system

Abstract

We provide the system. [Solution] A means of executing an automated medical interview process for medical support, A method for proposing disease names from medical interview data using natural language processing, A means of providing automatically generated medical records in an editable format, A means of storing medical records in a database and managing access rights, 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 persona chatbot control method 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] In the current medical field, long working hours and mental stress of doctors are major problems. Also, spending a lot of time on interviews and creating medical records not only reduces the medical treatment efficiency but may also impair the quality of medical services. Furthermore, due to the lack of consistent sharing of patient information between different medical institutions, the risk of duplicate diagnoses and medical errors is high. These problems are factors leading to an increase in medical costs and a decrease in patient satisfaction.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that implements an automated medical interview process for medical support. Specifically, it uses natural language processing to suggest appropriate disease names from interview data, thereby supporting physicians' diagnoses. It also provides automatically generated medical records in an editable format, reducing the burden on physicians. Furthermore, it securely stores patient information in a database and shares it with medical professionals in remote locations as needed, improving the consistency and efficiency of medical care. In this way, it simultaneously achieves improved operational efficiency in medical settings and enhanced quality of medical services.

[0006] An "automated medical interview process" refers to a series of procedures in which a computer system automatically generates questions and records answers in order to efficiently collect initial information from patients.

[0007] "Natural language processing" refers to the technology of understanding, interpreting, and generating human language using computers, and is particularly used in the analysis of medical data.

[0008] "Method for suggesting disease names" refers to a function that automatically lists possible disease names based on analyzed medical interview data and presents them to the doctor.

[0009] "Means of providing medical records in an editable format" refers to a function that provides automatically generated medical information in a format that allows physicians to manually modify or add to it.

[0010] "Means of saving to a database" refers to the function of securely and efficiently storing collected medical information in digital format and making it accessible in the future.

[0011] "Means of managing access rights" refers to authentication and permission setting functions that appropriately restrict access to stored medical information.

[0012] "Means of securely sharing information with healthcare professionals in remote locations" refers to communication methods that enable geographically separated healthcare professionals to view and use patient information in a secure manner. [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Next, an example of an embodiment of the 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 tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple 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 tagged RAM (Random Access Memory) 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 tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[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] The medical support system according to the present invention is installed in a medical facility and consists of a patient (the user), a terminal, and a server. Through this system, information is collected from the patient through an efficient interview process, and based on this information, disease names are suggested, and medical records are automatically generated and shared. The specific process is described below.

[0035] When a patient arrives at the hospital, the process begins with the patient logging in to a terminal located in the waiting room. Here, the patient answers questions related to their medical history. The terminal transmits these answers to a server in real time. The server analyzes the received data using natural language processing technology and lists possible diagnoses based on the information obtained. These suggested diagnoses are immediately returned to the terminal for the doctor to review before the examination.

[0036] At this stage, the server automatically generates an editable medical record based on the patient interview data and the suggested disease name. This medical record is designed so that doctors can correct or add information as needed. After the medical record is completed, the server stores the data in a secure database. This stored data can be accessed from anywhere by healthcare professionals with the necessary access rights.

[0037] As a concrete example, consider a patient who answers questions related to cold symptoms on a terminal. The terminal displays a screen for entering information such as the frequency of coughing and runny nose, and recent body temperature. Once the patient enters these fields, the terminal immediately sends the information to the server. The server analyzes it and suggests a diagnosis such as "cold," "influenza," or "allergic rhinitis." Based on this, an automatically generated medical record is created, which doctors can refer to during consultations to quickly assess the condition and plan treatment.

[0038] This system will improve the efficiency and accuracy of operations in medical settings and reduce the burden on doctors. Furthermore, consistent management and sharing of information will ensure uniformity of treatment across different medical institutions, improving the quality of medical services provided to patients.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The patient, as the user, logs in to a terminal located in the hospital waiting room. The terminal verifies the patient's authentication information on the input screen and authenticates the user.

[0042] Step 2:

[0043] The terminal displays a list of medical questionnaire questions received from the server. The patient, as the user, enters their symptoms and condition in response to the displayed questions.

[0044] Step 3:

[0045] The terminal sends the user's entered response data to the server in real time. The server collects and stores the received data.

[0046] Step 4:

[0047] The server analyzes the received medical interview data using a natural language processing algorithm and extracts possible disease names. It then organizes this information and generates diagnostic support information.

[0048] Step 5:

[0049] The server sends the generated list of disease names to terminals for healthcare professionals. This allows doctors to review the necessary information before a consultation.

[0050] Step 6:

[0051] The server automatically generates editable medical records based on the patient's questionnaire and a list of diseases. These generated medical records are then used by healthcare professionals to make any necessary corrections.

[0052] Step 7:

[0053] The server stores completed medical records in a secure database. This information is accessible only to authorized users and can be shared with other healthcare institutions.

[0054] (Example 1)

[0055] 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."

[0056] A challenge in the medical interview process at healthcare facilities is the inefficient collection, analysis, recording, and sharing of patient information. This increases the burden on medical staff and can lead to a lack of speed and accuracy in diagnosis. Furthermore, consistent information sharing between different healthcare institutions is difficult.

[0057] 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.

[0058] In this invention, the server includes means for executing an automated medical interview process for medical support, means for suggesting disease names from interview data using natural language processing, means for providing automatically generated medical records in an editable format, means for receiving patient input information via a terminal and transmitting it to the server, means for analyzing received data using a generation AI model, and means for displaying disease suggestions on the terminal in real time. This enables the efficient execution of the interview process and the automatic generation and consistent management of medical records. Furthermore, it enables the secure and rapid sharing of medical information, improving the efficiency and accuracy of operations in the medical field.

[0059] A "medical support system" is a set of technologies designed to streamline the medical treatment process in healthcare facilities and support the management and sharing of medical information.

[0060] An "automated medical interview process" is a system that electronically collects information from patients and efficiently provides data to assist in diagnosis.

[0061] "Natural language processing" is a technology that allows computers to understand and interpret human language, and it is used in the analysis of medical interview data.

[0062] "Disease name suggestion" is the process of listing possible disease names based on analyzed patient data.

[0063] "Automated medical records" refer to medical information created electronically based on patient interview data.

[0064] "Saving to a database" is the process of writing data to a digital medium for secure and efficient storage.

[0065] "Access rights management" is a method of restricting access to information to authorized users and is used to maintain the security of that information.

[0066] A "generative AI model" is an artificial intelligence technology that learns patterns based on large amounts of data and uses that knowledge to make predictions and judgments about new data.

[0067] A "terminal" is an electronic device that patients use to input medical records or to check their own data.

[0068] A "prompt" is a text containing questions or instructions given to an AI model, used to guide the model's analysis and generation processes.

[0069] This medical support system is implemented in medical facilities such as hospitals and clinics and consists mainly of users (patients), terminals, and a server. To use the system, patients first log in to a terminal installed in the waiting room upon arriving at the hospital. The terminal displays a series of questions related to the medical interview to the user, who then answers them. This device provides an easy-to-use interface for inputting symptoms and changes in physical condition that the user perceives.

[0070] The terminal transmits the received data to the server in real time. Standard communication technologies, such as the TCP / IP protocol, are used for this data transmission. The server utilizes generative AI models and natural language processing systems to analyze the information received from the patient. This AI model analyzes the data using prompt sentences. An example of a prompt sentence is, "Please suggest possible disease names based on the received symptom data."

[0071] The server returns the suggested disease name based on the analysis results to the terminal and displays it to the user and medical staff. This information can be reviewed by the doctor before the examination begins. Furthermore, the server automatically generates a medical record based on the interview data. This medical record is provided in an editable format, allowing the doctor to review its contents during the examination and add or modify information as needed. This data is stored securely in a database by the server and is accessible to healthcare professionals under appropriate access rights management.

[0072] For example, if a patient enters cold-related symptoms through a terminal, the terminal displays a screen for entering information such as cough frequency and body temperature. Once the user enters the necessary information, the server analyzes it using a generative AI model and suggests disease names such as "cold," "influenza," or "allergic rhinitis." A medical record is automatically generated based on this information and saved, allowing doctors to efficiently determine treatment plans during consultations.

[0073] The introduction of this system will standardize the processes of patient interviews and medical record management in healthcare institutions, improving the efficiency and accuracy of healthcare delivery. Furthermore, it will enable the secure and consistent management and sharing of medical information, allowing for unified treatment across different healthcare institutions.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The terminal displays a questionnaire to the user, who then enters information about their symptoms and physical condition on the screen. This data includes recent body temperature, cough frequency, and other symptoms. The terminal receives this information in input format and prepares it for transmission to the server. The output is the user's input data, which is used in the next processing step.

[0077] Step 2:

[0078] The terminal sends input data obtained from the user to the server. The data is transmitted using a secure and fast communication protocol. The input is the user's symptom data collected on the terminal, and the output is this data received by the server. In this step, the server receives the input data.

[0079] Step 3:

[0080] The server analyzes the user data it receives. This analysis utilizes a generative AI model and natural language processing techniques. The prompt "Please suggest possible disease names based on the received symptom data" is used to instruct the AI ​​model to analyze the data. The input is the user data sent to the server, and the output is a list of disease names suggested as a result of the analysis.

[0081] Step 4:

[0082] The terminal receives the analysis results from the server and displays suggested disease names to the user. The user and medical staff review this information and use it as a reference before the examination. The input is a list of disease names sent from the server, and the output is the suggested results displayed on the terminal. In this step, the server sends the analysis results, and the terminal receives those results.

[0083] Step 5:

[0084] The server automatically generates medical records based on patient interview data and suggested disease information. These records are provided in an editable format, allowing physicians to modify and add to them during consultations. Inputs are patient interview data and suggested disease names, while output is an editable medical record. The process includes the server generating the medical records and saving them to a database.

[0085] Step 6:

[0086] The server stores the generated medical records in a secure database, allowing authorized medical professionals to access these records from anywhere. The input is the medical records generated by the server, and the output is the medical records securely stored in the database. In this step, data storage and access management are performed as concrete actions.

[0087] (Application Example 1)

[0088] 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."

[0089] Traditional healthcare systems lack efficient ways to assess a patient's health status before they visit a medical institution and guide them to the appropriate facility. This makes it difficult for patients to avoid unnecessary tests and visits to the wrong departments, resulting in wasted medical resources. Furthermore, the lack of mechanisms for collecting and securely sharing patient health information with medical institutions in real time can hinder prompt medical responses.

[0090] 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.

[0091] In this invention, the server includes means for executing an automated medical interview process for medical support, means for suggesting disease names from interview data using natural language processing, and means for providing automatically generated medical records in an editable format. This enables efficient response and resource optimization in medical settings by allowing patients to check their health status at home in advance and be guided to visit the appropriate medical institution.

[0092] "Means for implementing an automated medical interview process for medical support" refers to a system that provides a tool for patients to input their health status in a questionnaire format before visiting a medical institution, and automatically compiles that data.

[0093] "A method for proposing disease names from medical interview data using natural language processing" is a technology that analyzes medical interview data collected from patients and lists appropriate disease names based on statistical data and algorithms.

[0094] "Means of providing automatically generated medical records in an editable format" refers to a function that automatically creates medical records based on analysis results and presents them in a format that allows healthcare professionals to input additional information or make corrections as needed.

[0095] "A means of storing medical records in a database and managing access rights" refers to a system that stores generated medical records in a secure location and controls who can access the data and when.

[0096] "Means for collecting and analyzing users' health information from remote measurement devices" refers to technology that captures and aggregates health data from devices used by users, analyzes that data, and evaluates their health status.

[0097] "A means of displaying guidance to appropriate medical institutions on a graphical device based on analysis results" refers to a method of identifying the most suitable medical institution or department for the user based on the analyzed data, and visually displaying that information on the user interface.

[0098] The embodiment of this invention is realized through the cooperation of various hardware and software. It is assumed that the server, terminal, and user device operate in conjunction with each other.

[0099] The server is built on a cloud-based data management system and performs real-time data processing. It is primarily based on AWS® Lambda, with backend logic implemented using Node.js. The collected medical interview data is analyzed using the Google® Cloud Natural Language API, a natural language processing platform, to quickly list appropriate disease names.

[0100] The terminal is a device used by patients to input data, primarily functioning as a smartphone or tablet. Flutter® is used for cross-platform development, providing a user-friendly interface. Data entered from the terminal is immediately sent to the server, enabling rapid analysis.

[0101] Users can input their health information into this system and receive appropriate medical guidance based on the analysis results. Remote monitoring devices such as smartwatches are also used to collect health information. This allows for monitoring of daily health conditions and provides guidance information to identify the medical institutions the patient needs.

