the reservation. Communication with the medical institution takes place for confirmation, and the reservation confirmation result is generated. The final result is notified to the user and the medical professional, completing the appointment. The input is the details of the provisional booking, and the output is the confirmed booking information.

[0496] (Application Example 1)

[0497] Next, we will explain Application Example 1. In the following explanation, 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."

[0498] In the medical diagnostic process, there is a challenge in that it is difficult for users to quickly and easily find the appropriate specialist, make an appointment, and even handle the payment of medical fees in a unified manner. Currently, many medical systems handle appointment scheduling and payment separately, which increases the burden on users and reduces the efficiency of medical services.

[0499] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0500] In this invention, the server includes means for receiving data on the user's health status and symptoms, means for generating a list of diseases using natural language processing based on the data, means for selecting an appropriate medical professional from registered data based on the list of diseases, means for obtaining information on the medical professional's available hours and notifying the user, and electronic payment means for settling medical fees based on the information. This enables the user to book an appointment with an appropriate specialist and settle medical fees immediately.

[0501] A "user" is someone who uses the system to input their health status and symptoms and receive medical services.

[0502] "Health status and symptom data" refers to information that represents the user's physical or mental condition or specific symptoms.

[0503] "Natural language processing" is a technology that allows computers to understand and process human language, and it is used to analyze input data on health conditions and symptoms.

[0504] The "disease list" is a list of possible diseases based on the user's symptoms, generated using natural language processing technology.

[0505] A "medical professional" is a healthcare worker, such as a physician, who is capable of diagnosing and treating specific diseases or symptoms.

[0506] A "data registry" is a database that stores past medical history, expert evaluation information, and other data, and is used for selecting medical professionals.

[0507] "Time information" refers to information indicating the time periods during which medical professionals are available to provide medical care, and this information is provided to users.

[0508] "Electronic payment methods" refer to means of settling medical fees online, enabling users to easily pay for medical services.

[0509] To implement this invention, the server receives data about the user's health status and symptoms. The user inputs their health status and specific symptoms using a device such as a smartphone. The device transmits this input information to the server via the network. The server analyzes the received data using natural language processing technology and generates a list of potential diseases.

[0510] The server selects an appropriate medical professional based on the generated disease list. This selection uses an algorithm based on past treatment history and medical professional evaluations, efficiently selecting the most suitable professional from the database. The server retrieves the available appointment times of the selected medical professional and notifies the user's terminal. The user can then select a desired date and time from the provided time information on their terminal and tentatively confirm the appointment.

[0511] Subsequently, the server confirms the provisional reservation with the medical institution and finalizes the reservation. A final reservation confirmation notice is sent to both the user and the medical professional. The cost of the selected treatment is displayed on the terminal, and the user completes the payment using electronic payment methods. In this process, the server provides treatment recommendations and electronic payment in an integrated manner, enabling the user to receive the service smoothly and efficiently.

[0512] For example, if a user experiences headaches and eye strain after prolonged computer work, they will be recommended an ophthalmologist, can select an appointment time, and pay the associated medical fees electronically. To support this process, the server utilizes a generative AI model to generate prompts such as: "The user has entered their symptoms in a smartphone app. Natural language processing will analyze the information, suggest the most suitable doctor and appointment, and complete the electronic payment for the medical fees. Please create an application program to achieve this."

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] Users input their health status and symptoms using a terminal. This input data includes specific symptoms and changes in physical condition. This data is initially processed on the terminal and then sent to the server in a formatted state.

[0516] Step 2:

[0517] The server receives data from the user. Based on this received data, it analyzes the information using natural language processing techniques. As a result of the analysis, a list of potential diseases is generated based on the input symptoms. The output is a list of estimated diseases.

[0518] Step 3:

[0519] The server selects appropriate medical professionals based on the generated disease list. This process involves accessing a database, considering medical history and professional evaluation information, and using a selection algorithm to identify the most suitable professional. The output provides information on the recommended medical professionals.

[0520] Step 4:

[0521] The server collects information on the availability of the selected medical professionals. The collected time information is organized into options that the user can book and is notified to the user's device. The user selects their desired appointment date and time from this information.

[0522] Step 5:

[0523] The user selects their desired appointment date and time on their device and makes a provisional reservation. The provisional reservation information is sent to the server for preliminary confirmation. In this step, the reservation time and the selected doctor's information are confirmed.

[0524] Step 6:

[0525] The server communicates with the medical institution based on the provisional booking information to perform a final booking confirmation. The final confirmed booking information is notified to both the user and the medical professional, and the booking is confirmed.

[0526] Step 7:

[0527] The server sends the confirmed medical fee to the user's terminal and prompts them to complete the payment using an electronic payment method. The user confirms the medical fee on their terminal and completes the electronic payment. As a result, the medical appointment and payment are integrated into a single process.

[0528] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0529] This invention begins when a user inputs their health status and symptoms into a terminal. The information entered by the user is transmitted to a server via a secure communication protocol. The server has the function to analyze the received data, and first analyzes the symptoms using natural language processing (NLP) technology. This analysis generates a list of possible diseases.

[0530] A distinctive feature of this invention is that the server incorporates an emotion engine that recognizes emotions from user input data. This emotion engine identifies emotions such as joy, anxiety, and pain from the text and voice input by the user, and adjusts subsequent processes based on that emotional state. For example, if the user's emotions of anxiety or pain are high, it is possible to prioritize the selection of a medical professional who can respond quickly.

[0531] Next, the server queries the database based on the emotion recognition results from the emotion engine and the generated list of diseases. The process then continues by selecting the most suitable medical professional based on past clinical experience and expert reviews. This ensures that the most appropriate medical service for the user's condition is provided quickly.

[0532] The server then extracts the available appointment times of the selected specialists and notifies the user's terminal. The user can select a preferred time slot from the appointment dates and times displayed on the terminal and tentatively confirm the reservation. Based on this tentative confirmation, the server confirms with the medical institution and makes the final reservation confirmation.

