system

Abstract

We provide the system. [Solution] A means by which the user inputs symptoms into an input terminal, The server uses a natural language processing engine to analyze the input symptoms and extract keywords. A server searches a disease database based on extracted keywords and lists related diseases. The server provides a means to assess the risk level of listed diseases and also provides information on other related symptoms. A means by which the server recommends medical institutions capable of providing specialized treatment based on the user's location information, The server provides a means of offering general treatment guidelines for each disease, The means by which the terminal displays this information to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 modern times, it is extremely important to identify the cause of symptoms and respond promptly at an appropriate medical institution. However, it is difficult for ordinary users to accurately judge their own symptoms, and the process of selecting an appropriate medical institution is also complicated. Therefore, there is a possibility of misselecting a medical institution or delaying treatment due to inappropriate self-diagnosis. To solve this problem, there is a need for a system that automatically analyzes the symptom information input by users, presents the possibility of appropriate diseases, and further provides the selection of a medical institution for early response and treatment guidelines.

Means for Solving the Problems

[0005] To solve the above-mentioned problems, the present invention provides the following means. First, means for the user to input symptoms into an input terminal. Next, means for the server to analyze the input symptoms and extract keywords using a natural language processing engine. Furthermore, means for the server to search a disease database based on the extracted keywords and list related diseases. For the listed diseases, means for the server to evaluate the degree of risk and provide other related symptoms. In addition, means for the server to recommend medical institutions capable of providing specialized treatment based on the user's location information. Finally, the system provides means for the server to provide general treatment guidelines for each disease, and means for the terminal to display this information to the user. This system allows the user to easily obtain information about possible diseases suitable for their symptoms, as well as appropriate medical institutions and treatment guidelines.

[0006] A "user" is an individual who uses the system to input symptoms and receives the information provided.

[0007] A "terminal" is a device used by users to input symptoms and display the provided information.

[0008] A "server" is a computing system that forms the core of a system, performing data processing, analysis, and information provision.

[0009] A "natural language processing engine" is a software tool that analyzes input text and understands its meaning.

[0010] "Keywords" are important words extracted by a natural language processing engine from the symptoms entered by the user.

[0011] A "disease database" is a collection of data that stores information about various diseases.

[0012] "Disease" is a general term for illnesses and ailments that cause abnormalities in the state or function of the body.

[0013] "Risk level" is an assessment of the risk associated with a particular disease.

[0014] "Location information" refers to geographical data that indicates the user's current location.

[0015] A "specialized medical institution" is a hospital or clinic with doctors who have specialized knowledge about specific diseases or symptoms.

[0016] "Treatment guidelines" are guidelines outlining standard treatment methods and countermeasures for a particular disease.

[0017] "Display" refers to the act of providing information processed by a system to the user visually.

[0018] A "Geographic Information System" is a system for managing and analyzing geographical data based on location information. [Brief explanation of the drawing]

[0019] [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]It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Further, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] 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).

[0026] 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."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] This invention is a system that lists possible diseases based on symptom information entered by the user, recommends appropriate medical institutions based on that list, and provides general treatment guidelines. The aim of this system is to help users quickly find out specific ways to deal with their symptoms.

[0041] System Configuration

[0042] This system consists mainly of the following elements.

[0043] User terminal: A device that allows the user to input symptoms and receive information. Examples include smartphones and personal computers.

[0044] Server: A central processing system that processes, analyzes, and provides information about data.

[0045] Natural Language Processing Engine: A software tool that analyzes symptoms entered by the user and extracts their meaning.

[0046] Disease database: A collection of data containing information about various diseases.

[0047] Geographic Information System: A system that searches for appropriate medical facilities based on the user's location information.

[0048] Program processing

[0049] User input

[0050] The user enters specific symptoms, such as "My back hurts," into the device. The device immediately sends the entered information to the server.

[0051] Natural Language Processing

[0052] The server passes the received text data to a natural language processing engine, which extracts keywords such as "back" and "pain." This process helps the system understand the entered symptoms and organize related information.

[0053] Disease Database Search

[0054] The server uses the extracted keywords to search the disease database. For example, diseases related to "back pain" might include "muscle pain," "lumbago," "kidney stones," and "shingles."

[0055] Risk assessment and symptom provision

[0056] For each listed disease, the server assesses its risk level. For example, "muscle pain" is rated as low risk, "lower back pain" as moderate risk, "kidney stones" as moderate risk, and "shingles" as high risk. Other symptoms associated with each disease (e.g., blood in the urine, rash, etc.) are also provided.

[0057] Recommended medical institutions

[0058] When a user allows their location to be shared, this location information is sent to the server via their device. The server uses a geographic information system to search for and recommend specialized medical facilities near the user's current location. For example, "Orthopedics," "Urology," and "Dermatology" may be listed.

[0059] Provision of treatment guidelines

[0060] The server retrieves general treatment guidelines for each disease from the database. For example, it might suggest that warm compresses and light exercise are effective for "muscle pain," rest and painkillers for "lumbago," hydration and a doctor's diagnosis for "kidney stones," and antiviral medication for "shingles."

[0061] Displaying Results

[0062] Ultimately, the terminal visually displays these processing results to the user. The user can see at a glance a list of possible diseases, risk assessments, related symptoms, recommended medical facilities, and treatment guidelines, enabling them to take quick and appropriate action.

[0063] Specific example

[0064] Simply by having the user type "My back hurts," the server assesses the likelihood and risk level of diseases related to the symptoms, and also suggests any other accompanying symptoms. Furthermore, it searches for appropriate medical facilities based on the user's location and provides concise and practical treatment guidelines. This entire process allows users to quickly receive appropriate medical advice.

[0065] Thus, the system of the present invention can support self-diagnosis of symptoms and improve the efficiency of selecting medical institutions.

[0066] The following describes the processing flow.

[0067] Step 1:

[0068] The user enters specific symptoms into the device, such as "My back hurts."

[0069] Step 2:

[0070] The terminal receives the entered symptoms and sends them to the server.

[0071] Step 3:

[0072] The server receives the text data and passes it to a natural language processing engine for analysis.

[0073] Step 4:

[0074] The server uses a natural language processing engine to extract keywords such as "back" and "painful."

[0075] Step 5:

[0076] The server searches the disease database based on the extracted keywords and lists related diseases.

[0077] Step 6:

[0078] The server assesses the risk level for each listed disease.

[0079] For example, "muscle pain (low risk)", "sudden lower back pain (medium risk)", "kidney stones (medium risk)", and "shingles (high risk)".

[0080] Step 7:

[0081] The server lists other symptoms associated with each disease.

[0082] For example, "muscle pain: persistent pain after a specific type of exercise," "lower back pain: sharp pain after a sudden movement," "kidney stones: blood in the urine, frequent urination," and "shingles: rash and pain."

[0083] Step 8:

[0084] The device requests the user's location information.

[0085] Step 9:

[0086] The user grants permission to share their location information.

[0087] Step 10:

[0088] The device sends the user's location information to the server.

[0089] Step 11:

[0090] The server uses geographic information systems based on location data to search for nearby specialized medical facilities.

[0091] For example, "orthopedics," "urology," and "dermatology."

[0092] Step 12:

[0093] The server retrieves general treatment guidelines for each disease from the database.

[0094] For example, "Muscle pain: Warm compresses and light exercise," "Lower back pain: Rest and pain medication," "Kidney stones: Hydration and doctor's diagnosis," "Shingles: Early administration of antiviral drugs."

[0095] Step 13:

[0096] The server sends the processing results to the terminal.

[0097] Step 14:

[0098] The device displays to the user a list of possible illnesses, a risk assessment, related symptoms, recommended medical facilities, and treatment guidelines.

[0099] Through this process, users can obtain appropriate medical information and countermeasures tailored to their symptoms.

[0100] (Example 1)

[0101] 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."

[0102] In modern society, it is crucial for users to obtain timely and appropriate medical information regarding their own symptoms. However, selecting a specialized medical institution and obtaining appropriate treatment guidelines often requires considerable time and effort. Traditional methods may rely on unreliable information for self-diagnosis, potentially leading to the selection of inappropriate medical institutions or the adoption of incorrect treatment plans. This poses a risk to the user's health.

[0103] 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.

[0104] In this invention, the server includes means for the user to input symptoms into an input device, means for analyzing the input symptoms using a natural language processing system and extracting keywords, means for searching a specialized database based on the extracted keywords and listing related diseases, means for evaluating the risk level of the listed diseases and providing other related symptoms, means for acquiring the user's location information and recommending medical institutions capable of providing specialized treatment, and means for providing general treatment guidelines for each disease. As a result, the user can quickly obtain appropriate medical information for their symptoms, and can efficiently select an appropriate medical institution and decide on a treatment plan.

[0105] An "input device" refers to a device used by a user to input symptoms. Specifically, this includes smartphones and personal computers.

[0106] A "computer on a network" refers to a central processing system that performs data processing, analysis, and information provision. It is commonly known as a server.

[0107] A "natural language processing system" refers to a software tool that analyzes text data entered by a user and extracts its meaning.

[0108] "Keywords" refer to important words or phrases extracted from the entered text. This helps identify the symptoms.

[0109] A "specialized database" refers to a collection of data containing information about various diseases. This includes information related to medical care and health.

[0110] "Listing" refers to displaying items related to a specific keyword in a list format.

[0111] "Risk level" refers to a standard for evaluating the risk level for a particular disease. It ranges from low risk to high risk.

[0112] "Location information" refers to data that indicates the user's current location. It is obtained using technologies such as GPS.

[0113] A "medical institution capable of providing specialized treatment" refers to a medical facility that has the ability to provide specialized treatment for a particular disease. This includes hospitals and clinics.

[0114] "Treatment guidelines" refer to information that outlines general treatment methods and coping strategies for specific diseases. This includes helpful advice for users.

[0115] This invention begins with the user inputting symptoms using an input device. A smartphone or personal computer can be used as the input device. The user inputs specific symptoms, such as "my back hurts," and the device then transmits this information to a server.

[0116] The server passes the received text data to a natural language processing system (e.g., a general-purpose natural language processing engine). This system extracts keywords from the input text and analyzes the meaning of the symptoms. For example, it can extract the keywords "back" and "pain" from the text.

[0117] The server then uses the extracted keywords to search specialized databases (e.g., medical databases). This search lists related conditions such as "muscle pain," "lower back pain," "kidney stones," and "shingles."

[0118] For each listed disease, the server assesses the risk level and provides information on other associated symptoms. For example, it might rate "muscle pain" as low risk, "lumbago" as moderate risk, "kidney stones" as moderate risk, and "shingles" as high risk. It also provides additional information on other associated symptoms, such as "hematuria" or "rash."

[0119] If the user allows location information to be shared, the device uses its GPS function to obtain information about its current location and sends it to the server. The server uses a geographic information system (e.g., a general-purpose geographic information system) to search for medical institutions near the user's current location that can provide specialized treatment. As a result, it recommends medical institutions such as "orthopedics," "urology," and "dermatology."

[0120] Next, the server retrieves general treatment guidelines for each disease from specialized databases (e.g., medical websites). Specifically, it suggests that warm compresses and light exercise are effective for "muscle pain," rest and painkillers for "lumbago," hydration and a doctor's diagnosis for "kidney stones," and antiviral medication for "shingles."

[0121] Ultimately, the device visually displays this information to the user. The user can see at a glance a list of possible illnesses, their risk levels, associated symptoms, recommended healthcare facilities, and treatment guidelines, enabling them to take quick and appropriate action.

[0122] Specific example

[0123] Simply by having the user type "My back hurts," the server assesses the likelihood and risk level of illnesses related to the symptoms, and also suggests any other accompanying symptoms. Furthermore, it searches for appropriate medical facilities based on the user's location and provides practical treatment guidelines. This entire process allows users to quickly receive appropriate medical advice.

[0124] Example of a prompt

[0125] "Please tell me about possible illnesses that could be causing back pain. Also, please explain the risk level, related symptoms, and treatment options in detail."

[0126] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0127] Step 1: User Input

[0128] The user uses an input device (smartphone or computer) to enter specific symptoms, such as "my back hurts." This input data is sent to the terminal in text format.

[0129] Step 2: Send a text message

[0130] The terminal immediately sends the entered text data to the server. This transmission is secure using the HTTPS protocol.

[0131] Input: Symptom text entered by the user (e.g., "My back hurts")

[0132] Output: Text data sent to the server

[0133] Step 3: Natural Language Processing

[0134] The server passes the received text data to a natural language processing system (e.g., a general-purpose natural language processing engine). The natural language processing system extracts important keywords (e.g., "back," "painful") from the input text and analyzes the context.

[0135] Input: Submitted text data

[0136] Output: Extracted keywords and contextual information

[0137] Step 4: Disease Database Search

[0138] The server uses the extracted keywords to search specialized databases (e.g., medical databases) and lists diseases associated with those keywords. For example, it might find diseases such as "muscle pain," "lower back pain," "kidney stones," and "shingles."

[0139] Input: Extracted keywords (e.g., "back", "pain")

[0140] Output: List of listed diseases

[0141] Step 5: Risk assessment and suggestion of additional symptoms

[0142] The server assesses the risk level for each listed disease and also provides additional related symptoms. A risk assessment algorithm is used for the assessment, and related symptoms such as "hematuria" and "rash" are also presented.

[0143] Input: List of listed diseases

[0144] Output: Risk assessment and associated additional symptoms

[0145] Step 6: Location Information Collection

[0146] When a user allows location information to be shared, the device uses its GPS function to obtain information about its current location and sends that data to the server. This data transmission also uses the HTTPS protocol.

[0147] Input: Permission to provide location information

[0148] Output: Acquired location data

[0149] Step 7: Proposal of Recommended Medical Institutions

[0150] The server uses the collected location information to access a geographic information system (e.g., a general-purpose geographic information system) to search for medical institutions near the user's current location that can provide specialized treatment. For example, medical institutions specializing in "orthopedics," "urology," and "dermatology" may be listed.

[0151] Input: Acquired location data

[0152] Output: List of recommended specialist medical institutions

[0153] Step 8: Provide general treatment guidelines

[0154] The server retrieves general treatment guidelines for each disease from specialized databases (e.g., specialist medical websites). Specifically, it recommends warm compresses and light exercise for muscle pain, rest and painkillers for acute lower back pain, hydration and a doctor's diagnosis for kidney stones, and antiviral medication for shingles.

[0155] Input: List of listed diseases

[0156] Output: Information on general treatment guidelines

[0157] Step 9: Displaying the results

[0158] Finally, the device visually displays these processing results to the user. The user can see at a glance a list of possible illnesses, risk assessments, related additional symptoms, recommended healthcare facilities, and treatment guidelines. This enables quick and appropriate medical consultation and response.

[0159] Input: All search and evaluation data

[0160] Output: Comprehensive information displayed to the user.

[0161] (Application Example 1)

[0162] 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."

[0163] Existing symptom diagnosis systems are limited to providing information such as disease listings and treatment guidelines, lacking a means to quickly and centrally manage subsequent medical service use, particularly payments for consultations and medication purchases. This often requires users to perform multiple operations, making the process cumbersome. To solve this problem, a system is needed that seamlessly integrates the user's process from entering symptoms to payment at the medical institution.

[0164] 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.

[0165] In this invention, the server includes means for enabling the user to make electronic payments for medical consultations and medication purchases, means for using a geographic information system to obtain the user's location information, and means for enabling electronic payments at recommended medical institutions. This allows the user to quickly and centrally perform a series of processes, from entering symptoms to paying for medical consultations.

[0166] An "input terminal" is a device used by users to input symptom information, and includes smartphones and personal computers.

[0167] A "natural language processing engine" is a software tool that analyzes text data entered by a user and extracts keywords.

[0168] A "disease database" is a collection of data containing detailed information about various diseases.

[0169] "Risk level" indicates an assessment of the risk for each listed disease, ranging from low risk to high risk.

[0170] "Location information" refers to data indicating the user's current location, and this information is used to search for appropriate medical facilities.

[0171] The method for recommending "medical institutions" is a function that suggests hospitals and clinics capable of providing specialized treatment based on the user's location information.

[0172] "Treatment guidelines" are information that provides general treatment methods and coping strategies for specific diseases.

[0173] "Electronic payment" refers to a method of making payments online for things like medical consultation fees and medication purchases.

[0174] A "Geographic Information System" is a system that acquires a user's location information and handles geographical data.

[0175] "Detailed data" refers to data that includes specific information about the listed diseases.

[0176] A "database for managing payment information" is a collection of data that centrally manages payment information related to medical consultation fees, medication purchases, and other related matters.

[0177] This invention integrates electronic payment functionality into a system that lists possible diseases based on symptom information entered by the user, recommends appropriate medical institutions based on that list, and provides general treatment guidelines. This system consists of the following main elements:

[0178] System Configuration

[0179] 1. User terminal: This is a device used by the user to input symptoms and receive diagnostic results, recommended medical institutions, and treatment guidelines. Examples include smartphones and personal computers.

[0180] 2. Server: A central processing system that processes, analyzes, and provides information on data. This server includes the following hardware and software:

[0181] 3. Natural Language Processing Engine: This is a software tool that analyzes symptoms entered by the user and extracts necessary keywords. Specifically, libraries such as spaCy and NLTK are used.

[0182] 4. Disease Database: This is a collection of data that stores information about various diseases. This allows users to search for related diseases based on the symptoms they enter.

[0183] 5. Geographic Information Systems: These are systems that acquire a user's location information and use it to search for appropriate medical facilities. The Geopy library is a concrete example.

[0184] 6. Electronic Payment System: This is a payment function that allows users to pay for medical consultations and purchase medications online. The Stripe API, for example, will be implemented.

[0185] System operation

[0186] 1. Inputting symptom information: The user enters specific symptoms (e.g., "My back hurts") on the device. The device immediately sends this information to the server.

[0187] 2. Natural Language Processing: The server passes the received text data to a natural language processing engine, which extracts keywords. This process helps the server understand the entered symptoms and organize relevant information.

[0188] 3. Disease Database Search: The server uses the extracted keywords to search the disease database and lists the relevant diseases.

[0189] 4. Risk assessment and provision of related symptoms: The server assesses the risk level for each listed disease and also provides other symptoms associated with each disease (e.g., blood in the urine, rash, etc.).

[0190] 5. Suggestion of Recommended Medical Institutions: When a user provides location information, the server uses a geographic information system to search for and recommend appropriate medical institutions.

[0191] 6. Provision of treatment guidelines: The server retrieves general treatment guidelines for each disease from the database and provides them to the user.

[0192] 7. Electronic payment function: Allows users to pay for medical consultations and medication purchases at recommended medical institutions using electronic payment.

[0193] Specific example

[0194] Simply by the user typing "back pain," the system provides a list of possible illnesses (e.g., "muscle pain," "lumbago," "kidney stones"), assesses their risk level, and suggests other related symptoms. Furthermore, it searches for appropriate medical facilities based on location information and offers the option to pay for consultations electronically.

[0195] Examples of prompt statements

[0196] The user entered "My back hurts." Display a list of possible illnesses, risk assessment, related symptoms, geographically nearby medical facilities, and general treatment guidelines, and also provide an option to pay for the consultation using electronic payment.

[0197] In this way, users can complete the entire process from input to payment quickly and centrally, making it possible to access medical services more smoothly.

[0198] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0199] Step 1: The user enters the symptoms into the device and sends them.

[0200] Users use devices such as smartphones or computers to input specific symptoms (for example, "My back hurts") and submit this information. The input data is sent to the server in text format.

[0201] Step 2: The server analyzes the symptoms using a natural language processing engine and extracts keywords.

[0202] The server passes the received text data to a natural language processing engine (e.g., spaCy or NLTK) to extract keywords related to the symptoms (e.g., "back," "pain"). This analysis converts the symptom information into structured data.

[0203] Step 3: The server searches the disease database based on the extracted keywords and lists the relevant diseases.

[0204] The server uses the extracted keywords to search the disease database and lists related diseases (e.g., "muscle pain," "lower back pain," "kidney stones," etc.). This list is generated and passed to the next processing step.

[0205] Step 4: Assess the risk level of the listed diseases and provide any other related symptoms.

[0206] The server assesses the risk level for each listed disease (from low risk to high risk) and provides other symptoms associated with each disease (e.g., "fatigue" for "muscle pain," "hematuria" for "kidney stones"). This information is then compiled together.

[0207] Step 5: The server searches for and recommends appropriate medical facilities based on the user's location information.

[0208] When a user allows location information to be shared, the location information sent from the device is sent to the server. The server uses a geographic information system (e.g., Geopy) to search for medical facilities near the user's current location and generates a list of recommended medical facilities.

[0209] Step 6: The server provides general treatment guidelines for each disease.

[0210] The server retrieves and compiles general treatment guidelines for each disease from a database (for example, "warm compresses" and "light exercise" for "muscle pain," and "rest and painkillers" for "lumbago").

[0211] Step 7: The device displays this information to the user.

[0212] The terminal visually displays information transmitted from the server to the user (a list of possible diseases, risk assessment, related symptoms, recommended medical facilities, and treatment guidelines). Based on this information, the user can decide on their next course of action.

[0213] Step 8: The user pays for consultation fees and medication purchases at the recommended medical institution using electronic payment.

[0214] After a user receives a medical consultation or purchases medication, they make a payment using an electronic payment system (e.g., Stripe). This operation is also performed from the terminal, and the payment information is sent to and recorded on the server.

