system

Abstract

We provide the system. [Solution] A device that accepts the health information entered by the user, A device that performs processing to recommend medical fields based on the health condition received, A device that provides information on telemedicine related to recommended medical fields, 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, and includes 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 the modern medical environment, it is difficult for users to quickly and surely select a medical field suitable for their health conditions, and there is a problem that it often takes a long time to receive appropriate medical treatment. As a result, users are at risk of leaving their deteriorating health conditions untreated. Also, there is a problem that it is difficult for users in situations where it is difficult to visit a hospital for face-to-face medical treatment or who live in remote areas to receive appropriate and prompt medical services.

Means for Solving the Problems

[0005] This invention provides a system that accepts health information entered by the user and recommends the most suitable medical field based on that information. Furthermore, by providing information on telemedicine related to the recommended medical field, users can quickly receive appropriate medical services from the comfort of their homes. In addition, by optimizing recommendations by referring to a database of past medical information based on the accepted health information and recommended medical field, the accuracy and convenience are improved. As a result, users can more easily receive appropriate medical care and manage their health optimally.

[0006] "User-entered health status" refers to information entered into the computer system by the user as a finger, indicating their current physical or mental state or symptoms.

[0007] A "device" is a component of hardware or software designed to perform a specific function or process.

[0008] "Medical field recommendation processing" refers to a series of calculation or decision-making processes that select and suggest appropriate medical departments and services based on the entered health status.

[0009] A "device that provides guidance for telemedicine" is a device designed to present information and procedures for using medical services online or through other means of communication.

[0010] "The process of referencing past medical information databases and optimizing recommendations" refers to a data analysis and calculation process that uses existing medical data to improve the accuracy and usefulness of treatment recommendations.

[0011] A "device for promoting subscriptions" is a device that provides information to users to increase their interest in a particular service and encourage them to subscribe. [Brief explanation of the drawing]

[0012] [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 the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system for quickly and accurately resolving health problems faced by users. By inputting their health status through the system, users can identify the appropriate medical field and receive corresponding telemedicine guidance. This enables many people to receive medical services without being constrained by time or distance.

[0034] The system provides an interface for users to input their health status through their device. For example, users can input specific symptoms such as "headache" or "stomach ache." The device receives this information and sends it to the server.

[0035] The server analyzes the received health data. Equipped with an AI engine, it refers to a database of past medical information to recommend the appropriate medical department based on the entered symptoms. For example, for a "headache," it recommends "neurology" or "internal medicine," and for "abdominal pain," it recommends "gastroenterology."

[0036] After the server identifies the recommended medical field, it sends that information back to the terminal. The terminal displays information about the medical department to the user and simultaneously guides them through telemedicine service options. This allows the user to consult online with a doctor in the selected medical department and receive a prescription if necessary.

[0037] As a concrete example, suppose a user enters the symptom "sore throat." The system sends this information to the server, which then processes it to recommend an ENT specialist. Subsequently, the recommendation is displayed on the user's terminal, and a guide is presented that allows the user to immediately consult online with an ENT doctor. In this way, the present invention enables the provision of appropriate medical services that meet the user's needs and significantly improves convenience.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user launches the application on their device and accesses the symptom input interface. There, the user enters their perceived health condition and specific symptoms as text. An interface is also provided that allows the user to select symptoms from a list.

[0041] Step 2:

[0042] The terminal receives symptom data entered by the user and sends it to the server. Before sending, it checks the format and content of the entered data and formats it if necessary. Data transmission uses a secure communication protocol (e.g., HTTPS) to ensure privacy.

[0043] Step 3:

[0044] The server analyzes the received symptom data. The AI ​​engine takes the symptom data as input and performs a process to identify the appropriate medical field by referring to past medical data and case information. Using a machine learning model, it identifies similar cases and recommends the most relevant medical department.

[0045] Step 4:

[0046] The server compiles the results for the recommended medical field and sends them back to the terminal. These results include information on the recommended medical department and related explanations. In addition, guidance information for using telemedicine services is also generated at the same time.

[0047] Step 5:

[0048] The terminal displays data received from the server to the user. The user can then review information on recommended medical departments and see options for immediate online consultations. This allows users to access necessary medical services regardless of geographical constraints.

[0049] (Example 1)

[0050] 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."

[0051] In modern medicine, quickly and accurately selecting the appropriate medical field is difficult for many people. In particular, geographical and time constraints make it difficult for users to receive appropriate medical support. Furthermore, current systems do not adequately optimize recommendations for medical fields based on user-entered health information, leaving challenges in providing effective medical support.

[0052] 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.

[0053] In this invention, the server includes means for acquiring health information entered by the user, means for performing analysis based on the acquired health information and recommending relevant medical fields, and means for providing electronic medical support related to the said medical fields. As a result, users can receive recommendations for appropriate medical fields quickly and accurately without geographical or temporal constraints, and medical support is provided more effectively.

[0054] "Health information" refers to data that shows the specific condition of a user's physical condition or symptoms.

[0055] A "medical field" refers to a specialized medical area that provides appropriate medical care and treatment for specific symptoms or health conditions.

[0056] "Electronic medical support" refers to the provision of medical services and information through remote digital platforms.

[0057] An "artificial intelligence model" is a technology based on algorithms that analyze large amounts of medical data to recommend the most suitable area of ​​treatment.

[0058] A "server" refers to a computer system used to process health information and make recommendations in the field of medical treatment.

[0059] "Users" refers to individuals who input health information through this system and receive suggestions for medical treatment areas.

[0060] This system provides an environment where users can remotely check their own health status and receive appropriate medical care quickly. To implement the system, terminals, servers, and generative AI models are used.

[0061] The terminal provides an interface for users to input health information. Specifically, mobile devices or computers are used as terminals, allowing users to input specific symptoms such as headaches or stomachaches through text input or selection of options. The terminal is responsible for transmitting the entered information to the server.

[0062] The server receives health information sent by the user and utilizes a generated AI model for analysis. The server stores a historical medical database, which can be used to recommend the most appropriate medical area. The AI ​​engine automatically selects a medical department and sends the results back to the terminal, including information on recommended departments and telemedicine services.

[0063] The generative AI model is an algorithm learned from input data, enabling rapid and accurate recommendations for medical areas. The goal is to ensure users receive the most appropriate medical support for their specific symptoms.

[0064] As a concrete example, suppose a user enters the symptom "sore throat." In this case, the device sends this information to the server, which uses a generative AI model to recommend an "otolaryngologist." The device receives this result and displays information about the medical department on the screen, and can also guide the user to related online medical service options.

[0065] As an example of a prompt, the generating AI model processes input such as "Return the recommended medical department based on the symptoms." This prompt allows the system to quickly process information and suggest the appropriate medical area.

[0066] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0067] Step 1:

[0068] The user enters health information using the terminal's interface. Specifically, they enter symptoms such as "headache" or "stomach ache" using text fields or selection options on the terminal's screen. After input, the terminal prepares to send this information to the next step. The input is the user's symptom data, and the output is data to be sent to the server.

[0069] Step 2:

[0070] The terminal transmits health information entered by the user to the server. A secure protocol is used for transmission to maintain data confidentiality. This ensures that user input information is delivered directly and safely to the server. The input is data related to the user's health status, and the output is the transfer of data to the server.

[0071] Step 3:

[0072] The server analyzes the received health information using a generating AI model. The server sends input data as prompts to the AI ​​engine and executes a process to recommend the most suitable medical field while referring to past medical databases. At this time, the AI ​​model selects a medical department based on the user's input. The input is symptom data sent from the terminal, and the output is information on the recommended medical field.

[0073] Step 4:

[0074] The server returns information about the medical treatment area and corresponding telemedicine services selected by the AI ​​engine to the terminal. Based on this returned data, the user is prepared to receive further medical support. The input is the recommendation result of the medical treatment area by the generative AI model, and the output is the information returned to the terminal.

[0075] Step 5:

[0076] The terminal displays information about the medical field received from the server to the user. The screen shows details of the selected medical department and available telemedicine service options. Specifically, the user can use links and reservation buttons to consult with a doctor online. The input is medical information from the server, and the output is information presented to the user.

[0077] (Application Example 1)

[0078] 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."

[0079] In the modern healthcare system, there is a challenge in that it is difficult for users to find the appropriate medical institution or department when they experience health problems. Furthermore, the inability to receive medical services without being restricted by time and location poses a significant hurdle for people who need prompt and appropriate medical care. This invention aims to solve these challenges in accessing medical care.

[0080] 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.

[0081] In this invention, the server includes functional means for receiving biometric information entered by the user, processing means for recommending a medical field based on the received biometric information, means for providing guidance on online medical consultations related to the recommended medical field, and data security means for ensuring secure information communication. This enables the user to quickly and appropriately identify a medical field and to use online medical consultations safely.

[0082] "Biometric information" is a term that refers to data related to a user's health status and symptoms.

[0083] The term "medical field" refers to specialized medical departments or fields of practice that address specific health conditions or symptoms.

[0084] "Online medical consultation" refers to a form of medical service in which doctors and patients conduct consultations and examinations remotely via the internet.

[0085] "Data security" refers to the technologies and methods used to protect users' personal data and medical information from unauthorized access and leakage.

[0086] The system implementing this invention mainly consists of a server, a terminal, and communication means. Users input biometric information using the terminal. The terminal is a computer device such as a smartphone or tablet. The input biometric information is securely transmitted to the server via the internet. The server is equipped with an AI engine using Python and TENSORFLOW®, which analyzes the received data. The AI ​​model uses generative AI and identifies the most appropriate medical area by referring to a database of past medical information.

[0087] Data security is guaranteed by encrypted communication using the SSL / TLS protocol. Once a recommendation from a medical department is obtained, it is sent back from the server to the terminal, which then displays instructions for online consultations on the screen. Users can then securely receive medical services based on this recommendation information.

[0088] As a concrete example, if a user enters biometric information such as "my vision is blurry" upon waking up in the morning, the device sends this information to a server. The server uses an AI engine to analyze a medical information database and sends a recommendation to the user for an ophthalmologist. The user can then choose to consult with an ophthalmologist online via the device.

[0089] Examples of prompts for a generative AI model include:

[0090] "Please enter the following symptoms: e.g., headache, stomach ache, sore throat."

[0091] In response to this, the system supports rapid access to medical care by recommending the appropriate medical field.

[0092] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0093] Step 1:

[0094] The user inputs biometric information through the terminal's interface. This input includes specific symptoms such as "headache" or "stomach ache." The input data is formatted by the terminal's data transmission program. The formatted input is then securely transmitted to the server via a communication module.

[0095] Step 2:

[0096] The server receives biometric information from the terminal in an encrypted format using the SSL / TLS protocol. On the server side, an AI engine using Python and TensorFlow analyzes the data. The AI ​​engine utilizes a database of historical medical information to identify the medical field based on the received symptoms. As a result of processing by this AI model, recommendation information for the appropriate medical department is generated.

[0097] Step 3:

[0098] The server sends back recommendation information for the identified medical department to the terminal. During this process, the data is again encrypted using the SSL / TLS protocol. The recommendation information includes the name of the medical department and options for related online medical services.

