System

Abstract

To provide a system for providing an environment for enabling a patient to easily understand his / her own disease state, and to quickly select optimal treatment.SOLUTION: The identification processing unit 290 of the data processing device 12 in the system receives and pre-processes the patient information, including the patient's age, gender, medical history, current symptoms, and desired course of treatment, analyzes the latest medical guidelines and relevant medical treatises, generates a treatment protocol, simulates treatment options using the generated treatment protocol and the patient information, and generates a customized treatment recommendation in consideration of the patient's desires and risk tolerance; The system has a cooperative function for providing the generated proposal to the patient, confirming the proposal contents by the patient, displaying interactive contents, sharing information with the family and friends of the patient, receiving feedback, and providing information and discussion with a medical worker, and determines and records a final treatment plan.SELECTED DRAWING: Figure 2

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] In the traditional medical environment, it is extremely difficult for patients and their families to understand medical options and make appropriate treatment decisions. The sheer volume of medical information and complex technical terminology often leave patients confused and anxious. As a result, they may lack understanding or misunderstand treatment, leading to suboptimal treatment choices. Furthermore, traditional second opinions rely on medical professionals, which takes time and often limits access. There is a need to resolve these issues and provide an environment that makes it easier for patients to understand their own medical condition and quickly select the most appropriate treatment. [Means for solving the problem]

[0005] The present invention provides an input means for patients to input their age, gender, medical history, current symptoms, and desired treatment course. Next, a data preparation means is used to receive and preprocess the input patient information. Then, a guideline analysis means is used to analyze the latest medical guidelines and related medical papers and generate a treatment protocol. An AI model means is provided to simulate treatment options based on this treatment protocol and patient information. Furthermore, a proposal generation means is used to generate a customized treatment proposal taking into account the patient's wishes and risk tolerance. This provides a provision means for providing the generated proposal to the patient, and a display means for the patient to confirm the proposal content and display interactive content. Furthermore, a sharing means is provided for sharing information with the patient's family and friends and receiving feedback, and a collaboration means is provided for providing information and holding discussions with medical professionals. These issues are resolved by building a system that includes a recording means for determining and recording the final treatment plan.

[0006] "Patient" refers to an individual who receives medical services.

[0007] "Input means" refers to a device or interface that allows a patient to provide their own information (such as age, gender, medical history, current symptoms, and desired treatment course) to the system.

[0008] "Data preparation tools" refers to programs and processes that preprocess input patient information and ensure data consistency and accuracy.

[0009] The "guideline analysis means" refers to a means for analyzing the latest medical guidelines and related medical papers and generating a treatment protocol based thereon.

[0010] "AI model tools" refers to artificial intelligence models or algorithms that simulate treatment options based on patient information and treatment protocols.

[0011] "Proposal Generator" refers to a program or algorithm that generates customized treatment proposals taking into account the patient's preferences and risk tolerance.

[0012] "Delivery means" refers to a method or interface for providing the generated treatment proposal to the patient.

[0013] "Display" refers to the device or screen on which the patient can view treatment suggestions and view interactive content.

[0014] "Sharing tools" refers to systems and processes for sharing patients' treatment recommendations with family and friends and receiving feedback.

[0015] "Collaboration methods" refer to platforms and communication methods for exchanging information and holding discussions with medical professionals.

[0016] "Recording means" refers to the system or database used to determine the final treatment plan and record its contents. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

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

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

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

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0032] 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.

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

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system that allows patients to input their medical history, symptoms, and treatment preferences, and then uses the latest medical guidelines and generative AI models to suggest optimal treatment options. Specific embodiments for implementing this system are described below.

[0039] Overall system overview

[0040] The server receives patient information, organizes the data, analyzes guidelines, and simulates treatment options using AI models to generate treatment proposals. The terminals are used by patients to input information and check the proposed treatment options. Users include patients themselves, their families, and medical professionals.

[0041] Program processing flow

[0042] Entering patient information

[0043] The user uses the terminal to input their age, gender, medical history, current symptoms, and desired treatment course, which includes an input form and an interface for clicking options.

[0044] Data preparation and preprocessing

[0045] The terminal sends the input data to the server, which then preprocesses the received data, specifically normalizing the data and checking for duplicates and inconsistencies.

[0046] Guidelines and literature analysis

[0047] The server analyzes the latest medical guidelines and relevant medical literature to generate a treatment protocol, which lists the best treatment options for the patient's condition.

[0048] AI-powered simulation of treatment options

[0049] The server uses a generative AI model to simulate treatment options based on patient information and treatment protocols, evaluating each option's suitability and predicted outcomes.