[0215] In this way, a series of steps allows users to handle everything from entering their symptoms to paying for medical consultations in one place.

[0216] 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.

[0217] This invention is a system that lists possible diseases based on symptom information entered by the user, recommends appropriate medical institutions based on that list, and also provides general treatment guidelines. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it achieves a more user-friendly approach.

[0218] System Configuration

[0219] This system mainly consists of the following elements:

[0220] User terminal: A device that allows the user to input symptoms and receive information. Examples include smartphones and personal computers.

[0221] Server: A central processing system that processes, analyzes, and provides information about data.

[0222] Natural Language Processing Engine: A software tool that analyzes symptoms entered by the user and extracts their meaning.

[0223] Emotion engine: A system that recognizes emotions based on user input and adjusts its response accordingly.

[0224] Disease database: A collection of data containing information about various diseases.

[0225] Geographic Information System: A system that searches for appropriate medical facilities based on the user's location information.

[0226] Program processing

[0227] User input

[0228] The user enters specific symptoms, such as "My back hurts," into the device. The device immediately sends the entered information to the server.

[0229] Natural Language Processing

[0230] The server passes the received text data to a natural language processing engine, which extracts keywords such as "back" and "pain." This process helps the system understand the entered symptoms and organize related information.

[0231] emotion recognition

[0232] The server passes the user's input data to the emotion engine, which recognizes the user's emotional state (e.g., stress, anxiety, calmness, etc.). The emotion engine analyzes the emotional state and sends the results back to the server.

[0233] Disease Database Search

[0234] The server uses the extracted keywords and recognized emotional states to search a disease database and list related illnesses. For example, illnesses related to "back pain" might include "muscle pain," "lumbago," "kidney stones," and "shingles."

[0235] Risk assessment and symptom provision

[0236] For each listed disease, the server assesses its risk level. For example, "muscle pain" is rated as low risk, "lower back pain" as moderate risk, "kidney stones" as moderate risk, and "shingles" as high risk. Other symptoms associated with each disease (e.g., blood in the urine, rash, etc.) are also provided.

[0237] Emotional response

[0238] The server adjusts how information is presented and the content of advice based on the recognized emotional state of the user. For example, if the user is feeling anxious, it prioritizes providing reassuring messages and support information.

[0239] Recommended medical institutions

[0240] When a user allows their location to be shared, this location information is sent to the server via their device. The server uses a geographic information system to search for and recommend specialized medical facilities near the user's current location. For example, "Orthopedics," "Urology," and "Dermatology" may be listed.

[0241] Provision of treatment guidelines

[0242] The server retrieves general treatment guidelines for each disease from the database. For example, it might suggest that warm compresses and light exercise are effective for "muscle pain," rest and painkillers for "lumbago," hydration and a doctor's diagnosis for "kidney stones," and antiviral medication for "shingles."

[0243] Displaying Results

[0244] Ultimately, the terminal visually displays these processing results to the user. The user can see at a glance a list of possible diseases, risk assessments, related symptoms, recommended medical facilities, and treatment guidelines, enabling them to take quick and appropriate action.

[0245] Specific example

[0246] When a user enters "My back hurts," the server uses a natural language processing engine to analyze the symptoms and an emotion engine to recognize the user's emotions. It then lists possible illnesses from a disease database and provides a risk assessment and symptom information tailored to the user's emotions. Based on location information, it searches for appropriate medical facilities and presents treatment guidelines suited to the user. To ensure the user feels comfortable seeking medical attention, it also provides emotionally-based advice and support information.

[0247] Thus, the system of the present invention can support self-diagnosis of symptoms while also taking into consideration the user's emotional state, and can improve the efficiency of selecting a medical institution.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The user enters specific symptoms into the device, such as "My back hurts."

[0251] Step 2:

[0252] The terminal receives the entered symptoms and sends them to the server.

[0253] Step 3:

[0254] The server receives the text data and passes it to a natural language processing engine for analysis.

[0255] Step 4:

[0256] The server uses a natural language processing engine to extract keywords such as "back" and "painful."

[0257] Step 5:

[0258] The server searches the disease database based on the extracted keywords and lists related diseases.

[0259] Step 6:

[0260] The server assesses the risk level for each listed disease.

[0261] For example, "muscle pain (low risk)", "sudden lower back pain (medium risk)", "kidney stones (medium risk)", and "shingles (high risk)".

[0262] Step 7:

[0263] The server lists other symptoms associated with each disease.

[0264] For example, "muscle pain: persistent pain after a specific type of exercise," "lower back pain: sharp pain after a sudden movement," "kidney stones: blood in the urine, frequent urination," and "shingles: rash and pain."

[0265] Step 8:

[0266] The server passes the user's input data to the emotion engine, which then recognizes the emotional state.

[0267] For example, it analyzes emotions such as "stress," "anxiety," and "calmness."

[0268] Step 9:

[0269] The server receives emotional states from the emotion engine and adjusts the information for the listed illnesses according to those emotions.

[0270] For example, for users who are feeling anxious, we provide reassuring messages and support information.

[0271] Step 10:

[0272] The device requests the user's location information.

[0273] Step 11:

[0274] The user grants permission to share their location information.

[0275] Step 12:

[0276] The device sends the user's location information to the server.

[0277] Step 13:

[0278] The server uses geographic information systems based on location data to search for nearby specialized medical facilities.

[0279] For example, "orthopedics," "urology," and "dermatology."

[0280] Step 14:

[0281] The server retrieves general treatment guidelines for each disease from the database.

[0282] For example, "muscle pain: warm compress and light exercise", "slipped disc: rest and pain relief prescription", "kidney stones: fluid intake and doctor's diagnosis", "shingles: early antiviral drug administration".

[0283] Step 15:

[0284] The server sends the processing result to the terminal.

[0285] Step 16:

[0286] The terminal displays to the user a list of possible diseases, risk assessment, related symptoms, recommended medical institutions, treatment guidelines, messages and support information according to emotional response.

[0287] Through this series of processes, the user can obtain appropriate medical information according to their own symptoms and corresponding countermeasures based on it. Thus, the system that takes into account the user's emotional state can provide a more personalized response to the user.

[0288] (Example 2)

[0289] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart device 14 is referred to as a "terminal".

[0290] In the conventional diagnostic support system, only diseases are listed based on the symptoms input by the user, and information providing considering the user's emotional state has not been provided. In addition, the provision of risk assessment and related symptoms for the listed diseases is also limited, and it is difficult to appropriately select from a plurality of medical institutions. To solve such problems, the present invention aims to develop a system that takes into account the user's emotional state and provides more accurate diagnostic support.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0292] In this invention, the server includes means for using a natural language processing engine to analyze the input symptoms and extract keywords, means for passing the user's input data to an emotion recognition engine to recognize the emotional state, and means for searching a disease database based on the extracted keywords and listing related diseases. This enables highly accurate diagnostic support while also taking into account the user's emotional state.

[0293] A "user" is an ordinary consumer or patient who uses the system to input symptom information.

[0294] An "input terminal" is a device used by users to input symptoms and receive information, and includes smartphones and personal computers.

[0295] A "server" is a central processing unit that processes, analyzes, and provides information about data.

[0296] A "natural language processing engine" is a software tool that analyzes text data entered by a user and extracts specific keywords.

[0297] An "emotion recognition engine" is a software system that analyzes a user's emotional state based on their input data.

[0298] A "disease database" is a collection of data that stores information about various diseases.

[0299] "Risk level" is an indicator used to assess the level of risk for a specified disease.

[0300] "Related symptoms" refer to other symptoms or signs that may be related to the user's main complaint.

[0301] "Location information" refers to data that indicates the user's current location, such as GPS data.

[0302] A "Geographic Information System" is a system that uses the location information of users to provide information that meets specific geographical conditions.

[0303] A "medical institution" refers to an organization or facility such as a hospital or clinic that treats diseases and injuries.

[0304] A "treatment guideline" refers to general treatment methods and countermeasures for a specific disease.

[0305] An "information presentation method" refers to the method and form of how to present information to users.

[0306] The "content of advice" indicates the advice and instructions provided to users.

[0307] The present invention is a system in which a user inputs symptom information, lists up possible diseases based on the information, recommends appropriate medical institutions, and further provides general treatment guidelines. For this purpose, by combining an emotion engine that recognizes the user's emotions, a more user-friendly response is realized.

[0308] This system is mainly composed of the following elements:

[0309] User terminal: A device through which a user inputs symptoms and receives information, such as a smartphone or a personal computer.

[0310] Server: A central processing system that processes, analyzes, and provides information.

[0311] Natural language processing engine: A software tool that analyzes the symptoms input by a user and extracts the meaning, such as Google (registered trademark) NLP API or IBM Watson (registered trademark).

[0312] Emotion engine: A system that recognizes emotions based on user input data and adjusts responses accordingly; Microsoft® Azure® Emotion API is an example of this.

[0313] Disease database: A collection of data containing information about various diseases.

[0314] Geographic Information Systems (GIS): These are systems that search for appropriate medical facilities based on the user's location information; the Google Maps API is an example of such a system.

[0315] The user uses a terminal to input specific symptoms (e.g., "My back hurts"). This information is immediately sent to the server. The server passes the received text data to a natural language processing engine, which extracts keywords (e.g., "back," "pain"). This process analyzes the entered symptoms and organizes the information. The server then passes the user's input data to an emotion recognition engine, which recognizes the emotional state (e.g., stress, anxiety, calmness, etc.). The results of this emotion analysis are sent back to the server.

[0316] Next, the server uses the extracted keywords and recognized emotional states to search a disease database and list related illnesses. For example, illnesses related to "back pain" might include "muscle pain," "lumbago," "kidney stones," and "shingles." Furthermore, the server assesses the risk level for each listed illness and also provides other related symptoms (e.g., blood in the urine, rash, etc.).

[0317] The server adjusts how information is presented and the content of advice based on the user's emotional state. For example, users who are feeling anxious will be given priority in receiving reassuring messages and supportive information. If the user allows location information to be shared, this location information is sent from the device to the server. The server uses a geographic information system to search for and recommend specialized medical facilities near the user's current location. Finally, the server retrieves general treatment guidelines for each disease from a disease database and presents them to the user.

[0318] For example, when a user enters "My back hurts," the device sends this to the server. The server analyzes the symptoms using a natural language processing engine and recognizes the user's emotions using an emotion engine. It then lists "muscle pain," "lower back pain," "kidney stones," and "shingles" from a disease database and evaluates the risk level and associated symptoms for each. Based on location information, it recommends "orthopedics" or "urology," and displays treatment guidelines such as applying a warm compress for "muscle pain" and staying hydrated for "kidney stones." Furthermore, it prioritizes providing reassuring messages to users who are feeling anxious, such as "Don't worry, early consultation will help."

[0319] Example of a prompt:

[0320] "When a user enters 'back pain,' please list possible illnesses and conduct a risk assessment. Additionally, display relevant treatment guidelines and recommended medical facilities based on location, and include messages to alleviate user anxiety."

[0321] Thus, the system of the present invention can support self-diagnosis of symptoms while also taking into consideration the user's emotional state, and can improve the efficiency of selecting a medical institution.

[0322] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0323] Step 1:

[0324] The user uses the terminal to input specific symptom information. For example, if the user inputs "My back hurts," the terminal immediately sends this information to the server. The input information is passed to the server as string data.

[0325] Step 2:

[0326] The server passes the received text data to a natural language processing engine. Here, a natural language processing engine such as Google NLP API or IBM Watson is used to extract keywords (e.g., "back" and "painful") from the text data. This analysis clarifies the meaning of the entered symptoms. The output is a list of keywords.

[0327] Step 3:

[0328] The server passes the extracted keywords to an emotion recognition engine (such as the Microsoft Azure Emotion API). The emotion recognition engine analyzes the user's emotional state (e.g., stress, anxiety, calmness, etc.) based on the user's input data. The output is the recognized emotional state and its intensity.

[0329] Step 4:

[0330] The server searches the disease database based on these keywords and emotional states. The disease database contains information on a variety of diseases, and diseases that match the keywords (e.g., "muscle pain," "lower back pain," "kidney stones," "shingles") are listed. The output is a list of the listed diseases.

[0331] Step 5:

[0332] The server assesses the risk level for each listed disease. Here, each disease is classified as having a risk level ranging from low risk to high risk. Additionally, it provides information on any additional symptoms associated with each disease (e.g., blood in the urine, rash, etc.). The output is a list of risk assessments and associated symptoms for each disease.

[0333] Step 6:

[0334] The server adjusts how information is presented and the content of advice based on the user's perceived emotional state. For example, a user feeling anxious will be given priority in receiving reassuring messages and support information. This involves modifying the presentation method and generating additional messages. The output consists of the adjusted information presentation method and additional messages.

[0335] Step 7:

[0336] When a user allows location information to be shared, the device sends that information to the server. The server uses a geographic information system (such as the Google Maps API) to search for medical facilities near the user's current location. The output is a list of recommended medical facilities.

[0337] Step 8:

[0338] The server retrieves general treatment guidelines for each disease from a disease database. For example, "muscle pain" might be described as requiring warm compresses or light exercise, while "kidney stones" might be described as requiring hydration and a doctor's diagnosis. The output is a list of treatment guidelines corresponding to each disease.

[0339] Step 9:

[0340] Ultimately, the device displays information to the user. This information, including a list of possible illnesses, risk assessments, related symptoms, recommended healthcare facilities, treatment guidelines, and additional emotionally-sensitive messages, is presented in a visual format for quick and easy viewing. This allows the user to take quick and appropriate action.

[0341] (Application Example 2)

[0342] 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".

[0343] In modern society, people are increasingly required to recognize symptoms in their daily lives and perform initial self-diagnosis. However, users often find it difficult to quickly find appropriate medical facilities and take time to understand what treatment is necessary for their symptoms. Furthermore, some symptoms require immediate medical attention, which can cause stress and anxiety. Therefore, there is a need for a system that allows users to quickly travel to appropriate medical facilities using autonomous vehicles.

[0344] 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.

[0345] In this invention, the server includes means for the user to input symptoms into an input terminal, means for the server to analyze the input symptoms and extract keywords using a natural language processing engine, means for the server to search a disease database based on the extracted keywords and list related diseases, means for the server to evaluate the risk level of the listed diseases and also provide other related symptoms, means for the server to recommend medical institutions capable of providing specialized treatment based on the user's location information, means for the server to provide general treatment guidelines for each disease, means for the terminal to display this information to the user, means for recognizing the user's emotions using an emotion engine and providing information appropriate to that state, and means for linking the route to the recommended medical institution to the navigation system of an autonomous vehicle. As a result, the user can receive a quick and appropriate response to their symptoms and travel to a medical institution safely using an autonomous vehicle.

[0346] 1. An "input terminal" is a device used by the user to input symptoms, and includes smartphones, tablets, etc.

[0347] 2. A "natural language processing engine" is a software tool that analyzes text data entered by a user and interprets its meaning.

[0348] 3. A "keyword" is a word or phrase with a specific meaning that is extracted from the symptom information entered by the user.

[0349] 4. A "disease database" is a collection of data in which information about various diseases is systematically stored.

[0350] 5. "Risk level" is an index that assesses the likelihood and severity of occurrence for each of the listed diseases.

[0351] 6. A "Geographic Information System" is a system that acquires a user's location information and searches for medical facilities based on geographical data.

[0352] 7. An "emotion engine" is a system that recognizes and analyzes the user's emotional state (stress, anxiety, calmness, etc.) from the user's input data.

[0353] 8. A "navigation system" is a system that guides an autonomous vehicle along a route to a specific destination.

[0354] 9. "Treatment guidelines" are information that summarizes the general treatment methods and coping strategies recommended for a specific disease.

[0355] 10. A "recommended medical institution" is a medical institution that can provide specialized treatment, suggested by the server based on the user's location information and symptoms.

[0356] This invention relates to a medical support system using an autonomous vehicle, and is a system that assists with navigation from symptom input to medical facilities. This system enables users to quickly input symptoms and provides appropriate medical facility suggestions and route guidance. Therefore, the system components include an input terminal, a server, an emotion engine, a disease database, a navigation system, and the like.

[0357] Hardware and software configuration

[0358] User terminal

[0359] The user terminal is a device such as a smartphone or a vehicle's infotainment system, used by the user to input symptoms. Voice input is also supported, utilizing a speech recognition engine (e.g., Google Speech API).

[0360] server

[0361] The servers are processing units located in the cloud and are responsible for the main functions of natural language processing, emotion recognition, and disease database search. The natural language processing engine is built using tools such as Hugging Face's Transformers and spaCy. The emotion engine similarly utilizes a pre-trained model from Transformers.

[0362] Disease Database

[0363] The disease database is a collection of data that stores detailed information about various diseases. The server searches this database based on symptom keywords and retrieves a list of related diseases.

[0364] Geographic Information Systems

[0365] Geographic information systems (e.g., Google Maps API) are used to obtain a user's current location and search for the most suitable medical facilities.

[0366] Navigation system

[0367] The navigation system is part of the autonomous vehicle and determines the route based on medical facility information provided from a server. Therefore, it also utilizes autonomous driving control software (e.g., Autoware).

[0368] System Operation Overview

[0369] 1. Input and analysis of symptoms:

[0370] When a user inputs a symptom through the in-car infotainment system (for example, "My back hurts"), a speech recognition engine converts this speech into text. A natural language processing engine then analyzes this text and extracts keywords related to the symptom.

[0371] 2. Emotion recognition:

[0372] The server uses an emotion engine to analyze the user's emotional state, along with the extracted keywords. For example, it can determine if the user is feeling anxious.

[0373] 3. Disease database search and risk assessment:

[0374] The server searches a disease database and lists diseases associated with the extracted keywords. Simultaneously, it assesses the risk level for each disease and provides information on other related symptoms.

[0375] 4. Provision of recommendations and treatment guidelines from medical institutions:

[0376] The server, having acquired the user's location information, uses a geographic information system to search for the most suitable medical facility for the user. Furthermore, it provides general treatment guidelines for various diseases.

[0377] 5. Displaying results and starting navigation:

[0378] The user's terminal displays a list of possible illnesses, a risk assessment, recommended medical facilities, and treatment guidelines. Finally, the navigation system coordinates the route to the recommended medical facility with the autonomous vehicle and begins navigation.

[0379] Specific examples and prompt statements

[0380] For example, if a user says "My back hurts," the system will perform the following actions.

[0381] Example of a prompt

[0382] User: "My back hurts."

[0383] System: "Analyzing symptoms and emotions..."

[0384] System: "Possible conditions include muscle pain, lumbago, kidney stones, and shingles. The recommended medical facility is your nearest orthopedic hospital."

[0385] System: "Starting autonomous driving to the destination medical facility."

[0386] In this way, this system can support self-diagnosis of symptoms while taking into account the user's emotional state, and improve the efficiency of selecting a medical institution. Users can travel to medical institutions safely through autonomous driving.

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

[0388] Step 1: The user voice-inputs the symptoms into the in-car infotainment system.

[0389] In terms of specific actions, the user inputs a symptom, such as "My back hurts," into the device by voice. The input data (voice) is converted into text data by a speech recognition engine (Google Speech API).

[0390] Input: Voice data ("My back hurts")

[0391] Output: Text data ("My back hurts")

[0392] Step 2: The server uses a natural language processing engine to parse the input text data.

[0393] The text data is sent to the server and analyzed by a natural language processing engine (such as Transformers or spaCy). Keywords ("back," "painful") are extracted.

[0394] Input: Text data ("My back hurts")

[0395] Output: Keywords ("back", "pain")

[0396] Step 3: The server uses the emotion engine to recognize the user's emotional state.

[0397] The extracted keywords and text data are passed to the emotion engine, which analyzes the user's emotional state (e.g., anxiety).

[0398] Input: Text data and keywords ("back pain", "back", "pain")

[0399] Output: Emotional state (anxiety)

[0400] Step 4: The server searches the disease database based on the extracted keywords.

[0401] The server uses keywords to search the disease database and lists related diseases (e.g., muscle pain, lumbago, kidney stones, shingles).

[0402] Input: Keywords ("back", "pain")

[0403] Output: Disease list (muscle pain, lumbago, kidney stones, shingles)

[0404] Step 5: The server assesses the risk level of the listed diseases and also provides any other associated symptoms.

[0405] The server assesses the risk level for each listed disease and also provides other symptoms that may be associated with each disease.

[0406] Input: Disease list (muscle pain, lumbago, kidney stones, shingles)

[0407] Output: Risk assessment and associated symptoms (muscle pain: low risk, lumbago: medium risk, kidney stones: medium risk, shingles: high risk)

[0408] Step 6: Based on the user's location information, the server recommends medical institutions that can provide specialized treatment.

[0409] The server obtains location information and uses a geographic information system (Google Maps API) to search for the most suitable medical facility. Recommended medical facilities include orthopedic hospitals, etc.

[0410] Input: Location information (latitude and longitude data)

[0411] Output: Recommended medical institutions (orthopedic hospitals, etc.)

[0412] Step 7: The server provides general treatment guidelines for each disease.

[0413] The server retrieves general treatment guidelines for the listed diseases from the database and provides them to the user.

[0414] Input: Disease list

[0415] Output: Treatment guidelines (Muscle pain: warm compresses, acute lower back pain: rest and painkillers, kidney stones: hydration, shingles: antiviral drugs, etc.)

[0416] Step 8: The device displays this information to the user.

