System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting diagnostic information, a means for analyzing a symptom by using the inputted diagnostic information, a means for presenting an optimal treatment policy based on the analysis result, a means for extracting a specialist corresponding to the presented treatment policy, and a means for automatically arranging an appointment with the extracted specialist.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Currently, many patients proceed with treatment based on the opinion of their primary care physician, but because there are few opportunities to compare different treatment plans, they may not receive the most appropriate treatment. Furthermore, the process of finding an appropriate specialist to obtain a second opinion, making an appointment, and then undergoing the examination is complicated and places a burden on patients. To solve these issues, there is a need for a system that proposes the most appropriate treatment plan based on diagnostic information, and automates matching with specialists and scheduling appointments. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention provides the following means: a system including means for inputting diagnostic information, means for analyzing symptoms using the input diagnostic information, means for presenting an optimal treatment plan based on the analysis results, means for extracting a specialist corresponding to the presented treatment plan, and means for automatically arranging an appointment with the extracted specialist. This allows patients to efficiently obtain a second opinion and receive treatment based on the optimal treatment plan.

[0006] "Diagnostic information" refers to information about the patient's health condition, such as the doctor's diagnosis, the patient's symptoms, and treatment history.

[0007] "Means for analyzing symptoms" refers to software or algorithms that evaluate and analyze a patient's current symptoms and condition based on the input diagnostic information.

[0008] "Means for presenting treatment plans" is a function that displays and proposes optimal treatment methods and treatment plans based on the results of symptom analysis.

[0009] The "means for extracting specialists" is a function for selecting from a database doctors who have the specialized knowledge and experience corresponding to the proposed treatment plan.

[0010] The "means for automatically arranging appointments" is a function that adjusts the appointment date with the selected specialist and automatically confirms the appointment using a communication means such as the Internet. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0012] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0015] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0016] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0017] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0019] [First embodiment]

[0020] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0021] 1, a 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.

[0022] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0024] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0025] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0027] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0029] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0031] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0032] The present invention is a system that proposes optimal treatment plans based on diagnostic information, and automates matching with specialists and scheduling appointments. This system is primarily composed of a server, terminals, and users.

[0033] System Overview

[0034] Entering diagnostic information

[0035] The user logs in to the system using an electronic device (smartphone or PC). After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. For example, the user might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect was observed."

[0036] Symptom analysis

[0037] The server receives the diagnostic information sent by the user and stores it in a database. The stored diagnostic information is then fed into an AI model for symptom analysis. This AI model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan.

[0038] Presentation of treatment plan

[0039] The server then sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may display a treatment plan such as "combined surgery and radiation therapy is recommended."

[0040] Identifying specialists

[0041] The server extracts the appropriate specialists from the database based on the proposed treatment plan. This list of specialists is organized for the user's convenience and sent to the terminal.

[0042] Arranging a medical appointment

[0043] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it displays, "Your appointment with Dr. Sato at Keio University Hospital on October 20th has been confirmed."

[0044] Specific examples

[0045] 1. User: Diagnosed with stomach cancer, chemotherapy has not been effective, and he / she would like to get a second opinion.

[0046] 2. Terminal: Log in to the system and enter the diagnosis and current symptoms.

[0047] 3. Server: Receives and stores diagnostic information, analyzes it using an AI model, and generates a treatment plan that recommends a combination of surgery and radiation therapy.

[0048] 4. Terminal: Displays the generated treatment plan to the user.

[0049] 5. Server: Based on the treatment plan, extract relevant specialists from the database and send them to the terminal.

[0050] 6. Terminal: Displays a list of specialists to the user and allows them to select the doctor they want.

[0051] 7. Server: Checks availability of selected specialists and arranges appointments.

[0052] 8. Terminal: Notifies the user of the reservation confirmation information and secures the appointment date.

[0053] In this way, this system can automatically propose optimal treatment plans and schedule appointments with specialists simply by having the user input diagnostic information, reducing the burden on patients and providing them with opportunities to receive better treatment.

[0054] The processing flow will be explained below.

[0055] Program processing steps

[0056] Step 1:

[0057] Users log in to the system using electronic terminals. This login information includes a user ID and password, and after authentication, users are able to access the system.

[0058] Step 2:

[0059] After logging in, the device displays an interface for entering diagnostic information, including text boxes and drop-down menus for entering symptoms and treatment history.

[0060] Step 3:

[0061] The user inputs diagnosis information (e.g., "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect has been observed") according to the interface.

[0062] Step 4:

[0063] The device sends the diagnostic information entered by the user to the server using an HTTP POST request.

[0064] Step 5:

[0065] The server stores the received diagnostic information in a database, including the user ID, symptoms, and treatment history.

[0066] Step 6:

[0067] The server passes the stored diagnostic information to an AI model, which then analyzes the symptoms using statistical data and the latest medical knowledge.

[0068] Step 7:

[0069] The server stores the optimal treatment plan generated by the AI ​​model in a database. For example, a treatment plan that suggests a combination of surgery and radiation therapy is recommended is generated.

[0070] Step 8:

[0071] The server sends the generated treatment plan to the terminal, and the data is sent as an HTTP response.

[0072] Step 9:

[0073] The terminal displays the received treatment plan to the user. For example, a message such as "Combined surgery and radiation therapy is recommended" is displayed.

[0074] Step 10:

[0075] The server extracts the appropriate specialists from the medical database based on the treatment plan, and organizes the extracted specialists' information (such as their names, affiliated hospitals, and specialties).

[0076] Step 11:

[0077] The server sends the extracted list of specialists to the terminal, which may include, for example, "Dr. Tanaka of the university hospital, Dr. Sato of the general hospital, and Dr. Takahashi of the clinic."

[0078] Step 12:

[0079] The terminal displays a list of specialists to the user and provides an interface for selecting the desired doctor.

[0080] Step 13:

[0081] The user selects the desired specialist from the list, for example, "Dr. Tanaka of the University Hospital."

[0082] Step 14:

[0083] The terminal sends the user's selection information to the server, again using an HTTP POST request.

[0084] Step 15:

[0085] The server accesses the appointment management system to check the availability of the selected specialist, and uses the system's API to obtain the specialist's schedule information.

[0086] Step 16:

[0087] The server automatically arranges appointments based on the schedule information and generates confirmed appointment information, such as "An appointment with Dr. Sato at Keio University Hospital has been confirmed for October 20, 2023."

[0088] Step 17:

[0089] The server sends the confirmed reservation information to the terminal using an HTTP response.

[0090] Step 18:

[0091] The terminal displays the received appointment information to the user and notifies the user that the appointment has been confirmed. The user can confirm the appointment date by checking this information.

[0092] Example 1

[0093] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0094] In conventional medical systems, the process from entering diagnostic information to making an appointment with a specialist is complicated, placing a significant burden on patients. In addition, there are cases where an appropriate treatment plan cannot be presented quickly, resulting in patients missing out on the opportunity to receive optimal treatment.

[0095] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0096] In this invention, the server includes: means for inputting diagnostic information using an electronic terminal; means for transmitting the input diagnostic information to the server and storing it; means for analyzing the stored diagnostic information using an AI model and analyzing symptoms; means for the server to generate an optimal treatment plan based on the analysis results and present it to the user; means for the server to extract a suitable specialist from a database based on the presented treatment plan; and means for displaying the extracted list of specialists to the user and automatically arranging an appointment with the selected specialist. This allows the user to easily complete a series of processes from inputting diagnostic information to making an appointment with a specialist, enabling the user to receive optimal treatment quickly.

[0097] An "electronic terminal" is an electronic device such as a smartphone or a personal computer that a user uses to input diagnostic information.

[0098] "Diagnostic information" is medically related information such as symptoms and treatment history provided by the user.

[0099] A "server" is a computer system that receives, stores, analyzes, and provides results to a user.

[0100] "Storing" refers to storing the received diagnostic information in a storage system such as a database.

[0101] An "AI model" is an artificial intelligence algorithm that analyzes diagnostic information based on statistical data and the latest medical knowledge, and estimates symptoms and treatment plans.

[0102] "Symptom analysis" refers to the use of AI models to analyze diagnostic information provided by users to gain a deeper understanding of their symptoms.

[0103] A "treatment plan" is a treatment method or procedure that is deemed optimal based on the results of symptom analysis.

[0104] A "specialist" is a medical professional with specialized knowledge and skills for a specific symptom or disease.

[0105] "Appointment" is the process of confirming a consultation date with a specialist selected by the user.

[0106] A "database" is an information system for efficiently managing diagnostic information and data related to specialists.

[0107] The present invention is a system that proposes optimal treatment plans based on diagnostic information, and automates matching with specialists and scheduling appointments. This system is primarily composed of a server, terminals, and users.

[0108] Entering diagnostic information

[0109] Users log in to the system using an electronic device (smartphone or PC). After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. For example, a user might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect has been observed." This entry can be made through a web browser or a mobile application.

[0110] Sending and storing diagnostic information

[0111] The terminal sends the entered diagnostic information to a server, which then stores the received diagnostic information in a dedicated database. This database can use a relational database management system such as MySQL (registered trademark) or MariaDB.

[0112] Analysis of diagnostic information

[0113] The server supplies the stored diagnostic information to an AI model. This AI model is built using machine learning frameworks such as TENSORFLOW (registered trademark) and PyTorch. The AI ​​model analyzes the diagnostic information and performs symptom analysis based on statistical data and the latest medical knowledge. For example, a treatment plan such as "combined surgery and radiation therapy is recommended" is generated.

[0114] Presentation of treatment plan

[0115] The server sends the generated treatment plan to the device, which then displays it to the user. For example, it may say, "A combination of surgery and radiation therapy is recommended." This allows the user to confirm the optimal treatment plan.

[0116] Identifying specialists

[0117] The server extracts the appropriate specialists from the database based on the treatment plan generated by the AI ​​model, and the extracted list of specialists is organized for the user's convenience and sent to the device.

[0118] Arranging a medical appointment

[0119] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it may say, "An appointment has been confirmed with a doctor at a certain university hospital on October 20th."

[0120] Specific examples

[0121] Below are some example prompts to illustrate the concept:

[0122] If a user is diagnosed with stomach cancer and chemotherapy is ineffective, please suggest the optimal treatment plan. The system will recommend surgery and radiation therapy, and include a function to list and schedule an appointment with an appropriate specialist. Please explain the specific processing steps and also add a flow for presenting the treatment plan and arranging an appointment with a specialist.

[0123] In this way, this system can automatically propose optimal treatment plans and schedule appointments with specialists simply by having the user input diagnostic information. This process reduces the burden on patients and provides them with opportunities to receive better treatment.

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1: Enter diagnostic information

[0126] Users log in to the system using a smartphone or PC. After logging in, the device displays an interface for entering diagnostic information. The user enters symptoms and treatment history on the interface. For example, they might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect has been observed."

[0127] Input: User's diagnosis information (symptoms, treatment history)

[0128] Output: Entered diagnostic information

[0129] Step 2: Send and save diagnostic information

[0130] The terminal sends the entered diagnostic information to the server, which then stores the received diagnostic information in a database, such as MySQL.

[0131] Input: Diagnostic information submitted by the user

[0132] Output: Diagnostic information stored in a database

[0133] Step 3: Analyze the diagnostic information

[0134] The server supplies the diagnostic information stored in the database to the AI ​​model. The AI ​​model is built using machine learning frameworks such as TensorFlow and performs analysis based on statistical data and the latest medical knowledge. The analysis results in the generation of an optimal treatment plan. For example, it may decide that a combination of surgery and radiation therapy is recommended.

[0135] Input: Saved diagnostic information

[0136] Output: Analyzed treatment plan

[0137] Step 4: Proposing a treatment plan

[0138] The server sends the generated treatment plan to the terminal, which then displays it to the user. For example, it may say, "Combined surgery and radiation therapy is recommended."

[0139] Input: Analyzed treatment plan

[0140] Output: A treatment plan presented to the user

[0141] Step 5: Specialist selection

[0142] The server extracts corresponding specialists from the database based on the generated treatment plan, generates a list of appropriate specialists, and transmits the list to the terminal.

[0143] Input: Analyzed treatment plan

[0144] Output: List of specialists

[0145] Step 6: Arrange an appointment

[0146] The user selects the doctor of their choice from a list of specialists displayed on the terminal. The terminal then sends information about the selected specialist to the server. The server then checks the availability of the selected specialist and automatically arranges the appointment. Once the appointment is confirmed, the appointment information is sent to the terminal and the user is notified. For example, a message may be displayed saying, "An appointment has been confirmed with a doctor at a certain university hospital on October 20th."

[0147] Input: User-selected specialist

[0148] Output: Confirmed reservation information and notification to the user

[0149] (Application example 1)

[0150] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0151] Conventional medical systems do not offer systems that can propose optimal treatment plans based on diagnostic information, match patients with specialists, and automate appointments. They also lack systems that can propose optimal meal plans based on health information or automate appointments with nutritionists. This has resulted in a lack of centralized, highly accurate recommendations and reservation arrangements, even in the fields of health and nutrition, requiring significant user effort. Furthermore, recommendations are not based on the latest knowledge or statistical data in each specialty, meaning the optimal choice for the user is not guaranteed. Therefore, there is a need for a system that can automate optimal recommendations and reservation arrangements based on diagnostic and health information, thereby reducing the burden on users.

[0152] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0153] In this invention, the server includes means for inputting diagnostic information, means for analyzing symptoms using the input diagnostic information, means for presenting an optimal treatment plan based on the analysis results, means for extracting a specialist corresponding to the presented treatment plan, means for automatically arranging a consultation appointment with the extracted specialist, means for inputting health information, means for analyzing an optimal meal plan and meal provision facility using the input health information, means for presenting an optimal meal plan and meal provision facility based on the analysis results, means for extracting a nutritionist corresponding to the presented meal plan and meal provision facility, and means for automatically arranging a consultation appointment with the extracted nutritionist. This enables optimal suggestions and reservation arrangements for the user based on both diagnostic information and health information.

[0154] "Diagnostic information" is medical information, such as symptoms and treatment history, that a user enters into the system.

[0155] "Health information" refers to information about dietary habits, such as health status, allergies, calorie restrictions, and dietary history, that a user inputs into the system.

[0156] "Symptom analysis" is a process that analyzes the user's symptoms using AI models and statistical data based on the input diagnostic information.

[0157] A "treatment policy" is a guideline for the optimal treatment method for the user, which is generated based on the results of symptom analysis.

[0158] A "specialist" is a doctor who has specialized knowledge and experience in a particular medical field.

[0159] "Appointment" is a procedure for making an appointment with a specialist selected by the user.

[0160] A "meal plan" is a meal suggestion suitable for a user that is generated based on health information.

[0161] "Meal Provider" means a dining establishment that offers a recommended meal plan to a user.

[0162] A "nutritionist" is a professional who provides appropriate nutritional management based on the user's health condition and meal plan.

[0163] "Consultation reservation" is a procedure for booking a consultation with a dietitian selected by the user.

[0164] The present invention is a system that presents optimal treatment guidelines and meal plans based on diagnostic and health information, and automatically arranges appointments with specialized doctors and nutritionists. This system is mainly composed of a server, terminals, and users.

[0165] Overall system configuration

[0166] 1. User operations

[0167] First, users access the system via a smartphone app or computer and log in. After logging in, an interface is displayed where they can enter diagnostic information (symptoms, treatment history) and health information (allergies, calorie restriction, dietary history).

[0168] 2. Data transmission and storage

[0169] The diagnostic and health information sent from the device is stored on a server, which manages it and provides it for analysis and retrieval as needed.

[0170] 3. Symptom and health information analysis

[0171] The server then feeds the stored diagnostic information into an AI model that analyzes the symptoms. The AI ​​model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan. It also generates an optimal diet plan based on the health information.

[0172] 4. Providing optimal treatment and dietary plans

[0173] The server then sends the generated treatment plan and meal plan to the device and displays it to the user. For example, it presents the optimal options for the user, such as "combined surgery and radiation therapy is recommended" or "low-calorie, gluten-free meal plan."

[0174] 5. Extraction and matching of specialized doctors and nutritionists

[0175] Based on the displayed treatment plan, the server extracts relevant specialists from the database and provides them to the user as a list. Similarly, based on the presented meal plan, the server extracts corresponding nutritionists and displays them to the user.

[0176] 6. Reservation Arrangements

[0177] Once the user selects the desired specialist and nutritionist, the server automatically checks their availability and arranges the reservation. Once the reservation is confirmed, the information is sent to the terminal and the user is notified.

[0178] Hardware and software used

[0179] Smartphone app / computer: Used for user interface and input data transmission.

[0180] Server: Database management and cloud processing.

[0181] AI models: Used to analyze diagnostic and health information and generate optimal treatment and diet plans.

[0182] Location API: Used to suggest dining facilities using GPS data.

[0183] Specific examples

[0184] 1. A user is diagnosed with stomach cancer and has not seen any effect from anti-cancer drug treatment, so they log in to the system. They enter their diagnosis, symptoms, and treatment history, as well as allergy information (e.g., gluten) and diet goals (e.g., low calorie).

[0185] 2. The server feeds the received diagnostic information into the AI ​​model and generates a treatment plan that recommends a combination of surgery and radiation therapy. It also generates a low-calorie, gluten-free meal plan based on the health information.

[0186] 3. The terminal displays the above treatment guidelines and meal plan to the user.

[0187] 4. The server extracts relevant specialists and corresponding nutritionists from the database and provides them to the user as a list.

[0188] 5. The terminal allows the user to select the specialist and nutritionist of their choice. The server checks availability, arranges the reservation, and notifies the user of the reservation confirmation.

[0189] Example prompts to input to the generative AI model

[0190] Provide user-entered health information (e.g., allergies, calorie restrictions, dietary history). Based on this information, suggest the best meal plan and restaurant for the user, then match with a professional nutritionist and make a reservation. In this step, process the necessary AI model and generate the API response.

[0191]

[0192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0193] Step 1:

[0194] Users log in to the system using a smartphone app or a computer. After logging in, an interface is displayed where they can enter diagnostic information (symptoms, treatment history) and health information (allergies, calorie restriction, dietary history). The entered information is sent from the device to the server.

[0195] Input: Diagnostic information, health information

[0196] Output: User data sent to the server

[0197] Step 2:

[0198] The server receives the diagnostic and health information sent by the user and stores it in a database for later analysis and retrieval.

[0199] Input: Data submitted by the user

[0200] Output: Data saved to database

[0201] Step 3:

[0202] The server then feeds the diagnostic information stored in the database to the AI ​​model, which analyzes the symptoms. The AI ​​model uses statistical data and the latest medical knowledge to generate the optimal treatment plan. It also analyzes the health information and generates the optimal meal plan.

[0203] Input: Stored diagnostic and health information

[0204] Output: Optimal treatment plan, optimal diet plan

[0205] Step 4:

[0206] The server then sends the treatment plan and meal plan generated by the AI ​​model to the device and displays it to the user, such as "Surgery and radiation therapy combined is recommended" or "A low-calorie, gluten-free meal plan."

[0207] Input: Optimal treatment plan, optimal diet plan

[0208] Output: what the user sees

[0209] Step 5:

[0210] The server extracts relevant specialists from the database based on the displayed treatment plan and sends them to the terminal.Similarly, it extracts corresponding nutritionists based on the presented meal plan and provides them to the user as a list.

[0211] Input: Optimal treatment plan, optimal diet plan

[0212] Output: List of specialists and nutritionists

[0213] Step 6:

[0214] The user selects the desired specialist and nutritionist from the provided list, and the selected information is sent from the terminal to the server.

[0215] Input: Specialist and nutritionist selection information

[0216] Output: Sends the selected data to the server

[0217] Step 7:

[0218] The server automatically checks the availability of the selected specialist and nutritionist and arranges the reservation. Once the reservation is confirmed, the information is sent to the terminal and the user is notified. For example, it may display "An appointment with a specialist has been confirmed for October 20th."

[0219] Input: User selection information

[0220] Output: Confirmed reservation information, notification to user

[0221] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0222] This invention combines a system that proposes optimal treatment plans based on diagnostic information, automates matching with specialists and scheduling appointments, and an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[0223] System Overview

[0224] Entering diagnostic information

[0225] A user logs in to the system using an electronic terminal. After logging in, an interface for inputting diagnostic information (symptoms, treatment history, etc.) is displayed. For example, the user may input, "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect was observed."

[0226] Symptom analysis

[0227] The server receives the diagnostic information sent by the user and stores it in a database. The stored diagnostic information is then fed into an AI model for symptom analysis. This AI model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan.

[0228] Presentation of treatment plan

[0229] The server then sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may display a treatment plan such as "combined surgery and radiation therapy is recommended."

[0230] Intervention by the Emotion Engine

[0231] The device's emotion engine analyzes the user's emotional state through their input and interactions. For example, the emotion engine analyzes the speed of text input, the pressure on the touchscreen, and the tone of the text to identify emotional states such as "anxiety" or "relief."

[0232] Emotion-Based Feedback

[0233] The server adjusts the treatment plan and specialist recommendation based on the emotional state analyzed by the emotion engine. For example, if the user is in an "anxious" state, more reassuring explanations and support will be provided.

