system

Abstract

We provide the system. [Solution] A means for users to register an account, A means for users to input their medical history and symptoms, A means for users to input specific details of their consultation, A means by which the server sends information received from the user to a generating AI for analysis, A means by which a generating AI generates optimal treatment suggestions based on analysis results, A means for the server to send the suggested results from the generated AI to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor 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] Due to the insurance coverage of infertility treatment, the number of patients receiving treatment is increasing. However, there is a problem that since there is no standardized treatment protocol, treatment proposals by each hospital and doctor are limited to those following a template. Therefore, it is difficult to provide personalized treatment proposals based on individual medical histories and symptoms, and it is difficult to provide the optimal treatment to patients. Solving this problem is an object of the present invention.

Means for Solving the Problems

[0005] The present invention solves the above problem with a system that includes the following means: a means for the user to register an account, a means for inputting medical history and symptoms, and a means for inputting specific consultation details. Furthermore, the system includes a means for the server to send the information received from the user to a generating AI for analysis, a means for the generating AI to generate an optimal treatment proposal based on the analysis results, and a means for the server to send the proposal results from the generating AI to the user. The system also further includes a means for the server to link the user's reservation information with a medical consultation system, a means for the user to input post-treatment feedback, and a means for providing feedback information to the generating AI to improve model accuracy. As a result, personalized treatment proposals based on individual medical history and the latest medical guidelines become possible, and the success rate of infertility treatment can be improved.

[0006] A "user" is an individual or similar entity that uses this system to provide information related to infertility treatment.

[0007] "Account registration" is the process by which a user enters the basic information and authentication information necessary to use the system and registers it with the system.

[0008] "Medical history" refers to records of medical consultations and treatments a user has received in the past, as well as related information.

[0009] "Symptoms" refer to information about the user's current health condition or specific health problems.

[0010] "Consultation details" refer to information in which users input specific questions, concerns, or anxieties into the system.

[0011] A "server" is a computing system that receives information sent by users, processes it, and communicates data with the generating AI.

[0012] "Generative AI" is a type of artificial intelligence that analyzes a user's medical history, symptoms, and the latest medical guidelines to generate optimal treatment suggestions.

[0013] "Analysis results" refer to the results obtained by the generating AI based on information provided by the user, and include specific treatment suggestions.

[0014] A "treatment suggestion" is a proposal created by the generating AI based on the analysis results, outlining specific treatment methods and next steps that should be taken for the user.

[0015] "Feedback" refers to the information that users input into the system about the effects and experience of the treatment they received after undergoing the suggested treatment.

[0016] The "medical consultation service system" is a separate system for making reservations and communicating with specialist doctors. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be described.

[0020] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention relates to a system for generating individualized treatment suggestions in infertility treatment, which can provide personalized treatment suggestions based on the user's medical history and symptoms. The specific program processing of the system is described below in natural language.

[0039] User registration and login

[0040] The terminal provides the user with an account registration screen. The user enters necessary information such as name, contact information, password, and past medical history. The terminal verifies this information and sends it to the server. The server stores the received information in its database and creates an account. Existing users enter their user ID and password on the login screen and access the system through the authentication process.

[0041] Entering patient information

[0042] The user enters detailed information about their medical history and current symptoms on the main screen. The device sends this information to the server, which then stores the received information in a database.

[0043] Enter your consultation details

[0044] The user enters their specific question into a text field. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." The terminal sends the input to the server, and the server saves the received information to a database.

[0045] Generating treatment proposals

[0046] The server sends the user's medical history, symptoms, and consultation details to the generating AI. The generating AI analyzes this information and generates optimal treatment suggestions by referring to the latest medical guidelines. The generating AI sends the suggested results back to the server, which then sends them to the terminal. The terminal displays the suggested results to the user.

[0047] Collaboration with medical consultation services

[0048] After the user reviews the treatment suggestions generated by the AI, if they wish to consult further, the device provides an option to "consult a specialist." The user enters their desired appointment date and time, and the device sends this information to the server. The server integrates the received appointment information with the medical consultation system. The server sends appointment confirmation information to the device, which then displays it to the user.

[0049] Post-treatment feedback

[0050] After a user receives treatment, the device provides a feedback input screen. The user inputs information about the treatment's effectiveness and experience, and sends this information to the server. The server stores the received feedback information in a database and provides it again to the generating AI, contributing to improving the model's accuracy.

[0051] Specific example

[0052] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have had problems with egg quality. Next, they enter their questions regarding the next steps in their infertility treatment.

[0053] The server sends this information to the AI, which analyzes it and generates a treatment suggestion stating, "Given the likely decline in egg quality due to age, egg donation should be considered." The server then sends this suggestion to user A, and the terminal displays it to user A.

[0054] If User A reviews the proposal and wishes to consult directly with a specialist, the terminal will provide a booking screen and confirm the booking in conjunction with the medical consultation system.

[0055] Through this series of processes, the system can propose the most suitable infertility treatment to the user based on their individual circumstances, thereby improving the success rate of the treatment.

[0056] The following describes the processing flow.

[0057] Step 1:

[0058] The device displays an account registration screen to the user. The user enters the necessary information, such as their name, contact information, password, and medical history.

[0059] Step 2:

[0060] The terminal verifies the entered information and sends it to the server. The server saves the information to its database and creates an account.

[0061] Step 3:

[0062] The user accesses the login screen and enters their user ID and password. The device sends this authentication information to the server.

[0063] Step 4:

[0064] The server compares the received authentication information with the database. If they match, it returns a login success message to the user and directs them to the main screen.

[0065] Step 5:

[0066] The user enters detailed information about their medical history and current symptoms on the main screen. The device then sends the entered information to the server.

[0067] Step 6:

[0068] The server saves the received information to a database. The database stores the user's medical history and symptoms.

[0069] Step 7:

[0070] The user enters specific details of their inquiry, such as "Please tell me about the next steps in infertility treatment." The device then sends the entered information to the server.

[0071] Step 8:

[0072] The server saves the received consultation details to a database and prepares the data for transmission to the generating AI.

[0073] Step 9:

[0074] The server sends the user's medical history, symptoms, and consultation details to the generating AI. The generating AI analyzes this information and generates optimal treatment suggestions based on the latest medical guidelines.

[0075] Step 10:

[0076] The generating AI sends its suggested results back to the server. The server receives these results and sends them to the user's device for display.

[0077] Step 11:

[0078] The device displays the suggested results to the user. If the user reviews the suggestions and wishes to discuss further, they access the reservation screen.

[0079] Step 12:

[0080] The user selects the "Consult a specialist" option and enters their desired appointment date and time. The device then sends the appointment information to the server.

[0081] Step 13:

[0082] The server receives the reservation information and links it with the medical consultation system. Once the linkage is complete, reservation confirmation information is sent back to the server.

[0083] Step 14:

[0084] The server sends reservation confirmation information to the terminal. The terminal displays the reservation confirmation information to the user, informing them that the reservation has been confirmed.

[0085] Step 15:

[0086] After the user receives treatment, the device provides a feedback input screen. The user enters information about the effectiveness and experience of the treatment.

[0087] Step 16:

[0088] The device sends feedback information to the server. The server stores the received feedback information in a database and provides it to the generating AI.

[0089] Step 17:

[0090] The generating AI improves the accuracy of the model based on feedback information. This will improve the accuracy of treatment suggestions for future users.

[0091] (Example 1)

[0092] 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."

[0093] In modern infertility treatment, it is difficult to propose personalized treatment methods based on each patient's unique symptoms and medical history, and general treatment methods tend to be used frequently. As a result, there are cases where the treatment is not sufficiently effective or where the patient's needs are not met. Furthermore, the lack of a system for properly collecting and utilizing post-treatment feedback makes it difficult to improve the quality of treatment.

[0094] 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.

[0095] In this invention, the server includes means for the user to register an account, means for the user to input medical history and symptoms, means for the user to input specific consultation details, means for the server to send the information received from the user to a generating AI for analysis, means for the generating AI to generate an optimal treatment proposal based on the analysis results, means for the server to send the proposal results from the generating AI to the user, and means for the terminal to display the proposal results to the user. This makes it possible to personalize and propose the optimal infertility treatment method according to the individual circumstances of the user.

[0096] A "user" is an individual who inputs their medical history and symptoms through the system and receives treatment suggestions.

[0097] "Account registration" is the process by which a user registers information such as their name, contact details, and password with the system.

[0098] "Medical history" refers to records of diagnoses and treatments a user has received in the past.

[0099] "Symptoms" refer to the specific health conditions or problems that a user is currently experiencing.

[0100] "Consultation content" refers to the specific questions or inquiries that users input through the system.

[0101] A "server" is a device that receives information from users and sends it to a generating AI for analysis.

[0102] "Generative AI" is artificial intelligence that analyzes received information and generates optimal treatment suggestions.

[0103] "Proposal results" refer to the content of the treatment suggestions generated by the AI ​​as a result of its analysis.

[0104] A "terminal" is a device used by users to input information or to display suggested results sent from a server.

[0105] "Appointment information" refers to information such as the date and time a user wishes to consult with a specialist.

[0106] The "Medical Consultation System" is a system that receives users' appointment information and coordinates with specialists.

[0107] "Feedback information" refers to information about the effectiveness and experience of treatment that users enter after treatment.

[0108] "Model accuracy" is an indicator that represents the accuracy and reliability of the analysis and suggestions of the generated AI.

[0109] "Medical guidelines" are standards that outline recommended treatment methods and procedures based on the latest medical knowledge.

[0110] The present invention is a system for generating individual treatment suggestions in infertility treatment, providing personalized treatment suggestions based on the user's medical history and symptoms. The following processes are performed as a concrete embodiment of this invention.

[0111] First, the user accesses the system through their device. The user enters information such as their name, contact information, password, and medical history on the account registration screen. The device checks the format of this information and sends it to the server. The server stores the received information in its database and creates an account. Existing users enter their user ID and password on the login screen and complete the authentication process.

[0112] The user enters detailed medical history and current symptoms on the main screen. The terminal checks the format of this information and sends it to the server. The server stores the received information in a database.

[0113] Next, the user enters their specific question. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." The terminal checks the format of the input and sends it to the server. The server saves the received question to its database.

[0114] This process involves the server sending the user's medical history, symptoms, and consultation details to a generating AI model. The generating AI model analyzes this information and generates optimal treatment suggestions by referring to the latest medical guidelines. The generating AI model sends its suggestions back to the server, which then sends them to the terminal. The terminal then displays the suggestions from the generating AI model to the user.

[0115] If the user reviews the treatment suggestions from the generated AI model and wishes to consult further, the terminal provides an option to "consult a specialist." When the user enters their desired appointment date and time, the terminal sends that information to the server. The server integrates the received appointment information with the medical consultation system and sends appointment confirmation information to the terminal. The terminal then displays the appointment confirmation information to the user.

[0116] After treatment, the user enters information about the treatment's effectiveness and experience through a feedback input screen. The device checks the format of the feedback and sends it to the server. The server stores the received feedback information in a database and provides it to the generated AI model, contributing to improving the model's accuracy.

[0117] Specific example

[0118] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have problems with egg quality. Next, they enter their questions regarding the next steps in their infertility treatment. The server sends this information to an AI model, which analyzes it and generates a treatment suggestion such as, "Given the decline in egg quality due to age, egg donation should be considered." The server sends this suggestion to User A, and the terminal displays it to User A. If User A reviews the suggestion and wishes to consult directly with a specialist, the terminal provides a booking screen and confirms the appointment in conjunction with the medical consultation system. Through this entire process, the system can provide users with optimal infertility treatment suggestions tailored to their individual circumstances, thereby improving the success rate of treatment.

[0119] Example of a prompt

[0120] Please describe the process of a system that takes user medical history, current symptoms, and specific consultation details as input, and then uses a generative AI model to generate optimal treatment suggestions.

[0121] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0122] Step 1:

[0123] The user opens the account registration screen and enters information such as name, contact information, password, and medical history. The device verifies this information and performs a format check. Input: User registration information. Output: User information that passed the format check.

[0124] Step 2:

[0125] The terminal sends user information that has passed format checks to the server. The server saves the received information to a database and creates an account. Input: Format-checked user information. Output: User information saved to the database.

[0126] Step 3:

[0127] An existing user enters their user ID and password on the login screen. The device sends this information to the server. Input: User ID and password. Output: Login request to the server.

[0128] Step 4:

[0129] The server verifies the received user ID and password against the information in the database. If authentication is successful, the session is started. Input: User ID and password. Output: Authentication result and session start.

[0130] Step 5:

[0131] The user enters detailed medical history and current symptoms on the main screen. The terminal checks this information for formatting and sends it to the server. Input: Medical history and symptoms. Output: Medical history and symptom information that has passed the formatting check.

[0132] Step 6:

[0133] The server saves the received medical history and symptom information to the database. Input: Format-checked medical history and symptom information. Output: Medical history and symptom information saved to the database.

[0134] Step 7:

[0135] The user enters their specific question. For example, they might enter, "Please tell me about the next steps in infertility treatment." The terminal checks the format of the input and sends it to the server. Input: Question. Output: Question that passed the format check.

[0136] Step 8:

[0137] The server saves the received consultation content to the database. Input: Format-checked consultation content. Output: Consultation content saved to the database.

[0138] Step 9:

[0139] The server sends the user's medical history, symptoms, and consultation details to a generating AI model. The generating AI model analyzes this information and, referencing the latest medical guidelines, generates optimal treatment suggestions. Input: Medical history, symptoms, consultation details. Output: Treatment suggestions generated by the generating AI.

[0140] Step 10:

[0141] The server receives the proposed results from the generated AI model and sends them to the terminal. Input: Treatment proposal from the generated AI. Output: Sending the proposed results to the terminal.

[0142] Step 11:

[0143] The terminal displays the received suggestion results to the user. Input: Suggestion results received from the server. Output: Display of suggestion results to the user.

[0144] Step 12:

[0145] If the user wishes to consult further, the device will offer an option to "Consult a specialist" and allow the user to enter their preferred appointment date and time. Input: Preferred appointment date and time. Output: Appointment information.

[0146] Step 13:

[0147] The terminal checks the format of the entered reservation request date and time and sends it to the server. Input: Format-checked reservation request date and time. Output: Reservation information sent to the server.

[0148] Step 14:

[0149] The server receives the reservation information and links it with the medical consultation system to receive reservation confirmation information. Input: Reservation information. Output: Reservation confirmation information from the medical consultation system.

[0150] Step 15:

[0151] The server sends reservation confirmation information to the terminal, and the terminal displays the reservation confirmation information to the user. Input: Reservation confirmation information. Output: Display of reservation confirmation information to the user.

[0152] Step 16:

[0153] After treatment, the user enters information about the treatment's effectiveness and experience on a feedback input screen. The device then performs a format check on the feedback content and sends it to the server. Input: Feedback content. Output: Feedback content that has passed the format check.

[0154] Step 17:

[0155] The server saves the received feedback information to a database and provides it to the generating AI model. Input: Format-checked feedback information. Output: Saving of feedback information to the database and providing it to the generating AI model.

[0156] These processing steps enable the system to generate optimal treatment suggestions based on the user's medical history and symptoms, thereby improving the success rate of treatment.

[0157] (Application Example 1)

[0158] 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."

[0159] In infertility treatment, the current system for providing patients with optimal treatment recommendations based on their individual medical history and symptoms is not adequately developed. Furthermore, there is a lack of integrated systems to efficiently and smoothly manage the entire process, including post-treatment appointments with specialists, payment of treatment fees, and feedback collection. As a result, patients often require significant time and effort to manage multiple procedures and information, which can delay the progress of their treatment.

[0160] 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.

[0161] In this invention, the server includes means for the user to register an account, means for the user to input medical history and symptoms, means for the user to input specific consultation details, means for the server to send the information received from the user to a generating AI for analysis, means for the generating AI to generate an optimal treatment proposal based on the analysis results, means for the server to send the proposal results from the generating AI to the user, means for the user to electronically pay for treatment, and means for managing appointments with medical institutions. As a result, patients can quickly receive optimal treatment proposals based on their individual medical history and symptoms, and it is also possible to centrally manage appointments with specialists, payment of treatment fees, and collection of post-treatment feedback.

[0162] "Account registration" is the process by which a user officially registers by entering personal information, passwords, and other details necessary to use the system.

[0163] "Medical history" refers to information including medical treatments, diagnoses, and medical history that a user has received in the past.

[0164] "Symptoms" refer to specific conditions or signs that indicate a health problem or ailment the user is currently experiencing.

[0165] "Consultation details" refer to information entered by the user in text format, expressing questions or wishes regarding specific medical policies or treatment methods.

[0166] A "server" is a computer system that receives data entered by users, sends it to a generating AI for analysis, and then returns and manages the results.

[0167] "Generative AI" is an artificial intelligence model that analyzes data based on received medical history, symptoms, and consultation content to generate optimal treatment suggestions.

[0168] "Analysis results" refer to the output, such as treatment suggestions and diagnostic results, obtained by the generating AI through its analysis of information received from the user.

[0169] A "treatment suggestion" is the optimal medical procedure or treatment method recommended to the user based on data analyzed by the generating AI.

[0170] "Electronic payment" refers to an electronic payment method that allows users to safely and efficiently pay for expenses such as medical treatment costs via the internet.

[0171] "Appointment management" is a function that allows users to input and manage appointments with medical institutions and specialists, and to adjust their schedules.

[0172] "Feedback" refers to information that users enter after treatment, including their impressions and evaluations of the treatment's effectiveness and their experience.

[0173] A system for implementing this invention consists of a server, a terminal, and a generated AI model. Specific embodiments for realizing this system are described below.

[0174] Hardware and software

[0175] 1. Hardware:

[0176] Smartphone (compatible with iOS and Android®)

[0177] Security module (TPM chip, etc.)

[0178] Server (equipped with high-performance processor and large memory capacity)

[0179] 2. Software:

[0180] Server-side: Python, Django, SQL database, OpenAI® API, Stripe API

[0181] Smartphone side: React Native

[0182] System operation

[0183] User registration and login

[0184] Users access a dedicated application from their smartphones and register an account. This process requires them to enter basic information such as their name, contact information, and password, as well as their past medical history. The registered information is sent from the device to the server and stored in the database. Existing users access the system by entering their user ID and password on the login screen and going through the authentication process.

[0185] Entering patient information

[0186] The user enters detailed information about their medical history and current symptoms on the main screen. This information is sent from the device to the server and stored in the database.

[0187] Enter your consultation details

[0188] The user enters their specific question into a text field. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." This information is also sent from the device to the server and stored in the database.

[0189] Generating treatment proposals

[0190] The server sends data to the generating AI based on the received medical history, symptoms, and consultation content. The generating AI analyzes this information and generates optimal treatment suggestions by referring to the latest medical guidelines. The generated treatment suggestions are sent from the server to the terminal and displayed to the user.

[0191] Electronic payment and reservation management

[0192] If the user reviews the treatment proposal and wishes to make an appointment with a specialist, the application displays an appointment entry screen. The user enters their desired date and time, and the server links this information with the medical institution's appointment management system. Furthermore, the Stripe API is used on the terminal to securely process the payment for the treatment costs associated with this appointment electronically.

[0193] Feedback Collection

[0194] After a user receives treatment, the device displays a feedback input screen where the user enters information about the treatment's effectiveness and their experience. This information is also sent to the server and provided to the AI, which helps improve the accuracy of treatment recommendations.

[0195] Specific example

[0196] For example, if user A wants to consult about the next steps in infertility treatment, after registering an account, they would enter "I have a problem with egg quality" in their medical history. Then, they would enter their consultation request, "Please tell me the next steps in infertility treatment." The server sends this information to the AI, which generates a suggestion that "because the quality of eggs may be declining due to age, egg donation should be considered." This suggestion is sent to user A, and they can then make an appointment with a specialist and pay for treatment costs all within the app.

[0197] Example of a prompt

[0198] "User's medical history and symptoms:

[0199] Medical history: {Medical history}

[0200] Symptom: {symptom}

[0201] Consultation details: {Consultation details}

[0202] Please generate the optimal treatment plan based on the latest medical guidelines.

[0203] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0204] Step 1:

[0205] The user enters necessary information such as their name, contact information, password, and medical history on the account registration screen. The device sends this information to the server, which stores the received information in a database. As a result, a new account for the user is created.

[0206] Input: Name, contact information, password, medical history

[0207] Data processing: Format the input information and send it to the server.

[0208] Output: User accounts stored in the database

[0209] Step 2:

[0210] The user enters their user ID and password on the login screen and accesses the system through the authentication process. If authentication is successful, the terminal redirects the user to the main screen.

[0211] Input: User ID, Password

[0212] Data processing: Verification of received authentication information

[0213] Output: User main screen after successful authentication

[0214] Step 3:

[0215] The user enters detailed information about their medical history and current symptoms on the main screen. The device sends this information to the server, which then stores the received information in a database.

[0216] Input: Medical history, symptoms

[0217] Data processing: Saving received information to a database

[0218] Output: Updated medical history and symptoms in the database

[0219] Step 4:

[0220] The user enters their specific inquiry into a text field, the device sends that information to the server, and the server saves the received information to a database.