[0533] Once a reservation is confirmed, the server sends a notification to both the user and the medical professional based on the confirmed reservation information. This notification includes the date, time, and location of the appointment, as well as details about the medical professional, allowing the user to schedule their appointment with confidence.

[0534] For example, if a user enters "I've had a severe headache since this morning and I'm very anxious," the server uses NLP technology to add "headache" to the list of conditions and simultaneously recognizes the emotion of "anxiety" through its emotion engine. Based on this data, the system determines that the situation may be urgent and prioritizes suggesting specialists who can provide emergency assistance. In addition, appropriate advice is also provided to reassure the user.

[0535] The following describes the processing flow.

[0536] Step 1:

[0537] Users input their health status and specific symptoms into the device. This input may include text or voice, and the data is stored on the device.

[0538] Step 2:

[0539] The terminal sends user input data to the server. Encrypted protocols are used to ensure data security during transmission.

[0540] Step 3:

[0541] The server analyzes the user data it receives. First, it uses natural language processing (NLP) to analyze the symptoms based on the input information and generates a list of possible diseases.

[0542] Step 4:

[0543] The server uses an emotion engine to recognize the user's emotions. This analyzes the text content from the input data and, as a result, identifies emotional states such as "anxiety," "relief," and "pain."

[0544] Step 5:

[0545] The server considers the emotion recognition results and the disease list to select the appropriate medical professional from the database. This selection is algorithmically based on past clinical experience and patient reviews to determine the best professional.

[0546] Step 6:

[0547] The server retrieves the available dates and times of the selected medical professionals from the database. This information is organized to best suit the user's time requirements.

[0548] Step 7:

[0549] Based on the information received from the server, the terminal displays the doctor's name, specialty, and possible appointment dates and times to the user. The user then selects their preferred appointment date and time from the options.

[0550] Step 8:

[0551] The terminal sends the user's selected reservation date and time to the server, which then uses that information to finalize the reservation with the medical institution.

[0552] Step 9:

[0553] The server confirms that the reservation has been made and sends a notification to both the user and the medical professional via email or message. This notification includes detailed information about the consultation.

[0554] (Example 2)

[0555] Next, we will describe Example 2. 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."

[0556] In modern society, there is a challenge in that users with diverse health conditions and symptoms often have difficulty receiving appropriate and timely medical services. Furthermore, there is a need to consider the user's emotional state and provide the most appropriate support. Traditional medical systems often lack sufficient automation in symptom analysis and specialist selection, and fail to adequately address emotional issues, leading to a decline in service quality.

[0557] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0558] In this invention, the server includes means for recognizing emotions from user input information, means for adjusting the process based on the recognized emotions, means for generating disease candidates using natural language processing, and means for selecting an appropriate specialist. This makes it possible to quickly provide optimal medical services tailored to the user's symptoms and mental state.

[0559] A "user" refers to an individual who uses this system and is the entity that inputs information about their health status and symptoms.

[0560] "Health status" refers to the user's physical and mental condition, and includes information analyzed by the system.

[0561] A "symptom" refers to a specific physical or mental phenomenon or state that the user perceives, and is the subject of analysis by the system.

[0562] "Means of receiving information" refers to a mechanism for transferring user-entered information to a server and processing it appropriately.

[0563] "Natural language processing" is a technology used on servers, which involves analyzing input text data to understand its meaning.

[0564] A "candidate disease" is a list of potential medical problems generated by natural language processing, and is treated as foundational information for determining appropriate medical treatment.

[0565] "Experts" refer to individuals or organizations qualified to perform medical-related duties and whose role is to provide medical services to users.

[0566] An "information storage device" refers to a mechanism used to store data within a server, including expert information and user history.

[0567] "Emotions" refer to the internal psychological state recognized from the data entered by the user, and are detected in order to adjust the processes within the system.

[0568] "Means of adjusting the process" refers to functions that appropriately modify the system's operation or service delivery method based on recognized emotions and analysis results.

[0569] This invention is a system that begins with the user inputting their health status and symptoms into a terminal. The user inputs symptoms and emotions in text format using a smartphone or personal computer. The entered information is securely transmitted to the server via the HTTPS protocol.

[0570] The server analyzes the received information using advanced natural language processing techniques. Specifically, it uses "natural language processing libraries" and "cloud-based natural language APIs" to analyze symptoms and generate disease candidates. At the same time, data on emotions entered by the user is also analyzed. "Emotion recognition engines" and "voice analysis software" are used to recognize emotions. This allows the system to adjust the process while taking the user's emotional state into consideration.

[0571] Next, the server searches the database based on the generated disease candidates and emotional assessments to identify the most suitable specialist. This specialist selection process uses a "database management system" that takes into account past medical records and specialist evaluations.

[0572] Information about the selected specialist and their available appointment times are sent from the server to the terminal. The user can select a desired appointment date and time from the provided time slots and make a provisional reservation. The server then coordinates the appointment time with the medical institution and confirms the final reservation. The confirmed reservation information is sent back to the terminal as a notification to both the user and the specialist. The entire system aims to support the user's health management quickly and efficiently.

[0573] As a concrete example, consider a case where a user inputs into their device, "I've had a terrible headache since this morning. I'm filled with anxiety." In this case, the server uses natural language processing to add "headache" to the list of illnesses and recognizes the emotion of "anxiety." Based on this, it prioritizes selecting a specialist who can provide emergency assistance and also provides appropriate advice to reassure the user.

[0574] An example of a prompt message would be something like, "Regarding the procedure for analyzing the health symptoms entered by the user, identifying emotions, and adjusting the treatment accordingly."

[0575] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0576] Step 1:

[0577] The user enters their health status and symptoms into the device. This input is in text or voice format, and the device converts it into digital data. This data then serves as the input for processing.