[0234] Identifying specialists

[0235] The server extracts the appropriate specialists from the database based on the treatment plan and emotional state. This list of specialists is organized taking into account the user's convenience and emotional state, and is sent to the terminal.

[0236] Arranging a medical appointment

[0237] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it will say, "Your appointment with Dr. Tanaka at the University Hospital on October 20th has been confirmed."

[0238] Specific examples

[0239] 1. User: Diagnosed with stomach cancer, chemotherapy has not been effective, and he / she would like to get a second opinion.

[0240] 2. Terminal: Log in to the system and enter the diagnosis and current symptoms.

[0241] 3. Server: Receives and stores diagnostic information, analyzes it using an AI model, and generates a treatment plan that recommends a combination of surgery and radiation therapy.

[0242] 4. Terminal: The generated treatment plan is displayed to the user. At the same time, the emotion engine analyzes the user's emotional state based on the user's input data and interactions.

[0243] 5. Server: Based on the emotional state obtained by the emotion engine, the treatment plan and list of specialists are adjusted and provided to the user in the most optimal way.

[0244] 6. Server: Based on the treatment plan, extract relevant specialists from the database and send them to the terminal.

[0245] 7. Terminal: The user is presented with a list of specialists and allowed to select the doctor of their choice. Support and explanations are provided according to the user's emotional state.

[0246] 8. Server: Checks availability of selected specialists and arranges appointments.

[0247] 9. Terminal: Notifies the user of the reservation confirmation information and secures the appointment date.

[0248] In this way, this system can automatically propose optimal treatment plans and schedule appointments with specialists simply by having the user input diagnostic information. Furthermore, by combining it with an emotion engine, it can provide support according to the user's emotional state, further reducing the burden on patients.

[0249] The processing flow will be explained below.

[0250] Program processing steps

[0251] Step 1:

[0252] A user logs in to the system using an electronic terminal by entering a user ID and password. The terminal then sends the entered information to the server, which then authenticates the user.

[0253] Step 2:

[0254] After logging in, the device displays an interface for entering diagnostic information, which includes text boxes and drop-down menus for entering symptoms and treatment history.

[0255] Step 3:

[0256] The user follows the interface to input diagnosis information, for example, "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect was observed."

[0257] Step 4:

[0258] The device sends the diagnostic information entered by the user to the server via an HTTP POST request.

[0259] Step 5:

[0260] The server stores the received diagnostic information in a database, including the user ID, symptoms, and treatment history.

[0261] Step 6:

[0262] The server passes the stored diagnostic information to an AI model, which analyzes the symptoms based on statistical data and the latest medical knowledge.

[0263] Step 7:

[0264] The server stores the optimal treatment plan generated by the AI ​​model in a database and sends the plan to the device. For example, the treatment plan may suggest that a combination of surgery and radiation therapy is recommended.

[0265] Step 8:

[0266] The terminal displays the received treatment plan to the user.

[0267] Step 9:

[0268] The device analyzes the user's operations (keyboard input, touch screen operation, etc.) and sends the data to the emotion engine, which analyzes the user's emotional state.

[0269] Step 10:

[0270] The server receives the emotional state from the emotion engine and adjusts the way it presents treatment plans and recommends specialists based on the user's feelings, such as "anxiety" or "relief."

[0271] Step 11:

[0272] Based on the adjusted information, the server extracts from the database a list of specialists who are suited to the treatment plan and the user's emotional state, and transmits this to the terminal.

[0273] Step 12:

[0274] The terminal displays a list of specialists to the user, including, for example, "Dr. Tanaka from the university hospital, Dr. Sato from the general hospital, and Dr. Takahashi from the clinic."

[0275] Step 13:

[0276] The user selects the doctor of their choice from a list of specialists, and the terminal then sends the information to the server.

[0277] Step 14:

[0278] The server checks the availability of the selected specialist via the reservation management system's API.

[0279] Step 15:

[0280] The server automatically arranges the reservation based on availability and generates confirmed reservation information, such as "An appointment with Dr. Tanaka at the University Hospital has been confirmed for October 20th."

[0281] Step 16:

[0282] The server transmits the confirmed reservation information to the terminal.

[0283] Step 17:

[0284] The terminal displays the received reservation information to the user and notifies the user that the reservation has been confirmed. By checking this information, the user can reserve a date to see a specialist.

[0285] This system's series of processes allows users to simply input their diagnosis information, and automatically recommend optimal treatment plans and schedule appointments with specialists. Furthermore, by combining it with an emotion engine, support can be provided according to the user's emotional state, reducing the burden on patients.

[0286] Example 2

[0287] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0288] Current medical systems make it difficult for users to input diagnostic information and then find the appropriate treatment plan or schedule an appointment with a specialist. Furthermore, they do not provide support that takes into account the user's emotional state, which can increase stress and anxiety for patients. This can result in delays in the transition to treatment and a decline in the quality of care.

[0289] The identification process 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 a means for inputting diagnostic information, a means for analyzing symptoms using the input diagnostic information, a means for presenting an optimal treatment plan based on the analysis results, a means for recognizing an emotional state, a means for adjusting the presentation of a treatment plan and the recommendation of a specialist based on the recognized emotional state, a means for extracting a specialist corresponding to the presented treatment plan, and a means for automatically arranging an appointment with the extracted specialist. As a result, by simply inputting diagnostic information by the user, an optimal treatment plan can be proposed, an appointment with a specialist can be automated, and support according to the emotional state can be provided, thereby reducing the burden on the patient.

[0290] "Diagnostic information" refers to medically relevant information such as symptoms and treatment history that a user enters into the system.

[0291] "Input means" refers to the interface and software that allows a user to input diagnostic information into the system through an electronic terminal.

[0292] The "analysis means" refers to technical means such as algorithms or AI models that the server uses to analyze symptoms using the input diagnostic information.

[0293] The "means for presenting" refers to a method and system for the server to display the optimal treatment plan to the user based on the results of symptom analysis.

[0294] "Emotional state" is data that reflects the user's psychological state and emotional response, and is the result of evaluating emotions such as anxiety and relief.

[0295] "Means for recognizing" refers to the technical means for analyzing a user's input and interactions using an emotion engine to understand the user's emotional state.

[0296] "Adjusting" refers to methods and systems for modifying treatment presentations or specialist recommendations based on the perceived emotional state.

[0297] The "means of extraction" refers to an algorithm or system that selects specialists from a database who are appropriate for the treatment plan.

[0298] "Means for arranging an appointment automatically" refers to a method and system that allows the system to automatically schedule an appointment with a specialist selected by the user.

[0299] This invention combines a system that proposes optimal treatment plans based on diagnostic information, automates matching with specialists and scheduling appointments, with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[0300] Entering diagnostic information

[0301] Users log in to the system using an electronic device, such as a smartphone or PC. After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. The information entered by the user might be, for example, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect has been observed." The software used is a web interface and input form that is accessed through a web browser.

[0302] Sending and receiving diagnostic information

[0303] The terminal sends the diagnostic information entered by the user to the server, which receives the information and stores it in an internal database. The hardware used is computers and servers in a data center, and the software used is a database management system.

[0304] Symptom analysis

[0305] The server then supplies the received diagnostic information to the AI ​​model, which then begins analyzing the symptoms. The AI ​​model then derives the optimal treatment plan based on the latest medical knowledge and statistical data. This AI model uses TensorFlow and PyTorch, among other technologies. Specifically, a Python script is executed to analyze the diagnostic information.

[0306] Generation and presentation of treatment plans

[0307] The server sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may suggest a treatment plan such as "surgery and radiation therapy are recommended." HTTP is used as the communication protocol, and the user interface displays the data.

[0308] Emotion analysis using an emotion engine

[0309] The device analyzes the user's input and interactions using an emotion engine to understand the user's emotional state. Specifically, the device analyzes things like the speed of text input and changes in touch pressure, and identifies emotions such as "anxiety" or "relief." The emotion engine uses EmotionAPI and other tools.

[0310] Emotion-Based Feedback

[0311] The server adjusts the treatment plan and specialist recommendation based on the emotional state analyzed by the emotion engine. For example, if the user is "anxious," it will provide a more reassuring explanation. An AI feedback system is used for this adjustment.

[0312] Identifying specialists

[0313] The server extracts the corresponding specialists from the database based on the treatment plan and emotional state. The extracted list of specialists is organized and sent to the terminal. The software used is a database management system and a matching algorithm.

[0314] Arranging a medical appointment

[0315] The user selects the doctor of their choice from a list of specialists displayed. The selection information is sent from the terminal to the server. The server automatically checks the specialist's availability and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, the message might say, "Your appointment with Dr. Tanaka at the university hospital on October 20th has been confirmed." Scheduling and notification systems are used for this purpose.

[0316] Specific examples

[0317] 1. The user logs in to the system and enters the diagnosis and current symptoms. For example, the user might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but it was ineffective."

[0318] 2. The server receives the diagnosis information and uses the AI ​​model to generate a treatment plan that recommends a combination of surgery and radiation therapy.

[0319] 3. The device displays the generated treatment plan to the user, and the emotion engine analyzes the user's emotions.

[0320] 4. The server adjusts the treatment plan and list of specialists based on the emotional state and provides them to the user.

[0321] 5. The server extracts specialists from the database based on the treatment plan and sends the list to the terminal.

[0322] 6. The device displays a list of specialists to the user and allows them to select the doctor they prefer. They provide support and explanations based on their emotional state.

[0323] 7. The server checks the availability of the selected specialist and arranges the appointment.

[0324] 8. The terminal notifies the user of the confirmed reservation information and asks for reconfirmation.

[0325] By using the above steps, the system can propose optimal treatment plans and automate specialist appointments simply by allowing the user to input diagnostic information. Furthermore, by using an emotion engine, it can provide support tailored to the user's emotional state, reducing the burden on patients.

[0326] Prompt Sentence Examples

[0327] "Please demonstrate a system in which a user inputs diagnostic information and an AI model generates an optimal treatment plan. Please also explain the specific steps for data input and analysis."

[0328] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0329] Step 1: Enter diagnostic information

[0330] Users log in to the system using a smartphone or PC. After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. The user enters information such as "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect was observed."

[0331] Input: Diagnostic information entered by the user on an electronic device

[0332] Output: Diagnostic information entered in the input form

[0333] Specific operations: Open the browser on the electronic device and access the system's login page. Enter your username and password and click the login button. Enter the diagnostic information in the form that is displayed next and click the submit button.

[0334] Step 2: Sending and Receiving Diagnostic Information

[0335] The terminal transmits the diagnostic information entered by the user to the server.

[0336] The server receives the diagnostic information sent from the terminal and stores it in an internal database.

[0337] Input: Diagnostic information that the device sends to the server

[0338] Output: Diagnostic information received by the server and stored in the database

[0339] Specific operation: Diagnostic information is sent from the device to the server as an HTTP POST request. The server receives the request and stores it in a database.

[0340] Step 3: Analyze the symptoms

[0341] The server then supplies the received diagnostic information to the AI ​​model, which then begins analyzing the symptoms. The AI ​​model then derives the optimal treatment plan based on the latest medical knowledge and statistical data.

[0342] Input: Diagnostic information stored in the database by the server

[0343] Output: Symptom analysis results by the AI ​​model

[0344] How it works: A Python script runs on the server, and an AI model analyzes the diagnostic information using the TensorFlow library.

[0345] Step 4: Generate and present a treatment plan

[0346] The server sends the treatment plan generated by the AI ​​model to the device.

[0347] The terminal displays this to the user, for example, "Combined surgery and radiation therapy is recommended."

[0348] Input: Symptom analysis results by AI model

[0349] Output: Treatment plan displayed on the terminal

[0350] Specific operation: The server sends the generated treatment plan to the terminal as an HTTP response. The terminal displays the received data on the screen.

[0351] Step 5: Emotion analysis using the emotion engine

[0352] The device analyzes the user's input and interactions with an emotion engine to understand the user's emotional state. For example, emotions such as "anxiety" or "relief" can be identified from changes in text input speed and touch pressure.

[0353] Input: User input and interaction data

[0354] Output: Emotion analysis results by the emotion engine

[0355] Specific operation: An emotion analysis library installed on the device analyzes the user's input data in real time and determines the user's emotional state (e.g., EmotionAPI).

[0356] Step 6: Emotional Feedback

[0357] The server adjusts the way it presents treatment plans and recommends specialists based on the emotional state analyzed by the emotion engine. For example, if the user is "anxious," it will provide more reassuring explanations.

[0358] Input: Sentiment analysis results

[0359] Output: List of tailored treatment plans and specialists

[0360] Specific operation: The server executes a script that creates and sends appropriate content to the user based on the user's emotional state.

[0361] Step 7: Specialist selection

[0362] The server extracts the most suitable doctors from a specialist database based on the treatment plan and emotional state, thereby generating a list of specialists suited to the user.

[0363] Input: Treatment plan and emotion analysis results

[0364] Output: List of specialists

[0365] Specific operation: The server executes an SQL query to generate a list of specialists.

[0366] Step 8: Arrange an appointment

[0367] The user selects the doctor of their choice from the displayed list of specialists, and the selection information is sent from the terminal to the server.

[0368] The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified.

[0369] Input: User's chosen specialist

[0370] Output: Appointment with a specialist

[0371] Specific operation: The user selects a specialist, and the selection information is sent from the terminal to the server. The server then accesses the specialist's appointment system via API, checks availability, and confirms the appointment.

[0372] Step 9: Notification of reservation results

[0373] The server transmits the confirmed reservation information to the terminal.

[0374] The terminal notifies the user of the reservation result. For example, it displays, "Your reservation with the doctor at the university hospital on October 20th has been confirmed."

[0375] Input: Confirmed reservation information

[0376] Output: Reservation confirmation

[0377] Specific operation: The server sends the reservation information to the terminal as an HTTP response. The terminal displays the received data on the screen and notifies the user.

[0378] (Application example 2)

[0379] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0380] Conventional medical systems do not take into account the user's emotional state when proposing treatment plans based on diagnostic information or when selecting and booking specialists, and therefore do not provide sufficient support, especially for patients experiencing anxiety or stress. Furthermore, the lack of accurate feedback based on the user's emotional state has led to issues such as reduced patient satisfaction and reduced efficiency in medical services.

[0381] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting diagnostic information, means for analyzing symptoms using the input diagnostic information, means for presenting an optimal treatment plan based on the analysis results, means for extracting a specialist corresponding to the presented treatment plan, means for automatically arranging an appointment with the extracted specialist, means for analyzing the user's emotional state, and means for adjusting the diagnostic results and the recommendation of a specialist based on the analyzed emotional state. This makes it possible to propose a treatment plan and make an appointment with a specialist based on the diagnostic information, as well as provide feedback and support according to the user's emotional state.

[0382] "Diagnostic information" is data that indicates the user's health condition, symptoms, treatment history, etc.

[0383] "Symptom analysis" is the act of evaluating symptoms based on input diagnostic information using statistical data and the latest medical knowledge.

[0384] A "treatment plan" is the optimal medical procedure or treatment method proposed based on the results of symptom analysis.

[0385] A "specialist" is a medical professional with specialized knowledge and experience in a particular medical field or treatment.

[0386] "Automatic appointment scheduling" is the process by which the system checks the availability of specialists and automatically schedules appointments.

[0387] "Emotional state" is information that indicates the user's psychological and emotional state.

[0388] "Emotion analysis" is the process of analyzing the user's input text, touchscreen usage data, writing tone, etc. to determine their emotional state.

[0389] "Feedback adjustment" refers to the act of changing or correcting diagnostic results, treatment plans, and specialist recommendations based on the results of emotion analysis.

[0390] This invention combines a system that proposes optimal treatment plans based on diagnostic information, automates matching with specialists and scheduling appointments, and an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[0391] System Overview

[0392] Entering diagnostic information

[0393] The user logs into the system using an electronic terminal. After logging in, an interface for inputting diagnostic information (symptoms, treatment history, etc.) is displayed. For example, the user might input, "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect was observed." This data is sent from the terminal to the server.

[0394] Symptom analysis

[0395] The server receives the diagnostic information sent by the user and stores it in a database. The stored diagnostic information is then fed into an AI model for symptom analysis. This AI model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan.

[0396] Presentation of treatment plan

[0397] The server then sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may display a treatment plan such as "combined surgery and radiation therapy is recommended."

[0398] Intervention by the Emotion Engine

[0399] The device's emotion engine analyzes the user's emotional state through their input and interactions, such as the speed of text entry, the pressure on the touchscreen, and the tone of the text, to identify emotional states such as "anxiety" or "relief."

[0400] Emotion-Based Feedback

[0401] The server adjusts the treatment plan and specialist recommendation based on the emotional state analyzed by the emotion engine. For example, if the user is in an "anxious" state, more reassuring explanations and support will be provided.

[0402] Identifying specialists

[0403] The server extracts the appropriate specialists from the database based on the treatment plan and emotional state. This list of specialists is organized taking into account the user's convenience and emotional state, and is sent to the terminal.

[0404] Arranging a medical appointment

[0405] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it will say, "Your appointment with Dr. Tanaka at the University Hospital on October 20th has been confirmed."

[0406] Specific Examples

[0407] For example, if a user is diagnosed with stomach cancer and wants to get a second opinion, they log in to the system and enter their diagnosis and current symptoms. The server receives this information, analyzes the symptoms using an AI model, and generates a treatment plan that recommends a combination of surgery and radiation therapy. Along with the treatment plan displayed on the device, an emotion engine analyzes the user's emotional state based on the data they entered. Based on the results, the server selects an appropriate specialist and provides them in the most optimal way for the user.

[0408] Example prompts to input to a generative AI model:

[0409] "Design an app that provides an online shopping experience that takes emotional state into account. Analyze the text entered by the user and determine their emotional state (positive, neutral, negative). Furthermore, determine the user's emotional state (calm, anxious) based on the pressure on the screen and the typing speed, and generate product suggestions and support messages accordingly."

[0410] In this way, this system can automatically suggest optimal treatment plans and schedule appointments with specialists simply by inputting diagnostic information. It can also further reduce the burden on patients by providing feedback and support that takes into account the user's emotional state.

[0411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0412] Step 1:

[0413] The user logs in to the system using an electronic terminal. A login screen appears and the user enters their username and password. The entered authentication information is sent to the server for authentication. If authentication is successful, a diagnostic information input interface is displayed. Here, the user enters diagnostic information such as their symptoms and treatment history.

[0414] Input: User name, password, diagnosis information (symptoms, treatment history, etc.)

[0415] Output: Diagnostic information input interface display, transmitted diagnostic information

[0416] Step 2:

[0417] The diagnostic information entered into the device is sent to the server, which stores the received diagnostic information in a database. The stored diagnostic information is then fed into an AI model to analyze the symptoms.

[0418] Input: Diagnostic information

[0419] Output: Diagnostic information stored in a database, diagnostic information fed to an AI model

[0420] Step 3:

[0421] The AI ​​model on the server analyzes the symptoms based on the received diagnostic information. It then uses statistical data and the latest medical knowledge to derive the optimal treatment plan. This treatment plan is then sent from the server to the device and displayed to the user.

[0422] Input: diagnostic information, statistical data, medical knowledge

[0423] Output: Treatment plan

[0424] Step 4:

[0425] The device displays the treatment plan received from the server to the user. At the same time, the emotion engine analyzes the user's input data (text entry speed, touchscreen pressure, and tone of writing). The analysis results in the user's emotional state (e.g., "anxiety" or "relief").

[0426] Input: Treatment plan, user input data (text entry speed, touchscreen pressure, writing tone)

[0427] Output: User's emotional state

[0428] Step 5:

[0429] The server receives the emotional state analyzed by the emotion engine and adjusts the treatment plan and specialist recommendation list based on the emotional state. For example, if the user is in an "anxious" state, the server will provide more detailed explanations of the treatment plan and prioritize recommendations of specialists who can relieve stress.

[0430] Input: Emotional state, treatment plan

[0431] Output: Coordinated treatment plan, specialist recommendation list

[0432] Step 6:

[0433] The list of specialists extracted by the server is organized taking into consideration the user's convenience and emotional state and sent to the terminal. The user selects the desired doctor from the list of specialists displayed on the terminal. This selection information is sent from the terminal to the server.

[0434] Input: Specialist list, emotional state

[0435] Output: User selection information

[0436] Step 7:

[0437] The server checks the availability of the specialist selected by the user and automatically arranges the appointment. Once the appointment is confirmed, the information (appointment date and time, location, doctor name, etc.) is sent to the terminal and notified to the user.

[0438] Input: User selection information, specialist availability

[0439] Output: Confirmed reservation information, reservation notification

[0440] Step 8:

[0441] The terminal displays the appointment confirmation information to the user, allowing them to confirm the consultation date, allowing the user to easily complete the appointment with the specialist.

[0442] Input: Confirmed reservation information

[0443] Output: Display reservation information

[0444] This series of processing steps allows users to simply input their diagnostic information, and automatically recommends optimal treatment options and schedules appointments with specialists. It also provides feedback and support based on the user's emotional state, reducing the burden on patients.