[0221] Input: Consultation details

[0222] Data processing: Classification and storage of consultation content

[0223] Output: Consultation details stored in the database

[0224] Step 5:

[0225] The server sends the user's medical history, symptoms, and consultation details to the AI. The AI ​​analyzes this information and generates optimal treatment suggestions.

[0226] Input: Medical history, symptoms, consultation details

[0227] Data processing: Data analysis using generative AI

[0228] Output: Treatment suggestion

[0229] Step 6:

[0230] The AI ​​generates treatment suggestions, which are received by the server and sent to the user's device. The device then displays the suggested results to the user.

[0231] Input: Treatment suggestions from generated AI

[0232] Data processing: Formatting and sending of proposal results

[0233] Output: Treatment suggestions displayed on the user's terminal

[0234] Step 7:

[0235] The user reviews the treatment proposal and enters their preferred appointment date and time using the appointment booking screen with the specialist. The terminal sends this information to the server, which then links the received appointment information with the medical institution's appointment management system.

[0236] Input: Desired date and time for reservation

[0237] Data processing: Sending reservation information and linking it with the management system.

[0238] Output: Booking confirmation information

[0239] Step 8:

[0240] To allow users to electronically pay for their treatment appointments, the Stripe API is used on their terminal to process the payment. The server confirms the success of the payment and notifies the user of the result.

[0241] Input: Payment information

[0242] Data processing: Payment processing using the Stripe API

[0243] Output: Payment completion notification

[0244] Step 9:

[0245] After a user receives treatment, the device displays a feedback input screen where the user enters information about the treatment's effectiveness and their experience. The device sends this information to a server, which stores the received feedback in a database and provides it to the generating AI.

[0246] Input: Feedback information

[0247] Data processing: Database storage of feedback information and provision to the generating AI.

[0248] Output: Improved generative AI model

[0249] 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.

[0250] This invention is a system for generating individualized treatment suggestions in infertility treatment, capable of providing personalized treatment suggestions based on the user's medical history and symptoms. Furthermore, by combining it with an emotion engine, it can recognize the user's emotions and provide treatment suggestions and psychological support based on those emotions. The specific program processing of the system is described below in natural language.

[0251] User registration and login

[0252] The terminal displays an account registration screen to the user. The user enters the necessary information, such as name, contact information, password, and past medical history. The terminal verifies this information and sends it to the server. The server saves the received information to its database and creates an account. Existing users enter their user ID and password on the login screen and access the system through the authentication process.

[0253] Entering patient information

[0254] The user enters detailed information about their medical history and current symptoms on the main screen. The device sends this information to the server, which then stores the received information in a database.

[0255] Enter your consultation details

[0256] The user enters their specific question into a text field. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." The terminal sends the input to the server, and the server saves the received information to a database.

[0257] Emotion recognition and the generation of treatment proposals

[0258] The server sends the user's input to the emotion engine, which analyzes the user's emotional state. The emotion engine identifies the user's emotional state (e.g., stress, anxiety, relief) and sends that information to the generating AI. The generating AI comprehensively analyzes the user's medical history, symptoms, consultation content, and emotional state to generate optimal treatment suggestions and psychological support. The generating AI sends the suggested results back to the server, which then sends them to the terminal. The terminal displays the suggested results to the user.

[0259] Collaboration with medical consultation services

[0260] After the user reviews the treatment suggestions generated by the AI ​​and wishes to consult further, they access the booking screen. The user selects the "Consult a specialist" option and enters their desired appointment date and time. The device sends the booking information to the server. The server integrates the received booking information with the medical consultation system. Once the integration is complete, booking confirmation information is sent back to the server. The server sends the booking confirmation information to the device, which then displays it to the user.

[0261] Post-treatment feedback

[0262] After a user receives treatment, the device provides a feedback input screen. The user inputs information about the effectiveness and experience of the treatment, as well as their emotional state. For example, they might input, "I was relieved when my pregnancy was confirmed after the treatment." The device sends the inputted feedback information to the server. The server stores the received feedback information in a database and provides it to the generating AI. The generating AI uses the feedback information to improve the accuracy of its model, thereby improving the accuracy of future treatment suggestions for the user.

[0263] Specific example

[0264] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have problems with egg quality. Next, they enter their questions about the next steps in their infertility treatment. The server sends this information to the emotion engine, which analyzes User A's emotional state (e.g., anxiety). The emotion engine sends the emotional data and medical history to the generating AI, which analyzes it and generates a treatment suggestion such as, "Given the age-related decline in egg quality, egg donation should be considered. We also recommend receiving psychological counseling to alleviate anxiety." The server sends this suggestion to User A, and the terminal displays it to User A.

[0265] If User A reviews the proposal and wishes to consult directly with a specialist, the terminal provides a reservation screen and confirms the reservation in conjunction with the medical consultation system. Through this process, the system can provide users with optimal infertility treatment proposals tailored to their individual circumstances and psychological support based on their emotional state, thereby improving the success rate of treatment.

[0266] The following describes the processing flow.

[0267] Step 1:

[0268] The device displays an account registration screen to the user. The user enters the necessary information, such as their name, contact information, password, and medical history.

[0269] Step 2:

[0270] The terminal verifies the entered information and sends it to the server. The server receives the information, saves it to the database, and creates the account.

[0271] Step 3:

[0272] The user accesses the login screen and enters their user ID and password. The device sends this authentication information to the server.

[0273] Step 4:

[0274] The server compares the received authentication information with the database. If they match, it returns a login success message to the user and directs them to the main screen.

[0275] Step 5:

[0276] The user enters detailed information about their medical history and current symptoms on the main screen. The device then sends the entered information to the server.

[0277] Step 6:

[0278] The server saves the received information to a database. The database stores the user's medical history and symptoms.

[0279] Step 7:

[0280] The user enters specific details of their inquiry, such as "Please tell me about the next steps in infertility treatment." The device then sends the entered information to the server.

[0281] Step 8:

[0282] The server saves the received consultation details to a database and sends them to the emotion engine.

[0283] Step 9:

[0284] The emotion engine analyzes the user's input content and identifies the emotional state (such as stress, anxiety, relief, etc.). The data of the emotional state is sent back to the server.

[0285] Step 10:

[0286] The server sends the emotional state data to the generative AI. The generative AI comprehensively analyzes the user's medical history, symptoms, consultation content, and emotional state, and generates an optimal treatment proposal and psychological support.

[0287] Step 11:

[0288] The generative AI sends the proposal result back to the server. The server receives this and sends the proposal result to the terminal.

[0289] Step 12:

[0290] The terminal displays the proposal result to the user. If the user checks the proposal content and wishes to have a more detailed consultation, they access the reservation screen.

[0291] Step 13:

[0292] The user selects the option of "Consult a specialist" and enters the desired reservation date and time. The terminal sends the reservation information to the server.

[0293] Step 14:

[0294] The server coordinates the received reservation information with the medical consultation window system. When the coordination is completed, the reservation confirmation information is sent back to the server.

[0295] Step 15:

[0296] The server sends the reservation confirmation information to the terminal. The terminal displays the reservation confirmation information to the user and notifies them that the reservation has been confirmed.

[0297] Step 16:

[0298] After the user receives treatment, the device provides a feedback input screen. The user enters information about the effectiveness and experience of the treatment, as well as their emotional state. For example, they might enter, "I was relieved when my pregnancy was confirmed after the treatment."

[0299] Step 17:

[0300] The device sends feedback information to the server. The server stores the received feedback information in a database and provides it to the generating AI.

[0301] Step 18:

[0302] The generating AI improves the accuracy of the model based on feedback information. This will improve the accuracy of treatment suggestions for future users.

[0303] (Example 2)

[0304] 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".

[0305] In infertility treatment, it is crucial to provide effective treatment suggestions based on each patient's medical history and current symptoms. However, conventional systems have the challenge of not being able to provide personalized treatment suggestions and psychological support that take into account each patient's emotional state. Furthermore, there is a lack of mechanisms to improve the accuracy of the generated AI model by utilizing post-treatment feedback information. As a result, it has been difficult to provide highly accurate treatment suggestions.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for transmitting the user's information to the emotion engine and analyzing the emotional state, means for transmitting the emotional data received from the emotion engine and the user's information to the generative AI for analysis, and means for the generative AI to generate an optimal treatment proposal and psychological support based on the user's medical history, symptoms, and emotional state. Thereby, it becomes possible to provide a highly accurate treatment proposal and psychological support based on each user's medical history and emotional state.

[0307] The "user" refers to an individual who inputs information related to infertility treatment using this system and receives treatment proposals and psychological support.

[0308] The "terminal" refers to a device such as a computer device or smartphone operated by the user, which provides an interface for the user to input information and check results.

[0309] The "server" refers to a central computer system that receives information transmitted from the terminal and performs processing, storage, and analysis.

[0310] The "medical history" refers to information regarding medical treatments, diagnostic results, and health status that the user has received in the past.

[0311] The "symptoms" refer to the problems or abnormal states related to health that the user is currently experiencing.

[0312] The "consultation content" refers to the specific questions or problems that the user wants to inquire about the system.

[0313] The "emotion engine" refers to a computer program for analyzing the user's emotional state (e.g., stress, anxiety, relief) based on the input content.

[0314] "Generative AI" refers to an artificial intelligence program that analyzes a user's medical history, symptoms, consultation content, and emotional state to automatically generate optimal treatment suggestions and psychological support.

[0315] "Proposal results" refers to information regarding treatment suggestions and psychological support created by the generating AI based on its analysis.

[0316] A "medical consultation service system" refers to a computer system used to manage appointments and consultations with specialists and counselors.

[0317] "Feedback" refers to users entering their thoughts and evaluations regarding the effectiveness and experience of treatment, as well as their emotional state.

[0318] "Model accuracy" refers to an indicator that shows the accuracy and appropriateness of the treatment suggestions and psychological support that the generated AI provides to the user.

[0319] This invention is a system that provides individualized treatment suggestions and psychological support in infertility treatment. The system aims to generate optimal treatment suggestions by comprehensively analyzing the user's medical history, symptoms, consultation content, and emotional state. Specific embodiments of this invention are described below.

[0320] User registration and login

[0321] The terminal displays an account registration screen to the user. The user enters necessary information such as name, contact information, password, and past medical history, and the terminal verifies this information and sends it to the server. The server stores the received information in its database and creates an account. Existing users enter their user ID and password on the login screen to authenticate and access the system.

[0322] Entering patient information

[0323] The user enters detailed information about their medical history and current symptoms on the main screen. The terminal sends this information to the server, which stores the received information in a database.

[0324] Enter your consultation details

[0325] The user enters their specific question into a text field. For example, they might enter, "Please tell me about the next steps in infertility treatment." The terminal sends the entered question to the server, and the server saves the received information in a database.

[0326] Emotion recognition and the generation of treatment proposals

[0327] The server sends the user's input to the emotion engine, which analyzes the user's emotional state. The emotion engine identifies the user's emotional state (e.g., stress, anxiety, relief) and sends this information to the generating AI. The generating AI comprehensively analyzes the user's medical history, symptoms, consultation content, and emotional state to generate optimal treatment suggestions and psychological support. The generating AI sends the suggested results back to the server, which then sends them to the terminal. The terminal displays the suggested results to the user.

[0328] Collaboration with medical consultation services

[0329] After the user reviews the treatment suggestions generated by the AI, if they wish to have a more detailed consultation, they access the reservation screen. The user selects the "Consult with a specialist" option and enters their desired reservation date and time. The device sends the reservation information to the server. The server links the received reservation information with the medical consultation system. Once the linkage is complete, reservation confirmation information is sent back to the server, which then sends the reservation confirmation information to the device, which displays it to the user.

[0330] Post-treatment feedback

[0331] After the user receives treatment, the device provides a feedback input screen. The user inputs information about the effectiveness and experience of the treatment, as well as their emotional state. For example, they might input, "I was relieved when my pregnancy was confirmed after the treatment." The device sends the input feedback information to the server. The server stores the received feedback information in a database and provides it to the generative AI. The generative AI uses the feedback information to improve the accuracy of its model.

[0332] Specific example

[0333] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have problems with egg quality. Next, they enter their questions about the next steps in their infertility treatment. The server sends this information to the emotion engine, which analyzes User A's emotional state (e.g., anxiety). The emotion engine sends the emotional data and medical history to the generating AI, which analyzes it and generates a treatment suggestion such as, "Given the age-related decline in egg quality, egg donation should be considered. We also recommend receiving psychological counseling to alleviate anxiety." The server sends this suggestion to User A, and the terminal displays it to User A.

[0334] Example of a prompt:

[0335] "The user's medical history is as follows: There are issues with egg quality. Current symptoms or concerns are as follows: Please advise on the next steps in fertility treatment. The user is currently experiencing anxiety. Based on this information, please provide optimal treatment suggestions and psychological support."

[0336] This invention makes it possible to provide highly accurate treatment suggestions and psychological support based on the user's medical history and emotional state.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1: User registration and login

[0339] Input: The user enters their name, contact information, password, and medical history on the account registration screen.

[0340] Operation: The terminal validates the input information and sends it to the server.

[0341] Data processing / calculation: The server receives validated information and saves it to the database.

[0342] Output: The server sends the account creation result back to the terminal and notifies the user that registration is complete.

[0343] Step 2: Enter patient information

[0344] Input: The user enters their medical history and current symptom information on the main screen.

[0345] Operation: The terminal sends the input information to the server.

[0346] Data processing / calculation: The server saves the information it receives to the database.

[0347] Output: The server notifies the terminal that the information has been saved and displays a confirmation message to the user.

[0348] Step 3: Enter your consultation details

[0349] Input: The user enters their question (e.g., "Please tell me about the next steps in infertility treatment") into the text field.

[0350] Operation: The terminal sends the input content to the server.

[0351] Data processing / calculation: The server saves the received consultation details to the database.

[0352] Output: The server notifies the terminal that the consultation details have been saved and displays a confirmation message to the user.

[0353] Step 4: Emotion recognition and generation of treatment proposals

[0354] Input: The server sends the user's medical history, symptoms, and consultation details to the emotion engine.

[0355] Operation: The emotion engine analyzes the emotional state.

[0356] Data processing / calculation: The emotion engine identifies emotional states (e.g., anxiety) and sends them to the generating AI.

[0357] Output: The generating AI generates treatment suggestions and psychological support based on the received data and sends the results back to the server.

[0358] Step 5: Providing treatment proposals

[0359] Input: The server receives the suggested results from the generated AI.

[0360] Operation: The server sends the suggestion results to the terminal.

[0361] Data Processing / Calculation: The server aggregates and organizes user-specific data and provides suggested results in an easy-to-understand format.

[0362] Output: The terminal displays the suggested results to the user. For example, a message such as, "Due to the possibility of a decline in egg quality due to age, egg donation should be considered. We also recommend receiving psychological counseling to alleviate anxiety," might be displayed.

[0363] Step 6: Collaboration with medical consultation services

[0364] Input: The user selects the "Consult a specialist" option and enters the appointment date and time.

[0365] Operation: The device sends reservation information to the server.

[0366] Data processing / calculation: The server sends the reservation information to the medical consultation system and confirms the reservation.

[0367] Output: The server sends reservation confirmation information to the terminal and displays it to the user.

[0368] Step 7: Post-treatment feedback

[0369] Input: The user enters feedback after treatment (e.g., "I was relieved when my pregnancy was confirmed after treatment").

[0370] Action: The device sends feedback information to the server.

[0371] Data processing / calculation: The server stores feedback information in a database and provides it to the generating AI.

[0372] Output: The server notifies the terminal that the feedback has been saved and displays a confirmation message to the user.

[0373] At each step, the user, terminal, and server work together to create a system that provides highly accurate treatment suggestions.

[0374] (Application Example 2)

[0375] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0376] In infertility treatment, personalized treatment suggestions based on the patient's medical history and symptoms are crucial, but traditional systems have lacked adequate psychological support. Furthermore, there is a need for an efficient method to analyze a patient's emotional state before they visit the clinic and to provide appropriate treatment suggestions and appointment scheduling.

[0377] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to register an account, means for the user to input medical history and symptoms, means for the user to input specific consultation details, means for the emotion engine to analyze the user's emotional state, means for the server to send the information received from the user to the generating AI for analysis, means for the generating AI to generate an optimal treatment proposal based on the analysis results, and means for the generating AI to display reservation information for face-to-face appointments to the user. This enables personalized treatment proposals and reservation coordination based on the user's emotional state.

[0378] "User" refers to an individual or group that uses the system, and in particular to those who receive consultations and treatment suggestions regarding infertility treatment.

[0379] "Account registration" refers to the process by which a user provides their basic information to the system and obtains a unique identification ID.

[0380] "Medical history" refers to a detailed record of treatments and diagnoses the user has received in the past.

[0381] "Symptoms" refer to the physical or mental problems or conditions that the user is currently experiencing.

[0382] "Consultation content" refers to the specific questions and requests that users ask through the system.

[0383] An "emotion engine" refers to software that analyzes user input data and identifies their emotional state.

[0384] A "server" refers to a remote computer system that processes and stores information received from users.

[0385] "Generative AI" refers to an artificial intelligence algorithm that automatically generates optimal treatment suggestions based on received data.

[0386] "Analysis results" refers to the output obtained after analyzing user data using a generative AI or emotion engine.

[0387] "Treatment suggestions" refer to specific treatment methods and support provided to the user based on their medical history, symptoms, consultation content, and emotional state.

[0388] "Reservation information" refers to data that users enter to book an in-person consultation with a specialist, indicating the date, time, and desired details.

[0389] The term "medical consultation service system" refers to a backend system for managing and processing appointments for consultations with medical professionals.

[0390] "Feedback" refers to data that represents the evaluation and opinions of users regarding the treatments and suggestions they received.

[0391] "Model accuracy" refers to an evaluation criterion that indicates the accuracy and suitability of the treatment suggestions provided by the generated AI.

[0392] This invention is a system for users to receive personalized treatment suggestions regarding infertility treatment, and detailed embodiments are shown below.

[0393] First, the user registers an account using their device. The user enters basic information such as their name, contact information, and past medical history. The entered information is sent from the device to the server and stored in the database.

[0394] Next, the user enters their medical history, current symptoms, and specific questions via their device. This information is also sent from the device to the server and stored in the database.

[0395] A key element of this system is the inclusion of an emotion engine, which analyzes the user's emotional state from their input data. The emotion engine analyzes the user's text input and behavioral data to identify their emotional state (e.g., anxiety, reassurance, etc.). This analysis result is then transmitted to the generating AI via a server.

[0396] The generating AI creates optimal treatment suggestions based on the user's medical history, symptoms, consultation content, and emotional state. The generating AI uses the latest medical guidelines and past patient data to generate specific and personalized treatment suggestions. The results of this AI analysis are transmitted from the server to the user's terminal and displayed.

[0397] Furthermore, if a user wishes to have an in-person consultation with a specialist, they enter reservation information based on the suggestions generated by the AI. The server then links this reservation information with the medical consultation system and sends reservation confirmation information to the user. This process allows users to receive consultation from a specialist at the appropriate time.

[0398] Furthermore, users provide feedback after treatment. This feedback information is sent to the server and provided to the generating AI. The generating AI uses this feedback data to improve the accuracy of its model. This continuous feedback loop allows the system to provide more accurate treatment suggestions.

[0399] As a concrete example, let's consider the case where User B uses the system for the first time. User B creates an account on their smartphone and enters their medical history regarding past infertility treatments. For example, they might enter that they have problems with ovarian function. Next, they enter a question about the next steps in their infertility treatment. Let's say they enter, "Please tell me about the next steps in my infertility treatment." The emotion engine analyzes User B's input to understand their anxiety and sends it to the generating AI. Based on the medical history and emotional state, the generating AI generates a treatment suggestion such as, "Ovarian dysfunction is suspected, so consider specific drug treatments. We also recommend counseling to reduce anxiety." The server sends this suggestion to User B, and it is displayed on their smartphone.

[0400] Examples of prompt statements include the following:

[0401] User's medical history: Female, 35 years old, underwent two artificial insemination procedures in the past three years. Has ovarian dysfunction.

[0402] Inquiry topic: I would like to know about the next steps in my treatment.

[0403] Emotional state: Anxiety.

[0404] Please generate treatment suggestions based on the generated AI.

[0405] This system allows users to receive personalized infertility treatment suggestions and psychological support based on their emotional state, potentially improving the success rate of treatment.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The user registers an account. The user uses their device to enter basic information such as their name, contact information, and medical history. The device sends the entered information to the server, which stores this information in a database. Input data: Name, contact information, medical history. Output: Notification of successful account creation.

[0409] Step 2:

[0410] The user inputs their medical history, current symptoms, and specific consultation details. The terminal sends this information to the server, which stores it in a database. Input data: medical history, symptoms, consultation details. Output: information is stored in the database.

[0411] Step 3:

[0412] The server sends the information received from the user to the emotion engine. The emotion engine analyzes the user's input data and identifies the emotional state (e.g., anxiety, reassurance). It then sends the analysis results back to the server. Input data: Medical history, consultation content. Output result: Analysis results of the emotional state.