[0578] Step 2:

[0579] The terminal sends the input digital data to the server via a secure communication protocol, such as HTTPS. This ensures the security of the data. Server reception is the output of this step.

[0580] Step 3:

[0581] The server analyzes the received digital data using a natural language processing engine. Specifically, it tokenizes the text data, extracts important keywords, performs grammatical analysis, and lists the symptoms. Through this process, the analyzed symptoms are output.

[0582] Step 4:

[0583] The server inputs the analyzed symptom data into the emotion recognition engine to analyze the user's emotions. Here, emotions such as "anxiety," "joy," and "pain" are identified through text analysis and voice analysis. This identification result becomes the output of the step.

[0584] Step 5:

[0585] The server searches the database for appropriate specialists based on the analyzed list of symptoms and emotional data. A selection algorithm, based on past clinical experience and evaluation information, generates a list of optimal specialists. This result is the output of this step.

[0586] Step 6:

[0587] The server retrieves the available appointment times of the selected specialist from the database and notifies the terminal. The user selects a convenient time from the times displayed on the terminal and generates a provisional booking. The provisional booking information is then sent to the server as input.

[0588] Step 7:

[0589] The server coordinates the appointment with the medical institution based on the received provisional booking information. Once the medical institution returns the final appointment time, the server confirms it. The final booking information is the output of this step.

[0590] Step 8:

[0591] The server resends the confirmed appointment information to the terminal, notifying both the user and the professional. The notification includes the appointment date and time, location, and professional details, allowing the user to prepare for the appointment. This transmission is the final output.

[0592] (Application Example 2)

[0593] Next, we will explain Application Example 2. In the following explanation, 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."

[0594] In today's world, many people need quick and accurate access to appropriate medical professionals for their health conditions and symptoms. However, systems that do not consider the emotional state of the user may not adequately respond to the urgency and anxiety they feel. Furthermore, it is necessary to go beyond simply generating disease lists and adjust the booking process so that users can receive medical services with peace of mind.

[0595] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0596] In this invention, the server includes a device for receiving information on the user's health status and symptoms, a device for generating a list of diseases using natural language processing technology based on the information, a device for selecting an appropriate medical professional from a data set based on the list of diseases, and a device for analyzing the user's emotions and adjusting the process based on the analysis results. This enables the rapid and accurate selection and booking of a medical professional that is in line with the user's health status and emotional state.

[0597] A "user" refers to an individual who inputs information about their health status and symptoms into the system.

[0598] "Health status and symptom information" refers to information in natural language or other formats that users input as data about their own physical condition.

[0599] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0600] A "list of diseases" is a list of possible illnesses and health problems generated using natural language processing technology.

[0601] A "medical professional" refers to a healthcare worker who is qualified to diagnose and treat specific diseases.

[0602] A "data set" refers to a database containing information about medical professionals.

[0603] A "device that analyzes emotions" refers to a device that recognizes emotions from information or voice input by the user and reflects the results in the process.

[0604] "Adjusting the process" means optimizing the procedures for consultations and appointments according to the user's emotions and circumstances.

[0605] "Rapid and accurate selection and booking of medical professionals" refers to quickly and appropriately selecting and booking medical professionals according to the user's condition.

[0606] The system that realizes this invention begins with the user inputting information about their health status and symptoms via a terminal. This data is transmitted to a server via the internet. The server analyzes the received information using natural language processing technology and generates a list of possible diseases. Possible natural language processing technologies used here include open-source NLP libraries such as "spaCy" and "NLTK". In addition, emotion recognition models using TensorFlow or PyTorch can be utilized to analyze emotions.

[0607] For example, if a user enters "I've had a severe headache since this morning and I'm very anxious," the server analyzes the input, generates a list of diseases that include "headache," and identifies the emotion of anxiety through emotion analysis. Based on this emotion, the server determines that a high-priority response is needed and selects a medical professional from the database who can respond quickly.

[0608] The user's device is notified of the available dates and times of the selected medical professional, and the user selects a preferred time from the displayed options. The final booking information obtained as a result of the selection is also shared with the medical professional.

[0609] An example of a prompt message would be that the user inputs "My back hurts from working at a desk for long hours," to which the system would respond with "We suggest you make an appointment with an orthopedic specialist."

[0610] This system allows users to receive medical services that are tailored to their health condition and emotional state quickly and accurately, while also enabling them to proceed with treatment with peace of mind.

[0611] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0612] Step 1:

[0613] The user uses their device to input information about their health status and symptoms. This input is in text format and is sent to the server via a secure communication protocol on the device. The input data might take the form of, for example, "I've had a severe headache since this morning, and I'm very worried."

[0614] Step 2:

[0615] The server analyzes the user's input data using natural language processing techniques. It extracts disease names and related keywords from the received text data using a natural language processing engine (e.g., spaCy) and generates a list of diseases. As a result, diseases such as "headache" are added to the list.

[0616] Step 3:

[0617] The server uses a generative AI model to analyze emotions from user input data. Based on the input text, it applies an emotion recognition model (e.g., an emotion analysis model using TensorFlow) to determine the emotion. The output is an emotion label such as "anxiety" or "pain." This allows the server to identify the user's emotional state.

[0618] Step 4:

[0619] The server uses the analysis results—a list of diseases and emotion labels—to query the data set and execute a process to select appropriate medical professionals. The selection prioritizes diseases and emotions based on their urgency; that is, it selects medical professionals who possess specialized knowledge of the identified diseases and can respond quickly.

[0620] Step 5:

[0621] The server extracts the available time slots for the selected medical professional and notifies the terminal of this information. The user selects a preferred time slot from the displayed dates and times on the terminal and makes a provisional reservation. The selected time slot is then sent to the server via the terminal.

[0622] Step 6:

[0623] Ultimately, the server verifies the provisional booking information with a specialist and confirms the appointment. Based on the confirmed booking information, notifications are sent to both the user and the medical specialist. These notifications include the date, time, location, and details of the medical specialist, allowing the user to schedule their appointment with confidence.