[0445] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0446] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0447] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0448] [Second embodiment]

[0449] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0450] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0451] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0452] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0453] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0454] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0455] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0456] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0457] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0458] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0459] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0460] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0461] The present invention is a system that proposes optimal treatment plans based on diagnostic information, and automates matching with specialists and scheduling appointments. This system is primarily composed of a server, terminals, and users.

[0462] System Overview

[0463] Entering diagnostic information

[0464] The user logs in to the system using an electronic device (smartphone or PC). After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. For example, the user might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect was observed."

[0465] Symptom analysis

[0466] The server receives the diagnostic information sent by the user and stores it in a database. The stored diagnostic information is then fed into an AI model for symptom analysis. This AI model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan.

[0467] Presentation of treatment plan

[0468] The server then sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may display a treatment plan such as "combined surgery and radiation therapy is recommended."

[0469] Identifying specialists

[0470] The server extracts the appropriate specialists from the database based on the proposed treatment plan. This list of specialists is organized for the user's convenience and sent to the terminal.

[0471] Arranging a medical appointment

[0472] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it displays, "Your appointment with Dr. Sato at Keio University Hospital on October 20th has been confirmed."

[0473] Specific examples

[0474] 1. User: Diagnosed with stomach cancer, chemotherapy has not been effective, and he / she would like to get a second opinion.

[0475] 2. Terminal: Log in to the system and enter the diagnosis and current symptoms.

[0476] 3. Server: Receives and stores diagnostic information, analyzes it using an AI model, and generates a treatment plan that recommends a combination of surgery and radiation therapy.

[0477] 4. Terminal: Displays the generated treatment plan to the user.

[0478] 5. Server: Based on the treatment plan, extract relevant specialists from the database and send them to the terminal.

[0479] 6. Terminal: Displays a list of specialists to the user and allows them to select the doctor they want.

[0480] 7. Server: Checks availability of selected specialists and arranges appointments.

[0481] 8. Terminal: Notifies the user of the reservation confirmation information and secures the appointment date.

[0482] In this way, this system can automatically propose optimal treatment plans and schedule appointments with specialists simply by having the user input diagnostic information, reducing the burden on patients and providing them with opportunities to receive better treatment.

[0483] The processing flow will be explained below.

[0484] Program processing steps

[0485] Step 1:

[0486] Users log in to the system using electronic terminals. This login information includes a user ID and password, and after authentication, users are able to access the system.

[0487] Step 2:

[0488] After logging in, the device displays an interface for entering diagnostic information, including text boxes and drop-down menus for entering symptoms and treatment history.

[0489] Step 3:

[0490] The user inputs diagnosis information (e.g., "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect has been observed") according to the interface.

[0491] Step 4:

[0492] The device sends the diagnostic information entered by the user to the server using an HTTP POST request.

[0493] Step 5:

[0494] The server stores the received diagnostic information in a database, including the user ID, symptoms, and treatment history.

[0495] Step 6:

[0496] The server passes the stored diagnostic information to an AI model, which then analyzes the symptoms using statistical data and the latest medical knowledge.

[0497] Step 7:

[0498] The server stores the optimal treatment plan generated by the AI ​​model in a database. For example, a treatment plan that suggests a combination of surgery and radiation therapy is recommended is generated.

[0499] Step 8:

[0500] The server sends the generated treatment plan to the terminal, and the data is sent as an HTTP response.

[0501] Step 9:

[0502] The terminal displays the received treatment plan to the user. For example, a message such as "Combined surgery and radiation therapy is recommended" is displayed.

[0503] Step 10:

[0504] The server extracts the appropriate specialists from the medical database based on the treatment plan, and organizes the extracted specialists' information (such as their names, affiliated hospitals, and specialties).

[0505] Step 11:

[0506] The server sends the extracted list of specialists to the terminal, which may include, for example, "Dr. Tanaka of the university hospital, Dr. Sato of the general hospital, and Dr. Takahashi of the clinic."

[0507] Step 12:

[0508] The terminal displays a list of specialists to the user and provides an interface for selecting the desired doctor.

[0509] Step 13:

[0510] The user selects the desired specialist from the list, for example, "Dr. Tanaka of the University Hospital."

[0511] Step 14:

[0512] The terminal sends the user's selection information to the server, again using an HTTP POST request.

[0513] Step 15:

[0514] The server accesses the appointment management system to check the availability of the selected specialist, and uses the system's API to obtain the specialist's schedule information.

[0515] Step 16:

[0516] The server automatically arranges appointments based on the schedule information and generates confirmed appointment information, such as "An appointment with Dr. Sato at Keio University Hospital has been confirmed for October 20, 2023."

[0517] Step 17:

[0518] The server sends the confirmed reservation information to the terminal using an HTTP response.

[0519] Step 18:

[0520] The terminal displays the received appointment information to the user and notifies the user that the appointment has been confirmed. The user can confirm the appointment date by checking this information.

[0521] Example 1

[0522] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0523] In conventional medical systems, the process from entering diagnostic information to making an appointment with a specialist is complicated, placing a significant burden on patients. In addition, there are cases where an appropriate treatment plan cannot be presented quickly, resulting in patients missing out on the opportunity to receive optimal treatment.

[0524] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0525] In this invention, the server includes: means for inputting diagnostic information using an electronic terminal; means for transmitting the input diagnostic information to the server and storing it; means for analyzing the stored diagnostic information using an AI model and analyzing symptoms; means for the server to generate an optimal treatment plan based on the analysis results and present it to the user; means for the server to extract a suitable specialist from a database based on the presented treatment plan; and means for displaying the extracted list of specialists to the user and automatically arranging an appointment with the selected specialist. This allows the user to easily complete a series of processes from inputting diagnostic information to making an appointment with a specialist, enabling the user to receive optimal treatment quickly.

[0526] An "electronic terminal" is an electronic device such as a smartphone or a personal computer that a user uses to input diagnostic information.

[0527] "Diagnostic information" is medically related information such as symptoms and treatment history provided by the user.

[0528] A "server" is a computer system that receives, stores, analyzes, and provides results to a user.

[0529] "Storing" refers to storing the received diagnostic information in a storage system such as a database.

[0530] An "AI model" is an artificial intelligence algorithm that analyzes diagnostic information based on statistical data and the latest medical knowledge, and estimates symptoms and treatment plans.

[0531] "Symptom analysis" refers to the use of AI models to analyze diagnostic information provided by users to gain a deeper understanding of their symptoms.

[0532] A "treatment plan" is a treatment method or procedure that is deemed optimal based on the results of symptom analysis.

[0533] A "specialist" is a medical professional with specialized knowledge and skills for a specific symptom or disease.

[0534] "Appointment" is the process of confirming a consultation date with a specialist selected by the user.

[0535] A "database" is an information system for efficiently managing diagnostic information and data related to specialists.

[0536] The present invention is a system that proposes optimal treatment plans based on diagnostic information, and automates matching with specialists and scheduling appointments. This system is primarily composed of a server, terminals, and users.

[0537] Entering diagnostic information

[0538] Users log in to the system using an electronic device (smartphone or PC). After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. For example, a user might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect has been observed." This entry can be made through a web browser or a mobile application.

[0539] Sending and storing diagnostic information

[0540] The terminal sends the entered diagnostic information to a server, which stores the received diagnostic information in a dedicated database. This database can use a relational database management system such as MySQL or MariaDB.

[0541] Analysis of diagnostic information

[0542] The server supplies the stored diagnostic information to an AI model, which is built using machine learning frameworks such as TensorFlow and PyTorch. The AI ​​model analyzes the diagnostic information and performs symptom analysis based on statistical data and the latest medical knowledge. For example, it generates a treatment plan such as "combined surgery and radiation therapy is recommended."

[0543] Presentation of treatment plan

[0544] The server sends the generated treatment plan to the device, which then displays it to the user. For example, it may say, "A combination of surgery and radiation therapy is recommended." This allows the user to confirm the optimal treatment plan.

[0545] Identifying specialists

[0546] The server extracts the appropriate specialists from the database based on the treatment plan generated by the AI ​​model, and the extracted list of specialists is organized for the user's convenience and sent to the device.

[0547] Arranging a medical appointment

[0548] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it may say, "An appointment has been confirmed with a doctor at a certain university hospital on October 20th."

[0549] Specific examples

[0550] Below are some example prompts to illustrate the concept:

[0551] If a user is diagnosed with stomach cancer and chemotherapy is ineffective, please suggest the optimal treatment plan. The system will recommend surgery and radiation therapy, and include a function to list and schedule an appointment with an appropriate specialist. Please explain the specific processing steps and also add a flow for presenting the treatment plan and arranging an appointment with a specialist.

[0552] In this way, this system can automatically propose optimal treatment plans and schedule appointments with specialists simply by having the user input diagnostic information. This process reduces the burden on patients and provides them with opportunities to receive better treatment.

[0553] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0554] Step 1: Enter diagnostic information

[0555] Users log in to the system using a smartphone or PC. After logging in, the device displays an interface for entering diagnostic information. The user enters symptoms and treatment history on the interface. For example, they might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect has been observed."

[0556] Input: User's diagnosis information (symptoms, treatment history)

[0557] Output: Entered diagnostic information

[0558] Step 2: Send and save diagnostic information

[0559] The terminal sends the entered diagnostic information to the server, which then stores the received diagnostic information in a database, such as MySQL.

[0560] Input: Diagnostic information submitted by the user

[0561] Output: Diagnostic information stored in a database

[0562] Step 3: Analyze the diagnostic information

[0563] The server supplies the diagnostic information stored in the database to the AI ​​model. The AI ​​model is built using machine learning frameworks such as TensorFlow and performs analysis based on statistical data and the latest medical knowledge. The analysis results in the generation of an optimal treatment plan. For example, it may decide that a combination of surgery and radiation therapy is recommended.

[0564] Input: Saved diagnostic information

[0565] Output: Analyzed treatment plan

[0566] Step 4: Proposing a treatment plan

[0567] The server sends the generated treatment plan to the terminal, which then displays it to the user. For example, it may say, "Combined surgery and radiation therapy is recommended."

[0568] Input: Analyzed treatment plan

[0569] Output: A treatment plan presented to the user

[0570] Step 5: Specialist selection

[0571] The server extracts corresponding specialists from the database based on the generated treatment plan, generates a list of appropriate specialists, and transmits the list to the terminal.

[0572] Input: Analyzed treatment plan

[0573] Output: List of specialists

[0574] Step 6: Arrange an appointment

[0575] The user selects the doctor of their choice from a list of specialists displayed on the terminal. The terminal then sends information about the selected specialist to the server. The server then checks the availability of the selected specialist and automatically arranges the appointment. Once the appointment is confirmed, the appointment information is sent to the terminal and the user is notified. For example, a message may be displayed saying, "An appointment has been confirmed with a doctor at a certain university hospital on October 20th."

[0576] Input: User-selected specialist

[0577] Output: Confirmed reservation information and notification to the user

[0578] (Application example 1)

[0579] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0580] Conventional medical systems do not offer systems that can propose optimal treatment plans based on diagnostic information, match patients with specialists, and automate appointments. They also lack systems that can propose optimal meal plans based on health information or automate appointments with nutritionists. This has resulted in a lack of centralized, highly accurate recommendations and reservation arrangements, even in the fields of health and nutrition, requiring significant user effort. Furthermore, recommendations are not based on the latest knowledge or statistical data in each specialty, meaning the optimal choice for the user is not guaranteed. Therefore, there is a need for a system that can automate optimal recommendations and reservation arrangements based on diagnostic and health information, thereby reducing the burden on users.

[0581] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0582] In this invention, the server includes means for inputting diagnostic information, means for analyzing symptoms using the input diagnostic information, means for presenting an optimal treatment plan based on the analysis results, means for extracting a specialist corresponding to the presented treatment plan, means for automatically arranging a consultation appointment with the extracted specialist, means for inputting health information, means for analyzing an optimal meal plan and meal provision facility using the input health information, means for presenting an optimal meal plan and meal provision facility based on the analysis results, means for extracting a nutritionist corresponding to the presented meal plan and meal provision facility, and means for automatically arranging a consultation appointment with the extracted nutritionist. This enables optimal suggestions and reservation arrangements for the user based on both diagnostic information and health information.

[0583] "Diagnostic information" is medical information, such as symptoms and treatment history, that a user enters into the system.

[0584] "Health information" refers to information about dietary habits, such as health status, allergies, calorie restrictions, and dietary history, that a user inputs into the system.

[0585] "Symptom analysis" is a process that analyzes the user's symptoms using AI models and statistical data based on the input diagnostic information.

[0586] A "treatment policy" is a guideline for the optimal treatment method for the user, which is generated based on the results of symptom analysis.

[0587] A "specialist" is a doctor who has specialized knowledge and experience in a particular medical field.

[0588] "Appointment" is a procedure for making an appointment with a specialist selected by the user.

[0589] A "meal plan" is a meal suggestion suitable for a user that is generated based on health information.

[0590] "Meal Provider" means a dining establishment that offers a recommended meal plan to a user.

[0591] A "nutritionist" is a professional who provides appropriate nutritional management based on the user's health condition and meal plan.

[0592] "Consultation reservation" is a procedure for booking a consultation with a dietitian selected by the user.

[0593] The present invention is a system that presents optimal treatment guidelines and meal plans based on diagnostic and health information, and automatically arranges appointments with specialized doctors and nutritionists. This system is mainly composed of a server, terminals, and users.

[0594] Overall system configuration

[0595] 1. User operations

[0596] First, users access the system via a smartphone app or computer and log in. After logging in, an interface is displayed where they can enter diagnostic information (symptoms, treatment history) and health information (allergies, calorie restriction, dietary history).

[0597] 2. Data transmission and storage

[0598] The diagnostic and health information sent from the device is stored on a server, which manages it and provides it for analysis and retrieval as needed.

[0599] 3. Symptom and health information analysis

[0600] The server then feeds the stored diagnostic information into an AI model that analyzes the symptoms. The AI ​​model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan. It also generates an optimal diet plan based on the health information.

[0601] 4. Providing optimal treatment and dietary plans

[0602] The server then sends the generated treatment plan and meal plan to the device and displays it to the user. For example, it presents the optimal options for the user, such as "combined surgery and radiation therapy is recommended" or "low-calorie, gluten-free meal plan."

[0603] 5. Extraction and matching of specialized doctors and nutritionists

[0604] Based on the displayed treatment plan, the server extracts relevant specialists from the database and provides them to the user as a list. Similarly, based on the presented meal plan, the server extracts corresponding nutritionists and displays them to the user.

[0605] 6. Reservation Arrangements

[0606] Once the user selects the desired specialist and nutritionist, the server automatically checks their availability and arranges the reservation. Once the reservation is confirmed, the information is sent to the terminal and the user is notified.

[0607] Hardware and software used

[0608] Smartphone app / computer: Used for user interface and input data transmission.

[0609] Server: Database management and cloud processing.

[0610] AI models: Used to analyze diagnostic and health information and generate optimal treatment and diet plans.

[0611] Location API: Used to suggest dining facilities using GPS data.

[0612] Specific examples

[0613] 1. A user is diagnosed with stomach cancer and has not seen any effect from anti-cancer drug treatment, so they log in to the system. They enter their diagnosis, symptoms, and treatment history, as well as allergy information (e.g., gluten) and diet goals (e.g., low calorie).

[0614] 2. The server feeds the received diagnostic information into the AI ​​model and generates a treatment plan that recommends a combination of surgery and radiation therapy. It also generates a low-calorie, gluten-free meal plan based on the health information.

[0615] 3. The terminal displays the above treatment guidelines and meal plan to the user.

[0616] 4. The server extracts relevant specialists and corresponding nutritionists from the database and provides them to the user as a list.

[0617] 5. The terminal allows the user to select the specialist and nutritionist of their choice. The server checks availability, arranges the reservation, and notifies the user of the reservation confirmation.

[0618] Example prompts to input to the generative AI model

[0619] Provide user-entered health information (e.g., allergies, calorie restrictions, dietary history). Based on this information, suggest the best meal plan and restaurant for the user, then match with a professional nutritionist and make a reservation. In this step, process the necessary AI model and generate the API response.

[0620]

[0621] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0622] Step 1:

[0623] Users log in to the system using a smartphone app or a computer. After logging in, an interface is displayed where they can enter diagnostic information (symptoms, treatment history) and health information (allergies, calorie restriction, dietary history). The entered information is sent from the device to the server.

[0624] Input: Diagnostic information, health information

[0625] Output: User data sent to the server

[0626] Step 2:

[0627] The server receives the diagnostic and health information sent by the user and stores it in a database for later analysis and retrieval.

[0628] Input: Data submitted by the user

[0629] Output: Data saved to database

[0630] Step 3:

[0631] The server then feeds the diagnostic information stored in the database to the AI ​​model, which analyzes the symptoms. The AI ​​model uses statistical data and the latest medical knowledge to generate the optimal treatment plan. It also analyzes the health information and generates the optimal meal plan.

[0632] Input: Stored diagnostic and health information

[0633] Output: Optimal treatment plan, optimal diet plan

[0634] Step 4:

[0635] The server then sends the treatment plan and meal plan generated by the AI ​​model to the device and displays it to the user, such as "Surgery and radiation therapy combined is recommended" or "A low-calorie, gluten-free meal plan."

[0636] Input: Optimal treatment plan, optimal diet plan

[0637] Output: what the user sees

[0638] Step 5:

[0639] The server extracts relevant specialists from the database based on the displayed treatment plan and sends them to the terminal.Similarly, it extracts corresponding nutritionists based on the presented meal plan and provides them to the user as a list.

[0640] Input: Optimal treatment plan, optimal diet plan

[0641] Output: List of specialists and nutritionists

[0642] Step 6:

[0643] The user selects the desired specialist and nutritionist from the provided list, and the selected information is sent from the terminal to the server.

[0644] Input: Specialist and nutritionist selection information

[0645] Output: Sends the selected data to the server

[0646] Step 7:

[0647] The server automatically checks the availability of the selected specialist and nutritionist and arranges the reservation. Once the reservation is confirmed, the information is sent to the terminal and the user is notified. For example, it may display "An appointment with a specialist has been confirmed for October 20th."

[0648] Input: User selection information

[0649] Output: Confirmed reservation information, notification to user

[0650] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0651] This invention combines a system that proposes optimal treatment plans based on diagnostic information, automates matching with specialists and scheduling appointments, and an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[0652] System Overview

[0653] Entering diagnostic information

[0654] A user logs in to the system using an electronic terminal. After logging in, an interface for inputting diagnostic information (symptoms, treatment history, etc.) is displayed. For example, the user may input, "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect was observed."

[0655] Symptom analysis

[0656] The server receives the diagnostic information sent by the user and stores it in a database. The stored diagnostic information is then fed into an AI model for symptom analysis. This AI model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan.

[0657] Presentation of treatment plan

[0658] The server then sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may display a treatment plan such as "combined surgery and radiation therapy is recommended."

[0659] Intervention by the Emotion Engine

[0660] The device's emotion engine analyzes the user's emotional state through their input and interactions. For example, the emotion engine analyzes the speed of text input, the pressure on the touchscreen, and the tone of the text to identify emotional states such as "anxiety" or "relief."

[0661] Emotion-Based Feedback

[0662] The server adjusts the treatment plan and specialist recommendation based on the emotional state analyzed by the emotion engine. For example, if the user is in an "anxious" state, more reassuring explanations and support will be provided.

[0663] Identifying specialists

[0664] The server extracts the appropriate specialists from the database based on the treatment plan and emotional state. This list of specialists is organized taking into account the user's convenience and emotional state, and is sent to the terminal.

[0665] Arranging a medical appointment

[0666] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it will say, "Your appointment with Dr. Tanaka at the University Hospital on October 20th has been confirmed."

[0667] Specific examples

[0668] 1. User: Diagnosed with stomach cancer, chemotherapy has not been effective, and he / she would like to get a second opinion.

[0669] 2. Terminal: Log in to the system and enter the diagnosis and current symptoms.

[0670] 3. Server: Receives and stores diagnostic information, analyzes it using an AI model, and generates a treatment plan that recommends a combination of surgery and radiation therapy.

[0671] 4. Terminal: The generated treatment plan is displayed to the user. At the same time, the emotion engine analyzes the user's emotional state based on the user's input data and interactions.

[0672] 5. Server: Based on the emotional state obtained by the emotion engine, the treatment plan and list of specialists are adjusted and provided to the user in the most optimal way.

[0673] 6. Server: Based on the treatment plan, extract relevant specialists from the database and send them to the terminal.

[0674] 7. Terminal: The user is presented with a list of specialists and allowed to select the doctor of their choice. Support and explanations are provided according to the user's emotional state.

[0675] 8. Server: Checks availability of selected specialists and arranges appointments.

[0676] 9. Terminal: Notifies the user of the reservation confirmation information and secures the appointment date.

[0677] In this way, this system can automatically propose optimal treatment plans and schedule appointments with specialists simply by having the user input diagnostic information. Furthermore, by combining it with an emotion engine, it can provide support according to the user's emotional state, further reducing the burden on patients.

[0678] The processing flow will be explained below.

[0679] Program processing steps

[0680] Step 1:

[0681] A user logs in to the system using an electronic terminal by entering a user ID and password. The terminal then sends the entered information to the server, which then authenticates the user.