[0102] As a concrete example, imagine a patient exhibiting cold symptoms entering their symptoms through a terminal app, and the analysis results would list possible diagnoses such as "cold," "influenza," and "allergic rhinitis." In this case, the system would use its database to display relevant medical institutions to the user along with map information.

[0103] As an example of a prompt message for the generative AI model, by inputting information in the format of "Please list the names of diseases that may be related to these symptoms," it becomes possible to receive suggestions for appropriate disease names.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] Users access a medical questionnaire application using their device and enter their answers to health-related questions. This data includes symptoms, body temperature, and past medical history. This information is transmitted to the server in real time.

[0107] Step 2:

[0108] The server processes the received data using the Google Cloud Natural Language API. This extracts important keywords from the medical history data and calculates possible disease names based on them. The output is a list of suggested disease names, which is immediately sent to the terminal.

[0109] Step 3:

[0110] The user reviews the suggested disease name on their device and enters any additional health information if necessary. This allows for more accurate analysis results. The new information is then sent back to the server.

[0111] Step 4:

[0112] The server performs natural language processing again based on the latest data and updates the list of disease names. Furthermore, it uses the analysis results to automatically generate medical records and converts them into an editable format that doctors can fill in and modify. This is then stored in a database.

[0113] Step 5:

[0114] The server also considers the user's location data and suggests appropriate nearby medical facilities. This includes a process that utilizes a Geographic Information System (GIS) to analyze the user's geographical location and select the most suitable medical facility. The output is sent to the terminal along with map information.

[0115] Step 6:

[0116] Based on the provided medical institution information, users can check maps on their smartphones or display devices and plan their visits. They can also use the information to directly make appointments with medical institutions.

[0117] This allows users to manage their health efficiently at home and receive appropriately guided medical support.

[0118] 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.

[0119] The system according to the present invention has a configuration that combines a user, a terminal, a server, and an emotion engine for the purpose of medical support. In addition to an efficient patient interview process, this system recognizes the patient's emotional state and provides medical support accordingly.

[0120] Patients, acting as users, log in to a terminal installed in the hospital waiting room and enter information about their symptoms. Similar to traditional processes, the terminal sends the information to a server, which uses natural language processing technology to analyze the data and extract possible disease names. This disease information is then sent to the terminals of healthcare professionals to assist in diagnosis.

[0121] The newly added emotion engine analyzes the patient's input speed, content, facial expressions, and tone of voice to assess their emotional state in real time. This information is fed back into the automated interview process, dynamically adjusting the way questions are asked and their order as needed. This allows for changes to questions that help patients relax if they are feeling tense or anxious.

[0122] As a concrete example, suppose a patient user is entering information about their cold symptoms on a terminal, and the emotion engine detects a state of tension. The terminal transmits this information to the server, which then adjusts the questions to promote relaxation. For example, a straightforward question like, "How long have your symptoms lasted?" can be changed to, "Have you done anything to help you relax when your symptoms appeared?"

[0123] Furthermore, the emotional data analyzed by the emotion engine is stored as supplementary information in the user's medical records. This information serves as important data for the examining physician to understand the patient's psychological state.

[0124] These features enhance efficiency and diagnostic accuracy in healthcare settings, improving the quality of services provided to patients. Furthermore, by exhibiting similar effects in healthcare facilities outside of hospitals, it contributes to improving the overall quality of healthcare.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The patient, as the user, logs in to a terminal installed in the waiting room of the medical facility. The terminal verifies the patient's authentication information on the input screen and performs user authentication.

[0128] Step 2:

[0129] The terminal displays a questionnaire to the user. A list of questions provided by the server is used. The user enters answers regarding their symptoms and condition.

[0130] Step 3:

[0131] The terminal transmits user input data to the server in real time. Input speed and touch pressure are also recorded during this process.

[0132] Step 4:

[0133] The server analyzes the received data using a natural language processing algorithm. It extracts possible disease names from the analysis results and sends them to the medical professional's terminal.

[0134] Step 5:

[0135] The emotion engine evaluates the user's emotional state based on input information from the device. Emotion recognition is performed through input speed, text content, and facial expression recognition.

[0136] Step 6:

[0137] The device dynamically adjusts the questionnaire questions based on the evaluation results of the emotion engine. For example, if tension is detected, the questions are changed to help the user relax.

[0138] Step 7:

[0139] The server stores emotional data as supplementary information to medical records, allowing doctors to refer to it later during consultations.

[0140] Step 8:

[0141] The server stores the generated medical records in a secure database. These records can be viewed and shared by healthcare professionals with access rights.

[0142] (Example 2)

[0143] 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 as the "terminal".

[0144] In modern healthcare settings, efficient and accurate diagnostic processes are required, but the consistency and accuracy of diagnoses are challenged because patients' emotional states can influence their answers during interviews. Furthermore, the proper storage and management of medical information and patients' emotional states, as well as efficient information sharing with healthcare professionals in remote locations, are also challenges.

[0145] 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.

[0146] In this invention, the server includes means for inputting patient information and executing an automated medical interview process, means for suggesting a medical condition from the interview data using natural language processing, and means for analyzing the patient's emotional state from the input data and dynamically adjusting the interview process. This enables appropriate interviews tailored to the patient's emotional state, improving the accuracy of diagnosis and the efficiency of the medical setting. Furthermore, it allows for the secure storage of medical information, including emotional data, and enables efficient information sharing with medical professionals in remote locations.

[0147] "Patient information" refers to identifiable medical data, including data on a patient's symptoms, diagnosis, or medical history.

[0148] An "automated medical interview process" is a procedure in which the system automatically conducts an appropriate medical interview based on the patient's input of their symptoms.

[0149] "Natural language processing" is a technology that allows computers to understand and analyze the language that humans speak naturally.

[0150] "Medical status" refers to the patient's health condition and potential illnesses based on interview data.

[0151] "Emotional state" refers to the patient's psychological state and includes states such as tension, anxiety, and relaxation.

[0152] "Dynamic adjustment" means that the system automatically changes to the optimal state according to the situation and conditions.

[0153] "Medical information" refers to all medical data related to a patient, including diagnostic results, treatment plans, and emotional data.

[0154] A "database" is a system that systematically stores large amounts of information, making it easy to access and search for.

[0155] This system, designed for medical support, consists of a user, terminal, server, and emotion engine. The main hardware includes an input terminal for user use and a server for data processing. The software includes a natural language processing engine and an emotion recognition engine.

[0156] The terminal provides an interface for the patient (user) to input information such as their symptoms. Users can input information into the terminal using methods such as a touchscreen or voice input. The entered information is securely encrypted and transmitted to the server.

[0157] The server analyzes this input data using natural language processing techniques. For example, it can use Python's natural language processing libraries, such as NLTK or spaCy. This analysis suggests possible medical conditions based on the symptoms. The suggested information is then provided to the terminal of the diagnostician.

[0158] Furthermore, the emotion engine analyzes elements such as the user's input speed, voice, and facial expressions in real time to assess their emotional state. Based on this assessment, the server can dynamically adjust the interview process. For example, if the user is tense, the questions can be changed to encourage relaxation.

[0159] As a concrete example, consider a case where a user enters symptoms of a cold. In this case, if the emotion engine detects the user's anxiety, the server will adjust the question to something like, "Did you do anything to relax when you experienced symptoms?" This process allows the user to participate in the consultation with greater peace of mind.

[0160] Examples of prompt messages include the following:

[0161] "If a user is feeling nervous, suggest some questions that might help them relax."

[0162] In this way, the system provides efficient and accurate medical support while responding to the patient's emotions.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] Users log in to a terminal in the waiting room and use a dedicated interface to enter their symptoms and basic personal information. This includes the patient's name, age, and specific symptoms (e.g., cough, fever). The terminal encrypts the entered information using the SSL / TLS protocol and sends it to the server.

[0166] Step 2:

[0167] The server receives data sent from the terminal. It analyzes the input data using a natural language processing engine, such as Python's NLTK or spaCy. Through analytics, it extracts possible medical conditions (e.g., cold, flu) from the entered symptoms and sends this information to the medical professional's terminal for diagnostic support. The output is a list of predicted diseases.

[0168] Step 3:

[0169] The emotion engine collects data such as typing speed, keyboard sounds, and facial expressions and voice tone from the camera and microphone while the user is typing on the device. Based on this data, the emotion engine evaluates the user's emotional state and identifies emotional states such as "tense" or "relaxed." The evaluation results are sent to the server.

[0170] Step 4:

[0171] The server receives emotional assessment data from the emotion engine and dynamically adjusts the ongoing interview process. Based on the emotional data, it performs actions such as changing the order of questions or softening the questions to encourage relaxation. A specific example of this action would be changing the question from "How often do your symptoms occur?" to "Have you done anything to relax when your symptoms occurred?"

[0172] Step 5:

[0173] The server ultimately stores the analyzed emotional data, along with the patient's medical history, as a digitized medical record. This record includes elements such as the patient's symptom information, predicted disease name, and emotional state. The stored data is referenced in subsequent diagnostic processes and treatment planning. As an output, a complete medical record is properly stored and accessible.

[0174] (Application Example 2)

[0175] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0176] Current medical support systems often struggle to consider patients' emotional states, which can compromise accuracy and efficiency. Furthermore, there is a lack of flexible systems in place to enhance the quality of psychological care in nursing facilities. Therefore, there is a need for systems that enable medical and care support tailored to the psychological state of patients and residents of nursing facilities.

[0177] 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.

[0178] In this invention, the server includes means for executing an automated medical interview process for medical support, means for suggesting disease names from interview data using natural language processing, and means for evaluating the psychological state of the subject using emotion analysis technology. This makes it possible to dynamically provide appropriate responses according to the psychological state of patients or residents of care facilities.

[0179] "Medical support" refers to technologies and processes used in medical settings to improve the efficiency and accuracy of diagnosis and treatment.

[0180] The "automated medical interview process" refers to a procedure in which the patient enters their symptoms into a terminal, and the system automatically proceeds with the interview based on that information.

[0181] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0182] "Suggesting a disease name" refers to the system presenting possible disease names based on the patient's medical history data.

[0183] "Emotional analysis technology" refers to methods for analyzing and evaluating an individual's emotional state from digital data.

[0184] "Assessing a psychological state" refers to measuring and judging an individual's mental health and emotional responses.

[0185] "Care support" refers to the processes and techniques used to provide physical and mental care to residents in care facilities.

[0186] "Dynamic adjustment" refers to changing the response in real time according to the situation.

[0187] A "system" refers to a structure in which multiple elements or processes are combined to achieve a specific purpose.

[0188] To implement the present invention, the medical support system consists of a server, a terminal, and a user.

[0189] The server first uses natural language processing technology to analyze the medical questionnaire data sent from the terminal, extracts possible disease names, and notifies the user. This improves the efficiency of diagnosis.

[0190] The terminal not only collects data entered by the user and sends it to the server, but also utilizes emotion analysis technology. It acquires various data such as the user's input speed, tension, facial expressions, and voice tone, and evaluates the user's psychological state in real time. This evaluation is provided by the emotion analysis engine. Based on this information, the terminal dynamically adjusts the content and order of the questionnaire questions, providing the user with a relaxing environment.

[0191] As a concrete example, consider a scenario where a user is entering information about cold symptoms on their device, and the emotion analysis engine detects a state of tension. In this case, the server changes the question, changing "How long have your symptoms been lasting?" to "Have you done anything to relax when the symptoms appeared?", thereby alleviating the user's tension.

[0192] Furthermore, the data obtained through emotion analysis is stored as medical records and used by healthcare professionals as important information to understand the user's psychological state. Based on this information, it becomes possible for nursing homes to provide care that is tailored to the emotions of their residents.

[0193] An example of a prompt might be, "Please suggest a suitable conversation topic if the sentiment analysis indicates high stress levels."

[0194] As described above, this system enables medical and nursing care support that takes into account the psychological state of individuals in various settings, and as a result, aims to improve the overall quality of medical and nursing care.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The device acquires data about symptoms and health conditions entered by the user. This input includes text descriptions of symptoms, voice tone, and videos of facial expressions. The device preprocesses this data and converts it into a format suitable for analysis.

[0198] Step 2:

[0199] The terminal prepares to send the acquired data to the server. Text data is prepared for natural language processing, and video and audio data is converted into a format suitable for the sentiment analysis engine. The input is processed data, and the output is the data sent to the server.

[0200] Step 3:

[0201] The server receives data sent from the terminal and analyzes the text data using a natural language processing engine. Here, it extracts possible disease names and notifies healthcare professionals of this information. The output is a list of disease names.

[0202] Step 4:

[0203] The server uses an emotion analysis engine to evaluate the user's emotional state. In this step, audio and video data are analyzed to determine the user's emotional state, such as whether they are tense or relaxed. The input is audio and video data, and the output is the evaluation result of the emotional state.