[0417] The device visually displays to the user a list of possible diseases, risk assessments, recommended medical facilities, and treatment guidelines.

[0418] Input: Risk assessment, recommended medical institutions, treatment guidelines

[0419] Output: Display (disease list, risk assessment, recommended medical facilities, treatment guidelines)

[0420] Step 9: The server links the route to the recommended medical facility to the autonomous vehicle's navigation system.

[0421] The server sends location information of recommended medical facilities to the navigation system, which then instructs the autonomous vehicle to begin route guidance.

[0422] Input: Location information of recommended medical institutions

[0423] Output: Route guidance for autonomous vehicles

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Second Embodiment]

[0428] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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".

[0440] This invention is a system that lists possible diseases based on symptom information entered by the user, recommends appropriate medical institutions based on that list, and provides general treatment guidelines. The aim of this system is to help users quickly find out specific ways to deal with their symptoms.

[0441] System Configuration

[0442] This system consists mainly of the following elements.

[0443] User terminal: A device that allows the user to input symptoms and receive information. Examples include smartphones and personal computers.

[0444] Server: A central processing system that processes, analyzes, and provides information about data.

[0445] Natural Language Processing Engine: A software tool that analyzes symptoms entered by the user and extracts their meaning.

[0446] Disease database: A collection of data containing information about various diseases.

[0447] Geographic Information System: A system that searches for appropriate medical facilities based on the user's location information.

[0448] Program processing

[0449] User input

[0450] The user enters specific symptoms, such as "My back hurts," into the device. The device immediately sends the entered information to the server.

[0451] Natural Language Processing

[0452] The server passes the received text data to a natural language processing engine, which extracts keywords such as "back" and "pain." This process helps the system understand the entered symptoms and organize related information.

[0453] Disease Database Search

[0454] The server uses the extracted keywords to search the disease database. For example, diseases related to "back pain" might include "muscle pain," "lumbago," "kidney stones," and "shingles."

[0455] Risk assessment and symptom provision

[0456] For each listed disease, the server assesses its risk level. For example, "muscle pain" is rated as low risk, "lower back pain" as moderate risk, "kidney stones" as moderate risk, and "shingles" as high risk. Other symptoms associated with each disease (e.g., blood in the urine, rash, etc.) are also provided.

[0457] Recommended medical institutions

[0458] When a user allows their location to be shared, this location information is sent to the server via their device. The server uses a geographic information system to search for and recommend specialized medical facilities near the user's current location. For example, "Orthopedics," "Urology," and "Dermatology" may be listed.

[0459] Provision of treatment guidelines

[0460] The server retrieves general treatment guidelines for each disease from the database. For example, it might suggest that warm compresses and light exercise are effective for "muscle pain," rest and painkillers for "lumbago," hydration and a doctor's diagnosis for "kidney stones," and antiviral medication for "shingles."

[0461] Displaying Results

[0462] Ultimately, the terminal visually displays these processing results to the user. The user can see at a glance a list of possible diseases, risk assessments, related symptoms, recommended medical facilities, and treatment guidelines, enabling them to take quick and appropriate action.

[0463] Specific example

[0464] Simply by having the user type "My back hurts," the server assesses the likelihood and risk level of diseases related to the symptoms, and also suggests any other accompanying symptoms. Furthermore, it searches for appropriate medical facilities based on the user's location and provides concise and practical treatment guidelines. This entire process allows users to quickly receive appropriate medical advice.

[0465] Thus, the system of the present invention can support self-diagnosis of symptoms and improve the efficiency of selecting medical institutions.

[0466] The following describes the processing flow.

[0467] Step 1:

[0468] The user enters specific symptoms into the device, such as "My back hurts."

[0469] Step 2:

[0470] The terminal receives the entered symptoms and sends them to the server.

[0471] Step 3:

[0472] The server receives the text data and passes it to a natural language processing engine for analysis.

[0473] Step 4:

[0474] The server uses a natural language processing engine to extract keywords such as "back" and "painful."

[0475] Step 5:

[0476] The server searches the disease database based on the extracted keywords and lists related diseases.

[0477] Step 6:

[0478] The server assesses the risk level for each listed disease.

[0479] For example, "muscle pain (low risk)", "sudden lower back pain (medium risk)", "kidney stones (medium risk)", and "shingles (high risk)".

[0480] Step 7:

[0481] The server lists other symptoms associated with each disease.

[0482] For example, "muscle pain: persistent pain after a specific type of exercise," "lower back pain: sharp pain after a sudden movement," "kidney stones: blood in the urine, frequent urination," and "shingles: rash and pain."

[0483] Step 8:

[0484] The device requests the user's location information.

[0485] Step 9:

[0486] The user grants permission to share their location information.

[0487] Step 10:

[0488] The device sends the user's location information to the server.

[0489] Step 11:

[0490] The server uses geographic information systems based on location data to search for nearby specialized medical facilities.

[0491] For example, "orthopedics," "urology," and "dermatology."

[0492] Step 12:

[0493] The server retrieves general treatment guidelines for each disease from the database.

[0494] For example, "Muscle pain: Warm compresses and light exercise," "Lower back pain: Rest and pain medication," "Kidney stones: Hydration and doctor's diagnosis," "Shingles: Early administration of antiviral drugs."

[0495] Step 13:

[0496] The server sends the processing results to the terminal.

[0497] Step 14:

[0498] The device displays to the user a list of possible illnesses, a risk assessment, related symptoms, recommended medical facilities, and treatment guidelines.

[0499] Through this process, users can obtain appropriate medical information and countermeasures tailored to their symptoms.

[0500] (Example 1)

[0501] 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".

[0502] In modern society, it is crucial for users to obtain timely and appropriate medical information regarding their own symptoms. However, selecting a specialized medical institution and obtaining appropriate treatment guidelines often requires considerable time and effort. Traditional methods may rely on unreliable information for self-diagnosis, potentially leading to the selection of inappropriate medical institutions or the adoption of incorrect treatment plans. This poses a risk to the user's health.

[0503] 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.

[0504] In this invention, the server includes means for the user to input symptoms into an input device, means for analyzing the input symptoms using a natural language processing system and extracting keywords, means for searching a specialized database based on the extracted keywords and listing related diseases, means for evaluating the risk level of the listed diseases and providing other related symptoms, means for acquiring the user's location information and recommending medical institutions capable of providing specialized treatment, and means for providing general treatment guidelines for each disease. As a result, the user can quickly obtain appropriate medical information for their symptoms, and can efficiently select an appropriate medical institution and decide on a treatment plan.

[0505] An "input device" refers to a device used by a user to input symptoms. Specifically, this includes smartphones and personal computers.

[0506] A "computer on a network" refers to a central processing system that performs data processing, analysis, and information provision. It is commonly known as a server.

[0507] A "natural language processing system" refers to a software tool that analyzes text data entered by a user and extracts its meaning.

[0508] "Keywords" refer to important words or phrases extracted from the entered text. This helps identify the symptoms.

[0509] A "specialized database" refers to a collection of data containing information about various diseases. This includes information related to medical care and health.

[0510] "Listing" refers to displaying items related to a specific keyword in a list format.

[0511] "Risk level" refers to a standard for evaluating the risk level for a particular disease. It ranges from low risk to high risk.

[0512] "Location information" refers to data that indicates the user's current location. It is obtained using technologies such as GPS.

[0513] A "medical institution capable of providing specialized treatment" refers to a medical facility that has the ability to provide specialized treatment for a particular disease. This includes hospitals and clinics.

[0514] "Treatment guidelines" refer to information that outlines general treatment methods and coping strategies for specific diseases. This includes helpful advice for users.

[0515] This invention begins with the user inputting symptoms using an input device. A smartphone or personal computer can be used as the input device. The user inputs specific symptoms, such as "my back hurts," and the device then transmits this information to a server.

[0516] The server passes the received text data to a natural language processing system (e.g., a general-purpose natural language processing engine). This system extracts keywords from the input text and analyzes the meaning of the symptoms. For example, it can extract the keywords "back" and "pain" from the text.

[0517] The server then uses the extracted keywords to search specialized databases (e.g., medical databases). This search lists related conditions such as "muscle pain," "lower back pain," "kidney stones," and "shingles."

[0518] For each listed disease, the server assesses the risk level and provides information on other associated symptoms. For example, it might rate "muscle pain" as low risk, "lumbago" as moderate risk, "kidney stones" as moderate risk, and "shingles" as high risk. It also provides additional information on other associated symptoms, such as "hematuria" or "rash."

[0519] If the user allows location information to be shared, the device uses its GPS function to obtain information about its current location and sends it to the server. The server uses a geographic information system (e.g., a general-purpose geographic information system) to search for medical institutions near the user's current location that can provide specialized treatment. As a result, it recommends medical institutions such as "orthopedics," "urology," and "dermatology."

[0520] Next, the server retrieves general treatment guidelines for each disease from specialized databases (e.g., medical websites). Specifically, it suggests that warm compresses and light exercise are effective for "muscle pain," rest and painkillers for "lumbago," hydration and a doctor's diagnosis for "kidney stones," and antiviral medication for "shingles."

[0521] Ultimately, the device visually displays this information to the user. The user can see at a glance a list of possible illnesses, their risk levels, associated symptoms, recommended healthcare facilities, and treatment guidelines, enabling them to take quick and appropriate action.

[0522] Specific example

[0523] Simply by having the user type "My back hurts," the server assesses the likelihood and risk level of illnesses related to the symptoms, and also suggests any other accompanying symptoms. Furthermore, it searches for appropriate medical facilities based on the user's location and provides practical treatment guidelines. This entire process allows users to quickly receive appropriate medical advice.

[0524] Example of a prompt

[0525] "Please tell me about possible illnesses that could be causing back pain. Also, please explain the risk level, related symptoms, and treatment options in detail."

[0526] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0527] Step 1: User Input

[0528] The user uses an input device (smartphone or computer) to enter specific symptoms, such as "my back hurts." This input data is sent to the terminal in text format.

[0529] Step 2: Send a text message

[0530] The terminal immediately sends the entered text data to the server. This transmission is secure using the HTTPS protocol.

[0531] Input: Symptom text entered by the user (e.g., "My back hurts")

[0532] Output: Text data sent to the server

[0533] Step 3: Natural Language Processing

[0534] The server passes the received text data to a natural language processing system (e.g., a general-purpose natural language processing engine). The natural language processing system extracts important keywords (e.g., "back," "painful") from the input text and analyzes the context.

[0535] Input: Submitted text data

[0536] Output: Extracted keywords and contextual information

[0537] Step 4: Disease Database Search

[0538] The server uses the extracted keywords to search specialized databases (e.g., medical databases) and lists diseases associated with those keywords. For example, it might find diseases such as "muscle pain," "lower back pain," "kidney stones," and "shingles."

[0539] Input: Extracted keywords (e.g., "back", "pain")

[0540] Output: List of listed diseases

[0541] Step 5: Risk assessment and suggestion of additional symptoms

[0542] The server assesses the risk level for each listed disease and also provides additional related symptoms. A risk assessment algorithm is used for the assessment, and related symptoms such as "hematuria" and "rash" are also presented.

[0543] Input: List of listed diseases

[0544] Output: Risk assessment and associated additional symptoms

[0545] Step 6: Location Information Collection

[0546] When a user allows location information to be shared, the device uses its GPS function to obtain information about its current location and sends that data to the server. This data transmission also uses the HTTPS protocol.

[0547] Input: Permission to provide location information

[0548] Output: Acquired location data

[0549] Step 7: Proposal of Recommended Medical Institutions

[0550] The server uses the collected location information to access a geographic information system (e.g., a general-purpose geographic information system) to search for medical institutions near the user's current location that can provide specialized treatment. For example, medical institutions specializing in "orthopedics," "urology," and "dermatology" may be listed.

[0551] Input: Acquired location data

[0552] Output: List of recommended specialist medical institutions

[0553] Step 8: Provide general treatment guidelines

[0554] The server retrieves general treatment guidelines for each disease from specialized databases (e.g., specialist medical websites). Specifically, it recommends warm compresses and light exercise for muscle pain, rest and painkillers for acute lower back pain, hydration and a doctor's diagnosis for kidney stones, and antiviral medication for shingles.

[0555] Input: List of listed diseases

[0556] Output: Information on general treatment guidelines

[0557] Step 9: Displaying the results

[0558] Finally, the device visually displays these processing results to the user. The user can see at a glance a list of possible illnesses, risk assessments, related additional symptoms, recommended healthcare facilities, and treatment guidelines. This enables quick and appropriate medical consultation and response.

[0559] Input: All search and evaluation data

[0560] Output: Comprehensive information displayed to the user.

[0561] (Application Example 1)

[0562] 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."

[0563] Existing symptom diagnosis systems are limited to providing information such as disease listings and treatment guidelines, lacking a means to quickly and centrally manage subsequent medical service use, particularly payments for consultations and medication purchases. This often requires users to perform multiple operations, making the process cumbersome. To solve this problem, a system is needed that seamlessly integrates the user's process from entering symptoms to payment at the medical institution.

[0564] 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.

[0565] In this invention, the server includes means for enabling the user to make electronic payments for medical consultations and medication purchases, means for using a geographic information system to obtain the user's location information, and means for enabling electronic payments at recommended medical institutions. This allows the user to quickly and centrally perform a series of processes, from entering symptoms to paying for medical consultations.

[0566] An "input terminal" is a device used by users to input symptom information, and includes smartphones and personal computers.

[0567] A "natural language processing engine" is a software tool that analyzes text data entered by a user and extracts keywords.

[0568] A "disease database" is a collection of data containing detailed information about various diseases.

[0569] "Risk level" indicates an assessment of the risk for each listed disease, ranging from low risk to high risk.

[0570] "Location information" refers to data indicating the user's current location, and this information is used to search for appropriate medical facilities.

[0571] The method for recommending "medical institutions" is a function that suggests hospitals and clinics capable of providing specialized treatment based on the user's location information.

[0572] "Treatment guidelines" are information that provides general treatment methods and coping strategies for specific diseases.

[0573] "Electronic payment" refers to a method of making payments online for things like medical consultation fees and medication purchases.

[0574] A "Geographic Information System" is a system that acquires a user's location information and handles geographical data.

[0575] "Detailed data" refers to data that includes specific information about the listed diseases.

[0576] A "database for managing payment information" is a collection of data that centrally manages payment information related to medical consultation fees, medication purchases, and other related matters.

[0577] This invention integrates electronic payment functionality into a system that lists possible diseases based on symptom information entered by the user, recommends appropriate medical institutions based on that list, and provides general treatment guidelines. This system consists of the following main elements:

[0578] System Configuration

[0579] 1. User terminal: This is a device used by the user to input symptoms and receive diagnostic results, recommended medical institutions, and treatment guidelines. Examples include smartphones and personal computers.

[0580] 2. Server: A central processing system that processes, analyzes, and provides information on data. This server includes the following hardware and software:

[0581] 3. Natural Language Processing Engine: This is a software tool that analyzes symptoms entered by the user and extracts necessary keywords. Specifically, libraries such as spaCy and NLTK are used.

[0582] 4. Disease Database: This is a collection of data that stores information about various diseases. This allows users to search for related diseases based on the symptoms they enter.

[0583] 5. Geographic Information Systems: These are systems that acquire a user's location information and use it to search for appropriate medical facilities. The Geopy library is a concrete example.

[0584] 6. Electronic Payment System: This is a payment function that allows users to pay for medical consultations and purchase medications online. The Stripe API, for example, will be implemented.

[0585] System operation

[0586] 1. Inputting symptom information: The user enters specific symptoms (e.g., "My back hurts") on the device. The device immediately sends this information to the server.

[0587] 2. Natural Language Processing: The server passes the received text data to a natural language processing engine, which extracts keywords. This process helps the server understand the entered symptoms and organize relevant information.

[0588] 3. Disease Database Search: The server uses the extracted keywords to search the disease database and lists the relevant diseases.

[0589] 4. Risk assessment and provision of related symptoms: The server assesses the risk level for each listed disease and also provides other symptoms associated with each disease (e.g., blood in the urine, rash, etc.).

[0590] 5. Suggestion of Recommended Medical Institutions: When a user provides location information, the server uses a geographic information system to search for and recommend appropriate medical institutions.

[0591] 6. Provision of treatment guidelines: The server retrieves general treatment guidelines for each disease from the database and provides them to the user.

[0592] 7. Electronic payment function: Allows users to pay for medical consultations and medication purchases at recommended medical institutions using electronic payment.

[0593] Specific example

[0594] Simply by the user typing "back pain," the system provides a list of possible illnesses (e.g., "muscle pain," "lumbago," "kidney stones"), assesses their risk level, and suggests other related symptoms. Furthermore, it searches for appropriate medical facilities based on location information and offers the option to pay for consultations electronically.

[0595] Examples of prompt statements

[0596] The user entered "My back hurts." Display a list of possible illnesses, risk assessment, related symptoms, geographically nearby medical facilities, and general treatment guidelines, and also provide an option to pay for the consultation using electronic payment.

[0597] In this way, users can complete the entire process from input to payment quickly and centrally, making it possible to access medical services more smoothly.

[0598] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0599] Step 1: The user enters the symptoms into the device and sends them.

[0600] Users use devices such as smartphones or computers to input specific symptoms (for example, "My back hurts") and submit this information. The input data is sent to the server in text format.

[0601] Step 2: The server analyzes the symptoms using a natural language processing engine and extracts keywords.

[0602] The server passes the received text data to a natural language processing engine (e.g., spaCy or NLTK) to extract keywords related to the symptoms (e.g., "back," "pain"). This analysis converts the symptom information into structured data.

[0603] Step 3: The server searches the disease database based on the extracted keywords and lists the relevant diseases.

[0604] The server uses the extracted keywords to search the disease database and lists related diseases (e.g., "muscle pain," "lower back pain," "kidney stones," etc.). This list is generated and passed to the next processing step.

[0605] Step 4: Assess the risk level of the listed diseases and provide any other related symptoms.

[0606] The server assesses the risk level for each listed disease (from low risk to high risk) and provides other symptoms associated with each disease (e.g., "fatigue" for "muscle pain," "hematuria" for "kidney stones"). This information is then compiled together.

[0607] Step 5: The server searches for and recommends appropriate medical facilities based on the user's location information.

[0608] When a user allows location information to be shared, the location information sent from the device is sent to the server. The server uses a geographic information system (e.g., Geopy) to search for medical facilities near the user's current location and generates a list of recommended medical facilities.

[0609] Step 6: The server provides general treatment guidelines for each disease.

[0610] The server retrieves and compiles general treatment guidelines for each disease from a database (for example, "warm compresses" and "light exercise" for "muscle pain," and "rest and painkillers" for "lumbago").

[0611] Step 7: The device displays this information to the user.

[0612] The terminal visually displays information transmitted from the server to the user (a list of possible diseases, risk assessment, related symptoms, recommended medical facilities, and treatment guidelines). Based on this information, the user can decide on their next course of action.

[0613] Step 8: The user pays for consultation fees and medication purchases at the recommended medical institution using electronic payment.

[0614] After a user receives a medical consultation or purchases medication, they make a payment using an electronic payment system (e.g., Stripe). This operation is also performed from the terminal, and the payment information is sent to and recorded on the server.

[0615] In this way, a series of steps allows users to handle everything from entering their symptoms to paying for medical consultations in one place.

[0616] 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.

[0617] This invention is a system that lists possible diseases based on symptom information entered by the user, recommends appropriate medical institutions based on that list, and also provides general treatment guidelines. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it achieves a more user-friendly approach.

[0618] System Configuration

[0619] This system mainly consists of the following elements:

[0620] User terminal: A device that allows the user to input symptoms and receive information. Examples include smartphones and personal computers.

[0621] Server: A central processing system that processes, analyzes, and provides information about data.

[0622] Natural Language Processing Engine: A software tool that analyzes symptoms entered by the user and extracts their meaning.

[0623] Emotion engine: A system that recognizes emotions based on user input and adjusts its response accordingly.

[0624] Disease database: A collection of data containing information about various diseases.

[0625] Geographic Information System: A system that searches for appropriate medical facilities based on the user's location information.

[0626] Program processing

[0627] User input

[0628] The user enters specific symptoms, such as "My back hurts," into the device. The device immediately sends the entered information to the server.

[0629] Natural Language Processing

[0630] The server passes the received text data to a natural language processing engine, which extracts keywords such as "back" and "pain." This process helps the system understand the entered symptoms and organize related information.

[0631] emotion recognition

[0632] The server passes the user's input data to the emotion engine, which recognizes the user's emotional state (e.g., stress, anxiety, calmness, etc.). The emotion engine analyzes the emotional state and sends the results back to the server.

[0633] Disease Database Search

[0634] The server uses the extracted keywords and recognized emotional states to search a disease database and list related illnesses. For example, illnesses related to "back pain" might include "muscle pain," "lumbago," "kidney stones," and "shingles."

[0635] Risk assessment and symptom provision

[0636] For each listed disease, the server assesses its risk level. For example, "muscle pain" is rated as low risk, "lower back pain" as moderate risk, "kidney stones" as moderate risk, and "shingles" as high risk. Other symptoms associated with each disease (e.g., blood in the urine, rash, etc.) are also provided.

[0637] Emotional response

[0638] The server adjusts how information is presented and the content of advice based on the recognized emotional state of the user. For example, if the user is feeling anxious, it prioritizes providing reassuring messages and support information.