[0413] Step 4:

[0414] The server sends emotional state data obtained from the emotion engine, along with the user's medical history and consultation details, to the generating AI. The generating AI then uses this information to create optimal treatment suggestions. Input data: medical history, symptoms, consultation details, emotional state. Output result: treatment suggestions.

[0415] Step 5:

[0416] The server receives treatment suggestions from the generated AI and sends them to the user's device. The device displays this information to the user. Input data: Treatment suggestions. Output result: Display of the suggested content.

[0417] Step 6:

[0418] If a user wishes to have an in-person consultation with a specialist, they enter reservation information based on the suggestions generated by the AI. The terminal sends this reservation information to the server, which then connects it with the medical consultation system. Once the connection is complete, the server sends reservation confirmation information to the user. Input data: Reservation information. Output result: Reservation confirmation information.

[0419] Step 7:

[0420] The user enters feedback after treatment. The device sends this feedback information to the server, which provides it to the generating AI. The generating AI uses this feedback data to improve the accuracy of its model. Input data: Feedback information. Output result: Improved model accuracy.

[0421] As a concrete example, the following prompt is sent to the generating AI:

[0422] User's medical history: Female, 35 years old, underwent two artificial insemination procedures in the past three years. Has ovarian dysfunction.

[0423] Inquiry topic: I would like to know about the next steps in my treatment.

[0424] Emotional state: Anxiety.

[0425] Please generate treatment suggestions based on the generated AI.

[0426] Through this process, users can receive personalized infertility treatment recommendations and psychological support based on their emotional state.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] [Second Embodiment]

[0431] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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".

[0443] This invention relates to a system for generating individualized treatment suggestions in infertility treatment, which can provide personalized treatment suggestions based on the user's medical history and symptoms. The specific program processing of the system is described below in natural language.

[0444] User registration and login

[0445] The terminal provides the user with an account registration screen. The user enters necessary information such as name, contact information, password, and past medical history. The terminal verifies this information and sends it to the server. The server stores the received information in its database and creates an account. Existing users enter their user ID and password on the login screen and access the system through the authentication process.

[0446] Entering patient information

[0447] The user enters detailed information about their medical history and current symptoms on the main screen. The device sends this information to the server, which then stores the received information in a database.

[0448] Enter your consultation details

[0449] The user enters their specific question into a text field. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." The terminal sends the input to the server, and the server saves the received information to a database.

[0450] Generating treatment proposals

[0451] The server sends the user's medical history, symptoms, and consultation details to the generating AI. The generating AI analyzes this information and generates optimal treatment suggestions by referring to the latest medical guidelines. The generating AI sends the suggested results back to the server, which then sends them to the terminal. The terminal displays the suggested results to the user.

[0452] Collaboration with medical consultation services

[0453] After the user reviews the treatment suggestions generated by the AI, if they wish to consult further, the device provides an option to "consult a specialist." The user enters their desired appointment date and time, and the device sends this information to the server. The server integrates the received appointment information with the medical consultation system. The server sends appointment confirmation information to the device, which then displays it to the user.

[0454] Post-treatment feedback

[0455] After a user receives treatment, the device provides a feedback input screen. The user inputs information about the treatment's effectiveness and experience, and sends this information to the server. The server stores the received feedback information in a database and provides it again to the generating AI, contributing to improving the model's accuracy.

[0456] Specific example

[0457] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have had problems with egg quality. Next, they enter their questions regarding the next steps in their infertility treatment.

[0458] The server sends this information to the AI, which analyzes it and generates a treatment suggestion stating, "Given the likely decline in egg quality due to age, egg donation should be considered." The server then sends this suggestion to user A, and the terminal displays it to user A.

[0459] If User A reviews the proposal and wishes to consult directly with a specialist, the terminal will provide a booking screen and confirm the booking in conjunction with the medical consultation system.

[0460] Through this series of processes, the system can propose the most suitable infertility treatment to the user based on their individual circumstances, thereby improving the success rate of the treatment.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] The device displays an account registration screen to the user. The user enters the necessary information, such as their name, contact information, password, and medical history.

[0464] Step 2:

[0465] The terminal verifies the entered information and sends it to the server. The server saves the information to its database and creates an account.

[0466] Step 3:

[0467] The user accesses the login screen and enters their user ID and password. The device sends this authentication information to the server.

[0468] Step 4:

[0469] The server compares the received authentication information with the database. If they match, it returns a login success message to the user and directs them to the main screen.

[0470] Step 5:

[0471] The user enters detailed information about their medical history and current symptoms on the main screen. The device then sends the entered information to the server.

[0472] Step 6:

[0473] The server saves the received information to a database. The database stores the user's medical history and symptoms.

[0474] Step 7:

[0475] The user enters specific details of their inquiry, such as "Please tell me about the next steps in infertility treatment." The device then sends the entered information to the server.

[0476] Step 8:

[0477] The server saves the received consultation details to a database and prepares the data for transmission to the generating AI.

[0478] Step 9:

[0479] The server sends the user's medical history, symptoms, and consultation details to the generating AI. The generating AI analyzes this information and generates optimal treatment suggestions based on the latest medical guidelines.

[0480] Step 10:

[0481] The generating AI sends its suggested results back to the server. The server receives these results and sends them to the user's device for display.

[0482] Step 11:

[0483] The device displays the suggested results to the user. If the user reviews the suggestions and wishes to discuss further, they access the reservation screen.

[0484] Step 12:

[0485] The user selects the "Consult a specialist" option and enters their desired appointment date and time. The device then sends the appointment information to the server.

[0486] Step 13:

[0487] The server receives the reservation information and links it with the medical consultation system. Once the linkage is complete, reservation confirmation information is sent back to the server.

[0488] Step 14:

[0489] The server sends reservation confirmation information to the terminal. The terminal displays the reservation confirmation information to the user, informing them that the reservation has been confirmed.

[0490] Step 15:

[0491] After the user receives treatment, the device provides a feedback input screen. The user enters information about the effectiveness and experience of the treatment.

[0492] Step 16:

[0493] The device sends feedback information to the server. The server stores the received feedback information in a database and provides it to the generating AI.

[0494] Step 17:

[0495] The generating AI improves the accuracy of the model based on feedback information. This will improve the accuracy of treatment suggestions for future users.

[0496] (Example 1)

[0497] 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".

[0498] In modern infertility treatment, it is difficult to propose personalized treatment methods based on each patient's unique symptoms and medical history, and general treatment methods tend to be used frequently. As a result, there are cases where the treatment is not sufficiently effective or where the patient's needs are not met. Furthermore, the lack of a system for properly collecting and utilizing post-treatment feedback makes it difficult to improve the quality of treatment.

[0499] 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.

[0500] In this invention, the server includes means for the user to register an account, means for the user to input medical history and symptoms, means for the user to input specific consultation details, means for the server to send the information received from the user to a generating AI for analysis, means for the generating AI to generate an optimal treatment proposal based on the analysis results, means for the server to send the proposal results from the generating AI to the user, and means for the terminal to display the proposal results to the user. This makes it possible to personalize and propose the optimal infertility treatment method according to the individual circumstances of the user.

[0501] A "user" is an individual who inputs their medical history and symptoms through the system and receives treatment suggestions.

[0502] "Account registration" is the process by which a user registers information such as their name, contact details, and password with the system.

[0503] "Medical history" refers to records of diagnoses and treatments a user has received in the past.

[0504] "Symptoms" refer to the specific health conditions or problems that a user is currently experiencing.

[0505] "Consultation content" refers to the specific questions or inquiries that users input through the system.

[0506] A "server" is a device that receives information from users and sends it to a generating AI for analysis.

[0507] "Generative AI" is artificial intelligence that analyzes received information and generates optimal treatment suggestions.

[0508] "Proposal results" refer to the content of the treatment suggestions generated by the AI ​​as a result of its analysis.

[0509] A "terminal" is a device used by users to input information or to display suggested results sent from a server.

[0510] "Appointment information" refers to information such as the date and time a user wishes to consult with a specialist.

[0511] The "Medical Consultation System" is a system that receives users' appointment information and coordinates with specialists.

[0512] "Feedback information" refers to information about the effectiveness and experience of treatment that users enter after treatment.

[0513] "Model accuracy" is an indicator that represents the accuracy and reliability of the analysis and suggestions of the generated AI.

[0514] "Medical guidelines" are standards that outline recommended treatment methods and procedures based on the latest medical knowledge.

[0515] The present invention is a system for generating individual treatment suggestions in infertility treatment, providing personalized treatment suggestions based on the user's medical history and symptoms. The following processes are performed as a concrete embodiment of this invention.

[0516] First, the user accesses the system through their device. The user enters information such as their name, contact information, password, and medical history on the account registration screen. The device checks the format of this information and sends it to the server. The server stores the received information in its database and creates an account. Existing users enter their user ID and password on the login screen and complete the authentication process.

[0517] The user enters detailed medical history and current symptoms on the main screen. The terminal checks the format of this information and sends it to the server. The server stores the received information in a database.

[0518] Next, the user enters their specific question. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." The terminal checks the format of the input and sends it to the server. The server saves the received question to its database.

[0519] This process involves the server sending the user's medical history, symptoms, and consultation details to a generating AI model. The generating AI model analyzes this information and generates optimal treatment suggestions by referring to the latest medical guidelines. The generating AI model sends its suggestions back to the server, which then sends them to the terminal. The terminal then displays the suggestions from the generating AI model to the user.

[0520] If the user reviews the treatment suggestions from the generated AI model and wishes to consult further, the terminal provides an option to "consult a specialist." When the user enters their desired appointment date and time, the terminal sends that information to the server. The server integrates the received appointment information with the medical consultation system and sends appointment confirmation information to the terminal. The terminal then displays the appointment confirmation information to the user.

[0521] After treatment, the user enters information about the treatment's effectiveness and experience through a feedback input screen. The device checks the format of the feedback and sends it to the server. The server stores the received feedback information in a database and provides it to the generated AI model, contributing to improving the model's accuracy.

[0522] Specific example

[0523] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have problems with egg quality. Next, they enter their questions regarding the next steps in their infertility treatment. The server sends this information to an AI model, which analyzes it and generates a treatment suggestion such as, "Given the decline in egg quality due to age, egg donation should be considered." The server sends this suggestion to User A, and the terminal displays it to User A. If User A reviews the suggestion and wishes to consult directly with a specialist, the terminal provides a booking screen and confirms the appointment in conjunction with the medical consultation system. Through this entire process, the system can provide users with optimal infertility treatment suggestions tailored to their individual circumstances, thereby improving the success rate of treatment.

[0524] Example of a prompt

[0525] Please describe the process of a system that takes user medical history, current symptoms, and specific consultation details as input, and then uses a generative AI model to generate optimal treatment suggestions.

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

[0527] Step 1:

[0528] The user opens the account registration screen and enters information such as name, contact information, password, and medical history. The device verifies this information and performs a format check. Input: User registration information. Output: User information that passed the format check.

[0529] Step 2:

[0530] The terminal sends user information that has passed format checks to the server. The server saves the received information to a database and creates an account. Input: Format-checked user information. Output: User information saved to the database.

[0531] Step 3:

[0532] An existing user enters their user ID and password on the login screen. The device sends this information to the server. Input: User ID and password. Output: Login request to the server.

[0533] Step 4:

[0534] The server verifies the received user ID and password against the information in the database. If authentication is successful, the session is started. Input: User ID and password. Output: Authentication result and session start.

[0535] Step 5:

[0536] The user enters detailed medical history and current symptoms on the main screen. The terminal checks this information for formatting and sends it to the server. Input: Medical history and symptoms. Output: Medical history and symptom information that has passed the formatting check.

[0537] Step 6:

[0538] The server saves the received medical history and symptom information to the database. Input: Format-checked medical history and symptom information. Output: Medical history and symptom information saved to the database.

[0539] Step 7:

[0540] The user enters their specific question. For example, they might enter, "Please tell me about the next steps in infertility treatment." The terminal checks the format of the input and sends it to the server. Input: Question. Output: Question that passed the format check.

[0541] Step 8:

[0542] The server saves the received consultation content to the database. Input: Format-checked consultation content. Output: Consultation content saved to the database.

[0543] Step 9:

[0544] The server sends the user's medical history, symptoms, and consultation details to a generating AI model. The generating AI model analyzes this information and, referencing the latest medical guidelines, generates optimal treatment suggestions. Input: Medical history, symptoms, consultation details. Output: Treatment suggestions generated by the generating AI.

[0545] Step 10:

[0546] The server receives the proposed results from the generated AI model and sends them to the terminal. Input: Treatment proposal from the generated AI. Output: Sending the proposed results to the terminal.

[0547] Step 11:

[0548] The terminal displays the received suggestion results to the user. Input: Suggestion results received from the server. Output: Display of suggestion results to the user.

[0549] Step 12:

[0550] If the user wishes to consult further, the device will offer an option to "Consult a specialist" and allow the user to enter their preferred appointment date and time. Input: Preferred appointment date and time. Output: Appointment information.

[0551] Step 13:

[0552] The terminal checks the format of the entered reservation request date and time and sends it to the server. Input: Format-checked reservation request date and time. Output: Reservation information sent to the server.

[0553] Step 14:

[0554] The server receives the reservation information and links it with the medical consultation system to receive reservation confirmation information. Input: Reservation information. Output: Reservation confirmation information from the medical consultation system.

[0555] Step 15:

[0556] The server sends reservation confirmation information to the terminal, and the terminal displays the reservation confirmation information to the user. Input: Reservation confirmation information. Output: Display of reservation confirmation information to the user.

[0557] Step 16:

[0558] After treatment, the user enters information about the treatment's effectiveness and experience on a feedback input screen. The device then performs a format check on the feedback content and sends it to the server. Input: Feedback content. Output: Feedback content that has passed the format check.

[0559] Step 17:

[0560] The server saves the received feedback information to a database and provides it to the generating AI model. Input: Format-checked feedback information. Output: Saving of feedback information to the database and providing it to the generating AI model.

[0561] These processing steps enable the system to generate optimal treatment suggestions based on the user's medical history and symptoms, thereby improving the success rate of treatment.

[0562] (Application Example 1)

[0563] 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."

[0564] In infertility treatment, the current system for providing patients with optimal treatment recommendations based on their individual medical history and symptoms is not adequately developed. Furthermore, there is a lack of integrated systems to efficiently and smoothly manage the entire process, including post-treatment appointments with specialists, payment of treatment fees, and feedback collection. As a result, patients often require significant time and effort to manage multiple procedures and information, which can delay the progress of their treatment.

[0565] 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.

[0566] In this invention, the server includes means for the user to register an account, means for the user to input medical history and symptoms, means for the user to input specific consultation details, means for the server to send the information received from the user to a generating AI for analysis, means for the generating AI to generate an optimal treatment proposal based on the analysis results, means for the server to send the proposal results from the generating AI to the user, means for the user to electronically pay for treatment, and means for managing appointments with medical institutions. As a result, patients can quickly receive optimal treatment proposals based on their individual medical history and symptoms, and it is also possible to centrally manage appointments with specialists, payment of treatment fees, and collection of post-treatment feedback.

[0567] "Account registration" is the process by which a user officially registers by entering personal information, passwords, and other details necessary to use the system.

[0568] "Medical history" refers to information including medical treatments, diagnoses, and medical history that a user has received in the past.

[0569] "Symptoms" refer to specific conditions or signs that indicate a health problem or ailment the user is currently experiencing.

[0570] "Consultation details" refer to information entered by the user in text format, expressing questions or wishes regarding specific medical policies or treatment methods.

[0571] A "server" is a computer system that receives data entered by users, sends it to a generating AI for analysis, and then returns and manages the results.

[0572] "Generative AI" is an artificial intelligence model that analyzes data based on received medical history, symptoms, and consultation content to generate optimal treatment suggestions.

[0573] "Analysis results" refer to the output, such as treatment suggestions and diagnostic results, obtained by the generating AI through its analysis of information received from the user.

[0574] A "treatment suggestion" is the optimal medical procedure or treatment method recommended to the user based on data analyzed by the generating AI.

[0575] "Electronic payment" refers to an electronic payment method that allows users to safely and efficiently pay for expenses such as medical treatment costs via the internet.

[0576] "Appointment management" is a function that allows users to input and manage appointments with medical institutions and specialists, and to adjust their schedules.

[0577] "Feedback" refers to information that users enter after treatment, including their impressions and evaluations of the treatment's effectiveness and their experience.

[0578] A system for implementing this invention consists of a server, a terminal, and a generated AI model. Specific embodiments for realizing this system are described below.

[0579] Hardware and software

[0580] 1. Hardware:

[0581] Smartphones (compatible with iOS and Android)

[0582] Security module (TPM chip, etc.)

[0583] Server (equipped with high-performance processor and large memory capacity)

[0584] 2. Software:

[0585] Server-side: Python, Django, SQL database, OpenAI API, Stripe API

[0586] Smartphone side: React Native

[0587] System operation

[0588] User registration and login

[0589] Users access a dedicated application from their smartphones and register an account. This process requires them to enter basic information such as their name, contact information, and password, as well as their past medical history. The registered information is sent from the device to the server and stored in the database. Existing users access the system by entering their user ID and password on the login screen and going through the authentication process.

[0590] Entering patient information

[0591] The user enters detailed information about their medical history and current symptoms on the main screen. This information is sent from the device to the server and stored in the database.

[0592] Enter your consultation details

[0593] The user enters their specific question into a text field. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." This information is also sent from the device to the server and stored in the database.

[0594] Generating treatment proposals

[0595] The server sends data to the generating AI based on the received medical history, symptoms, and consultation content. The generating AI analyzes this information and generates optimal treatment suggestions by referring to the latest medical guidelines. The generated treatment suggestions are sent from the server to the terminal and displayed to the user.

[0596] Electronic payment and reservation management

[0597] If the user reviews the treatment proposal and wishes to make an appointment with a specialist, the application displays an appointment entry screen. The user enters their desired date and time, and the server links this information with the medical institution's appointment management system. Furthermore, the Stripe API is used on the terminal to securely process the payment for the treatment costs associated with this appointment electronically.

[0598] Feedback Collection

[0599] After a user receives treatment, the device displays a feedback input screen where the user enters information about the treatment's effectiveness and their experience. This information is also sent to the server and provided to the AI, which helps improve the accuracy of treatment recommendations.

[0600] Specific example

[0601] For example, if user A wants to consult about the next steps in infertility treatment, after registering an account, they would enter "I have a problem with egg quality" in their medical history. Then, they would enter their consultation request, "Please tell me the next steps in infertility treatment." The server sends this information to the AI, which generates a suggestion that "because the quality of eggs may be declining due to age, egg donation should be considered." This suggestion is sent to user A, and they can then make an appointment with a specialist and pay for treatment costs all within the app.

[0602] Example of a prompt

[0603] "User's medical history and symptoms:

[0604] Medical history: {Medical history}

[0605] Symptom: {symptom}

[0606] Consultation details: {Consultation details}

[0607] Please generate the optimal treatment plan based on the latest medical guidelines.

[0608] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0609] Step 1:

[0610] The user enters necessary information such as their name, contact information, password, and medical history on the account registration screen. The device sends this information to the server, which stores the received information in a database. As a result, a new account for the user is created.

[0611] Input: Name, contact information, password, medical history

[0612] Data processing: Format the input information and send it to the server.

[0613] Output: User accounts stored in the database

[0614] Step 2:

[0615] The user enters their user ID and password on the login screen and accesses the system through the authentication process. If authentication is successful, the terminal redirects the user to the main screen.

[0616] Input: User ID, Password

[0617] Data processing: Verification of received authentication information

[0618] Output: User main screen after successful authentication

[0619] Step 3:

[0620] The user enters detailed information about their medical history and current symptoms on the main screen. The device sends this information to the server, which then stores the received information in a database.

[0621] Input: Medical history, symptoms

[0622] Data processing: Saving received information to a database

[0623] Output: Updated medical history and symptoms in the database

[0624] Step 4:

[0625] The user enters their specific inquiry into a text field, the device sends that information to the server, and the server saves the received information to a database.

[0626] Input: Consultation details

[0627] Data processing: Classification and storage of consultation content

[0628] Output: Consultation details stored in the database

[0629] Step 5:

[0630] The server sends the user's medical history, symptoms, and consultation details to the AI. The AI ​​analyzes this information and generates optimal treatment suggestions.

[0631] Input: Medical history, symptoms, consultation details

[0632] Data processing: Data analysis using generative AI

[0633] Output: Treatment suggestion

[0634] Step 6:

[0635] The AI ​​generates treatment suggestions, which are received by the server and sent to the user's device. The device then displays the suggested results to the user.

[0636] Input: Treatment suggestions from generated AI

[0637] Data processing: Formatting and sending of proposal results

[0638] Output: Treatment suggestions displayed on the user's terminal

[0639] Step 7:

[0640] The user reviews the treatment proposal and enters their preferred appointment date and time using the appointment booking screen with the specialist. The terminal sends this information to the server, which then links the received appointment information with the medical institution's appointment management system.