[0204] Step 5:

[0205] The server dynamically adjusts the questionnaire based on the results of the emotion analysis. For example, if the patient is highly anxious, the server will soften the wording of the questions. The output is a list of the adjusted questions.

[0206] Step 6:

[0207] The device presents the user with carefully designed questions and displays a form for entering answers. This process reduces the user's psychological stress and encourages them to answer in a relaxed state. The output is the user's response data.

[0208] Step 7:

[0209] The server ultimately formats the data returned by the user as a medical record and stores it in a database. Emotional data is also recorded, which can be used by healthcare professionals for future diagnoses. The output is a set of medical record data.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] The medical support system according to the present invention is installed in a medical facility and consists of a patient (the user), a terminal, and a server. Through this system, information is collected from the patient through an efficient interview process, and based on this information, disease names are suggested, and medical records are automatically generated and shared. The specific process is described below.

[0227] When a patient arrives at the hospital, the process begins with the patient logging in to a terminal located in the waiting room. Here, the patient answers questions related to their medical history. The terminal transmits these answers to a server in real time. The server analyzes the received data using natural language processing technology and lists possible diagnoses based on the information obtained. These suggested diagnoses are immediately returned to the terminal for the doctor to review before the examination.

[0228] At this stage, the server automatically generates an editable medical record based on the patient interview data and the suggested disease name. This medical record is designed so that doctors can correct or add information as needed. After the medical record is completed, the server stores the data in a secure database. This stored data can be accessed from anywhere by healthcare professionals with the necessary access rights.

[0229] As a concrete example, consider a patient who answers questions related to cold symptoms on a terminal. The terminal displays a screen for entering information such as the frequency of coughing and runny nose, and recent body temperature. Once the patient enters these fields, the terminal immediately sends the information to the server. The server analyzes it and suggests a diagnosis such as "cold," "influenza," or "allergic rhinitis." Based on this, an automatically generated medical record is created, which doctors can refer to during consultations to quickly assess the condition and plan treatment.

[0230] This system will improve the efficiency and accuracy of operations in medical settings and reduce the burden on doctors. Furthermore, consistent management and sharing of information will ensure uniformity of treatment across different medical institutions, improving the quality of medical services provided to patients.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The patient, as the user, logs in to a terminal located in the hospital waiting room. The terminal verifies the patient's authentication information on the input screen and authenticates the user.

[0234] Step 2:

[0235] The terminal displays a list of medical questionnaire questions received from the server. The patient, as the user, enters their symptoms and condition in response to the displayed questions.

[0236] Step 3:

[0237] The terminal sends the user's entered response data to the server in real time. The server collects and stores the received data.

[0238] Step 4:

[0239] The server analyzes the received medical interview data using a natural language processing algorithm and extracts possible disease names. It then organizes this information and generates diagnostic support information.

[0240] Step 5:

[0241] The server sends the generated list of disease names to terminals for healthcare professionals. This allows doctors to review the necessary information before a consultation.

[0242] Step 6:

[0243] The server automatically generates editable medical records based on the patient's questionnaire and a list of diseases. These generated medical records are then used by healthcare professionals to make any necessary corrections.

[0244] Step 7:

[0245] The server stores completed medical records in a secure database. This information is accessible only to authorized users and can be shared with other healthcare institutions.

[0246] (Example 1)

[0247] 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."

[0248] A challenge in the medical interview process at healthcare facilities is the inefficient collection, analysis, recording, and sharing of patient information. This increases the burden on medical staff and can lead to a lack of speed and accuracy in diagnosis. Furthermore, consistent information sharing between different healthcare institutions is difficult.

[0249] 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.

[0250] In this invention, the server includes means for executing an automated medical interview process for medical support, means for suggesting disease names from interview data using natural language processing, means for providing automatically generated medical records in an editable format, means for receiving patient input information via a terminal and transmitting it to the server, means for analyzing received data using a generation AI model, and means for displaying disease suggestions on the terminal in real time. This enables the efficient execution of the interview process and the automatic generation and consistent management of medical records. Furthermore, it enables the secure and rapid sharing of medical information, improving the efficiency and accuracy of operations in the medical field.

[0251] A "medical support system" is a set of technologies designed to streamline the medical treatment process in healthcare facilities and support the management and sharing of medical information.

[0252] An "automated medical interview process" is a system that electronically collects information from patients and efficiently provides data to assist in diagnosis.

[0253] "Natural language processing" is a technology that allows computers to understand and interpret human language, and it is used in the analysis of medical interview data.

[0254] "Disease name suggestion" is the process of listing possible disease names based on analyzed patient data.

[0255] "Automated medical records" refer to medical information created electronically based on patient interview data.

[0256] "Saving to a database" is the process of writing data to a digital medium for secure and efficient storage.

[0257] "Access rights management" is a method of restricting access to information to authorized users and is used to maintain the security of that information.

[0258] A "generative AI model" is an artificial intelligence technology that learns patterns based on large amounts of data and uses that knowledge to make predictions and judgments about new data.

[0259] A "terminal" is an electronic device that patients use to input medical records or to check their own data.

[0260] A "prompt" is a text containing questions or instructions given to an AI model, used to guide the model's analysis and generation processes.

[0261] This medical support system is implemented in medical facilities such as hospitals and clinics and consists mainly of users (patients), terminals, and a server. To use the system, patients first log in to a terminal installed in the waiting room upon arriving at the hospital. The terminal displays a series of questions related to the medical interview to the user, who then answers them. This device provides an easy-to-use interface for inputting symptoms and changes in physical condition that the user perceives.

[0262] The terminal transmits the received data to the server in real time. Standard communication technologies, such as the TCP / IP protocol, are used for this data transmission. The server utilizes generative AI models and natural language processing systems to analyze the information received from the patient. This AI model analyzes the data using prompt sentences. An example of a prompt sentence is, "Please suggest possible disease names based on the received symptom data."

[0263] The server returns the suggested disease name based on the analysis results to the terminal and displays it to the user and medical staff. This information can be reviewed by the doctor before the examination begins. Furthermore, the server automatically generates a medical record based on the interview data. This medical record is provided in an editable format, allowing the doctor to review its contents during the examination and add or modify information as needed. This data is stored securely in a database by the server and is accessible to healthcare professionals under appropriate access rights management.

[0264] For example, if a patient enters cold-related symptoms through a terminal, the terminal displays a screen for entering information such as cough frequency and body temperature. Once the user enters the necessary information, the server analyzes it using a generative AI model and suggests disease names such as "cold," "influenza," or "allergic rhinitis." A medical record is automatically generated based on this information and saved, allowing doctors to efficiently determine treatment plans during consultations.

[0265] The introduction of this system will standardize the processes of patient interviews and medical record management in healthcare institutions, improving the efficiency and accuracy of healthcare delivery. Furthermore, it will enable the secure and consistent management and sharing of medical information, allowing for unified treatment across different healthcare institutions.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The terminal displays a questionnaire to the user, who then enters information about their symptoms and physical condition on the screen. This data includes recent body temperature, cough frequency, and other symptoms. The terminal receives this information in input format and prepares it for transmission to the server. The output is the user's input data, which is used in the next processing step.

[0269] Step 2:

[0270] The terminal sends input data obtained from the user to the server. The data is transmitted using a secure and fast communication protocol. The input is the user's symptom data collected on the terminal, and the output is this data received by the server. In this step, the server receives the input data.

[0271] Step 3:

[0272] The server analyzes the user data it receives. This analysis utilizes a generative AI model and natural language processing techniques. The prompt "Please suggest possible disease names based on the received symptom data" is used to instruct the AI ​​model to analyze the data. The input is the user data sent to the server, and the output is a list of disease names suggested as a result of the analysis.

[0273] Step 4:

[0274] The terminal receives the analysis results from the server and displays suggested disease names to the user. The user and medical staff review this information and use it as a reference before the examination. The input is a list of disease names sent from the server, and the output is the suggested results displayed on the terminal. In this step, the server sends the analysis results, and the terminal receives those results.

[0275] Step 5:

[0276] The server automatically generates medical records based on patient interview data and suggested disease information. These records are provided in an editable format, allowing physicians to modify and add to them during consultations. Inputs are patient interview data and suggested disease names, while output is an editable medical record. The process includes the server generating the medical records and saving them to a database.

[0277] Step 6:

[0278] The server stores the generated medical records in a secure database, allowing authorized medical professionals to access these records from anywhere. The input is the medical records generated by the server, and the output is the medical records securely stored in the database. In this step, data storage and access management are performed as concrete actions.

[0279] (Application Example 1)

[0280] 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."

[0281] Traditional healthcare systems lack efficient ways to assess a patient's health status before they visit a medical institution and guide them to the appropriate facility. This makes it difficult for patients to avoid unnecessary tests and visits to the wrong departments, resulting in wasted medical resources. Furthermore, the lack of mechanisms for collecting and securely sharing patient health information with medical institutions in real time can hinder prompt medical responses.

[0282] 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.

[0283] In this invention, the server includes means for executing an automatic interview process for medical support, means for proposing a disease name from interview data using natural language processing, and means for providing the automatically generated medical record in an editable format. As a result, patients can check their health status at home in advance and be guided to visit an appropriate medical institution, enabling efficient response and resource optimization in the medical field.

[0284] The "means for executing an automatic interview process for medical support" is a system that provides a tool for patients to input their health status in an interview format before visiting a medical institution and automatically aggregates the data.

[0285] The "means for proposing a disease name from interview data using natural language processing" is a technology that analyzes the interview data collected from patients and lists appropriate disease names based on statistical data and algorithms.

[0286] The "means for providing the automatically generated medical record in an editable format" is a function that automatically creates a medical record based on the analysis results and presents it in a format that allows medical staff to input additional information or make corrections as needed.

[0287] The "means for storing the medical record in a database and managing access rights" is a system that stores the generated medical record in a secure location and controls who can access the data and when.

[0288] The "means for collecting and analyzing the user's health information from a remote measurement device" is a technology that captures and aggregates health data from the device used by the user and analyzes the data to evaluate the health status.

[0289] The "means for presenting guidance to an appropriate medical institution on a display device based on the analysis results" is a method that identifies the optimal medical institution and department for the user based on the analyzed data and visually displays the information on the user interface.

[0290] The embodiment of this invention is realized through the cooperation of various hardware and software. It is assumed that the server, terminal, and user device operate in conjunction with each other.

[0291] The server is built on a cloud-based data management system and performs real-time data processing. It primarily uses AWS Lambda as its foundation, with backend logic implemented using Node.js. The collected patient interview data is analyzed using the Google Cloud Natural Language API, a natural language processing platform, to quickly list appropriate disease names.

[0292] The terminal is a device used by patients to input data, primarily functioning as a smartphone or tablet. Flutter is used for cross-platform development, providing a user-friendly interface. Data entered from the terminal is immediately sent to the server, allowing for rapid analysis.

[0293] Users can input their health information into this system and receive appropriate medical guidance based on the analysis results. Remote monitoring devices such as smartwatches are also used to collect health information. This allows for monitoring of daily health conditions and provides guidance information to identify the medical institutions the patient needs.

[0294] As a concrete example, imagine a patient exhibiting cold symptoms entering their symptoms through a terminal app, and the analysis results would list possible diagnoses such as "cold," "influenza," and "allergic rhinitis." In this case, the system would use its database to display relevant medical institutions to the user along with map information.

[0295] As an example of a prompt message for the generative AI model, by inputting information in the format of "Please list the names of diseases that may be related to these symptoms," it becomes possible to receive suggestions for appropriate disease names.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] Users access a medical questionnaire application using their device and enter their answers to health-related questions. This data includes symptoms, body temperature, and past medical history. This information is transmitted to the server in real time.

[0299] Step 2:

[0300] The server processes the received data using the Google Cloud Natural Language API. This extracts important keywords from the medical history data and calculates possible disease names based on them. The output is a list of suggested disease names, which is immediately sent to the terminal.

[0301] Step 3:

[0302] The user reviews the suggested disease name on their device and enters any additional health information if necessary. This allows for more accurate analysis results. The new information is then sent back to the server.

[0303] Step 4:

[0304] The server performs natural language processing again based on the latest data and updates the list of disease names. Furthermore, it uses the analysis results to automatically generate medical records and converts them into an editable format that doctors can fill in and modify. This is then stored in a database.

[0305] Step 5:

[0306] The server also takes into account the user's location data and proposes appropriate nearby medical institutions. This includes a process of using a Geographic Information System (GIS) to analyze the user's geographical location and select the optimal medical institution. The output is sent to the terminal together with the map information.

[0307] Step 6:

[0308] Based on the guided medical institution information, the user can check the map on a smartphone or a graphic display device and make a plan for seeing a doctor. It is also possible to make a reservation directly with the medical institution using the guidance information.