[0639] Recommended medical institutions

[0640] When a user allows their location to be shared, this location information is sent to the server via their device. The server uses a geographic information system to search for and recommend specialized medical facilities near the user's current location. For example, "Orthopedics," "Urology," and "Dermatology" may be listed.

[0641] Provision of treatment guidelines

[0642] The server retrieves general treatment guidelines for each disease from the database. For example, it might suggest that warm compresses and light exercise are effective for "muscle pain," rest and painkillers for "lumbago," hydration and a doctor's diagnosis for "kidney stones," and antiviral medication for "shingles."

[0643] Displaying Results

[0644] Ultimately, the terminal visually displays these processing results to the user. The user can see at a glance a list of possible diseases, risk assessments, related symptoms, recommended medical facilities, and treatment guidelines, enabling them to take quick and appropriate action.

[0645] Specific example

[0646] When a user enters "My back hurts," the server uses a natural language processing engine to analyze the symptoms and an emotion engine to recognize the user's emotions. It then lists possible illnesses from a disease database and provides a risk assessment and symptom information tailored to the user's emotions. Based on location information, it searches for appropriate medical facilities and presents treatment guidelines suited to the user. To ensure the user feels comfortable seeking medical attention, it also provides emotionally-based advice and support information.

[0647] Thus, the system of the present invention can support self-diagnosis of symptoms while also taking into consideration the user's emotional state, and can improve the efficiency of selecting a medical institution.

[0648] The following describes the processing flow.

[0649] Step 1:

[0650] The user enters specific symptoms into the device, such as "My back hurts."

[0651] Step 2:

[0652] The terminal receives the entered symptoms and sends them to the server.

[0653] Step 3:

[0654] The server receives the text data and passes it to a natural language processing engine for analysis.

[0655] Step 4:

[0656] The server uses a natural language processing engine to extract keywords such as "back" and "painful."

[0657] Step 5:

[0658] The server searches the disease database based on the extracted keywords and lists related diseases.

[0659] Step 6:

[0660] The server assesses the risk level for each listed disease.

[0661] For example, "muscle pain (low risk)", "sudden lower back pain (medium risk)", "kidney stones (medium risk)", and "shingles (high risk)".

[0662] Step 7:

[0663] The server lists other symptoms associated with each disease.

[0664] For example, "muscle pain: persistent pain after a specific type of exercise," "lower back pain: sharp pain after a sudden movement," "kidney stones: blood in the urine, frequent urination," and "shingles: rash and pain."

[0665] Step 8:

[0666] The server passes the user's input data to the emotion engine, which then recognizes the emotional state.

[0667] For example, it analyzes emotions such as "stress," "anxiety," and "calmness."

[0668] Step 9:

[0669] The server receives emotional states from the emotion engine and adjusts the information for the listed illnesses according to those emotions.

[0670] For example, for users who are feeling anxious, we provide reassuring messages and support information.

[0671] Step 10:

[0672] The device requests the user's location information.

[0673] Step 11:

[0674] The user grants permission to share their location information.

[0675] Step 12:

[0676] The device sends the user's location information to the server.

[0677] Step 13:

[0678] The server uses geographic information systems based on location data to search for nearby specialized medical facilities.

[0679] For example, "orthopedics," "urology," and "dermatology."

[0680] Step 14:

[0681] The server retrieves general treatment guidelines for each disease from the database.

[0682] For example, "Muscle pain: Warm compresses and light exercise," "Lower back pain: Rest and pain medication," "Kidney stones: Hydration and doctor's diagnosis," "Shingles: Early administration of antiviral drugs."

[0683] Step 15:

[0684] The server sends the processing results to the terminal.

[0685] Step 16:

[0686] The device displays to the user a list of possible illnesses and risk assessments, related symptoms, recommended medical facilities, treatment guidelines, and emotionally responsive messages and support information.

[0687] Through this process, users can obtain appropriate medical information and corresponding countermeasures tailored to their symptoms. In this way, a system that takes into account the user's emotional state can provide a more personalized response.

[0688] (Example 2)

[0689] 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".

[0690] Conventional diagnostic support systems merely list diseases based on symptoms entered by the user, without providing information that takes into account the user's emotional state. Furthermore, risk assessments for the listed diseases and the provision of related symptoms are limited, making it difficult to make an appropriate selection from multiple medical institutions. To solve these problems, the present invention aims to develop a system that takes the user's emotional state into account and provides more accurate diagnostic support.

[0691] 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.

[0692] In this invention, the server includes means for using a natural language processing engine to analyze the input symptoms and extract keywords, means for passing the user's input data to an emotion recognition engine to recognize the emotional state, and means for searching a disease database based on the extracted keywords and listing related diseases. This enables highly accurate diagnostic support while also taking into account the user's emotional state.

[0693] A "user" is an ordinary consumer or patient who uses the system to input symptom information.

[0694] An "input terminal" is a device used by users to input symptoms and receive information, and includes smartphones and personal computers.

[0695] A "server" is a central processing unit that processes, analyzes, and provides information about data.

[0696] A "natural language processing engine" is a software tool that analyzes text data entered by a user and extracts specific keywords.

[0697] An "emotion recognition engine" is a software system that analyzes a user's emotional state based on their input data.

[0698] A "disease database" is a collection of data that stores information about various diseases.

[0699] "Risk level" is an indicator used to assess the level of risk for a specified disease.

[0700] "Related symptoms" refer to other symptoms or signs that may be related to the user's main complaint.

[0701] "Location information" refers to data that indicates the user's current location, such as GPS data.

[0702] A "Geographic Information System" is a system that uses a user's location information to provide information that matches specific geographical conditions.

[0703] A "medical institution" refers to an organization or facility that provides treatment for illnesses and injuries, such as a hospital or clinic.

[0704] "Treatment guidelines" refer to general treatment methods and strategies for a specific disease.

[0705] "Information presentation method" refers to the techniques and formats used to show information to users.

[0706] "The content of the advice" refers to the advice and instructions provided to the user.

[0707] This invention is a system in which a user inputs symptom information, and based on that information, it lists possible diseases, recommends appropriate medical institutions, and provides general treatment guidelines. To achieve this, it incorporates an emotion engine that recognizes the user's emotions, thereby realizing a more user-friendly approach.

[0708] This system mainly consists of the following elements:

[0709] User terminal: A device used by the user to input symptoms and receive information; this includes smartphones and personal computers.

[0710] Server: A central processing system that processes, analyzes, and provides information about data.

[0711] Natural Language Processing Engine: A software tool that analyzes symptoms entered by a user and extracts meaning; examples include Google NLP API and IBM Watson.

[0712] Emotion engine: A system that recognizes emotions based on user input data and adjusts responses accordingly; Microsoft Azure Emotion API is an example of this.

[0713] Disease database: A collection of data containing information about various diseases.

[0714] Geographic Information Systems (GIS): These are systems that search for appropriate medical facilities based on the user's location information; the Google Maps API is an example of such a system.

[0715] The user uses a terminal to input specific symptoms (e.g., "My back hurts"). This information is immediately sent to the server. The server passes the received text data to a natural language processing engine, which extracts keywords (e.g., "back," "pain"). This process analyzes the entered symptoms and organizes the information. The server then passes the user's input data to an emotion recognition engine, which recognizes the emotional state (e.g., stress, anxiety, calmness, etc.). The results of this emotion analysis are sent back to the server.

[0716] Next, the server uses the extracted keywords and recognized emotional states to search a disease database and list related illnesses. For example, illnesses related to "back pain" might include "muscle pain," "lumbago," "kidney stones," and "shingles." Furthermore, the server assesses the risk level for each listed illness and also provides other related symptoms (e.g., blood in the urine, rash, etc.).

[0717] The server adjusts how information is presented and the content of advice based on the user's emotional state. For example, users who are feeling anxious will be given priority in receiving reassuring messages and supportive information. If the user allows location information to be shared, this location information is sent from the device to the server. The server uses a geographic information system to search for and recommend specialized medical facilities near the user's current location. Finally, the server retrieves general treatment guidelines for each disease from a disease database and presents them to the user.

[0718] For example, when a user enters "My back hurts," the device sends this to the server. The server analyzes the symptoms using a natural language processing engine and recognizes the user's emotions using an emotion engine. It then lists "muscle pain," "lower back pain," "kidney stones," and "shingles" from a disease database and evaluates the risk level and associated symptoms for each. Based on location information, it recommends "orthopedics" or "urology," and displays treatment guidelines such as applying a warm compress for "muscle pain" and staying hydrated for "kidney stones." Furthermore, it prioritizes providing reassuring messages to users who are feeling anxious, such as "Don't worry, early consultation will help."

[0719] Example of a prompt:

[0720] "When a user enters 'back pain,' please list possible illnesses and conduct a risk assessment. Additionally, display relevant treatment guidelines and recommended medical facilities based on location, and include messages to alleviate user anxiety."

[0721] Thus, the system of the present invention can support self-diagnosis of symptoms while also taking into consideration the user's emotional state, and can improve the efficiency of selecting a medical institution.

[0722] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0723] Step 1:

[0724] The user uses the terminal to input specific symptom information. For example, if the user inputs "My back hurts," the terminal immediately sends this information to the server. The input information is passed to the server as string data.

[0725] Step 2:

[0726] The server passes the received text data to a natural language processing engine. Here, a natural language processing engine such as Google NLP API or IBM Watson is used to extract keywords (e.g., "back" and "painful") from the text data. This analysis clarifies the meaning of the entered symptoms. The output is a list of keywords.

[0727] Step 3:

[0728] The server passes the extracted keywords to an emotion recognition engine (such as the Microsoft Azure Emotion API). The emotion recognition engine analyzes the user's emotional state (e.g., stress, anxiety, calmness, etc.) based on the user's input data. The output is the recognized emotional state and its intensity.

[0729] Step 4:

[0730] The server searches the disease database based on these keywords and emotional states. The disease database contains information on a variety of diseases, and diseases that match the keywords (e.g., "muscle pain," "lower back pain," "kidney stones," "shingles") are listed. The output is a list of the listed diseases.

[0731] Step 5:

[0732] The server assesses the risk level for each listed disease. Here, each disease is classified as having a risk level ranging from low risk to high risk. Additionally, it provides information on any additional symptoms associated with each disease (e.g., blood in the urine, rash, etc.). The output is a list of risk assessments and associated symptoms for each disease.

[0733] Step 6:

[0734] The server adjusts how information is presented and the content of advice based on the user's perceived emotional state. For example, a user feeling anxious will be given priority in receiving reassuring messages and support information. This involves modifying the presentation method and generating additional messages. The output consists of the adjusted information presentation method and additional messages.

[0735] Step 7:

[0736] When a user allows location information to be shared, the device sends that information to the server. The server uses a geographic information system (such as the Google Maps API) to search for medical facilities near the user's current location. The output is a list of recommended medical facilities.

[0737] Step 8:

[0738] The server retrieves general treatment guidelines for each disease from a disease database. For example, "muscle pain" might be described as requiring warm compresses or light exercise, while "kidney stones" might be described as requiring hydration and a doctor's diagnosis. The output is a list of treatment guidelines corresponding to each disease.

[0739] Step 9:

[0740] Ultimately, the device displays information to the user. This information, including a list of possible illnesses, risk assessments, related symptoms, recommended healthcare facilities, treatment guidelines, and additional emotionally-sensitive messages, is presented in a visual format for quick and easy viewing. This allows the user to take quick and appropriate action.

[0741] (Application Example 2)

[0742] 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."

[0743] In modern society, people are increasingly required to recognize symptoms in their daily lives and perform initial self-diagnosis. However, users often find it difficult to quickly find appropriate medical facilities and take time to understand what treatment is necessary for their symptoms. Furthermore, some symptoms require immediate medical attention, which can cause stress and anxiety. Therefore, there is a need for a system that allows users to quickly travel to appropriate medical facilities using autonomous vehicles.

[0744] 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.

[0745] In this invention, the server includes means for the user to input symptoms into an input terminal, means for the server to analyze the input symptoms and extract keywords using a natural language processing engine, means for the server to search a disease database based on the extracted keywords and list related diseases, means for the server to evaluate the risk level of the listed diseases and also provide other related symptoms, means for the server to recommend medical institutions capable of providing specialized treatment based on the user's location information, means for the server to provide general treatment guidelines for each disease, means for the terminal to display this information to the user, means for recognizing the user's emotions using an emotion engine and providing information appropriate to that state, and means for linking the route to the recommended medical institution to the navigation system of an autonomous vehicle. As a result, the user can receive a quick and appropriate response to their symptoms and travel to a medical institution safely using an autonomous vehicle.

[0746] 1. An "input terminal" is a device used by the user to input symptoms, and includes smartphones, tablets, etc.

[0747] 2. A "natural language processing engine" is a software tool that analyzes text data entered by a user and interprets its meaning.

[0748] 3. A "keyword" is a word or phrase with a specific meaning that is extracted from the symptom information entered by the user.

[0749] 4. A "disease database" is a collection of data in which information about various diseases is systematically stored.

[0750] 5. "Risk level" is an index that assesses the likelihood and severity of occurrence for each of the listed diseases.

[0751] 6. A "Geographic Information System" is a system that acquires a user's location information and searches for medical facilities based on geographical data.

[0752] 7. An "emotion engine" is a system that recognizes and analyzes the user's emotional state (stress, anxiety, calmness, etc.) from the user's input data.

[0753] 8. A "navigation system" is a system that guides an autonomous vehicle along a route to a specific destination.

[0754] 9. "Treatment guidelines" are information that summarizes the general treatment methods and coping strategies recommended for a specific disease.

[0755] 10. A "recommended medical institution" is a medical institution that can provide specialized treatment, suggested by the server based on the user's location information and symptoms.

[0756] This invention relates to a medical support system using an autonomous vehicle, and is a system that assists with navigation from symptom input to medical facilities. This system enables users to quickly input symptoms and provides appropriate medical facility suggestions and route guidance. Therefore, the system components include an input terminal, a server, an emotion engine, a disease database, a navigation system, and the like.

[0757] Hardware and software configuration

[0758] User terminal

[0759] The user terminal is a device such as a smartphone or a vehicle's infotainment system, used by the user to input symptoms. Voice input is also supported, utilizing a speech recognition engine (e.g., Google Speech API).

[0760] server

[0761] The servers are processing units located in the cloud and are responsible for the main functions of natural language processing, emotion recognition, and disease database search. The natural language processing engine is built using tools such as Hugging Face's Transformers and spaCy. The emotion engine similarly utilizes a pre-trained model from Transformers.

[0762] Disease Database

[0763] The disease database is a collection of data that stores detailed information about various diseases. The server searches this database based on symptom keywords and retrieves a list of related diseases.

[0764] Geographic Information Systems

[0765] Geographic information systems (e.g., Google Maps API) are used to obtain a user's current location and search for the most suitable medical facilities.

[0766] Navigation system

[0767] The navigation system is part of the autonomous vehicle and determines the route based on medical facility information provided from a server. Therefore, it also utilizes autonomous driving control software (e.g., Autoware).

[0768] System Operation Overview

[0769] 1. Input and analysis of symptoms:

[0770] When a user inputs a symptom through the in-car infotainment system (for example, "My back hurts"), a speech recognition engine converts this speech into text. A natural language processing engine then analyzes this text and extracts keywords related to the symptom.

[0771] 2. Emotion recognition:

[0772] The server uses an emotion engine to analyze the user's emotional state, along with the extracted keywords. For example, it can determine if the user is feeling anxious.

[0773] 3. Disease database search and risk assessment:

[0774] The server searches a disease database and lists diseases associated with the extracted keywords. Simultaneously, it assesses the risk level for each disease and provides information on other related symptoms.

[0775] 4. Provision of recommendations and treatment guidelines from medical institutions:

[0776] The server, having acquired the user's location information, uses a geographic information system to search for the most suitable medical facility for the user. Furthermore, it provides general treatment guidelines for various diseases.

[0777] 5. Displaying results and starting navigation:

[0778] The user's terminal displays a list of possible illnesses, a risk assessment, recommended medical facilities, and treatment guidelines. Finally, the navigation system coordinates the route to the recommended medical facility with the autonomous vehicle and begins navigation.

[0779] Specific examples and prompt statements

[0780] For example, if a user says "My back hurts," the system will perform the following actions.

[0781] Example of a prompt

[0782] User: "My back hurts."

[0783] System: "Analyzing symptoms and emotions..."

[0784] System: "Possible conditions include muscle pain, lumbago, kidney stones, and shingles. The recommended medical facility is your nearest orthopedic hospital."

[0785] System: "Starting autonomous driving to the destination medical facility."

[0786] In this way, this system can support self-diagnosis of symptoms while taking into account the user's emotional state, and improve the efficiency of selecting a medical institution. Users can travel to medical institutions safely through autonomous driving.

[0787] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0788] Step 1: The user voice-inputs the symptoms into the in-car infotainment system.

[0789] In terms of specific actions, the user inputs a symptom, such as "My back hurts," into the device by voice. The input data (voice) is converted into text data by a speech recognition engine (Google Speech API).

[0790] Input: Voice data ("My back hurts")

[0791] Output: Text data ("My back hurts")

[0792] Step 2: The server uses a natural language processing engine to parse the input text data.

[0793] The text data is sent to the server and analyzed by a natural language processing engine (such as Transformers or spaCy). Keywords ("back," "painful") are extracted.

[0794] Input: Text data ("My back hurts")

[0795] Output: Keywords ("back", "pain")

[0796] Step 3: The server uses the emotion engine to recognize the user's emotional state.

[0797] The extracted keywords and text data are passed to the emotion engine, which analyzes the user's emotional state (e.g., anxiety).

[0798] Input: Text data and keywords ("back pain", "back", "pain")

[0799] Output: Emotional state (anxiety)

[0800] Step 4: The server searches the disease database based on the extracted keywords.

[0801] The server uses keywords to search the disease database and lists related diseases (e.g., muscle pain, lumbago, kidney stones, shingles).

[0802] Input: Keywords ("back", "pain")

[0803] Output: Disease list (muscle pain, lumbago, kidney stones, shingles)

[0804] Step 5: The server assesses the risk level of the listed diseases and also provides any other associated symptoms.

[0805] The server assesses the risk level for each listed disease and also provides other symptoms that may be associated with each disease.

[0806] Input: Disease list (muscle pain, lumbago, kidney stones, shingles)

[0807] Output: Risk assessment and associated symptoms (muscle pain: low risk, lumbago: medium risk, kidney stones: medium risk, shingles: high risk)

[0808] Step 6: Based on the user's location information, the server recommends medical institutions that can provide specialized treatment.

[0809] The server obtains location information and uses a geographic information system (Google Maps API) to search for the most suitable medical facility. Recommended medical facilities include orthopedic hospitals, etc.

[0810] Input: Location information (latitude and longitude data)

[0811] Output: Recommended medical institutions (orthopedic hospitals, etc.)

[0812] Step 7: The server provides general treatment guidelines for each disease.

[0813] The server retrieves general treatment guidelines for the listed diseases from the database and provides them to the user.

[0814] Input: Disease list

[0815] Output: Treatment guidelines (Muscle pain: warm compresses, acute lower back pain: rest and painkillers, kidney stones: hydration, shingles: antiviral drugs, etc.)

[0816] Step 8: The device displays this information to the user.

[0817] The device visually displays to the user a list of possible diseases, risk assessments, recommended medical facilities, and treatment guidelines.

[0818] Input: Risk assessment, recommended medical institutions, treatment guidelines

[0819] Output: Display (disease list, risk assessment, recommended medical facilities, treatment guidelines)

[0820] Step 9: The server links the route to the recommended medical facility to the autonomous vehicle's navigation system.

[0821] The server sends location information of recommended medical facilities to the navigation system, which then instructs the autonomous vehicle to begin route guidance.

[0822] Input: Location information of recommended medical institutions

[0823] Output: Route guidance for autonomous vehicles

[0824] 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.

[0825] 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.

[0826] 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.

[0827] [Third Embodiment]

[0828] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0829] 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.

[0830] 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).

[0831] 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.

[0832] 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.

[0833] 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).

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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".

[0840] This invention is a system that lists possible diseases based on symptom information entered by the user, recommends appropriate medical institutions based on that list, and provides general treatment guidelines. The aim of this system is to help users quickly find out specific ways to deal with their symptoms.

[0841] System Configuration

[0842] This system consists mainly of the following elements.

[0843] User terminal: A device that allows the user to input symptoms and receive information. Examples include smartphones and personal computers.

[0844] Server: A central processing system that processes, analyzes, and provides information about data.

[0845] Natural Language Processing Engine: A software tool that analyzes symptoms entered by the user and extracts their meaning.

[0846] Disease database: A collection of data containing information about various diseases.

[0847] Geographic Information System: A system that searches for appropriate medical facilities based on the user's location information.

[0848] Program processing

[0849] User input

[0850] The user enters specific symptoms, such as "My back hurts," into the device. The device immediately sends the entered information to the server.

[0851] Natural Language Processing

[0852] The server passes the received text data to a natural language processing engine, which extracts keywords such as "back" and "pain." This process helps the system understand the entered symptoms and organize related information.

[0853] Disease Database Search

[0854] The server uses the extracted keywords to search the disease database. For example, diseases related to "back pain" might include "muscle pain," "lumbago," "kidney stones," and "shingles."

[0855] Risk assessment and symptom provision

[0856] For each listed disease, the server assesses its risk level. For example, "muscle pain" is rated as low risk, "lower back pain" as moderate risk, "kidney stones" as moderate risk, and "shingles" as high risk. Other symptoms associated with each disease (e.g., blood in the urine, rash, etc.) are also provided.