[0099] Step 4:

[0100] The device decrypts the recommendation information received from the server and displays it on the screen in a user-friendly format. The displayed information includes action buttons for online medical services the user can select, and links to initiate online consultations with doctors. Based on this information, the user can select and access appropriate medical services.

[0101] 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.

[0102] This invention is a system that allows users to input their health status and then provides medical recommendations that take into account their emotions based on that information. The system is equipped with an emotion engine that can recognize the user's emotions through text and voice analysis from the input information.

[0103] When the system is first used, the terminal provides the user with an interface to input their health status. Here, the user can choose to input specific symptoms as text or describe them verbally. The terminal converts this input information into digital data and sends it to the server.

[0104] The server first analyzes the received data using an emotion engine to recognize the user's emotional state. For example, if it performs a linguistic sentiment analysis of the input content and determines that the user may be experiencing anxiety, it uses that emotional information to select a medical department. This can then lead to the recommendation of a department capable of providing faster and more attentive care than usual.

[0105] Next, the server uses an AI engine to refer to past medical data and case information to determine the appropriate medical field for the symptoms. It then makes recommendations that take emotional information into account and prepares the system for guiding patients to telemedicine.

[0106] The results of the completed medical department recommendations and information on telemedicine are sent to the terminal. The terminal presents this to the user and, based on the emotional information received, displays encouraging and supportive messages as needed. For example, a user feeling anxious will be shown a reassuring message and guidance on online medical consultations appropriate to their situation.

[0107] As a concrete example, consider the case of a user complaining of "stomach pain." If the system determines that this user is feeling anxious about the pain, it will recommend a telemedicine service that allows for quick contact with a doctor, along with a gastroenterologist. Furthermore, it will provide supportive messages to alleviate the anxiety. In this way, the present invention realizes optimal medical support that also takes the user's psychological state into consideration.

[0108] The following describes the processing flow.

[0109] Step 1:

[0110] The user launches the application on their device and accesses the health status input screen. Here, the user can input their current symptoms and health status in text or voice.

[0111] Step 2:

[0112] The terminal receives health data entered by the user and converts it into a digital format. The converted data is then sent to the server.

[0113] Step 3:

[0114] The server passes the received data to the emotion engine, which recognizes the user's emotions from the text and audio data. The emotion engine performs language analysis and voice tone analysis to determine, for example, that the user is feeling "anxious."

[0115] Step 4:

[0116] The server then uses its AI engine to determine the recommended medical field based on the recognized emotional data and input health status data. The AI ​​engine also refers to past medical data and takes into account treatments that require attention due to emotional factors.

[0117] Step 5:

[0118] The server compiles information on the finalized medical field and related telemedicine services, and sends it to the terminal. This information includes the name of the medical department and instructions on how to use telemedicine.

[0119] Step 6:

[0120] The terminal displays information from the server to the user. In addition to information on recommended medical departments, it also provides emotionally sensitive encouraging and supportive messages. For example, a user feeling anxious might see a message such as, "Let's talk to a doctor right away so you can feel at ease."

[0121] Step 7:

[0122] Users can review the provided information and choose to use telemedicine services. After making their selection, the device accesses the telemedicine platform and schedules an online consultation with a doctor.

[0123] (Example 2)

[0124] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0125] In recent years, there has been a growing need to quickly and accurately assess the health status of individual users and provide appropriate medical services based on that assessment. However, conventional systems have difficulty recommending medical services that take into account the emotional state of users, and thus have a problem in that they cannot adequately reduce the psychological burden on users.

[0126] 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.

[0127] In this invention, the server includes terminal means for receiving biometric information entered by the user, analysis means for performing emotional analysis from the received biometric information to understand the user's emotional state, and recommendation means for recommending medical services based on the analyzed emotional state and biometric information. This makes it possible to provide optimal medical services tailored to the user's psychological state.

[0128] "Biometric information" refers to data that includes numerical values ​​and descriptions related to the user's health status and physical functions.

[0129] "Terminal means" refers to equipment or devices that allow users to input biometric information, and is a device that can input and transmit information.

[0130] "Emotional analysis" is a process that evaluates and recognizes the user's emotions and mental state based on the input information.

[0131] "Analysis means" refers to a system component equipped with functions and technologies for emotional analysis of biological information.

[0132] "Recommendation methods" refer to systems and methods that select and present medical services suitable for the user based on analyzed emotional state and biometric information.

[0133] "Remote healthcare" refers to health management and medical support services provided across physical distances.

[0134] This invention is a system in which a user inputs their own biometric information, and based on that information, it performs emotional analysis and recommends medical services. Specifically, it uses a terminal, a server, and software to link them together.

[0135] First, the user inputs biometric information using a device. These devices include PCs, smartphones, and tablets, and are equipped with text input and voice input capabilities. The user can provide information to the system by entering their physical symptoms in text or describing them verbally.

[0136] The device transmits biometric information as digital data to the server. The server is equipped with an emotion engine and an AI engine, which primarily perform analysis and recommendation processing. The emotion engine recognizes the user's emotional state based on the received data. For example, it detects words such as "worry" or "anxiety" from the input text and determines that they are related to the user's emotional state.

[0137] Next, the AI ​​engine references a database of past health information and recommends appropriate medical services based on the analyzed emotional state and biometric data. This recommendation aims for a swift and accurate medical response; for example, if the problem is related to the digestive system, it will recommend a gastroenterologist.

[0138] Finally, the terminal displays information from the server to the user. This includes information on recommended medical departments and guidance on remote healthcare services. In addition, messages that provide reassurance tailored to the user's emotional state are also displayed.

[0139] As a concrete example, for a user who is worried because they have a stomach ache, the system recommends a gastroenterologist and guides them to the option of an online consultation. An example of a prompt message for the generating AI model would be, "Suggest the most suitable medical service for the symptom 'stomach ache' and emotion 'anxiety'."

[0140] Thus, this invention is designed with the aim of providing optimal medical support by comprehensively considering the user's biometric information and emotional state.

[0141] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0142] Step 1:

[0143] The user enters biometric information into the device. The user can use text input to describe their health condition in detail, or choose voice input to describe their symptoms verbally. The entered information is recorded on the device as text data or digital audio data.

[0144] Step 2:

[0145] The terminal prepares to send the input data to the server. While text data may be sent directly to the server, voice data is typically converted to text using speech recognition technology. The converted data is then sent to the server using a secure communication protocol.

[0146] Step 3:

[0147] The server processes the received data and first performs emotional analysis using an emotion engine. It extracts emotion-related keywords and expressions from the input data, analyzes their linguistic characteristics, and evaluates the user's emotions. This process determines the user's emotional state, such as being tense, anxious, or calm.

[0148] Step 4:

[0149] The server uses an AI engine to recommend medical services based on the results of emotion analysis and biometric information. The server searches past medical databases to determine the appropriate medical department for the symptoms and emotional state. For example, for abdominal pain and anxiety, it would present gastroenterology and telemedicine as options.

[0150] Step 5:

[0151] The server sends departmental recommendations and remote healthcare information to the terminal. The server also adds support messages based on the user's emotional state. This allows the terminal to present the user with a message that includes recommended medical services and emotional considerations.

[0152] Step 6:

[0153] The terminal displays recommended information received from the server to the user. Specifically, it shows details about medical departments, links to make appointments, and emotionally reassuring messages. Users can use this information to book medical services as needed and receive prompt assistance.

[0154] (Application Example 2)

[0155] 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".

[0156] The current medical support system recommends medical departments without considering the user's feelings, resulting in a lack of processes that provide reassurance. Furthermore, the effective use of telemedicine is not being adequately promoted. Therefore, there is a need for recommendations of appropriate medical fields that address the user's feelings, and for the provision of support based on those feelings.

[0157] 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.

[0158] In this invention, the server includes means for recognizing the health status and emotions entered by the user, means for recommending medical fields based on the entered health status and emotions, and means for providing telemedicine guidance related to the recommended medical fields and generating support messages that correspond to the emotions. This makes it possible to provide appropriate medical support and a sense of security that is sensitive to the user's psychological state.

[0159] A "user" is an individual who inputs their health status and emotions through the system.

[0160] "Health status" refers to the conditions or symptoms related to an individual's physical and mental health.

[0161] "Emotions" refer to the psychological states or reactions that individuals express.

[0162] The "medical field" refers to a specialized area of ​​medical practice suited to specific symptoms or health conditions.

[0163] "Telemedicine" refers to medical services provided remotely using communication technologies such as the internet.

[0164] "Emotionally responsive support messages" refer to encouraging and reassuring messages provided according to the user's emotional state.

[0165] A "server" is a computer device that processes digital information and manages the entire system.

[0166] To implement this invention, multiple technical elements are combined to construct a system. The server uses speech recognition and text analysis software to recognize the user's health status and emotions. Specifically, speech recognition technology such as Google® Speech-to-Text is used to convert speech data into text. In addition, natural language processing libraries such as Hugging Face Transformers are used to analyze emotions from the text data.

[0167] The server uses machine learning algorithms to recommend a medical department based on the analyzed health status and emotional data. In this process, machine learning libraries such as Scikit-learn are used to build a model that combines historical medical information data and emotional data to determine the most appropriate medical department.

[0168] Furthermore, the server utilizes the OpenAI® GPT model to generate emotionally responsive support messages. This allows for the creation and timely delivery of reassuring messages when users are experiencing anxiety or stress.

[0169] For example, if a user enters "I have a headache and I'm a little worried," the system will recommend a neurologist and provide a recommendation message such as "A neurologist would be appropriate. You can make an appointment for a consultation immediately," as well as a reassuring message such as "Don't worry, a specialist will support you."

[0170] In this way, it becomes possible to quickly provide optimal medical support that takes the user's emotions into consideration.

[0171] An example of a prompt message for a generative AI model is: "This user is experiencing a headache and is feeling anxious. Please generate a hospital recommendation and a reassuring message."

[0172] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0173] Step 1:

[0174] The terminal receives input from the user regarding their health status and emotions. The user provides this information via voice or text. This input data is converted into a digital format on the terminal and sent to the server as text data using speech recognition software.

[0175] Step 2:

[0176] The server processes the received text data using a speech analysis engine to extract the user's emotions. Specifically, it uses natural language processing tools to analyze emotional indicators in the text and identify emotions such as anxiety or reassurance. This analysis result is then sent to the next processing step as user emotion information.

[0177] Step 3:

[0178] The server uses machine learning models to recommend the appropriate medical field based on the received health status and emotional information. Leveraging the Scikit-learn library, it algorithmically analyzes historical medical data and current input data. This determines the most relevant medical department and sends that information to the next step.

[0179] Step 4:

[0180] The server uses a generative AI model to generate emotionally responsive support messages. Based on OpenAI GPT, the model constructs reassuring messages and specific support tailored to the user's emotional state. The generated messages, along with recommended medical information, are sent to the device.