[0050] Generate customized treatment recommendations

[0051] The server takes into account the patient's preferences and risk tolerance and generates a customized treatment proposal, including details of the treatment, expected effects, side effects, and duration of treatment.

[0052] Providing and confirming proposals

[0053] The device then provides the generated treatment proposal to the user, who can then review and deepen their understanding of the proposal through interactive screens and videos.

[0054] Share information with family and friends

[0055] Users can share their treatment suggestions with family and friends, allowing them to receive feedback from others.

[0056] Collaboration with medical professionals

[0057] The server provides a discussion function for sharing treatment suggestions with medical professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[0058] Deciding and recording the final treatment plan

[0059] The user, family, friends, and medical professionals decide on the final treatment plan, and the server records this decision and notifies all parties with relevant information.

[0060] Specific examples

[0061] For example, in the case of a 42-year-old male patient with hypertension and diabetes, the process would proceed as follows:

[0062] 1. The patient enters his / her age (42 years old), gender (male), medical history (high blood pressure, diabetes), current symptoms, and treatment preferences into the terminal.

[0063] 2. The terminal sends the entered information to the server.

[0064] 3. The server pre-processes the data, normalizing it and checking for inconsistencies.

[0065] 4. The server analyzes the latest medical guidelines and related papers to generate the optimal treatment protocol for the patient.

[0066] 5. The server uses the generative AI model to simulate treatment options and evaluate the suitability of each option.

[0067] 6. The server takes into account the patient's preferences and risk tolerance, generates a customized treatment proposal, and sends the details to the device.

[0068] 7. The device visually displays treatment suggestions to help patients understand them.

[0069] 8. The patient shares information with family members and gets their opinions.

[0070] 9. Provide a means for the server to collaborate with medical professionals for discussion and to incorporate expert opinions.

[0071] 10. The patient, family, friends, and healthcare professionals decide on the final treatment plan, which is recorded by the server.

[0072] The above is an embodiment of the present invention, which allows patients to gain a deeper understanding of their own condition and to select the most appropriate treatment quickly and accurately.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user inputs his / her age, sex, medical history, current symptoms, and desired treatment course via the terminal.

[0076] Specifically, the data is entered by filling in the necessary information in the input form and pressing the send button.

[0077] Step 2:

[0078] The terminal transmits the input data to the server.

[0079] The data is encrypted and transmitted over a secure connection.

[0080] Step 3:

[0081] The server receives the transmitted data and stores it in a data database.

[0082] Checks the received data and inserts it into the appropriate tables in the database.

[0083] Step 4:

[0084] The server performs pre-processing, which includes data normalization, duplicate checks, and consistency checks.

[0085] Standardize data formats, remove duplicate records, and check for outliers and inconsistencies.

[0086] Step 5:

[0087] The server analyzes the latest medical guidelines and related medical literature and generates treatment protocols.

[0088] Database queries are performed to extract relevant guidelines and papers, and analytical algorithms are used to create treatment protocols.

[0089] Step 6:

[0090] The server uses a generative AI model to simulate treatment options.

[0091] The generative AI model is run using patient information and treatment protocols as input to simulate multiple treatment options.

[0092] Step 7:

[0093] The server generates a customized treatment proposal based on the simulation results, taking into account the patient's preferences and risk tolerance.

[0094] The simulation results are analyzed to select the treatment plan that best suits the patient's wishes and risk profile.

[0095] Step 8:

[0096] The server transmits the generated treatment proposal to the terminal.

[0097] The suggestions are formatted into an interactive format and sent to the patient's device.

[0098] Step 9:

[0099] The terminal visually displays the received treatment suggestions.

[0100] Interactive images and instructional videos are used to detail treatment options.

[0101] Step 10:

[0102] Users share their treatment suggestions with family and friends.

[0103] Use the share feature to send information via email or message.

[0104] Step 11:

[0105] The device again sends feedback from family and friends to the server.

[0106] The received feedback is sent to the server and stored in a database.

[0107] Step 12:

[0108] The server provides information to medical professionals, including the patient's primary care physician, and provides an environment for discussion.

[0109] Proposals are shared and discussion functions are provided through an interface for medical professionals.

[0110] Step 13:

[0111] The user, family, and healthcare professionals share and discuss information to determine the final treatment plan.

[0112] Discussions are held in real time using the chat and video conferencing functions provided by the server.

[0113] Step 14:

[0114] The server determines and records the final treatment plan.

[0115] The confirmed treatment plan will be recorded in the database and notified to all parties.

[0116] These are the specific processing steps of the system, which allow patients to receive support in making optimal medical choices.

[0117] Example 1

[0118] 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."