[0309] In this way, the user can efficiently manage their health at home and receive appropriately guided medical support.

[0310] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0311] The system according to the present invention has a configuration that combines a user, a terminal, a server, and an emotion engine for the purpose of medical support. In this system, in addition to an efficient consultation process by the patient, the emotional state of the patient is recognized and corresponding medical support is provided.

[0312] The patient who is the user logs in to the terminal installed in the hospital waiting room and inputs information about their symptoms. Similar to the conventional process, the terminal sends the information to the server, and the server analyzes the data using natural language processing technology and extracts possible disease names. This disease information is notified to the terminal of medical staff to assist in diagnosis.

[0313] The newly added emotion engine analyzes the patient's input speed, content, facial expressions, and tone of voice to assess their emotional state in real time. This information is fed back into the automated interview process, dynamically adjusting the way questions are asked and their order as needed. This allows for changes to questions that help patients relax if they are feeling tense or anxious.

[0314] As a concrete example, suppose a patient user is entering information about their cold symptoms on a terminal, and the emotion engine detects a state of tension. The terminal transmits this information to the server, which then adjusts the questions to promote relaxation. For example, a straightforward question like, "How long have your symptoms lasted?" can be changed to, "Have you done anything to help you relax when your symptoms appeared?"

[0315] Furthermore, the emotional data analyzed by the emotion engine is stored as supplementary information in the user's medical records. This information serves as important data for the examining physician to understand the patient's psychological state.

[0316] These features enhance efficiency and diagnostic accuracy in healthcare settings, improving the quality of services provided to patients. Furthermore, by exhibiting similar effects in healthcare facilities outside of hospitals, it contributes to improving the overall quality of healthcare.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The patient, as the user, logs in to a terminal installed in the waiting room of the medical facility. The terminal verifies the patient's authentication information on the input screen and performs user authentication.

[0320] Step 2:

[0321] The terminal displays a questionnaire to the user. A list of questions provided by the server is used. The user enters answers regarding their symptoms and condition.

[0322] Step 3:

[0323] The terminal transmits user input data to the server in real time. Input speed and touch pressure are also recorded during this process.

[0324] Step 4:

[0325] The server analyzes the received data using a natural language processing algorithm. It extracts possible disease names from the analysis results and sends them to the medical professional's terminal.

[0326] Step 5:

[0327] The emotion engine evaluates the user's emotional state based on input information from the device. Emotion recognition is performed through input speed, text content, and facial expression recognition.

[0328] Step 6:

[0329] The device dynamically adjusts the questionnaire questions based on the evaluation results of the emotion engine. For example, if tension is detected, the questions are changed to help the user relax.

[0330] Step 7:

[0331] The server stores emotional data as supplementary information to medical records, allowing doctors to refer to it later during consultations.

[0332] Step 8:

[0333] The server stores the generated medical records in a secure database. These records can be viewed and shared by healthcare professionals with access rights.

[0334] (Example 2)

[0335] 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".

[0336] In modern healthcare settings, efficient and accurate diagnostic processes are required, but the consistency and accuracy of diagnoses are challenged because patients' emotional states can influence their answers during interviews. Furthermore, the proper storage and management of medical information and patients' emotional states, as well as efficient information sharing with healthcare professionals in remote locations, are also challenges.

[0337] 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.

[0338] In this invention, the server includes means for inputting patient information and executing an automated medical interview process, means for suggesting a medical condition from the interview data using natural language processing, and means for analyzing the patient's emotional state from the input data and dynamically adjusting the interview process. This enables appropriate interviews tailored to the patient's emotional state, improving the accuracy of diagnosis and the efficiency of the medical setting. Furthermore, it allows for the secure storage of medical information, including emotional data, and enables efficient information sharing with medical professionals in remote locations.

[0339] "Patient information" refers to identifiable medical data, including data on a patient's symptoms, diagnosis, or medical history.

[0340] An "automated medical interview process" is a procedure in which the system automatically conducts an appropriate medical interview based on the patient's input of their symptoms.

[0341] "Natural language processing" is a technology that allows computers to understand and analyze the language that humans speak naturally.

[0342] "Medical status" refers to the patient's health condition and potential illnesses based on interview data.

[0343] "Emotional state" refers to the patient's psychological state and includes states such as tension, anxiety, and relaxation.

[0344] "Dynamic adjustment" means that the system automatically changes to the optimal state according to the situation and conditions.

[0345] "Medical information" refers to all medical data related to a patient, including diagnostic results, treatment plans, and emotional data.

[0346] A "database" is a system that systematically stores large amounts of information, making it easy to access and search for.

[0347] This system, designed for medical support, consists of a user, terminal, server, and emotion engine. The main hardware includes an input terminal for user use and a server for data processing. The software includes a natural language processing engine and an emotion recognition engine.

[0348] The terminal provides an interface for the patient (user) to input information such as their symptoms. Users can input information into the terminal using methods such as a touchscreen or voice input. The entered information is securely encrypted and transmitted to the server.

[0349] The server analyzes this input data using natural language processing techniques. For example, it can use Python's natural language processing libraries, such as NLTK or spaCy. This analysis suggests possible medical conditions based on the symptoms. The suggested information is then provided to the terminal of the diagnostician.

[0350] Furthermore, the emotion engine analyzes elements such as the user's input speed, voice, and facial expressions in real time to assess their emotional state. Based on this assessment, the server can dynamically adjust the interview process. For example, if the user is tense, the questions can be changed to encourage relaxation.

[0351] As a concrete example, consider a case where a user enters symptoms of a cold. In this case, if the emotion engine detects the user's anxiety, the server will adjust the question to something like, "Did you do anything to relax when you experienced symptoms?" This process allows the user to participate in the consultation with greater peace of mind.

[0352] Examples of prompt messages include the following:

[0353] "If a user is feeling nervous, suggest some questions that might help them relax."

[0354] In this way, the system provides efficient and accurate medical support while responding to the patient's emotions.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] Users log in to a terminal in the waiting room and use a dedicated interface to enter their symptoms and basic personal information. This includes the patient's name, age, and specific symptoms (e.g., cough, fever). The terminal encrypts the entered information using the SSL / TLS protocol and sends it to the server.

[0358] Step 2:

[0359] The server receives data sent from the terminal. It analyzes the input data using a natural language processing engine, such as Python's NLTK or spaCy. Through analytics, it extracts possible medical conditions (e.g., cold, flu) from the entered symptoms and sends this information to the medical professional's terminal for diagnostic support. The output is a list of predicted diseases.

[0360] Step 3:

[0361] The emotion engine collects data such as typing speed, keyboard sounds, and facial expressions and voice tone from the camera and microphone while the user is typing on the device. Based on this data, the emotion engine evaluates the user's emotional state and identifies emotional states such as "tense" or "relaxed." The evaluation results are sent to the server.

[0362] Step 4:

[0363] The server receives emotional assessment data from the emotion engine and dynamically adjusts the ongoing interview process. Based on the emotional data, it performs actions such as changing the order of questions or softening the questions to encourage relaxation. A specific example of this action would be changing the question from "How often do your symptoms occur?" to "Have you done anything to relax when your symptoms occurred?"

[0364] Step 5:

[0365] The server ultimately stores the analyzed emotional data, along with the patient's medical history, as a digitized medical record. This record includes elements such as the patient's symptom information, predicted disease name, and emotional state. The stored data is referenced in subsequent diagnostic processes and treatment planning. As an output, a complete medical record is properly stored and accessible.

[0366] (Application Example 2)

[0367] 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."

[0368] Current medical support systems often struggle to consider patients' emotional states, which can compromise accuracy and efficiency. Furthermore, there is a lack of flexible systems in place to enhance the quality of psychological care in nursing facilities. Therefore, there is a need for systems that enable medical and care support tailored to the psychological state of patients and residents of nursing facilities.

[0369] 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.

[0370] In this invention, the server includes means for executing an automated medical interview process for medical support, means for suggesting disease names from interview data using natural language processing, and means for evaluating the psychological state of the subject using emotion analysis technology. This makes it possible to dynamically provide appropriate responses according to the psychological state of patients or residents of care facilities.

[0371] "Medical support" refers to technologies and processes used in medical settings to improve the efficiency and accuracy of diagnosis and treatment.

[0372] The "automated medical interview process" refers to a procedure in which the patient enters their symptoms into a terminal, and the system automatically proceeds with the interview based on that information.

[0373] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0374] "Suggesting a disease name" refers to the system presenting possible disease names based on the patient's medical history data.

[0375] "Emotional analysis technology" refers to methods for analyzing and evaluating an individual's emotional state from digital data.

[0376] "Assessing a psychological state" refers to measuring and judging an individual's mental health and emotional responses.

[0377] "Care support" refers to the processes and techniques used to provide physical and mental care to residents in care facilities.

[0378] "Dynamic adjustment" refers to changing the response in real time according to the situation.

[0379] A "system" refers to a structure in which multiple elements or processes are combined to achieve a specific purpose.

[0380] To implement the present invention, the medical support system consists of a server, a terminal, and a user.

[0381] The server first uses natural language processing technology to analyze the medical questionnaire data sent from the terminal, extracts possible disease names, and notifies the user. This improves the efficiency of diagnosis.

[0382] The terminal not only collects data entered by the user and sends it to the server, but also utilizes emotion analysis technology. It acquires various data such as the user's input speed, tension, facial expressions, and voice tone, and evaluates the user's psychological state in real time. This evaluation is provided by the emotion analysis engine. Based on this information, the terminal dynamically adjusts the content and order of the questionnaire questions, providing the user with a relaxing environment.

[0383] As a concrete example, consider a scenario where a user is entering information about cold symptoms on their device, and the emotion analysis engine detects a state of tension. In this case, the server changes the question, changing "How long have your symptoms been lasting?" to "Have you done anything to relax when the symptoms appeared?", thereby alleviating the user's tension.

[0384] Furthermore, the data obtained through emotion analysis is stored as medical records and used by healthcare professionals as important information to understand the user's psychological state. Based on this information, it becomes possible for nursing homes to provide care that is tailored to the emotions of their residents.

[0385] An example of a prompt might be, "Please suggest a suitable conversation topic if the sentiment analysis indicates high stress levels."

[0386] As described above, this system aims to enable medical and nursing care support that takes into account the psychological state of individuals in various settings, thereby improving the overall quality of medical and nursing care.

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] The device acquires data about symptoms and health conditions entered by the user. This input includes text descriptions of symptoms, voice tone, and videos of facial expressions. The device preprocesses this data and converts it into a format suitable for analysis.

[0390] Step 2:

[0391] The terminal prepares to send the acquired data to the server. Text data is prepared for natural language processing, and video and audio data is converted into a format suitable for the sentiment analysis engine. The input is processed data, and the output is the data sent to the server.

[0392] Step 3:

[0393] The server receives data sent from the terminal and analyzes the text data using a natural language processing engine. Here, it extracts possible disease names and notifies healthcare professionals of this information. The output is a list of disease names.

[0394] Step 4:

[0395] The server uses an emotion analysis engine to evaluate the user's emotional state. In this step, audio and video data are analyzed to determine the user's emotional state, such as whether they are tense or relaxed. The input is audio and video data, and the output is the evaluation result of the emotional state.

[0396] Step 5:

[0397] The server dynamically adjusts the questionnaire based on the results of the emotion analysis. For example, if the patient is highly anxious, the server will soften the wording of the questions. The output is a list of the adjusted questions.

[0398] Step 6:

[0399] The device presents the user with carefully designed questions and displays a form for entering answers. This process reduces the user's psychological stress and encourages them to answer in a relaxed state. The output is the user's response data.

[0400] Step 7:

[0401] The server ultimately formats the data returned by the user as a medical record and stores it in a database. Emotional data is also recorded, which can be used by healthcare professionals for future diagnoses. The output is a set of medical record data.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] The medical support system according to the present invention is installed in a medical facility and consists of a patient (the user), a terminal, and a server. Through this system, information is collected from the patient through an efficient interview process, and based on this information, disease names are suggested, and medical records are automatically generated and shared. The specific process is described below.

[0419] When a patient arrives at the hospital, the process begins with the patient logging in to a terminal located in the waiting room. Here, the patient answers questions related to their medical history. The terminal transmits these answers to a server in real time. The server analyzes the received data using natural language processing technology and lists possible diagnoses based on the information obtained. These suggested diagnoses are immediately returned to the terminal for the doctor to review before the examination.

[0420] At this stage, the server automatically generates an editable medical record based on the patient interview data and the suggested disease name. This medical record is designed so that doctors can correct or add information as needed. After the medical record is completed, the server stores the data in a secure database. This stored data can be accessed from anywhere by healthcare professionals with the necessary access rights.