[0181] Step 5:

[0182] The device displays medical recommendations and support messages received from the server to the user. Specifically, it displays messages on the screen or conveys information verbally through a voice assistant. This allows users to receive medical support that takes their emotions into consideration.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Second Embodiment]

[0187] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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".

[0199] This invention is a system for quickly and accurately resolving health problems faced by users. By inputting their health status through the system, users can identify the appropriate medical field and receive corresponding telemedicine guidance. This enables many people to receive medical services without being constrained by time or distance.

[0200] The system provides an interface for users to input their health status through their device. For example, users can input specific symptoms such as "headache" or "stomach ache." The device receives this information and sends it to the server.

[0201] The server analyzes the received health data. Equipped with an AI engine, it refers to a database of past medical information to recommend the appropriate medical department based on the entered symptoms. For example, for a "headache," it recommends "neurology" or "internal medicine," and for "abdominal pain," it recommends "gastroenterology."

[0202] After the server identifies the recommended medical field, it sends that information back to the terminal. The terminal displays information about the medical department to the user and simultaneously guides them through telemedicine service options. This allows the user to consult online with a doctor in the selected medical department and receive a prescription if necessary.

[0203] As a concrete example, suppose a user enters the symptom "sore throat." The system sends this information to the server, which then processes it to recommend an ENT specialist. Subsequently, the recommendation is displayed on the user's terminal, and a guide is presented that allows the user to immediately consult online with an ENT doctor. In this way, the present invention enables the provision of appropriate medical services that meet the user's needs and significantly improves convenience.

[0204] The following describes the processing flow.

[0205] Step 1:

[0206] The user launches the application on their device and accesses the symptom input interface. There, the user enters their perceived health condition and specific symptoms as text. An interface is also provided that allows the user to select symptoms from a list.

[0207] Step 2:

[0208] The terminal receives symptom data entered by the user and sends it to the server. Before sending, it checks the format and content of the entered data and formats it if necessary. Data transmission uses a secure communication protocol (e.g., HTTPS) to ensure privacy.

[0209] Step 3:

[0210] The server analyzes the received symptom data. The AI ​​engine takes the symptom data as input and performs a process to identify the appropriate medical field by referring to past medical data and case information. Using a machine learning model, it identifies similar cases and recommends the most relevant medical department.

[0211] Step 4:

[0212] The server compiles the results for the recommended medical field and sends them back to the terminal. These results include information on the recommended medical department and related explanations. In addition, guidance information for using telemedicine services is also generated at the same time.

[0213] Step 5:

[0214] The terminal displays data received from the server to the user. The user can then review information on recommended medical departments and see options for immediate online consultations. This allows users to access necessary medical services regardless of geographical constraints.

[0215] (Example 1)

[0216] 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."

[0217] In modern medicine, quickly and accurately selecting the appropriate medical field is difficult for many people. In particular, geographical and time constraints make it difficult for users to receive appropriate medical support. Furthermore, current systems do not adequately optimize recommendations for medical fields based on user-entered health information, leaving challenges in providing effective medical support.

[0218] 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.

[0219] In this invention, the server includes means for acquiring health information entered by the user, means for performing analysis based on the acquired health information and recommending relevant medical fields, and means for providing electronic medical support related to the said medical fields. As a result, users can receive recommendations for appropriate medical fields quickly and accurately without geographical or temporal constraints, and medical support is provided more effectively.

[0220] "Health information" refers to data that shows the specific condition of a user's physical condition or symptoms.

[0221] A "medical field" refers to a specialized medical area that provides appropriate medical care and treatment for specific symptoms or health conditions.

[0222] "Electronic medical support" refers to the provision of medical services and information through remote digital platforms.

[0223] An "artificial intelligence model" is a technology based on algorithms that analyze large amounts of medical data to recommend the most suitable area of ​​treatment.

[0224] A "server" refers to a computer system used to process health information and make recommendations in the field of medical treatment.

[0225] "Users" refers to individuals who input health information through this system and receive suggestions for medical treatment areas.

[0226] This system provides an environment where users can remotely check their own health status and receive appropriate medical care quickly. To implement the system, terminals, servers, and generative AI models are used.

[0227] The terminal provides an interface for users to input health information. Specifically, mobile devices or computers are used as terminals, allowing users to input specific symptoms such as headaches or stomachaches through text input or selection of options. The terminal is responsible for transmitting the entered information to the server.

[0228] The server receives health information sent by the user and utilizes a generated AI model for analysis. The server stores a historical medical database, which can be used to recommend the most appropriate medical area. The AI ​​engine automatically selects a medical department and sends the results back to the terminal, including information on recommended departments and telemedicine services.

[0229] The generative AI model is an algorithm learned from input data, enabling rapid and accurate recommendations for medical areas. The goal is to ensure users receive the most appropriate medical support for their specific symptoms.

[0230] As a concrete example, suppose a user enters the symptom "sore throat." In this case, the device sends this information to the server, which uses a generative AI model to recommend an "otolaryngologist." The device receives this result and displays information about the medical department on the screen, and can also guide the user to related online medical service options.

[0231] As an example of a prompt, the generating AI model processes input such as "Return the recommended medical department based on the symptoms." This prompt allows the system to quickly process information and suggest the appropriate medical area.

[0232] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0233] Step 1:

[0234] The user enters health information using the terminal's interface. Specifically, they enter symptoms such as "headache" or "stomach ache" using text fields or selection options on the terminal's screen. After input, the terminal prepares to send this information to the next step. The input is the user's symptom data, and the output is data to be sent to the server.

[0235] Step 2:

[0236] The terminal transmits health information entered by the user to the server. A secure protocol is used for transmission to maintain data confidentiality. This ensures that user input information is delivered directly and safely to the server. The input is data related to the user's health status, and the output is the transfer of data to the server.

[0237] Step 3:

[0238] The server analyzes the received health information using a generating AI model. The server sends input data as prompts to the AI ​​engine and executes a process to recommend the most suitable medical field while referring to past medical databases. At this time, the AI ​​model selects a medical department based on the user's input. The input is symptom data sent from the terminal, and the output is information on the recommended medical field.

[0239] Step 4:

[0240] The server returns information about the medical treatment area and corresponding telemedicine services selected by the AI ​​engine to the terminal. Based on this returned data, the user is prepared to receive further medical support. The input is the recommendation result of the medical treatment area by the generative AI model, and the output is the information returned to the terminal.

[0241] Step 5:

[0242] The terminal displays information about the medical field received from the server to the user. The screen shows details of the selected medical department and available telemedicine service options. Specifically, the user can use links and reservation buttons to consult with a doctor online. The input is medical information from the server, and the output is information presented to the user.

[0243] (Application Example 1)

[0244] 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."

[0245] In the modern healthcare system, there is a challenge in that it is difficult for users to find the appropriate medical institution or department when they experience health problems. Furthermore, the inability to receive medical services without being restricted by time and location poses a significant hurdle for people who need prompt and appropriate medical care. This invention aims to solve these challenges in accessing medical care.

[0246] 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.

[0247] In this invention, the server includes functional means for receiving biometric information entered by the user, processing means for recommending a medical field based on the received biometric information, means for providing guidance on online medical consultations related to the recommended medical field, and data security means for ensuring secure information communication. This enables the user to quickly and appropriately identify a medical field and to use online medical consultations safely.

[0248] "Biometric information" is a term that refers to data related to a user's health status and symptoms.

[0249] The term "medical field" refers to specialized medical departments or fields of practice that address specific health conditions or symptoms.

[0250] "Online medical consultation" refers to a form of medical service in which doctors and patients conduct consultations and examinations remotely via the internet.

[0251] "Data security" refers to the technologies and methods used to protect users' personal data and medical information from unauthorized access and leakage.

[0252] The system implementing this invention mainly consists of a server, a terminal, and communication means. Users input biometric information using the terminal. The terminal is a computer device such as a smartphone or tablet. The input biometric information is securely transmitted to the server via the internet. The server is equipped with an AI engine using Python and TensorFlow, which analyzes the received data. The AI ​​model uses generative AI and identifies the most appropriate medical area by referring to a database of historical medical information.

[0253] Data security is guaranteed by encrypted communication using the SSL / TLS protocol. Once a recommendation from a medical department is obtained, it is sent back from the server to the terminal, which then displays instructions for online consultations on the screen. Users can then securely receive medical services based on this recommendation information.

[0254] As a concrete example, if a user enters biometric information such as "my vision is blurry" upon waking up in the morning, the device sends this information to a server. The server uses an AI engine to analyze a medical information database and sends a recommendation to the user for an ophthalmologist. The user can then choose to consult with an ophthalmologist online via the device.

[0255] Examples of prompts for a generative AI model include:

[0256] "Please enter the following symptoms: e.g., headache, stomach ache, sore throat."

[0257] In response to this, the system supports rapid access to medical care by recommending the appropriate medical field.

[0258] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0259] Step 1:

[0260] The user inputs biometric information through the terminal's interface. This input includes specific symptoms such as "headache" or "stomach ache." The input data is formatted by the terminal's data transmission program. The formatted input is then securely transmitted to the server via a communication module.

[0261] Step 2:

[0262] The server receives biometric information from the terminal in an encrypted format using the SSL / TLS protocol. On the server side, an AI engine using Python and TensorFlow analyzes the data. The AI ​​engine utilizes a database of historical medical information to identify the medical field based on the received symptoms. As a result of processing by this AI model, recommendation information for the appropriate medical department is generated.

[0263] Step 3:

[0264] The server sends back recommendation information for the identified medical department to the terminal. During this process, the data is again encrypted using the SSL / TLS protocol. The recommendation information includes the name of the medical department and options for related online medical services.

[0265] Step 4:

[0266] The device decrypts the recommendation information received from the server and displays it on the screen in a user-friendly format. The displayed information includes action buttons for online medical services the user can select, and links to initiate online consultations with doctors. Based on this information, the user can select and access appropriate medical services.

[0267] 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.

[0268] This invention is a system that allows users to input their health status and then provides medical recommendations that take into account their emotions based on that information. The system is equipped with an emotion engine that can recognize the user's emotions through text and voice analysis from the input information.

[0269] When the system is first used, the terminal provides the user with an interface to input their health status. Here, the user can choose to input specific symptoms as text or describe them verbally. The terminal converts this input information into digital data and sends it to the server.

[0270] The server first analyzes the received data using an emotion engine to recognize the user's emotional state. For example, if it performs a linguistic sentiment analysis of the input content and determines that the user may be experiencing anxiety, it uses that emotional information to select a medical department. This can then lead to the recommendation of a department capable of providing faster and more attentive care than usual.

[0271] Next, the server uses an AI engine to refer to past medical data and case information to determine the appropriate medical field for the symptoms. It then makes recommendations that take emotional information into account and prepares the system for guiding patients to telemedicine.

[0272] The results of the completed medical department recommendations and information on telemedicine are sent to the terminal. The terminal presents this to the user and, based on the emotional information received, displays encouraging and supportive messages as needed. For example, a user feeling anxious will be shown a reassuring message and guidance on online medical consultations appropriate to their situation.