[0682] Step 2:

[0683] After logging in, the device displays an interface for entering diagnostic information, which includes text boxes and drop-down menus for entering symptoms and treatment history.

[0684] Step 3:

[0685] The user follows the interface to input diagnosis information, for example, "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect was observed."

[0686] Step 4:

[0687] The device sends the diagnostic information entered by the user to the server via an HTTP POST request.

[0688] Step 5:

[0689] The server stores the received diagnostic information in a database, including the user ID, symptoms, and treatment history.

[0690] Step 6:

[0691] The server passes the stored diagnostic information to an AI model, which analyzes the symptoms based on statistical data and the latest medical knowledge.

[0692] Step 7:

[0693] The server stores the optimal treatment plan generated by the AI ​​model in a database and sends the plan to the device. For example, the treatment plan may suggest that a combination of surgery and radiation therapy is recommended.

[0694] Step 8:

[0695] The terminal displays the received treatment plan to the user.

[0696] Step 9:

[0697] The device analyzes the user's operations (keyboard input, touch screen operation, etc.) and sends the data to the emotion engine, which analyzes the user's emotional state.

[0698] Step 10:

[0699] The server receives the emotional state from the emotion engine and adjusts the way it presents treatment plans and recommends specialists based on the user's feelings, such as "anxiety" or "relief."

[0700] Step 11:

[0701] Based on the adjusted information, the server extracts from the database a list of specialists who are suited to the treatment plan and the user's emotional state, and transmits this to the terminal.

[0702] Step 12:

[0703] The terminal displays a list of specialists to the user, including, for example, "Dr. Tanaka from the university hospital, Dr. Sato from the general hospital, and Dr. Takahashi from the clinic."

[0704] Step 13:

[0705] The user selects the doctor of their choice from a list of specialists, and the terminal then sends the information to the server.

[0706] Step 14:

[0707] The server checks the availability of the selected specialist via the reservation management system's API.

[0708] Step 15:

[0709] The server automatically arranges the reservation based on availability and generates confirmed reservation information, such as "An appointment with Dr. Tanaka at the University Hospital has been confirmed for October 20th."

[0710] Step 16:

[0711] The server transmits the confirmed reservation information to the terminal.

[0712] Step 17:

[0713] The terminal displays the received reservation information to the user and notifies the user that the reservation has been confirmed. By checking this information, the user can reserve a date to see a specialist.

[0714] This system's series of processes allows users to simply input their diagnosis information, and automatically recommend optimal treatment plans and schedule appointments with specialists. Furthermore, by combining it with an emotion engine, support can be provided according to the user's emotional state, reducing the burden on patients.

[0715] Example 2

[0716] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0717] Current medical systems make it difficult for users to input diagnostic information and then find the appropriate treatment plan or schedule an appointment with a specialist. Furthermore, they do not provide support that takes into account the user's emotional state, which can increase stress and anxiety for patients. This can result in delays in the transition to treatment and a decline in the quality of care.

[0718] The identification process 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 a means for inputting diagnostic information, a means for analyzing symptoms using the input diagnostic information, a means for presenting an optimal treatment plan based on the analysis results, a means for recognizing an emotional state, a means for adjusting the presentation of a treatment plan and the recommendation of a specialist based on the recognized emotional state, a means for extracting a specialist corresponding to the presented treatment plan, and a means for automatically arranging an appointment with the extracted specialist. As a result, by simply inputting diagnostic information by the user, an optimal treatment plan can be proposed, an appointment with a specialist can be automated, and support according to the emotional state can be provided, thereby reducing the burden on the patient.

[0719] "Diagnostic information" refers to medically relevant information such as symptoms and treatment history that a user enters into the system.

[0720] "Input means" refers to the interface and software that allows a user to input diagnostic information into the system through an electronic terminal.

[0721] The "analysis means" refers to technical means such as algorithms or AI models that the server uses to analyze symptoms using the input diagnostic information.

[0722] The "means for presenting" refers to a method and system for the server to display the optimal treatment plan to the user based on the results of symptom analysis.

[0723] "Emotional state" is data that reflects the user's psychological state and emotional response, and is the result of evaluating emotions such as anxiety and relief.

[0724] "Means for recognizing" refers to the technical means for analyzing a user's input and interactions using an emotion engine to understand the user's emotional state.

[0725] "Adjusting" refers to methods and systems for modifying treatment presentations or specialist recommendations based on the perceived emotional state.

[0726] The "means of extraction" refers to an algorithm or system that selects specialists from a database who are appropriate for the treatment plan.

[0727] "Means for arranging an appointment automatically" refers to a method and system that allows the system to automatically schedule an appointment with a specialist selected by the user.

[0728] This invention combines a system that proposes optimal treatment plans based on diagnostic information, automates matching with specialists and scheduling appointments, with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[0729] Entering diagnostic information

[0730] Users log in to the system using an electronic device, such as a smartphone or PC. After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. The information entered by the user might be, for example, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect has been observed." The software used is a web interface and input form that is accessed through a web browser.

[0731] Sending and receiving diagnostic information

[0732] The terminal sends the diagnostic information entered by the user to the server, which receives the information and stores it in an internal database. The hardware used is computers and servers in a data center, and the software used is a database management system.

[0733] Symptom analysis

[0734] The server then supplies the received diagnostic information to the AI ​​model, which then begins analyzing the symptoms. The AI ​​model then derives the optimal treatment plan based on the latest medical knowledge and statistical data. This AI model uses TensorFlow and PyTorch, among other technologies. Specifically, a Python script is executed to analyze the diagnostic information.

[0735] Generation and presentation of treatment plans

[0736] The server sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may suggest a treatment plan such as "surgery and radiation therapy are recommended." HTTP is used as the communication protocol, and the user interface displays the data.

[0737] Emotion analysis using an emotion engine

[0738] The device analyzes the user's input and interactions using an emotion engine to understand the user's emotional state. Specifically, the device analyzes things like the speed of text input and changes in touch pressure, and identifies emotions such as "anxiety" or "relief." The emotion engine uses EmotionAPI and other tools.

[0739] Emotion-Based Feedback

[0740] The server adjusts the treatment plan and specialist recommendation based on the emotional state analyzed by the emotion engine. For example, if the user is "anxious," it will provide a more reassuring explanation. An AI feedback system is used for this adjustment.

[0741] Identifying specialists

[0742] The server extracts the corresponding specialists from the database based on the treatment plan and emotional state. The extracted list of specialists is organized and sent to the terminal. The software used is a database management system and a matching algorithm.

[0743] Arranging a medical appointment

[0744] The user selects the doctor of their choice from a list of specialists displayed. The selection information is sent from the terminal to the server. The server automatically checks the specialist's availability and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, the message might say, "Your appointment with Dr. Tanaka at the university hospital on October 20th has been confirmed." Scheduling and notification systems are used for this purpose.

[0745] Specific examples

[0746] 1. The user logs in to the system and enters the diagnosis and current symptoms. For example, the user might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but it was ineffective."

[0747] 2. The server receives the diagnosis information and uses the AI ​​model to generate a treatment plan that recommends a combination of surgery and radiation therapy.

[0748] 3. The device displays the generated treatment plan to the user, and the emotion engine analyzes the user's emotions.

[0749] 4. The server adjusts the treatment plan and list of specialists based on the emotional state and provides them to the user.

[0750] 5. The server extracts specialists from the database based on the treatment plan and sends the list to the terminal.

[0751] 6. The device displays a list of specialists to the user and allows them to select the doctor they prefer. They provide support and explanations based on their emotional state.

[0752] 7. The server checks the availability of the selected specialist and arranges the appointment.

[0753] 8. The terminal notifies the user of the confirmed reservation information and asks for reconfirmation.

[0754] By using the above steps, the system can propose optimal treatment plans and automate specialist appointments simply by allowing the user to input diagnostic information. Furthermore, by using an emotion engine, it can provide support tailored to the user's emotional state, reducing the burden on patients.

[0755] Prompt Sentence Examples

[0756] "Please demonstrate a system in which a user inputs diagnostic information and an AI model generates an optimal treatment plan. Please also explain the specific steps for data input and analysis."

[0757] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0758] Step 1: Enter diagnostic information

[0759] Users log in to the system using a smartphone or PC. After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. The user enters information such as "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect was observed."

[0760] Input: Diagnostic information entered by the user on an electronic device

[0761] Output: Diagnostic information entered in the input form

[0762] Specific operations: Open the browser on the electronic device and access the system's login page. Enter your username and password and click the login button. Enter the diagnostic information in the form that is displayed next and click the submit button.

[0763] Step 2: Sending and Receiving Diagnostic Information

[0764] The terminal transmits the diagnostic information entered by the user to the server.

[0765] The server receives the diagnostic information sent from the terminal and stores it in an internal database.

[0766] Input: Diagnostic information that the device sends to the server

[0767] Output: Diagnostic information received by the server and stored in the database

[0768] Specific operation: Diagnostic information is sent from the device to the server as an HTTP POST request. The server receives the request and stores it in a database.

[0769] Step 3: Analyze the symptoms

[0770] The server then supplies the received diagnostic information to the AI ​​model, which then begins analyzing the symptoms. The AI ​​model then derives the optimal treatment plan based on the latest medical knowledge and statistical data.

[0771] Input: Diagnostic information stored in the database by the server

[0772] Output: Symptom analysis results by the AI ​​model

[0773] How it works: A Python script runs on the server, and an AI model analyzes the diagnostic information using the TensorFlow library.

[0774] Step 4: Generate and present a treatment plan

[0775] The server sends the treatment plan generated by the AI ​​model to the device.

[0776] The terminal displays this to the user, for example, "Combined surgery and radiation therapy is recommended."

[0777] Input: Symptom analysis results by AI model

[0778] Output: Treatment plan displayed on the terminal

[0779] Specific operation: The server sends the generated treatment plan to the terminal as an HTTP response. The terminal displays the received data on the screen.

[0780] Step 5: Emotion analysis using the emotion engine

[0781] The device analyzes the user's input and interactions with an emotion engine to understand the user's emotional state. For example, emotions such as "anxiety" or "relief" can be identified from changes in text input speed and touch pressure.

[0782] Input: User input and interaction data

[0783] Output: Emotion analysis results by the emotion engine

[0784] Specific operation: An emotion analysis library installed on the device analyzes the user's input data in real time and determines the user's emotional state (e.g., EmotionAPI).

[0785] Step 6: Emotional Feedback

[0786] The server adjusts the way it presents treatment plans and recommends specialists based on the emotional state analyzed by the emotion engine. For example, if the user is "anxious," it will provide more reassuring explanations.

[0787] Input: Sentiment analysis results

[0788] Output: List of tailored treatment plans and specialists

[0789] Specific operation: The server executes a script that creates and sends appropriate content to the user based on the user's emotional state.

[0790] Step 7: Specialist selection

[0791] The server extracts the most suitable doctors from a specialist database based on the treatment plan and emotional state, thereby generating a list of specialists suited to the user.

[0792] Input: Treatment plan and emotion analysis results

[0793] Output: List of specialists

[0794] Specific operation: The server executes an SQL query to generate a list of specialists.

[0795] Step 8: Arrange an appointment

[0796] The user selects the doctor of their choice from the displayed list of specialists, and the selection information is sent from the terminal to the server.

[0797] The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified.

[0798] Input: User's chosen specialist

[0799] Output: Appointment with a specialist

[0800] Specific operation: The user selects a specialist, and the selection information is sent from the terminal to the server. The server then accesses the specialist's appointment system via API, checks availability, and confirms the appointment.

[0801] Step 9: Notification of reservation results

[0802] The server transmits the confirmed reservation information to the terminal.

[0803] The terminal notifies the user of the reservation result. For example, it displays, "Your reservation with the doctor at the university hospital on October 20th has been confirmed."

[0804] Input: Confirmed reservation information

[0805] Output: Reservation confirmation

[0806] Specific operation: The server sends the reservation information to the terminal as an HTTP response. The terminal displays the received data on the screen and notifies the user.

[0807] (Application example 2)

[0808] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0809] Conventional medical systems do not take into account the user's emotional state when proposing treatment plans based on diagnostic information or when selecting and booking specialists, and therefore do not provide sufficient support, especially for patients experiencing anxiety or stress. Furthermore, the lack of accurate feedback based on the user's emotional state has led to issues such as reduced patient satisfaction and reduced efficiency in medical services.

[0810] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting diagnostic information, means for analyzing symptoms using the input diagnostic information, means for presenting an optimal treatment plan based on the analysis results, means for extracting a specialist corresponding to the presented treatment plan, means for automatically arranging an appointment with the extracted specialist, means for analyzing the user's emotional state, and means for adjusting the diagnostic results and the recommendation of a specialist based on the analyzed emotional state. This makes it possible to propose a treatment plan and make an appointment with a specialist based on the diagnostic information, as well as provide feedback and support according to the user's emotional state.

[0811] "Diagnostic information" is data that indicates the user's health condition, symptoms, treatment history, etc.

[0812] "Symptom analysis" is the act of evaluating symptoms based on input diagnostic information using statistical data and the latest medical knowledge.

[0813] A "treatment plan" is the optimal medical procedure or treatment method proposed based on the results of symptom analysis.

[0814] A "specialist" is a medical professional with specialized knowledge and experience in a particular medical field or treatment.

[0815] "Automatic appointment scheduling" is the process by which the system checks the availability of specialists and automatically schedules appointments.

[0816] "Emotional state" is information that indicates the user's psychological and emotional state.

[0817] "Emotion analysis" is the process of analyzing the user's input text, touchscreen usage data, writing tone, etc. to determine their emotional state.

[0818] "Feedback adjustment" refers to the act of changing or correcting diagnostic results, treatment plans, and specialist recommendations based on the results of emotion analysis.

[0819] This invention combines a system that proposes optimal treatment plans based on diagnostic information, automates matching with specialists and scheduling appointments, and an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[0820] System Overview

[0821] Entering diagnostic information

[0822] The user logs into the system using an electronic terminal. After logging in, an interface for inputting diagnostic information (symptoms, treatment history, etc.) is displayed. For example, the user might input, "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect was observed." This data is sent from the terminal to the server.

[0823] Symptom analysis

[0824] The server receives the diagnostic information sent by the user and stores it in a database. The stored diagnostic information is then fed into an AI model for symptom analysis. This AI model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan.

[0825] Presentation of treatment plan

[0826] The server then sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may display a treatment plan such as "combined surgery and radiation therapy is recommended."

[0827] Intervention by the Emotion Engine

[0828] The device's emotion engine analyzes the user's emotional state through their input and interactions, such as the speed of text entry, the pressure on the touchscreen, and the tone of the text, to identify emotional states such as "anxiety" or "relief."

[0829] Emotion-Based Feedback

[0830] The server adjusts the treatment plan and specialist recommendation based on the emotional state analyzed by the emotion engine. For example, if the user is in an "anxious" state, more reassuring explanations and support will be provided.

[0831] Identifying specialists

[0832] The server extracts the appropriate specialists from the database based on the treatment plan and emotional state. This list of specialists is organized taking into account the user's convenience and emotional state, and is sent to the terminal.

[0833] Arranging a medical appointment

[0834] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it will say, "Your appointment with Dr. Tanaka at the University Hospital on October 20th has been confirmed."

[0835] Specific Examples

[0836] For example, if a user is diagnosed with stomach cancer and wants to get a second opinion, they log in to the system and enter their diagnosis and current symptoms. The server receives this information, analyzes the symptoms using an AI model, and generates a treatment plan that recommends a combination of surgery and radiation therapy. Along with the treatment plan displayed on the device, an emotion engine analyzes the user's emotional state based on the data they entered. Based on the results, the server selects an appropriate specialist and provides them in the most optimal way for the user.

[0837] Example prompts to input to a generative AI model:

[0838] "Design an app that provides an online shopping experience that takes emotional state into account. Analyze the text entered by the user and determine their emotional state (positive, neutral, negative). Furthermore, determine the user's emotional state (calm, anxious) based on the pressure on the screen and the typing speed, and generate product suggestions and support messages accordingly."

[0839] In this way, this system can automatically suggest optimal treatment plans and schedule appointments with specialists simply by inputting diagnostic information. It can also further reduce the burden on patients by providing feedback and support that takes into account the user's emotional state.

[0840] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0841] Step 1:

[0842] The user logs in to the system using an electronic terminal. A login screen appears and the user enters their username and password. The entered authentication information is sent to the server for authentication. If authentication is successful, a diagnostic information input interface is displayed. Here, the user enters diagnostic information such as their symptoms and treatment history.

[0843] Input: User name, password, diagnosis information (symptoms, treatment history, etc.)

[0844] Output: Diagnostic information input interface display, transmitted diagnostic information

[0845] Step 2:

[0846] The diagnostic information entered into the device is sent to the server, which stores the received diagnostic information in a database. The stored diagnostic information is then fed into an AI model to analyze the symptoms.

[0847] Input: Diagnostic information

[0848] Output: Diagnostic information stored in a database, diagnostic information fed to an AI model

[0849] Step 3:

[0850] The AI ​​model on the server analyzes the symptoms based on the received diagnostic information. It then uses statistical data and the latest medical knowledge to derive the optimal treatment plan. This treatment plan is then sent from the server to the device and displayed to the user.

[0851] Input: diagnostic information, statistical data, medical knowledge

[0852] Output: Treatment plan

[0853] Step 4:

[0854] The device displays the treatment plan received from the server to the user. At the same time, the emotion engine analyzes the user's input data (text entry speed, touchscreen pressure, and tone of writing). The analysis results in the user's emotional state (e.g., "anxiety" or "relief").

[0855] Input: Treatment plan, user input data (text entry speed, touchscreen pressure, writing tone)

[0856] Output: User's emotional state

[0857] Step 5:

[0858] The server receives the emotional state analyzed by the emotion engine and adjusts the treatment plan and specialist recommendation list based on the emotional state. For example, if the user is in an "anxious" state, the server will provide more detailed explanations of the treatment plan and prioritize recommendations of specialists who can relieve stress.

[0859] Input: Emotional state, treatment plan

[0860] Output: Coordinated treatment plan, specialist recommendation list

[0861] Step 6:

[0862] The list of specialists extracted by the server is organized taking into consideration the user's convenience and emotional state and sent to the terminal. The user selects the desired doctor from the list of specialists displayed on the terminal. This selection information is sent from the terminal to the server.

[0863] Input: Specialist list, emotional state

[0864] Output: User selection information

[0865] Step 7:

[0866] The server checks the availability of the specialist selected by the user and automatically arranges the appointment. Once the appointment is confirmed, the information (appointment date and time, location, doctor name, etc.) is sent to the terminal and notified to the user.

[0867] Input: User selection information, specialist availability

[0868] Output: Confirmed reservation information, reservation notification

[0869] Step 8:

[0870] The terminal displays the appointment confirmation information to the user, allowing them to confirm the consultation date, allowing the user to easily complete the appointment with the specialist.

[0871] Input: Confirmed reservation information

[0872] Output: Display reservation information

[0873] This series of processing steps allows users to simply input their diagnostic information, and automatically recommends optimal treatment options and schedules appointments with specialists. It also provides feedback and support based on the user's emotional state, reducing the burden on patients.

[0874] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0875] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0876] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0877] [Third embodiment]

[0878] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0879] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0880] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0881] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0882] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0883] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0884] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0885] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0886] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0887] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0888] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0889] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0890] The present invention is a system that proposes optimal treatment plans based on diagnostic information, and automates matching with specialists and scheduling appointments. This system is primarily composed of a server, terminals, and users.

[0891] System Overview

[0892] Entering diagnostic information

[0893] The user logs in to the system using an electronic device (smartphone or PC). After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. For example, the user might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect was observed."

[0894] Symptom analysis

[0895] The server receives the diagnostic information sent by the user and stores it in a database. The stored diagnostic information is then fed into an AI model for symptom analysis. This AI model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan.

[0896] Presentation of treatment plan

[0897] The server then sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may display a treatment plan such as "combined surgery and radiation therapy is recommended."

[0898] Identifying specialists

[0899] The server extracts the appropriate specialists from the database based on the proposed treatment plan. This list of specialists is organized for the user's convenience and sent to the terminal.

[0900] Arranging a medical appointment

[0901] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it displays, "Your appointment with Dr. Sato at Keio University Hospital on October 20th has been confirmed."

[0902] Specific examples

[0903] 1. User: Diagnosed with stomach cancer, chemotherapy has not been effective, and he / she would like to get a second opinion.

[0904] 2. Terminal: Log in to the system and enter the diagnosis and current symptoms.

[0905] 3. Server: Receives and stores diagnostic information, analyzes it using an AI model, and generates a treatment plan that recommends a combination of surgery and radiation therapy.

[0906] 4. Terminal: Displays the generated treatment plan to the user.

[0907] 5. Server: Based on the treatment plan, extract relevant specialists from the database and send them to the terminal.

[0908] 6. Terminal: Displays a list of specialists to the user and allows them to select the doctor they want.

[0909] 7. Server: Checks availability of selected specialists and arranges appointments.

[0910] 8. Terminal: Notifies the user of the reservation confirmation information and secures the appointment date.

[0911] In this way, this system can automatically propose optimal treatment plans and schedule appointments with specialists simply by having the user input diagnostic information, reducing the burden on patients and providing them with opportunities to receive better treatment.