[0624] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0625] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0626] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0627] [Fourth Embodiment]

[0628] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0629] As shown in Figure 7, the 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.

[0630] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0631] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0632] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0633] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0634] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0635] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0636] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0637] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0638] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0639] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0640] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0641] This invention begins with a user inputting their health status and symptoms into a terminal. The information entered by the user is transmitted to a server via the network. The server receives this information and analyzes it using natural language processing technology. This analysis generates a list of potential diseases.

[0642] Based on the generated disease list, the server accesses a database and selects the most suitable medical professional based on past treatment records and reviews from other healthcare professionals. This selection process utilizes an algorithm to efficiently choose the professional best suited to the user's specific symptoms.

[0643] The server then retrieves the available dates and times of the selected medical professional from the database and notifies the terminal of this information. The user selects a desired date and time from the available dates and times provided on the terminal and tentatively confirms the reservation.

[0644] The server uses the provisionally confirmed reservation information to make a final confirmation with the medical institution and then confirms the reservation. A final reservation confirmation notice is sent to both the user and the medical professional. This allows the user to receive medical consultation and treatment quickly and smoothly.

[0645] For example, if a user enters symptoms such as "headache" and "eye pain," the server analyzes this information and estimates conditions such as "tension headache" or "eye strain." Then, based on past medical data, it matches the user with an appropriate specialist, such as an internist or ophthalmologist, and suggests available appointment times. In this way, users can quickly find the most suitable specialist and efficiently schedule appointments.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The user enters their health status and symptoms into the device. The entered information includes specific symptoms and details of their physical ailments.

[0649] Step 2:

[0650] The terminal sends user input data to the server. A secure communication protocol is applied to this data transmission.

[0651] Step 3:

[0652] The server analyzes the received data. Natural language processing (NLP) techniques are used to extract potential disease indications from the entered symptoms.

[0653] Step 4:

[0654] The server accesses a database of medical professionals based on the generated disease list. It then selects the most suitable medical professional candidate based on their area of ​​expertise, past clinical experience, and reviews.

[0655] Step 5:

[0656] The server retrieves the available dates and times of the selected specialist and uses that information to generate candidate dates and times for appointment bookings.

[0657] Step 6:

[0658] The terminal displays the user with a list of available appointment dates and times received from the server. The user selects their preferred date and time and confirms the appointment request on the terminal.

[0659] Step 7:

[0660] A reservation request is sent from the terminal to the server. The server receives this request and initiates the final confirmation process with the medical institution.

[0661] Step 8:

[0662] After the server completes the final confirmation of the reservation, it sends a reservation confirmation notice to the user and the healthcare professional. This notice includes the reservation time, healthcare professional information, and instructions on how to access the consultation.

[0663] (Example 1)

[0664] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0665] In modern society, users are required to quickly and accurately communicate their health status and symptoms to specialists and receive appropriate medical services. However, finding the right medical specialist and efficiently scheduling appointments is difficult. There is a need for a system that can solve these problems.

[0666] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0667] In this invention, the server includes means for receiving information on the user's health status and symptoms, means for generating a list of diseases using natural language processing with a generative AI model, and means for selecting an appropriate medical professional from a data collection device. This enables the user to quickly select an appropriate medical professional and efficiently book an appointment.

[0668] A "user" is someone who inputs their health status and symptoms into the system and receives medical services.

[0669] "Health status and symptom information" refers to data that users input into the system indicating their own physical and mental condition.

[0670] A "generative AI model" is an artificial intelligence technology that utilizes natural language processing in data analysis, understanding and analyzing the meaning of input text.

[0671] "Natural language processing" is a technology that understands text data as human language and extracts its meaning.

[0672] A "disease list" is a list of potential illnesses and physical problems based on the user's health information, obtained as a result of natural language processing.

[0673] A "medical professional" is a professional who has been trained to diagnose and treat specific diseases or health problems.

[0674] A "data collection device" is a database system that stores information about medical professionals and past medical records.

[0675] "Available consultation times" refers to the time period during which medical professionals can provide consultations to users.

[0676] "Communication" refers to the process of transmitting information to users and medical professionals, using the internet and other telecommunication methods.

[0677] A "recommendation algorithm" is a computational method that uses past medical records and evaluation information to select the most suitable medical professional for a user.

[0678] To implement this invention, the user first inputs their health status and symptoms using a terminal. Once the user inputs their symptoms via a dedicated application on their smartphone or computer, this information is transmitted to a server via the internet. After receiving this information, the server analyzes the data using natural language processing technology with a generative AI model. This analysis can utilize natural language processing services such as Google Cloud Natural Language API or Amazon Comprehend.

[0679] Based on the analysis results, the server generates a list of potential diseases. Using this list, the server accesses a data aggregation device to select appropriate medical professionals. Here, a recommendation algorithm is used based on past medical records and evaluation information of medical professionals. The server retrieves the available appointment times of the selected medical professionals from the database and sends this information to the user's terminal. This allows the user to select a preferred appointment time from the notified available times and make a provisional booking.

[0680] For example, if a user reports "headache" and "eye pain," the server analyzes this information and estimates potential conditions such as "tension headache" or "eye strain." Based on this estimation, it then selects the appropriate specialist, such as an internist or ophthalmologist, and suggests an available appointment time. This process allows the user to receive appropriate and prompt medical assistance.

[0681] An example of a prompt message is, "Please provide a specific example and detailed description of a system that analyzes a user's health information and recommends the most suitable medical services." This would enable the system to efficiently analyze the user's health status and provide the optimal treatment plan.

[0682] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0683] Step 1:

[0684] Users input their health status and symptoms into the device. Specifically, this involves using a dedicated smartphone application to enter symptoms such as "headache" or "eye pain" into text boxes. The data entered is text-based information indicating the user's health status and symptoms.