[0273] As a concrete example, consider the case of a user complaining of "stomach pain." If the system determines that this user is feeling anxious about the pain, it will recommend a telemedicine service that allows for quick contact with a doctor, along with a gastroenterologist. Furthermore, it will provide supportive messages to alleviate the anxiety. In this way, the present invention realizes optimal medical support that also takes the user's psychological state into consideration.

[0274] The following describes the processing flow.

[0275] Step 1:

[0276] The user launches the application on their device and accesses the health status input screen. Here, the user can input their current symptoms and health status in text or voice.

[0277] Step 2:

[0278] The terminal receives health data entered by the user and converts it into a digital format. The converted data is then sent to the server.

[0279] Step 3:

[0280] The server passes the received data to the emotion engine, which recognizes the user's emotions from the text and audio data. The emotion engine performs language analysis and voice tone analysis to determine, for example, that the user is feeling "anxious."

[0281] Step 4:

[0282] The server then utilizes the AI engine to determine the recommended medical field based on the recognized emotion data and the input health status data. The AI engine refers to past medical data and also takes into account medical treatments that require attention due to emotions.

[0283] Step 5:

[0284] The server prepares guidance on telemedicine services related to the information on the finally determined medical field and transmits it to the terminal. These contents include the name of the medical department and the procedure for using telemedicine.

[0285] Step 6:

[0286] The terminal displays the information from the server to the user. In addition to the information on the recommended medical department, encouragement and support messages considering emotions are also provided. For example, for a user feeling anxious, a message such as "Let's talk to a doctor soon to feel at ease" is displayed.

[0287] Step 7:

[0288] The user can check the provided information and choose to use the telemedicine service. After the selection, the terminal accesses the telemedicine platform and schedules an online consultation with a doctor.

[0289] (Example 2)

[0290] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] In recent years, there has been a demand to quickly and accurately grasp the health status of individual users and provide appropriate medical services based on it. However, in conventional systems, it is difficult to recommend medical services considering the emotional state of users, and there is a problem that the psychological burden of users cannot be sufficiently reduced.

[0292] 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.

[0293] In this invention, the server includes terminal means for receiving biometric information entered by the user, analysis means for performing emotional analysis from the received biometric information to understand the user's emotional state, and recommendation means for recommending medical services based on the analyzed emotional state and biometric information. This makes it possible to provide optimal medical services tailored to the user's psychological state.

[0294] "Biometric information" refers to data that includes numerical values ​​and descriptions related to the user's health status and physical functions.

[0295] "Terminal means" refers to equipment or devices that allow users to input biometric information, and is a device that can input and transmit information.

[0296] "Emotional analysis" is a process that evaluates and recognizes the user's emotions and mental state based on the input information.

[0297] "Analysis means" refers to a system component equipped with functions and technologies for emotional analysis of biological information.

[0298] "Recommendation methods" refer to systems and methods that select and present medical services suitable for the user based on analyzed emotional state and biometric information.

[0299] "Remote healthcare" refers to health management and medical support services provided across physical distances.

[0300] This invention is a system in which a user inputs their own biometric information, and based on that information, it performs emotional analysis and recommends medical services. Specifically, it uses a terminal, a server, and software to link them together.

[0301] First, the user inputs biometric information using a terminal. The terminals include PCs, smartphones, tablets, etc., which are equipped with text input functions and voice input functions. The user can provide information to the system by inputting their physical symptoms in text or explaining them by voice.

[0302] The terminal transmits the biometric information to the server as digital data. The server is equipped with an emotion engine and an AI engine, which mainly perform analysis and recommendation processes. The emotion engine recognizes the user's emotional state based on the received data. For example, it detects words such as "worried" or "uneasy" from the input text and determines that it is related to the user's emotional state.

[0303] Next, the AI engine refers to the past health information database and recommends a suitable medical service based on the analyzed emotional state and biometric information. This recommendation aims for a quick and accurate medical response. For example, if it is a digestive problem, it recommends a specialist in gastroenterology.

[0304] Finally, the terminal presents the information from the server to the user. This includes information on the recommended department for diagnosis and treatment and guidance on remote healthcare services. Furthermore, a message that gives a sense of reassurance according to the user's emotional state is also displayed.

[0305] As a specific example, for a user who "has a stomachache and is worried", the system recommends gastroenterology and guides the options for online diagnosis and treatment. An example of a prompt sentence for the generative AI model is "Propose the optimal medical service for the symptom'stomachache' and the emotion 'uneasy'."

[0306] In this way, this invention is designed with the aim of comprehensively considering the user's biometric information and emotional state and providing optimal medical support.

[0307] The flow of the specific process in Example 2 will be described using FIG. 13.

[0308] Step 1:

[0309] The user enters biometric information into the device. The user can use text input to describe their health condition in detail, or choose voice input to describe their symptoms verbally. The entered information is recorded on the device as text data or digital audio data.

[0310] Step 2:

[0311] The terminal prepares to send the input data to the server. While text data may be sent directly to the server, voice data is typically converted to text using speech recognition technology. The converted data is then sent to the server using a secure communication protocol.

[0312] Step 3:

[0313] The server processes the received data and first performs emotional analysis using an emotion engine. It extracts emotion-related keywords and expressions from the input data, analyzes their linguistic characteristics, and evaluates the user's emotions. This process determines the user's emotional state, such as being tense, anxious, or calm.

[0314] Step 4:

[0315] The server uses an AI engine to recommend medical services based on the results of emotion analysis and biometric information. The server searches past medical databases to determine the appropriate medical department for the symptoms and emotional state. For example, for abdominal pain and anxiety, it would present gastroenterology and telemedicine as options.

[0316] Step 5:

[0317] The server sends departmental recommendations and remote healthcare information to the terminal. The server also adds support messages based on the user's emotional state. This allows the terminal to present the user with a message that includes recommended medical services and emotional considerations.

[0318] Step 6:

[0319] The terminal displays recommended information received from the server to the user. Specifically, it shows details about medical departments, links to make appointments, and emotionally reassuring messages. Users can use this information to book medical services as needed and receive prompt assistance.

[0320] (Application Example 2)

[0321] 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."

[0322] The current medical support system recommends medical departments without considering the user's feelings, resulting in a lack of processes that provide reassurance. Furthermore, the effective use of telemedicine is not being adequately promoted. Therefore, there is a need for recommendations of appropriate medical fields that address the user's feelings, and for the provision of support based on those feelings.

[0323] 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.

[0324] In this invention, the server includes means for recognizing the health status and emotions entered by the user, means for recommending medical fields based on the entered health status and emotions, and means for providing telemedicine guidance related to the recommended medical fields and generating support messages that correspond to the emotions. This makes it possible to provide appropriate medical support and a sense of security that is sensitive to the user's psychological state.

[0325] A "user" is an individual who inputs their health status and emotions through the system.

[0326] "Health status" refers to the conditions or symptoms related to an individual's physical and mental health.

[0327] "Emotions" refer to the psychological states or reactions that individuals express.

[0328] The "medical field" refers to a specialized area of ​​medical practice suited to specific symptoms or health conditions.

[0329] "Telemedicine" refers to medical services provided remotely using communication technologies such as the internet.

[0330] "Emotionally responsive support messages" refer to encouraging and reassuring messages provided according to the user's emotional state.

[0331] A "server" is a computer device that processes digital information and manages the entire system.

[0332] To implement this invention, multiple technical elements are combined to construct a system. The server uses speech recognition and text analysis software to recognize the user's health status and emotions. Specifically, speech recognition technology such as Google Speech-to-Text is used to convert speech data into text. In addition, natural language processing libraries such as Hugging Face Transformers are used to analyze emotions from the text data.

[0333] The server uses machine learning algorithms to recommend a medical department based on the analyzed health status and emotional data. In this process, machine learning libraries such as Scikit-learn are used to build a model that combines historical medical information data and emotional data to determine the most appropriate medical department.

[0334] Furthermore, the server utilizes the OpenAI GPT model to generate emotionally responsive support messages. This allows for the creation and timely delivery of reassuring messages when users are feeling anxious or stressed.

[0335] For example, if a user enters "I have a headache and I'm a little worried," the system will recommend a neurologist and provide a recommendation message such as "A neurologist would be appropriate. You can make an appointment for a consultation immediately," as well as a reassuring message such as "Don't worry, a specialist will support you."

[0336] In this way, it becomes possible to quickly provide optimal medical support that takes the user's emotions into consideration.

[0337] An example of a prompt message for a generative AI model is: "This user is experiencing a headache and is feeling anxious. Please generate a hospital recommendation and a reassuring message."

[0338] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0339] Step 1:

[0340] The terminal receives input from the user regarding their health status and emotions. The user provides this information via voice or text. This input data is converted into a digital format on the terminal and sent to the server as text data using speech recognition software.

[0341] Step 2:

[0342] The server processes the received text data using a speech analysis engine to extract the user's emotions. Specifically, it uses natural language processing tools to analyze emotional indicators in the text and identify emotions such as anxiety or reassurance. This analysis result is then sent to the next processing step as user emotion information.

[0343] Step 3:

[0344] The server uses machine learning models to recommend the appropriate medical field based on the received health status and emotional information. Leveraging the Scikit-learn library, it algorithmically analyzes historical medical data and current input data. This determines the most relevant medical department and sends that information to the next step.

[0345] Step 4:

[0346] The server uses a generative AI model to generate emotionally responsive support messages. Based on OpenAI GPT, the model constructs reassuring messages and specific support tailored to the user's emotional state. The generated messages, along with recommended medical information, are sent to the device.

[0347] Step 5:

[0348] The device displays medical recommendations and support messages received from the server to the user. Specifically, it displays messages on the screen or conveys information verbally through a voice assistant. This allows users to receive medical support that takes their emotions into consideration.

[0349] 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.

[0350] 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.

[0351] 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.

[0352] [Third Embodiment]

[0353] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0354] 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.

[0355] 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).

[0356] 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.

[0357] 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.

[0358] 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).

[0359] 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.

[0360] 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.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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".

[0365] This invention is a system for quickly and accurately resolving health problems faced by users. By inputting their health status through the system, users can identify the appropriate medical field and receive corresponding telemedicine guidance. This enables many people to receive medical services without being constrained by time or distance.

[0366] The system provides an interface for users to input their health status through their device. For example, users can input specific symptoms such as "headache" or "stomach ache." The device receives this information and sends it to the server.

[0367] The server analyzes the received health data. Equipped with an AI engine, it refers to a database of past medical information to recommend the appropriate medical department based on the entered symptoms. For example, for a "headache," it recommends "neurology" or "internal medicine," and for "abdominal pain," it recommends "gastroenterology."

[0368] After the server identifies the recommended medical field, it sends that information back to the terminal. The terminal displays information about the medical department to the user and simultaneously guides them through telemedicine service options. This allows the user to consult online with a doctor in the selected medical department and receive a prescription if necessary.

[0369] As a concrete example, suppose a user enters the symptom "sore throat." The system sends this information to the server, which then processes it to recommend an ENT specialist. Subsequently, the recommendation is displayed on the user's terminal, and a guide is presented that allows the user to immediately consult online with an ENT doctor. In this way, the present invention enables the provision of appropriate medical services that meet the user's needs and significantly improves convenience.