[0912] The processing flow will be explained below.

[0913] Program processing steps

[0914] Step 1:

[0915] Users log in to the system using electronic terminals. This login information includes a user ID and password, and after authentication, users are able to access the system.

[0916] Step 2:

[0917] After logging in, the device displays an interface for entering diagnostic information, including text boxes and drop-down menus for entering symptoms and treatment history.

[0918] Step 3:

[0919] The user inputs diagnosis information (e.g., "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect has been observed") according to the interface.

[0920] Step 4:

[0921] The device sends the diagnostic information entered by the user to the server using an HTTP POST request.

[0922] Step 5:

[0923] The server stores the received diagnostic information in a database, including the user ID, symptoms, and treatment history.

[0924] Step 6:

[0925] The server passes the stored diagnostic information to an AI model, which then analyzes the symptoms using statistical data and the latest medical knowledge.

[0926] Step 7:

[0927] The server stores the optimal treatment plan generated by the AI ​​model in a database. For example, a treatment plan that suggests a combination of surgery and radiation therapy is recommended is generated.

[0928] Step 8:

[0929] The server sends the generated treatment plan to the terminal, and the data is sent as an HTTP response.

[0930] Step 9:

[0931] The terminal displays the received treatment plan to the user. For example, a message such as "Combined surgery and radiation therapy is recommended" is displayed.

[0932] Step 10:

[0933] The server extracts the appropriate specialists from the medical database based on the treatment plan, and organizes the extracted specialists' information (such as their names, affiliated hospitals, and specialties).

[0934] Step 11:

[0935] The server sends the extracted list of specialists to the terminal, which may include, for example, "Dr. Tanaka of the university hospital, Dr. Sato of the general hospital, and Dr. Takahashi of the clinic."

[0936] Step 12:

[0937] The terminal displays a list of specialists to the user and provides an interface for selecting the desired doctor.

[0938] Step 13:

[0939] The user selects the desired specialist from the list, for example, "Dr. Tanaka of the University Hospital."

[0940] Step 14:

[0941] The terminal sends the user's selection information to the server, again using an HTTP POST request.

[0942] Step 15:

[0943] The server accesses the appointment management system to check the availability of the selected specialist, and uses the system's API to obtain the specialist's schedule information.

[0944] Step 16:

[0945] The server automatically arranges appointments based on the schedule information and generates confirmed appointment information, such as "An appointment with Dr. Sato at Keio University Hospital has been confirmed for October 20, 2023."

[0946] Step 17:

[0947] The server sends the confirmed reservation information to the terminal using an HTTP response.

[0948] Step 18:

[0949] The terminal displays the received appointment information to the user and notifies the user that the appointment has been confirmed. The user can confirm the appointment date by checking this information.

[0950] Example 1

[0951] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0952] In conventional medical systems, the process from entering diagnostic information to making an appointment with a specialist is complicated, placing a significant burden on patients. In addition, there are cases where an appropriate treatment plan cannot be presented quickly, resulting in patients missing out on the opportunity to receive optimal treatment.

[0953] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0954] In this invention, the server includes: means for inputting diagnostic information using an electronic terminal; means for transmitting the input diagnostic information to the server and storing it; means for analyzing the stored diagnostic information using an AI model and analyzing symptoms; means for the server to generate an optimal treatment plan based on the analysis results and present it to the user; means for the server to extract a suitable specialist from a database based on the presented treatment plan; and means for displaying the extracted list of specialists to the user and automatically arranging an appointment with the selected specialist. This allows the user to easily complete a series of processes from inputting diagnostic information to making an appointment with a specialist, enabling the user to receive optimal treatment quickly.

[0955] An "electronic terminal" is an electronic device such as a smartphone or a personal computer that a user uses to input diagnostic information.

[0956] "Diagnostic information" is medically related information such as symptoms and treatment history provided by the user.

[0957] A "server" is a computer system that receives, stores, analyzes, and provides results to a user.

[0958] "Storing" refers to storing the received diagnostic information in a storage system such as a database.

[0959] An "AI model" is an artificial intelligence algorithm that analyzes diagnostic information based on statistical data and the latest medical knowledge, and estimates symptoms and treatment plans.

[0960] "Symptom analysis" refers to the use of AI models to analyze diagnostic information provided by users to gain a deeper understanding of their symptoms.

[0961] A "treatment plan" is a treatment method or procedure that is deemed optimal based on the results of symptom analysis.

[0962] A "specialist" is a medical professional with specialized knowledge and skills for a specific symptom or disease.

[0963] "Appointment" is the process of confirming a consultation date with a specialist selected by the user.

[0964] A "database" is an information system for efficiently managing diagnostic information and data related to specialists.

[0965] The present invention is a system that proposes optimal treatment plans based on diagnostic information, and automates matching with specialists and scheduling appointments. This system is primarily composed of a server, terminals, and users.

[0966] Entering diagnostic information

[0967] Users log in to the system using an electronic device (smartphone or PC). After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. For example, a user might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect has been observed." This entry can be made through a web browser or a mobile application.

[0968] Sending and storing diagnostic information

[0969] The terminal sends the entered diagnostic information to a server, which stores the received diagnostic information in a dedicated database. This database can use a relational database management system such as MySQL or MariaDB.

[0970] Analysis of diagnostic information

[0971] The server supplies the stored diagnostic information to an AI model, which is built using machine learning frameworks such as TensorFlow and PyTorch. The AI ​​model analyzes the diagnostic information and performs symptom analysis based on statistical data and the latest medical knowledge. For example, it generates a treatment plan such as "combined surgery and radiation therapy is recommended."

[0972] Presentation of treatment plan

[0973] The server sends the generated treatment plan to the device, which then displays it to the user. For example, it may say, "A combination of surgery and radiation therapy is recommended." This allows the user to confirm the optimal treatment plan.

[0974] Identifying specialists

[0975] The server extracts the appropriate specialists from the database based on the treatment plan generated by the AI ​​model, and the extracted list of specialists is organized for the user's convenience and sent to the device.

[0976] Arranging a medical appointment

[0977] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it may say, "An appointment has been confirmed with a doctor at a certain university hospital on October 20th."

[0978] Specific examples

[0979] Below are some example prompts to illustrate the concept:

[0980] If a user is diagnosed with stomach cancer and chemotherapy is ineffective, please suggest the optimal treatment plan. The system will recommend surgery and radiation therapy, and include a function to list and schedule an appointment with an appropriate specialist. Please explain the specific processing steps and also add a flow for presenting the treatment plan and arranging an appointment with a specialist.

[0981] In this way, this system can automatically propose optimal treatment plans and schedule appointments with specialists simply by having the user input diagnostic information. This process reduces the burden on patients and provides them with opportunities to receive better treatment.

[0982] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0983] Step 1: Enter diagnostic information

[0984] Users log in to the system using a smartphone or PC. After logging in, the device displays an interface for entering diagnostic information. The user enters symptoms and treatment history on the interface. For example, they might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect has been observed."

[0985] Input: User's diagnosis information (symptoms, treatment history)

[0986] Output: Entered diagnostic information

[0987] Step 2: Send and save diagnostic information

[0988] The terminal sends the entered diagnostic information to the server, which then stores the received diagnostic information in a database, such as MySQL.

[0989] Input: Diagnostic information submitted by the user

[0990] Output: Diagnostic information stored in a database

[0991] Step 3: Analyze the diagnostic information

[0992] The server supplies the diagnostic information stored in the database to the AI ​​model. The AI ​​model is built using machine learning frameworks such as TensorFlow and performs analysis based on statistical data and the latest medical knowledge. The analysis results in the generation of an optimal treatment plan. For example, it may decide that a combination of surgery and radiation therapy is recommended.

[0993] Input: Saved diagnostic information

[0994] Output: Analyzed treatment plan

[0995] Step 4: Proposing a treatment plan

[0996] The server sends the generated treatment plan to the terminal, which then displays it to the user. For example, it may say, "Combined surgery and radiation therapy is recommended."

[0997] Input: Analyzed treatment plan

[0998] Output: A treatment plan presented to the user

[0999] Step 5: Specialist selection

[1000] The server extracts corresponding specialists from the database based on the generated treatment plan, generates a list of appropriate specialists, and transmits the list to the terminal.

[1001] Input: Analyzed treatment plan

[1002] Output: List of specialists

[1003] Step 6: Arrange an appointment

[1004] The user selects the doctor of their choice from a list of specialists displayed on the terminal. The terminal then sends information about the selected specialist to the server. The server then checks the availability of the selected specialist and automatically arranges the appointment. Once the appointment is confirmed, the appointment information is sent to the terminal and the user is notified. For example, a message may be displayed saying, "An appointment has been confirmed with a doctor at a certain university hospital on October 20th."

[1005] Input: User-selected specialist

[1006] Output: Confirmed reservation information and notification to the user

[1007] (Application example 1)

[1008] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1009] Conventional medical systems do not offer systems that can propose optimal treatment plans based on diagnostic information, match patients with specialists, and automate appointments. They also lack systems that can propose optimal meal plans based on health information or automate appointments with nutritionists. This has resulted in a lack of centralized, highly accurate recommendations and reservation arrangements, even in the fields of health and nutrition, requiring significant user effort. Furthermore, recommendations are not based on the latest knowledge or statistical data in each specialty, meaning the optimal choice for the user is not guaranteed. Therefore, there is a need for a system that can automate optimal recommendations and reservation arrangements based on diagnostic and health information, thereby reducing the burden on users.

[1010] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1011] In this invention, the server includes means for inputting diagnostic information, means for analyzing symptoms using the input diagnostic information, means for presenting an optimal treatment plan based on the analysis results, means for extracting a specialist corresponding to the presented treatment plan, means for automatically arranging a consultation appointment with the extracted specialist, means for inputting health information, means for analyzing an optimal meal plan and meal provision facility using the input health information, means for presenting an optimal meal plan and meal provision facility based on the analysis results, means for extracting a nutritionist corresponding to the presented meal plan and meal provision facility, and means for automatically arranging a consultation appointment with the extracted nutritionist. This enables optimal suggestions and reservation arrangements for the user based on both diagnostic information and health information.

[1012] "Diagnostic information" is medical information, such as symptoms and treatment history, that a user enters into the system.

[1013] "Health information" refers to information about dietary habits, such as health status, allergies, calorie restrictions, and dietary history, that a user inputs into the system.

[1014] "Symptom analysis" is a process that analyzes the user's symptoms using AI models and statistical data based on the input diagnostic information.

[1015] A "treatment policy" is a guideline for the optimal treatment method for the user, which is generated based on the results of symptom analysis.

[1016] A "specialist" is a doctor who has specialized knowledge and experience in a particular medical field.

[1017] "Appointment" is a procedure for making an appointment with a specialist selected by the user.

[1018] A "meal plan" is a meal suggestion suitable for a user that is generated based on health information.

[1019] "Meal Provider" means a dining establishment that offers a recommended meal plan to a user.

[1020] A "nutritionist" is a professional who provides appropriate nutritional management based on the user's health condition and meal plan.

[1021] "Consultation reservation" is a procedure for booking a consultation with a dietitian selected by the user.

[1022] The present invention is a system that presents optimal treatment guidelines and meal plans based on diagnostic and health information, and automatically arranges appointments with specialized doctors and nutritionists. This system is mainly composed of a server, terminals, and users.

[1023] Overall system configuration

[1024] 1. User operations

[1025] First, users access the system via a smartphone app or computer and log in. After logging in, an interface is displayed where they can enter diagnostic information (symptoms, treatment history) and health information (allergies, calorie restriction, dietary history).

[1026] 2. Data transmission and storage

[1027] The diagnostic and health information sent from the device is stored on a server, which manages it and provides it for analysis and retrieval as needed.

[1028] 3. Symptom and health information analysis

[1029] The server then feeds the stored diagnostic information into an AI model that analyzes the symptoms. The AI ​​model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan. It also generates an optimal diet plan based on the health information.

[1030] 4. Providing optimal treatment and dietary plans

[1031] The server then sends the generated treatment plan and meal plan to the device and displays it to the user. For example, it presents the optimal options for the user, such as "combined surgery and radiation therapy is recommended" or "low-calorie, gluten-free meal plan."

[1032] 5. Extraction and matching of specialized doctors and nutritionists

[1033] Based on the displayed treatment plan, the server extracts relevant specialists from the database and provides them to the user as a list. Similarly, based on the presented meal plan, the server extracts corresponding nutritionists and displays them to the user.

[1034] 6. Reservation Arrangements

[1035] Once the user selects the desired specialist and nutritionist, the server automatically checks their availability and arranges the reservation. Once the reservation is confirmed, the information is sent to the terminal and the user is notified.

[1036] Hardware and software used

[1037] Smartphone app / computer: Used for user interface and input data transmission.

[1038] Server: Database management and cloud processing.

[1039] AI models: Used to analyze diagnostic and health information and generate optimal treatment and diet plans.

[1040] Location API: Used to suggest dining facilities using GPS data.

[1041] Specific examples

[1042] 1. A user is diagnosed with stomach cancer and has not seen any effect from anti-cancer drug treatment, so they log in to the system. They enter their diagnosis, symptoms, and treatment history, as well as allergy information (e.g., gluten) and diet goals (e.g., low calorie).

[1043] 2. The server feeds the received diagnostic information into the AI ​​model and generates a treatment plan that recommends a combination of surgery and radiation therapy. It also generates a low-calorie, gluten-free meal plan based on the health information.

[1044] 3. The terminal displays the above treatment guidelines and meal plan to the user.

[1045] 4. The server extracts relevant specialists and corresponding nutritionists from the database and provides them to the user as a list.

[1046] 5. The terminal allows the user to select the specialist and nutritionist of their choice. The server checks availability, arranges the reservation, and notifies the user of the reservation confirmation.

[1047] Example prompts to input to the generative AI model

[1048] Provide user-entered health information (e.g., allergies, calorie restrictions, dietary history). Based on this information, suggest the best meal plan and restaurant for the user, then match with a professional nutritionist and make a reservation. In this step, process the necessary AI model and generate the API response.

[1049]

[1050] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1051] Step 1:

[1052] Users log in to the system using a smartphone app or a computer. After logging in, an interface is displayed where they can enter diagnostic information (symptoms, treatment history) and health information (allergies, calorie restriction, dietary history). The entered information is sent from the device to the server.

[1053] Input: Diagnostic information, health information

[1054] Output: User data sent to the server

[1055] Step 2:

[1056] The server receives the diagnostic and health information sent by the user and stores it in a database for later analysis and retrieval.

[1057] Input: Data submitted by the user

[1058] Output: Data saved to database

[1059] Step 3:

[1060] The server then feeds the diagnostic information stored in the database to the AI ​​model, which analyzes the symptoms. The AI ​​model uses statistical data and the latest medical knowledge to generate the optimal treatment plan. It also analyzes the health information and generates the optimal meal plan.

[1061] Input: Stored diagnostic and health information

[1062] Output: Optimal treatment plan, optimal diet plan

[1063] Step 4:

[1064] The server then sends the treatment plan and meal plan generated by the AI ​​model to the device and displays it to the user, such as "Surgery and radiation therapy combined is recommended" or "A low-calorie, gluten-free meal plan."

[1065] Input: Optimal treatment plan, optimal diet plan

[1066] Output: what the user sees

[1067] Step 5:

[1068] The server extracts relevant specialists from the database based on the displayed treatment plan and sends them to the terminal.Similarly, it extracts corresponding nutritionists based on the presented meal plan and provides them to the user as a list.

[1069] Input: Optimal treatment plan, optimal diet plan

[1070] Output: List of specialists and nutritionists

[1071] Step 6:

[1072] The user selects the desired specialist and nutritionist from the provided list, and the selected information is sent from the terminal to the server.

[1073] Input: Specialist and nutritionist selection information

[1074] Output: Sends the selected data to the server

[1075] Step 7:

[1076] The server automatically checks the availability of the selected specialist and nutritionist and arranges the reservation. Once the reservation is confirmed, the information is sent to the terminal and the user is notified. For example, it may display "An appointment with a specialist has been confirmed for October 20th."

[1077] Input: User selection information

[1078] Output: Confirmed reservation information, notification to user

[1079] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1080] This invention combines a system that proposes optimal treatment plans based on diagnostic information, automates matching with specialists and scheduling appointments, and an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[1081] System Overview

[1082] Entering diagnostic information

[1083] A user logs in to the system using an electronic terminal. After logging in, an interface for inputting diagnostic information (symptoms, treatment history, etc.) is displayed. For example, the user may input, "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect was observed."

[1084] Symptom analysis

[1085] The server receives the diagnostic information sent by the user and stores it in a database. The stored diagnostic information is then fed into an AI model for symptom analysis. This AI model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan.

[1086] Presentation of treatment plan

[1087] The server then sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may display a treatment plan such as "combined surgery and radiation therapy is recommended."

[1088] Intervention by the Emotion Engine

[1089] The device's emotion engine analyzes the user's emotional state through their input and interactions. For example, the emotion engine analyzes the speed of text input, the pressure on the touchscreen, and the tone of the text to identify emotional states such as "anxiety" or "relief."

[1090] Emotion-Based Feedback

[1091] The server adjusts the treatment plan and specialist recommendation based on the emotional state analyzed by the emotion engine. For example, if the user is in an "anxious" state, more reassuring explanations and support will be provided.

[1092] Identifying specialists

[1093] The server extracts the appropriate specialists from the database based on the treatment plan and emotional state. This list of specialists is organized taking into account the user's convenience and emotional state, and is sent to the terminal.

[1094] Arranging a medical appointment

[1095] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it will say, "Your appointment with Dr. Tanaka at the University Hospital on October 20th has been confirmed."

[1096] Specific examples

[1097] 1. User: Diagnosed with stomach cancer, chemotherapy has not been effective, and he / she would like to get a second opinion.

[1098] 2. Terminal: Log in to the system and enter the diagnosis and current symptoms.

[1099] 3. Server: Receives and stores diagnostic information, analyzes it using an AI model, and generates a treatment plan that recommends a combination of surgery and radiation therapy.

[1100] 4. Terminal: The generated treatment plan is displayed to the user. At the same time, the emotion engine analyzes the user's emotional state based on the user's input data and interactions.

[1101] 5. Server: Based on the emotional state obtained by the emotion engine, the treatment plan and list of specialists are adjusted and provided to the user in the most optimal way.

[1102] 6. Server: Based on the treatment plan, extract relevant specialists from the database and send them to the terminal.

[1103] 7. Terminal: The user is presented with a list of specialists and allowed to select the doctor of their choice. Support and explanations are provided according to the user's emotional state.

[1104] 8. Server: Checks availability of selected specialists and arranges appointments.

[1105] 9. Terminal: Notifies the user of the reservation confirmation information and secures the appointment date.

[1106] In this way, this system can automatically propose optimal treatment plans and schedule appointments with specialists simply by having the user input diagnostic information. Furthermore, by combining it with an emotion engine, it can provide support according to the user's emotional state, further reducing the burden on patients.

[1107] The processing flow will be explained below.

[1108] Program processing steps

[1109] Step 1:

[1110] A user logs in to the system using an electronic terminal by entering a user ID and password. The terminal then sends the entered information to the server, which then authenticates the user.

[1111] Step 2:

[1112] After logging in, the device displays an interface for entering diagnostic information, which includes text boxes and drop-down menus for entering symptoms and treatment history.

[1113] Step 3:

[1114] The user follows the interface to input diagnosis information, for example, "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect was observed."

[1115] Step 4:

[1116] The device sends the diagnostic information entered by the user to the server via an HTTP POST request.

[1117] Step 5:

[1118] The server stores the received diagnostic information in a database, including the user ID, symptoms, and treatment history.

[1119] Step 6:

[1120] The server passes the stored diagnostic information to an AI model, which analyzes the symptoms based on statistical data and the latest medical knowledge.

[1121] Step 7:

[1122] The server stores the optimal treatment plan generated by the AI ​​model in a database and sends the plan to the device. For example, the treatment plan may suggest that a combination of surgery and radiation therapy is recommended.

[1123] Step 8:

[1124] The terminal displays the received treatment plan to the user.

[1125] Step 9:

[1126] The device analyzes the user's operations (keyboard input, touch screen operation, etc.) and sends the data to the emotion engine, which analyzes the user's emotional state.

[1127] Step 10:

[1128] The server receives the emotional state from the emotion engine and adjusts the way it presents treatment plans and recommends specialists based on the user's feelings, such as "anxiety" or "relief."

[1129] Step 11:

[1130] Based on the adjusted information, the server extracts from the database a list of specialists who are suited to the treatment plan and the user's emotional state, and transmits this to the terminal.

[1131] Step 12:

[1132] The terminal displays a list of specialists to the user, including, for example, "Dr. Tanaka from the university hospital, Dr. Sato from the general hospital, and Dr. Takahashi from the clinic."

[1133] Step 13:

[1134] The user selects the doctor of their choice from a list of specialists, and the terminal then sends the information to the server.

[1135] Step 14:

[1136] The server checks the availability of the selected specialist via the reservation management system's API.

[1137] Step 15:

[1138] The server automatically arranges the reservation based on availability and generates confirmed reservation information, such as "An appointment with Dr. Tanaka at the University Hospital has been confirmed for October 20th."