[0641] Input: Desired date and time for reservation

[0642] Data processing: Sending reservation information and linking it with the management system.

[0643] Output: Booking confirmation information

[0644] Step 8:

[0645] To allow users to electronically pay for their treatment appointments, the Stripe API is used on their terminal to process the payment. The server confirms the success of the payment and notifies the user of the result.

[0646] Input: Payment information

[0647] Data processing: Payment processing using the Stripe API

[0648] Output: Payment completion notification

[0649] Step 9:

[0650] After a user receives treatment, the device displays a feedback input screen where the user enters information about the treatment's effectiveness and their experience. The device sends this information to a server, which stores the received feedback in a database and provides it to the generating AI.

[0651] Input: Feedback information

[0652] Data processing: Database storage of feedback information and provision to the generating AI.

[0653] Output: Improved generative AI model

[0654] 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.

[0655] This invention is a system for generating individualized treatment suggestions in infertility treatment, capable of providing personalized treatment suggestions based on the user's medical history and symptoms. Furthermore, by combining it with an emotion engine, it can recognize the user's emotions and provide treatment suggestions and psychological support based on those emotions. The specific program processing of the system is described below in natural language.

[0656] User registration and login

[0657] The terminal displays an account registration screen to the user. The user enters the necessary information, such as name, contact information, password, and past medical history. The terminal verifies this information and sends it to the server. The server saves the received information to its database and creates an account. Existing users enter their user ID and password on the login screen and access the system through the authentication process.

[0658] Entering patient information

[0659] The user enters detailed information about their medical history and current symptoms on the main screen. The device sends this information to the server, which then stores the received information in a database.

[0660] Enter your consultation details

[0661] The user enters their specific question into a text field. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." The terminal sends the input to the server, and the server saves the received information to a database.

[0662] Emotion recognition and the generation of treatment proposals

[0663] The server sends the user's input to the emotion engine, which analyzes the user's emotional state. The emotion engine identifies the user's emotional state (e.g., stress, anxiety, relief) and sends that information to the generating AI. The generating AI comprehensively analyzes the user's medical history, symptoms, consultation content, and emotional state to generate optimal treatment suggestions and psychological support. The generating AI sends the suggested results back to the server, which then sends them to the terminal. The terminal displays the suggested results to the user.

[0664] Collaboration with medical consultation services

[0665] After the user reviews the treatment suggestions generated by the AI ​​and wishes to consult further, they access the booking screen. The user selects the "Consult a specialist" option and enters their desired appointment date and time. The device sends the booking information to the server. The server integrates the received booking information with the medical consultation system. Once the integration is complete, booking confirmation information is sent back to the server. The server sends the booking confirmation information to the device, which then displays it to the user.

[0666] Post-treatment feedback

[0667] After a user receives treatment, the device provides a feedback input screen. The user inputs information about the effectiveness and experience of the treatment, as well as their emotional state. For example, they might input, "I was relieved when my pregnancy was confirmed after the treatment." The device sends the inputted feedback information to the server. The server stores the received feedback information in a database and provides it to the generating AI. The generating AI uses the feedback information to improve the accuracy of its model, thereby improving the accuracy of future treatment suggestions for the user.

[0668] Specific example

[0669] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have problems with egg quality. Next, they enter their questions about the next steps in their infertility treatment. The server sends this information to the emotion engine, which analyzes User A's emotional state (e.g., anxiety). The emotion engine sends the emotional data and medical history to the generating AI, which analyzes it and generates a treatment suggestion such as, "Given the age-related decline in egg quality, egg donation should be considered. We also recommend receiving psychological counseling to alleviate anxiety." The server sends this suggestion to User A, and the terminal displays it to User A.

[0670] If User A reviews the proposal and wishes to consult directly with a specialist, the terminal provides a reservation screen and confirms the reservation in conjunction with the medical consultation system. Through this process, the system can provide users with optimal infertility treatment proposals tailored to their individual circumstances and psychological support based on their emotional state, thereby improving the success rate of treatment.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] The device displays an account registration screen to the user. The user enters the necessary information, such as their name, contact information, password, and medical history.

[0674] Step 2:

[0675] The terminal verifies the entered information and sends it to the server. The server receives the information, saves it to the database, and creates the account.

[0676] Step 3:

[0677] The user accesses the login screen and enters their user ID and password. The device sends this authentication information to the server.

[0678] Step 4:

[0679] The server compares the received authentication information with the database. If they match, it returns a login success message to the user and directs them to the main screen.

[0680] Step 5:

[0681] The user enters detailed information about their medical history and current symptoms on the main screen. The device then sends the entered information to the server.

[0682] Step 6:

[0683] The server saves the received information to a database. The database stores the user's medical history and symptoms.

[0684] Step 7:

[0685] The user enters specific details of their inquiry, such as "Please tell me about the next steps in infertility treatment." The device then sends the entered information to the server.

[0686] Step 8:

[0687] The server saves the received consultation details to a database and sends them to the emotion engine.

[0688] Step 9:

[0689] The emotion engine analyzes the user's input and identifies their emotional state (stress, anxiety, reassurance, etc.). It then sends the emotional state data back to the server.

[0690] Step 10:

[0691] The server sends emotional state data to the generating AI. The generating AI comprehensively analyzes the user's medical history, symptoms, consultation content, and emotional state to generate optimal treatment suggestions and psychological support.

[0692] Step 11:

[0693] The generating AI sends its suggested results back to the server. The server receives these results and sends them to the terminal.

[0694] Step 12:

[0695] The device displays the suggested results to the user. If the user reviews the suggestions and wishes to discuss further, they access the reservation screen.

[0696] Step 13:

[0697] The user selects the "Consult a specialist" option and enters their desired appointment date and time. The device then sends the appointment information to the server.

[0698] Step 14:

[0699] The server receives the reservation information and links it with the medical consultation system. Once the linkage is complete, reservation confirmation information is sent back to the server.

[0700] Step 15:

[0701] The server sends reservation confirmation information to the terminal. The terminal displays the reservation confirmation information to the user, informing them that the reservation has been confirmed.

[0702] Step 16:

[0703] After the user receives treatment, the device provides a feedback input screen. The user enters information about the effectiveness and experience of the treatment, as well as their emotional state. For example, they might enter, "I was relieved when my pregnancy was confirmed after the treatment."

[0704] Step 17:

[0705] The device sends feedback information to the server. The server stores the received feedback information in a database and provides it to the generating AI.

[0706] Step 18:

[0707] The generating AI improves the accuracy of the model based on feedback information. This will improve the accuracy of treatment suggestions for future users.

[0708] (Example 2)

[0709] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0710] In infertility treatment, it is crucial to provide effective treatment suggestions based on each patient's medical history and current symptoms. However, conventional systems have the challenge of not being able to provide personalized treatment suggestions and psychological support that take into account each patient's emotional state. Furthermore, there is a lack of mechanisms to improve the accuracy of the generated AI model by utilizing post-treatment feedback information. As a result, it has been difficult to provide highly accurate treatment suggestions.

[0711] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for transmitting user information to an emotion engine and analyzing the emotional state, means for transmitting the emotional data received from the emotion engine and user information to a generating AI and having it perform analysis, and means for the generating AI to generate optimal treatment suggestions and psychological support based on the user's medical history, symptoms, and emotional state. This makes it possible to provide highly accurate treatment suggestions and psychological support based on each user's medical history and emotional state.

[0712] A "user" refers to an individual who uses this system to input information about infertility treatment and receive treatment suggestions and psychological support.

[0713] A "terminal" refers to a computer device or smartphone operated by a user, providing an interface for the user to input information and check results.

[0714] A "server" refers to a central computer system that receives, processes, stores, and analyzes information transmitted from terminals.

[0715] "Medical history" refers to information about medical treatments, diagnoses, and health status that a user has received in the past.

[0716] "Symptoms" refer to the health problems or discomforts that the user is currently experiencing.

[0717] "Consultation content" refers to specific questions or problems that users want to ask the system about.

[0718] An "emotion engine" refers to a computer program that analyzes a user's emotional state (e.g., stress, anxiety, reassurance) based on their input.

[0719] "Generative AI" refers to an artificial intelligence program that analyzes a user's medical history, symptoms, consultation content, and emotional state to automatically generate optimal treatment suggestions and psychological support.

[0720] "Proposal results" refers to information regarding treatment suggestions and psychological support created by the generating AI based on its analysis.

[0721] A "medical consultation service system" refers to a computer system used to manage appointments and consultations with specialists and counselors.

[0722] "Feedback" refers to users entering their thoughts and evaluations regarding the effectiveness and experience of treatment, as well as their emotional state.

[0723] "Model accuracy" refers to an indicator that shows the accuracy and appropriateness of the treatment suggestions and psychological support that the generated AI provides to the user.

[0724] This invention is a system that provides individualized treatment suggestions and psychological support in infertility treatment. The system aims to generate optimal treatment suggestions by comprehensively analyzing the user's medical history, symptoms, consultation content, and emotional state. Specific embodiments of this invention are described below.

[0725] User registration and login

[0726] The terminal displays an account registration screen to the user. The user enters necessary information such as name, contact information, password, and past medical history, and the terminal verifies this information and sends it to the server. The server stores the received information in its database and creates an account. Existing users enter their user ID and password on the login screen to authenticate and access the system.

[0727] Entering patient information

[0728] The user enters detailed information about their medical history and current symptoms on the main screen. The terminal sends this information to the server, which stores the received information in a database.

[0729] Enter your consultation details

[0730] The user enters their specific question into a text field. For example, they might enter, "Please tell me about the next steps in infertility treatment." The terminal sends the entered question to the server, and the server saves the received information in a database.

[0731] Emotion recognition and the generation of treatment proposals

[0732] The server sends the user's input to the emotion engine, which analyzes the user's emotional state. The emotion engine identifies the user's emotional state (e.g., stress, anxiety, relief) and sends this information to the generating AI. The generating AI comprehensively analyzes the user's medical history, symptoms, consultation content, and emotional state to generate optimal treatment suggestions and psychological support. The generating AI sends the suggested results back to the server, which then sends them to the terminal. The terminal displays the suggested results to the user.

[0733] Collaboration with medical consultation services

[0734] After the user reviews the treatment suggestions generated by the AI, if they wish to have a more detailed consultation, they access the reservation screen. The user selects the "Consult with a specialist" option and enters their desired reservation date and time. The device sends the reservation information to the server. The server links the received reservation information with the medical consultation system. Once the linkage is complete, reservation confirmation information is sent back to the server, which then sends the reservation confirmation information to the device, which displays it to the user.

[0735] Post-treatment feedback

[0736] After the user receives treatment, the device provides a feedback input screen. The user inputs information about the effectiveness and experience of the treatment, as well as their emotional state. For example, they might input, "I was relieved when my pregnancy was confirmed after the treatment." The device sends the input feedback information to the server. The server stores the received feedback information in a database and provides it to the generative AI. The generative AI uses the feedback information to improve the accuracy of its model.

[0737] Specific example

[0738] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have problems with egg quality. Next, they enter their questions about the next steps in their infertility treatment. The server sends this information to the emotion engine, which analyzes User A's emotional state (e.g., anxiety). The emotion engine sends the emotional data and medical history to the generating AI, which analyzes it and generates a treatment suggestion such as, "Given the age-related decline in egg quality, egg donation should be considered. We also recommend receiving psychological counseling to alleviate anxiety." The server sends this suggestion to User A, and the terminal displays it to User A.

[0739] Example of a prompt:

[0740] "The user's medical history is as follows: There are issues with egg quality. Current symptoms or concerns are as follows: Please advise on the next steps in fertility treatment. The user is currently experiencing anxiety. Based on this information, please provide optimal treatment suggestions and psychological support."

[0741] This invention makes it possible to provide highly accurate treatment suggestions and psychological support based on the user's medical history and emotional state.

[0742] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0743] Step 1: User registration and login

[0744] Input: The user enters their name, contact information, password, and medical history on the account registration screen.

[0745] Operation: The terminal validates the input information and sends it to the server.

[0746] Data processing / calculation: The server receives validated information and saves it to the database.

[0747] Output: The server sends the account creation result back to the terminal and notifies the user that registration is complete.

[0748] Step 2: Enter patient information

[0749] Input: The user enters their medical history and current symptom information on the main screen.

[0750] Operation: The terminal sends the input information to the server.

[0751] Data processing / calculation: The server saves the information it receives to the database.

[0752] Output: The server notifies the terminal that the information has been saved and displays a confirmation message to the user.

[0753] Step 3: Enter your consultation details

[0754] Input: The user enters their question (e.g., "Please tell me about the next steps in infertility treatment") into the text field.

[0755] Operation: The terminal sends the input content to the server.

[0756] Data processing / calculation: The server saves the received consultation details to the database.

[0757] Output: The server notifies the terminal that the consultation details have been saved and displays a confirmation message to the user.

[0758] Step 4: Emotion recognition and generation of treatment proposals

[0759] Input: The server sends the user's medical history, symptoms, and consultation details to the emotion engine.

[0760] Operation: The emotion engine analyzes the emotional state.

[0761] Data processing / calculation: The emotion engine identifies emotional states (e.g., anxiety) and sends them to the generating AI.

[0762] Output: The generating AI generates treatment suggestions and psychological support based on the received data and sends the results back to the server.

[0763] Step 5: Providing treatment proposals

[0764] Input: The server receives the suggested results from the generated AI.

[0765] Operation: The server sends the suggestion results to the terminal.

[0766] Data Processing / Calculation: The server aggregates and organizes user-specific data and provides suggested results in an easy-to-understand format.

[0767] Output: The terminal displays the suggested results to the user. For example, a message such as, "Due to the possibility of a decline in egg quality due to age, egg donation should be considered. We also recommend receiving psychological counseling to alleviate anxiety," might be displayed.

[0768] Step 6: Collaboration with medical consultation services

[0769] Input: The user selects the "Consult a specialist" option and enters the appointment date and time.

[0770] Operation: The device sends reservation information to the server.

[0771] Data processing / calculation: The server sends the reservation information to the medical consultation system and confirms the reservation.

[0772] Output: The server sends reservation confirmation information to the terminal and displays it to the user.

[0773] Step 7: Post-treatment feedback

[0774] Input: The user enters feedback after treatment (e.g., "I was relieved when my pregnancy was confirmed after treatment").

[0775] Action: The device sends feedback information to the server.

[0776] Data processing / calculation: The server stores feedback information in a database and provides it to the generating AI.

[0777] Output: The server notifies the terminal that the feedback has been saved and displays a confirmation message to the user.

[0778] At each step, the user, terminal, and server work together to create a system that provides highly accurate treatment suggestions.

[0779] (Application Example 2)

[0780] 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."

[0781] In infertility treatment, personalized treatment suggestions based on the patient's medical history and symptoms are crucial, but traditional systems have lacked adequate psychological support. Furthermore, there is a need for an efficient method to analyze a patient's emotional state before they visit the clinic and to provide appropriate treatment suggestions and appointment scheduling.

[0782] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to register an account, means for the user to input medical history and symptoms, means for the user to input specific consultation details, means for the emotion engine to analyze the user's emotional state, means for the server to send the information received from the user to the generating AI for analysis, means for the generating AI to generate an optimal treatment proposal based on the analysis results, and means for the generating AI to display reservation information for face-to-face appointments to the user. This enables personalized treatment proposals and reservation coordination based on the user's emotional state.

[0783] "User" refers to an individual or group that uses the system, and in particular to those who receive consultations and treatment suggestions regarding infertility treatment.

[0784] "Account registration" refers to the process by which a user provides their basic information to the system and obtains a unique identification ID.

[0785] "Medical history" refers to a detailed record of treatments and diagnoses the user has received in the past.

[0786] "Symptoms" refer to the physical or mental problems or conditions that the user is currently experiencing.

[0787] "Consultation content" refers to the specific questions and requests that users ask through the system.

[0788] An "emotion engine" refers to software that analyzes user input data and identifies their emotional state.

[0789] A "server" refers to a remote computer system that processes and stores information received from users.

[0790] "Generative AI" refers to an artificial intelligence algorithm that automatically generates optimal treatment suggestions based on received data.

[0791] "Analysis results" refers to the output obtained after analyzing user data using a generative AI or emotion engine.

[0792] "Treatment suggestions" refer to specific treatment methods and support provided to the user based on their medical history, symptoms, consultation content, and emotional state.

[0793] "Reservation information" refers to data that users enter to book an in-person consultation with a specialist, indicating the date, time, and desired details.

[0794] The term "medical consultation service system" refers to a backend system for managing and processing appointments for consultations with medical professionals.

[0795] "Feedback" refers to data that represents the evaluation and opinions of users regarding the treatments and suggestions they received.

[0796] "Model accuracy" refers to an evaluation criterion that indicates the accuracy and suitability of the treatment suggestions provided by the generated AI.

[0797] This invention is a system for users to receive personalized treatment suggestions regarding infertility treatment, and detailed embodiments are shown below.

[0798] First, the user registers an account using their device. The user enters basic information such as their name, contact information, and past medical history. The entered information is sent from the device to the server and stored in the database.

[0799] Next, the user enters their medical history, current symptoms, and specific questions via their device. This information is also sent from the device to the server and stored in the database.

[0800] A key element of this system is the inclusion of an emotion engine, which analyzes the user's emotional state from their input data. The emotion engine analyzes the user's text input and behavioral data to identify their emotional state (e.g., anxiety, reassurance, etc.). This analysis result is then transmitted to the generating AI via a server.

[0801] The generating AI creates optimal treatment suggestions based on the user's medical history, symptoms, consultation content, and emotional state. The generating AI uses the latest medical guidelines and past patient data to generate specific and personalized treatment suggestions. The results of this AI analysis are transmitted from the server to the user's terminal and displayed.

[0802] Furthermore, if a user wishes to have an in-person consultation with a specialist, they enter reservation information based on the suggestions generated by the AI. The server then links this reservation information with the medical consultation system and sends reservation confirmation information to the user. This process allows users to receive consultation from a specialist at the appropriate time.

[0803] Furthermore, users provide feedback after treatment. This feedback information is sent to the server and provided to the generating AI. The generating AI uses this feedback data to improve the accuracy of its model. This continuous feedback loop allows the system to provide more accurate treatment suggestions.

[0804] As a concrete example, let's consider the case where User B uses the system for the first time. User B creates an account on their smartphone and enters their medical history regarding past infertility treatments. For example, they might enter that they have problems with ovarian function. Next, they enter a question about the next steps in their infertility treatment. Let's say they enter, "Please tell me about the next steps in my infertility treatment." The emotion engine analyzes User B's input to understand their anxiety and sends it to the generating AI. Based on the medical history and emotional state, the generating AI generates a treatment suggestion such as, "Ovarian dysfunction is suspected, so consider specific drug treatments. We also recommend counseling to reduce anxiety." The server sends this suggestion to User B, and it is displayed on their smartphone.

[0805] Examples of prompt statements include the following:

[0806] User's medical history: Female, 35 years old, underwent two artificial insemination procedures in the past three years. Has ovarian dysfunction.

[0807] Inquiry topic: I would like to know about the next steps in my treatment.

[0808] Emotional state: Anxiety.

[0809] Please generate treatment suggestions based on the generated AI.

[0810] This system allows users to receive personalized infertility treatment suggestions and psychological support based on their emotional state, potentially improving the success rate of treatment.

[0811] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0812] Step 1:

[0813] The user registers an account. The user uses their device to enter basic information such as their name, contact information, and medical history. The device sends the entered information to the server, which stores this information in a database. Input data: Name, contact information, medical history. Output: Notification of successful account creation.

[0814] Step 2:

[0815] The user inputs their medical history, current symptoms, and specific consultation details. The terminal sends this information to the server, which stores it in a database. Input data: medical history, symptoms, consultation details. Output: information is stored in the database.

[0816] Step 3:

[0817] The server sends the information received from the user to the emotion engine. The emotion engine analyzes the user's input data and identifies the emotional state (e.g., anxiety, reassurance). It then sends the analysis results back to the server. Input data: Medical history, consultation content. Output result: Analysis results of the emotional state.

[0818] Step 4:

[0819] The server sends emotional state data obtained from the emotion engine, along with the user's medical history and consultation details, to the generating AI. The generating AI then uses this information to create optimal treatment suggestions. Input data: medical history, symptoms, consultation details, emotional state. Output result: treatment suggestions.

[0820] Step 5:

[0821] The server receives treatment suggestions from the generated AI and sends them to the user's device. The device displays this information to the user. Input data: Treatment suggestions. Output result: Display of the suggested content.

[0822] Step 6:

[0823] If a user wishes to have an in-person consultation with a specialist, they enter reservation information based on the suggestions generated by the AI. The terminal sends this reservation information to the server, which then connects it with the medical consultation system. Once the connection is complete, the server sends reservation confirmation information to the user. Input data: Reservation information. Output result: Reservation confirmation information.

[0824] Step 7:

[0825] The user enters feedback after treatment. The device sends this feedback information to the server, which provides it to the generating AI. The generating AI uses this feedback data to improve the accuracy of its model. Input data: Feedback information. Output result: Improved model accuracy.