[0370] The following describes the processing flow.

[0371] Step 1:

[0372] The user launches the application on their device and accesses the symptom input interface. There, the user enters their perceived health condition and specific symptoms as text. An interface is also provided that allows the user to select symptoms from a list.

[0373] Step 2:

[0374] The terminal receives symptom data entered by the user and sends it to the server. Before sending, it checks the format and content of the entered data and formats it if necessary. Data transmission uses a secure communication protocol (e.g., HTTPS) to ensure privacy.

[0375] Step 3:

[0376] The server analyzes the received symptom data. The AI ​​engine takes the symptom data as input and performs a process to identify the appropriate medical field by referring to past medical data and case information. Using a machine learning model, it identifies similar cases and recommends the most relevant medical department.

[0377] Step 4:

[0378] The server compiles the results for the recommended medical field and sends them back to the terminal. These results include information on the recommended medical department and related explanations. In addition, guidance information for using telemedicine services is also generated at the same time.

[0379] Step 5:

[0380] The terminal displays data received from the server to the user. The user can then review information on recommended medical departments and see options for immediate online consultations. This allows users to access necessary medical services regardless of geographical constraints.

[0381] (Example 1)

[0382] 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."

[0383] In modern medicine, quickly and accurately selecting the appropriate medical field is difficult for many people. In particular, geographical and time constraints make it difficult for users to receive appropriate medical support. Furthermore, current systems do not adequately optimize recommendations for medical fields based on user-entered health information, leaving challenges in providing effective medical support.

[0384] 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.

[0385] In this invention, the server includes means for acquiring health information entered by the user, means for performing analysis based on the acquired health information and recommending relevant medical fields, and means for providing electronic medical support related to the said medical fields. As a result, users can receive recommendations for appropriate medical fields quickly and accurately without geographical or temporal constraints, and medical support is provided more effectively.

[0386] "Health information" refers to data that shows the specific condition of a user's physical condition or symptoms.

[0387] A "medical field" refers to a specialized medical area that provides appropriate medical care and treatment for specific symptoms or health conditions.

[0388] "Electronic medical support" refers to the provision of medical services and information through remote digital platforms.

[0389] An "artificial intelligence model" is a technology based on algorithms that analyze large amounts of medical data to recommend the most suitable area of ​​treatment.

[0390] A "server" refers to a computer system used to process health information and make recommendations in the field of medical treatment.

[0391] "Users" refers to individuals who input health information through this system and receive suggestions for medical treatment areas.

[0392] This system provides an environment where users can remotely check their own health status and receive appropriate medical care quickly. To implement the system, terminals, servers, and generative AI models are used.

[0393] The terminal provides an interface for users to input health information. Specifically, mobile devices or computers are used as terminals, allowing users to input specific symptoms such as headaches or stomachaches through text input or selection of options. The terminal is responsible for transmitting the entered information to the server.

[0394] The server receives health information sent by the user and utilizes a generated AI model for analysis. The server stores a historical medical database, which can be used to recommend the most appropriate medical area. The AI ​​engine automatically selects a medical department and sends the results back to the terminal, including information on recommended departments and telemedicine services.

[0395] The generative AI model is an algorithm learned from input data, enabling rapid and accurate recommendations for medical areas. The goal is to ensure users receive the most appropriate medical support for their specific symptoms.

[0396] As a concrete example, suppose a user enters the symptom "sore throat." In this case, the device sends this information to the server, which uses a generative AI model to recommend an "otolaryngologist." The device receives this result and displays information about the medical department on the screen, and can also guide the user to related online medical service options.

[0397] As an example of a prompt, the generating AI model processes input such as "Return the recommended medical department based on the symptoms." This prompt allows the system to quickly process information and suggest the appropriate medical area.

[0398] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0399] Step 1:

[0400] The user enters health information using the terminal's interface. Specifically, they enter symptoms such as "headache" or "stomach ache" using text fields or selection options on the terminal's screen. After input, the terminal prepares to send this information to the next step. The input is the user's symptom data, and the output is data to be sent to the server.

[0401] Step 2:

[0402] The terminal transmits health information entered by the user to the server. A secure protocol is used for transmission to maintain data confidentiality. This ensures that user input information is delivered directly and safely to the server. The input is data related to the user's health status, and the output is the transfer of data to the server.

[0403] Step 3:

[0404] The server analyzes the received health information using a generating AI model. The server sends input data as prompts to the AI ​​engine and executes a process to recommend the most suitable medical field while referring to past medical databases. At this time, the AI ​​model selects a medical department based on the user's input. The input is symptom data sent from the terminal, and the output is information on the recommended medical field.

[0405] Step 4:

[0406] The server returns information about the medical treatment area and corresponding telemedicine services selected by the AI ​​engine to the terminal. Based on this returned data, the user is prepared to receive further medical support. The input is the recommendation result of the medical treatment area by the generative AI model, and the output is the information returned to the terminal.

[0407] Step 5:

[0408] The terminal displays information about the medical field received from the server to the user. The screen shows details of the selected medical department and available telemedicine service options. Specifically, the user can use links and reservation buttons to consult with a doctor online. The input is medical information from the server, and the output is information presented to the user.

[0409] (Application Example 1)

[0410] 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."

[0411] In the modern healthcare system, there is a challenge in that it is difficult for users to find the appropriate medical institution or department when they experience health problems. Furthermore, the inability to receive medical services without being restricted by time and location poses a significant hurdle for people who need prompt and appropriate medical care. This invention aims to solve these challenges in accessing medical care.

[0412] 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.

[0413] In this invention, the server includes functional means for receiving biometric information entered by the user, processing means for recommending a medical field based on the received biometric information, means for providing guidance on online medical consultations related to the recommended medical field, and data security means for ensuring secure information communication. This enables the user to quickly and appropriately identify a medical field and to use online medical consultations safely.

[0414] "Biometric information" is a term that refers to data related to a user's health status and symptoms.

[0415] The term "medical field" refers to specialized medical departments or fields of practice that address specific health conditions or symptoms.

[0416] "Online medical consultation" refers to a form of medical service in which doctors and patients conduct consultations and examinations remotely via the internet.

[0417] "Data security" refers to the technologies and methods used to protect users' personal data and medical information from unauthorized access and leakage.

[0418] The system implementing this invention mainly consists of a server, a terminal, and communication means. Users input biometric information using the terminal. The terminal is a computer device such as a smartphone or tablet. The input biometric information is securely transmitted to the server via the internet. The server is equipped with an AI engine using Python and TensorFlow, which analyzes the received data. The AI ​​model uses generative AI and identifies the most appropriate medical area by referring to a database of historical medical information.

[0419] Data security is guaranteed by encrypted communication using the SSL / TLS protocol. Once a recommendation from a medical department is obtained, it is sent back from the server to the terminal, which then displays instructions for online consultations on the screen. Users can then securely receive medical services based on this recommendation information.

[0420] As a concrete example, if a user enters biometric information such as "my vision is blurry" upon waking up in the morning, the device sends this information to a server. The server uses an AI engine to analyze a medical information database and sends a recommendation to the user for an ophthalmologist. The user can then choose to consult with an ophthalmologist online via the device.

[0421] Examples of prompts for a generative AI model include:

[0422] "Please enter the following symptoms: e.g., headache, stomach ache, sore throat."

[0423] In response to this, the system supports rapid access to medical care by recommending the appropriate medical field.

[0424] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0425] Step 1:

[0426] The user inputs biometric information through the terminal's interface. This input includes specific symptoms such as "headache" or "stomach ache." The input data is formatted by the terminal's data transmission program. The formatted input is then securely transmitted to the server via a communication module.

[0427] Step 2:

[0428] The server receives biometric information from the terminal in an encrypted format using the SSL / TLS protocol. On the server side, an AI engine using Python and TensorFlow analyzes the data. The AI ​​engine utilizes a database of historical medical information to identify the medical field based on the received symptoms. As a result of processing by this AI model, recommendation information for the appropriate medical department is generated.

[0429] Step 3:

[0430] The server sends back recommendation information for the identified medical department to the terminal. During this process, the data is again encrypted using the SSL / TLS protocol. The recommendation information includes the name of the medical department and options for related online medical services.

[0431] Step 4:

[0432] The device decrypts the recommendation information received from the server and displays it on the screen in a user-friendly format. The displayed information includes action buttons for online medical services the user can select, and links to initiate online consultations with doctors. Based on this information, the user can select and access appropriate medical services.

[0433] 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.

[0434] This invention is a system that allows users to input their health status and then provides medical recommendations that take into account their emotions based on that information. The system is equipped with an emotion engine that can recognize the user's emotions through text and voice analysis from the input information.

[0435] When the system is first used, the terminal provides the user with an interface to input their health status. Here, the user can choose to input specific symptoms as text or describe them verbally. The terminal converts this input information into digital data and sends it to the server.

[0436] The server first analyzes the received data using an emotion engine to recognize the user's emotional state. For example, if it performs a linguistic sentiment analysis of the input content and determines that the user may be experiencing anxiety, it uses that emotional information to select a medical department. This can then lead to the recommendation of a department capable of providing faster and more attentive care than usual.

[0437] Next, the server uses an AI engine to refer to past medical data and case information to determine the appropriate medical field for the symptoms. It then makes recommendations that take emotional information into account and prepares the system for guiding patients to telemedicine.

[0438] The results of the completed medical department recommendations and information on telemedicine are sent to the terminal. The terminal presents this to the user and, based on the emotional information received, displays encouraging and supportive messages as needed. For example, a user feeling anxious will be shown a reassuring message and guidance on online medical consultations appropriate to their situation.

[0439] As a concrete example, consider the case of a user complaining of "stomach pain." If the system determines that this user is feeling anxious about the pain, it will recommend a telemedicine service that allows for quick contact with a doctor, along with a gastroenterologist. Furthermore, it will provide supportive messages to alleviate the anxiety. In this way, the present invention realizes optimal medical support that also takes the user's psychological state into consideration.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The user launches the application on their device and accesses the health status input screen. Here, the user can input their current symptoms and health status in text or voice.

[0443] Step 2:

[0444] The terminal receives health data entered by the user and converts it into a digital format. The converted data is then sent to the server.

[0445] Step 3:

[0446] The server passes the received data to the emotion engine, which recognizes the user's emotions from the text and audio data. The emotion engine performs language analysis and voice tone analysis to determine, for example, that the user is feeling "anxious."

[0447] Step 4:

[0448] The server then uses its AI engine to determine the recommended medical field based on the recognized emotional data and input health status data. The AI ​​engine also refers to past medical data and takes into account treatments that require attention due to emotional factors.

[0449] Step 5:

[0450] The server compiles information on the finalized medical field and related telemedicine services, and sends it to the terminal. This information includes the name of the medical department and instructions on how to use telemedicine.