[0421] As a concrete example, consider a patient who answers questions related to cold symptoms on a terminal. The terminal displays a screen for entering information such as the frequency of coughing and runny nose, and recent body temperature. Once the patient enters these fields, the terminal immediately sends the information to the server. The server analyzes it and suggests a diagnosis such as "cold," "influenza," or "allergic rhinitis." Based on this, an automatically generated medical record is created, which doctors can refer to during consultations to quickly assess the condition and plan treatment.

[0422] This system will improve the efficiency and accuracy of operations in medical settings and reduce the burden on doctors. Furthermore, consistent management and sharing of information will ensure uniformity of treatment across different medical institutions, improving the quality of medical services provided to patients.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The patient, as the user, logs in to a terminal located in the hospital waiting room. The terminal verifies the patient's authentication information on the input screen and authenticates the user.

[0426] Step 2:

[0427] The terminal displays a list of medical questionnaire questions received from the server. The patient, as the user, enters their symptoms and condition in response to the displayed questions.

[0428] Step 3:

[0429] The terminal sends the user's entered response data to the server in real time. The server collects and stores the received data.

[0430] Step 4:

[0431] The server analyzes the received medical interview data using a natural language processing algorithm and extracts possible disease names. It then organizes this information and generates diagnostic support information.

[0432] Step 5:

[0433] The server sends the generated list of disease names to terminals for healthcare professionals. This allows doctors to review the necessary information before a consultation.

[0434] Step 6:

[0435] The server automatically generates editable medical records based on the patient's questionnaire and a list of diseases. These generated medical records are then used by healthcare professionals to make any necessary corrections.

[0436] Step 7:

[0437] The server stores completed medical records in a secure database. This information is accessible only to authorized users and can be shared with other healthcare institutions.

[0438] (Example 1)

[0439] 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."

[0440] A challenge in the medical interview process at healthcare facilities is the inefficient collection, analysis, recording, and sharing of patient information. This increases the burden on medical staff and can lead to a lack of speed and accuracy in diagnosis. Furthermore, consistent information sharing between different healthcare institutions is difficult.

[0441] 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.

[0442] In this invention, the server includes means for executing an automated medical interview process for medical support, means for suggesting disease names from interview data using natural language processing, means for providing automatically generated medical records in an editable format, means for receiving patient input information via a terminal and transmitting it to the server, means for analyzing received data using a generation AI model, and means for displaying disease suggestions on the terminal in real time. This enables the efficient execution of the interview process and the automatic generation and consistent management of medical records. Furthermore, it enables the secure and rapid sharing of medical information, improving the efficiency and accuracy of operations in the medical field.

[0443] A "medical support system" is a set of technologies designed to streamline the medical treatment process in healthcare facilities and support the management and sharing of medical information.

[0444] An "automated medical interview process" is a system that electronically collects information from patients and efficiently provides data to assist in diagnosis.

[0445] "Natural language processing" is a technology that allows computers to understand and interpret human language, and it is used in the analysis of medical interview data.

[0446] "Disease name suggestion" is the process of listing possible disease names based on analyzed patient data.

[0447] "Automated medical records" refer to medical information created electronically based on patient interview data.

[0448] "Saving to a database" is the process of writing data to a digital medium for secure and efficient storage.

[0449] "Access rights management" is a method of restricting access to information to authorized users and is used to maintain the security of that information.

[0450] A "generative AI model" is an artificial intelligence technology that learns patterns based on large amounts of data and uses that knowledge to make predictions and judgments about new data.

[0451] A "terminal" is an electronic device that patients use to input medical records or to check their own data.

[0452] A "prompt" is a text containing questions or instructions given to an AI model, used to guide the model's analysis and generation processes.

[0453] This medical support system is implemented in medical facilities such as hospitals and clinics and consists mainly of users (patients), terminals, and a server. To use the system, patients first log in to a terminal installed in the waiting room upon arriving at the hospital. The terminal displays a series of questions related to the medical interview to the user, who then answers them. This device provides an easy-to-use interface for inputting symptoms and changes in physical condition that the user perceives.

[0454] The terminal transmits the received data to the server in real time. Standard communication technologies, such as the TCP / IP protocol, are used for this data transmission. The server utilizes generative AI models and natural language processing systems to analyze the information received from the patient. This AI model analyzes the data using prompt sentences. An example of a prompt sentence is, "Please suggest possible disease names based on the received symptom data."

[0455] The server returns the suggested disease name based on the analysis results to the terminal and displays it to the user and medical staff. This information can be reviewed by the doctor before the examination begins. Furthermore, the server automatically generates a medical record based on the interview data. This medical record is provided in an editable format, allowing the doctor to review its contents during the examination and add or modify information as needed. This data is stored securely in a database by the server and is accessible to healthcare professionals under appropriate access rights management.

[0456] For example, if a patient enters cold-related symptoms through a terminal, the terminal displays a screen for entering information such as cough frequency and body temperature. Once the user enters the necessary information, the server analyzes it using a generative AI model and suggests disease names such as "cold," "influenza," or "allergic rhinitis." A medical record is automatically generated based on this information and saved, allowing doctors to efficiently determine treatment plans during consultations.

[0457] The introduction of this system will standardize the processes of patient interviews and medical record management in healthcare institutions, improving the efficiency and accuracy of healthcare delivery. Furthermore, it will enable the secure and consistent management and sharing of medical information, allowing for unified treatment across different healthcare institutions.

[0458] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0459] Step 1:

[0460] The terminal displays a questionnaire to the user, who then enters information about their symptoms and physical condition on the screen. This data includes recent body temperature, cough frequency, and other symptoms. The terminal receives this information in input format and prepares it for transmission to the server. The output is the user's input data, which is used in the next processing step.

[0461] Step 2:

[0462] The terminal sends input data obtained from the user to the server. The data is transmitted using a secure and fast communication protocol. The input is the user's symptom data collected on the terminal, and the output is this data received by the server. In this step, the server receives the input data.

[0463] Step 3:

[0464] The server analyzes the user data it receives. This analysis utilizes a generative AI model and natural language processing techniques. The prompt "Please suggest possible disease names based on the received symptom data" is used to instruct the AI ​​model to analyze the data. The input is the user data sent to the server, and the output is a list of disease names suggested as a result of the analysis.

[0465] Step 4:

[0466] The terminal receives the analysis results from the server and displays suggested disease names to the user. The user and medical staff review this information and use it as a reference before the examination. The input is a list of disease names sent from the server, and the output is the suggested results displayed on the terminal. In this step, the server sends the analysis results, and the terminal receives those results.

[0467] Step 5:

[0468] The server automatically generates medical records based on patient interview data and suggested disease information. These records are provided in an editable format, allowing physicians to modify and add to them during consultations. Inputs are patient interview data and suggested disease names, while output is an editable medical record. The process includes the server generating the medical records and saving them to a database.

[0469] Step 6:

[0470] The server stores the generated medical records in a secure database, allowing authorized medical professionals to access these records from anywhere. The input is the medical records generated by the server, and the output is the medical records securely stored in the database. In this step, data storage and access management are performed as concrete actions.

[0471] (Application Example 1)

[0472] 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."

[0473] Traditional healthcare systems lack efficient ways to assess a patient's health status before they visit a medical institution and guide them to the appropriate facility. This makes it difficult for patients to avoid unnecessary tests and visits to the wrong departments, resulting in wasted medical resources. Furthermore, the lack of mechanisms for collecting and securely sharing patient health information with medical institutions in real time can hinder prompt medical responses.

[0474] 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.

[0475] In this invention, the server includes means for executing an automated medical interview process for medical support, means for suggesting disease names from interview data using natural language processing, and means for providing automatically generated medical records in an editable format. This enables efficient response and resource optimization in medical settings by allowing patients to check their health status at home in advance and be guided to visit the appropriate medical institution.

[0476] "Means for implementing an automated medical interview process for medical support" refers to a system that provides a tool for patients to input their health status in a questionnaire format before visiting a medical institution, and automatically compiles that data.

[0477] "A method for proposing disease names from medical interview data using natural language processing" is a technology that analyzes medical interview data collected from patients and lists appropriate disease names based on statistical data and algorithms.

[0478] "Means of providing automatically generated medical records in an editable format" refers to a function that automatically creates medical records based on analysis results and presents them in a format that allows healthcare professionals to input additional information or make corrections as needed.

[0479] "A means of storing medical records in a database and managing access rights" refers to a system that stores generated medical records in a secure location and controls who can access the data and when.

[0480] "Means for collecting and analyzing users' health information from remote measurement devices" refers to technology that captures and aggregates health data from devices used by users, analyzes that data, and evaluates their health status.

[0481] "A means of displaying guidance to appropriate medical institutions on a graphical device based on analysis results" refers to a method of identifying the most suitable medical institution or department for the user based on the analyzed data, and visually displaying that information on the user interface.

[0482] The embodiment of this invention is realized through the cooperation of various hardware and software. It is assumed that the server, terminal, and user device operate in conjunction with each other.

[0483] The server is built on a cloud-based data management system and performs real-time data processing. It primarily uses AWS Lambda as its foundation, with backend logic implemented using Node.js. The collected patient interview data is analyzed using the Google Cloud Natural Language API, a natural language processing platform, to quickly list appropriate disease names.

[0484] The terminal is a device used by patients to input data, primarily functioning as a smartphone or tablet. Flutter is used for cross-platform development, providing a user-friendly interface. Data entered from the terminal is immediately sent to the server, allowing for rapid analysis.

[0485] Users can input their health information into this system and receive appropriate medical guidance based on the analysis results. Remote monitoring devices such as smartwatches are also used to collect health information. This allows for monitoring of daily health conditions and provides guidance information to identify the medical institutions the patient needs.

[0486] As a concrete example, imagine a patient exhibiting cold symptoms entering their symptoms through a terminal app, and the analysis results would list possible diagnoses such as "cold," "influenza," and "allergic rhinitis." In this case, the system would use its database to display relevant medical institutions to the user along with map information.

[0487] As an example of a prompt message for the generative AI model, by inputting information in the format of "Please list the names of diseases that may be related to these symptoms," it becomes possible to receive suggestions for appropriate disease names.

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] Users access a medical questionnaire application using their device and enter their answers to health-related questions. This data includes symptoms, body temperature, and past medical history. This information is transmitted to the server in real time.

[0491] Step 2:

[0492] The server processes the received data using the Google Cloud Natural Language API. This extracts important keywords from the medical history data and calculates possible disease names based on them. The output is a list of suggested disease names, which is immediately sent to the terminal.

[0493] Step 3:

[0494] The user reviews the suggested disease name on their device and enters any additional health information if necessary. This allows for more accurate analysis results. The new information is then sent back to the server.

[0495] Step 4:

[0496] The server performs natural language processing again based on the latest data and updates the list of disease names. Furthermore, it uses the analysis results to automatically generate medical records and converts them into an editable format that doctors can fill in and modify. This is then stored in a database.

[0497] Step 5:

[0498] The server also considers the user's location data and suggests appropriate nearby medical facilities. This includes a process that utilizes a Geographic Information System (GIS) to analyze the user's geographical location and select the most suitable medical facility. The output is sent to the terminal along with map information.

[0499] Step 6:

[0500] Based on the provided medical institution information, users can check maps on their smartphones or display devices and plan their visits. They can also use the information to directly make appointments with medical institutions.

[0501] This allows users to manage their health efficiently at home and receive appropriately guided medical support.

[0502] 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.

[0503] The system according to the present invention has a configuration that combines a user, a terminal, a server, and an emotion engine for the purpose of medical support. In addition to an efficient patient interview process, this system recognizes the patient's emotional state and provides medical support accordingly.

[0504] Patients, acting as users, log in to a terminal installed in the hospital waiting room and enter information about their symptoms. Similar to traditional processes, the terminal sends the information to a server, which uses natural language processing technology to analyze the data and extract possible disease names. This disease information is then sent to the terminals of healthcare professionals to assist in diagnosis.

[0505] The newly added emotion engine analyzes the patient's input speed, content, facial expressions, and tone of voice to assess their emotional state in real time. This information is fed back into the automated interview process, dynamically adjusting the way questions are asked and their order as needed. This allows for changes to questions that help patients relax if they are feeling tense or anxious.

[0506] As a concrete example, suppose a patient user is entering information about their cold symptoms on a terminal, and the emotion engine detects a state of tension. The terminal transmits this information to the server, which then adjusts the questions to promote relaxation. For example, a straightforward question like, "How long have your symptoms lasted?" can be changed to, "Have you done anything to help you relax when your symptoms appeared?"

[0507] Furthermore, the emotional data analyzed by the emotion engine is stored as supplementary information in the user's medical records. This information serves as important data for the examining physician to understand the patient's psychological state.