[1139] Step 16:

[1140] The server transmits the confirmed reservation information to the terminal.

[1141] Step 17:

[1142] The terminal displays the received reservation information to the user and notifies the user that the reservation has been confirmed. By checking this information, the user can reserve a date to see a specialist.

[1143] This system's series of processes allows users to simply input their diagnosis information, and automatically recommend optimal treatment plans and schedule appointments with specialists. Furthermore, by combining it with an emotion engine, support can be provided according to the user's emotional state, reducing the burden on patients.

[1144] Example 2

[1145] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1146] Current medical systems make it difficult for users to input diagnostic information and then find the appropriate treatment plan or schedule an appointment with a specialist. Furthermore, they do not provide support that takes into account the user's emotional state, which can increase stress and anxiety for patients. This can result in delays in the transition to treatment and a decline in the quality of care.

[1147] The identification process 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 a means for inputting diagnostic information, a means for analyzing symptoms using the input diagnostic information, a means for presenting an optimal treatment plan based on the analysis results, a means for recognizing an emotional state, a means for adjusting the presentation of a treatment plan and the recommendation of a specialist based on the recognized emotional state, a means for extracting a specialist corresponding to the presented treatment plan, and a means for automatically arranging an appointment with the extracted specialist. As a result, by simply inputting diagnostic information by the user, an optimal treatment plan can be proposed, an appointment with a specialist can be automated, and support according to the emotional state can be provided, thereby reducing the burden on the patient.

[1148] "Diagnostic information" refers to medically relevant information such as symptoms and treatment history that a user enters into the system.

[1149] "Input means" refers to the interface and software that allows a user to input diagnostic information into the system through an electronic terminal.

[1150] The "analysis means" refers to technical means such as algorithms or AI models that the server uses to analyze symptoms using the input diagnostic information.

[1151] The "means for presenting" refers to a method and system for the server to display the optimal treatment plan to the user based on the results of symptom analysis.

[1152] "Emotional state" is data that reflects the user's psychological state and emotional response, and is the result of evaluating emotions such as anxiety and relief.

[1153] "Means for recognizing" refers to the technical means for analyzing a user's input and interactions using an emotion engine to understand the user's emotional state.

[1154] "Adjusting" refers to methods and systems for modifying treatment presentations or specialist recommendations based on the perceived emotional state.

[1155] The "means of extraction" refers to an algorithm or system that selects specialists from a database who are appropriate for the treatment plan.

[1156] "Means for arranging an appointment automatically" refers to a method and system that allows the system to automatically schedule an appointment with a specialist selected by the user.

[1157] This invention combines a system that proposes optimal treatment plans based on diagnostic information, automates matching with specialists and scheduling appointments, with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[1158] Entering diagnostic information

[1159] Users log in to the system using an electronic device, such as a smartphone or PC. After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. The information entered by the user might be, for example, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect has been observed." The software used is a web interface and input form that is accessed through a web browser.

[1160] Sending and receiving diagnostic information

[1161] The terminal sends the diagnostic information entered by the user to the server, which receives the information and stores it in an internal database. The hardware used is computers and servers in a data center, and the software used is a database management system.

[1162] Symptom analysis

[1163] The server then supplies the received diagnostic information to the AI ​​model, which then begins analyzing the symptoms. The AI ​​model then derives the optimal treatment plan based on the latest medical knowledge and statistical data. This AI model uses TensorFlow and PyTorch, among other technologies. Specifically, a Python script is executed to analyze the diagnostic information.

[1164] Generation and presentation of treatment plans

[1165] The server sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may suggest a treatment plan such as "surgery and radiation therapy are recommended." HTTP is used as the communication protocol, and the user interface displays the data.

[1166] Emotion analysis using an emotion engine

[1167] The device analyzes the user's input and interactions using an emotion engine to understand the user's emotional state. Specifically, the device analyzes things like the speed of text input and changes in touch pressure, and identifies emotions such as "anxiety" or "relief." The emotion engine uses EmotionAPI and other tools.

[1168] Emotion-Based Feedback

[1169] The server adjusts the treatment plan and specialist recommendation based on the emotional state analyzed by the emotion engine. For example, if the user is "anxious," it will provide a more reassuring explanation. An AI feedback system is used for this adjustment.

[1170] Identifying specialists

[1171] The server extracts the corresponding specialists from the database based on the treatment plan and emotional state. The extracted list of specialists is organized and sent to the terminal. The software used is a database management system and a matching algorithm.

[1172] Arranging a medical appointment

[1173] The user selects the doctor of their choice from a list of specialists displayed. The selection information is sent from the terminal to the server. The server automatically checks the specialist's availability and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, the message might say, "Your appointment with Dr. Tanaka at the university hospital on October 20th has been confirmed." Scheduling and notification systems are used for this purpose.

[1174] Specific examples

[1175] 1. The user logs in to the system and enters the diagnosis and current symptoms. For example, the user might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but it was ineffective."

[1176] 2. The server receives the diagnosis information and uses the AI ​​model to generate a treatment plan that recommends a combination of surgery and radiation therapy.

[1177] 3. The device displays the generated treatment plan to the user, and the emotion engine analyzes the user's emotions.

[1178] 4. The server adjusts the treatment plan and list of specialists based on the emotional state and provides them to the user.

[1179] 5. The server extracts specialists from the database based on the treatment plan and sends the list to the terminal.

[1180] 6. The device displays a list of specialists to the user and allows them to select the doctor they prefer. They provide support and explanations based on their emotional state.

[1181] 7. The server checks the availability of the selected specialist and arranges the appointment.

[1182] 8. The terminal notifies the user of the confirmed reservation information and asks for reconfirmation.

[1183] By using the above steps, the system can propose optimal treatment plans and automate specialist appointments simply by allowing the user to input diagnostic information. Furthermore, by using an emotion engine, it can provide support tailored to the user's emotional state, reducing the burden on patients.

[1184] Prompt Sentence Examples

[1185] "Please demonstrate a system in which a user inputs diagnostic information and an AI model generates an optimal treatment plan. Please also explain the specific steps for data input and analysis."

[1186] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1187] Step 1: Enter diagnostic information

[1188] Users log in to the system using a smartphone or PC. After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. The user enters information such as "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect was observed."

[1189] Input: Diagnostic information entered by the user on an electronic device

[1190] Output: Diagnostic information entered in the input form

[1191] Specific operations: Open the browser on the electronic device and access the system's login page. Enter your username and password and click the login button. Enter the diagnostic information in the form that is displayed next and click the submit button.

[1192] Step 2: Sending and Receiving Diagnostic Information

[1193] The terminal transmits the diagnostic information entered by the user to the server.

[1194] The server receives the diagnostic information sent from the terminal and stores it in an internal database.

[1195] Input: Diagnostic information that the device sends to the server

[1196] Output: Diagnostic information received by the server and stored in the database

[1197] Specific operation: Diagnostic information is sent from the device to the server as an HTTP POST request. The server receives the request and stores it in a database.

[1198] Step 3: Analyze the symptoms

[1199] The server then supplies the received diagnostic information to the AI ​​model, which then begins analyzing the symptoms. The AI ​​model then derives the optimal treatment plan based on the latest medical knowledge and statistical data.

[1200] Input: Diagnostic information stored in the database by the server

[1201] Output: Symptom analysis results by the AI ​​model

[1202] How it works: A Python script runs on the server, and an AI model analyzes the diagnostic information using the TensorFlow library.

[1203] Step 4: Generate and present a treatment plan

[1204] The server sends the treatment plan generated by the AI ​​model to the device.

[1205] The terminal displays this to the user, for example, "Combined surgery and radiation therapy is recommended."

[1206] Input: Symptom analysis results by AI model

[1207] Output: Treatment plan displayed on the terminal

[1208] Specific operation: The server sends the generated treatment plan to the terminal as an HTTP response. The terminal displays the received data on the screen.

[1209] Step 5: Emotion analysis using the emotion engine

[1210] The device analyzes the user's input and interactions with an emotion engine to understand the user's emotional state. For example, emotions such as "anxiety" or "relief" can be identified from changes in text input speed and touch pressure.

[1211] Input: User input and interaction data

[1212] Output: Emotion analysis results by the emotion engine

[1213] Specific operation: An emotion analysis library installed on the device analyzes the user's input data in real time and determines the user's emotional state (e.g., EmotionAPI).

[1214] Step 6: Emotional Feedback

[1215] The server adjusts the way it presents treatment plans and recommends specialists based on the emotional state analyzed by the emotion engine. For example, if the user is "anxious," it will provide more reassuring explanations.

[1216] Input: Sentiment analysis results

[1217] Output: List of tailored treatment plans and specialists

[1218] Specific operation: The server executes a script that creates and sends appropriate content to the user based on the user's emotional state.

[1219] Step 7: Specialist selection

[1220] The server extracts the most suitable doctors from a specialist database based on the treatment plan and emotional state, thereby generating a list of specialists suited to the user.

[1221] Input: Treatment plan and emotion analysis results

[1222] Output: List of specialists

[1223] Specific operation: The server executes an SQL query to generate a list of specialists.

[1224] Step 8: Arrange an appointment

[1225] The user selects the doctor of their choice from the displayed list of specialists, and the selection information is sent from the terminal to the server.

[1226] The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified.

[1227] Input: User's chosen specialist

[1228] Output: Appointment with a specialist

[1229] Specific operation: The user selects a specialist, and the selection information is sent from the terminal to the server. The server then accesses the specialist's appointment system via API, checks availability, and confirms the appointment.

[1230] Step 9: Notification of reservation results

[1231] The server transmits the confirmed reservation information to the terminal.

[1232] The terminal notifies the user of the reservation result. For example, it displays, "Your reservation with the doctor at the university hospital on October 20th has been confirmed."

[1233] Input: Confirmed reservation information

[1234] Output: Reservation confirmation

[1235] Specific operation: The server sends the reservation information to the terminal as an HTTP response. The terminal displays the received data on the screen and notifies the user.

[1236] (Application example 2)

[1237] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1238] Conventional medical systems do not take into account the user's emotional state when proposing treatment plans based on diagnostic information or when selecting and booking specialists, and therefore do not provide sufficient support, especially for patients experiencing anxiety or stress. Furthermore, the lack of accurate feedback based on the user's emotional state has led to issues such as reduced patient satisfaction and reduced efficiency in medical services.

[1239] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting diagnostic information, means for analyzing symptoms using the input diagnostic information, means for presenting an optimal treatment plan based on the analysis results, means for extracting a specialist corresponding to the presented treatment plan, means for automatically arranging an appointment with the extracted specialist, means for analyzing the user's emotional state, and means for adjusting the diagnostic results and the recommendation of a specialist based on the analyzed emotional state. This makes it possible to propose a treatment plan and make an appointment with a specialist based on the diagnostic information, as well as provide feedback and support according to the user's emotional state.

[1240] "Diagnostic information" is data that indicates the user's health condition, symptoms, treatment history, etc.

[1241] "Symptom analysis" is the act of evaluating symptoms based on input diagnostic information using statistical data and the latest medical knowledge.

[1242] A "treatment plan" is the optimal medical procedure or treatment method proposed based on the results of symptom analysis.

[1243] A "specialist" is a medical professional with specialized knowledge and experience in a particular medical field or treatment.

[1244] "Automatic appointment scheduling" is the process by which the system checks the availability of specialists and automatically schedules appointments.

[1245] "Emotional state" is information that indicates the user's psychological and emotional state.

[1246] "Emotion analysis" is the process of analyzing the user's input text, touchscreen usage data, writing tone, etc. to determine their emotional state.

[1247] "Feedback adjustment" refers to the act of changing or correcting diagnostic results, treatment plans, and specialist recommendations based on the results of emotion analysis.

[1248] This invention combines a system that proposes optimal treatment plans based on diagnostic information, automates matching with specialists and scheduling appointments, and an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[1249] System Overview

[1250] Entering diagnostic information

[1251] The user logs into the system using an electronic terminal. After logging in, an interface for inputting diagnostic information (symptoms, treatment history, etc.) is displayed. For example, the user might input, "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect was observed." This data is sent from the terminal to the server.

[1252] Symptom analysis

[1253] The server receives the diagnostic information sent by the user and stores it in a database. The stored diagnostic information is then fed into an AI model for symptom analysis. This AI model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan.

[1254] Presentation of treatment plan

[1255] The server then sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may display a treatment plan such as "combined surgery and radiation therapy is recommended."

[1256] Intervention by the Emotion Engine

[1257] The device's emotion engine analyzes the user's emotional state through their input and interactions, such as the speed of text entry, the pressure on the touchscreen, and the tone of the text, to identify emotional states such as "anxiety" or "relief."

[1258] Emotion-Based Feedback

[1259] The server adjusts the treatment plan and specialist recommendation based on the emotional state analyzed by the emotion engine. For example, if the user is in an "anxious" state, more reassuring explanations and support will be provided.

[1260] Identifying specialists

[1261] The server extracts the appropriate specialists from the database based on the treatment plan and emotional state. This list of specialists is organized taking into account the user's convenience and emotional state, and is sent to the terminal.

[1262] Arranging a medical appointment

[1263] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it will say, "Your appointment with Dr. Tanaka at the University Hospital on October 20th has been confirmed."

[1264] Specific Examples

[1265] For example, if a user is diagnosed with stomach cancer and wants to get a second opinion, they log in to the system and enter their diagnosis and current symptoms. The server receives this information, analyzes the symptoms using an AI model, and generates a treatment plan that recommends a combination of surgery and radiation therapy. Along with the treatment plan displayed on the device, an emotion engine analyzes the user's emotional state based on the data they entered. Based on the results, the server selects an appropriate specialist and provides them in the most optimal way for the user.

[1266] Example prompts to input to a generative AI model:

[1267] "Design an app that provides an online shopping experience that takes emotional state into account. Analyze the text entered by the user and determine their emotional state (positive, neutral, negative). Furthermore, determine the user's emotional state (calm, anxious) based on the pressure on the screen and the typing speed, and generate product suggestions and support messages accordingly."

[1268] In this way, this system can automatically suggest optimal treatment plans and schedule appointments with specialists simply by inputting diagnostic information. It can also further reduce the burden on patients by providing feedback and support that takes into account the user's emotional state.

[1269] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1270] Step 1:

[1271] The user logs in to the system using an electronic terminal. A login screen appears and the user enters their username and password. The entered authentication information is sent to the server for authentication. If authentication is successful, a diagnostic information input interface is displayed. Here, the user enters diagnostic information such as their symptoms and treatment history.

[1272] Input: User name, password, diagnosis information (symptoms, treatment history, etc.)

[1273] Output: Diagnostic information input interface display, transmitted diagnostic information

[1274] Step 2:

[1275] The diagnostic information entered into the device is sent to the server, which stores the received diagnostic information in a database. The stored diagnostic information is then fed into an AI model to analyze the symptoms.

[1276] Input: Diagnostic information

[1277] Output: Diagnostic information stored in a database, diagnostic information fed to an AI model

[1278] Step 3:

[1279] The AI ​​model on the server analyzes the symptoms based on the received diagnostic information. It then uses statistical data and the latest medical knowledge to derive the optimal treatment plan. This treatment plan is then sent from the server to the device and displayed to the user.

[1280] Input: diagnostic information, statistical data, medical knowledge

[1281] Output: Treatment plan

[1282] Step 4:

[1283] The device displays the treatment plan received from the server to the user. At the same time, the emotion engine analyzes the user's input data (text entry speed, touchscreen pressure, and tone of writing). The analysis results in the user's emotional state (e.g., "anxiety" or "relief").

[1284] Input: Treatment plan, user input data (text entry speed, touchscreen pressure, writing tone)

[1285] Output: User's emotional state

[1286] Step 5:

[1287] The server receives the emotional state analyzed by the emotion engine and adjusts the treatment plan and specialist recommendation list based on the emotional state. For example, if the user is in an "anxious" state, the server will provide more detailed explanations of the treatment plan and prioritize recommendations of specialists who can relieve stress.

[1288] Input: Emotional state, treatment plan

[1289] Output: Coordinated treatment plan, specialist recommendation list

[1290] Step 6:

[1291] The list of specialists extracted by the server is organized taking into consideration the user's convenience and emotional state and sent to the terminal. The user selects the desired doctor from the list of specialists displayed on the terminal. This selection information is sent from the terminal to the server.

[1292] Input: Specialist list, emotional state

[1293] Output: User selection information

[1294] Step 7:

[1295] The server checks the availability of the specialist selected by the user and automatically arranges the appointment. Once the appointment is confirmed, the information (appointment date and time, location, doctor name, etc.) is sent to the terminal and notified to the user.

[1296] Input: User selection information, specialist availability

[1297] Output: Confirmed reservation information, reservation notification

[1298] Step 8:

[1299] The terminal displays the appointment confirmation information to the user, allowing them to confirm the consultation date, allowing the user to easily complete the appointment with the specialist.

[1300] Input: Confirmed reservation information

[1301] Output: Display reservation information

[1302] This series of processing steps allows users to simply input their diagnostic information, and automatically recommends optimal treatment options and schedules appointments with specialists. It also provides feedback and support based on the user's emotional state, reducing the burden on patients.

[1303] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1304] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1305] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1306] [Fourth embodiment]

[1307] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1308] 7, a 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.

[1309] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1310] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1311] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1312] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1313] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1314] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1315] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1316] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1317] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1318] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1319] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1320] The present invention is a system that proposes optimal treatment plans based on diagnostic information, and automates matching with specialists and scheduling appointments. This system is primarily composed of a server, terminals, and users.

[1321] System Overview

[1322] Entering diagnostic information

[1323] The user logs in to the system using an electronic device (smartphone or PC). After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. For example, the user might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect was observed."

[1324] Symptom analysis

[1325] The server receives the diagnostic information sent by the user and stores it in a database. The stored diagnostic information is then fed into an AI model for symptom analysis. This AI model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan.

[1326] Presentation of treatment plan

[1327] The server then sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may display a treatment plan such as "combined surgery and radiation therapy is recommended."

[1328] Identifying specialists

[1329] The server extracts the appropriate specialists from the database based on the proposed treatment plan. This list of specialists is organized for the user's convenience and sent to the terminal.

[1330] Arranging a medical appointment

[1331] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it displays, "Your appointment with Dr. Sato at Keio University Hospital on October 20th has been confirmed."

[1332] Specific examples

[1333] 1. User: Diagnosed with stomach cancer, chemotherapy has not been effective, and he / she would like to get a second opinion.

[1334] 2. Terminal: Log in to the system and enter the diagnosis and current symptoms.

[1335] 3. Server: Receives and stores diagnostic information, analyzes it using an AI model, and generates a treatment plan that recommends a combination of surgery and radiation therapy.

[1336] 4. Terminal: Displays the generated treatment plan to the user.

[1337] 5. Server: Based on the treatment plan, extract relevant specialists from the database and send them to the terminal.

[1338] 6. Terminal: Displays a list of specialists to the user and allows them to select the doctor they want.

[1339] 7. Server: Checks availability of selected specialists and arranges appointments.

[1340] 8. Terminal: Notifies the user of the reservation confirmation information and secures the appointment date.

[1341] In this way, this system can automatically propose optimal treatment plans and schedule appointments with specialists simply by having the user input diagnostic information, reducing the burden on patients and providing them with opportunities to receive better treatment.

[1342] The processing flow will be explained below.

[1343] Program processing steps

[1344] Step 1:

[1345] Users log in to the system using electronic terminals. This login information includes a user ID and password, and after authentication, users are able to access the system.

[1346] Step 2:

[1347] After logging in, the device displays an interface for entering diagnostic information, including text boxes and drop-down menus for entering symptoms and treatment history.

[1348] Step 3:

[1349] The user inputs diagnosis information (e.g., "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect has been observed") according to the interface.

[1350] Step 4:

[1351] The device sends the diagnostic information entered by the user to the server using an HTTP POST request.

[1352] Step 5:

[1353] The server stores the received diagnostic information in a database, including the user ID, symptoms, and treatment history.

[1354] Step 6:

[1355] The server passes the stored diagnostic information to an AI model, which then analyzes the symptoms using statistical data and the latest medical knowledge.

[1356] Step 7:

[1357] The server stores the optimal treatment plan generated by the AI ​​model in a database. For example, a treatment plan that suggests a combination of surgery and radiation therapy is recommended is generated.

[1358] Step 8:

[1359] The server sends the generated treatment plan to the terminal, and the data is sent as an HTTP response.

[1360] Step 9:

[1361] The terminal displays the received treatment plan to the user. For example, a message such as "Combined surgery and radiation therapy is recommended" is displayed.

[1362] Step 10:

[1363] The server extracts the appropriate specialists from the medical database based on the treatment plan, and organizes the extracted specialists' information (such as their names, affiliated hospitals, and specialties).

[1364] Step 11:

[1365] The server sends the extracted list of specialists to the terminal, which may include, for example, "Dr. Tanaka of the university hospital, Dr. Sato of the general hospital, and Dr. Takahashi of the clinic."

[1366] Step 12:

[1367] The terminal displays a list of specialists to the user and provides an interface for selecting the desired doctor.