[0451] Step 6:

[0452] The terminal displays information from the server to the user. In addition to information on recommended medical departments, it also provides emotionally sensitive encouraging and supportive messages. For example, a user feeling anxious might see a message such as, "Let's talk to a doctor right away so you can feel at ease."

[0453] Step 7:

[0454] Users can review the provided information and choose to use telemedicine services. After making their selection, the device accesses the telemedicine platform and schedules an online consultation with a doctor.

[0455] (Example 2)

[0456] 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."

[0457] In recent years, there has been a growing need to quickly and accurately assess the health status of individual users and provide appropriate medical services based on that assessment. However, conventional systems have difficulty recommending medical services that take into account the emotional state of users, and thus have a problem in that they cannot adequately reduce the psychological burden on users.

[0458] 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.

[0459] In this invention, the server includes terminal means for receiving biometric information entered by the user, analysis means for performing emotional analysis from the received biometric information to understand the user's emotional state, and recommendation means for recommending medical services based on the analyzed emotional state and biometric information. This makes it possible to provide optimal medical services tailored to the user's psychological state.

[0460] "Biometric information" refers to data that includes numerical values ​​and descriptions related to the user's health status and physical functions.

[0461] "Terminal means" refers to equipment or devices that allow users to input biometric information, and is a device that can input and transmit information.

[0462] "Emotional analysis" is a process that evaluates and recognizes the user's emotions and mental state based on the input information.

[0463] "Analysis means" refers to a system component equipped with functions and technologies for emotional analysis of biological information.

[0464] "Recommendation methods" refer to systems and methods that select and present medical services suitable for the user based on analyzed emotional state and biometric information.

[0465] "Remote healthcare" refers to health management and medical support services provided across physical distances.

[0466] This invention is a system in which a user inputs their own biometric information, and based on that information, it performs emotional analysis and recommends medical services. Specifically, it uses a terminal, a server, and software to link them together.

[0467] First, the user inputs biometric information using a device. These devices include PCs, smartphones, and tablets, and are equipped with text input and voice input capabilities. The user can provide information to the system by entering their physical symptoms in text or describing them verbally.

[0468] The device transmits biometric information as digital data to the server. The server is equipped with an emotion engine and an AI engine, which primarily perform analysis and recommendation processing. The emotion engine recognizes the user's emotional state based on the received data. For example, it detects words such as "worry" or "anxiety" from the input text and determines that they are related to the user's emotional state.

[0469] Next, the AI ​​engine references a database of past health information and recommends appropriate medical services based on the analyzed emotional state and biometric data. This recommendation aims for a swift and accurate medical response; for example, if the problem is related to the digestive system, it will recommend a gastroenterologist.

[0470] Finally, the terminal displays information from the server to the user. This includes information on recommended medical departments and guidance on remote healthcare services. In addition, messages that provide reassurance tailored to the user's emotional state are also displayed.

[0471] As a concrete example, for a user who is worried because they have a stomach ache, the system recommends a gastroenterologist and guides them to the option of an online consultation. An example of a prompt message for the generating AI model would be, "Suggest the most suitable medical service for the symptom 'stomach ache' and emotion 'anxiety'."

[0472] Thus, this invention is designed with the aim of providing optimal medical support by comprehensively considering the user's biometric information and emotional state.

[0473] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0474] Step 1:

[0475] The user enters biometric information into the device. The user can use text input to describe their health condition in detail, or choose voice input to describe their symptoms verbally. The entered information is recorded on the device as text data or digital audio data.

[0476] Step 2:

[0477] The terminal prepares to send the input data to the server. While text data may be sent directly to the server, voice data is typically converted to text using speech recognition technology. The converted data is then sent to the server using a secure communication protocol.

[0478] Step 3:

[0479] The server processes the received data and first performs emotional analysis using an emotion engine. It extracts emotion-related keywords and expressions from the input data, analyzes their linguistic characteristics, and evaluates the user's emotions. This process determines the user's emotional state, such as being tense, anxious, or calm.

[0480] Step 4:

[0481] The server uses an AI engine to recommend medical services based on the results of emotion analysis and biometric information. The server searches past medical databases to determine the appropriate medical department for the symptoms and emotional state. For example, for abdominal pain and anxiety, it would present gastroenterology and telemedicine as options.

[0482] Step 5:

[0483] The server sends departmental recommendations and remote healthcare information to the terminal. The server also adds support messages based on the user's emotional state. This allows the terminal to present the user with a message that includes recommended medical services and emotional considerations.

[0484] Step 6:

[0485] The terminal displays recommended information received from the server to the user. Specifically, it shows details about medical departments, links to make appointments, and emotionally reassuring messages. Users can use this information to book medical services as needed and receive prompt assistance.

[0486] (Application Example 2)

[0487] 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."

[0488] The current medical support system recommends medical departments without considering the user's feelings, resulting in a lack of processes that provide reassurance. Furthermore, the effective use of telemedicine is not being adequately promoted. Therefore, there is a need for recommendations of appropriate medical fields that address the user's feelings, and for the provision of support based on those feelings.

[0489] 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.

[0490] In this invention, the server includes means for recognizing the health status and emotions entered by the user, means for recommending medical fields based on the entered health status and emotions, and means for providing telemedicine guidance related to the recommended medical fields and generating support messages that correspond to the emotions. This makes it possible to provide appropriate medical support and a sense of security that is sensitive to the user's psychological state.

[0491] A "user" is an individual who inputs their health status and emotions through the system.

[0492] "Health status" refers to the conditions or symptoms related to an individual's physical and mental health.

[0493] "Emotions" refer to the psychological states or reactions that individuals express.

[0494] The "medical field" refers to a specialized area of ​​medical practice suited to specific symptoms or health conditions.

[0495] "Telemedicine" refers to medical services provided remotely using communication technologies such as the internet.

[0496] "Emotionally responsive support messages" refer to encouraging and reassuring messages provided according to the user's emotional state.

[0497] A "server" is a computer device that processes digital information and manages the entire system.

[0498] To implement this invention, multiple technical elements are combined to construct a system. The server uses speech recognition and text analysis software to recognize the user's health status and emotions. Specifically, speech recognition technology such as Google Speech-to-Text is used to convert speech data into text. In addition, natural language processing libraries such as Hugging Face Transformers are used to analyze emotions from the text data.

[0499] The server uses machine learning algorithms to recommend a medical department based on the analyzed health status and emotional data. In this process, machine learning libraries such as Scikit-learn are used to build a model that combines historical medical information data and emotional data to determine the most appropriate medical department.

[0500] Furthermore, the server utilizes the OpenAI GPT model to generate emotionally responsive support messages. This allows for the creation and timely delivery of reassuring messages when users are feeling anxious or stressed.

[0501] For example, if a user enters "I have a headache and I'm a little worried," the system will recommend a neurologist and provide a recommendation message such as "A neurologist would be appropriate. You can make an appointment for a consultation immediately," as well as a reassuring message such as "Don't worry, a specialist will support you."

[0502] In this way, it becomes possible to quickly provide optimal medical support that takes the user's emotions into consideration.

[0503] An example of a prompt message for a generative AI model is: "This user is experiencing a headache and is feeling anxious. Please generate a hospital recommendation and a reassuring message."

[0504] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0505] Step 1:

[0506] The terminal receives input from the user regarding their health status and emotions. The user provides this information via voice or text. This input data is converted into a digital format on the terminal and sent to the server as text data using speech recognition software.

[0507] Step 2:

[0508] The server processes the received text data using a speech analysis engine to extract the user's emotions. Specifically, it uses natural language processing tools to analyze emotional indicators in the text and identify emotions such as anxiety or reassurance. This analysis result is then sent to the next processing step as user emotion information.

[0509] Step 3:

[0510] The server uses machine learning models to recommend the appropriate medical field based on the received health status and emotional information. Leveraging the Scikit-learn library, it algorithmically analyzes historical medical data and current input data. This determines the most relevant medical department and sends that information to the next step.

[0511] Step 4:

[0512] The server uses a generative AI model to generate emotionally responsive support messages. Based on OpenAI GPT, the model constructs reassuring messages and specific support tailored to the user's emotional state. The generated messages, along with recommended medical information, are sent to the device.

[0513] Step 5:

[0514] The device displays medical recommendations and support messages received from the server to the user. Specifically, it displays messages on the screen or conveys information verbally through a voice assistant. This allows users to receive medical support that takes their emotions into consideration.

[0515] 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.

[0516] 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.

[0517] 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.

[0518] [Fourth Embodiment]

[0519] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0520] 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.

[0521] 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).

[0522] 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.

[0523] 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.

[0524] 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).

[0525] 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.

[0526] 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.

[0527] 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.

[0528] 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.

[0529] 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.

[0530] 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.

[0531] 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".

[0532] This invention is a system for quickly and accurately resolving health problems faced by users. By inputting their health status through the system, users can identify the appropriate medical field and receive corresponding telemedicine guidance. This enables many people to receive medical services without being constrained by time or distance.

[0533] The system provides an interface for users to input their health status through their device. For example, users can input specific symptoms such as "headache" or "stomach ache." The device receives this information and sends it to the server.

[0534] The server analyzes the received health data. Equipped with an AI engine, it refers to a database of past medical information to recommend the appropriate medical department based on the entered symptoms. For example, for a "headache," it recommends "neurology" or "internal medicine," and for "abdominal pain," it recommends "gastroenterology."

[0535] After the server identifies the recommended medical field, it sends that information back to the terminal. The terminal displays information about the medical department to the user and simultaneously guides them through telemedicine service options. This allows the user to consult online with a doctor in the selected medical department and receive a prescription if necessary.

[0536] As a concrete example, suppose a user enters the symptom "sore throat." The system sends this information to the server, which then processes it to recommend an ENT specialist. Subsequently, the recommendation is displayed on the user's terminal, and a guide is presented that allows the user to immediately consult online with an ENT doctor. In this way, the present invention enables the provision of appropriate medical services that meet the user's needs and significantly improves convenience.

[0537] The following describes the processing flow.

[0538] Step 1:

[0539] The user launches the application on their device and accesses the symptom input interface. There, the user enters their perceived health condition and specific symptoms as text. An interface is also provided that allows the user to select symptoms from a list.

[0540] Step 2:

[0541] The terminal receives symptom data entered by the user and sends it to the server. Before sending, it checks the format and content of the entered data and formats it if necessary. Data transmission uses a secure communication protocol (e.g., HTTPS) to ensure privacy.

[0542] Step 3:

[0543] The server analyzes the received symptom data. The AI ​​engine takes the symptom data as input and performs a process to identify the appropriate medical field by referring to past medical data and case information. Using a machine learning model, it identifies similar cases and recommends the most relevant medical department.

[0544] Step 4:

[0545] The server compiles the results for the recommended medical field and sends them back to the terminal. These results include information on the recommended medical department and related explanations. In addition, guidance information for using telemedicine services is also generated at the same time.

[0546] Step 5:

[0547] The terminal displays data received from the server to the user. The user can then review information on recommended medical departments and see options for immediate online consultations. This allows users to access necessary medical services regardless of geographical constraints.

[0548] (Example 1)

[0549] 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".