[0857] Recommended medical institutions

[0858] When a user allows their location to be shared, this location information is sent to the server via their device. The server uses a geographic information system to search for and recommend specialized medical facilities near the user's current location. For example, "Orthopedics," "Urology," and "Dermatology" may be listed.

[0859] Provision of treatment guidelines

[0860] The server retrieves general treatment guidelines for each disease from the database. For example, it might suggest that warm compresses and light exercise are effective for "muscle pain," rest and painkillers for "lumbago," hydration and a doctor's diagnosis for "kidney stones," and antiviral medication for "shingles."

[0861] Displaying Results

[0862] Ultimately, the terminal visually displays these processing results to the user. The user can see at a glance a list of possible diseases, risk assessments, related symptoms, recommended medical facilities, and treatment guidelines, enabling them to take quick and appropriate action.

[0863] Specific example

[0864] Simply by having the user type "My back hurts," the server assesses the likelihood and risk level of diseases related to the symptoms, and also suggests any other accompanying symptoms. Furthermore, it searches for appropriate medical facilities based on the user's location and provides concise and practical treatment guidelines. This entire process allows users to quickly receive appropriate medical advice.

[0865] Thus, the system of the present invention can support self-diagnosis of symptoms and improve the efficiency of selecting medical institutions.

[0866] The following describes the processing flow.

[0867] Step 1:

[0868] The user enters specific symptoms into the device, such as "My back hurts."

[0869] Step 2:

[0870] The terminal receives the entered symptoms and sends them to the server.

[0871] Step 3:

[0872] The server receives the text data and passes it to a natural language processing engine for analysis.

[0873] Step 4:

[0874] The server uses a natural language processing engine to extract keywords such as "back" and "painful."

[0875] Step 5:

[0876] The server searches the disease database based on the extracted keywords and lists related diseases.

[0877] Step 6:

[0878] The server assesses the risk level for each listed disease.

[0879] For example, "muscle pain (low risk)", "sudden lower back pain (medium risk)", "kidney stones (medium risk)", and "shingles (high risk)".

[0880] Step 7:

[0881] The server lists other symptoms associated with each disease.

[0882] For example, "muscle pain: persistent pain after a specific type of exercise," "lower back pain: sharp pain after a sudden movement," "kidney stones: blood in the urine, frequent urination," and "shingles: rash and pain."

[0883] Step 8:

[0884] The device requests the user's location information.

[0885] Step 9:

[0886] The user grants permission to share their location information.

[0887] Step 10:

[0888] The device sends the user's location information to the server.

[0889] Step 11:

[0890] The server uses geographic information systems based on location data to search for nearby specialized medical facilities.

[0891] For example, "orthopedics," "urology," and "dermatology."

[0892] Step 12:

[0893] The server retrieves general treatment guidelines for each disease from the database.

[0894] For example, "Muscle pain: Warm compresses and light exercise," "Lower back pain: Rest and pain medication," "Kidney stones: Hydration and doctor's diagnosis," "Shingles: Early administration of antiviral drugs."

[0895] Step 13:

[0896] The server sends the processing results to the terminal.

[0897] Step 14:

[0898] The device displays to the user a list of possible illnesses, a risk assessment, related symptoms, recommended medical facilities, and treatment guidelines.

[0899] Through this process, users can obtain appropriate medical information and countermeasures tailored to their symptoms.

[0900] (Example 1)

[0901] 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."

[0902] In modern society, it is crucial for users to obtain timely and appropriate medical information regarding their own symptoms. However, selecting a specialized medical institution and obtaining appropriate treatment guidelines often requires considerable time and effort. Traditional methods may rely on unreliable information for self-diagnosis, potentially leading to the selection of inappropriate medical institutions or the adoption of incorrect treatment plans. This poses a risk to the user's health.

[0903] 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.

[0904] In this invention, the server includes means for the user to input symptoms into an input device, means for analyzing the input symptoms using a natural language processing system and extracting keywords, means for searching a specialized database based on the extracted keywords and listing related diseases, means for evaluating the risk level of the listed diseases and providing other related symptoms, means for acquiring the user's location information and recommending medical institutions capable of providing specialized treatment, and means for providing general treatment guidelines for each disease. As a result, the user can quickly obtain appropriate medical information for their symptoms, and can efficiently select an appropriate medical institution and decide on a treatment plan.

[0905] An "input device" refers to a device used by a user to input symptoms. Specifically, this includes smartphones and personal computers.

[0906] A "computer on a network" refers to a central processing system that performs data processing, analysis, and information provision. It is commonly known as a server.

[0907] A "natural language processing system" refers to a software tool that analyzes text data entered by a user and extracts its meaning.

[0908] "Keywords" refer to important words or phrases extracted from the entered text. This helps identify the symptoms.

[0909] A "specialized database" refers to a collection of data containing information about various diseases. This includes information related to medical care and health.

[0910] "Listing" refers to displaying items related to a specific keyword in a list format.

[0911] "Risk level" refers to a standard for evaluating the risk level for a particular disease. It ranges from low risk to high risk.

[0912] "Location information" refers to data that indicates the user's current location. It is obtained using technologies such as GPS.

[0913] A "medical institution capable of providing specialized treatment" refers to a medical facility that has the ability to provide specialized treatment for a particular disease. This includes hospitals and clinics.

[0914] "Treatment guidelines" refer to information that outlines general treatment methods and coping strategies for specific diseases. This includes helpful advice for users.

[0915] This invention begins with the user inputting symptoms using an input device. A smartphone or personal computer can be used as the input device. The user inputs specific symptoms, such as "my back hurts," and the device then transmits this information to a server.

[0916] The server passes the received text data to a natural language processing system (e.g., a general-purpose natural language processing engine). This system extracts keywords from the input text and analyzes the meaning of the symptoms. For example, it can extract the keywords "back" and "pain" from the text.

[0917] The server then uses the extracted keywords to search specialized databases (e.g., medical databases). This search lists related conditions such as "muscle pain," "lower back pain," "kidney stones," and "shingles."

[0918] For each listed disease, the server assesses the risk level and provides information on other associated symptoms. For example, it might rate "muscle pain" as low risk, "lumbago" as moderate risk, "kidney stones" as moderate risk, and "shingles" as high risk. It also provides additional information on other associated symptoms, such as "hematuria" or "rash."

[0919] If the user allows location information to be shared, the device uses its GPS function to obtain information about its current location and sends it to the server. The server uses a geographic information system (e.g., a general-purpose geographic information system) to search for medical institutions near the user's current location that can provide specialized treatment. As a result, it recommends medical institutions such as "orthopedics," "urology," and "dermatology."

[0920] Next, the server retrieves general treatment guidelines for each disease from specialized databases (e.g., medical websites). Specifically, it suggests that warm compresses and light exercise are effective for "muscle pain," rest and painkillers for "lumbago," hydration and a doctor's diagnosis for "kidney stones," and antiviral medication for "shingles."

[0921] Ultimately, the device visually displays this information to the user. The user can see at a glance a list of possible illnesses, their risk levels, associated symptoms, recommended healthcare facilities, and treatment guidelines, enabling them to take quick and appropriate action.

[0922] Specific example

[0923] Simply by having the user type "My back hurts," the server assesses the likelihood and risk level of illnesses related to the symptoms, and also suggests any other accompanying symptoms. Furthermore, it searches for appropriate medical facilities based on the user's location and provides practical treatment guidelines. This entire process allows users to quickly receive appropriate medical advice.

[0924] Example of a prompt

[0925] "Please tell me about possible illnesses that could be causing back pain. Also, please explain the risk level, related symptoms, and treatment options in detail."

[0926] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0927] Step 1: User Input

[0928] The user uses an input device (smartphone or computer) to enter specific symptoms, such as "my back hurts." This input data is sent to the terminal in text format.

[0929] Step 2: Send a text message

[0930] The terminal immediately sends the entered text data to the server. This transmission is secure using the HTTPS protocol.

[0931] Input: Symptom text entered by the user (e.g., "My back hurts")

[0932] Output: Text data sent to the server

[0933] Step 3: Natural Language Processing

[0934] The server passes the received text data to a natural language processing system (e.g., a general-purpose natural language processing engine). The natural language processing system extracts important keywords (e.g., "back," "painful") from the input text and analyzes the context.

[0935] Input: Submitted text data

[0936] Output: Extracted keywords and contextual information

[0937] Step 4: Disease Database Search

[0938] The server uses the extracted keywords to search specialized databases (e.g., medical databases) and lists diseases associated with those keywords. For example, it might find diseases such as "muscle pain," "lower back pain," "kidney stones," and "shingles."

[0939] Input: Extracted keywords (e.g., "back", "pain")

[0940] Output: List of listed diseases

[0941] Step 5: Risk assessment and suggestion of additional symptoms

[0942] The server assesses the risk level for each listed disease and also provides additional related symptoms. A risk assessment algorithm is used for the assessment, and related symptoms such as "hematuria" and "rash" are also presented.

[0943] Input: List of listed diseases

[0944] Output: Risk assessment and associated additional symptoms

[0945] Step 6: Location Information Collection

[0946] When a user allows location information to be shared, the device uses its GPS function to obtain information about its current location and sends that data to the server. This data transmission also uses the HTTPS protocol.

[0947] Input: Permission to provide location information

[0948] Output: Acquired location data

[0949] Step 7: Proposal of Recommended Medical Institutions

[0950] The server uses the collected location information to access a geographic information system (e.g., a general-purpose geographic information system) to search for medical institutions near the user's current location that can provide specialized treatment. For example, medical institutions specializing in "orthopedics," "urology," and "dermatology" may be listed.

[0951] Input: Acquired location data

[0952] Output: List of recommended specialist medical institutions

[0953] Step 8: Provide general treatment guidelines

[0954] The server retrieves general treatment guidelines for each disease from specialized databases (e.g., specialist medical websites). Specifically, it recommends warm compresses and light exercise for muscle pain, rest and painkillers for acute lower back pain, hydration and a doctor's diagnosis for kidney stones, and antiviral medication for shingles.

[0955] Input: List of listed diseases

[0956] Output: Information on general treatment guidelines

[0957] Step 9: Displaying the results

[0958] Finally, the device visually displays these processing results to the user. The user can see at a glance a list of possible illnesses, risk assessments, related additional symptoms, recommended healthcare facilities, and treatment guidelines. This enables quick and appropriate medical consultation and response.

[0959] Input: All search and evaluation data

[0960] Output: Comprehensive information displayed to the user.

[0961] (Application Example 1)

[0962] 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."

[0963] Existing symptom diagnosis systems are limited to providing information such as disease listings and treatment guidelines, lacking a means to quickly and centrally manage subsequent medical service use, particularly payments for consultations and medication purchases. This often requires users to perform multiple operations, making the process cumbersome. To solve this problem, a system is needed that seamlessly integrates the user's process from entering symptoms to payment at the medical institution.

[0964] 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.

[0965] In this invention, the server includes means for enabling the user to make electronic payments for medical consultations and medication purchases, means for using a geographic information system to obtain the user's location information, and means for enabling electronic payments at recommended medical institutions. This allows the user to quickly and centrally perform a series of processes, from entering symptoms to paying for medical consultations.

[0966] An "input terminal" is a device used by users to input symptom information, and includes smartphones and personal computers.

[0967] A "natural language processing engine" is a software tool that analyzes text data entered by a user and extracts keywords.

[0968] A "disease database" is a collection of data containing detailed information about various diseases.

[0969] "Risk level" indicates an assessment of the risk for each listed disease, ranging from low risk to high risk.

[0970] "Location information" refers to data indicating the user's current location, and this information is used to search for appropriate medical facilities.

[0971] The method for recommending "medical institutions" is a function that suggests hospitals and clinics capable of providing specialized treatment based on the user's location information.

[0972] "Treatment guidelines" are information that provides general treatment methods and coping strategies for specific diseases.

[0973] "Electronic payment" refers to a method of making payments online for things like medical consultation fees and medication purchases.

[0974] A "Geographic Information System" is a system that acquires a user's location information and handles geographical data.

[0975] "Detailed data" refers to data that includes specific information about the listed diseases.

[0976] A "database for managing payment information" is a collection of data that centrally manages payment information related to medical consultation fees, medication purchases, and other related matters.

[0977] This invention integrates electronic payment functionality into a system that lists possible diseases based on symptom information entered by the user, recommends appropriate medical institutions based on that list, and provides general treatment guidelines. This system consists of the following main elements:

[0978] System Configuration

[0979] 1. User terminal: This is a device used by the user to input symptoms and receive diagnostic results, recommended medical institutions, and treatment guidelines. Examples include smartphones and personal computers.

[0980] 2. Server: A central processing system that processes, analyzes, and provides information on data. This server includes the following hardware and software:

[0981] 3. Natural Language Processing Engine: This is a software tool that analyzes symptoms entered by the user and extracts necessary keywords. Specifically, libraries such as spaCy and NLTK are used.

[0982] 4. Disease Database: This is a collection of data that stores information about various diseases. This allows users to search for related diseases based on the symptoms they enter.

[0983] 5. Geographic Information Systems: These are systems that acquire a user's location information and use it to search for appropriate medical facilities. The Geopy library is a concrete example.

[0984] 6. Electronic Payment System: This is a payment function that allows users to pay for medical consultations and purchase medications online. The Stripe API, for example, will be implemented.

[0985] System operation

[0986] 1. Inputting symptom information: The user enters specific symptoms (e.g., "My back hurts") on the device. The device immediately sends this information to the server.

[0987] 2. Natural Language Processing: The server passes the received text data to a natural language processing engine, which extracts keywords. This process helps the server understand the entered symptoms and organize relevant information.

[0988] 3. Disease Database Search: The server uses the extracted keywords to search the disease database and lists the relevant diseases.

[0989] 4. Risk assessment and provision of related symptoms: The server assesses the risk level for each listed disease and also provides other symptoms associated with each disease (e.g., blood in the urine, rash, etc.).

[0990] 5. Suggestion of Recommended Medical Institutions: When a user provides location information, the server uses a geographic information system to search for and recommend appropriate medical institutions.

[0991] 6. Provision of treatment guidelines: The server retrieves general treatment guidelines for each disease from the database and provides them to the user.

[0992] 7. Electronic payment function: Allows users to pay for medical consultations and medication purchases at recommended medical institutions using electronic payment.

[0993] Specific example

[0994] Simply by the user typing "back pain," the system provides a list of possible illnesses (e.g., "muscle pain," "lumbago," "kidney stones"), assesses their risk level, and suggests other related symptoms. Furthermore, it searches for appropriate medical facilities based on location information and offers the option to pay for consultations electronically.

[0995] Examples of prompt statements

[0996] The user entered "My back hurts." Display a list of possible illnesses, risk assessment, related symptoms, geographically nearby medical facilities, and general treatment guidelines, and also provide an option to pay for the consultation using electronic payment.

[0997] In this way, users can complete the entire process from input to payment quickly and centrally, making it possible to access medical services more smoothly.

[0998] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0999] Step 1: The user enters the symptoms into the device and sends them.

[1000] Users use devices such as smartphones or computers to input specific symptoms (for example, "My back hurts") and submit this information. The input data is sent to the server in text format.

[1001] Step 2: The server analyzes the symptoms using a natural language processing engine and extracts keywords.

[1002] The server passes the received text data to a natural language processing engine (e.g., spaCy or NLTK) to extract keywords related to the symptoms (e.g., "back," "pain"). This analysis converts the symptom information into structured data.

[1003] Step 3: The server searches the disease database based on the extracted keywords and lists the relevant diseases.

[1004] The server uses the extracted keywords to search the disease database and lists related diseases (e.g., "muscle pain," "lower back pain," "kidney stones," etc.). This list is generated and passed to the next processing step.

[1005] Step 4: Assess the risk level of the listed diseases and provide any other related symptoms.

[1006] The server assesses the risk level for each listed disease (from low risk to high risk) and provides other symptoms associated with each disease (e.g., "fatigue" for "muscle pain," "hematuria" for "kidney stones"). This information is then compiled together.

[1007] Step 5: The server searches for and recommends appropriate medical facilities based on the user's location information.

[1008] When a user allows location information to be shared, the location information sent from the device is sent to the server. The server uses a geographic information system (e.g., Geopy) to search for medical facilities near the user's current location and generates a list of recommended medical facilities.

[1009] Step 6: The server provides general treatment guidelines for each disease.

[1010] The server retrieves and compiles general treatment guidelines for each disease from a database (for example, "warm compresses" and "light exercise" for "muscle pain," and "rest and painkillers" for "lumbago").

[1011] Step 7: The device displays this information to the user.

[1012] The terminal visually displays information transmitted from the server to the user (a list of possible diseases, risk assessment, related symptoms, recommended medical facilities, and treatment guidelines). Based on this information, the user can decide on their next course of action.

[1013] Step 8: The user pays for consultation fees and medication purchases at the recommended medical institution using electronic payment.

[1014] After a user receives a medical consultation or purchases medication, they make a payment using an electronic payment system (e.g., Stripe). This operation is also performed from the terminal, and the payment information is sent to and recorded on the server.

[1015] In this way, a series of steps allows users to handle everything from entering their symptoms to paying for medical consultations in one place.

[1016] 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.

[1017] This invention is a system that lists possible diseases based on symptom information entered by the user, recommends appropriate medical institutions based on that list, and also provides general treatment guidelines. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it achieves a more user-friendly approach.

[1018] System Configuration

[1019] This system mainly consists of the following elements:

[1020] User terminal: A device that allows the user to input symptoms and receive information. Examples include smartphones and personal computers.

[1021] Server: A central processing system that processes, analyzes, and provides information about data.

[1022] Natural Language Processing Engine: A software tool that analyzes symptoms entered by the user and extracts their meaning.

[1023] Emotion engine: A system that recognizes emotions based on user input and adjusts its response accordingly.

[1024] Disease database: A collection of data containing information about various diseases.

[1025] Geographic Information System: A system that searches for appropriate medical facilities based on the user's location information.

[1026] Program processing

[1027] User input

[1028] The user enters specific symptoms, such as "My back hurts," into the device. The device immediately sends the entered information to the server.

[1029] Natural Language Processing

[1030] The server passes the received text data to a natural language processing engine, which extracts keywords such as "back" and "pain." This process helps the system understand the entered symptoms and organize related information.

[1031] emotion recognition

[1032] The server passes the user's input data to the emotion engine, which recognizes the user's emotional state (e.g., stress, anxiety, calmness, etc.). The emotion engine analyzes the emotional state and sends the results back to the server.

[1033] Disease Database Search

[1034] The server uses the extracted keywords and recognized emotional states to search a disease database and list related illnesses. For example, illnesses related to "back pain" might include "muscle pain," "lumbago," "kidney stones," and "shingles."

[1035] Risk assessment and symptom provision

[1036] For each listed disease, the server assesses its risk level. For example, "muscle pain" is rated as low risk, "lower back pain" as moderate risk, "kidney stones" as moderate risk, and "shingles" as high risk. Other symptoms associated with each disease (e.g., blood in the urine, rash, etc.) are also provided.

[1037] Emotional response

[1038] The server adjusts how information is presented and the content of advice based on the recognized emotional state of the user. For example, if the user is feeling anxious, it prioritizes providing reassuring messages and support information.

[1039] Recommended medical institutions

[1040] When a user allows their location to be shared, this location information is sent to the server via their device. The server uses a geographic information system to search for and recommend specialized medical facilities near the user's current location. For example, "orthopedics," "urology," and "dermatology" may be listed.

[1041] Provision of treatment guidelines

[1042] The server retrieves general treatment guidelines for each disease from the database. For example, it might suggest that warm compresses and light exercise are effective for "muscle pain," rest and painkillers for "lumbago," hydration and a doctor's diagnosis for "kidney stones," and antiviral medication for "shingles."

[1043] Displaying Results

[1044] Ultimately, the terminal visually displays these processing results to the user. The user can see at a glance a list of possible diseases, risk assessments, related symptoms, recommended medical facilities, and treatment guidelines, enabling them to take quick and appropriate action.

[1045] Specific example

[1046] When a user enters "My back hurts," the server uses a natural language processing engine to analyze the symptoms and an emotion engine to recognize the user's emotions. It then lists possible illnesses from a disease database and provides a risk assessment and symptom information tailored to the user's emotions. Based on location information, it searches for appropriate medical facilities and presents treatment guidelines suited to the user. To ensure the user feels comfortable seeking medical attention, it also provides emotionally-based advice and support information.

[1047] Thus, the system of the present invention can support self-diagnosis of symptoms while also taking into consideration the user's emotional state, and can improve the efficiency of selecting a medical institution.

[1048] The following describes the processing flow.

[1049] Step 1:

[1050] The user enters specific symptoms into the device, such as "My back hurts."

[1051] Step 2:

[1052] The terminal receives the entered symptoms and sends them to the server.

[1053] Step 3:

[1054] The server receives the text data and passes it to a natural language processing engine for analysis.

[1055] Step 4:

[1056] The server uses a natural language processing engine to extract keywords such as "back" and "painful."

[1057] Step 5:

[1058] The server searches the disease database based on the extracted keywords and lists related diseases.

[1059] Step 6:

[1060] The server assesses the risk level for each listed disease.

[1061] For example, "muscle pain (low risk)", "sudden lower back pain (medium risk)", "kidney stones (medium risk)", and "shingles (high risk)".