[1368] Step 13:

[1369] The user selects the desired specialist from the list, for example, "Dr. Tanaka of the University Hospital."

[1370] Step 14:

[1371] The terminal sends the user's selection information to the server, again using an HTTP POST request.

[1372] Step 15:

[1373] The server accesses the appointment management system to check the availability of the selected specialist, and uses the system's API to obtain the specialist's schedule information.

[1374] Step 16:

[1375] The server automatically arranges appointments based on the schedule information and generates confirmed appointment information, such as "An appointment with Dr. Sato at Keio University Hospital has been confirmed for October 20, 2023."

[1376] Step 17:

[1377] The server sends the confirmed reservation information to the terminal using an HTTP response.

[1378] Step 18:

[1379] The terminal displays the received appointment information to the user and notifies the user that the appointment has been confirmed. The user can confirm the appointment date by checking this information.

[1380] Example 1

[1381] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1382] In conventional medical systems, the process from entering diagnostic information to making an appointment with a specialist is complicated, placing a significant burden on patients. In addition, there are cases where an appropriate treatment plan cannot be presented quickly, resulting in patients missing out on the opportunity to receive optimal treatment.

[1383] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1384] In this invention, the server includes: means for inputting diagnostic information using an electronic terminal; means for transmitting the input diagnostic information to the server and storing it; means for analyzing the stored diagnostic information using an AI model and analyzing symptoms; means for the server to generate an optimal treatment plan based on the analysis results and present it to the user; means for the server to extract a suitable specialist from a database based on the presented treatment plan; and means for displaying the extracted list of specialists to the user and automatically arranging an appointment with the selected specialist. This allows the user to easily complete a series of processes from inputting diagnostic information to making an appointment with a specialist, enabling the user to receive optimal treatment quickly.

[1385] An "electronic terminal" is an electronic device such as a smartphone or a personal computer that a user uses to input diagnostic information.

[1386] "Diagnostic information" is medically related information such as symptoms and treatment history provided by the user.

[1387] A "server" is a computer system that receives, stores, analyzes, and provides results to a user.

[1388] "Storing" refers to storing the received diagnostic information in a storage system such as a database.

[1389] An "AI model" is an artificial intelligence algorithm that analyzes diagnostic information based on statistical data and the latest medical knowledge, and estimates symptoms and treatment plans.

[1390] "Symptom analysis" refers to the use of AI models to analyze diagnostic information provided by users to gain a deeper understanding of their symptoms.

[1391] A "treatment plan" is a treatment method or procedure that is deemed optimal based on the results of symptom analysis.

[1392] A "specialist" is a medical professional with specialized knowledge and skills for a specific symptom or disease.

[1393] "Appointment" is the process of confirming a consultation date with a specialist selected by the user.

[1394] A "database" is an information system for efficiently managing diagnostic information and data related to specialists.

[1395] The present invention is a system that proposes optimal treatment plans based on diagnostic information, and automates matching with specialists and scheduling appointments. This system is primarily composed of a server, terminals, and users.

[1396] Entering diagnostic information

[1397] Users log in to the system using an electronic device (smartphone or PC). After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. For example, a user might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect has been observed." This entry can be made through a web browser or a mobile application.

[1398] Sending and storing diagnostic information

[1399] The terminal sends the entered diagnostic information to a server, which stores the received diagnostic information in a dedicated database. This database can use a relational database management system such as MySQL or MariaDB.

[1400] Analysis of diagnostic information

[1401] The server supplies the stored diagnostic information to an AI model, which is built using machine learning frameworks such as TensorFlow and PyTorch. The AI ​​model analyzes the diagnostic information and performs symptom analysis based on statistical data and the latest medical knowledge. For example, it generates a treatment plan such as "combined surgery and radiation therapy is recommended."

[1402] Presentation of treatment plan

[1403] The server sends the generated treatment plan to the device, which then displays it to the user. For example, it may say, "A combination of surgery and radiation therapy is recommended." This allows the user to confirm the optimal treatment plan.

[1404] Identifying specialists

[1405] The server extracts the appropriate specialists from the database based on the treatment plan generated by the AI ​​model, and the extracted list of specialists is organized for the user's convenience and sent to the device.

[1406] Arranging a medical appointment

[1407] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it may say, "An appointment has been confirmed with a doctor at a certain university hospital on October 20th."

[1408] Specific examples

[1409] Below are some example prompts to illustrate the concept:

[1410] If a user is diagnosed with stomach cancer and chemotherapy is ineffective, please suggest the optimal treatment plan. The system will recommend surgery and radiation therapy, and include a function to list and schedule an appointment with an appropriate specialist. Please explain the specific processing steps and also add a flow for presenting the treatment plan and arranging an appointment with a specialist.

[1411] In this way, this system can automatically propose optimal treatment plans and schedule appointments with specialists simply by having the user input diagnostic information. This process reduces the burden on patients and provides them with opportunities to receive better treatment.

[1412] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1413] Step 1: Enter diagnostic information

[1414] Users log in to the system using a smartphone or PC. After logging in, the device displays an interface for entering diagnostic information. The user enters symptoms and treatment history on the interface. For example, they might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect has been observed."

[1415] Input: User's diagnosis information (symptoms, treatment history)

[1416] Output: Entered diagnostic information

[1417] Step 2: Send and save diagnostic information

[1418] The terminal sends the entered diagnostic information to the server, which then stores the received diagnostic information in a database, such as MySQL.

[1419] Input: Diagnostic information submitted by the user

[1420] Output: Diagnostic information stored in a database

[1421] Step 3: Analyze the diagnostic information

[1422] The server supplies the diagnostic information stored in the database to the AI ​​model. The AI ​​model is built using machine learning frameworks such as TensorFlow and performs analysis based on statistical data and the latest medical knowledge. The analysis results in the generation of an optimal treatment plan. For example, it may decide that a combination of surgery and radiation therapy is recommended.

[1423] Input: Saved diagnostic information

[1424] Output: Analyzed treatment plan

[1425] Step 4: Proposing a treatment plan

[1426] The server sends the generated treatment plan to the terminal, which then displays it to the user. For example, it may say, "Combined surgery and radiation therapy is recommended."

[1427] Input: Analyzed treatment plan

[1428] Output: A treatment plan presented to the user

[1429] Step 5: Specialist selection

[1430] The server extracts corresponding specialists from the database based on the generated treatment plan, generates a list of appropriate specialists, and transmits the list to the terminal.

[1431] Input: Analyzed treatment plan

[1432] Output: List of specialists

[1433] Step 6: Arrange an appointment

[1434] The user selects the doctor of their choice from a list of specialists displayed on the terminal. The terminal then sends information about the selected specialist to the server. The server then checks the availability of the selected specialist and automatically arranges the appointment. Once the appointment is confirmed, the appointment information is sent to the terminal and the user is notified. For example, a message may be displayed saying, "An appointment has been confirmed with a doctor at a certain university hospital on October 20th."

[1435] Input: User-selected specialist

[1436] Output: Confirmed reservation information and notification to the user

[1437] (Application example 1)

[1438] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1439] Conventional medical systems do not offer systems that can propose optimal treatment plans based on diagnostic information, match patients with specialists, and automate appointments. They also lack systems that can propose optimal meal plans based on health information or automate appointments with nutritionists. This has resulted in a lack of centralized, highly accurate recommendations and reservation arrangements, even in the fields of health and nutrition, requiring significant user effort. Furthermore, recommendations are not based on the latest knowledge or statistical data in each specialty, meaning the optimal choice for the user is not guaranteed. Therefore, there is a need for a system that can automate optimal recommendations and reservation arrangements based on diagnostic and health information, thereby reducing the burden on users.

[1440] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1441] In this invention, the server includes means for inputting diagnostic information, means for analyzing symptoms using the input diagnostic information, means for presenting an optimal treatment plan based on the analysis results, means for extracting a specialist corresponding to the presented treatment plan, means for automatically arranging a consultation appointment with the extracted specialist, means for inputting health information, means for analyzing an optimal meal plan and meal provision facility using the input health information, means for presenting an optimal meal plan and meal provision facility based on the analysis results, means for extracting a nutritionist corresponding to the presented meal plan and meal provision facility, and means for automatically arranging a consultation appointment with the extracted nutritionist. This enables optimal suggestions and reservation arrangements for the user based on both diagnostic information and health information.

[1442] "Diagnostic information" is medical information, such as symptoms and treatment history, that a user enters into the system.

[1443] "Health information" refers to information about dietary habits, such as health status, allergies, calorie restrictions, and dietary history, that a user inputs into the system.

[1444] "Symptom analysis" is a process that analyzes the user's symptoms using AI models and statistical data based on the input diagnostic information.

[1445] A "treatment policy" is a guideline for the optimal treatment method for the user, which is generated based on the results of symptom analysis.

[1446] A "specialist" is a doctor who has specialized knowledge and experience in a particular medical field.

[1447] "Appointment" is a procedure for making an appointment with a specialist selected by the user.

[1448] A "meal plan" is a meal suggestion suitable for a user that is generated based on health information.

[1449] "Meal Provider" means a dining establishment that offers a recommended meal plan to a user.

[1450] A "nutritionist" is a professional who provides appropriate nutritional management based on the user's health condition and meal plan.

[1451] "Consultation reservation" is a procedure for booking a consultation with a dietitian selected by the user.

[1452] The present invention is a system that presents optimal treatment guidelines and meal plans based on diagnostic and health information, and automatically arranges appointments with specialized doctors and nutritionists. This system is mainly composed of a server, terminals, and users.

[1453] Overall system configuration

[1454] 1. User operations

[1455] First, users access the system via a smartphone app or computer and log in. After logging in, an interface is displayed where they can enter diagnostic information (symptoms, treatment history) and health information (allergies, calorie restriction, dietary history).

[1456] 2. Data transmission and storage

[1457] The diagnostic and health information sent from the device is stored on a server, which manages it and provides it for analysis and retrieval as needed.

[1458] 3. Symptom and health information analysis

[1459] The server then feeds the stored diagnostic information into an AI model that analyzes the symptoms. The AI ​​model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan. It also generates an optimal diet plan based on the health information.

[1460] 4. Providing optimal treatment and dietary plans

[1461] The server then sends the generated treatment plan and meal plan to the device and displays it to the user. For example, it presents the optimal options for the user, such as "combined surgery and radiation therapy is recommended" or "low-calorie, gluten-free meal plan."

[1462] 5. Extraction and matching of specialized doctors and nutritionists

[1463] Based on the displayed treatment plan, the server extracts relevant specialists from the database and provides them to the user as a list. Similarly, based on the presented meal plan, the server extracts corresponding nutritionists and displays them to the user.

[1464] 6. Reservation Arrangements

[1465] Once the user selects the desired specialist and nutritionist, the server automatically checks their availability and arranges the reservation. Once the reservation is confirmed, the information is sent to the terminal and the user is notified.

[1466] Hardware and software used

[1467] Smartphone app / computer: Used for user interface and input data transmission.

[1468] Server: Database management and cloud processing.

[1469] AI models: Used to analyze diagnostic and health information and generate optimal treatment and diet plans.

[1470] Location API: Used to suggest dining facilities using GPS data.

[1471] Specific examples

[1472] 1. A user is diagnosed with stomach cancer and has not seen any effect from anti-cancer drug treatment, so they log in to the system. They enter their diagnosis, symptoms, and treatment history, as well as allergy information (e.g., gluten) and diet goals (e.g., low calorie).

[1473] 2. The server feeds the received diagnostic information into the AI ​​model and generates a treatment plan that recommends a combination of surgery and radiation therapy. It also generates a low-calorie, gluten-free meal plan based on the health information.

[1474] 3. The terminal displays the above treatment guidelines and meal plan to the user.

[1475] 4. The server extracts relevant specialists and corresponding nutritionists from the database and provides them to the user as a list.

[1476] 5. The terminal allows the user to select the specialist and nutritionist of their choice. The server checks availability, arranges the reservation, and notifies the user of the reservation confirmation.

[1477] Example prompts to input to the generative AI model

[1478] Provide user-entered health information (e.g., allergies, calorie restrictions, dietary history). Based on this information, suggest the best meal plan and restaurant for the user, then match with a professional nutritionist and make a reservation. In this step, process the necessary AI model and generate the API response.

[1479]

[1480] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1481] Step 1:

[1482] Users log in to the system using a smartphone app or a computer. After logging in, an interface is displayed where they can enter diagnostic information (symptoms, treatment history) and health information (allergies, calorie restriction, dietary history). The entered information is sent from the device to the server.

[1483] Input: Diagnostic information, health information

[1484] Output: User data sent to the server

[1485] Step 2:

[1486] The server receives the diagnostic and health information sent by the user and stores it in a database for later analysis and retrieval.

[1487] Input: Data submitted by the user

[1488] Output: Data saved to database

[1489] Step 3:

[1490] The server then feeds the diagnostic information stored in the database to the AI ​​model, which analyzes the symptoms. The AI ​​model uses statistical data and the latest medical knowledge to generate the optimal treatment plan. It also analyzes the health information and generates the optimal meal plan.

[1491] Input: Stored diagnostic and health information

[1492] Output: Optimal treatment plan, optimal diet plan

[1493] Step 4:

[1494] The server then sends the treatment plan and meal plan generated by the AI ​​model to the device and displays it to the user, such as "Surgery and radiation therapy combined is recommended" or "A low-calorie, gluten-free meal plan."

[1495] Input: Optimal treatment plan, optimal diet plan

[1496] Output: what the user sees

[1497] Step 5:

[1498] The server extracts relevant specialists from the database based on the displayed treatment plan and sends them to the terminal.Similarly, it extracts corresponding nutritionists based on the presented meal plan and provides them to the user as a list.

[1499] Input: Optimal treatment plan, optimal diet plan

[1500] Output: List of specialists and nutritionists

[1501] Step 6:

[1502] The user selects the desired specialist and nutritionist from the provided list, and the selected information is sent from the terminal to the server.

[1503] Input: Specialist and nutritionist selection information

[1504] Output: Sends the selected data to the server

[1505] Step 7:

[1506] The server automatically checks the availability of the selected specialist and nutritionist and arranges the reservation. Once the reservation is confirmed, the information is sent to the terminal and the user is notified. For example, it may display "An appointment with a specialist has been confirmed for October 20th."

[1507] Input: User selection information

[1508] Output: Confirmed reservation information, notification to user

[1509] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1510] This invention combines a system that proposes optimal treatment plans based on diagnostic information, automates matching with specialists and scheduling appointments, and an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[1511] System Overview

[1512] Entering diagnostic information

[1513] A user logs in to the system using an electronic terminal. After logging in, an interface for inputting diagnostic information (symptoms, treatment history, etc.) is displayed. For example, the user may input, "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect was observed."

[1514] Symptom analysis

[1515] The server receives the diagnostic information sent by the user and stores it in a database. The stored diagnostic information is then fed into an AI model for symptom analysis. This AI model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan.

[1516] Presentation of treatment plan

[1517] The server then sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may display a treatment plan such as "combined surgery and radiation therapy is recommended."

[1518] Intervention by the Emotion Engine

[1519] The device's emotion engine analyzes the user's emotional state through their input and interactions. For example, the emotion engine analyzes the speed of text input, the pressure on the touchscreen, and the tone of the text to identify emotional states such as "anxiety" or "relief."

[1520] Emotion-Based Feedback

[1521] The server adjusts the treatment plan and specialist recommendation based on the emotional state analyzed by the emotion engine. For example, if the user is in an "anxious" state, more reassuring explanations and support will be provided.

[1522] Identifying specialists

[1523] The server extracts the appropriate specialists from the database based on the treatment plan and emotional state. This list of specialists is organized taking into account the user's convenience and emotional state, and is sent to the terminal.

[1524] Arranging a medical appointment

[1525] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it will say, "Your appointment with Dr. Tanaka at the University Hospital on October 20th has been confirmed."

[1526] Specific examples

[1527] 1. User: Diagnosed with stomach cancer, chemotherapy has not been effective, and he / she would like to get a second opinion.

[1528] 2. Terminal: Log in to the system and enter the diagnosis and current symptoms.

[1529] 3. Server: Receives and stores diagnostic information, analyzes it using an AI model, and generates a treatment plan that recommends a combination of surgery and radiation therapy.

[1530] 4. Terminal: The generated treatment plan is displayed to the user. At the same time, the emotion engine analyzes the user's emotional state based on the user's input data and interactions.

[1531] 5. Server: Based on the emotional state obtained by the emotion engine, the treatment plan and list of specialists are adjusted and provided to the user in the most optimal way.

[1532] 6. Server: Based on the treatment plan, extract relevant specialists from the database and send them to the terminal.

[1533] 7. Terminal: The user is presented with a list of specialists and allowed to select the doctor of their choice. Support and explanations are provided according to the user's emotional state.

[1534] 8. Server: Checks availability of selected specialists and arranges appointments.

[1535] 9. Terminal: Notifies the user of the reservation confirmation information and secures the appointment date.

[1536] In this way, this system can automatically propose optimal treatment plans and schedule appointments with specialists simply by having the user input diagnostic information. Furthermore, by combining it with an emotion engine, it can provide support according to the user's emotional state, further reducing the burden on patients.

[1537] The processing flow will be explained below.

[1538] Program processing steps

[1539] Step 1:

[1540] A user logs in to the system using an electronic terminal by entering a user ID and password. The terminal then sends the entered information to the server, which then authenticates the user.

[1541] Step 2:

[1542] After logging in, the device displays an interface for entering diagnostic information, which includes text boxes and drop-down menus for entering symptoms and treatment history.

[1543] Step 3:

[1544] The user follows the interface to input diagnosis information, for example, "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect was observed."

[1545] Step 4:

[1546] The device sends the diagnostic information entered by the user to the server via an HTTP POST request.

[1547] Step 5:

[1548] The server stores the received diagnostic information in a database, including the user ID, symptoms, and treatment history.

[1549] Step 6:

[1550] The server passes the stored diagnostic information to an AI model, which analyzes the symptoms based on statistical data and the latest medical knowledge.

[1551] Step 7:

[1552] The server stores the optimal treatment plan generated by the AI ​​model in a database and sends the plan to the device. For example, the treatment plan may suggest that a combination of surgery and radiation therapy is recommended.

[1553] Step 8:

[1554] The terminal displays the received treatment plan to the user.

[1555] Step 9:

[1556] The device analyzes the user's operations (keyboard input, touch screen operation, etc.) and sends the data to the emotion engine, which analyzes the user's emotional state.

[1557] Step 10:

[1558] The server receives the emotional state from the emotion engine and adjusts the way it presents treatment plans and recommends specialists based on the user's feelings, such as "anxiety" or "relief."

[1559] Step 11:

[1560] Based on the adjusted information, the server extracts from the database a list of specialists who are suited to the treatment plan and the user's emotional state, and transmits this to the terminal.

[1561] Step 12:

[1562] The terminal displays a list of specialists to the user, including, for example, "Dr. Tanaka from the university hospital, Dr. Sato from the general hospital, and Dr. Takahashi from the clinic."

[1563] Step 13:

[1564] The user selects the doctor of their choice from a list of specialists, and the terminal then sends the information to the server.

[1565] Step 14:

[1566] The server checks the availability of the selected specialist via the reservation management system's API.

[1567] Step 15:

[1568] The server automatically arranges the reservation based on availability and generates confirmed reservation information, such as "An appointment with Dr. Tanaka at the University Hospital has been confirmed for October 20th."

[1569] Step 16:

[1570] The server transmits the confirmed reservation information to the terminal.

[1571] Step 17:

[1572] The terminal displays the received reservation information to the user and notifies the user that the reservation has been confirmed. By checking this information, the user can reserve a date to see a specialist.

[1573] This system's series of processes allows users to simply input their diagnosis information, and automatically recommend optimal treatment plans and schedule appointments with specialists. Furthermore, by combining it with an emotion engine, support can be provided according to the user's emotional state, reducing the burden on patients.

[1574] Example 2

[1575] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1576] Current medical systems make it difficult for users to input diagnostic information and then find the appropriate treatment plan or schedule an appointment with a specialist. Furthermore, they do not provide support that takes into account the user's emotional state, which can increase stress and anxiety for patients. This can result in delays in the transition to treatment and a decline in the quality of care.

[1577] The identification process 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 a means for inputting diagnostic information, a means for analyzing symptoms using the input diagnostic information, a means for presenting an optimal treatment plan based on the analysis results, a means for recognizing an emotional state, a means for adjusting the presentation of a treatment plan and the recommendation of a specialist based on the recognized emotional state, a means for extracting a specialist corresponding to the presented treatment plan, and a means for automatically arranging an appointment with the extracted specialist. As a result, by simply inputting diagnostic information by the user, an optimal treatment plan can be proposed, an appointment with a specialist can be automated, and support according to the emotional state can be provided, thereby reducing the burden on the patient.

[1578] "Diagnostic information" refers to medically relevant information such as symptoms and treatment history that a user enters into the system.

[1579] "Input means" refers to the interface and software that allows a user to input diagnostic information into the system through an electronic terminal.

[1580] The "analysis means" refers to technical means such as algorithms or AI models that the server uses to analyze symptoms using the input diagnostic information.