[0550] In modern medicine, quickly and accurately selecting the appropriate medical field is difficult for many people. In particular, geographical and time constraints make it difficult for users to receive appropriate medical support. Furthermore, current systems do not adequately optimize recommendations for medical fields based on user-entered health information, leaving challenges in providing effective medical support.

[0551] 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.

[0552] In this invention, the server includes means for acquiring health information entered by the user, means for performing analysis based on the acquired health information and recommending relevant medical fields, and means for providing electronic medical support related to the said medical fields. As a result, users can receive recommendations for appropriate medical fields quickly and accurately without geographical or temporal constraints, and medical support is provided more effectively.

[0553] "Health information" refers to data that shows the specific condition of a user's physical condition or symptoms.

[0554] A "medical field" refers to a specialized medical area that provides appropriate medical care and treatment for specific symptoms or health conditions.

[0555] "Electronic medical support" refers to the provision of medical services and information through remote digital platforms.

[0556] An "artificial intelligence model" is a technology based on algorithms that analyze large amounts of medical data to recommend the most suitable area of ​​treatment.

[0557] A "server" refers to a computer system used to process health information and make recommendations in the field of medical treatment.

[0558] "Users" refers to individuals who input health information through this system and receive suggestions for medical treatment areas.

[0559] This system provides an environment where users can remotely check their own health status and receive appropriate medical care quickly. To implement the system, terminals, servers, and generative AI models are used.

[0560] The terminal provides an interface for users to input health information. Specifically, mobile devices or computers are used as terminals, allowing users to input specific symptoms such as headaches or stomachaches through text input or selection of options. The terminal is responsible for transmitting the entered information to the server.

[0561] The server receives health information sent by the user and utilizes a generated AI model for analysis. The server stores a historical medical database, which can be used to recommend the most appropriate medical area. The AI ​​engine automatically selects a medical department and sends the results back to the terminal, including information on recommended departments and telemedicine services.

[0562] The generative AI model is an algorithm learned from input data, enabling rapid and accurate recommendations for medical areas. The goal is to ensure users receive the most appropriate medical support for their specific symptoms.

[0563] As a concrete example, suppose a user enters the symptom "sore throat." In this case, the device sends this information to the server, which uses a generative AI model to recommend an "otolaryngologist." The device receives this result and displays information about the medical department on the screen, and can also guide the user to related online medical service options.

[0564] As an example of a prompt, the generating AI model processes input such as "Return the recommended medical department based on the symptoms." This prompt allows the system to quickly process information and suggest the appropriate medical area.

[0565] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0566] Step 1:

[0567] The user enters health information using the terminal's interface. Specifically, they enter symptoms such as "headache" or "stomach ache" using text fields or selection options on the terminal's screen. After input, the terminal prepares to send this information to the next step. The input is the user's symptom data, and the output is data to be sent to the server.

[0568] Step 2:

[0569] The terminal transmits health information entered by the user to the server. A secure protocol is used for transmission to maintain data confidentiality. This ensures that user input information is delivered directly and safely to the server. The input is data related to the user's health status, and the output is the transfer of data to the server.

[0570] Step 3:

[0571] The server analyzes the received health information using a generating AI model. The server sends input data as prompts to the AI ​​engine and executes a process to recommend the most suitable medical field while referring to past medical databases. At this time, the AI ​​model selects a medical department based on the user's input. The input is symptom data sent from the terminal, and the output is information on the recommended medical field.

[0572] Step 4:

[0573] The server returns information about the medical treatment area and corresponding telemedicine services selected by the AI ​​engine to the terminal. Based on this returned data, the user is prepared to receive further medical support. The input is the recommendation result of the medical treatment area by the generative AI model, and the output is the information returned to the terminal.

[0574] Step 5:

[0575] The terminal displays information about the medical field received from the server to the user. The screen shows details of the selected medical department and available telemedicine service options. Specifically, the user can use links and reservation buttons to consult with a doctor online. The input is medical information from the server, and the output is information presented to the user.

[0576] (Application Example 1)

[0577] 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".

[0578] In the modern healthcare system, there is a challenge in that it is difficult for users to find the appropriate medical institution or department when they experience health problems. Furthermore, the inability to receive medical services without being restricted by time and location poses a significant hurdle for people who need prompt and appropriate medical care. This invention aims to solve these challenges in accessing medical care.

[0579] 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.

[0580] In this invention, the server includes functional means for receiving biometric information entered by the user, processing means for recommending a medical field based on the received biometric information, means for providing guidance on online medical consultations related to the recommended medical field, and data security means for ensuring secure information communication. This enables the user to quickly and appropriately identify a medical field and to use online medical consultations safely.

[0581] "Biometric information" is a term that refers to data related to a user's health status and symptoms.

[0582] The term "medical field" refers to specialized medical departments or fields of practice that address specific health conditions or symptoms.

[0583] "Online medical consultation" refers to a form of medical service in which doctors and patients conduct consultations and examinations remotely via the internet.

[0584] "Data security" refers to the technologies and methods used to protect users' personal data and medical information from unauthorized access and leakage.

[0585] The system implementing this invention mainly consists of a server, a terminal, and communication means. Users input biometric information using the terminal. The terminal is a computer device such as a smartphone or tablet. The input biometric information is securely transmitted to the server via the internet. The server is equipped with an AI engine using Python and TensorFlow, which analyzes the received data. The AI ​​model uses generative AI and identifies the most appropriate medical area by referring to a database of historical medical information.

[0586] Data security is guaranteed by encrypted communication using the SSL / TLS protocol. Once a recommendation from a medical department is obtained, it is sent back from the server to the terminal, which then displays instructions for online consultations on the screen. Users can then securely receive medical services based on this recommendation information.

[0587] As a concrete example, if a user enters biometric information such as "my vision is blurry" upon waking up in the morning, the device sends this information to a server. The server uses an AI engine to analyze a medical information database and sends a recommendation to the user for an ophthalmologist. The user can then choose to consult with an ophthalmologist online via the device.

[0588] Examples of prompts for a generative AI model include:

[0589] "Please enter the following symptoms: e.g., headache, stomach ache, sore throat."

[0590] In response to this, the system supports rapid access to medical care by recommending the appropriate medical field.

[0591] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0592] Step 1:

[0593] The user inputs biometric information through the terminal's interface. This input includes specific symptoms such as "headache" or "stomach ache." The input data is formatted by the terminal's data transmission program. The formatted input is then securely transmitted to the server via a communication module.

[0594] Step 2:

[0595] The server receives biometric information from the terminal in an encrypted format using the SSL / TLS protocol. On the server side, an AI engine using Python and TensorFlow analyzes the data. The AI ​​engine utilizes a database of historical medical information to identify the medical field based on the received symptoms. As a result of processing by this AI model, recommendation information for the appropriate medical department is generated.

[0596] Step 3:

[0597] The server sends back recommendation information for the identified medical department to the terminal. During this process, the data is again encrypted using the SSL / TLS protocol. The recommendation information includes the name of the medical department and options for related online medical services.

[0598] Step 4:

[0599] The device decrypts the recommendation information received from the server and displays it on the screen in a user-friendly format. The displayed information includes action buttons for online medical services the user can select, and links to initiate online consultations with doctors. Based on this information, the user can select and access appropriate medical services.

[0600] 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.

[0601] This invention is a system that allows users to input their health status and then provides medical recommendations that take into account their emotions based on that information. The system is equipped with an emotion engine that can recognize the user's emotions through text and voice analysis from the input information.

[0602] When the system is first used, the terminal provides the user with an interface to input their health status. Here, the user can choose to input specific symptoms as text or describe them verbally. The terminal converts this input information into digital data and sends it to the server.

[0603] The server first analyzes the received data using an emotion engine to recognize the user's emotional state. For example, if it performs a linguistic sentiment analysis of the input content and determines that the user may be experiencing anxiety, it uses that emotional information to select a medical department. This can then lead to the recommendation of a department capable of providing faster and more attentive care than usual.

[0604] Next, the server uses an AI engine to refer to past medical data and case information to determine the appropriate medical field for the symptoms. It then makes recommendations that take emotional information into account and prepares the system for guiding patients to telemedicine.

[0605] The results of the completed medical department recommendations and information on telemedicine are sent to the terminal. The terminal presents this to the user and, based on the emotional information received, displays encouraging and supportive messages as needed. For example, a user feeling anxious will be shown a reassuring message and guidance on online medical consultations appropriate to their situation.

[0606] As a concrete example, consider the case of a user complaining of "stomach pain." If the system determines that this user is feeling anxious about the pain, it will recommend a telemedicine service that allows for quick contact with a doctor, along with a gastroenterologist. Furthermore, it will provide supportive messages to alleviate the anxiety. In this way, the present invention realizes optimal medical support that also takes the user's psychological state into consideration.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] The user launches the application on their device and accesses the health status input screen. Here, the user can input their current symptoms and health status in text or voice.

[0610] Step 2:

[0611] The terminal receives health data entered by the user and converts it into a digital format. The converted data is then sent to the server.

[0612] Step 3:

[0613] The server passes the received data to the emotion engine, which recognizes the user's emotions from the text and audio data. The emotion engine performs language analysis and voice tone analysis to determine, for example, that the user is feeling "anxious."

[0614] Step 4:

[0615] The server then uses its AI engine to determine the recommended medical field based on the recognized emotional data and input health status data. The AI ​​engine also refers to past medical data and takes into account treatments that require attention due to emotional factors.

[0616] Step 5:

[0617] The server compiles information on the finalized medical field and related telemedicine services, and sends it to the terminal. This information includes the name of the medical department and instructions on how to use telemedicine.

[0618] Step 6:

[0619] The terminal displays information from the server to the user. In addition to information on recommended medical departments, it also provides emotionally sensitive encouraging and supportive messages. For example, a user feeling anxious might see a message such as, "Let's talk to a doctor right away so you can feel at ease."

[0620] Step 7:

[0621] Users can review the provided information and choose to use telemedicine services. After making their selection, the device accesses the telemedicine platform and schedules an online consultation with a doctor.

[0622] (Example 2)

[0623] 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".

[0624] In recent years, there has been a growing need to quickly and accurately assess the health status of individual users and provide appropriate medical services based on that assessment. However, conventional systems have difficulty recommending medical services that take into account the emotional state of users, and thus have a problem in that they cannot adequately reduce the psychological burden on users.

[0625] 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.

[0626] In this invention, the server includes terminal means for receiving biometric information entered by the user, analysis means for performing emotional analysis from the received biometric information to understand the user's emotional state, and recommendation means for recommending medical services based on the analyzed emotional state and biometric information. This makes it possible to provide optimal medical services tailored to the user's psychological state.

[0627] "Biometric information" refers to data that includes numerical values ​​and descriptions related to the user's health status and physical functions.

[0628] "Terminal means" refers to equipment or devices that allow users to input biometric information, and is a device that can input and transmit information.

[0629] "Emotional analysis" is a process that evaluates and recognizes the user's emotions and mental state based on the input information.