[0826] As a concrete example, the following prompt is sent to the generating AI:

[0827] User's medical history: Female, 35 years old, underwent two artificial insemination procedures in the past three years. Has ovarian dysfunction.

[0828] Inquiry topic: I would like to know about the next steps in my treatment.

[0829] Emotional state: Anxiety.

[0830] Please generate treatment suggestions based on the generated AI.

[0831] Through this process, users can receive personalized infertility treatment recommendations and psychological support based on their emotional state.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] [Third Embodiment]

[0836] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0837] 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.

[0838] 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).

[0839] 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.

[0840] 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.

[0841] 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).

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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".

[0848] This invention relates to a system for generating individualized treatment suggestions in infertility treatment, which can provide personalized treatment suggestions based on the user's medical history and symptoms. The specific program processing of the system is described below in natural language.

[0849] User registration and login

[0850] The terminal provides the user with an account registration screen. The user enters necessary information such as name, contact information, password, and past medical history. The terminal verifies this information and sends it to the server. The server stores the received information in its database and creates an account. Existing users enter their user ID and password on the login screen and access the system through the authentication process.

[0851] Entering patient information

[0852] The user enters detailed information about their medical history and current symptoms on the main screen. The device sends this information to the server, which then stores the received information in a database.

[0853] Enter your consultation details

[0854] The user enters their specific question into a text field. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." The terminal sends the input to the server, and the server saves the received information to a database.

[0855] Generating treatment proposals

[0856] The server sends the user's medical history, symptoms, and consultation details to the generating AI. The generating AI analyzes this information and generates optimal treatment suggestions by referring to the latest medical guidelines. The generating AI sends the suggested results back to the server, which then sends them to the terminal. The terminal displays the suggested results to the user.

[0857] Collaboration with medical consultation services

[0858] After the user reviews the treatment suggestions generated by the AI, if they wish to consult further, the device provides an option to "consult a specialist." The user enters their desired appointment date and time, and the device sends this information to the server. The server integrates the received appointment information with the medical consultation system. The server sends appointment confirmation information to the device, which then displays it to the user.

[0859] Post-treatment feedback

[0860] After a user receives treatment, the device provides a feedback input screen. The user inputs information about the treatment's effectiveness and experience, and sends this information to the server. The server stores the received feedback information in a database and provides it again to the generating AI, contributing to improving the model's accuracy.

[0861] Specific example

[0862] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have had problems with egg quality. Next, they enter their questions regarding the next steps in their infertility treatment.

[0863] The server sends this information to the AI, which analyzes it and generates a treatment suggestion stating, "Given the likely decline in egg quality due to age, egg donation should be considered." The server then sends this suggestion to user A, and the terminal displays it to user A.

[0864] If User A reviews the proposal and wishes to consult directly with a specialist, the terminal will provide a booking screen and confirm the booking in conjunction with the medical consultation system.

[0865] Through this series of processes, the system can propose the most suitable infertility treatment to the user based on their individual circumstances, thereby improving the success rate of the treatment.

[0866] The following describes the processing flow.

[0867] Step 1:

[0868] The device displays an account registration screen to the user. The user enters the necessary information, such as their name, contact information, password, and medical history.

[0869] Step 2:

[0870] The terminal verifies the entered information and sends it to the server. The server saves the information to its database and creates an account.

[0871] Step 3:

[0872] The user accesses the login screen and enters their user ID and password. The device sends this authentication information to the server.

[0873] Step 4:

[0874] The server compares the received authentication information with the database. If they match, it returns a login success message to the user and directs them to the main screen.

[0875] Step 5:

[0876] The user enters detailed information about their medical history and current symptoms on the main screen. The device then sends the entered information to the server.

[0877] Step 6:

[0878] The server saves the received information to a database. The database stores the user's medical history and symptoms.

[0879] Step 7:

[0880] The user enters specific details of their inquiry, such as "Please tell me about the next steps in infertility treatment." The device then sends the entered information to the server.

[0881] Step 8:

[0882] The server saves the received consultation details to a database and prepares the data for transmission to the generating AI.

[0883] Step 9:

[0884] The server sends the user's medical history, symptoms, and consultation details to the generating AI. The generating AI analyzes this information and generates optimal treatment suggestions based on the latest medical guidelines.

[0885] Step 10:

[0886] The generating AI sends its suggested results back to the server. The server receives these results and sends them to the user's device for display.

[0887] Step 11:

[0888] The device displays the suggested results to the user. If the user reviews the suggestions and wishes to discuss further, they access the reservation screen.

[0889] Step 12:

[0890] The user selects the "Consult a specialist" option and enters their desired appointment date and time. The device then sends the appointment information to the server.

[0891] Step 13:

[0892] The server receives the reservation information and links it with the medical consultation system. Once the linkage is complete, reservation confirmation information is sent back to the server.

[0893] Step 14:

[0894] The server sends reservation confirmation information to the terminal. The terminal displays the reservation confirmation information to the user, informing them that the reservation has been confirmed.

[0895] Step 15:

[0896] After the user receives treatment, the device provides a feedback input screen. The user enters information about the effectiveness and experience of the treatment.

[0897] Step 16:

[0898] The device sends feedback information to the server. The server stores the received feedback information in a database and provides it to the generating AI.

[0899] Step 17:

[0900] The generating AI improves the accuracy of the model based on feedback information. This will improve the accuracy of treatment suggestions for future users.

[0901] (Example 1)

[0902] 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."

[0903] In modern infertility treatment, it is difficult to propose personalized treatment methods based on each patient's unique symptoms and medical history, and general treatment methods tend to be used frequently. As a result, there are cases where the treatment is not sufficiently effective or where the patient's needs are not met. Furthermore, the lack of a system for properly collecting and utilizing post-treatment feedback makes it difficult to improve the quality of treatment.

[0904] 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.

[0905] In this invention, the server includes means for the user to register an account, means for the user to input medical history and symptoms, means for the user to input specific consultation details, means for the server to send the information received from the user to a generating AI for analysis, means for the generating AI to generate an optimal treatment proposal based on the analysis results, means for the server to send the proposal results from the generating AI to the user, and means for the terminal to display the proposal results to the user. This makes it possible to personalize and propose the optimal infertility treatment method according to the individual circumstances of the user.

[0906] A "user" is an individual who inputs their medical history and symptoms through the system and receives treatment suggestions.

[0907] "Account registration" is the process by which a user registers information such as their name, contact details, and password with the system.

[0908] "Medical history" refers to records of diagnoses and treatments a user has received in the past.

[0909] "Symptoms" refer to the specific health conditions or problems that a user is currently experiencing.

[0910] "Consultation content" refers to the specific questions or inquiries that users input through the system.

[0911] A "server" is a device that receives information from users and sends it to a generating AI for analysis.

[0912] "Generative AI" is artificial intelligence that analyzes received information and generates optimal treatment suggestions.

[0913] "Proposal results" refer to the content of the treatment suggestions generated by the AI ​​as a result of its analysis.

[0914] A "terminal" is a device used by users to input information or to display suggested results sent from a server.

[0915] "Appointment information" refers to information such as the date and time a user wishes to consult with a specialist.

[0916] The "Medical Consultation System" is a system that receives users' appointment information and coordinates with specialists.

[0917] "Feedback information" refers to information about the effectiveness and experience of treatment that users enter after treatment.

[0918] "Model accuracy" is an indicator that represents the accuracy and reliability of the analysis and suggestions of the generated AI.

[0919] "Medical guidelines" are standards that outline recommended treatment methods and procedures based on the latest medical knowledge.

[0920] The present invention is a system for generating individual treatment suggestions in infertility treatment, providing personalized treatment suggestions based on the user's medical history and symptoms. The following processes are performed as a concrete embodiment of this invention.

[0921] First, the user accesses the system through their device. The user enters information such as their name, contact information, password, and medical history on the account registration screen. The device checks the format of this information and sends it to the server. The server stores the received information in its database and creates an account. Existing users enter their user ID and password on the login screen and complete the authentication process.

[0922] The user enters detailed medical history and current symptoms on the main screen. The terminal checks the format of this information and sends it to the server. The server stores the received information in a database.

[0923] Next, the user enters their specific question. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." The terminal checks the format of the input and sends it to the server. The server saves the received question to its database.

[0924] This process involves the server sending the user's medical history, symptoms, and consultation details to a generating AI model. The generating AI model analyzes this information and generates optimal treatment suggestions by referring to the latest medical guidelines. The generating AI model sends its suggestions back to the server, which then sends them to the terminal. The terminal then displays the suggestions from the generating AI model to the user.

[0925] If the user reviews the treatment suggestions from the generated AI model and wishes to consult further, the terminal provides an option to "consult a specialist." When the user enters their desired appointment date and time, the terminal sends that information to the server. The server integrates the received appointment information with the medical consultation system and sends appointment confirmation information to the terminal. The terminal then displays the appointment confirmation information to the user.

[0926] After treatment, the user enters information about the treatment's effectiveness and experience through a feedback input screen. The device checks the format of the feedback and sends it to the server. The server stores the received feedback information in a database and provides it to the generated AI model, contributing to improving the model's accuracy.

[0927] Specific example

[0928] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have problems with egg quality. Next, they enter their questions regarding the next steps in their infertility treatment. The server sends this information to an AI model, which analyzes it and generates a treatment suggestion such as, "Given the decline in egg quality due to age, egg donation should be considered." The server sends this suggestion to User A, and the terminal displays it to User A. If User A reviews the suggestion and wishes to consult directly with a specialist, the terminal provides a booking screen and confirms the appointment in conjunction with the medical consultation system. Through this entire process, the system can provide users with optimal infertility treatment suggestions tailored to their individual circumstances, thereby improving the success rate of treatment.

[0929] Example of a prompt

[0930] Please describe the process of a system that takes user medical history, current symptoms, and specific consultation details as input, and then uses a generative AI model to generate optimal treatment suggestions.

[0931] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0932] Step 1:

[0933] The user opens the account registration screen and enters information such as name, contact information, password, and medical history. The device verifies this information and performs a format check. Input: User registration information. Output: User information that passed the format check.

[0934] Step 2:

[0935] The terminal sends user information that has passed format checks to the server. The server saves the received information to a database and creates an account. Input: Format-checked user information. Output: User information saved to the database.

[0936] Step 3:

[0937] An existing user enters their user ID and password on the login screen. The device sends this information to the server. Input: User ID and password. Output: Login request to the server.

[0938] Step 4:

[0939] The server verifies the received user ID and password against the information in the database. If authentication is successful, the session is started. Input: User ID and password. Output: Authentication result and session start.

[0940] Step 5:

[0941] The user enters detailed medical history and current symptoms on the main screen. The terminal checks this information for formatting and sends it to the server. Input: Medical history and symptoms. Output: Medical history and symptom information that has passed the formatting check.

[0942] Step 6:

[0943] The server saves the received medical history and symptom information to the database. Input: Format-checked medical history and symptom information. Output: Medical history and symptom information saved to the database.

[0944] Step 7:

[0945] The user enters their specific question. For example, they might enter, "Please tell me about the next steps in infertility treatment." The terminal checks the format of the input and sends it to the server. Input: Question. Output: Question that passed the format check.

[0946] Step 8:

[0947] The server saves the received consultation content to the database. Input: Format-checked consultation content. Output: Consultation content saved to the database.

[0948] Step 9:

[0949] The server sends the user's medical history, symptoms, and consultation details to a generating AI model. The generating AI model analyzes this information and, referencing the latest medical guidelines, generates optimal treatment suggestions. Input: Medical history, symptoms, consultation details. Output: Treatment suggestions generated by the generating AI.

[0950] Step 10:

[0951] The server receives the proposed results from the generated AI model and sends them to the terminal. Input: Treatment proposal from the generated AI. Output: Sending the proposed results to the terminal.

[0952] Step 11:

[0953] The terminal displays the received suggestion results to the user. Input: Suggestion results received from the server. Output: Display of suggestion results to the user.

[0954] Step 12:

[0955] If the user wishes to consult further, the device will offer an option to "Consult a specialist" and allow the user to enter their preferred appointment date and time. Input: Preferred appointment date and time. Output: Appointment information.

[0956] Step 13:

[0957] The terminal checks the format of the entered reservation request date and time and sends it to the server. Input: Format-checked reservation request date and time. Output: Reservation information sent to the server.

[0958] Step 14:

[0959] The server receives the reservation information and links it with the medical consultation system to receive reservation confirmation information. Input: Reservation information. Output: Reservation confirmation information from the medical consultation system.

[0960] Step 15:

[0961] The server sends reservation confirmation information to the terminal, and the terminal displays the reservation confirmation information to the user. Input: Reservation confirmation information. Output: Display of reservation confirmation information to the user.

[0962] Step 16:

[0963] After treatment, the user enters information about the treatment's effectiveness and experience on a feedback input screen. The device then performs a format check on the feedback content and sends it to the server. Input: Feedback content. Output: Feedback content that has passed the format check.

[0964] Step 17:

[0965] The server saves the received feedback information to a database and provides it to the generating AI model. Input: Format-checked feedback information. Output: Saving of feedback information to the database and providing it to the generating AI model.

[0966] These processing steps enable the system to generate optimal treatment suggestions based on the user's medical history and symptoms, thereby improving the success rate of treatment.

[0967] (Application Example 1)

[0968] 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."

[0969] In infertility treatment, the current system for providing patients with optimal treatment recommendations based on their individual medical history and symptoms is not adequately developed. Furthermore, there is a lack of integrated systems to efficiently and smoothly manage the entire process, including post-treatment appointments with specialists, payment of treatment fees, and feedback collection. As a result, patients often require significant time and effort to manage multiple procedures and information, which can delay the progress of their treatment.

[0970] 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.

[0971] In this invention, the server includes means for the user to register an account, means for the user to input medical history and symptoms, means for the user to input specific consultation details, means for the server to send the information received from the user to a generating AI for analysis, means for the generating AI to generate an optimal treatment proposal based on the analysis results, means for the server to send the proposal results from the generating AI to the user, means for the user to electronically pay for treatment, and means for managing appointments with medical institutions. As a result, patients can quickly receive optimal treatment proposals based on their individual medical history and symptoms, and it is also possible to centrally manage appointments with specialists, payment of treatment fees, and collection of post-treatment feedback.

[0972] "Account registration" is the process by which a user officially registers by entering personal information, passwords, and other details necessary to use the system.

[0973] "Medical history" refers to information including medical treatments, diagnoses, and medical history that a user has received in the past.

[0974] "Symptoms" refer to specific conditions or signs that indicate a health problem or ailment the user is currently experiencing.

[0975] "Consultation details" refer to information entered by the user in text format, expressing questions or wishes regarding specific medical policies or treatment methods.

[0976] A "server" is a computer system that receives data entered by users, sends it to a generating AI for analysis, and then returns and manages the results.

[0977] "Generative AI" is an artificial intelligence model that analyzes data based on received medical history, symptoms, and consultation content to generate optimal treatment suggestions.

[0978] "Analysis results" refer to the output, such as treatment suggestions and diagnostic results, obtained by the generating AI through its analysis of information received from the user.

[0979] A "treatment suggestion" is the optimal medical procedure or treatment method recommended to the user based on data analyzed by the generating AI.

[0980] "Electronic payment" refers to an electronic payment method that allows users to safely and efficiently pay for expenses such as medical treatment costs via the internet.

[0981] "Appointment management" is a function that allows users to input and manage appointments with medical institutions and specialists, and to adjust their schedules.

[0982] "Feedback" refers to information that users enter after treatment, including their impressions and evaluations of the treatment's effectiveness and their experience.

[0983] A system for implementing this invention consists of a server, a terminal, and a generated AI model. Specific embodiments for realizing this system are described below.

[0984] Hardware and software

[0985] 1. Hardware:

[0986] Smartphones (compatible with iOS and Android)

[0987] Security module (TPM chip, etc.)

[0988] Server (equipped with high-performance processor and large memory capacity)

[0989] 2. Software:

[0990] Server-side: Python, Django, SQL database, OpenAI API, Stripe API

[0991] Smartphone side: React Native

[0992] System operation

[0993] User registration and login

[0994] Users access a dedicated application from their smartphones and register an account. This process requires them to enter basic information such as their name, contact information, and password, as well as their past medical history. The registered information is sent from the device to the server and stored in the database. Existing users access the system by entering their user ID and password on the login screen and going through the authentication process.

[0995] Entering patient information

[0996] The user enters detailed information about their medical history and current symptoms on the main screen. This information is sent from the device to the server and stored in the database.

[0997] Enter your consultation details

[0998] The user enters their specific question into a text field. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." This information is also sent from the device to the server and stored in the database.

[0999] Generating treatment proposals

[1000] The server sends data to the generating AI based on the received medical history, symptoms, and consultation content. The generating AI analyzes this information and generates optimal treatment suggestions by referring to the latest medical guidelines. The generated treatment suggestions are sent from the server to the terminal and displayed to the user.

[1001] Electronic payment and reservation management

[1002] If the user reviews the treatment proposal and wishes to make an appointment with a specialist, the application displays an appointment entry screen. The user enters their desired date and time, and the server links this information with the medical institution's appointment management system. Furthermore, the Stripe API is used on the terminal to securely process the payment for the treatment costs associated with this appointment electronically.

[1003] Feedback Collection

[1004] After a user receives treatment, the device displays a feedback input screen where the user enters information about the treatment's effectiveness and their experience. This information is also sent to the server and provided to the AI, which helps improve the accuracy of treatment recommendations.

[1005] Specific example

[1006] For example, if user A wants to consult about the next steps in infertility treatment, after registering an account, they would enter "I have a problem with egg quality" in their medical history. Then, they would enter their consultation request, "Please tell me the next steps in infertility treatment." The server sends this information to the AI, which generates a suggestion that "because the quality of eggs may be declining due to age, egg donation should be considered." This suggestion is sent to user A, and they can then make an appointment with a specialist and pay for treatment costs all within the app.

[1007] Example of a prompt

[1008] "User's medical history and symptoms:

[1009] Medical history: {Medical history}

[1010] Symptom: {symptom}

[1011] Consultation details: {Consultation details}

[1012] Please generate the optimal treatment plan based on the latest medical guidelines.

[1013] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1014] Step 1:

[1015] The user enters necessary information such as their name, contact information, password, and medical history on the account registration screen. The device sends this information to the server, which stores the received information in a database. As a result, a new account for the user is created.

[1016] Input: Name, contact information, password, medical history

[1017] Data processing: Format the input information and send it to the server.

[1018] Output: User accounts stored in the database

[1019] Step 2:

[1020] The user enters their user ID and password on the login screen and accesses the system through the authentication process. If authentication is successful, the terminal redirects the user to the main screen.

[1021] Input: User ID, Password

[1022] Data processing: Verification of received authentication information

[1023] Output: User main screen after successful authentication

[1024] Step 3:

[1025] The user enters detailed information about their medical history and current symptoms on the main screen. The device sends this information to the server, which then stores the received information in a database.

[1026] Input: Medical history, symptoms

[1027] Data processing: Saving received information to a database

[1028] Output: Updated medical history and symptoms in the database

[1029] Step 4:

[1030] The user enters their specific inquiry into a text field, the device sends that information to the server, and the server saves the received information to a database.

[1031] Input: Consultation details

[1032] Data processing: Classification and storage of consultation content

[1033] Output: Consultation details stored in the database

[1034] Step 5:

[1035] The server sends the user's medical history, symptoms, and consultation details to the AI. The AI ​​analyzes this information and generates optimal treatment suggestions.

[1036] Input: Medical history, symptoms, consultation details

[1037] Data processing: Data analysis using generative AI

[1038] Output: Treatment suggestion

[1039] Step 6:

[1040] The AI ​​generates treatment suggestions, which are received by the server and sent to the user's device. The device then displays the suggested results to the user.

[1041] Input: Treatment suggestions from generated AI

[1042] Data processing: Formatting and sending of proposal results

[1043] Output: Treatment suggestions displayed on the user's terminal

[1044] Step 7:

[1045] The user reviews the treatment proposal and enters their preferred appointment date and time using the appointment booking screen with the specialist. The terminal sends this information to the server, which then links the received appointment information with the medical institution's appointment management system.

[1046] Input: Desired date and time for reservation

[1047] Data processing: Sending reservation information and linking it with the management system.

[1048] Output: Booking confirmation information

[1049] Step 8:

[1050] To allow users to electronically pay for their treatment appointments, the Stripe API is used on their terminal to process the payment. The server confirms the success of the payment and notifies the user of the result.

[1051] Input: Payment information

[1052] Data processing: Payment processing using the Stripe API

[1053] Output: Payment completion notification

[1054] Step 9:

[1055] After a user receives treatment, the device displays a feedback input screen where the user enters information about the treatment's effectiveness and their experience. The device sends this information to a server, which stores the received feedback in a database and provides it to the generating AI.

[1056] Input: Feedback information

[1057] Data processing: Database storage of feedback information and provision to the generating AI.

[1058] Output: Improved generative AI model

[1059] 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.