[1581] The "means for presenting" refers to a method and system for the server to display the optimal treatment plan to the user based on the results of symptom analysis.

[1582] "Emotional state" is data that reflects the user's psychological state and emotional response, and is the result of evaluating emotions such as anxiety and relief.

[1583] "Means for recognizing" refers to the technical means for analyzing a user's input and interactions using an emotion engine to understand the user's emotional state.

[1584] "Adjusting" refers to methods and systems for modifying treatment presentations or specialist recommendations based on the perceived emotional state.

[1585] The "means of extraction" refers to an algorithm or system that selects specialists from a database who are appropriate for the treatment plan.

[1586] "Means for arranging an appointment automatically" refers to a method and system that allows the system to automatically schedule an appointment with a specialist selected by the user.

[1587] This invention combines a system that proposes optimal treatment plans based on diagnostic information, automates matching with specialists and scheduling appointments, with an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[1588] Entering diagnostic information

[1589] Users log in to the system using an electronic device, such as a smartphone or PC. After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. The information entered by the user might be, for example, "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect has been observed." The software used is a web interface and input form that is accessed through a web browser.

[1590] Sending and receiving diagnostic information

[1591] The terminal sends the diagnostic information entered by the user to the server, which receives the information and stores it in an internal database. The hardware used is computers and servers in a data center, and the software used is a database management system.

[1592] Symptom analysis

[1593] The server then supplies the received diagnostic information to the AI ​​model, which then begins analyzing the symptoms. The AI ​​model then derives the optimal treatment plan based on the latest medical knowledge and statistical data. This AI model uses TensorFlow and PyTorch, among other technologies. Specifically, a Python script is executed to analyze the diagnostic information.

[1594] Generation and presentation of treatment plans

[1595] The server sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may suggest a treatment plan such as "surgery and radiation therapy are recommended." HTTP is used as the communication protocol, and the user interface displays the data.

[1596] Emotion analysis using an emotion engine

[1597] The device analyzes the user's input and interactions using an emotion engine to understand the user's emotional state. Specifically, the device analyzes things like the speed of text input and changes in touch pressure, and identifies emotions such as "anxiety" or "relief." The emotion engine uses EmotionAPI and other tools.

[1598] Emotion-Based Feedback

[1599] The server adjusts the treatment plan and specialist recommendation based on the emotional state analyzed by the emotion engine. For example, if the user is "anxious," it will provide a more reassuring explanation. An AI feedback system is used for this adjustment.

[1600] Identifying specialists

[1601] The server extracts the corresponding specialists from the database based on the treatment plan and emotional state. The extracted list of specialists is organized and sent to the terminal. The software used is a database management system and a matching algorithm.

[1602] Arranging a medical appointment

[1603] The user selects the doctor of their choice from a list of specialists displayed. The selection information is sent from the terminal to the server. The server automatically checks the specialist's availability and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, the message might say, "Your appointment with Dr. Tanaka at the university hospital on October 20th has been confirmed." Scheduling and notification systems are used for this purpose.

[1604] Specific examples

[1605] 1. The user logs in to the system and enters the diagnosis and current symptoms. For example, the user might enter, "I was diagnosed with stomach cancer and underwent chemotherapy, but it was ineffective."

[1606] 2. The server receives the diagnosis information and uses the AI ​​model to generate a treatment plan that recommends a combination of surgery and radiation therapy.

[1607] 3. The device displays the generated treatment plan to the user, and the emotion engine analyzes the user's emotions.

[1608] 4. The server adjusts the treatment plan and list of specialists based on the emotional state and provides them to the user.

[1609] 5. The server extracts specialists from the database based on the treatment plan and sends the list to the terminal.

[1610] 6. The device displays a list of specialists to the user and allows them to select the doctor they prefer. They provide support and explanations based on their emotional state.

[1611] 7. The server checks the availability of the selected specialist and arranges the appointment.

[1612] 8. The terminal notifies the user of the confirmed reservation information and asks for reconfirmation.

[1613] By using the above steps, the system can propose optimal treatment plans and automate specialist appointments simply by allowing the user to input diagnostic information. Furthermore, by using an emotion engine, it can provide support tailored to the user's emotional state, reducing the burden on patients.

[1614] Prompt Sentence Examples

[1615] "Please demonstrate a system in which a user inputs diagnostic information and an AI model generates an optimal treatment plan. Please also explain the specific steps for data input and analysis."

[1616] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1617] Step 1: Enter diagnostic information

[1618] Users log in to the system using a smartphone or PC. After logging in, an interface for entering diagnostic information (symptoms, treatment history, etc.) is displayed. The user enters information such as "I was diagnosed with stomach cancer and underwent chemotherapy, but no effect was observed."

[1619] Input: Diagnostic information entered by the user on an electronic device

[1620] Output: Diagnostic information entered in the input form

[1621] Specific operations: Open the browser on the electronic device and access the system's login page. Enter your username and password and click the login button. Enter the diagnostic information in the form that is displayed next and click the submit button.

[1622] Step 2: Sending and Receiving Diagnostic Information

[1623] The terminal transmits the diagnostic information entered by the user to the server.

[1624] The server receives the diagnostic information sent from the terminal and stores it in an internal database.

[1625] Input: Diagnostic information that the device sends to the server

[1626] Output: Diagnostic information received by the server and stored in the database

[1627] Specific operation: Diagnostic information is sent from the device to the server as an HTTP POST request. The server receives the request and stores it in a database.

[1628] Step 3: Analyze the symptoms

[1629] The server then supplies the received diagnostic information to the AI ​​model, which then begins analyzing the symptoms. The AI ​​model then derives the optimal treatment plan based on the latest medical knowledge and statistical data.

[1630] Input: Diagnostic information stored in the database by the server

[1631] Output: Symptom analysis results by the AI ​​model

[1632] How it works: A Python script runs on the server, and an AI model analyzes the diagnostic information using the TensorFlow library.

[1633] Step 4: Generate and present a treatment plan

[1634] The server sends the treatment plan generated by the AI ​​model to the device.

[1635] The terminal displays this to the user, for example, "Combined surgery and radiation therapy is recommended."

[1636] Input: Symptom analysis results by AI model

[1637] Output: Treatment plan displayed on the terminal

[1638] Specific operation: The server sends the generated treatment plan to the terminal as an HTTP response. The terminal displays the received data on the screen.

[1639] Step 5: Emotion analysis using the emotion engine

[1640] The device analyzes the user's input and interactions with an emotion engine to understand the user's emotional state. For example, emotions such as "anxiety" or "relief" can be identified from changes in text input speed and touch pressure.

[1641] Input: User input and interaction data

[1642] Output: Emotion analysis results by the emotion engine

[1643] Specific operation: An emotion analysis library installed on the device analyzes the user's input data in real time and determines the user's emotional state (e.g., EmotionAPI).

[1644] Step 6: Emotional Feedback

[1645] The server adjusts the way it presents treatment plans and recommends specialists based on the emotional state analyzed by the emotion engine. For example, if the user is "anxious," it will provide more reassuring explanations.

[1646] Input: Sentiment analysis results

[1647] Output: List of tailored treatment plans and specialists

[1648] Specific operation: The server executes a script that creates and sends appropriate content to the user based on the user's emotional state.

[1649] Step 7: Specialist selection

[1650] The server extracts the most suitable doctors from a specialist database based on the treatment plan and emotional state, thereby generating a list of specialists suited to the user.

[1651] Input: Treatment plan and emotion analysis results

[1652] Output: List of specialists

[1653] Specific operation: The server executes an SQL query to generate a list of specialists.

[1654] Step 8: Arrange an appointment

[1655] The user selects the doctor of their choice from the displayed list of specialists, and the selection information is sent from the terminal to the server.

[1656] The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified.

[1657] Input: User's chosen specialist

[1658] Output: Appointment with a specialist

[1659] Specific operation: The user selects a specialist, and the selection information is sent from the terminal to the server. The server then accesses the specialist's appointment system via API, checks availability, and confirms the appointment.

[1660] Step 9: Notification of reservation results

[1661] The server transmits the confirmed reservation information to the terminal.

[1662] The terminal notifies the user of the reservation result. For example, it displays, "Your reservation with the doctor at the university hospital on October 20th has been confirmed."

[1663] Input: Confirmed reservation information

[1664] Output: Reservation confirmation

[1665] Specific operation: The server sends the reservation information to the terminal as an HTTP response. The terminal displays the received data on the screen and notifies the user.

[1666] (Application example 2)

[1667] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1668] Conventional medical systems do not take into account the user's emotional state when proposing treatment plans based on diagnostic information or when selecting and booking specialists, and therefore do not provide sufficient support, especially for patients experiencing anxiety or stress. Furthermore, the lack of accurate feedback based on the user's emotional state has led to issues such as reduced patient satisfaction and reduced efficiency in medical services.

[1669] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting diagnostic information, means for analyzing symptoms using the input diagnostic information, means for presenting an optimal treatment plan based on the analysis results, means for extracting a specialist corresponding to the presented treatment plan, means for automatically arranging an appointment with the extracted specialist, means for analyzing the user's emotional state, and means for adjusting the diagnostic results and the recommendation of a specialist based on the analyzed emotional state. This makes it possible to propose a treatment plan and make an appointment with a specialist based on the diagnostic information, as well as provide feedback and support according to the user's emotional state.

[1670] "Diagnostic information" is data that indicates the user's health condition, symptoms, treatment history, etc.

[1671] "Symptom analysis" is the act of evaluating symptoms based on input diagnostic information using statistical data and the latest medical knowledge.

[1672] A "treatment plan" is the optimal medical procedure or treatment method proposed based on the results of symptom analysis.

[1673] A "specialist" is a medical professional with specialized knowledge and experience in a particular medical field or treatment.

[1674] "Automatic appointment scheduling" is the process by which the system checks the availability of specialists and automatically schedules appointments.

[1675] "Emotional state" is information that indicates the user's psychological and emotional state.

[1676] "Emotion analysis" is the process of analyzing the user's input text, touchscreen usage data, writing tone, etc. to determine their emotional state.

[1677] "Feedback adjustment" refers to the act of changing or correcting diagnostic results, treatment plans, and specialist recommendations based on the results of emotion analysis.

[1678] This invention combines a system that proposes optimal treatment plans based on diagnostic information, automates matching with specialists and scheduling appointments, and an emotion engine that recognizes the user's emotions. This system is composed of a server, a terminal, and a user.

[1679] System Overview

[1680] Entering diagnostic information

[1681] The user logs into the system using an electronic terminal. After logging in, an interface for inputting diagnostic information (symptoms, treatment history, etc.) is displayed. For example, the user might input, "I was diagnosed with stomach cancer and underwent anti-cancer drug treatment, but no effect was observed." This data is sent from the terminal to the server.

[1682] Symptom analysis

[1683] The server receives the diagnostic information sent by the user and stores it in a database. The stored diagnostic information is then fed into an AI model for symptom analysis. This AI model, based on statistical data and the latest medical knowledge, derives the optimal treatment plan.

[1684] Presentation of treatment plan

[1685] The server then sends the treatment plan generated by the AI ​​model to the device, which then displays it to the user. For example, the device may display a treatment plan such as "combined surgery and radiation therapy is recommended."

[1686] Intervention by the Emotion Engine

[1687] The device's emotion engine analyzes the user's emotional state through their input and interactions, such as the speed of text entry, the pressure on the touchscreen, and the tone of the text, to identify emotional states such as "anxiety" or "relief."

[1688] Emotion-Based Feedback

[1689] The server adjusts the treatment plan and specialist recommendation based on the emotional state analyzed by the emotion engine. For example, if the user is in an "anxious" state, more reassuring explanations and support will be provided.

[1690] Identifying specialists

[1691] The server extracts the appropriate specialists from the database based on the treatment plan and emotional state. This list of specialists is organized taking into account the user's convenience and emotional state, and is sent to the terminal.

[1692] Arranging a medical appointment

[1693] The user selects the doctor of their choice from a list of specialists. The selected information is sent from the terminal to the server. The server automatically checks the availability of the selected specialist and arranges the appointment. Once the appointment is confirmed, the information is sent to the terminal and the user is notified. For example, it will say, "Your appointment with Dr. Tanaka at the University Hospital on October 20th has been confirmed."

[1694] Specific Examples

[1695] For example, if a user is diagnosed with stomach cancer and wants to get a second opinion, they log in to the system and enter their diagnosis and current symptoms. The server receives this information, analyzes the symptoms using an AI model, and generates a treatment plan that recommends a combination of surgery and radiation therapy. Along with the treatment plan displayed on the device, an emotion engine analyzes the user's emotional state based on the data they entered. Based on the results, the server selects an appropriate specialist and provides them in the most optimal way for the user.

[1696] Example prompts to input to a generative AI model:

[1697] "Design an app that provides an online shopping experience that takes emotional state into account. Analyze the text entered by the user and determine their emotional state (positive, neutral, negative). Furthermore, determine the user's emotional state (calm, anxious) based on the pressure on the screen and the typing speed, and generate product suggestions and support messages accordingly."

[1698] In this way, this system can automatically suggest optimal treatment plans and schedule appointments with specialists simply by inputting diagnostic information. It can also further reduce the burden on patients by providing feedback and support that takes into account the user's emotional state.

[1699] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1700] Step 1:

[1701] The user logs in to the system using an electronic terminal. A login screen appears and the user enters their username and password. The entered authentication information is sent to the server for authentication. If authentication is successful, a diagnostic information input interface is displayed. Here, the user enters diagnostic information such as their symptoms and treatment history.

[1702] Input: User name, password, diagnosis information (symptoms, treatment history, etc.)

[1703] Output: Diagnostic information input interface display, transmitted diagnostic information

[1704] Step 2:

[1705] The diagnostic information entered into the device is sent to the server, which stores the received diagnostic information in a database. The stored diagnostic information is then fed into an AI model to analyze the symptoms.

[1706] Input: Diagnostic information

[1707] Output: Diagnostic information stored in a database, diagnostic information fed to an AI model

[1708] Step 3:

[1709] The AI ​​model on the server analyzes the symptoms based on the received diagnostic information. It then uses statistical data and the latest medical knowledge to derive the optimal treatment plan. This treatment plan is then sent from the server to the device and displayed to the user.

[1710] Input: diagnostic information, statistical data, medical knowledge

[1711] Output: Treatment plan

[1712] Step 4:

[1713] The device displays the treatment plan received from the server to the user. At the same time, the emotion engine analyzes the user's input data (text entry speed, touchscreen pressure, and tone of writing). The analysis results in the user's emotional state (e.g., "anxiety" or "relief").

[1714] Input: Treatment plan, user input data (text entry speed, touchscreen pressure, writing tone)

[1715] Output: User's emotional state

[1716] Step 5:

[1717] The server receives the emotional state analyzed by the emotion engine and adjusts the treatment plan and specialist recommendation list based on the emotional state. For example, if the user is in an "anxious" state, the server will provide more detailed explanations of the treatment plan and prioritize recommendations of specialists who can relieve stress.

[1718] Input: Emotional state, treatment plan

[1719] Output: Coordinated treatment plan, specialist recommendation list

[1720] Step 6:

[1721] The list of specialists extracted by the server is organized taking into consideration the user's convenience and emotional state and sent to the terminal. The user selects the desired doctor from the list of specialists displayed on the terminal. This selection information is sent from the terminal to the server.

[1722] Input: Specialist list, emotional state

[1723] Output: User selection information

[1724] Step 7:

[1725] The server checks the availability of the specialist selected by the user and automatically arranges the appointment. Once the appointment is confirmed, the information (appointment date and time, location, doctor name, etc.) is sent to the terminal and notified to the user.

[1726] Input: User selection information, specialist availability

[1727] Output: Confirmed reservation information, reservation notification

[1728] Step 8:

[1729] The terminal displays the appointment confirmation information to the user, allowing them to confirm the consultation date, allowing the user to easily complete the appointment with the specialist.

[1730] Input: Confirmed reservation information

[1731] Output: Display reservation information

[1732] This series of processing steps allows users to simply input their diagnostic information, and automatically recommends optimal treatment options and schedules appointments with specialists. It also provides feedback and support based on the user's emotional state, reducing the burden on patients.

[1733] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1734] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1735] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1736] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1737] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1738] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1739] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1740] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1741] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1742] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1743] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1744] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1745] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1746] 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.

[1747] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1748] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1749] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1750] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1751] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1752] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1753] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1754] The following is further disclosed regarding the above embodiment.

[1755] (Claim 1)

[1756] a means for inputting diagnostic information;

[1757] means for analyzing symptoms using the input diagnostic information;

[1758] A means for presenting an optimal treatment plan based on the analysis results;

[1759] a means for extracting a specialist corresponding to the presented treatment plan;

[1760] The system includes a means for automatically arranging an appointment with the extracted specialist.

[1761] (Claim 2)

[1762] The system of claim 1 , wherein the diagnostic information includes symptoms and treatment history.

[1763] (Claim 3)

[1764] 2. The system of claim 1, wherein the symptom analysis is based on statistical data and current medical knowledge.

[1765] "Example 1"

[1766] (Claim 1)

[1767] means for inputting diagnostic information using an electronic terminal;

[1768] means for transmitting the input diagnostic information to a server and storing the information;

[1769] A means for analyzing the stored diagnostic information using an AI model and analyzing symptoms;

[1770] A means for the server to generate an optimal treatment plan based on the analysis results and present it to the user;

[1771] A means for the server to extract a corresponding specialist from a database based on the presented treatment plan;

[1772] The system includes a means for displaying the extracted list of specialists to the user and automatically arranging an appointment with the selected specialist.

[1773] (Claim 2)

[1774] The system of claim 1 , wherein the diagnostic information includes symptoms and treatment history.

[1775] (Claim 3)

[1776] 2. The system of claim 1, wherein the symptom analysis is based on statistical data and the latest medical knowledge and analyzed by an AI model.

[1777] "Application Example 1"

[1778] (Claim 1)

[1779] a means for inputting diagnostic information;

[1780] means for analyzing symptoms using the input diagnostic information;

[1781] A means for presenting an optimal treatment plan based on the analysis results;

[1782] a means for extracting a specialist corresponding to the presented treatment plan;

[1783] A means for automatically arranging an appointment with the extracted specialist;

[1784] a means for inputting health information;

[1785] A means for analyzing an optimal meal plan and meal provision facility using the input health information;

[1786] A means for presenting an optimal meal plan and meal provision facility based on the analysis results;

[1787] A means for extracting a nutritionist corresponding to the presented meal plan and the meal provision facility;

[1788] The system includes a means for automatically arranging a consultation appointment with the extracted nutritionist.

[1789] (Claim 2)

[1790] The system of claim 1 , wherein the diagnostic information and the health information includes symptoms, medical history, allergy information, calorie restriction, and dietary history.

[1791] (Claim 3)

[1792] 2. The system of claim 1, wherein the symptom analysis and the meal plan analysis are based on statistical data and current medical and nutritional knowledge.

[1793] "Example 2: Combining Emotion Engines"

[1794] (Claim 1)

[1795] a means for inputting diagnostic information;

[1796] means for analyzing symptoms using the input diagnostic information;

[1797] A means for presenting an optimal treatment plan based on the analysis results;

[1798] a means for recognizing an emotional state;

[1799] means for adjusting treatment recommendations and specialist recommendations based on the recognized emotional state;

[1800] a means for extracting a specialist corresponding to the presented treatment plan;

[1801] The system includes a means for automatically arranging an appointment with the extracted specialist.

[1802] (Claim 2)

[1803] the diagnostic information includes symptoms and treatment history;

[1804] 10. The system of claim 1, wherein the recognition of the emotional state is performed through user input or interaction.

[1805] (Claim 3)

[1806] The symptom analysis is based on statistical data and the latest medical knowledge,

[1807] The system of claim 1, wherein the treatment plan is presented using a generative AI model.

[1808] "Application example 2 when combining emotion engines"

[1809] (Claim 1)

[1810] a means for inputting diagnostic information;

[1811] means for analyzing symptoms using the input diagnostic information;

[1812] A means for presenting an optimal treatment plan based on the analysis results;

[1813] a means for extracting a specialist corresponding to the presented treatment plan;

[1814] A means for automatically arranging an appointment with the extracted specialist;

[1815] means for analyzing the emotional state of a user;

[1816] means for adjusting a diagnosis or a specialist recommendation based on the analyzed emotional state;

[1817] A system including:

[1818] (Claim 2)

[1819] The system of claim 1 , wherein the diagnostic information includes symptoms and treatment history.

[1820] (Claim 3)

[1821] 2. The system of claim 1, wherein the symptom analysis is based on statistical data and current medical knowledge.

[1822] (Claim 4)

[1823] 10. The system of claim 1, wherein the emotional state analysis is based on text input, touchscreen pressure, and writing tone. [Explanation of symbols]

[1824] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting diagnostic information; means for analyzing symptoms using the input diagnostic information; A means for presenting an optimal treatment plan based on the analysis results; a means for extracting a specialist corresponding to the presented treatment plan; The system includes a means for automatically arranging an appointment with the extracted specialist.

2. The system of claim 1 , wherein the diagnostic information includes symptoms and treatment history.

3. 2. The system of claim 1, wherein the symptom analysis is based on statistical data and current medical knowledge.

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