[0508] These features enhance efficiency and diagnostic accuracy in healthcare settings, improving the quality of services provided to patients. Furthermore, by exhibiting similar effects in healthcare facilities outside of hospitals, it contributes to improving the overall quality of healthcare.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The patient, as the user, logs in to a terminal installed in the waiting room of the medical facility. The terminal verifies the patient's authentication information on the input screen and performs user authentication.

[0512] Step 2:

[0513] The terminal displays a questionnaire to the user. A list of questions provided by the server is used. The user enters answers regarding their symptoms and condition.

[0514] Step 3:

[0515] The terminal transmits user input data to the server in real time. Input speed and touch pressure are also recorded during this process.

[0516] Step 4:

[0517] The server analyzes the received data using a natural language processing algorithm. It extracts possible disease names from the analysis results and sends them to the medical professional's terminal.

[0518] Step 5:

[0519] The emotion engine evaluates the user's emotional state based on input information from the device. Emotion recognition is performed through input speed, text content, and facial expression recognition.

[0520] Step 6:

[0521] The device dynamically adjusts the questionnaire questions based on the evaluation results of the emotion engine. For example, if tension is detected, the questions are changed to help the user relax.

[0522] Step 7:

[0523] The server stores emotional data as supplementary information to medical records, allowing doctors to refer to it later during consultations.

[0524] Step 8:

[0525] The server stores the generated medical records in a secure database. These records can be viewed and shared by healthcare professionals with access rights.

[0526] (Example 2)

[0527] 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."

[0528] In modern healthcare settings, efficient and accurate diagnostic processes are required, but the consistency and accuracy of diagnoses are challenged because patients' emotional states can influence their answers during interviews. Furthermore, the proper storage and management of medical information and patients' emotional states, as well as efficient information sharing with healthcare professionals in remote locations, are also challenges.

[0529] 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.

[0530] In this invention, the server includes means for inputting patient information and executing an automated medical interview process, means for suggesting a medical condition from the interview data using natural language processing, and means for analyzing the patient's emotional state from the input data and dynamically adjusting the interview process. This enables appropriate interviews tailored to the patient's emotional state, improving the accuracy of diagnosis and the efficiency of the medical setting. Furthermore, it allows for the secure storage of medical information, including emotional data, and enables efficient information sharing with medical professionals in remote locations.

[0531] "Patient information" refers to identifiable medical data, including data on a patient's symptoms, diagnosis, or medical history.

[0532] An "automated medical interview process" is a procedure in which the system automatically conducts an appropriate medical interview based on the patient's input of their symptoms.

[0533] "Natural language processing" is a technology that allows computers to understand and analyze the language that humans speak naturally.

[0534] "Medical status" refers to the patient's health condition and potential illnesses based on interview data.

[0535] "Emotional state" refers to the patient's psychological state and includes states such as tension, anxiety, and relaxation.

[0536] "Dynamic adjustment" means that the system automatically changes to the optimal state according to the situation and conditions.

[0537] "Medical information" refers to all medical data related to a patient, including diagnostic results, treatment plans, and emotional data.

[0538] A "database" is a system that systematically stores large amounts of information, making it easy to access and search for.

[0539] This system, designed for medical support, consists of a user, terminal, server, and emotion engine. The main hardware includes an input terminal for user use and a server for data processing. The software includes a natural language processing engine and an emotion recognition engine.

[0540] The terminal provides an interface for the patient (user) to input information such as their symptoms. Users can input information into the terminal using methods such as a touchscreen or voice input. The entered information is securely encrypted and transmitted to the server.

[0541] The server analyzes this input data using natural language processing techniques. For example, it can use Python's natural language processing libraries, such as NLTK or spaCy. This analysis suggests possible medical conditions based on the symptoms. The suggested information is then provided to the terminal of the diagnostician.

[0542] Furthermore, the emotion engine analyzes elements such as the user's input speed, voice, and facial expressions in real time to assess their emotional state. Based on this assessment, the server can dynamically adjust the interview process. For example, if the user is tense, the questions can be changed to encourage relaxation.

[0543] As a concrete example, consider a case where a user enters symptoms of a cold. In this case, if the emotion engine detects the user's anxiety, the server will adjust the question to something like, "Did you do anything to relax when you experienced symptoms?" This process allows the user to participate in the consultation with greater peace of mind.

[0544] Examples of prompt messages include the following:

[0545] "If a user is feeling nervous, suggest some questions that might help them relax."

[0546] In this way, the system provides efficient and accurate medical support while responding to the patient's emotions.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] Users log in to a terminal in the waiting room and use a dedicated interface to enter their symptoms and basic personal information. This includes the patient's name, age, and specific symptoms (e.g., cough, fever). The terminal encrypts the entered information using the SSL / TLS protocol and sends it to the server.

[0550] Step 2:

[0551] The server receives data sent from the terminal. It analyzes the input data using a natural language processing engine, such as Python's NLTK or spaCy. Through analytics, it extracts possible medical conditions (e.g., cold, flu) from the entered symptoms and sends this information to the medical professional's terminal for diagnostic support. The output is a list of predicted diseases.

[0552] Step 3:

[0553] The emotion engine collects data such as typing speed, keyboard sounds, and facial expressions and voice tone from the camera and microphone while the user is typing on the device. Based on this data, the emotion engine evaluates the user's emotional state and identifies emotional states such as "tense" or "relaxed." The evaluation results are sent to the server.

[0554] Step 4:

[0555] The server receives emotional assessment data from the emotion engine and dynamically adjusts the ongoing interview process. Based on the emotional data, it performs actions such as changing the order of questions or softening the questions to encourage relaxation. A specific example of this action would be changing the question from "How often do your symptoms occur?" to "Have you done anything to relax when your symptoms occurred?"

[0556] Step 5:

[0557] The server ultimately stores the analyzed emotional data, along with the patient's medical history, as a digitized medical record. This record includes elements such as the patient's symptom information, predicted disease name, and emotional state. The stored data is referenced in subsequent diagnostic processes and treatment planning. As an output, a complete medical record is properly stored and accessible.

[0558] (Application Example 2)

[0559] 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."

[0560] Current medical support systems often struggle to consider patients' emotional states, which can compromise accuracy and efficiency. Furthermore, there is a lack of flexible systems in place to enhance the quality of psychological care in nursing facilities. Therefore, there is a need for systems that enable medical and care support tailored to the psychological state of patients and residents of nursing facilities.

[0561] 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.

[0562] In this invention, the server includes means for executing an automated medical interview process for medical support, means for suggesting disease names from interview data using natural language processing, and means for evaluating the psychological state of the subject using emotion analysis technology. This makes it possible to dynamically provide appropriate responses according to the psychological state of patients or residents of care facilities.

[0563] "Medical support" refers to technologies and processes used in medical settings to improve the efficiency and accuracy of diagnosis and treatment.

[0564] The "automated medical interview process" refers to a procedure in which the patient enters their symptoms into a terminal, and the system automatically proceeds with the interview based on that information.

[0565] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0566] "Suggesting a disease name" refers to the system presenting possible disease names based on the patient's medical history data.

[0567] "Emotional analysis technology" refers to methods for analyzing and evaluating an individual's emotional state from digital data.

[0568] "Assessing a psychological state" refers to measuring and judging an individual's mental health and emotional responses.

[0569] "Care support" refers to the processes and techniques used to provide physical and mental care to residents in care facilities.

[0570] "Dynamic adjustment" refers to changing the response in real time according to the situation.

[0571] A "system" refers to a structure in which multiple elements or processes are combined to achieve a specific purpose.

[0572] To implement the present invention, the medical support system consists of a server, a terminal, and a user.

[0573] The server first uses natural language processing technology to analyze the medical questionnaire data sent from the terminal, extracts possible disease names, and notifies the user. This improves the efficiency of diagnosis.

[0574] The terminal not only collects data entered by the user and sends it to the server, but also utilizes emotion analysis technology. It acquires various data such as the user's input speed, tension, facial expressions, and voice tone, and evaluates the user's psychological state in real time. This evaluation is provided by the emotion analysis engine. Based on this information, the terminal dynamically adjusts the content and order of the questionnaire questions, providing the user with a relaxing environment.

[0575] As a concrete example, consider a scenario where a user is entering information about cold symptoms on their device, and the emotion analysis engine detects a state of tension. In this case, the server changes the question, changing "How long have your symptoms been lasting?" to "Have you done anything to relax when the symptoms appeared?", thereby alleviating the user's tension.

[0576] Furthermore, the data obtained through emotion analysis is stored as medical records and used by healthcare professionals as important information to understand the user's psychological state. Based on this information, it becomes possible for nursing homes to provide care that is tailored to the emotions of their residents.

[0577] An example of a prompt might be, "Please suggest a suitable conversation topic if the sentiment analysis indicates high stress levels."

[0578] As described above, this system enables medical and nursing care support that takes into account the psychological state of individuals in various settings, and as a result, aims to improve the overall quality of medical and nursing care.

[0579] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0580] Step 1:

[0581] The device acquires data about symptoms and health conditions entered by the user. This input includes text descriptions of symptoms, voice tone, and videos of facial expressions. The device preprocesses this data and converts it into a format suitable for analysis.

[0582] Step 2:

[0583] The terminal prepares to send the acquired data to the server. Text data is prepared for natural language processing, and video and audio data is converted into a format suitable for the sentiment analysis engine. The input is processed data, and the output is the data sent to the server.

[0584] Step 3:

[0585] The server receives data sent from the terminal and analyzes the text data using a natural language processing engine. Here, it extracts possible disease names and notifies healthcare professionals of this information. The output is a list of disease names.

[0586] Step 4:

[0587] The server uses an emotion analysis engine to evaluate the user's emotional state. In this step, audio and video data are analyzed to determine the user's emotional state, such as whether they are tense or relaxed. The input is audio and video data, and the output is the evaluation result of the emotional state.

[0588] Step 5:

[0589] The server dynamically adjusts the questionnaire based on the results of the emotion analysis. For example, if the patient is highly anxious, the server will soften the wording of the questions. The output is a list of the adjusted questions.

[0590] Step 6:

[0591] The device presents the user with carefully designed questions and displays a form for entering answers. This process reduces the user's psychological stress and encourages them to answer in a relaxed state. The output is the user's response data.

[0592] Step 7:

[0593] The server ultimately formats the data returned by the user as a medical record and stores it in a database. Emotional data is also recorded, which can be used by healthcare professionals for future diagnoses. The output is a set of medical record data.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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".

[0611] The medical support system according to the present invention is installed in a medical facility and consists of a patient (the user), a terminal, and a server. Through this system, information is collected from the patient through an efficient interview process, and based on this information, disease names are suggested, and medical records are automatically generated and shared. The specific process is described below.

[0612] When a patient arrives at the hospital, the process begins with the patient logging in to a terminal located in the waiting room. Here, the patient answers questions related to their medical history. The terminal transmits these answers to a server in real time. The server analyzes the received data using natural language processing technology and lists possible diagnoses based on the information obtained. These suggested diagnoses are immediately returned to the terminal for the doctor to review before the examination.

[0613] At this stage, the server automatically generates an editable medical record based on the patient interview data and the suggested disease name. This medical record is designed so that doctors can correct or add information as needed. After the medical record is completed, the server stores the data in a secure database. This stored data can be accessed from anywhere by healthcare professionals with the necessary access rights.

[0614] As a concrete example, consider a patient who answers questions related to cold symptoms on a terminal. The terminal displays a screen for entering information such as the frequency of coughing and runny nose, and recent body temperature. Once the patient enters these fields, the terminal immediately sends the information to the server. The server analyzes it and suggests a diagnosis such as "cold," "influenza," or "allergic rhinitis." Based on this, an automatically generated medical record is created, which doctors can refer to during consultations to quickly assess the condition and plan treatment.

[0615] This system will improve the efficiency and accuracy of operations in medical settings and reduce the burden on doctors. Furthermore, consistent management and sharing of information will ensure uniformity of treatment across different medical institutions, improving the quality of medical services provided to patients.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The patient, as the user, logs in to a terminal located in the hospital waiting room. The terminal verifies the patient's authentication information on the input screen and authenticates the user.

[0619] Step 2:

[0620] The terminal displays a list of medical questionnaire questions received from the server. The patient, as the user, enters their symptoms and condition in response to the displayed questions.

[0621] Step 3:

[0622] The terminal sends the user's entered response data to the server in real time. The server collects and stores the received data.

[0623] Step 4:

[0624] The server analyzes the received medical interview data using a natural language processing algorithm and extracts possible disease names. It then organizes this information and generates diagnostic support information.

[0625] Step 5:

[0626] The server sends the generated list of disease names to terminals for healthcare professionals. This allows doctors to review the necessary information before a consultation.