[1062] Step 7:

[1063] The server lists other symptoms associated with each disease.

[1064] For example, "muscle pain: persistent pain after a specific type of exercise," "lower back pain: sharp pain after a sudden movement," "kidney stones: blood in the urine, frequent urination," and "shingles: rash and pain."

[1065] Step 8:

[1066] The server passes the user's input data to the emotion engine, which then recognizes the emotional state.

[1067] For example, it analyzes emotions such as "stress," "anxiety," and "calmness."

[1068] Step 9:

[1069] The server receives emotional states from the emotion engine and adjusts the information for the listed illnesses according to those emotions.

[1070] For example, for users who are feeling anxious, we provide reassuring messages and support information.

[1071] Step 10:

[1072] The device requests the user's location information.

[1073] Step 11:

[1074] The user grants permission to share their location information.

[1075] Step 12:

[1076] The device sends the user's location information to the server.

[1077] Step 13:

[1078] The server uses geographic information systems based on location data to search for nearby specialized medical facilities.

[1079] For example, "orthopedics," "urology," and "dermatology."

[1080] Step 14:

[1081] The server retrieves general treatment guidelines for each disease from the database.

[1082] For example, "Muscle pain: Warm compresses and light exercise," "Lower back pain: Rest and pain medication," "Kidney stones: Hydration and doctor's diagnosis," "Shingles: Early administration of antiviral drugs."

[1083] Step 15:

[1084] The server sends the processing results to the terminal.

[1085] Step 16:

[1086] The device displays to the user a list of possible illnesses and risk assessments, related symptoms, recommended medical facilities, treatment guidelines, and emotionally responsive messages and support information.

[1087] Through this process, users can obtain appropriate medical information and corresponding countermeasures tailored to their symptoms. In this way, a system that takes into account the user's emotional state can provide a more personalized response.

[1088] (Example 2)

[1089] 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."

[1090] Conventional diagnostic support systems merely list diseases based on symptoms entered by the user, without providing information that takes into account the user's emotional state. Furthermore, risk assessments for the listed diseases and the provision of related symptoms are limited, making it difficult to make an appropriate selection from multiple medical institutions. To solve these problems, the present invention aims to develop a system that takes the user's emotional state into account and provides more accurate diagnostic support.

[1091] 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.

[1092] In this invention, the server includes means for using a natural language processing engine to analyze the input symptoms and extract keywords, means for passing the user's input data to an emotion recognition engine to recognize the emotional state, and means for searching a disease database based on the extracted keywords and listing related diseases. This enables highly accurate diagnostic support while also taking into account the user's emotional state.

[1093] A "user" is an ordinary consumer or patient who uses the system to input symptom information.

[1094] An "input terminal" is a device used by users to input symptoms and receive information, and includes smartphones and personal computers.

[1095] A "server" is a central processing unit that processes, analyzes, and provides information about data.

[1096] A "natural language processing engine" is a software tool that analyzes text data entered by a user and extracts specific keywords.

[1097] An "emotion recognition engine" is a software system that analyzes a user's emotional state based on their input data.

[1098] A "disease database" is a collection of data that stores information about various diseases.

[1099] "Risk level" is an indicator used to assess the level of risk for a specified disease.

[1100] "Related symptoms" refer to other symptoms or signs that may be related to the user's main complaint.

[1101] "Location information" refers to data that indicates the user's current location, such as GPS data.

[1102] A "Geographic Information System" is a system that uses a user's location information to provide information that matches specific geographical conditions.

[1103] A "medical institution" refers to an organization or facility that provides treatment for illnesses and injuries, such as a hospital or clinic.

[1104] "Treatment guidelines" refer to general treatment methods and strategies for a specific disease.

[1105] "Information presentation method" refers to the techniques and formats used to show information to users.

[1106] "The content of the advice" refers to the advice and instructions provided to the user.

[1107] This invention is a system in which a user inputs symptom information, and based on that information, it lists possible diseases, recommends appropriate medical institutions, and provides general treatment guidelines. To achieve this, it incorporates an emotion engine that recognizes the user's emotions, thereby realizing a more user-friendly approach.

[1108] This system mainly consists of the following elements:

[1109] User terminal: A device used by the user to input symptoms and receive information; this includes smartphones and personal computers.

[1110] Server: A central processing system that processes, analyzes, and provides information about data.

[1111] Natural Language Processing Engine: A software tool that analyzes symptoms entered by a user and extracts meaning; examples include Google NLP API and IBM Watson.

[1112] Emotion engine: A system that recognizes emotions based on user input data and adjusts responses accordingly; Microsoft Azure Emotion API is an example of this.

[1113] Disease database: A collection of data containing information about various diseases.

[1114] Geographic Information Systems (GIS): These are systems that search for appropriate medical facilities based on the user's location information; the Google Maps API is an example of such a system.

[1115] The user uses a terminal to input specific symptoms (e.g., "My back hurts"). This information is immediately sent to the server. The server passes the received text data to a natural language processing engine, which extracts keywords (e.g., "back," "pain"). This process analyzes the entered symptoms and organizes the information. The server then passes the user's input data to an emotion recognition engine, which recognizes the emotional state (e.g., stress, anxiety, calmness, etc.). The results of this emotion analysis are sent back to the server.

[1116] Next, the server uses the extracted keywords and recognized emotional states to search a disease database and list related illnesses. For example, illnesses related to "back pain" might include "muscle pain," "lumbago," "kidney stones," and "shingles." Furthermore, the server assesses the risk level for each listed illness and also provides other related symptoms (e.g., blood in the urine, rash, etc.).

[1117] The server adjusts how information is presented and the content of advice based on the user's emotional state. For example, users who are feeling anxious will be given priority in receiving reassuring messages and supportive information. If the user allows location information to be shared, this location information is sent from the device to the server. The server uses a geographic information system to search for and recommend specialized medical facilities near the user's current location. Finally, the server retrieves general treatment guidelines for each disease from a disease database and presents them to the user.

[1118] For example, when a user enters "My back hurts," the device sends this to the server. The server analyzes the symptoms using a natural language processing engine and recognizes the user's emotions using an emotion engine. It then lists "muscle pain," "lower back pain," "kidney stones," and "shingles" from a disease database and evaluates the risk level and associated symptoms for each. Based on location information, it recommends "orthopedics" or "urology," and displays treatment guidelines such as applying a warm compress for "muscle pain" and staying hydrated for "kidney stones." Furthermore, it prioritizes providing reassuring messages to users who are feeling anxious, such as "Don't worry, early consultation will help."

[1119] Example of a prompt:

[1120] "When a user enters 'back pain,' please list possible illnesses and conduct a risk assessment. Additionally, display relevant treatment guidelines and recommended medical facilities based on location, and include messages to alleviate user anxiety."

[1121] Thus, the system of the present invention can support self-diagnosis of symptoms while also taking into consideration the user's emotional state, and can improve the efficiency of selecting a medical institution.

[1122] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1123] Step 1:

[1124] The user uses the terminal to input specific symptom information. For example, if the user inputs "My back hurts," the terminal immediately sends this information to the server. The input information is passed to the server as string data.

[1125] Step 2:

[1126] The server passes the received text data to a natural language processing engine. Here, a natural language processing engine such as Google NLP API or IBM Watson is used to extract keywords (e.g., "back" and "painful") from the text data. This analysis clarifies the meaning of the entered symptoms. The output is a list of keywords.

[1127] Step 3:

[1128] The server passes the extracted keywords to an emotion recognition engine (such as the Microsoft Azure Emotion API). The emotion recognition engine analyzes the user's emotional state (e.g., stress, anxiety, calmness, etc.) based on the user's input data. The output is the recognized emotional state and its intensity.

[1129] Step 4:

[1130] The server searches the disease database based on these keywords and emotional states. The disease database contains information on a variety of diseases, and diseases that match the keywords (e.g., "muscle pain," "lower back pain," "kidney stones," "shingles") are listed. The output is a list of the listed diseases.

[1131] Step 5:

[1132] The server assesses the risk level for each listed disease. Here, each disease is classified as having a risk level ranging from low risk to high risk. Additionally, it provides information on any additional symptoms associated with each disease (e.g., blood in the urine, rash, etc.). The output is a list of risk assessments and associated symptoms for each disease.

[1133] Step 6:

[1134] The server adjusts how information is presented and the content of advice based on the user's perceived emotional state. For example, a user feeling anxious will be given priority in receiving reassuring messages and support information. This involves modifying the presentation method and generating additional messages. The output consists of the adjusted information presentation method and additional messages.

[1135] Step 7:

[1136] When a user allows location information to be shared, the device sends that information to the server. The server uses a geographic information system (such as the Google Maps API) to search for medical facilities near the user's current location. The output is a list of recommended medical facilities.

[1137] Step 8:

[1138] The server retrieves general treatment guidelines for each disease from a disease database. For example, "muscle pain" might be described as requiring warm compresses or light exercise, while "kidney stones" might be described as requiring hydration and a doctor's diagnosis. The output is a list of treatment guidelines corresponding to each disease.

[1139] Step 9:

[1140] Ultimately, the device displays information to the user. This information, including a list of possible illnesses, risk assessments, related symptoms, recommended healthcare facilities, treatment guidelines, and additional emotionally-sensitive messages, is presented in a visual format for quick and easy viewing. This allows the user to take quick and appropriate action.

[1141] (Application Example 2)

[1142] 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."

[1143] In modern society, people are increasingly required to recognize symptoms in their daily lives and perform initial self-diagnosis. However, users often find it difficult to quickly find appropriate medical facilities and take time to understand what treatment is necessary for their symptoms. Furthermore, some symptoms require immediate medical attention, which can cause stress and anxiety. Therefore, there is a need for a system that allows users to quickly travel to appropriate medical facilities using autonomous vehicles.

[1144] 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.

[1145] In this invention, the server includes means for the user to input symptoms into an input terminal, means for the server to analyze the input symptoms and extract keywords using a natural language processing engine, means for the server to search a disease database based on the extracted keywords and list related diseases, means for the server to evaluate the risk level of the listed diseases and also provide other related symptoms, means for the server to recommend medical institutions capable of providing specialized treatment based on the user's location information, means for the server to provide general treatment guidelines for each disease, means for the terminal to display this information to the user, means for recognizing the user's emotions using an emotion engine and providing information appropriate to that state, and means for linking the route to the recommended medical institution to the navigation system of an autonomous vehicle. As a result, the user can receive a quick and appropriate response to their symptoms and travel to a medical institution safely using an autonomous vehicle.

[1146] 1. An "input terminal" is a device used by the user to input symptoms, and includes smartphones, tablets, etc.

[1147] 2. A "natural language processing engine" is a software tool that analyzes text data entered by a user and interprets its meaning.

[1148] 3. A "keyword" is a word or phrase with a specific meaning that is extracted from the symptom information entered by the user.

[1149] 4. A "disease database" is a collection of data in which information about various diseases is systematically stored.

[1150] 5. "Risk level" is an index that assesses the likelihood and severity of occurrence for each of the listed diseases.

[1151] 6. A "Geographic Information System" is a system that acquires a user's location information and searches for medical facilities based on geographical data.

[1152] 7. An "emotion engine" is a system that recognizes and analyzes the user's emotional state (stress, anxiety, calmness, etc.) from the user's input data.

[1153] 8. A "navigation system" is a system that guides an autonomous vehicle along a route to a specific destination.

[1154] 9. "Treatment guidelines" are information that summarizes the general treatment methods and coping strategies recommended for a specific disease.

[1155] 10. A "recommended medical institution" is a medical institution that can provide specialized treatment, suggested by the server based on the user's location information and symptoms.

[1156] This invention relates to a medical support system using an autonomous vehicle, and is a system that assists with navigation from symptom input to medical facilities. This system enables users to quickly input symptoms and provides appropriate medical facility suggestions and route guidance. Therefore, the system components include an input terminal, a server, an emotion engine, a disease database, a navigation system, and the like.

[1157] Hardware and software configuration

[1158] User terminal

[1159] The user terminal is a device such as a smartphone or a vehicle's infotainment system, used by the user to input symptoms. Voice input is also supported, utilizing a speech recognition engine (e.g., Google Speech API).

[1160] server

[1161] The servers are processing units located in the cloud and are responsible for the main functions of natural language processing, emotion recognition, and disease database search. The natural language processing engine is built using tools such as Hugging Face's Transformers and spaCy. The emotion engine similarly utilizes a pre-trained model from Transformers.

[1162] Disease Database

[1163] The disease database is a collection of data that stores detailed information about various diseases. The server searches this database based on symptom keywords and retrieves a list of related diseases.

[1164] Geographic Information Systems

[1165] Geographic information systems (e.g., Google Maps API) are used to obtain a user's current location and search for the most suitable medical facilities.

[1166] Navigation system

[1167] The navigation system is part of the autonomous vehicle and determines the route based on medical facility information provided from a server. Therefore, it also utilizes autonomous driving control software (e.g., Autoware).

[1168] System Operation Overview

[1169] 1. Input and analysis of symptoms:

[1170] When a user inputs a symptom through the in-car infotainment system (for example, "My back hurts"), a speech recognition engine converts this speech into text. A natural language processing engine then analyzes this text and extracts keywords related to the symptom.

[1171] 2. Emotion recognition:

[1172] The server uses an emotion engine to analyze the user's emotional state, along with the extracted keywords. For example, it can determine if the user is feeling anxious.

[1173] 3. Disease database search and risk assessment:

[1174] The server searches a disease database and lists diseases associated with the extracted keywords. Simultaneously, it assesses the risk level for each disease and provides information on other related symptoms.

[1175] 4. Provision of recommendations and treatment guidelines from medical institutions:

[1176] The server, having acquired the user's location information, uses a geographic information system to search for the most suitable medical facility for the user. Furthermore, it provides general treatment guidelines for various diseases.

[1177] 5. Displaying results and starting navigation:

[1178] The user's terminal displays a list of possible illnesses, a risk assessment, recommended medical facilities, and treatment guidelines. Finally, the navigation system coordinates the route to the recommended medical facility with the autonomous vehicle and begins navigation.

[1179] Specific examples and prompt statements

[1180] For example, if a user says "My back hurts," the system will perform the following actions.

[1181] Example of a prompt

[1182] User: "My back hurts."

[1183] System: "Analyzing symptoms and emotions..."

[1184] System: "Possible conditions include muscle pain, lumbago, kidney stones, and shingles. The recommended medical facility is your nearest orthopedic hospital."

[1185] System: "Starting autonomous driving to the destination medical facility."

[1186] In this way, this system can support self-diagnosis of symptoms while taking into account the user's emotional state, and improve the efficiency of selecting a medical institution. Users can travel to medical institutions safely through autonomous driving.

[1187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1188] Step 1: The user voice-inputs the symptoms into the in-car infotainment system.

[1189] In terms of specific actions, the user inputs a symptom, such as "My back hurts," into the device by voice. The input data (voice) is converted into text data by a speech recognition engine (Google Speech API).

[1190] Input: Voice data ("My back hurts")

[1191] Output: Text data ("My back hurts")

[1192] Step 2: The server uses a natural language processing engine to parse the input text data.

[1193] The text data is sent to the server and analyzed by a natural language processing engine (such as Transformers or spaCy). Keywords ("back," "painful") are extracted.

[1194] Input: Text data ("My back hurts")

[1195] Output: Keywords ("back", "pain")

[1196] Step 3: The server uses the emotion engine to recognize the user's emotional state.

[1197] The extracted keywords and text data are passed to the emotion engine, which analyzes the user's emotional state (e.g., anxiety).

[1198] Input: Text data and keywords ("back pain", "back", "pain")

[1199] Output: Emotional state (anxiety)

[1200] Step 4: The server searches the disease database based on the extracted keywords.

[1201] The server uses keywords to search the disease database and lists related diseases (e.g., muscle pain, lumbago, kidney stones, shingles).

[1202] Input: Keywords ("back", "pain")

[1203] Output: Disease list (muscle pain, lumbago, kidney stones, shingles)

[1204] Step 5: The server assesses the risk level of the listed diseases and also provides any other associated symptoms.

[1205] The server assesses the risk level for each listed disease and also provides other symptoms that may be associated with each disease.

[1206] Input: Disease list (muscle pain, lumbago, kidney stones, shingles)

[1207] Output: Risk assessment and associated symptoms (muscle pain: low risk, lumbago: medium risk, kidney stones: medium risk, shingles: high risk)

[1208] Step 6: Based on the user's location information, the server recommends medical institutions that can provide specialized treatment.

[1209] The server obtains location information and uses a geographic information system (Google Maps API) to search for the most suitable medical facility. Recommended medical facilities include orthopedic hospitals, etc.

[1210] Input: Location information (latitude and longitude data)

[1211] Output: Recommended medical institutions (orthopedic hospitals, etc.)

[1212] Step 7: The server provides general treatment guidelines for each disease.

[1213] The server retrieves general treatment guidelines for the listed diseases from the database and provides them to the user.

[1214] Input: Disease list

[1215] Output: Treatment guidelines (Muscle pain: warm compresses, acute lower back pain: rest and painkillers, kidney stones: hydration, shingles: antiviral drugs, etc.)

[1216] Step 8: The device displays this information to the user.

[1217] The device visually displays to the user a list of possible diseases, risk assessments, recommended medical facilities, and treatment guidelines.

[1218] Input: Risk assessment, recommended medical institutions, treatment guidelines

[1219] Output: Display (disease list, risk assessment, recommended medical facilities, treatment guidelines)

[1220] Step 9: The server links the route to the recommended medical facility to the autonomous vehicle's navigation system.

[1221] The server sends location information of recommended medical facilities to the navigation system, which then instructs the autonomous vehicle to begin route guidance.

[1222] Input: Location information of recommended medical institutions

[1223] Output: Route guidance for autonomous vehicles

[1224] 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.

[1225] 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.

[1226] 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.

[1227] [Fourth Embodiment]

[1228] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1229] 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.

[1230] 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).

[1231] 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.

[1232] 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.

[1233] 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).

[1234] 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.

[1235] 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.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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".

[1241] This invention is a system that lists possible diseases based on symptom information entered by the user, recommends appropriate medical institutions based on that list, and provides general treatment guidelines. The aim of this system is to help users quickly find out specific ways to deal with their symptoms.

[1242] System Configuration

[1243] This system consists mainly of the following elements.

[1244] User terminal: A device that allows the user to input symptoms and receive information. Examples include smartphones and personal computers.

[1245] Server: A central processing system that processes, analyzes, and provides information about data.

[1246] Natural Language Processing Engine: A software tool that analyzes symptoms entered by the user and extracts their meaning.

[1247] Disease database: A collection of data containing information about various diseases.

[1248] Geographic Information System: A system that searches for appropriate medical facilities based on the user's location information.

[1249] Program processing

[1250] User input

[1251] The user enters specific symptoms, such as "My back hurts," into the device. The device immediately sends the entered information to the server.

[1252] Natural Language Processing

[1253] The server passes the received text data to a natural language processing engine, which extracts keywords such as "back" and "pain." This process helps the system understand the entered symptoms and organize related information.

[1254] Disease Database Search

[1255] The server uses the extracted keywords to search the disease database. For example, diseases related to "back pain" might include "muscle pain," "lumbago," "kidney stones," and "shingles."

[1256] Risk assessment and symptom provision

[1257] For each listed disease, the server assesses its risk level. For example, "muscle pain" is rated as low risk, "lower back pain" as moderate risk, "kidney stones" as moderate risk, and "shingles" as high risk. Other symptoms associated with each disease (e.g., blood in the urine, rash, etc.) are also provided.

[1258] Recommended medical institutions

[1259] When a user allows their location to be shared, this location information is sent to the server via their device. The server uses a geographic information system to search for and recommend specialized medical facilities near the user's current location. For example, "Orthopedics," "Urology," and "Dermatology" may be listed.

[1260] Provision of treatment guidelines

[1261] The server retrieves general treatment guidelines for each disease from the database. For example, it might suggest that warm compresses and light exercise are effective for "muscle pain," rest and painkillers for "lumbago," hydration and a doctor's diagnosis for "kidney stones," and antiviral medication for "shingles."

[1262] Displaying Results

[1263] Ultimately, the terminal visually displays these processing results to the user. The user can see at a glance a list of possible diseases, risk assessments, related symptoms, recommended medical facilities, and treatment guidelines, enabling them to take quick and appropriate action.

[1264] Specific example

[1265] Simply by having the user type "My back hurts," the server assesses the likelihood and risk level of diseases related to the symptoms, and also suggests any other accompanying symptoms. Furthermore, it searches for appropriate medical facilities based on the user's location and provides concise and practical treatment guidelines. This entire process allows users to quickly receive appropriate medical advice.

[1266] Thus, the system of the present invention can support self-diagnosis of symptoms and improve the efficiency of selecting medical institutions.

[1267] The following describes the processing flow.

[1268] Step 1:

[1269] The user enters specific symptoms into the device, such as "My back hurts."

[1270] Step 2:

[1271] The terminal receives the entered symptoms and sends them to the server.

[1272] Step 3:

[1273] The server receives the text data and passes it to a natural language processing engine for analysis.

[1274] Step 4:

[1275] The server uses a natural language processing engine to extract keywords such as "back" and "painful."

[1276] Step 5:

[1277] The server searches the disease database based on the extracted keywords and lists related diseases.

[1278] Step 6:

[1279] The server assesses the risk level for each listed disease.