[1060] This invention is a system for generating individualized treatment suggestions in infertility treatment, capable of providing personalized treatment suggestions based on the user's medical history and symptoms. Furthermore, by combining it with an emotion engine, it can recognize the user's emotions and provide treatment suggestions and psychological support based on those emotions. The specific program processing of the system is described below in natural language.

[1061] User registration and login

[1062] The terminal displays an account registration screen to the user. The user enters the necessary information, such as name, contact information, password, and past medical history. The terminal verifies this information and sends it to the server. The server saves the received information to its database and creates an account. Existing users enter their user ID and password on the login screen and access the system through the authentication process.

[1063] Entering patient information

[1064] The user enters detailed information about their medical history and current symptoms on the main screen. The device sends this information to the server, which then stores the received information in a database.

[1065] Enter your consultation details

[1066] The user enters their specific question into a text field. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." The terminal sends the input to the server, and the server saves the received information to a database.

[1067] Emotion recognition and the generation of treatment proposals

[1068] The server sends the user's input to the emotion engine, which analyzes the user's emotional state. The emotion engine identifies the user's emotional state (e.g., stress, anxiety, relief) and sends that information to the generating AI. The generating AI comprehensively analyzes the user's medical history, symptoms, consultation content, and emotional state to generate optimal treatment suggestions and psychological support. The generating AI sends the suggested results back to the server, which then sends them to the terminal. The terminal displays the suggested results to the user.

[1069] Collaboration with medical consultation services

[1070] After the user reviews the treatment suggestions generated by the AI ​​and wishes to consult further, they access the booking screen. The user selects the "Consult a specialist" option and enters their desired appointment date and time. The device sends the booking information to the server. The server integrates the received booking information with the medical consultation system. Once the integration is complete, booking confirmation information is sent back to the server. The server sends the booking confirmation information to the device, which then displays it to the user.

[1071] Post-treatment feedback

[1072] After a user receives treatment, the device provides a feedback input screen. The user inputs information about the effectiveness and experience of the treatment, as well as their emotional state. For example, they might input, "I was relieved when my pregnancy was confirmed after the treatment." The device sends the inputted feedback information to the server. The server stores the received feedback information in a database and provides it to the generating AI. The generating AI uses the feedback information to improve the accuracy of its model, thereby improving the accuracy of future treatment suggestions for the user.

[1073] Specific example

[1074] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have problems with egg quality. Next, they enter their questions about the next steps in their infertility treatment. The server sends this information to the emotion engine, which analyzes User A's emotional state (e.g., anxiety). The emotion engine sends the emotional data and medical history to the generating AI, which analyzes it and generates a treatment suggestion such as, "Given the age-related decline in egg quality, egg donation should be considered. We also recommend receiving psychological counseling to alleviate anxiety." The server sends this suggestion to User A, and the terminal displays it to User A.

[1075] If User A reviews the proposal and wishes to consult directly with a specialist, the terminal provides a reservation screen and confirms the reservation in conjunction with the medical consultation system. Through this process, the system can provide users with optimal infertility treatment proposals tailored to their individual circumstances and psychological support based on their emotional state, thereby improving the success rate of treatment.

[1076] The following describes the processing flow.

[1077] Step 1:

[1078] The device displays an account registration screen to the user. The user enters the necessary information, such as their name, contact information, password, and medical history.

[1079] Step 2:

[1080] The terminal verifies the entered information and sends it to the server. The server receives the information, saves it to the database, and creates the account.

[1081] Step 3:

[1082] The user accesses the login screen and enters their user ID and password. The device sends this authentication information to the server.

[1083] Step 4:

[1084] The server compares the received authentication information with the database. If they match, it returns a login success message to the user and directs them to the main screen.

[1085] Step 5:

[1086] The user enters detailed information about their medical history and current symptoms on the main screen. The device then sends the entered information to the server.

[1087] Step 6:

[1088] The server saves the received information to a database. The database stores the user's medical history and symptoms.

[1089] Step 7:

[1090] The user enters specific details of their inquiry, such as "Please tell me about the next steps in infertility treatment." The device then sends the entered information to the server.

[1091] Step 8:

[1092] The server saves the received consultation details to a database and sends them to the emotion engine.

[1093] Step 9:

[1094] The emotion engine analyzes the user's input and identifies their emotional state (stress, anxiety, reassurance, etc.). It then sends the emotional state data back to the server.

[1095] Step 10:

[1096] The server sends emotional state data to the generating AI. The generating AI comprehensively analyzes the user's medical history, symptoms, consultation content, and emotional state to generate optimal treatment suggestions and psychological support.

[1097] Step 11:

[1098] The generating AI sends its suggested results back to the server. The server receives these results and sends them to the terminal.

[1099] Step 12:

[1100] The device displays the suggested results to the user. If the user reviews the suggestions and wishes to discuss further, they access the reservation screen.

[1101] Step 13:

[1102] The user selects the "Consult a specialist" option and enters their desired appointment date and time. The device then sends the appointment information to the server.

[1103] Step 14:

[1104] The server receives the reservation information and links it with the medical consultation system. Once the linkage is complete, reservation confirmation information is sent back to the server.

[1105] Step 15:

[1106] The server sends reservation confirmation information to the terminal. The terminal displays the reservation confirmation information to the user, informing them that the reservation has been confirmed.

[1107] Step 16:

[1108] After the user receives treatment, the device provides a feedback input screen. The user enters information about the effectiveness and experience of the treatment, as well as their emotional state. For example, they might enter, "I was relieved when my pregnancy was confirmed after the treatment."

[1109] Step 17:

[1110] The device sends feedback information to the server. The server stores the received feedback information in a database and provides it to the generating AI.

[1111] Step 18:

[1112] The generating AI improves the accuracy of the model based on feedback information. This will improve the accuracy of treatment suggestions for future users.

[1113] (Example 2)

[1114] 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."

[1115] In infertility treatment, it is crucial to provide effective treatment suggestions based on each patient's medical history and current symptoms. However, conventional systems have the challenge of not being able to provide personalized treatment suggestions and psychological support that take into account each patient's emotional state. Furthermore, there is a lack of mechanisms to improve the accuracy of the generated AI model by utilizing post-treatment feedback information. As a result, it has been difficult to provide highly accurate treatment suggestions.

[1116] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for transmitting user information to an emotion engine and analyzing the emotional state, means for transmitting the emotional data received from the emotion engine and user information to a generating AI and having it perform analysis, and means for the generating AI to generate optimal treatment suggestions and psychological support based on the user's medical history, symptoms, and emotional state. This makes it possible to provide highly accurate treatment suggestions and psychological support based on each user's medical history and emotional state.

[1117] A "user" refers to an individual who uses this system to input information about infertility treatment and receive treatment suggestions and psychological support.

[1118] A "terminal" refers to a computer device or smartphone operated by a user, providing an interface for the user to input information and check results.

[1119] A "server" refers to a central computer system that receives, processes, stores, and analyzes information transmitted from terminals.

[1120] "Medical history" refers to information about medical treatments, diagnoses, and health status that a user has received in the past.

[1121] "Symptoms" refer to the health problems or discomforts that the user is currently experiencing.

[1122] "Consultation content" refers to specific questions or problems that users want to ask the system about.

[1123] An "emotion engine" refers to a computer program that analyzes a user's emotional state (e.g., stress, anxiety, reassurance) based on their input.

[1124] "Generative AI" refers to an artificial intelligence program that analyzes a user's medical history, symptoms, consultation content, and emotional state to automatically generate optimal treatment suggestions and psychological support.

[1125] "Proposal results" refers to information regarding treatment suggestions and psychological support created by the generating AI based on its analysis.

[1126] A "medical consultation service system" refers to a computer system used to manage appointments and consultations with specialists and counselors.

[1127] "Feedback" refers to users entering their thoughts and evaluations regarding the effectiveness and experience of treatment, as well as their emotional state.

[1128] "Model accuracy" refers to an indicator that shows the accuracy and appropriateness of the treatment suggestions and psychological support that the generated AI provides to the user.

[1129] This invention is a system that provides individualized treatment suggestions and psychological support in infertility treatment. The system aims to generate optimal treatment suggestions by comprehensively analyzing the user's medical history, symptoms, consultation content, and emotional state. Specific embodiments of this invention are described below.

[1130] User registration and login

[1131] The terminal displays an account registration screen to the user. The user enters necessary information such as name, contact information, password, and past medical history, and the terminal verifies this information and sends it to the server. The server stores the received information in its database and creates an account. Existing users enter their user ID and password on the login screen to authenticate and access the system.

[1132] Entering patient information

[1133] The user enters detailed information about their medical history and current symptoms on the main screen. The terminal sends this information to the server, which stores the received information in a database.

[1134] Enter your consultation details

[1135] The user enters their specific question into a text field. For example, they might enter, "Please tell me about the next steps in infertility treatment." The terminal sends the entered question to the server, and the server saves the received information in a database.

[1136] Emotion recognition and the generation of treatment proposals

[1137] The server sends the user's input to the emotion engine, which analyzes the user's emotional state. The emotion engine identifies the user's emotional state (e.g., stress, anxiety, relief) and sends this information to the generating AI. The generating AI comprehensively analyzes the user's medical history, symptoms, consultation content, and emotional state to generate optimal treatment suggestions and psychological support. The generating AI sends the suggested results back to the server, which then sends them to the terminal. The terminal displays the suggested results to the user.

[1138] Collaboration with medical consultation services

[1139] After the user reviews the treatment suggestions generated by the AI, if they wish to have a more detailed consultation, they access the reservation screen. The user selects the "Consult with a specialist" option and enters their desired reservation date and time. The device sends the reservation information to the server. The server links the received reservation information with the medical consultation system. Once the linkage is complete, reservation confirmation information is sent back to the server, which then sends the reservation confirmation information to the device, which displays it to the user.

[1140] Post-treatment feedback

[1141] After the user receives treatment, the device provides a feedback input screen. The user inputs information about the effectiveness and experience of the treatment, as well as their emotional state. For example, they might input, "I was relieved when my pregnancy was confirmed after the treatment." The device sends the input feedback information to the server. The server stores the received feedback information in a database and provides it to the generative AI. The generative AI uses the feedback information to improve the accuracy of its model.

[1142] Specific example

[1143] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have problems with egg quality. Next, they enter their questions about the next steps in their infertility treatment. The server sends this information to the emotion engine, which analyzes User A's emotional state (e.g., anxiety). The emotion engine sends the emotional data and medical history to the generating AI, which analyzes it and generates a treatment suggestion such as, "Given the age-related decline in egg quality, egg donation should be considered. We also recommend receiving psychological counseling to alleviate anxiety." The server sends this suggestion to User A, and the terminal displays it to User A.

[1144] Example of a prompt:

[1145] "The user's medical history is as follows: There are issues with egg quality. Current symptoms or concerns are as follows: Please advise on the next steps in fertility treatment. The user is currently experiencing anxiety. Based on this information, please provide optimal treatment suggestions and psychological support."

[1146] This invention makes it possible to provide highly accurate treatment suggestions and psychological support based on the user's medical history and emotional state.

[1147] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1148] Step 1: User registration and login

[1149] Input: The user enters their name, contact information, password, and medical history on the account registration screen.

[1150] Operation: The terminal validates the input information and sends it to the server.

[1151] Data processing / calculation: The server receives validated information and saves it to the database.

[1152] Output: The server sends the account creation result back to the terminal and notifies the user that registration is complete.

[1153] Step 2: Enter patient information

[1154] Input: The user enters their medical history and current symptom information on the main screen.

[1155] Operation: The terminal sends the input information to the server.

[1156] Data processing / calculation: The server saves the information it receives to the database.

[1157] Output: The server notifies the terminal that the information has been saved and displays a confirmation message to the user.

[1158] Step 3: Enter your consultation details

[1159] Input: The user enters their question (e.g., "Please tell me about the next steps in infertility treatment") into the text field.

[1160] Operation: The terminal sends the input content to the server.

[1161] Data processing / calculation: The server saves the received consultation details to the database.

[1162] Output: The server notifies the terminal that the consultation details have been saved and displays a confirmation message to the user.

[1163] Step 4: Emotion recognition and generation of treatment proposals

[1164] Input: The server sends the user's medical history, symptoms, and consultation details to the emotion engine.

[1165] Operation: The emotion engine analyzes the emotional state.

[1166] Data processing / calculation: The emotion engine identifies emotional states (e.g., anxiety) and sends them to the generating AI.

[1167] Output: The generating AI generates treatment suggestions and psychological support based on the received data and sends the results back to the server.

[1168] Step 5: Providing treatment proposals

[1169] Input: The server receives the suggested results from the generated AI.

[1170] Operation: The server sends the suggestion results to the terminal.

[1171] Data Processing / Calculation: The server aggregates and organizes user-specific data and provides suggested results in an easy-to-understand format.

[1172] Output: The terminal displays the suggested results to the user. For example, a message such as, "Due to the possibility of a decline in egg quality due to age, egg donation should be considered. We also recommend receiving psychological counseling to alleviate anxiety," might be displayed.

[1173] Step 6: Collaboration with medical consultation services

[1174] Input: The user selects the "Consult a specialist" option and enters the appointment date and time.

[1175] Operation: The device sends reservation information to the server.

[1176] Data processing / calculation: The server sends the reservation information to the medical consultation system and confirms the reservation.

[1177] Output: The server sends reservation confirmation information to the terminal and displays it to the user.

[1178] Step 7: Post-treatment feedback

[1179] Input: The user enters feedback after treatment (e.g., "I was relieved when my pregnancy was confirmed after treatment").

[1180] Action: The device sends feedback information to the server.

[1181] Data processing / calculation: The server stores feedback information in a database and provides it to the generating AI.

[1182] Output: The server notifies the terminal that the feedback has been saved and displays a confirmation message to the user.

[1183] At each step, the user, terminal, and server work together to create a system that provides highly accurate treatment suggestions.

[1184] (Application Example 2)

[1185] 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."

[1186] In infertility treatment, personalized treatment suggestions based on the patient's medical history and symptoms are crucial, but traditional systems have lacked adequate psychological support. Furthermore, there is a need for an efficient method to analyze a patient's emotional state before they visit the clinic and to provide appropriate treatment suggestions and appointment scheduling.

[1187] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to register an account, means for the user to input medical history and symptoms, means for the user to input specific consultation details, means for the emotion engine to analyze the user's emotional state, means for the server to send the information received from the user to the generating AI for analysis, means for the generating AI to generate an optimal treatment proposal based on the analysis results, and means for the generating AI to display reservation information for face-to-face appointments to the user. This enables personalized treatment proposals and reservation coordination based on the user's emotional state.

[1188] "User" refers to an individual or group that uses the system, and in particular to those who receive consultations and treatment suggestions regarding infertility treatment.

[1189] "Account registration" refers to the process by which a user provides their basic information to the system and obtains a unique identification ID.

[1190] "Medical history" refers to a detailed record of treatments and diagnoses the user has received in the past.

[1191] "Symptoms" refer to the physical or mental problems or conditions that the user is currently experiencing.

[1192] "Consultation content" refers to the specific questions and requests that users ask through the system.

[1193] An "emotion engine" refers to software that analyzes user input data and identifies their emotional state.

[1194] A "server" refers to a remote computer system that processes and stores information received from users.

[1195] "Generative AI" refers to an artificial intelligence algorithm that automatically generates optimal treatment suggestions based on received data.

[1196] "Analysis results" refers to the output obtained after analyzing user data using a generative AI or emotion engine.

[1197] "Treatment suggestions" refer to specific treatment methods and support provided to the user based on their medical history, symptoms, consultation content, and emotional state.

[1198] "Reservation information" refers to data that users enter to book an in-person consultation with a specialist, indicating the date, time, and desired details.

[1199] The term "medical consultation service system" refers to a backend system for managing and processing appointments for consultations with medical professionals.

[1200] "Feedback" refers to data that represents the evaluation and opinions of users regarding the treatments and suggestions they received.

[1201] "Model accuracy" refers to an evaluation criterion that indicates the accuracy and suitability of the treatment suggestions provided by the generated AI.

[1202] This invention is a system for users to receive personalized treatment suggestions regarding infertility treatment, and detailed embodiments are shown below.

[1203] First, the user registers an account using their device. The user enters basic information such as their name, contact information, and past medical history. The entered information is sent from the device to the server and stored in the database.

[1204] Next, the user enters their medical history, current symptoms, and specific questions via their device. This information is also sent from the device to the server and stored in the database.

[1205] A key element of this system is the inclusion of an emotion engine, which analyzes the user's emotional state from their input data. The emotion engine analyzes the user's text input and behavioral data to identify their emotional state (e.g., anxiety, reassurance, etc.). This analysis result is then transmitted to the generating AI via a server.

[1206] The generating AI creates optimal treatment suggestions based on the user's medical history, symptoms, consultation content, and emotional state. The generating AI uses the latest medical guidelines and past patient data to generate specific and personalized treatment suggestions. The results of this AI analysis are transmitted from the server to the user's terminal and displayed.

[1207] Furthermore, if a user wishes to have an in-person consultation with a specialist, they enter reservation information based on the suggestions generated by the AI. The server then links this reservation information with the medical consultation system and sends reservation confirmation information to the user. This process allows users to receive consultation from a specialist at the appropriate time.

[1208] Furthermore, users provide feedback after treatment. This feedback information is sent to the server and provided to the generating AI. The generating AI uses this feedback data to improve the accuracy of its model. This continuous feedback loop allows the system to provide more accurate treatment suggestions.

[1209] As a concrete example, let's consider the case where User B uses the system for the first time. User B creates an account on their smartphone and enters their medical history regarding past infertility treatments. For example, they might enter that they have problems with ovarian function. Next, they enter a question about the next steps in their infertility treatment. Let's say they enter, "Please tell me about the next steps in my infertility treatment." The emotion engine analyzes User B's input to understand their anxiety and sends it to the generating AI. Based on the medical history and emotional state, the generating AI generates a treatment suggestion such as, "Ovarian dysfunction is suspected, so consider specific drug treatments. We also recommend counseling to reduce anxiety." The server sends this suggestion to User B, and it is displayed on their smartphone.

[1210] Examples of prompt statements include the following:

[1211] User's medical history: Female, 35 years old, underwent two artificial insemination procedures in the past three years. Has ovarian dysfunction.

[1212] Inquiry topic: I would like to know about the next steps in my treatment.

[1213] Emotional state: Anxiety.

[1214] Please generate treatment suggestions based on the generated AI.

[1215] This system allows users to receive personalized infertility treatment suggestions and psychological support based on their emotional state, potentially improving the success rate of treatment.

[1216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1217] Step 1:

[1218] The user registers an account. The user uses their device to enter basic information such as their name, contact information, and medical history. The device sends the entered information to the server, which stores this information in a database. Input data: Name, contact information, medical history. Output: Notification of successful account creation.

[1219] Step 2:

[1220] The user inputs their medical history, current symptoms, and specific consultation details. The terminal sends this information to the server, which stores it in a database. Input data: medical history, symptoms, consultation details. Output: information is stored in the database.

[1221] Step 3:

[1222] The server sends the information received from the user to the emotion engine. The emotion engine analyzes the user's input data and identifies the emotional state (e.g., anxiety, reassurance). It then sends the analysis results back to the server. Input data: Medical history, consultation content. Output result: Analysis results of the emotional state.

[1223] Step 4:

[1224] The server sends emotional state data obtained from the emotion engine, along with the user's medical history and consultation details, to the generating AI. The generating AI then uses this information to create optimal treatment suggestions. Input data: medical history, symptoms, consultation details, emotional state. Output result: treatment suggestions.

[1225] Step 5:

[1226] The server receives treatment suggestions from the generated AI and sends them to the user's device. The device displays this information to the user. Input data: Treatment suggestions. Output result: Display of the suggested content.

[1227] Step 6:

[1228] If a user wishes to have an in-person consultation with a specialist, they enter reservation information based on the suggestions generated by the AI. The terminal sends this reservation information to the server, which then connects it with the medical consultation system. Once the connection is complete, the server sends reservation confirmation information to the user. Input data: Reservation information. Output result: Reservation confirmation information.

[1229] Step 7:

[1230] The user enters feedback after treatment. The device sends this feedback information to the server, which provides it to the generating AI. The generating AI uses this feedback data to improve the accuracy of its model. Input data: Feedback information. Output result: Improved model accuracy.

[1231] As a concrete example, the following prompt is sent to the generating AI:

[1232] User's medical history: Female, 35 years old, underwent two artificial insemination procedures in the past three years. Has ovarian dysfunction.

[1233] Inquiry topic: I would like to know about the next steps in my treatment.

[1234] Emotional state: Anxiety.

[1235] Please generate treatment suggestions based on the generated AI.

[1236] Through this process, users can receive personalized infertility treatment recommendations and psychological support based on their emotional state.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] [Fourth Embodiment]

[1241] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1242] 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.

[1243] 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).

[1244] 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.

[1245] 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.

[1246] 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).

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] 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.

[1253] 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".

[1254] This invention relates to a system for generating individualized treatment suggestions in infertility treatment, which can provide personalized treatment suggestions based on the user's medical history and symptoms. The specific program processing of the system is described below in natural language.