[0627] Step 6:

[0628] The server automatically generates editable medical records based on the patient's questionnaire and a list of diseases. These generated medical records are then used by healthcare professionals to make any necessary corrections.

[0629] Step 7:

[0630] The server stores completed medical records in a secure database. This information is accessible only to authorized users and can be shared with other healthcare institutions.

[0631] (Example 1)

[0632] 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".

[0633] A challenge in the medical interview process at healthcare facilities is the inefficient collection, analysis, recording, and sharing of patient information. This increases the burden on medical staff and can lead to a lack of speed and accuracy in diagnosis. Furthermore, consistent information sharing between different healthcare institutions is difficult.

[0634] 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.

[0635] In this invention, the server includes means for executing an automated medical interview process for medical support, means for suggesting disease names from interview data using natural language processing, means for providing automatically generated medical records in an editable format, means for receiving patient input information via a terminal and transmitting it to the server, means for analyzing received data using a generation AI model, and means for displaying disease suggestions on the terminal in real time. This enables the efficient execution of the interview process and the automatic generation and consistent management of medical records. Furthermore, it enables the secure and rapid sharing of medical information, improving the efficiency and accuracy of operations in the medical field.

[0636] A "medical support system" is a set of technologies designed to streamline the medical treatment process in healthcare facilities and support the management and sharing of medical information.

[0637] An "automated medical interview process" is a system that electronically collects information from patients and efficiently provides data to assist in diagnosis.

[0638] "Natural language processing" is a technology that allows computers to understand and interpret human language, and it is used in the analysis of medical interview data.

[0639] "Disease name suggestion" is the process of listing possible disease names based on analyzed patient data.

[0640] "Automated medical records" refer to medical information created electronically based on patient interview data.

[0641] "Saving to a database" is the process of writing data to a digital medium for secure and efficient storage.

[0642] "Access rights management" is a method of restricting access to information to authorized users and is used to maintain the security of that information.

[0643] A "generative AI model" is an artificial intelligence technology that learns patterns based on large amounts of data and uses that knowledge to make predictions and judgments about new data.

[0644] A "terminal" is an electronic device that patients use to input medical records or to check their own data.

[0645] A "prompt" is a text containing questions or instructions given to an AI model, used to guide the model's analysis and generation processes.

[0646] This medical support system is implemented in medical facilities such as hospitals and clinics and consists mainly of users (patients), terminals, and a server. To use the system, patients first log in to a terminal installed in the waiting room upon arriving at the hospital. The terminal displays a series of questions related to the medical interview to the user, who then answers them. This device provides an easy-to-use interface for inputting symptoms and changes in physical condition that the user perceives.

[0647] The terminal transmits the received data to the server in real time. Standard communication technologies, such as the TCP / IP protocol, are used for this data transmission. The server utilizes generative AI models and natural language processing systems to analyze the information received from the patient. This AI model analyzes the data using prompt sentences. An example of a prompt sentence is, "Please suggest possible disease names based on the received symptom data."

[0648] The server returns the suggested disease name based on the analysis results to the terminal and displays it to the user and medical staff. This information can be reviewed by the doctor before the examination begins. Furthermore, the server automatically generates a medical record based on the interview data. This medical record is provided in an editable format, allowing the doctor to review its contents during the examination and add or modify information as needed. This data is stored securely in a database by the server and is accessible to healthcare professionals under appropriate access rights management.

[0649] For example, if a patient enters cold-related symptoms through a terminal, the terminal displays a screen for entering information such as cough frequency and body temperature. Once the user enters the necessary information, the server analyzes it using a generative AI model and suggests disease names such as "cold," "influenza," or "allergic rhinitis." A medical record is automatically generated based on this information and saved, allowing doctors to efficiently determine treatment plans during consultations.

[0650] The introduction of this system will standardize the processes of patient interviews and medical record management in healthcare institutions, improving the efficiency and accuracy of healthcare delivery. Furthermore, it will enable the secure and consistent management and sharing of medical information, allowing for unified treatment across different healthcare institutions.

[0651] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0652] Step 1:

[0653] The terminal displays a questionnaire to the user, who then enters information about their symptoms and physical condition on the screen. This data includes recent body temperature, cough frequency, and other symptoms. The terminal receives this information in input format and prepares it for transmission to the server. The output is the user's input data, which is used in the next processing step.

[0654] Step 2:

[0655] The terminal sends input data obtained from the user to the server. The data is transmitted using a secure and fast communication protocol. The input is the user's symptom data collected on the terminal, and the output is this data received by the server. In this step, the server receives the input data.

[0656] Step 3:

[0657] The server analyzes the user data it receives. This analysis utilizes a generative AI model and natural language processing techniques. The prompt "Please suggest possible disease names based on the received symptom data" is used to instruct the AI ​​model to analyze the data. The input is the user data sent to the server, and the output is a list of disease names suggested as a result of the analysis.

[0658] Step 4:

[0659] The terminal receives the analysis results from the server and displays suggested disease names to the user. The user and medical staff review this information and use it as a reference before the examination. The input is a list of disease names sent from the server, and the output is the suggested results displayed on the terminal. In this step, the server sends the analysis results, and the terminal receives those results.

[0660] Step 5:

[0661] The server automatically generates medical records based on patient interview data and suggested disease information. These records are provided in an editable format, allowing physicians to modify and add to them during consultations. Inputs are patient interview data and suggested disease names, while output is an editable medical record. The process includes the server generating the medical records and saving them to a database.

[0662] Step 6:

[0663] The server stores the generated medical records in a secure database, allowing authorized medical professionals to access these records from anywhere. The input is the medical records generated by the server, and the output is the medical records securely stored in the database. In this step, data storage and access management are performed as concrete actions.

[0664] (Application Example 1)

[0665] 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".

[0666] Traditional healthcare systems lack efficient ways to assess a patient's health status before they visit a medical institution and guide them to the appropriate facility. This makes it difficult for patients to avoid unnecessary tests and visits to the wrong departments, resulting in wasted medical resources. Furthermore, the lack of mechanisms for collecting and securely sharing patient health information with medical institutions in real time can hinder prompt medical responses.

[0667] 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.

[0668] In this invention, the server includes means for executing an automated medical interview process for medical support, means for suggesting disease names from interview data using natural language processing, and means for providing automatically generated medical records in an editable format. This enables efficient response and resource optimization in medical settings by allowing patients to check their health status at home in advance and be guided to visit the appropriate medical institution.

[0669] "Means for implementing an automated medical interview process for medical support" refers to a system that provides a tool for patients to input their health status in a questionnaire format before visiting a medical institution, and automatically compiles that data.

[0670] "A method for proposing disease names from medical interview data using natural language processing" is a technology that analyzes medical interview data collected from patients and lists appropriate disease names based on statistical data and algorithms.

[0671] "Means of providing automatically generated medical records in an editable format" refers to a function that automatically creates medical records based on analysis results and presents them in a format that allows healthcare professionals to input additional information or make corrections as needed.

[0672] "A means of storing medical records in a database and managing access rights" refers to a system that stores generated medical records in a secure location and controls who can access the data and when.

[0673] "Means for collecting and analyzing users' health information from remote measurement devices" refers to technology that captures and aggregates health data from devices used by users, analyzes that data, and evaluates their health status.

[0674] "A means of displaying guidance to appropriate medical institutions on a graphical device based on analysis results" refers to a method of identifying the most suitable medical institution or department for the user based on the analyzed data, and visually displaying that information on the user interface.

[0675] The embodiment of this invention is realized through the cooperation of various hardware and software. It is assumed that the server, terminal, and user device operate in conjunction with each other.

[0676] The server is built on a cloud-based data management system and performs real-time data processing. It primarily uses AWS Lambda as its foundation, with backend logic implemented using Node.js. The collected patient interview data is analyzed using the Google Cloud Natural Language API, a natural language processing platform, to quickly list appropriate disease names.

[0677] The terminal is a device used by patients to input data, primarily functioning as a smartphone or tablet. Flutter is used for cross-platform development, providing a user-friendly interface. Data entered from the terminal is immediately sent to the server, allowing for rapid analysis.

[0678] Users can input their health information into this system and receive appropriate medical guidance based on the analysis results. Remote monitoring devices such as smartwatches are also used to collect health information. This allows for monitoring of daily health conditions and provides guidance information to identify the medical institutions the patient needs.

[0679] As a concrete example, imagine a patient exhibiting cold symptoms entering their symptoms through a terminal app, and the analysis results would list possible diagnoses such as "cold," "influenza," and "allergic rhinitis." In this case, the system would use its database to display relevant medical institutions to the user along with map information.

[0680] As an example of a prompt message for the generative AI model, by inputting information in the format of "Please list the names of diseases that may be related to these symptoms," it becomes possible to receive suggestions for appropriate disease names.

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] Users access a medical questionnaire application using their device and enter their answers to health-related questions. This data includes symptoms, body temperature, and past medical history. This information is transmitted to the server in real time.

[0684] Step 2:

[0685] The server processes the received data using the Google Cloud Natural Language API. This extracts important keywords from the medical history data and calculates possible disease names based on them. The output is a list of suggested disease names, which is immediately sent to the terminal.

[0686] Step 3:

[0687] The user reviews the suggested disease name on their device and enters any additional health information if necessary. This allows for more accurate analysis results. The new information is then sent back to the server.

[0688] Step 4:

[0689] The server performs natural language processing again based on the latest data and updates the list of disease names. Furthermore, it uses the analysis results to automatically generate medical records and converts them into an editable format that doctors can fill in and modify. This is then stored in a database.

[0690] Step 5:

[0691] The server also considers the user's location data and suggests appropriate nearby medical facilities. This includes a process that utilizes a Geographic Information System (GIS) to analyze the user's geographical location and select the most suitable medical facility. The output is sent to the terminal along with map information.

[0692] Step 6:

[0693] Based on the provided medical institution information, users can check maps on their smartphones or display devices and plan their visits. They can also use the information to directly make appointments with medical institutions.

[0694] This allows users to manage their health efficiently at home and receive appropriately guided medical support.

[0695] 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.

[0696] The system according to the present invention has a configuration that combines a user, a terminal, a server, and an emotion engine for the purpose of medical support. In addition to an efficient patient interview process, this system recognizes the patient's emotional state and provides medical support accordingly.

[0697] Patients, acting as users, log in to a terminal installed in the hospital waiting room and enter information about their symptoms. Similar to traditional processes, the terminal sends the information to a server, which uses natural language processing technology to analyze the data and extract possible disease names. This disease information is then sent to the terminals of healthcare professionals to assist in diagnosis.

[0698] The newly added emotion engine analyzes the patient's input speed, content, facial expressions, and tone of voice to assess their emotional state in real time. This information is fed back into the automated interview process, dynamically adjusting the way questions are asked and their order as needed. This allows for changes to questions that help patients relax if they are feeling tense or anxious.

[0699] As a concrete example, suppose a patient user is entering information about their cold symptoms on a terminal, and the emotion engine detects a state of tension. The terminal transmits this information to the server, which then adjusts the questions to promote relaxation. For example, a straightforward question like, "How long have your symptoms lasted?" can be changed to, "Have you done anything to help you relax when your symptoms appeared?"

[0700] Furthermore, the emotional data analyzed by the emotion engine is stored as supplementary information in the user's medical records. This information serves as important data for the examining physician to understand the patient's psychological state.

[0701] These features enhance efficiency and diagnostic accuracy in healthcare settings, improving the quality of services provided to patients. Furthermore, by exhibiting similar effects in healthcare facilities outside of hospitals, it contributes to improving the overall quality of healthcare.

[0702] The following describes the processing flow.

[0703] Step 1:

[0704] The patient, as the user, logs in to a terminal installed in the waiting room of the medical facility. The terminal verifies the patient's authentication information on the input screen and performs user authentication.

[0705] Step 2:

[0706] The terminal displays a questionnaire to the user. A list of questions provided by the server is used. The user enters answers regarding their symptoms and condition.

[0707] Step 3:

[0708] The terminal transmits user input data to the server in real time. Input speed and touch pressure are also recorded during this process.

[0709] Step 4:

[0710] The server analyzes the received data using a natural language processing algorithm. It extracts possible disease names from the analysis results and sends them to the medical professional's terminal.

[0711] Step 5:

[0712] The emotion engine evaluates the user's emotional state based on input information from the device. Emotion recognition is performed through input speed, text content, and facial expression recognition.

[0713] Step 6:

[0714] The device dynamically adjusts the questionnaire questions based on the evaluation results of the emotion engine. For example, if tension is detected, the questions are changed to help the user relax.

[0715] Step 7:

[0716] The server stores emotional data as supplementary information to medical records, allowing doctors to refer to it later during consultations.