[1280] For example, "muscle pain (low risk)", "sudden lower back pain (medium risk)", "kidney stones (medium risk)", and "shingles (high risk)".

[1281] Step 7:

[1282] The server lists other symptoms associated with each disease.

[1283] For example, "muscle pain: persistent pain after a specific type of exercise," "lower back pain: sharp pain after a sudden movement," "kidney stones: blood in the urine, frequent urination," and "shingles: rash and pain."

[1284] Step 8:

[1285] The device requests the user's location information.

[1286] Step 9:

[1287] The user grants permission to share their location information.

[1288] Step 10:

[1289] The device sends the user's location information to the server.

[1290] Step 11:

[1291] The server uses geographic information systems based on location data to search for nearby specialized medical facilities.

[1292] For example, "orthopedics," "urology," and "dermatology."

[1293] Step 12:

[1294] The server retrieves general treatment guidelines for each disease from the database.

[1295] For example, "Muscle pain: Warm compresses and light exercise," "Lower back pain: Rest and pain medication," "Kidney stones: Hydration and doctor's diagnosis," "Shingles: Early administration of antiviral drugs."

[1296] Step 13:

[1297] The server sends the processing results to the terminal.

[1298] Step 14:

[1299] The device displays to the user a list of possible illnesses, a risk assessment, related symptoms, recommended medical facilities, and treatment guidelines.

[1300] Through this process, users can obtain appropriate medical information and countermeasures tailored to their symptoms.

[1301] (Example 1)

[1302] 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".

[1303] In modern society, it is crucial for users to obtain timely and appropriate medical information regarding their own symptoms. However, selecting a specialized medical institution and obtaining appropriate treatment guidelines often requires considerable time and effort. Traditional methods may rely on unreliable information for self-diagnosis, potentially leading to the selection of inappropriate medical institutions or the adoption of incorrect treatment plans. This poses a risk to the user's health.

[1304] 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.

[1305] In this invention, the server includes means for the user to input symptoms into an input device, means for analyzing the input symptoms using a natural language processing system and extracting keywords, means for searching a specialized database based on the extracted keywords and listing related diseases, means for evaluating the risk level of the listed diseases and providing other related symptoms, means for acquiring the user's location information and recommending medical institutions capable of providing specialized treatment, and means for providing general treatment guidelines for each disease. As a result, the user can quickly obtain appropriate medical information for their symptoms, and can efficiently select an appropriate medical institution and decide on a treatment plan.

[1306] An "input device" refers to a device used by a user to input symptoms. Specifically, this includes smartphones and personal computers.

[1307] A "computer on a network" refers to a central processing system that performs data processing, analysis, and information provision. It is commonly known as a server.

[1308] A "natural language processing system" refers to a software tool that analyzes text data entered by a user and extracts its meaning.

[1309] "Keywords" refer to important words or phrases extracted from the entered text. This helps identify the symptoms.

[1310] A "specialized database" refers to a collection of data containing information about various diseases. This includes information related to medical care and health.

[1311] "Listing" refers to displaying items related to a specific keyword in a list format.

[1312] "Risk level" refers to a standard for evaluating the risk level for a particular disease. It ranges from low risk to high risk.

[1313] "Location information" refers to data that indicates the user's current location. It is obtained using technologies such as GPS.

[1314] A "medical institution capable of providing specialized treatment" refers to a medical facility that has the ability to provide specialized treatment for a particular disease. This includes hospitals and clinics.

[1315] "Treatment guidelines" refer to information that outlines general treatment methods and coping strategies for specific diseases. This includes helpful advice for users.

[1316] This invention begins with the user inputting symptoms using an input device. A smartphone or personal computer can be used as the input device. The user inputs specific symptoms, such as "my back hurts," and the device then transmits this information to a server.

[1317] The server passes the received text data to a natural language processing system (e.g., a general-purpose natural language processing engine). This system extracts keywords from the input text and analyzes the meaning of the symptoms. For example, it can extract the keywords "back" and "pain" from the text.

[1318] The server then uses the extracted keywords to search specialized databases (e.g., medical databases). This search lists related conditions such as "muscle pain," "lower back pain," "kidney stones," and "shingles."

[1319] For each listed disease, the server assesses the risk level and provides information on other associated symptoms. For example, it might rate "muscle pain" as low risk, "lumbago" as moderate risk, "kidney stones" as moderate risk, and "shingles" as high risk. It also provides additional information on other associated symptoms, such as "hematuria" or "rash."

[1320] If the user allows location information to be shared, the device uses its GPS function to obtain information about its current location and sends it to the server. The server uses a geographic information system (e.g., a general-purpose geographic information system) to search for medical institutions near the user's current location that can provide specialized treatment. As a result, it recommends medical institutions such as "orthopedics," "urology," and "dermatology."

[1321] Next, the server retrieves general treatment guidelines for each disease from specialized databases (e.g., medical websites). Specifically, it suggests that warm compresses and light exercise are effective for "muscle pain," rest and painkillers for "lumbago," hydration and a doctor's diagnosis for "kidney stones," and antiviral medication for "shingles."

[1322] Ultimately, the device visually displays this information to the user. The user can see at a glance a list of possible illnesses, their risk levels, associated symptoms, recommended healthcare facilities, and treatment guidelines, enabling them to take quick and appropriate action.

[1323] Specific example

[1324] Simply by having the user type "My back hurts," the server assesses the likelihood and risk level of illnesses related to the symptoms, and also suggests any other accompanying symptoms. Furthermore, it searches for appropriate medical facilities based on the user's location and provides practical treatment guidelines. This entire process allows users to quickly receive appropriate medical advice.

[1325] Example of a prompt

[1326] "Please tell me about possible illnesses that could be causing back pain. Also, please explain the risk level, related symptoms, and treatment options in detail."

[1327] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1328] Step 1: User Input

[1329] The user uses an input device (smartphone or computer) to enter specific symptoms, such as "my back hurts." This input data is sent to the terminal in text format.

[1330] Step 2: Send a text message

[1331] The terminal immediately sends the entered text data to the server. This transmission is secure using the HTTPS protocol.

[1332] Input: Symptom text entered by the user (e.g., "My back hurts")

[1333] Output: Text data sent to the server

[1334] Step 3: Natural Language Processing

[1335] The server passes the received text data to a natural language processing system (e.g., a general-purpose natural language processing engine). The natural language processing system extracts important keywords (e.g., "back," "painful") from the input text and analyzes the context.

[1336] Input: Submitted text data

[1337] Output: Extracted keywords and contextual information

[1338] Step 4: Disease Database Search

[1339] The server uses the extracted keywords to search specialized databases (e.g., medical databases) and lists diseases associated with those keywords. For example, it might find diseases such as "muscle pain," "lower back pain," "kidney stones," and "shingles."

[1340] Input: Extracted keywords (e.g., "back", "pain")

[1341] Output: List of listed diseases

[1342] Step 5: Risk assessment and suggestion of additional symptoms

[1343] The server assesses the risk level for each listed disease and also provides additional related symptoms. A risk assessment algorithm is used for the assessment, and related symptoms such as "hematuria" and "rash" are also presented.

[1344] Input: List of listed diseases

[1345] Output: Risk assessment and associated additional symptoms

[1346] Step 6: Location Information Collection

[1347] When a user allows location information to be shared, the device uses its GPS function to obtain information about its current location and sends that data to the server. This data transmission also uses the HTTPS protocol.

[1348] Input: Permission to provide location information

[1349] Output: Acquired location data

[1350] Step 7: Proposal of Recommended Medical Institutions

[1351] The server uses the collected location information to access a geographic information system (e.g., a general-purpose geographic information system) to search for medical institutions near the user's current location that can provide specialized treatment. For example, medical institutions specializing in "orthopedics," "urology," and "dermatology" may be listed.

[1352] Input: Acquired location data

[1353] Output: List of recommended specialist medical institutions

[1354] Step 8: Provide general treatment guidelines

[1355] The server retrieves general treatment guidelines for each disease from specialized databases (e.g., specialist medical websites). Specifically, it recommends warm compresses and light exercise for muscle pain, rest and painkillers for acute lower back pain, hydration and a doctor's diagnosis for kidney stones, and antiviral medication for shingles.

[1356] Input: List of listed diseases

[1357] Output: Information on general treatment guidelines

[1358] Step 9: Displaying the results

[1359] Finally, the device visually displays these processing results to the user. The user can see at a glance a list of possible illnesses, risk assessments, related additional symptoms, recommended healthcare facilities, and treatment guidelines. This enables quick and appropriate medical consultation and response.

[1360] Input: All search and evaluation data

[1361] Output: Comprehensive information displayed to the user.

[1362] (Application Example 1)

[1363] 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".

[1364] Existing symptom diagnosis systems are limited to providing information such as disease listings and treatment guidelines, lacking a means to quickly and centrally manage subsequent medical service use, particularly payments for consultations and medication purchases. This often requires users to perform multiple operations, making the process cumbersome. To solve this problem, a system is needed that seamlessly integrates the user's process from entering symptoms to payment at the medical institution.

[1365] 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.

[1366] In this invention, the server includes means for enabling the user to make electronic payments for medical consultations and medication purchases, means for using a geographic information system to obtain the user's location information, and means for enabling electronic payments at recommended medical institutions. This allows the user to quickly and centrally perform a series of processes, from entering symptoms to paying for medical consultations.

[1367] An "input terminal" is a device used by users to input symptom information, and includes smartphones and personal computers.

[1368] A "natural language processing engine" is a software tool that analyzes text data entered by a user and extracts keywords.

[1369] A "disease database" is a collection of data containing detailed information about various diseases.

[1370] "Risk level" indicates an assessment of the risk for each listed disease, ranging from low risk to high risk.

[1371] "Location information" refers to data indicating the user's current location, and this information is used to search for appropriate medical facilities.

[1372] The method for recommending "medical institutions" is a function that suggests hospitals and clinics capable of providing specialized treatment based on the user's location information.

[1373] "Treatment guidelines" are information that provides general treatment methods and coping strategies for specific diseases.

[1374] "Electronic payment" refers to a method of making payments online for things like medical consultation fees and medication purchases.

[1375] A "Geographic Information System" is a system that acquires a user's location information and handles geographical data.

[1376] "Detailed data" refers to data that includes specific information about the listed diseases.

[1377] A "database for managing payment information" is a collection of data that centrally manages payment information related to medical consultation fees, medication purchases, and other related matters.

[1378] This invention integrates electronic payment functionality into a system that lists possible diseases based on symptom information entered by the user, recommends appropriate medical institutions based on that list, and provides general treatment guidelines. This system consists of the following main elements:

[1379] System Configuration

[1380] 1. User terminal: This is a device used by the user to input symptoms and receive diagnostic results, recommended medical institutions, and treatment guidelines. Examples include smartphones and personal computers.

[1381] 2. Server: A central processing system that processes, analyzes, and provides information on data. This server includes the following hardware and software:

[1382] 3. Natural Language Processing Engine: This is a software tool that analyzes symptoms entered by the user and extracts necessary keywords. Specifically, libraries such as spaCy and NLTK are used.

[1383] 4. Disease Database: This is a collection of data that stores information about various diseases. This allows users to search for related diseases based on the symptoms they enter.

[1384] 5. Geographic Information Systems: These are systems that acquire a user's location information and use it to search for appropriate medical facilities. The Geopy library is a concrete example.

[1385] 6. Electronic Payment System: This is a payment function that allows users to pay for medical consultations and purchase medications online. The Stripe API, for example, will be implemented.

[1386] System operation

[1387] 1. Inputting symptom information: The user enters specific symptoms (e.g., "My back hurts") on the device. The device immediately sends this information to the server.

[1388] 2. Natural Language Processing: The server passes the received text data to a natural language processing engine, which extracts keywords. This process helps the server understand the entered symptoms and organize relevant information.

[1389] 3. Disease Database Search: The server uses the extracted keywords to search the disease database and lists the relevant diseases.

[1390] 4. Risk assessment and provision of related symptoms: The server assesses the risk level for each listed disease and also provides other symptoms associated with each disease (e.g., blood in the urine, rash, etc.).

[1391] 5. Suggestion of Recommended Medical Institutions: When a user provides location information, the server uses a geographic information system to search for and recommend appropriate medical institutions.

[1392] 6. Provision of treatment guidelines: The server retrieves general treatment guidelines for each disease from the database and provides them to the user.

[1393] 7. Electronic payment function: Allows users to pay for medical consultations and medication purchases at recommended medical institutions using electronic payment.

[1394] Specific example

[1395] Simply by the user typing "back pain," the system provides a list of possible illnesses (e.g., "muscle pain," "lumbago," "kidney stones"), assesses their risk level, and suggests other related symptoms. Furthermore, it searches for appropriate medical facilities based on location information and offers the option to pay for consultations electronically.

[1396] Examples of prompt statements

[1397] The user entered "My back hurts." Display a list of possible illnesses, risk assessment, related symptoms, geographically nearby medical facilities, and general treatment guidelines, and also provide an option to pay for the consultation using electronic payment.

[1398] In this way, users can complete the entire process from input to payment quickly and centrally, making it possible to access medical services more smoothly.

[1399] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1400] Step 1: The user enters the symptoms into the device and sends them.

[1401] Users use devices such as smartphones or computers to input specific symptoms (for example, "My back hurts") and submit this information. The input data is sent to the server in text format.

[1402] Step 2: The server analyzes the symptoms using a natural language processing engine and extracts keywords.

[1403] The server passes the received text data to a natural language processing engine (e.g., spaCy or NLTK) to extract keywords related to the symptoms (e.g., "back," "pain"). This analysis converts the symptom information into structured data.

[1404] Step 3: The server searches the disease database based on the extracted keywords and lists the relevant diseases.

[1405] The server uses the extracted keywords to search the disease database and lists related diseases (e.g., "muscle pain," "lower back pain," "kidney stones," etc.). This list is generated and passed to the next processing step.

[1406] Step 4: Assess the risk level of the listed diseases and provide any other related symptoms.

[1407] The server assesses the risk level for each listed disease (from low risk to high risk) and provides other symptoms associated with each disease (e.g., "fatigue" for "muscle pain," "hematuria" for "kidney stones"). This information is then compiled together.

[1408] Step 5: The server searches for and recommends appropriate medical facilities based on the user's location information.

[1409] When a user allows location information to be shared, the location information sent from the device is sent to the server. The server uses a geographic information system (e.g., Geopy) to search for medical facilities near the user's current location and generates a list of recommended medical facilities.

[1410] Step 6: The server provides general treatment guidelines for each disease.

[1411] The server retrieves and compiles general treatment guidelines for each disease from a database (for example, "warm compresses" and "light exercise" for "muscle pain," and "rest and painkillers" for "lumbago").

[1412] Step 7: The device displays this information to the user.

[1413] The terminal visually displays information transmitted from the server to the user (a list of possible diseases, risk assessment, related symptoms, recommended medical facilities, and treatment guidelines). Based on this information, the user can decide on their next course of action.

[1414] Step 8: The user pays for consultation fees and medication purchases at the recommended medical institution using electronic payment.

[1415] After a user receives a medical consultation or purchases medication, they make a payment using an electronic payment system (e.g., Stripe). This operation is also performed from the terminal, and the payment information is sent to and recorded on the server.

[1416] In this way, a series of steps allows users to handle everything from entering their symptoms to paying for medical consultations in one place.

[1417] 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.

[1418] This invention is a system that lists possible diseases based on symptom information entered by the user, recommends appropriate medical institutions based on that list, and also provides general treatment guidelines. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it achieves a more user-friendly approach.

[1419] System Configuration

[1420] This system mainly consists of the following elements:

[1421] User terminal: A device that allows the user to input symptoms and receive information. Examples include smartphones and personal computers.

[1422] Server: A central processing system that processes, analyzes, and provides information about data.

[1423] Natural Language Processing Engine: A software tool that analyzes symptoms entered by the user and extracts their meaning.

[1424] Emotion engine: A system that recognizes emotions based on user input and adjusts its response accordingly.

[1425] Disease database: A collection of data containing information about various diseases.

[1426] Geographic Information System: A system that searches for appropriate medical facilities based on the user's location information.

[1427] Program processing

[1428] User input

[1429] The user enters specific symptoms, such as "My back hurts," into the device. The device immediately sends the entered information to the server.

[1430] Natural Language Processing

[1431] The server passes the received text data to a natural language processing engine, which extracts keywords such as "back" and "pain." This process helps the system understand the entered symptoms and organize related information.

[1432] emotion recognition

[1433] The server passes the user's input data to the emotion engine, which recognizes the user's emotional state (e.g., stress, anxiety, calmness, etc.). The emotion engine analyzes the emotional state and sends the results back to the server.

[1434] Disease Database Search

[1435] The server uses the extracted keywords and recognized emotional states to search a disease database and list related illnesses. For example, illnesses related to "back pain" might include "muscle pain," "lumbago," "kidney stones," and "shingles."

[1436] Risk assessment and symptom provision

[1437] For each listed disease, the server assesses its risk level. For example, "muscle pain" is rated as low risk, "lower back pain" as moderate risk, "kidney stones" as moderate risk, and "shingles" as high risk. Other symptoms associated with each disease (e.g., blood in the urine, rash, etc.) are also provided.

[1438] Emotional response

[1439] The server adjusts how information is presented and the content of advice based on the recognized emotional state of the user. For example, if the user is feeling anxious, it prioritizes providing reassuring messages and support information.

[1440] Recommended medical institutions

[1441] When a user allows their location to be shared, this location information is sent to the server via their device. The server uses a geographic information system to search for and recommend specialized medical facilities near the user's current location. For example, "Orthopedics," "Urology," and "Dermatology" may be listed.

[1442] Provision of treatment guidelines

[1443] The server retrieves general treatment guidelines for each disease from the database. For example, it might suggest that warm compresses and light exercise are effective for "muscle pain," rest and painkillers for "lumbago," hydration and a doctor's diagnosis for "kidney stones," and antiviral medication for "shingles."

[1444] Displaying Results

[1445] Ultimately, the terminal visually displays these processing results to the user. The user can see at a glance a list of possible diseases, risk assessments, related symptoms, recommended medical facilities, and treatment guidelines, enabling them to take quick and appropriate action.

[1446] Specific example

[1447] When a user enters "My back hurts," the server uses a natural language processing engine to analyze the symptoms and an emotion engine to recognize the user's emotions. It then lists possible illnesses from a disease database and provides a risk assessment and symptom information tailored to the user's emotions. Based on location information, it searches for appropriate medical facilities and presents treatment guidelines suited to the user. To ensure the user feels comfortable seeking medical attention, it also provides emotionally-based advice and support information.

[1448] Thus, the system of the present invention can support self-diagnosis of symptoms while also taking into consideration the user's emotional state, and can improve the efficiency of selecting a medical institution.

[1449] The following describes the processing flow.

[1450] Step 1:

[1451] The user enters specific symptoms into the device, such as "My back hurts."

[1452] Step 2:

[1453] The terminal receives the entered symptoms and sends them to the server.

[1454] Step 3:

[1455] The server receives the text data and passes it to a natural language processing engine for analysis.

[1456] Step 4:

[1457] The server uses a natural language processing engine to extract keywords such as "back" and "painful."

[1458] Step 5:

[1459] The server searches the disease database based on the extracted keywords and lists related diseases.

[1460] Step 6:

[1461] The server assesses the risk level for each listed disease.

[1462] For example, "muscle pain (low risk)", "sudden lower back pain (medium risk)", "kidney stones (medium risk)", and "shingles (high risk)".

[1463] Step 7:

[1464] The server lists other symptoms associated with each disease.

[1465] For example, "muscle pain: persistent pain after a specific type of exercise," "lower back pain: sharp pain after a sudden movement," "kidney stones: blood in the urine, frequent urination," and "shingles: rash and pain."

[1466] Step 8:

[1467] The server passes the user's input data to the emotion engine, which then recognizes the emotional state.

[1468] For example, it analyzes emotions such as "stress," "anxiety," and "calmness."

[1469] Step 9:

[1470] The server receives emotional states from the emotion engine and adjusts the information for the listed illnesses according to those emotions.

[1471] For example, for users who are feeling anxious, we provide reassuring messages and support information.

[1472] Step 10:

[1473] The device requests the user's location information.

[1474] Step 11:

[1475] The user grants permission to share their location information.

[1476] Step 12:

[1477] The device sends the user's location information to the server.

[1478] Step 13:

[1479] The server uses geographic information systems based on location data to search for nearby specialized medical facilities.

[1480] For example, "orthopedics," "urology," and "dermatology."

[1481] Step 14:

[1482] The server retrieves general treatment guidelines for each disease from the database.

[1483] For example, "Muscle pain: Warm compresses and light exercise," "Lower back pain: Rest and pain medication," "Kidney stones: Hydration and doctor's diagnosis," "Shingles: Early administration of antiviral drugs."

[1484] Step 15:

[1485] The server sends the processing results to the terminal.

[1486] Step 16:

[1487] The device displays to the user a list of possible illnesses and risk assessments, related symptoms, recommended medical facilities, treatment guidelines, and emotionally responsive messages and support information.

[1488] Through this process, users can obtain appropriate medical information and corresponding countermeasures tailored to their symptoms. In this way, a system that takes into account the user's emotional state can provide a more personalized response.

[1489] (Example 2)

[1490] 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".

[1491] Conventional diagnostic support systems merely list diseases based on symptoms entered by the user, without providing information that takes into account the user's emotional state. Furthermore, risk assessments for the listed diseases and the provision of related symptoms are limited, making it difficult to make an appropriate selection from multiple medical institutions. To solve these problems, the present invention aims to develop a system that takes the user's emotional state into account and provides more accurate diagnostic support.