[0685] Step 2:

[0686] The terminal sends the input data to the server. The data is transmitted over the internet and securely transferred using the HTTPS protocol. The input is text data, and the data is delivered to the server as output.

[0687] Step 3:

[0688] The server uses natural language processing to analyze the received data. It analyzes the input text using a generative AI model and extracts keywords and phrases. The input is text data submitted by the user, and the output is a list of key items and symptoms generated based on the analyzed information.

[0689] Step 4:

[0690] The server generates a list of potential diseases based on the analysis results. For example, natural language processing is used to list possible disease names such as "tension headache" and "eye strain." This process uses machine learning algorithms. The input is the analysis information obtained in the previous step, and the output is a list of estimated disease names.

[0691] Step 5:

[0692] The server selects appropriate medical professionals from the data aggregation device based on the disease list. A recommendation algorithm is used, utilizing past medical records and evaluation information of medical professionals. The input is the generated disease list, and the output is the selected medical professional information.

[0693] Step 6:

[0694] The server collects the available appointment times of selected medical professionals and sends this information to the user's terminal. The server retrieves the availability of medical professionals from the database in real time and sends emails or push notifications to the user. The input is the medical professional's data, and the output is the available appointment time presented to the user.

[0695] Step 7:

[0696] The user selects a desired time from the displayed available appointment times and makes a provisional reservation. The provisional reservation is completed when the user clicks the selection button on the terminal. The input is the available appointment times notified by the server, and the output is the user's selected preferred appointment time.

[0697] Step 8:

[0698] The server uses the provisional booking information to perform a final confirmation with the medical institution and finalize the reservation. Communication with the medical institution takes place for confirmation, and the reservation confirmation result is generated. The final result is notified to the user and the medical professional, completing the appointment. The input is the details of the provisional booking, and the output is the confirmed booking information.

[0699] (Application Example 1)

[0700] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] In the medical diagnostic process, there is a challenge in that it is difficult for users to quickly and easily find the appropriate specialist, make an appointment, and even handle the payment of medical fees in a unified manner. Currently, many medical systems handle appointment scheduling and payment separately, which increases the burden on users and reduces the efficiency of medical services.

[0702] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0703] In this invention, the server includes means for receiving data on the user's health status and symptoms, means for generating a list of diseases using natural language processing based on the data, means for selecting an appropriate medical professional from registered data based on the list of diseases, means for obtaining information on the medical professional's available hours and notifying the user, and electronic payment means for settling medical fees based on the information. This enables the user to book an appointment with an appropriate specialist and settle medical fees immediately.

[0704] A "user" is someone who uses the system to input their health status and symptoms and receive medical services.

[0705] "Health status and symptom data" refers to information that represents the user's physical or mental condition or specific symptoms.

[0706] "Natural language processing" is a technology that allows computers to understand and process human language, and it is used to analyze input data on health conditions and symptoms.

[0707] The "disease list" is a list of possible diseases based on the user's symptoms, generated using natural language processing technology.

[0708] A "medical professional" is a healthcare worker, such as a physician, who is capable of diagnosing and treating specific diseases or symptoms.

[0709] A "data registry" is a database that stores past medical history, expert evaluation information, and other data, and is used for selecting medical professionals.

[0710] "Time information" refers to information indicating the time periods during which medical professionals are available to provide medical care, and this information is provided to users.

[0711] "Electronic payment methods" refer to means of settling medical fees online, enabling users to easily pay for medical services.

[0712] To implement this invention, the server receives data about the user's health status and symptoms. The user inputs their health status and specific symptoms using a device such as a smartphone. The device transmits this input information to the server via the network. The server analyzes the received data using natural language processing technology and generates a list of potential diseases.

[0713] The server selects an appropriate medical professional based on the generated disease list. This selection uses an algorithm based on past treatment history and medical professional evaluations, efficiently selecting the most suitable professional from the database. The server retrieves the available appointment times of the selected medical professional and notifies the user's terminal. The user can then select a desired date and time from the provided time information on their terminal and tentatively confirm the appointment.

[0714] Subsequently, the server confirms the provisional reservation with the medical institution and finalizes the reservation. A final reservation confirmation notice is sent to both the user and the medical professional. The cost of the selected treatment is displayed on the terminal, and the user completes the payment using electronic payment methods. In this process, the server provides treatment recommendations and electronic payment in an integrated manner, enabling the user to receive the service smoothly and efficiently.

[0715] For example, if a user experiences headaches and eye strain after prolonged computer work, they will be recommended an ophthalmologist, can select an appointment time, and pay the associated medical fees electronically. To support this process, the server utilizes a generative AI model to generate prompts such as: "The user has entered their symptoms in a smartphone app. Natural language processing will analyze the information, suggest the most suitable doctor and appointment, and complete the electronic payment for the medical fees. Please create an application program to achieve this."

[0716] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0717] Step 1:

[0718] Users input their health status and symptoms using a terminal. This input data includes specific symptoms and changes in physical condition. This data is initially processed on the terminal and then sent to the server in a formatted state.

[0719] Step 2:

[0720] The server receives data from the user. Based on this received data, it analyzes the information using natural language processing techniques. As a result of the analysis, a list of potential diseases is generated based on the input symptoms. The output is a list of estimated diseases.

[0721] Step 3:

[0722] The server selects appropriate medical professionals based on the generated disease list. This process involves accessing a database, considering medical history and professional evaluation information, and using a selection algorithm to identify the most suitable professional. The output provides information on the recommended medical professionals.

[0723] Step 4:

[0724] The server collects information on the availability of the selected medical professionals. The collected time information is organized into options that the user can book and is notified to the user's device. The user selects their desired appointment date and time from this information.

[0725] Step 5:

[0726] The user selects their desired appointment date and time on their device and makes a provisional reservation. The provisional reservation information is sent to the server for preliminary confirmation. In this step, the reservation time and the selected doctor's information are confirmed.