[1255] User registration and login

[1256] The terminal provides the user with an account registration screen. The user enters necessary information such as name, contact information, password, and past medical history. The terminal verifies this information and sends it to the server. The server stores the received information in its database and creates an account. Existing users enter their user ID and password on the login screen and access the system through the authentication process.

[1257] Entering patient information

[1258] The user enters detailed information about their medical history and current symptoms on the main screen. The device sends this information to the server, which then stores the received information in a database.

[1259] Enter your consultation details

[1260] The user enters their specific question into a text field. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." The terminal sends the input to the server, and the server saves the received information to a database.

[1261] Generating treatment proposals

[1262] The server sends the user's medical history, symptoms, and consultation details to the generating AI. The generating AI analyzes this information and generates optimal treatment suggestions by referring to the latest medical guidelines. The generating AI sends the suggested results back to the server, which then sends them to the terminal. The terminal displays the suggested results to the user.

[1263] Collaboration with medical consultation services

[1264] After the user reviews the treatment suggestions generated by the AI, if they wish to consult further, the device provides an option to "consult a specialist." The user enters their desired appointment date and time, and the device sends this information to the server. The server integrates the received appointment information with the medical consultation system. The server sends appointment confirmation information to the device, which then displays it to the user.

[1265] Post-treatment feedback

[1266] After a user receives treatment, the device provides a feedback input screen. The user inputs information about the treatment's effectiveness and experience, and sends this information to the server. The server stores the received feedback information in a database and provides it again to the generating AI, contributing to improving the model's accuracy.

[1267] Specific example

[1268] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have had problems with egg quality. Next, they enter their questions regarding the next steps in their infertility treatment.

[1269] The server sends this information to the AI, which analyzes it and generates a treatment suggestion stating, "Given the likely decline in egg quality due to age, egg donation should be considered." The server then sends this suggestion to user A, and the terminal displays it to user A.

[1270] If User A reviews the proposal and wishes to consult directly with a specialist, the terminal will provide a booking screen and confirm the booking in conjunction with the medical consultation system.

[1271] Through this series of processes, the system can propose the most suitable infertility treatment to the user based on their individual circumstances, thereby improving the success rate of the treatment.

[1272] The following describes the processing flow.

[1273] Step 1:

[1274] The device displays an account registration screen to the user. The user enters the necessary information, such as their name, contact information, password, and medical history.

[1275] Step 2:

[1276] The terminal verifies the entered information and sends it to the server. The server saves the information to its database and creates an account.

[1277] Step 3:

[1278] The user accesses the login screen and enters their user ID and password. The device sends this authentication information to the server.

[1279] Step 4:

[1280] The server compares the received authentication information with the database. If they match, it returns a login success message to the user and directs them to the main screen.

[1281] Step 5:

[1282] The user enters detailed information about their medical history and current symptoms on the main screen. The device then sends the entered information to the server.

[1283] Step 6:

[1284] The server saves the received information to a database. The database stores the user's medical history and symptoms.

[1285] Step 7:

[1286] The user enters specific details of their inquiry, such as "Please tell me about the next steps in infertility treatment." The device then sends the entered information to the server.

[1287] Step 8:

[1288] The server saves the received consultation details to a database and prepares the data for transmission to the generating AI.

[1289] Step 9:

[1290] The server sends the user's medical history, symptoms, and consultation details to the generating AI. The generating AI analyzes this information and generates optimal treatment suggestions based on the latest medical guidelines.

[1291] Step 10:

[1292] The generating AI sends its suggested results back to the server. The server receives these results and sends them to the user's device for display.

[1293] Step 11:

[1294] The device displays the suggested results to the user. If the user reviews the suggestions and wishes to discuss further, they access the reservation screen.

[1295] Step 12:

[1296] The user selects the "Consult a specialist" option and enters their desired appointment date and time. The device then sends the appointment information to the server.

[1297] Step 13:

[1298] The server receives the reservation information and links it with the medical consultation system. Once the linkage is complete, reservation confirmation information is sent back to the server.

[1299] Step 14:

[1300] The server sends reservation confirmation information to the terminal. The terminal displays the reservation confirmation information to the user, informing them that the reservation has been confirmed.

[1301] Step 15:

[1302] After the user receives treatment, the device provides a feedback input screen. The user enters information about the effectiveness and experience of the treatment.

[1303] Step 16:

[1304] The device sends feedback information to the server. The server stores the received feedback information in a database and provides it to the generating AI.

[1305] Step 17:

[1306] The generating AI improves the accuracy of the model based on feedback information. This will improve the accuracy of treatment suggestions for future users.

[1307] (Example 1)

[1308] 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".

[1309] In modern infertility treatment, it is difficult to propose personalized treatment methods based on each patient's unique symptoms and medical history, and general treatment methods tend to be used frequently. As a result, there are cases where the treatment is not sufficiently effective or where the patient's needs are not met. Furthermore, the lack of a system for properly collecting and utilizing post-treatment feedback makes it difficult to improve the quality of treatment.

[1310] 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.

[1311] In this invention, the server includes means for the user to register an account, means for the user to input medical history and symptoms, means for the user to input specific consultation details, means for the server to send the information received from the user to a generating AI for analysis, means for the generating AI to generate an optimal treatment proposal based on the analysis results, means for the server to send the proposal results from the generating AI to the user, and means for the terminal to display the proposal results to the user. This makes it possible to personalize and propose the optimal infertility treatment method according to the individual circumstances of the user.

[1312] A "user" is an individual who inputs their medical history and symptoms through the system and receives treatment suggestions.

[1313] "Account registration" is the process by which a user registers information such as their name, contact details, and password with the system.

[1314] "Medical history" refers to records of diagnoses and treatments a user has received in the past.

[1315] "Symptoms" refer to the specific health conditions or problems that a user is currently experiencing.

[1316] "Consultation content" refers to the specific questions or inquiries that users input through the system.

[1317] A "server" is a device that receives information from users and sends it to a generating AI for analysis.

[1318] "Generative AI" is artificial intelligence that analyzes received information and generates optimal treatment suggestions.

[1319] "Proposal results" refer to the content of the treatment suggestions generated by the AI ​​as a result of its analysis.

[1320] A "terminal" is a device used by users to input information or to display suggested results sent from a server.

[1321] "Appointment information" refers to information such as the date and time a user wishes to consult with a specialist.

[1322] The "Medical Consultation System" is a system that receives users' appointment information and coordinates with specialists.

[1323] "Feedback information" refers to information about the effectiveness and experience of treatment that users enter after treatment.

[1324] "Model accuracy" is an indicator that represents the accuracy and reliability of the analysis and suggestions of the generated AI.

[1325] "Medical guidelines" are standards that outline recommended treatment methods and procedures based on the latest medical knowledge.

[1326] The present invention is a system for generating individual treatment suggestions in infertility treatment, providing personalized treatment suggestions based on the user's medical history and symptoms. The following processes are performed as a concrete embodiment of this invention.

[1327] First, the user accesses the system through their device. The user enters information such as their name, contact information, password, and medical history on the account registration screen. The device checks the format of this information and sends it to the server. The server stores the received information in its database and creates an account. Existing users enter their user ID and password on the login screen and complete the authentication process.

[1328] The user enters detailed medical history and current symptoms on the main screen. The terminal checks the format of this information and sends it to the server. The server stores the received information in a database.

[1329] Next, the user enters their specific question. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." The terminal checks the format of the input and sends it to the server. The server saves the received question to its database.

[1330] This process involves the server sending the user's medical history, symptoms, and consultation details to a generating AI model. The generating AI model analyzes this information and generates optimal treatment suggestions by referring to the latest medical guidelines. The generating AI model sends its suggestions back to the server, which then sends them to the terminal. The terminal then displays the suggestions from the generating AI model to the user.

[1331] If the user reviews the treatment suggestions from the generated AI model and wishes to consult further, the terminal provides an option to "consult a specialist." When the user enters their desired appointment date and time, the terminal sends that information to the server. The server integrates the received appointment information with the medical consultation system and sends appointment confirmation information to the terminal. The terminal then displays the appointment confirmation information to the user.

[1332] After treatment, the user enters information about the treatment's effectiveness and experience through a feedback input screen. The device checks the format of the feedback and sends it to the server. The server stores the received feedback information in a database and provides it to the generated AI model, contributing to improving the model's accuracy.

[1333] Specific example

[1334] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have problems with egg quality. Next, they enter their questions regarding the next steps in their infertility treatment. The server sends this information to an AI model, which analyzes it and generates a treatment suggestion such as, "Given the decline in egg quality due to age, egg donation should be considered." The server sends this suggestion to User A, and the terminal displays it to User A. If User A reviews the suggestion and wishes to consult directly with a specialist, the terminal provides a booking screen and confirms the appointment in conjunction with the medical consultation system. Through this entire process, the system can provide users with optimal infertility treatment suggestions tailored to their individual circumstances, thereby improving the success rate of treatment.

[1335] Example of a prompt

[1336] Please describe the process of a system that takes user medical history, current symptoms, and specific consultation details as input, and then uses a generative AI model to generate optimal treatment suggestions.

[1337] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1338] Step 1:

[1339] The user opens the account registration screen and enters information such as name, contact information, password, and medical history. The device verifies this information and performs a format check. Input: User registration information. Output: User information that passed the format check.

[1340] Step 2:

[1341] The terminal sends user information that has passed format checks to the server. The server saves the received information to a database and creates an account. Input: Format-checked user information. Output: User information saved to the database.

[1342] Step 3:

[1343] An existing user enters their user ID and password on the login screen. The device sends this information to the server. Input: User ID and password. Output: Login request to the server.

[1344] Step 4:

[1345] The server verifies the received user ID and password against the information in the database. If authentication is successful, the session is started. Input: User ID and password. Output: Authentication result and session start.

[1346] Step 5:

[1347] The user enters detailed medical history and current symptoms on the main screen. The terminal checks this information for formatting and sends it to the server. Input: Medical history and symptoms. Output: Medical history and symptom information that has passed the formatting check.

[1348] Step 6:

[1349] The server saves the received medical history and symptom information to the database. Input: Format-checked medical history and symptom information. Output: Medical history and symptom information saved to the database.

[1350] Step 7:

[1351] The user enters their specific question. For example, they might enter, "Please tell me about the next steps in infertility treatment." The terminal checks the format of the input and sends it to the server. Input: Question. Output: Question that passed the format check.

[1352] Step 8:

[1353] The server saves the received consultation content to the database. Input: Format-checked consultation content. Output: Consultation content saved to the database.

[1354] Step 9:

[1355] The server sends the user's medical history, symptoms, and consultation details to a generating AI model. The generating AI model analyzes this information and, referencing the latest medical guidelines, generates optimal treatment suggestions. Input: Medical history, symptoms, consultation details. Output: Treatment suggestions generated by the generating AI.

[1356] Step 10:

[1357] The server receives the proposed results from the generated AI model and sends them to the terminal. Input: Treatment proposal from the generated AI. Output: Sending the proposed results to the terminal.

[1358] Step 11:

[1359] The terminal displays the received suggestion results to the user. Input: Suggestion results received from the server. Output: Display of suggestion results to the user.

[1360] Step 12:

[1361] If the user wishes to consult further, the device will offer an option to "Consult a specialist" and allow the user to enter their preferred appointment date and time. Input: Preferred appointment date and time. Output: Appointment information.

[1362] Step 13:

[1363] The terminal checks the format of the entered reservation request date and time and sends it to the server. Input: Format-checked reservation request date and time. Output: Reservation information sent to the server.

[1364] Step 14:

[1365] The server receives the reservation information and links it with the medical consultation system to receive reservation confirmation information. Input: Reservation information. Output: Reservation confirmation information from the medical consultation system.

[1366] Step 15:

[1367] The server sends reservation confirmation information to the terminal, and the terminal displays the reservation confirmation information to the user. Input: Reservation confirmation information. Output: Display of reservation confirmation information to the user.

[1368] Step 16:

[1369] After treatment, the user enters information about the treatment's effectiveness and experience on a feedback input screen. The device then performs a format check on the feedback content and sends it to the server. Input: Feedback content. Output: Feedback content that has passed the format check.

[1370] Step 17:

[1371] The server saves the received feedback information to a database and provides it to the generating AI model. Input: Format-checked feedback information. Output: Saving of feedback information to the database and providing it to the generating AI model.

[1372] These processing steps enable the system to generate optimal treatment suggestions based on the user's medical history and symptoms, thereby improving the success rate of treatment.

[1373] (Application Example 1)

[1374] 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".

[1375] In infertility treatment, the current system for providing patients with optimal treatment recommendations based on their individual medical history and symptoms is not adequately developed. Furthermore, there is a lack of integrated systems to efficiently and smoothly manage the entire process, including post-treatment appointments with specialists, payment of treatment fees, and feedback collection. As a result, patients often require significant time and effort to manage multiple procedures and information, which can delay the progress of their treatment.

[1376] 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.

[1377] In this invention, the server includes means for the user to register an account, means for the user to input medical history and symptoms, means for the user to input specific consultation details, means for the server to send the information received from the user to a generating AI for analysis, means for the generating AI to generate an optimal treatment proposal based on the analysis results, means for the server to send the proposal results from the generating AI to the user, means for the user to electronically pay for treatment, and means for managing appointments with medical institutions. As a result, patients can quickly receive optimal treatment proposals based on their individual medical history and symptoms, and it is also possible to centrally manage appointments with specialists, payment of treatment fees, and collection of post-treatment feedback.

[1378] "Account registration" is the process by which a user officially registers by entering personal information, passwords, and other details necessary to use the system.

[1379] "Medical history" refers to information including medical treatments, diagnoses, and medical history that a user has received in the past.

[1380] "Symptoms" refer to specific conditions or signs that indicate a health problem or ailment the user is currently experiencing.

[1381] "Consultation details" refer to information entered by the user in text format, expressing questions or wishes regarding specific medical policies or treatment methods.

[1382] A "server" is a computer system that receives data entered by users, sends it to a generating AI for analysis, and then returns and manages the results.

[1383] "Generative AI" is an artificial intelligence model that analyzes data based on received medical history, symptoms, and consultation content to generate optimal treatment suggestions.

[1384] "Analysis results" refer to the output, such as treatment suggestions and diagnostic results, obtained by the generating AI through its analysis of information received from the user.

[1385] A "treatment suggestion" is the optimal medical procedure or treatment method recommended to the user based on data analyzed by the generating AI.

[1386] "Electronic payment" refers to an electronic payment method that allows users to safely and efficiently pay for expenses such as medical treatment costs via the internet.

[1387] "Appointment management" is a function that allows users to input and manage appointments with medical institutions and specialists, and to adjust their schedules.

[1388] "Feedback" refers to information that users enter after treatment, including their impressions and evaluations of the treatment's effectiveness and their experience.

[1389] A system for implementing this invention consists of a server, a terminal, and a generated AI model. Specific embodiments for realizing this system are described below.

[1390] Hardware and software

[1391] 1. Hardware:

[1392] Smartphones (compatible with iOS and Android)

[1393] Security module (TPM chip, etc.)

[1394] Server (equipped with high-performance processor and large memory capacity)

[1395] 2. Software:

[1396] Server-side: Python, Django, SQL database, OpenAI API, Stripe API

[1397] Smartphone side: React Native

[1398] System operation

[1399] User registration and login

[1400] Users access a dedicated application from their smartphones and register an account. This process requires them to enter basic information such as their name, contact information, and password, as well as their past medical history. The registered information is sent from the device to the server and stored in the database. Existing users access the system by entering their user ID and password on the login screen and going through the authentication process.

[1401] Entering patient information

[1402] The user enters detailed information about their medical history and current symptoms on the main screen. This information is sent from the device to the server and stored in the database.

[1403] Enter your consultation details

[1404] The user enters their specific question into a text field. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." This information is also sent from the device to the server and stored in the database.

[1405] Generating treatment proposals

[1406] The server sends data to the generating AI based on the received medical history, symptoms, and consultation content. The generating AI analyzes this information and generates optimal treatment suggestions by referring to the latest medical guidelines. The generated treatment suggestions are sent from the server to the terminal and displayed to the user.

[1407] Electronic payment and reservation management

[1408] If the user reviews the treatment proposal and wishes to make an appointment with a specialist, the application displays an appointment entry screen. The user enters their desired date and time, and the server links this information with the medical institution's appointment management system. Furthermore, the Stripe API is used on the terminal to securely process the payment for the treatment costs associated with this appointment electronically.

[1409] Feedback Collection

[1410] After a user receives treatment, the device displays a feedback input screen where the user enters information about the treatment's effectiveness and their experience. This information is also sent to the server and provided to the AI, which helps improve the accuracy of treatment recommendations.

[1411] Specific example

[1412] For example, if user A wants to consult about the next steps in infertility treatment, after registering an account, they would enter "I have a problem with egg quality" in their medical history. Then, they would enter their consultation request, "Please tell me the next steps in infertility treatment." The server sends this information to the AI, which generates a suggestion that "because the quality of eggs may be declining due to age, egg donation should be considered." This suggestion is sent to user A, and they can then make an appointment with a specialist and pay for treatment costs all within the app.

[1413] Example of a prompt

[1414] "User's medical history and symptoms:

[1415] Medical history: {Medical history}

[1416] Symptom: {symptom}

[1417] Consultation details: {Consultation details}

[1418] Please generate the optimal treatment plan based on the latest medical guidelines.

[1419] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1420] Step 1:

[1421] The user enters necessary information such as their name, contact information, password, and medical history on the account registration screen. The device sends this information to the server, which stores the received information in a database. As a result, a new account for the user is created.

[1422] Input: Name, contact information, password, medical history

[1423] Data processing: Format the input information and send it to the server.

[1424] Output: User accounts stored in the database

[1425] Step 2:

[1426] The user enters their user ID and password on the login screen and accesses the system through the authentication process. If authentication is successful, the terminal redirects the user to the main screen.

[1427] Input: User ID, Password

[1428] Data processing: Verification of received authentication information

[1429] Output: User main screen after successful authentication

[1430] Step 3:

[1431] The user enters detailed information about their medical history and current symptoms on the main screen. The device sends this information to the server, which then stores the received information in a database.

[1432] Input: Medical history, symptoms

[1433] Data processing: Saving received information to a database

[1434] Output: Updated medical history and symptoms in the database

[1435] Step 4:

[1436] The user enters their specific inquiry into a text field, the device sends that information to the server, and the server saves the received information to a database.

[1437] Input: Consultation details

[1438] Data processing: Classification and storage of consultation content

[1439] Output: Consultation details stored in the database

[1440] Step 5:

[1441] The server sends the user's medical history, symptoms, and consultation details to the AI. The AI ​​analyzes this information and generates optimal treatment suggestions.

[1442] Input: Medical history, symptoms, consultation details

[1443] Data processing: Data analysis using generative AI

[1444] Output: Treatment suggestion

[1445] Step 6:

[1446] The AI ​​generates treatment suggestions, which are received by the server and sent to the user's device. The device then displays the suggested results to the user.

[1447] Input: Treatment suggestions from generated AI

[1448] Data processing: Formatting and sending of proposal results

[1449] Output: Treatment suggestions displayed on the user's terminal

[1450] Step 7:

[1451] The user reviews the treatment proposal and enters their preferred appointment date and time using the appointment booking screen with the specialist. The terminal sends this information to the server, which then links the received appointment information with the medical institution's appointment management system.

[1452] Input: Desired date and time for reservation

[1453] Data processing: Sending reservation information and linking it with the management system.

[1454] Output: Booking confirmation information

[1455] Step 8:

[1456] To allow users to electronically pay for their treatment appointments, the Stripe API is used on their terminal to process the payment. The server confirms the success of the payment and notifies the user of the result.

[1457] Input: Payment information

[1458] Data processing: Payment processing using the Stripe API

[1459] Output: Payment completion notification

[1460] Step 9:

[1461] After a user receives treatment, the device displays a feedback input screen where the user enters information about the treatment's effectiveness and their experience. The device sends this information to a server, which stores the received feedback in a database and provides it to the generating AI.

[1462] Input: Feedback information

[1463] Data processing: Database storage of feedback information and provision to the generating AI.

[1464] Output: Improved generative AI model

[1465] 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.

[1466] This invention is a system for generating individualized treatment suggestions in infertility treatment, capable of providing personalized treatment suggestions based on the user's medical history and symptoms. Furthermore, by combining it with an emotion engine, it can recognize the user's emotions and provide treatment suggestions and psychological support based on those emotions. The specific program processing of the system is described below in natural language.

[1467] User registration and login

[1468] The terminal displays an account registration screen to the user. The user enters the necessary information, such as name, contact information, password, and past medical history. The terminal verifies this information and sends it to the server. The server saves the received information to its database and creates an account. Existing users enter their user ID and password on the login screen and access the system through the authentication process.

[1469] Entering patient information

[1470] The user enters detailed information about their medical history and current symptoms on the main screen. The device sends this information to the server, which then stores the received information in a database.