[1492] 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.

[1493] In this invention, the server includes means for using a natural language processing engine to analyze the input symptoms and extract keywords, means for passing the user's input data to an emotion recognition engine to recognize the emotional state, and means for searching a disease database based on the extracted keywords and listing related diseases. This enables highly accurate diagnostic support while also taking into account the user's emotional state.

[1494] A "user" is an ordinary consumer or patient who uses the system to input symptom information.

[1495] An "input terminal" is a device used by users to input symptoms and receive information, and includes smartphones and personal computers.

[1496] A "server" is a central processing unit that processes, analyzes, and provides information about data.

[1497] A "natural language processing engine" is a software tool that analyzes text data entered by a user and extracts specific keywords.

[1498] An "emotion recognition engine" is a software system that analyzes a user's emotional state based on their input data.

[1499] A "disease database" is a collection of data that stores information about various diseases.

[1500] "Risk level" is an indicator used to assess the level of risk for a specified disease.

[1501] "Related symptoms" refer to other symptoms or signs that may be related to the user's main complaint.

[1502] "Location information" refers to data that indicates the user's current location, such as GPS data.

[1503] A "Geographic Information System" is a system that uses a user's location information to provide information that matches specific geographical conditions.

[1504] A "medical institution" refers to an organization or facility that provides treatment for illnesses and injuries, such as a hospital or clinic.

[1505] "Treatment guidelines" refer to general treatment methods and strategies for a specific disease.

[1506] "Information presentation method" refers to the techniques and formats used to show information to users.

[1507] "The content of the advice" refers to the advice and instructions provided to the user.

[1508] This invention is a system in which a user inputs symptom information, and based on that information, it lists possible diseases, recommends appropriate medical institutions, and provides general treatment guidelines. To achieve this, it incorporates an emotion engine that recognizes the user's emotions, thereby realizing a more user-friendly approach.

[1509] This system mainly consists of the following elements:

[1510] User terminal: A device used by the user to input symptoms and receive information; this includes smartphones and personal computers.

[1511] Server: A central processing system that processes, analyzes, and provides information about data.

[1512] Natural Language Processing Engine: A software tool that analyzes symptoms entered by a user and extracts meaning; examples include Google NLP API and IBM Watson.

[1513] Emotion engine: A system that recognizes emotions based on user input data and adjusts responses accordingly; Microsoft Azure Emotion API is an example of this.

[1514] Disease database: A collection of data containing information about various diseases.

[1515] Geographic Information Systems (GIS): These are systems that search for appropriate medical facilities based on the user's location information; the Google Maps API is an example of such a system.

[1516] The user uses a terminal to input specific symptoms (e.g., "My back hurts"). This information is immediately sent to the server. The server passes the received text data to a natural language processing engine, which extracts keywords (e.g., "back," "pain"). This process analyzes the entered symptoms and organizes the information. The server then passes the user's input data to an emotion recognition engine, which recognizes the emotional state (e.g., stress, anxiety, calmness, etc.). The results of this emotion analysis are sent back to the server.

[1517] Next, the server uses the extracted keywords and recognized emotional states to search a disease database and list related illnesses. For example, illnesses related to "back pain" might include "muscle pain," "lumbago," "kidney stones," and "shingles." Furthermore, the server assesses the risk level for each listed illness and also provides other related symptoms (e.g., blood in the urine, rash, etc.).

[1518] The server adjusts how information is presented and the content of advice based on the user's emotional state. For example, users who are feeling anxious will be given priority in receiving reassuring messages and supportive information. If the user allows location information to be shared, this location information is sent from the device to the server. The server uses a geographic information system to search for and recommend specialized medical facilities near the user's current location. Finally, the server retrieves general treatment guidelines for each disease from a disease database and presents them to the user.

[1519] For example, when a user enters "My back hurts," the device sends this to the server. The server analyzes the symptoms using a natural language processing engine and recognizes the user's emotions using an emotion engine. It then lists "muscle pain," "lower back pain," "kidney stones," and "shingles" from a disease database and evaluates the risk level and associated symptoms for each. Based on location information, it recommends "orthopedics" or "urology," and displays treatment guidelines such as applying a warm compress for "muscle pain" and staying hydrated for "kidney stones." Furthermore, it prioritizes providing reassuring messages to users who are feeling anxious, such as "Don't worry, early consultation will help."

[1520] Example of a prompt:

[1521] "When a user enters 'back pain,' please list possible illnesses and conduct a risk assessment. Additionally, display relevant treatment guidelines and recommended medical facilities based on location, and include messages to alleviate user anxiety."

[1522] Thus, the system of the present invention can support self-diagnosis of symptoms while also taking into consideration the user's emotional state, and can improve the efficiency of selecting a medical institution.

[1523] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1524] Step 1:

[1525] The user uses the terminal to input specific symptom information. For example, if the user inputs "My back hurts," the terminal immediately sends this information to the server. The input information is passed to the server as string data.

[1526] Step 2:

[1527] The server passes the received text data to a natural language processing engine. Here, a natural language processing engine such as Google NLP API or IBM Watson is used to extract keywords (e.g., "back" and "painful") from the text data. This analysis clarifies the meaning of the entered symptoms. The output is a list of keywords.

[1528] Step 3:

[1529] The server passes the extracted keywords to an emotion recognition engine (such as the Microsoft Azure Emotion API). The emotion recognition engine analyzes the user's emotional state (e.g., stress, anxiety, calmness, etc.) based on the user's input data. The output is the recognized emotional state and its intensity.

[1530] Step 4:

[1531] The server searches the disease database based on these keywords and emotional states. The disease database contains information on a variety of diseases, and diseases that match the keywords (e.g., "muscle pain," "lower back pain," "kidney stones," "shingles") are listed. The output is a list of the listed diseases.

[1532] Step 5:

[1533] The server assesses the risk level for each listed disease. Here, each disease is classified as having a risk level ranging from low risk to high risk. Additionally, it provides information on any additional symptoms associated with each disease (e.g., blood in the urine, rash, etc.). The output is a list of risk assessments and associated symptoms for each disease.

[1534] Step 6:

[1535] The server adjusts how information is presented and the content of advice based on the user's perceived emotional state. For example, a user feeling anxious will be given priority in receiving reassuring messages and support information. This involves modifying the presentation method and generating additional messages. The output consists of the adjusted information presentation method and additional messages.

[1536] Step 7:

[1537] When a user allows location information to be shared, the device sends that information to the server. The server uses a geographic information system (such as the Google Maps API) to search for medical facilities near the user's current location. The output is a list of recommended medical facilities.

[1538] Step 8:

[1539] The server retrieves general treatment guidelines for each disease from a disease database. For example, "muscle pain" might be described as requiring warm compresses or light exercise, while "kidney stones" might be described as requiring hydration and a doctor's diagnosis. The output is a list of treatment guidelines corresponding to each disease.

[1540] Step 9:

[1541] Ultimately, the device displays information to the user. This information, including a list of possible illnesses, risk assessments, related symptoms, recommended healthcare facilities, treatment guidelines, and additional emotionally-sensitive messages, is presented in a visual format for quick and easy viewing. This allows the user to take quick and appropriate action.

[1542] (Application Example 2)

[1543] 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".

[1544] In modern society, people are increasingly required to recognize symptoms in their daily lives and perform initial self-diagnosis. However, users often find it difficult to quickly find appropriate medical facilities and take time to understand what treatment is necessary for their symptoms. Furthermore, some symptoms require immediate medical attention, which can cause stress and anxiety. Therefore, there is a need for a system that allows users to quickly travel to appropriate medical facilities using autonomous vehicles.

[1545] 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.

[1546] In this invention, the server includes means for the user to input symptoms into an input terminal, means for the server to analyze the input symptoms and extract keywords using a natural language processing engine, means for the server to search a disease database based on the extracted keywords and list related diseases, means for the server to evaluate the risk level of the listed diseases and also provide other related symptoms, means for the server to recommend medical institutions capable of providing specialized treatment based on the user's location information, means for the server to provide general treatment guidelines for each disease, means for the terminal to display this information to the user, means for recognizing the user's emotions using an emotion engine and providing information appropriate to that state, and means for linking the route to the recommended medical institution to the navigation system of an autonomous vehicle. As a result, the user can receive a quick and appropriate response to their symptoms and travel to a medical institution safely using an autonomous vehicle.

[1547] 1. An "input terminal" is a device used by the user to input symptoms, and includes smartphones, tablets, etc.

[1548] 2. A "natural language processing engine" is a software tool that analyzes text data entered by a user and interprets its meaning.

[1549] 3. A "keyword" is a word or phrase with a specific meaning that is extracted from the symptom information entered by the user.

[1550] 4. A "disease database" is a collection of data in which information about various diseases is systematically stored.

[1551] 5. "Risk level" is an index that assesses the likelihood and severity of occurrence for each of the listed diseases.

[1552] 6. A "Geographic Information System" is a system that acquires a user's location information and searches for medical facilities based on geographical data.

[1553] 7. An "emotion engine" is a system that recognizes and analyzes the user's emotional state (stress, anxiety, calmness, etc.) from the user's input data.

[1554] 8. A "navigation system" is a system that guides an autonomous vehicle along a route to a specific destination.

[1555] 9. "Treatment guidelines" are information that summarizes the general treatment methods and coping strategies recommended for a specific disease.

[1556] 10. A "recommended medical institution" is a medical institution that can provide specialized treatment, suggested by the server based on the user's location information and symptoms.

[1557] This invention relates to a medical support system using an autonomous vehicle, and is a system that assists with navigation from symptom input to medical facilities. This system enables users to quickly input symptoms and provides appropriate medical facility suggestions and route guidance. Therefore, the system components include an input terminal, a server, an emotion engine, a disease database, a navigation system, and the like.

[1558] Hardware and software configuration

[1559] User terminal

[1560] The user terminal is a device such as a smartphone or a vehicle's infotainment system, used by the user to input symptoms. Voice input is also supported, utilizing a speech recognition engine (e.g., Google Speech API).

[1561] server

[1562] The servers are processing units located in the cloud and are responsible for the main functions of natural language processing, emotion recognition, and disease database search. The natural language processing engine is built using tools such as Hugging Face's Transformers and spaCy. The emotion engine similarly utilizes a pre-trained model from Transformers.

[1563] Disease Database

[1564] The disease database is a collection of data that stores detailed information about various diseases. The server searches this database based on symptom keywords and retrieves a list of related diseases.

[1565] Geographic Information Systems

[1566] Geographic information systems (e.g., Google Maps API) are used to obtain a user's current location and search for the most suitable medical facilities.

[1567] Navigation system

[1568] The navigation system is part of the autonomous vehicle and determines the route based on medical facility information provided from a server. Therefore, it also utilizes autonomous driving control software (e.g., Autoware).

[1569] System Operation Overview

[1570] 1. Input and analysis of symptoms:

[1571] When a user inputs a symptom through the in-car infotainment system (for example, "My back hurts"), a speech recognition engine converts this speech into text. A natural language processing engine then analyzes this text and extracts keywords related to the symptom.

[1572] 2. Emotion recognition:

[1573] The server uses an emotion engine to analyze the user's emotional state, along with the extracted keywords. For example, it can determine if the user is feeling anxious.

[1574] 3. Disease database search and risk assessment:

[1575] The server searches a disease database and lists diseases associated with the extracted keywords. Simultaneously, it assesses the risk level for each disease and provides information on other related symptoms.

[1576] 4. Provision of recommendations and treatment guidelines from medical institutions:

[1577] The server, having acquired the user's location information, uses a geographic information system to search for the most suitable medical facility for the user. Furthermore, it provides general treatment guidelines for various diseases.

[1578] 5. Displaying results and starting navigation:

[1579] The user's terminal displays a list of possible illnesses, a risk assessment, recommended medical facilities, and treatment guidelines. Finally, the navigation system coordinates the route to the recommended medical facility with the autonomous vehicle and begins navigation.

[1580] Specific examples and prompt statements

[1581] For example, if a user says "My back hurts," the system will perform the following actions.

[1582] Example of a prompt

[1583] User: "My back hurts."

[1584] System: "Analyzing symptoms and emotions..."

[1585] System: "Possible conditions include muscle pain, lumbago, kidney stones, and shingles. The recommended medical facility is your nearest orthopedic hospital."

[1586] System: "Starting autonomous driving to the destination medical facility."

[1587] In this way, this system can support self-diagnosis of symptoms while taking into account the user's emotional state, and improve the efficiency of selecting a medical institution. Users can travel to medical institutions safely through autonomous driving.

[1588] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1589] Step 1: The user voice-inputs the symptoms into the in-car infotainment system.

[1590] In terms of specific actions, the user inputs a symptom, such as "My back hurts," into the device by voice. The input data (voice) is converted into text data by a speech recognition engine (Google Speech API).

[1591] Input: Voice data ("My back hurts")

[1592] Output: Text data ("My back hurts")

[1593] Step 2: The server uses a natural language processing engine to parse the input text data.

[1594] The text data is sent to the server and analyzed by a natural language processing engine (such as Transformers or spaCy). Keywords ("back," "painful") are extracted.

[1595] Input: Text data ("My back hurts")

[1596] Output: Keywords ("back", "pain")

[1597] Step 3: The server uses the emotion engine to recognize the user's emotional state.

[1598] The extracted keywords and text data are passed to the emotion engine, which analyzes the user's emotional state (e.g., anxiety).

[1599] Input: Text data and keywords ("back pain", "back", "pain")

[1600] Output: Emotional state (anxiety)

[1601] Step 4: The server searches the disease database based on the extracted keywords.

[1602] The server uses keywords to search the disease database and lists related diseases (e.g., muscle pain, lumbago, kidney stones, shingles).

[1603] Input: Keywords ("back", "pain")

[1604] Output: Disease list (muscle pain, lumbago, kidney stones, shingles)

[1605] Step 5: The server assesses the risk level of the listed diseases and also provides any other associated symptoms.

[1606] The server assesses the risk level for each listed disease and also provides other symptoms that may be associated with each disease.

[1607] Input: Disease list (muscle pain, lumbago, kidney stones, shingles)

[1608] Output: Risk assessment and associated symptoms (muscle pain: low risk, lumbago: medium risk, kidney stones: medium risk, shingles: high risk)

[1609] Step 6: Based on the user's location information, the server recommends medical institutions that can provide specialized treatment.

[1610] The server obtains location information and uses a geographic information system (Google Maps API) to search for the most suitable medical facility. Recommended medical facilities include orthopedic hospitals, etc.

[1611] Input: Location information (latitude and longitude data)

[1612] Output: Recommended medical institutions (orthopedic hospitals, etc.)

[1613] Step 7: The server provides general treatment guidelines for each disease.

[1614] The server retrieves general treatment guidelines for the listed diseases from the database and provides them to the user.

[1615] Input: Disease list

[1616] Output: Treatment guidelines (Muscle pain: warm compresses, acute lower back pain: rest and painkillers, kidney stones: hydration, shingles: antiviral drugs, etc.)

[1617] Step 8: The device displays this information to the user.

[1618] The device visually displays to the user a list of possible diseases, risk assessments, recommended medical facilities, and treatment guidelines.

[1619] Input: Risk assessment, recommended medical institutions, treatment guidelines

[1620] Output: Display (disease list, risk assessment, recommended medical facilities, treatment guidelines)

[1621] Step 9: The server links the route to the recommended medical facility to the autonomous vehicle's navigation system.

[1622] The server sends location information of recommended medical facilities to the navigation system, which then instructs the autonomous vehicle to begin route guidance.

[1623] Input: Location information of recommended medical institutions

[1624] Output: Route guidance for autonomous vehicles

[1625] 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.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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."

[1634] 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.

[1635] 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.

[1636] 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.

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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.

[1646] The following is further disclosed regarding the embodiments described above.

[1647] (Claim 1)

[1648] A means for the user to input symptoms into an input terminal,

[1649] The server uses a natural language processing engine to analyze the input symptoms and extract keywords.

[1650] A server searches a disease database based on extracted keywords and lists related diseases.

[1651] The server provides a means to assess the risk level of listed diseases and also provides information on other related symptoms.

[1652] A means by which the server recommends medical institutions capable of providing specialized treatment based on the user's location information,

[1653] The server provides a means of offering general treatment guidelines for each disease,

[1654] The means by which the terminal displays this information to the user,

[1655] A system that includes this.

[1656] (Claim 2)

[1657] The system according to claim 1, wherein the server uses a geographic information system to obtain the user's location information.

[1658] (Claim 3)

[1659] The system according to claim 1, wherein the server includes means for obtaining detailed data of the listed diseases from a database.

[1660] "Example 1"

[1661] (Claim 1)

[1662] A means by which the user inputs symptoms into an input device,

[1663] A means by which computers on a network use a natural language processing system to analyze input symptoms and extract keywords,

[1664] A means of having computers on a network search specialized databases based on extracted keywords and list related diseases,

[1665] A means of assessing the risk level of listed diseases using computers on a network, and also providing information on other related symptoms,

[1666] A means by which computers on the network recommend medical institutions capable of providing specialized treatment based on the user's location information,

[1667] A means by which computers on a network provide general treatment guidelines for each disease,

[1668] The input device provides means for displaying this information to the user,

[1669] A system that includes this.

[1670] (Claim 2)

[1671] The system according to claim 1, wherein a computer on the network uses a geographic information system to obtain the user's location information.

[1672] (Claim 3)

[1673] The system according to claim 1, wherein a computer on the network includes means for obtaining detailed data of the listed diseases from a database.

[1674] "Application Example 1"

[1675] (Claim 1)

[1676] A means for the user to input symptoms into an input terminal,

[1677] The server uses a natural language processing engine to analyze the input symptoms and extract keywords.

[1678] A server searches a disease database based on extracted keywords and lists related diseases.

[1679] The server provides a means to assess the risk level of listed diseases and also provides information on other related symptoms.

[1680] A means by which the server recommends medical institutions capable of providing specialized treatment based on the user's location information,

[1681] The server provides a means of offering general treatment guidelines for each disease,

[1682] The means by which the terminal displays this information to the user,

[1683] A means to enable users to pay for medical consultations and purchase medications electronically,

[1684] A system that includes this.

[1685] (Claim 2)

[1686] The system according to claim 1, comprising means for the server to use a geographic information system to obtain the user's location information, and means for enabling payment at recommended medical facilities to be made by electronic payment.

[1687] (Claim 3)

[1688] The system according to claim 1, comprising a server, means for retrieving detailed data of listed diseases from a database, and a database for managing payment information for medical consultation fees.

[1689] "Example 2 of combining an emotion engine"

[1690] (Claim 1)

[1691] A means for the user to input symptoms into an input terminal,

[1692] The server uses a natural language processing engine to analyze the input symptoms and extract keywords.

[1693] The server passes user input data to an emotion recognition engine and has a means of recognizing the emotional state.

[1694] A server searches a disease database based on extracted keywords and lists related diseases.

[1695] The server provides a means to assess the risk level of listed diseases and also provides information on other related symptoms.

[1696] A means by which the server recommends medical institutions capable of providing specialized treatment based on the user's location information,

[1697] The server provides a means of offering general treatment guidelines for each disease,

[1698] A means by which the server adjusts the way information is presented and the content of advice according to the emotional state,

[1699] The means by which the terminal displays this information to the user,

[1700] A system that includes this.

[1701] (Claim 2)

[1702] The system according to claim 1, wherein the server uses a geographic information system to obtain the user's location information.

[1703] (Claim 3)

[1704] The system according to claim 1, comprising means for the server to retrieve detailed data of listed diseases from a database.

[1705] "Application example 2 of combining emotional engines"

[1706] (Claim 1)

[1707] A means for the user to input symptoms into an input terminal,

[1708] The server uses a natural language processing engine to analyze the input symptoms and extract keywords.

[1709] A server searches a disease database based on extracted keywords and lists related diseases.

[1710] The server provides a means to assess the risk level of listed diseases and also provides information on other related symptoms.

[1711] A means by which the server recommends medical institutions capable of providing specialized treatment based on the user's location information,

[1712] The server provides a means of offering general treatment guidelines for each disease,

[1713] The means by which the terminal displays this information to the user,

[1714] A means of recognizing a user's emotions using an emotion engine and presenting information appropriate to that state,

[1715] A means of linking the route to recommended medical facilities to the navigation system of an autonomous vehicle,

[1716] A system that includes this.

[1717] (Claim 2)

[1718] The system according to claim 1, wherein the server uses a geographic information system to obtain the user's location information.

[1719] (Claim 3)

[1720] The system according to claim 1, wherein the server includes means for obtaining detailed data of the listed diseases from a database. [Explanation of Symbols]

[1721] 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 for the user to input symptoms into an input terminal, The server uses a natural language processing engine to analyze the input symptoms and extract keywords. A server searches a disease database based on extracted keywords and lists related diseases. The server provides a means to assess the risk level of listed diseases and also provides information on other related symptoms. A means by which the server recommends medical institutions capable of providing specialized treatment based on the user's location information, The server provides a means of offering general treatment guidelines for each disease, The means by which the terminal displays this information to the user, A system that includes this.

2. The system according to claim 1, wherein the server uses a geographic information system to obtain the user's location information.

3. The system according to claim 1, wherein the server includes means for obtaining detailed data of the listed diseases from a database.

Images