[0727] Step 6:

[0728] The server communicates with the medical institution based on the provisional booking information to perform a final booking confirmation. The final confirmed booking information is notified to both the user and the medical professional, and the booking is confirmed.

[0729] Step 7:

[0730] The server sends the confirmed medical fee to the user's terminal and prompts them to complete the payment using an electronic payment method. The user confirms the medical fee on their terminal and completes the electronic payment. As a result, the medical appointment and payment are integrated into a single process.

[0731] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0732] This invention begins when a user inputs their health status and symptoms into a terminal. The information entered by the user is transmitted to a server via a secure communication protocol. The server has the function to analyze the received data, and first analyzes the symptoms using natural language processing (NLP) technology. This analysis generates a list of possible diseases.

[0733] A distinctive feature of this invention is that the server incorporates an emotion engine that recognizes emotions from user input data. This emotion engine identifies emotions such as joy, anxiety, and pain from the text and voice input by the user, and adjusts subsequent processes based on that emotional state. For example, if the user's emotions of anxiety or pain are high, it is possible to prioritize the selection of a medical professional who can respond quickly.

[0734] Next, the server queries the database based on the emotion recognition results from the emotion engine and the generated list of diseases. The process then continues by selecting the most suitable medical professional based on past clinical experience and expert reviews. This ensures that the most appropriate medical service for the user's condition is provided quickly.

[0735] The server then extracts the available appointment times of the selected specialists and notifies the user's terminal. The user can select a preferred time slot from the appointment dates and times displayed on the terminal and tentatively confirm the reservation. Based on this tentative confirmation, the server confirms with the medical institution and makes the final reservation confirmation.

[0736] Once a reservation is confirmed, the server sends a notification to both the user and the medical professional based on the confirmed reservation information. This notification includes the date, time, and location of the appointment, as well as details about the medical professional, allowing the user to schedule their appointment with confidence.

[0737] For example, if a user enters "I've had a severe headache since this morning and I'm very anxious," the server uses NLP technology to add "headache" to the list of conditions and simultaneously recognizes the emotion of "anxiety" through its emotion engine. Based on this data, the system determines that the situation may be urgent and prioritizes suggesting specialists who can provide emergency assistance. In addition, appropriate advice is also provided to reassure the user.

[0738] The following describes the processing flow.

[0739] Step 1:

[0740] Users input their health status and specific symptoms into the device. This input may include text or voice, and the data is stored on the device.

[0741] Step 2:

[0742] The terminal sends user input data to the server. Encrypted protocols are used to ensure data security during transmission.

[0743] Step 3:

[0744] The server analyzes the user data it receives. First, it uses natural language processing (NLP) to analyze the symptoms based on the input information and generates a list of possible diseases.

[0745] Step 4:

[0746] The server uses an emotion engine to recognize the user's emotions. This analyzes the text content from the input data and, as a result, identifies emotional states such as "anxiety," "relief," and "pain."

[0747] Step 5:

[0748] The server considers the emotion recognition results and the disease list to select the appropriate medical professional from the database. This selection is algorithmically based on past clinical experience and patient reviews to determine the best professional.

[0749] Step 6:

[0750] The server retrieves the available dates and times of the selected medical professionals from the database. This information is organized to best suit the user's time requirements.

[0751] Step 7:

[0752] Based on the information received from the server, the terminal displays the doctor's name, specialty, and possible appointment dates and times to the user. The user then selects their preferred appointment date and time from the options.

[0753] Step 8:

[0754] The terminal sends the user's selected reservation date and time to the server, which then uses that information to finalize the reservation with the medical institution.

[0755] Step 9:

[0756] The server confirms that the reservation has been made and sends a notification to both the user and the medical professional via email or message. This notification includes detailed information about the consultation.

[0757] (Example 2)

[0758] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0759] In modern society, there is a challenge in that users with diverse health conditions and symptoms often have difficulty receiving appropriate and timely medical services. Furthermore, there is a need to consider the user's emotional state and provide the most appropriate support. Traditional medical systems often lack sufficient automation in symptom analysis and specialist selection, and fail to adequately address emotional issues, leading to a decline in service quality.

[0760] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0761] In this invention, the server includes means for recognizing emotions from user input information, means for adjusting the process based on the recognized emotions, means for generating disease candidates using natural language processing, and means for selecting an appropriate specialist. This makes it possible to quickly provide optimal medical services tailored to the user's symptoms and mental state.

[0762] A "user" refers to an individual who uses this system and is the entity that inputs information about their health status and symptoms.

[0763] "Health status" refers to the user's physical and mental condition, and includes information analyzed by the system.

[0764] A "symptom" refers to a specific physical or mental phenomenon or state that the user perceives, and is the subject of analysis by the system.

[0765] "Means of receiving information" refers to a mechanism for transferring user-entered information to a server and processing it appropriately.

[0766] "Natural language processing" is a technology used on servers, which involves analyzing input text data to understand its meaning.

[0767] A "candidate disease" is a list of potential medical problems generated by natural language processing, and is treated as foundational information for determining appropriate medical treatment.

[0768] "Experts" refer to individuals or organizations qualified to perform medical-related duties and whose role is to provide medical services to users.

[0769] An "information storage device" refers to a mechanism used to store data within a server, including expert information and user history.

[0770] "Emotions" refer to the internal psychological state recognized from the data entered by the user, and are detected in order to adjust the processes within the system.

[0771] "Means of adjusting the process" refers to functions that appropriately modify the system's operation or service delivery method based on recognized emotions and analysis results.

[0772] This invention is a system that begins with the user inputting their health status and symptoms into a terminal. The user inputs symptoms and emotions in text format using a smartphone or personal computer. The entered information is securely transmitted to the server via the HTTPS protocol.

[0773] The server analyzes the received information using advanced natural language processing techniques. Specifically, it uses "natural language processing libraries" and "cloud-based natural language APIs" to analyze symptoms and generate disease candidates. At the same time, data on emotions entered by the user is also analyzed. "Emotion recognition engines" and "voice analysis software" are used to recognize emotions. This allows the system to adjust the process while taking the user's emotional state into consideration.