[0717] Step 8:

[0718] The server stores the generated medical records in a secure database. These records can be viewed and shared by healthcare professionals with access rights.

[0719] (Example 2)

[0720] 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".

[0721] In modern healthcare settings, efficient and accurate diagnostic processes are required, but the consistency and accuracy of diagnoses are challenged because patients' emotional states can influence their answers during interviews. Furthermore, the proper storage and management of medical information and patients' emotional states, as well as efficient information sharing with healthcare professionals in remote locations, are also challenges.

[0722] 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.

[0723] In this invention, the server includes means for inputting patient information and executing an automated medical interview process, means for suggesting a medical condition from the interview data using natural language processing, and means for analyzing the patient's emotional state from the input data and dynamically adjusting the interview process. This enables appropriate interviews tailored to the patient's emotional state, improving the accuracy of diagnosis and the efficiency of the medical setting. Furthermore, it allows for the secure storage of medical information, including emotional data, and enables efficient information sharing with medical professionals in remote locations.

[0724] "Patient information" refers to identifiable medical data, including data on a patient's symptoms, diagnosis, or medical history.

[0725] An "automated medical interview process" is a procedure in which the system automatically conducts an appropriate medical interview based on the patient's input of their symptoms.

[0726] "Natural language processing" is a technology that allows computers to understand and analyze the language that humans speak naturally.

[0727] "Medical status" refers to the patient's health condition and potential illnesses based on interview data.

[0728] "Emotional state" refers to the patient's psychological state and includes states such as tension, anxiety, and relaxation.

[0729] "Dynamic adjustment" means that the system automatically changes to the optimal state according to the situation and conditions.

[0730] "Medical information" refers to all medical data related to a patient, including diagnostic results, treatment plans, and emotional data.

[0731] A "database" is a system that systematically stores large amounts of information, making it easy to access and search for.

[0732] This system, designed for medical support, consists of a user, terminal, server, and emotion engine. The main hardware includes an input terminal for user use and a server for data processing. The software includes a natural language processing engine and an emotion recognition engine.

[0733] The terminal provides an interface for the patient (user) to input information such as their symptoms. Users can input information into the terminal using methods such as a touchscreen or voice input. The entered information is securely encrypted and transmitted to the server.

[0734] The server analyzes this input data using natural language processing techniques. For example, it can use Python's natural language processing libraries, such as NLTK or spaCy. This analysis suggests possible medical conditions based on the symptoms. The suggested information is then provided to the terminal of the diagnostician.

[0735] Furthermore, the emotion engine analyzes elements such as the user's input speed, voice, and facial expressions in real time to assess their emotional state. Based on this assessment, the server can dynamically adjust the interview process. For example, if the user is tense, the questions can be changed to encourage relaxation.

[0736] As a concrete example, consider a case where a user enters symptoms of a cold. In this case, if the emotion engine detects the user's anxiety, the server will adjust the question to something like, "Did you do anything to relax when you experienced symptoms?" This process allows the user to participate in the consultation with greater peace of mind.

[0737] Examples of prompt messages include the following:

[0738] "If a user is feeling nervous, suggest some questions that might help them relax."

[0739] In this way, the system provides efficient and accurate medical support while responding to the patient's emotions.

[0740] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0741] Step 1:

[0742] Users log in to a terminal in the waiting room and use a dedicated interface to enter their symptoms and basic personal information. This includes the patient's name, age, and specific symptoms (e.g., cough, fever). The terminal encrypts the entered information using the SSL / TLS protocol and sends it to the server.

[0743] Step 2:

[0744] The server receives data sent from the terminal. It analyzes the input data using a natural language processing engine, such as Python's NLTK or spaCy. Through analytics, it extracts possible medical conditions (e.g., cold, flu) from the entered symptoms and sends this information to the medical professional's terminal for diagnostic support. The output is a list of predicted diseases.

[0745] Step 3:

[0746] The emotion engine collects data such as typing speed, keyboard sounds, and facial expressions and voice tone from the camera and microphone while the user is typing on the device. Based on this data, the emotion engine evaluates the user's emotional state and identifies emotional states such as "tense" or "relaxed." The evaluation results are sent to the server.

[0747] Step 4:

[0748] The server receives emotional assessment data from the emotion engine and dynamically adjusts the ongoing interview process. Based on the emotional data, it performs actions such as changing the order of questions or softening the questions to encourage relaxation. A specific example of this action would be changing the question from "How often do your symptoms occur?" to "Have you done anything to relax when your symptoms occurred?"

[0749] Step 5:

[0750] The server ultimately stores the analyzed emotional data, along with the patient's medical history, as a digitized medical record. This record includes elements such as the patient's symptom information, predicted disease name, and emotional state. The stored data is referenced in subsequent diagnostic processes and treatment planning. As an output, a complete medical record is properly stored and accessible.

[0751] (Application Example 2)

[0752] 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".

[0753] Current medical support systems often struggle to consider patients' emotional states, which can compromise accuracy and efficiency. Furthermore, there is a lack of flexible systems in place to enhance the quality of psychological care in nursing facilities. Therefore, there is a need for systems that enable medical and care support tailored to the psychological state of patients and residents of nursing facilities.

[0754] 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.

[0755] In this invention, the server includes means for executing an automated medical interview process for medical support, means for suggesting disease names from interview data using natural language processing, and means for evaluating the psychological state of the subject using emotion analysis technology. This makes it possible to dynamically provide appropriate responses according to the psychological state of patients or residents of care facilities.

[0756] "Medical support" refers to technologies and processes used in medical settings to improve the efficiency and accuracy of diagnosis and treatment.

[0757] The "automated medical interview process" refers to a procedure in which the patient enters their symptoms into a terminal, and the system automatically proceeds with the interview based on that information.

[0758] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0759] "Suggesting a disease name" refers to the system presenting possible disease names based on the patient's medical history data.

[0760] "Emotional analysis technology" refers to methods for analyzing and evaluating an individual's emotional state from digital data.

[0761] "Assessing a psychological state" refers to measuring and judging an individual's mental health and emotional responses.

[0762] "Care support" refers to the processes and techniques used to provide physical and mental care to residents in care facilities.

[0763] "Dynamic adjustment" refers to changing the response in real time according to the situation.

[0764] A "system" refers to a structure in which multiple elements or processes are combined to achieve a specific purpose.

[0765] To implement the present invention, the medical support system consists of a server, a terminal, and a user.

[0766] The server first uses natural language processing technology to analyze the medical questionnaire data sent from the terminal, extracts possible disease names, and notifies the user. This improves the efficiency of diagnosis.

[0767] The terminal not only collects data entered by the user and sends it to the server, but also utilizes emotion analysis technology. It acquires various data such as the user's input speed, tension, facial expressions, and voice tone, and evaluates the user's psychological state in real time. This evaluation is provided by the emotion analysis engine. Based on this information, the terminal dynamically adjusts the content and order of the questionnaire questions, providing the user with a relaxing environment.

[0768] As a concrete example, consider a scenario where a user is entering information about cold symptoms on their device, and the emotion analysis engine detects a state of tension. In this case, the server changes the question, changing "How long have your symptoms been lasting?" to "Have you done anything to relax when the symptoms appeared?", thereby alleviating the user's tension.

[0769] Furthermore, the data obtained through emotion analysis is stored as medical records and used by healthcare professionals as important information to understand the user's psychological state. Based on this information, it becomes possible for nursing homes to provide care that is tailored to the emotions of their residents.

[0770] An example of a prompt might be, "Please suggest a suitable conversation topic if the sentiment analysis indicates high stress levels."

[0771] As described above, this system enables medical and nursing care support that takes into account the psychological state of individuals in various settings, and as a result, aims to improve the overall quality of medical and nursing care.

[0772] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0773] Step 1:

[0774] The device acquires data about symptoms and health conditions entered by the user. This input includes text descriptions of symptoms, voice tone, and videos of facial expressions. The device preprocesses this data and converts it into a format suitable for analysis.

[0775] Step 2:

[0776] The terminal prepares to send the acquired data to the server. Text data is prepared for natural language processing, and video and audio data is converted into a format suitable for the sentiment analysis engine. The input is processed data, and the output is the data sent to the server.

[0777] Step 3:

[0778] The server receives data sent from the terminal and analyzes the text data using a natural language processing engine. Here, it extracts possible disease names and notifies healthcare professionals of this information. The output is a list of disease names.

[0779] Step 4:

[0780] The server uses an emotion analysis engine to evaluate the user's emotional state. In this step, audio and video data are analyzed to determine the user's emotional state, such as whether they are tense or relaxed. The input is audio and video data, and the output is the evaluation result of the emotional state.

[0781] Step 5:

[0782] The server dynamically adjusts the questionnaire based on the results of the emotion analysis. For example, if the patient is highly anxious, the server will soften the wording of the questions. The output is a list of the adjusted questions.

[0783] Step 6:

[0784] The device presents the user with carefully designed questions and displays a form for entering answers. This process reduces the user's psychological stress and encourages them to answer in a relaxed state. The output is the user's response data.

[0785] Step 7:

[0786] The server ultimately formats the data returned by the user as a medical record and stores it in a database. Emotional data is also recorded, which can be used by healthcare professionals for future diagnoses. The output is a set of medical record data.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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."

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] The following is further disclosed regarding the embodiments described above.

[0809] (Claim 1)

[0810] A means of executing an automated medical interview process for medical support,

[0811] A method for proposing disease names from medical interview data using natural language processing,

[0812] A means of providing automatically generated medical records in an editable format,

[0813] A means of storing medical records in a database and managing access rights,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, comprising means for securely sharing medical information with medical professionals in remote locations.

[0817] (Claim 3)

[0818] The system according to claim 1, comprising means for controlling access to a patient's medical information based on unique identification information.

[0819] "Example 1"

[0820] (Claim 1)

[0821] A means of executing an automated medical interview process for medical support,

[0822] A method for proposing disease names from medical interview data using natural language processing,

[0823] A means of providing automatically generated medical records in an editable format,

[0824] A means of storing medical records in a database and managing access rights,

[0825] A means of receiving information entered by the patient via a terminal and sending it to a server,

[0826] A means of analyzing received data using a generative AI model,

[0827] A means of displaying disease suggestions on a terminal in real time,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, comprising means for securely sharing medical information with medical professionals in remote locations.

[0831] (Claim 3)

[0832] The system according to claim 1, comprising means for controlling access to a patient's medical information based on unique identification information.

[0833] "Application Example 1"

[0834] (Claim 1)

[0835] A means of executing an automated medical interview process for medical support,

[0836] A method for proposing disease names from medical interview data using natural language processing,

[0837] A means of providing automatically generated medical records in an editable format,

[0838] A means of storing medical records in a database and managing access rights,

[0839] A means of collecting and analyzing users' health information from remote monitoring devices,

[0840] A means for displaying guidance to an appropriate medical institution on a graphical device based on the analysis results,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, comprising means for securely sharing medical information with medical professionals in remote locations.

[0844] (Claim 3)

[0845] The system according to claim 1, comprising means for controlling access to a patient's medical information based on unique identification information.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] A means of inputting patient information and executing an automated medical interview process,

[0849] A method for proposing medical status from medical interview data using natural language processing,

[0850] A means of analyzing the patient's emotional state from input data and dynamically adjusting the interview process,

[0851] A means of recording emotional data along with medical information and providing it in an editable format,

[0852] A means of storing medical information in a database and managing access rights,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, comprising means for securely sharing medical information with medical professionals in remote locations and linking emotional data.

[0856] (Claim 3)

[0857] The system according to claim 1, comprising means for controlling access to a patient's medical information based on unique authentication information.

[0858] "Application example 2 when combining with an emotional engine"

[0859] (Claim 1)

[0860] A means of executing an automated medical interview process for medical support,

[0861] A method for proposing disease names from medical interview data using natural language processing,

[0862] A means of providing automatically generated medical records in an editable format,

[0863] A means of storing medical records in a database and managing access rights,

[0864] A means of evaluating the psychological state of subjects using emotion analysis technology and dynamically adjusting the content of the questionnaire based on that evaluation,

[0865] A means of using data based on emotion analysis in care support,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, comprising means for securely sharing medical information with medical professionals in remote locations.

[0869] (Claim 3)

[0870] The system according to claim 1, comprising means for controlling access to a patient's medical information based on unique identification information. [Explanation of symbols]

[0871] 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 executing an automated medical interview process for medical support, A method for proposing disease names from medical interview data using natural language processing, A means of providing automatically generated medical records in an editable format, A means of storing medical records in a database and managing access rights, A system that includes this.

2. The system according to claim 1, comprising means for securely sharing medical information with medical professionals in remote locations.

3. The system according to claim 1, comprising means for controlling access to a patient's medical information based on unique identification information.

Images