[1471] Enter your consultation details

[1472] The user enters their specific question into a text field. For example, they might enter a question like, "Please tell me about the next steps in infertility treatment." The terminal sends the input to the server, and the server saves the received information to a database.

[1473] Emotion recognition and the generation of treatment proposals

[1474] The server sends the user's input to the emotion engine, which analyzes the user's emotional state. The emotion engine identifies the user's emotional state (e.g., stress, anxiety, relief) and sends that information to the generating AI. The generating AI comprehensively analyzes the user's medical history, symptoms, consultation content, and emotional state to generate optimal treatment suggestions and psychological support. The generating AI sends the suggested results back to the server, which then sends them to the terminal. The terminal displays the suggested results to the user.

[1475] Collaboration with medical consultation services

[1476] After the user reviews the treatment suggestions generated by the AI ​​and wishes to consult further, they access the booking screen. The user selects the "Consult a specialist" option and enters their desired appointment date and time. The device sends the booking information to the server. The server integrates the received booking information with the medical consultation system. Once the integration is complete, booking confirmation information is sent back to the server. The server sends the booking confirmation information to the device, which then displays it to the user.

[1477] Post-treatment feedback

[1478] After a user receives treatment, the device provides a feedback input screen. The user inputs information about the effectiveness and experience of the treatment, as well as their emotional state. For example, they might input, "I was relieved when my pregnancy was confirmed after the treatment." The device sends the inputted feedback information to the server. The server stores the received feedback information in a database and provides it to the generating AI. The generating AI uses the feedback information to improve the accuracy of its model, thereby improving the accuracy of future treatment suggestions for the user.

[1479] Specific example

[1480] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have problems with egg quality. Next, they enter their questions about the next steps in their infertility treatment. The server sends this information to the emotion engine, which analyzes User A's emotional state (e.g., anxiety). The emotion engine sends the emotional data and medical history to the generating AI, which analyzes it and generates a treatment suggestion such as, "Given the age-related decline in egg quality, egg donation should be considered. We also recommend receiving psychological counseling to alleviate anxiety." The server sends this suggestion to User A, and the terminal displays it to User A.

[1481] If User A reviews the proposal and wishes to consult directly with a specialist, the terminal provides a reservation screen and confirms the reservation in conjunction with the medical consultation system. Through this process, the system can provide users with optimal infertility treatment proposals tailored to their individual circumstances and psychological support based on their emotional state, thereby improving the success rate of treatment.

[1482] The following describes the processing flow.

[1483] Step 1:

[1484] The device displays an account registration screen to the user. The user enters the necessary information, such as their name, contact information, password, and medical history.

[1485] Step 2:

[1486] The terminal verifies the entered information and sends it to the server. The server receives the information, saves it to the database, and creates the account.

[1487] Step 3:

[1488] The user accesses the login screen and enters their user ID and password. The device sends this authentication information to the server.

[1489] Step 4:

[1490] The server compares the received authentication information with the database. If they match, it returns a login success message to the user and directs them to the main screen.

[1491] Step 5:

[1492] The user enters detailed information about their medical history and current symptoms on the main screen. The device then sends the entered information to the server.

[1493] Step 6:

[1494] The server saves the received information to a database. The database stores the user's medical history and symptoms.

[1495] Step 7:

[1496] The user enters specific details of their inquiry, such as "Please tell me about the next steps in infertility treatment." The device then sends the entered information to the server.

[1497] Step 8:

[1498] The server saves the received consultation details to a database and sends them to the emotion engine.

[1499] Step 9:

[1500] The emotion engine analyzes the user's input and identifies their emotional state (stress, anxiety, reassurance, etc.). It then sends the emotional state data back to the server.

[1501] Step 10:

[1502] The server sends emotional state data to the generating AI. The generating AI comprehensively analyzes the user's medical history, symptoms, consultation content, and emotional state to generate optimal treatment suggestions and psychological support.

[1503] Step 11:

[1504] The generating AI sends its suggested results back to the server. The server receives these results and sends them to the terminal.

[1505] Step 12:

[1506] The device displays the suggested results to the user. If the user reviews the suggestions and wishes to discuss further, they access the reservation screen.

[1507] Step 13:

[1508] The user selects the "Consult a specialist" option and enters their desired appointment date and time. The device then sends the appointment information to the server.

[1509] Step 14:

[1510] The server receives the reservation information and links it with the medical consultation system. Once the linkage is complete, reservation confirmation information is sent back to the server.

[1511] Step 15:

[1512] The server sends reservation confirmation information to the terminal. The terminal displays the reservation confirmation information to the user, informing them that the reservation has been confirmed.

[1513] Step 16:

[1514] After the user receives treatment, the device provides a feedback input screen. The user enters information about the effectiveness and experience of the treatment, as well as their emotional state. For example, they might enter, "I was relieved when my pregnancy was confirmed after the treatment."

[1515] Step 17:

[1516] The device sends feedback information to the server. The server stores the received feedback information in a database and provides it to the generating AI.

[1517] Step 18:

[1518] The generating AI improves the accuracy of the model based on feedback information. This will improve the accuracy of treatment suggestions for future users.

[1519] (Example 2)

[1520] 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".

[1521] In infertility treatment, it is crucial to provide effective treatment suggestions based on each patient's medical history and current symptoms. However, conventional systems have the challenge of not being able to provide personalized treatment suggestions and psychological support that take into account each patient's emotional state. Furthermore, there is a lack of mechanisms to improve the accuracy of the generated AI model by utilizing post-treatment feedback information. As a result, it has been difficult to provide highly accurate treatment suggestions.

[1522] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for transmitting user information to an emotion engine and analyzing the emotional state, means for transmitting the emotional data received from the emotion engine and user information to a generating AI and having it perform analysis, and means for the generating AI to generate optimal treatment suggestions and psychological support based on the user's medical history, symptoms, and emotional state. This makes it possible to provide highly accurate treatment suggestions and psychological support based on each user's medical history and emotional state.

[1523] A "user" refers to an individual who uses this system to input information about infertility treatment and receive treatment suggestions and psychological support.

[1524] A "terminal" refers to a computer device or smartphone operated by a user, providing an interface for the user to input information and check results.

[1525] A "server" refers to a central computer system that receives, processes, stores, and analyzes information transmitted from terminals.

[1526] "Medical history" refers to information about medical treatments, diagnoses, and health status that a user has received in the past.

[1527] "Symptoms" refer to the health problems or discomforts that the user is currently experiencing.

[1528] "Consultation content" refers to specific questions or problems that users want to ask the system about.

[1529] An "emotion engine" refers to a computer program that analyzes a user's emotional state (e.g., stress, anxiety, reassurance) based on their input.

[1530] "Generative AI" refers to an artificial intelligence program that analyzes a user's medical history, symptoms, consultation content, and emotional state to automatically generate optimal treatment suggestions and psychological support.

[1531] "Proposal results" refers to information regarding treatment suggestions and psychological support created by the generating AI based on its analysis.

[1532] A "medical consultation service system" refers to a computer system used to manage appointments and consultations with specialists and counselors.

[1533] "Feedback" refers to users entering their thoughts and evaluations regarding the effectiveness and experience of treatment, as well as their emotional state.

[1534] "Model accuracy" refers to an indicator that shows the accuracy and appropriateness of the treatment suggestions and psychological support that the generated AI provides to the user.

[1535] This invention is a system that provides individualized treatment suggestions and psychological support in infertility treatment. The system aims to generate optimal treatment suggestions by comprehensively analyzing the user's medical history, symptoms, consultation content, and emotional state. Specific embodiments of this invention are described below.

[1536] User registration and login

[1537] The terminal displays an account registration screen to the user. The user enters necessary information such as name, contact information, password, and past medical history, and the terminal verifies this information and sends it to the server. The server stores the received information in its database and creates an account. Existing users enter their user ID and password on the login screen to authenticate and access the system.

[1538] Entering patient information

[1539] The user enters detailed information about their medical history and current symptoms on the main screen. The terminal sends this information to the server, which stores the received information in a database.

[1540] Enter your consultation details

[1541] The user enters their specific question into a text field. For example, they might enter, "Please tell me about the next steps in infertility treatment." The terminal sends the entered question to the server, and the server saves the received information in a database.

[1542] Emotion recognition and the generation of treatment proposals

[1543] The server sends the user's input to the emotion engine, which analyzes the user's emotional state. The emotion engine identifies the user's emotional state (e.g., stress, anxiety, relief) and sends this information to the generating AI. The generating AI comprehensively analyzes the user's medical history, symptoms, consultation content, and emotional state to generate optimal treatment suggestions and psychological support. The generating AI sends the suggested results back to the server, which then sends them to the terminal. The terminal displays the suggested results to the user.

[1544] Collaboration with medical consultation services

[1545] After the user reviews the treatment suggestions generated by the AI, if they wish to have a more detailed consultation, they access the reservation screen. The user selects the "Consult with a specialist" option and enters their desired reservation date and time. The device sends the reservation information to the server. The server links the received reservation information with the medical consultation system. Once the linkage is complete, reservation confirmation information is sent back to the server, which then sends the reservation confirmation information to the device, which displays it to the user.

[1546] Post-treatment feedback

[1547] After the user receives treatment, the device provides a feedback input screen. The user inputs information about the effectiveness and experience of the treatment, as well as their emotional state. For example, they might input, "I was relieved when my pregnancy was confirmed after the treatment." The device sends the input feedback information to the server. The server stores the received feedback information in a database and provides it to the generative AI. The generative AI uses the feedback information to improve the accuracy of its model.

[1548] Specific example

[1549] When User A uses the system for the first time, they create an account and enter their past infertility treatment history. For example, they might enter that they have problems with egg quality. Next, they enter their questions about the next steps in their infertility treatment. The server sends this information to the emotion engine, which analyzes User A's emotional state (e.g., anxiety). The emotion engine sends the emotional data and medical history to the generating AI, which analyzes it and generates a treatment suggestion such as, "Given the age-related decline in egg quality, egg donation should be considered. We also recommend receiving psychological counseling to alleviate anxiety." The server sends this suggestion to User A, and the terminal displays it to User A.

[1550] Example of a prompt:

[1551] "The user's medical history is as follows: There are issues with egg quality. Current symptoms or concerns are as follows: Please advise on the next steps in fertility treatment. The user is currently experiencing anxiety. Based on this information, please provide optimal treatment suggestions and psychological support."

[1552] This invention makes it possible to provide highly accurate treatment suggestions and psychological support based on the user's medical history and emotional state.

[1553] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1554] Step 1: User registration and login

[1555] Input: The user enters their name, contact information, password, and medical history on the account registration screen.

[1556] Operation: The terminal validates the input information and sends it to the server.

[1557] Data processing / calculation: The server receives validated information and saves it to the database.

[1558] Output: The server sends the account creation result back to the terminal and notifies the user that registration is complete.

[1559] Step 2: Enter patient information

[1560] Input: The user enters their medical history and current symptom information on the main screen.

[1561] Operation: The terminal sends the input information to the server.

[1562] Data processing / calculation: The server saves the information it receives to the database.

[1563] Output: The server notifies the terminal that the information has been saved and displays a confirmation message to the user.

[1564] Step 3: Enter your consultation details

[1565] Input: The user enters their question (e.g., "Please tell me about the next steps in infertility treatment") into the text field.

[1566] Operation: The terminal sends the input content to the server.

[1567] Data processing / calculation: The server saves the received consultation details to the database.

[1568] Output: The server notifies the terminal that the consultation details have been saved and displays a confirmation message to the user.

[1569] Step 4: Emotion recognition and generation of treatment proposals

[1570] Input: The server sends the user's medical history, symptoms, and consultation details to the emotion engine.

[1571] Operation: The emotion engine analyzes the emotional state.

[1572] Data processing / calculation: The emotion engine identifies emotional states (e.g., anxiety) and sends them to the generating AI.

[1573] Output: The generating AI generates treatment suggestions and psychological support based on the received data and sends the results back to the server.

[1574] Step 5: Providing treatment proposals

[1575] Input: The server receives the suggested results from the generated AI.

[1576] Operation: The server sends the suggestion results to the terminal.

[1577] Data Processing / Calculation: The server aggregates and organizes user-specific data and provides suggested results in an easy-to-understand format.

[1578] Output: The terminal displays the suggested results to the user. For example, a message such as, "Due to the possibility of a decline in egg quality due to age, egg donation should be considered. We also recommend receiving psychological counseling to alleviate anxiety," might be displayed.

[1579] Step 6: Collaboration with medical consultation services

[1580] Input: The user selects the "Consult a specialist" option and enters the appointment date and time.

[1581] Operation: The device sends reservation information to the server.

[1582] Data processing / calculation: The server sends the reservation information to the medical consultation system and confirms the reservation.

[1583] Output: The server sends reservation confirmation information to the terminal and displays it to the user.

[1584] Step 7: Post-treatment feedback

[1585] Input: The user enters feedback after treatment (e.g., "I was relieved when my pregnancy was confirmed after treatment").

[1586] Action: The device sends feedback information to the server.

[1587] Data processing / calculation: The server stores feedback information in a database and provides it to the generating AI.

[1588] Output: The server notifies the terminal that the feedback has been saved and displays a confirmation message to the user.

[1589] At each step, the user, terminal, and server work together to create a system that provides highly accurate treatment suggestions.

[1590] (Application Example 2)

[1591] 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".

[1592] In infertility treatment, personalized treatment suggestions based on the patient's medical history and symptoms are crucial, but traditional systems have lacked adequate psychological support. Furthermore, there is a need for an efficient method to analyze a patient's emotional state before they visit the clinic and to provide appropriate treatment suggestions and appointment scheduling.

[1593] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to register an account, means for the user to input medical history and symptoms, means for the user to input specific consultation details, means for the emotion engine to analyze the user's emotional state, means for the server to send the information received from the user to the generating AI for analysis, means for the generating AI to generate an optimal treatment proposal based on the analysis results, and means for the generating AI to display reservation information for face-to-face appointments to the user. This enables personalized treatment proposals and reservation coordination based on the user's emotional state.

[1594] "User" refers to an individual or group that uses the system, and in particular to those who receive consultations and treatment suggestions regarding infertility treatment.

[1595] "Account registration" refers to the process by which a user provides their basic information to the system and obtains a unique identification ID.

[1596] "Medical history" refers to a detailed record of treatments and diagnoses the user has received in the past.

[1597] "Symptoms" refer to the physical or mental problems or conditions that the user is currently experiencing.

[1598] "Consultation content" refers to the specific questions and requests that users ask through the system.

[1599] An "emotion engine" refers to software that analyzes user input data and identifies their emotional state.

[1600] A "server" refers to a remote computer system that processes and stores information received from users.

[1601] "Generative AI" refers to an artificial intelligence algorithm that automatically generates optimal treatment suggestions based on received data.

[1602] "Analysis results" refers to the output obtained after analyzing user data using a generative AI or emotion engine.

[1603] "Treatment suggestions" refer to specific treatment methods and support provided to the user based on their medical history, symptoms, consultation content, and emotional state.

[1604] "Reservation information" refers to data that users enter to book an in-person consultation with a specialist, indicating the date, time, and desired details.

[1605] The term "medical consultation service system" refers to a backend system for managing and processing appointments for consultations with medical professionals.

[1606] "Feedback" refers to data that represents the evaluation and opinions of users regarding the treatments and suggestions they received.

[1607] "Model accuracy" refers to an evaluation criterion that indicates the accuracy and suitability of the treatment suggestions provided by the generated AI.

[1608] This invention is a system for users to receive personalized treatment suggestions regarding infertility treatment, and detailed embodiments are shown below.

[1609] First, the user registers an account using their device. The user enters basic information such as their name, contact information, and past medical history. The entered information is sent from the device to the server and stored in the database.

[1610] Next, the user enters their medical history, current symptoms, and specific questions via their device. This information is also sent from the device to the server and stored in the database.

[1611] A key element of this system is the inclusion of an emotion engine, which analyzes the user's emotional state from their input data. The emotion engine analyzes the user's text input and behavioral data to identify their emotional state (e.g., anxiety, reassurance, etc.). This analysis result is then transmitted to the generating AI via a server.

[1612] The generating AI creates optimal treatment suggestions based on the user's medical history, symptoms, consultation content, and emotional state. The generating AI uses the latest medical guidelines and past patient data to generate specific and personalized treatment suggestions. The results of this AI analysis are transmitted from the server to the user's terminal and displayed.

[1613] Furthermore, if a user wishes to have an in-person consultation with a specialist, they enter reservation information based on the suggestions generated by the AI. The server then links this reservation information with the medical consultation system and sends reservation confirmation information to the user. This process allows users to receive consultation from a specialist at the appropriate time.

[1614] Furthermore, users provide feedback after treatment. This feedback information is sent to the server and provided to the generating AI. The generating AI uses this feedback data to improve the accuracy of its model. This continuous feedback loop allows the system to provide more accurate treatment suggestions.

[1615] As a concrete example, let's consider the case where User B uses the system for the first time. User B creates an account on their smartphone and enters their medical history regarding past infertility treatments. For example, they might enter that they have problems with ovarian function. Next, they enter a question about the next steps in their infertility treatment. Let's say they enter, "Please tell me about the next steps in my infertility treatment." The emotion engine analyzes User B's input to understand their anxiety and sends it to the generating AI. Based on the medical history and emotional state, the generating AI generates a treatment suggestion such as, "Ovarian dysfunction is suspected, so consider specific drug treatments. We also recommend counseling to reduce anxiety." The server sends this suggestion to User B, and it is displayed on their smartphone.

[1616] Examples of prompt statements include the following:

[1617] User's medical history: Female, 35 years old, underwent two artificial insemination procedures in the past three years. Has ovarian dysfunction.

[1618] Inquiry topic: I would like to know about the next steps in my treatment.

[1619] Emotional state: Anxiety.

[1620] Please generate treatment suggestions based on the generated AI.

[1621] This system allows users to receive personalized infertility treatment suggestions and psychological support based on their emotional state, potentially improving the success rate of treatment.

[1622] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1623] Step 1:

[1624] The user registers an account. The user uses their device to enter basic information such as their name, contact information, and medical history. The device sends the entered information to the server, which stores this information in a database. Input data: Name, contact information, medical history. Output: Notification of successful account creation.

[1625] Step 2:

[1626] The user inputs their medical history, current symptoms, and specific consultation details. The terminal sends this information to the server, which stores it in a database. Input data: medical history, symptoms, consultation details. Output: information is stored in the database.

[1627] Step 3:

[1628] The server sends the information received from the user to the emotion engine. The emotion engine analyzes the user's input data and identifies the emotional state (e.g., anxiety, reassurance). It then sends the analysis results back to the server. Input data: Medical history, consultation content. Output result: Analysis results of the emotional state.

[1629] Step 4:

[1630] The server sends emotional state data obtained from the emotion engine, along with the user's medical history and consultation details, to the generating AI. The generating AI then uses this information to create optimal treatment suggestions. Input data: medical history, symptoms, consultation details, emotional state. Output result: treatment suggestions.

[1631] Step 5:

[1632] The server receives treatment suggestions from the generated AI and sends them to the user's device. The device displays this information to the user. Input data: Treatment suggestions. Output result: Display of the suggested content.

[1633] Step 6:

[1634] If a user wishes to have an in-person consultation with a specialist, they enter reservation information based on the suggestions generated by the AI. The terminal sends this reservation information to the server, which then connects it with the medical consultation system. Once the connection is complete, the server sends reservation confirmation information to the user. Input data: Reservation information. Output result: Reservation confirmation information.

[1635] Step 7:

[1636] The user enters feedback after treatment. The device sends this feedback information to the server, which provides it to the generating AI. The generating AI uses this feedback data to improve the accuracy of its model. Input data: Feedback information. Output result: Improved model accuracy.

[1637] As a concrete example, the following prompt is sent to the generating AI:

[1638] User's medical history: Female, 35 years old, underwent two artificial insemination procedures in the past three years. Has ovarian dysfunction.

[1639] Inquiry topic: I would like to know about the next steps in my treatment.

[1640] Emotional state: Anxiety.

[1641] Please generate treatment suggestions based on the generated AI.

[1642] Through this process, users can receive personalized infertility treatment recommendations and psychological support based on their emotional state.

[1643] 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.

[1644] 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.

[1645] 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 robot 414.

[1646] 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.

[1647] 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.

[1648] 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.

[1649] 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.

[1650] 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.

[1651] 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."

[1652] 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.

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] 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 m...

Claims

1. The means by which users can register an account, A means for users to input their medical history and symptoms, A means for users to input specific details of their consultation, A means by which the server sends information received from the user to a generating AI for analysis, A means by which a generating AI generates optimal treatment suggestions based on analysis results, A means for the server to send the suggested results from the generated AI to the user, A system that includes this.

2. A means for the server to link user reservation information with the medical consultation system, A means by which the server sends reservation confirmation information to the user, A means for users to input post-treatment feedback, A means by which the server provides feedback information to the generating AI to improve model accuracy, The system according to claim 1, further comprising:

3. The system according to claim 1, comprising means for a generating AI to analyze the user's medical history, symptoms, and the latest medical guidelines to propose an individualized treatment method.

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