[0774] Next, the server searches the database based on the generated disease candidates and emotional assessments to identify the most suitable specialist. This specialist selection process uses a "database management system" that takes into account past medical records and specialist evaluations.

[0775] Information about the selected specialist and their available appointment times are sent from the server to the terminal. The user can select a desired appointment date and time from the provided time slots and make a provisional reservation. The server then coordinates the appointment time with the medical institution and confirms the final reservation. The confirmed reservation information is sent back to the terminal as a notification to both the user and the specialist. The entire system aims to support the user's health management quickly and efficiently.

[0776] As a concrete example, consider a case where a user inputs into their device, "I've had a terrible headache since this morning. I'm filled with anxiety." In this case, the server uses natural language processing to add "headache" to the list of illnesses and recognizes the emotion of "anxiety." Based on this, it prioritizes selecting a specialist who can provide emergency assistance and also provides appropriate advice to reassure the user.

[0777] An example of a prompt message would be something like, "Regarding the procedure for analyzing the health symptoms entered by the user, identifying emotions, and adjusting the treatment accordingly."

[0778] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0779] Step 1:

[0780] The user enters their health status and symptoms into the device. This input is in text or voice format, and the device converts it into digital data. This data then serves as the input for processing.

[0781] Step 2:

[0782] The terminal sends the input digital data to the server via a secure communication protocol, such as HTTPS. This ensures the security of the data. Server reception is the output of this step.

[0783] Step 3:

[0784] The server analyzes the received digital data using a natural language processing engine. Specifically, it tokenizes the text data, extracts important keywords, performs grammatical analysis, and lists the symptoms. Through this process, the analyzed symptoms are output.

[0785] Step 4:

[0786] The server inputs the analyzed symptom data into the emotion recognition engine to analyze the user's emotions. Here, emotions such as "anxiety," "joy," and "pain" are identified through text analysis and voice analysis. This identification result becomes the output of the step.

[0787] Step 5:

[0788] The server searches the database for appropriate specialists based on the analyzed list of symptoms and emotional data. A selection algorithm, based on past clinical experience and evaluation information, generates a list of optimal specialists. This result is the output of this step.

[0789] Step 6:

[0790] The server retrieves the available appointment times of the selected specialist from the database and notifies the terminal. The user selects a convenient time from the times displayed on the terminal and generates a provisional booking. The provisional booking information is then sent to the server as input.

[0791] Step 7:

[0792] The server coordinates the appointment with the medical institution based on the received provisional booking information. Once the medical institution returns the final appointment time, the server confirms it. The final booking information is the output of this step.

[0793] Step 8:

[0794] The server resends the confirmed appointment information to the terminal, notifying both the user and the professional. The notification includes the appointment date and time, location, and professional details, allowing the user to prepare for the appointment. This transmission is the final output.

[0795] (Application Example 2)

[0796] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0797] In today's world, many people need quick and accurate access to appropriate medical professionals for their health conditions and symptoms. However, systems that do not consider the emotional state of the user may not adequately respond to the urgency and anxiety they feel. Furthermore, it is necessary to go beyond simply generating disease lists and adjust the booking process so that users can receive medical services with peace of mind.

[0798] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0799] In this invention, the server includes a device for receiving information on the user's health status and symptoms, a device for generating a list of diseases using natural language processing technology based on the information, a device for selecting an appropriate medical professional from a data set based on the list of diseases, and a device for analyzing the user's emotions and adjusting the process based on the analysis results. This enables the rapid and accurate selection and booking of a medical professional that is in line with the user's health status and emotional state.

[0800] A "user" refers to an individual who inputs information about their health status and symptoms into the system.

[0801] "Health status and symptom information" refers to information in natural language or other formats that users input as data about their own physical condition.

[0802] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0803] A "list of diseases" is a list of possible illnesses and health problems generated using natural language processing technology.

[0804] A "medical professional" refers to a healthcare worker who is qualified to diagnose and treat specific diseases.

[0805] A "data set" refers to a database containing information about medical professionals.

[0806] A "device that analyzes emotions" refers to a device that recognizes emotions from information or voice input by the user and reflects the results in the process.

[0807] "Adjusting the process" means optimizing the procedures for consultations and appointments according to the user's emotions and circumstances.

[0808] "Rapid and accurate selection and booking of medical professionals" refers to quickly and appropriately selecting and booking medical professionals according to the user's condition.

[0809] The system that realizes this invention begins with the user inputting information about their health status and symptoms via a terminal. This data is transmitted to a server via the internet. The server analyzes the received information using natural language processing technology and generates a list of possible diseases. Possible natural language processing technologies used here include open-source NLP libraries such as "spaCy" and "NLTK". In addition, emotion recognition models using TensorFlow or PyTorch can be utilized to analyze emotions.

[0810] For example, if a user enters "I've had a severe headache since this morning and I'm very anxious," the server analyzes the input, generates a list of diseases that include "headache," and identifies the emotion of anxiety through emotion analysis. Based on this emotion, the server determines that a high-priority response is needed and selects a medical professional from the database who can respond quickly.

[0811] The user's device is notified of the available dates and times of the selected medical professional, and the user selects a preferred time from the displayed options. The final booking information obtained as a result of the selection is also shared with the medical professional.

[0812] An example of a prompt message would be that the user inputs "My back hurts from working at a desk for long hours," to which the system would respond with "We suggest you make an appointment with an orthopedic specialist."

[0813] This system allows users to receive medical services that are tailored to their health condition and emotional state quickly and accurately, while also enabling them to proceed with treatment with peace of mind.

[0814] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0815] Step 1:

[0816] The user uses their device to input information about their health status and symptoms. This input is in text format and is sent to the server via a secure communication protocol on the device. The input data might take the form of, for example, "I've had a severe headache since this morning, and I'm very worried."