[0630] "Analysis means" refers to a system component equipped with functions and technologies for emotional analysis of biological information.

[0631] "Recommendation methods" refer to systems and methods that select and present medical services suitable for the user based on analyzed emotional state and biometric information.

[0632] "Remote healthcare" refers to health management and medical support services provided across physical distances.

[0633] This invention is a system in which a user inputs their own biometric information, and based on that information, it performs emotional analysis and recommends medical services. Specifically, it uses a terminal, a server, and software to link them together.

[0634] First, the user inputs biometric information using a device. These devices include PCs, smartphones, and tablets, and are equipped with text input and voice input capabilities. The user can provide information to the system by entering their physical symptoms in text or describing them verbally.

[0635] The device transmits biometric information as digital data to the server. The server is equipped with an emotion engine and an AI engine, which primarily perform analysis and recommendation processing. The emotion engine recognizes the user's emotional state based on the received data. For example, it detects words such as "worry" or "anxiety" from the input text and determines that they are related to the user's emotional state.

[0636] Next, the AI ​​engine references a database of past health information and recommends appropriate medical services based on the analyzed emotional state and biometric data. This recommendation aims for a swift and accurate medical response; for example, if the problem is related to the digestive system, it will recommend a gastroenterologist.

[0637] Finally, the terminal displays information from the server to the user. This includes information on recommended medical departments and guidance on remote healthcare services. In addition, messages that provide reassurance tailored to the user's emotional state are also displayed.

[0638] As a concrete example, for a user who is worried because they have a stomach ache, the system recommends a gastroenterologist and guides them to the option of an online consultation. An example of a prompt message for the generating AI model would be, "Suggest the most suitable medical service for the symptom 'stomach ache' and emotion 'anxiety'."

[0639] Thus, this invention is designed with the aim of providing optimal medical support by comprehensively considering the user's biometric information and emotional state.

[0640] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0641] Step 1:

[0642] The user enters biometric information into the device. The user can use text input to describe their health condition in detail, or choose voice input to describe their symptoms verbally. The entered information is recorded on the device as text data or digital audio data.

[0643] Step 2:

[0644] The terminal prepares to send the input data to the server. While text data may be sent directly to the server, voice data is typically converted to text using speech recognition technology. The converted data is then sent to the server using a secure communication protocol.

[0645] Step 3:

[0646] The server processes the received data and first performs emotional analysis using an emotion engine. It extracts emotion-related keywords and expressions from the input data, analyzes their linguistic characteristics, and evaluates the user's emotions. This process determines the user's emotional state, such as being tense, anxious, or calm.

[0647] Step 4:

[0648] The server uses an AI engine to recommend medical services based on the results of emotion analysis and biometric information. The server searches past medical databases to determine the appropriate medical department for the symptoms and emotional state. For example, for abdominal pain and anxiety, it would present gastroenterology and telemedicine as options.

[0649] Step 5:

[0650] The server sends departmental recommendations and remote healthcare information to the terminal. The server also adds support messages based on the user's emotional state. This allows the terminal to present the user with a message that includes recommended medical services and emotional considerations.

[0651] Step 6:

[0652] The terminal displays recommended information received from the server to the user. Specifically, it shows details about medical departments, links to make appointments, and emotionally reassuring messages. Users can use this information to book medical services as needed and receive prompt assistance.

[0653] (Application Example 2)

[0654] 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".

[0655] The current medical support system recommends medical departments without considering the user's feelings, resulting in a lack of processes that provide reassurance. Furthermore, the effective use of telemedicine is not being adequately promoted. Therefore, there is a need for recommendations of appropriate medical fields that address the user's feelings, and for the provision of support based on those feelings.

[0656] 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.

[0657] In this invention, the server includes means for recognizing the health status and emotions entered by the user, means for recommending medical fields based on the entered health status and emotions, and means for providing telemedicine guidance related to the recommended medical fields and generating support messages that correspond to the emotions. This makes it possible to provide appropriate medical support and a sense of security that is sensitive to the user's psychological state.

[0658] A "user" is an individual who inputs their health status and emotions through the system.

[0659] "Health status" refers to the conditions or symptoms related to an individual's physical and mental health.

[0660] "Emotions" refer to the psychological states or reactions that individuals express.

[0661] The "medical field" refers to a specialized area of ​​medical practice suited to specific symptoms or health conditions.

[0662] "Telemedicine" refers to medical services provided remotely using communication technologies such as the internet.

[0663] "Emotionally responsive support messages" refer to encouraging and reassuring messages provided according to the user's emotional state.

[0664] A "server" is a computer device that processes digital information and manages the entire system.

[0665] To implement this invention, multiple technical elements are combined to construct a system. The server uses speech recognition and text analysis software to recognize the user's health status and emotions. Specifically, speech recognition technology such as Google Speech-to-Text is used to convert speech data into text. In addition, natural language processing libraries such as Hugging Face Transformers are used to analyze emotions from the text data.

[0666] The server uses machine learning algorithms to recommend a medical department based on the analyzed health status and emotional data. In this process, machine learning libraries such as Scikit-learn are used to build a model that combines historical medical information data and emotional data to determine the most appropriate medical department.

[0667] Furthermore, the server utilizes the OpenAI GPT model to generate emotionally responsive support messages. This allows for the creation and timely delivery of reassuring messages when users are feeling anxious or stressed.

[0668] For example, if a user enters "I have a headache and I'm a little worried," the system will recommend a neurologist and provide a recommendation message such as "A neurologist would be appropriate. You can make an appointment for a consultation immediately," as well as a reassuring message such as "Don't worry, a specialist will support you."

[0669] In this way, it becomes possible to quickly provide optimal medical support that takes the user's emotions into consideration.

[0670] An example of a prompt message for a generative AI model is: "This user is experiencing a headache and is feeling anxious. Please generate a hospital recommendation and a reassuring message."

[0671] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0672] Step 1:

[0673] The terminal receives input from the user regarding their health status and emotions. The user provides this information via voice or text. This input data is converted into a digital format on the terminal and sent to the server as text data using speech recognition software.

[0674] Step 2:

[0675] The server processes the received text data using a speech analysis engine to extract the user's emotions. Specifically, it uses natural language processing tools to analyze emotional indicators in the text and identify emotions such as anxiety or reassurance. This analysis result is then sent to the next processing step as user emotion information.

[0676] Step 3:

[0677] The server uses machine learning models to recommend the appropriate medical field based on the received health status and emotional information. Leveraging the Scikit-learn library, it algorithmically analyzes historical medical data and current input data. This determines the most relevant medical department and sends that information to the next step.

[0678] Step 4:

[0679] The server uses a generative AI model to generate emotionally responsive support messages. Based on OpenAI GPT, the model constructs reassuring messages and specific support tailored to the user's emotional state. The generated messages, along with recommended medical information, are sent to the device.

[0680] Step 5:

[0681] The device displays medical recommendations and support messages received from the server to the user. Specifically, it displays messages on the screen or conveys information verbally through a voice assistant. This allows users to receive medical support that takes their emotions into consideration.

[0682] 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.

[0683] 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.

[0684] 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.

[0685] 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.

[0686] 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.

[0687] 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.

[0688] 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.

[0689] 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 based, for example, 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.

[0690] 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."

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] The following is further disclosed regarding the embodiments described above.

[0704] (Claim 1)

[0705] A device that accepts the health information entered by the user,

[0706] A device that performs processing to recommend medical fields based on the health condition received,

[0707] A device that provides information on telemedicine related to recommended medical fields,

[0708] A system that includes this.

[0709] (Claim 2)

[0710] The system according to claim 1, which performs a process to optimize recommendations by referring to a past medical information database based on the received health status and recommended medical field.

[0711] (Claim 3)

[0712] The system according to claim 1, further comprising a device that recommends medical services to users and simultaneously encourages them to subscribe to telemedicine services.

[0713] "Example 1"

[0714] (Claim 1)

[0715] A means of obtaining health information entered by the user,

[0716] Based on the acquired health information, a means of analysis is performed to recommend relevant medical fields,

[0717] Means for providing electronic medical support related to the aforementioned medical field,

[0718] A method for using artificial intelligence to refer to past medical records and generate recommendations,

[0719] A means of presenting users with information on the field of medical treatment and options for electronic medical consultation,

[0720] A system that includes this.

[0721] (Claim 2)

[0722] The system according to claim 1, wherein an artificial intelligence model dynamically optimizes the medical field corresponding to the user's health information.

[0723] (Claim 3)

[0724] The system according to claim 1, further comprising means for providing procedural support for users to conduct electronic medical consultations.

[0725] "Application Example 1"

[0726] (Claim 1)

[0727] A functional means for receiving biometric information entered by the user,

[0728] A processing method that recommends medical fields based on the received biometric information,

[0729] A means of providing guidance on online medical consultations related to recommended medical fields,

[0730] Data security measures for ensuring secure information communication,

[0731] A system that includes this.

[0732] (Claim 2)

[0733] The system according to claim 1, which performs a process to optimize recommendations by referring to a past medical information database based on the received biological information and the recommended medical field.

[0734] (Claim 3)

[0735] The system according to claim 1, further comprising a device that provides users with recommendations in the medical field and encourages them to participate in using online medical consultation services.

[0736] "Example 2 of combining an emotion engine"

[0737] (Claim 1)

[0738] A terminal device that receives biometric information entered by the user,

[0739] An analytical method that performs emotional analysis from received biometric information to understand the user's emotional state,

[0740] A recommendation system that recommends medical services based on analyzed emotional state and biometric information,

[0741] A means of providing information on remote healthcare related to recommended medical services,

[0742] A system that includes this.

[0743] (Claim 2)

[0744] The system according to claim 1, which performs a process to optimize recommendations by referring to a past health information database based on the received biometric and emotional data.

[0745] (Claim 3)

[0746] The system according to claim 1, further comprising a function that recommends medical services to users and simultaneously encourages them to subscribe to remote healthcare solutions.

[0747] "Application example 2 when combining with an emotional engine"

[0748] (Claim 1)

[0749] A means of recognizing the health status and emotions entered by the user,

[0750] A means of recommending medical services based on the entered health status and emotions,

[0751] It provides telemedicine guidance related to recommended medical fields and a means of generating emotionally resonant support messages.

[0752] A system that includes this.

[0753] (Claim 2)

[0754] The system according to claim 1, which optimizes recommendations by referring to a past medical information database based on the health status and emotional information received, and further processes the generation of support messages that correspond to the emotional state.

[0755] (Claim 3)

[0756] The system according to claim 1, further comprising means for recommending medical services to users while simultaneously promoting subscription to emotionally sensitive telemedicine services. [Explanation of Symbols]

[0757] 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 device that accepts the health information entered by the user, A device that performs processing to recommend medical fields based on the health condition received, A device that provides information on telemedicine related to recommended medical fields, A system that includes this.

2. The system according to claim 1, which performs a process to optimize recommendations by referring to a past medical information database based on the health condition and recommended medical field received.

3. The system according to claim 1, further comprising a device that recommends medical services to users and simultaneously encourages them to subscribe to telemedicine services.

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