[0817] Step 2:

[0818] The server analyzes the user's input data using natural language processing techniques. It extracts disease names and related keywords from the received text data using a natural language processing engine (e.g., spaCy) and generates a list of diseases. As a result, diseases such as "headache" are added to the list.

[0819] Step 3:

[0820] The server uses a generative AI model to analyze emotions from user input data. Based on the input text, it applies an emotion recognition model (e.g., an emotion analysis model using TensorFlow) to determine the emotion. The output is an emotion label such as "anxiety" or "pain." This allows the server to identify the user's emotional state.

[0821] Step 4:

[0822] The server uses the analysis results—a list of diseases and emotion labels—to query the data set and execute a process to select appropriate medical professionals. The selection prioritizes diseases and emotions based on their urgency; that is, it selects medical professionals who possess specialized knowledge of the identified diseases and can respond quickly.

[0823] Step 5:

[0824] The server extracts the available time slots for the selected medical professional and notifies the terminal of this information. The user selects a preferred time slot from the displayed dates and times on the terminal and makes a provisional reservation. The selected time slot is then sent to the server via the terminal.

[0825] Step 6:

[0826] Ultimately, the server verifies the provisional booking information with a specialist and confirms the appointment. Based on the confirmed booking information, notifications are sent to both the user and the medical specialist. These notifications include the date, time, location, and details of the medical specialist, allowing the user to schedule their appointment with confidence.

[0827] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0828] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0829] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0830] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0831] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0832] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0833] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0834] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0835] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0836] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0837] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0838] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0839] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0840] 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.

[0841] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0842] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0843] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0844] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0845] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0846] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0847] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0848] The following is further disclosed regarding the embodiments described above.

[0849] (Claim 1)

[0850] A means of receiving data on the user's health status and symptoms,

[0851] A means for generating a list of diseases using natural language processing based on the aforementioned data,

[0852] A means for selecting an appropriate medical professional from a database based on the aforementioned list of diseases,

[0853] A means for obtaining the available dates and times of the aforementioned medical professional and notifying the user,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, comprising an algorithm that takes into account past clinical performance and reviews by medical professionals when selecting the aforementioned medical professionals.

[0857] (Claim 3)

[0858] The system according to claim 1, further comprising means for sending a reservation confirmation notice to both the user and the medical professional.

[0859] "Example 1"

[0860] (Claim 1)

[0861] A means of receiving information on the user's health status and symptoms,

[0862] A means for generating a list of diseases using natural language processing with a generative AI model based on the aforementioned information,

[0863] A means for selecting an appropriate medical professional from the aforementioned disease list using a data collection device,

[0864] A means for collecting and communicating the available times of the aforementioned medical professionals to the user,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, comprising a recommendation algorithm that uses past medical records and evaluation information of medical professionals in the selection of the aforementioned medical professionals.

[0868] (Claim 3)

[0869] The system according to claim 1, further comprising means for sending a reservation confirmation notification to both the user and the medical professional via a communication network.

[0870] "Application Example 1"

[0871] (Claim 1)

[0872] A means of receiving data on the user's health status and symptoms,

[0873] A means for generating a list of diseases using natural language processing based on the aforementioned data,

[0874] A means for selecting an appropriate medical professional from the registered data based on the aforementioned disease list,

[0875] A means for obtaining information on the availability of the aforementioned medical professional and notifying the user,

[0876] An electronic payment method for settling medical expenses based on the aforementioned information,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, comprising a calculation procedure that takes into account past medical history and evaluations of medical professionals when selecting the aforementioned medical professionals.

[0880] (Claim 3)

[0881] The system according to claim 1, further comprising means for sending a reservation confirmation notice to both the user and the medical professional.

[0882] "Example 2 of combining an emotion engine"

[0883] (Claim 1)

[0884] A means of receiving information on the user's health status and symptoms,

[0885] A means for generating disease candidates using natural language processing based on the aforementioned information,

[0886] A means for selecting an appropriate specialist from an information storage device based on the aforementioned disease candidate,

[0887] A means for obtaining the available dates and times of the aforementioned specialists and notifying the user,

[0888] A means of recognizing emotions from user input information,

[0889] Means for adjusting the process based on the recognized emotions,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, comprising a calculation method that takes into account past medical records and the evaluation of the experts when selecting the aforementioned experts.

[0893] (Claim 3)

[0894] The system according to claim 1, further comprising means for sending a reservation confirmation notice to both the user and the expert.

[0895] "Application example 2 when combining with an emotional engine"

[0896] (Claim 1)

[0897] A device that receives information on the user's health status and symptoms,

[0898] A device that generates a list of diseases using natural language processing technology based on the aforementioned information,

[0899] A device for selecting an appropriate medical professional from a data set based on the aforementioned list of diseases,

[0900] A device that obtains the available consultation times of the aforementioned medical professionals and informs the user of those times,

[0901] A device that analyzes user emotions and adjusts the process based on the analysis results,

[0902] A device that quickly makes appointments with medical professionals based on the analysis of symptoms and emotions,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, comprising an algorithm that takes into account past clinical performance and reviews by medical professionals when selecting the aforementioned medical professionals.

[0906] (Claim 3)

[0907] The system according to claim 1, further comprising means for sending a reservation confirmation notice to both the user and the medical professional. [Explanation of symbols]

[0908] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving data on the user's health status and symptoms, A means for generating a list of diseases using natural language processing based on the aforementioned data, A means for selecting an appropriate medical professional from a database based on the aforementioned list of diseases, A means for obtaining the available dates and times of the aforementioned medical professional and notifying the user, A system that includes this.

2. The system according to claim 1, comprising an algorithm that takes into account past clinical performance and reviews by medical professionals when selecting the aforementioned medical professionals.

3. The system according to claim 1, further comprising means for sending a reservation confirmation notice to both the user and the medical professional.

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