[0119] Conventional medical systems have difficulty instantly generating effective and appropriate treatment proposals based on the medical history and symptoms entered by the patient themselves. They also face challenges in providing customized treatment proposals that reflect the patient's wishes and insufficient collaboration with family and medical professionals. As a result, it has been difficult for patients to gain a deep understanding of their condition and quickly select the optimal treatment.

[0120] 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.

[0121] In this invention, the server includes: an input means for a patient to input their age, gender, medical history, current symptoms, and desired treatment plan; a data preparation means for receiving the input patient information and performing preprocessing such as normalization and inconsistency checks; a guideline analysis means for analyzing the latest medical guidelines and related literature and generating a treatment protocol; a generative AI model means for simulating treatment options using the generated treatment protocol and patient information; a proposal generation means for generating a customized treatment proposal taking into account the patient's wishes and risk tolerance; a provision means for providing the generated proposal to the patient and displaying visual and interactive content; a sharing means for sharing information with the patient's family and friends and receiving feedback; a collaboration means for providing information and discussions with medical professionals; and a recording means for determining and recording a final treatment plan. This enables patients to deeply understand their condition and quickly and accurately select the optimal treatment.

[0122] The "input means" is a means for a patient to input information such as age, sex, medical history, current symptoms, and desired treatment course.

[0123] The "data preparation means" is a means for receiving input patient information and performing pre-processing such as normalization and inconsistency checks.

[0124] The "guideline analysis means" is a means for analyzing the latest medical guidelines and related literature and generating treatment protocols.

[0125] The "generative AI model means" is a means for simulating treatment options using the generated treatment protocol and patient information.

[0126] A "proposal generator" is a means for generating a customized treatment proposal, taking into account the patient's preferences and risk tolerance.

[0127] The "delivery means" is a means for providing the generated suggestions to the patient and displaying visual and interactive content.

[0128] "Sharing tools" are ways to share information with the patient's family and friends and receive feedback.

[0129] "Collaboration methods" are means for providing information and holding discussions with medical professionals.

[0130] "Recording means" is a means for determining and recording the final treatment plan.

[0131] MODE FOR CARRYING OUT THE INVENTION

[0132] This system utilizes the latest medical guidelines and generative AI models to suggest optimal treatment options based on patient-entered information. A specific implementation of the entire system is described below.

[0133] System Overview

[0134] The server receives patient information, organizes the data, analyzes guidelines, and simulates treatment options using a generative AI model to generate treatment proposals. The terminal is used by patients to input information and check the proposed treatment options. Users include patients themselves, their families, and medical professionals.

[0135] Hardware and software used

[0136] Hardware: Servers, devices (PCs, tablets, smartphones)

[0137] Software: Database management system, generative AI model, interactive interface

[0138] Processing flow

[0139] 1. Enter patient information

[0140] The user inputs their age, gender, medical history, current symptoms, and desired treatment plan via a terminal, using an interface where they click on text boxes and options.

[0141] Examples:

[0142] The user inputs that he is a 42-year-old male suffering from high blood pressure and diabetes, and describes his current symptoms as slight fever and dizziness. He also inputs that he wishes to receive drug therapy as his treatment preference.

[0143] 2. Data preparation and preprocessing

[0144] The terminal sends the entered data to the server, which receives the data and performs normalization, duplication checks, and inconsistency checks.

[0145] Examples:

[0146] The server normalizes the entered age data of "42 years old" and checks for duplicate information about "high blood pressure" and "diabetes."

[0147] 3. Analysis of guidelines and literature

[0148] The server analyzes the latest medical guidelines and related literature to generate the optimal treatment plan for the patient's condition. This analysis includes searching guideline databases and related papers to extract the optimal treatment plan.

[0149] Examples:

[0150] The server searches the guideline database for the latest treatments for "high blood pressure" and "diabetes" and generates a treatment protocol appropriate for the patient.

[0151] 4. AI-powered simulation of treatment options

[0152] The server uses the generative AI model to simulate treatment options based on treatment protocols and patient information, and the results are evaluated for suitability and predicted outcomes for each treatment option.

[0153] Examples:

[0154] The generative AI model simulates treatment options based on the information of "42-year-old male, high blood pressure, diabetes, slight fever, dizziness" and evaluates suitability.

[0155] 5. Generating customized treatment suggestions

[0156] The server generates a customized treatment proposal that takes into account the patient's preferences (e.g., drug therapy) and risk tolerance. The proposal includes details of the treatment, expected effects, side effects, and treatment duration.

[0157] Examples:

[0158] The server generates treatment plans with minimal side effects, focusing on drug therapy, and proposes details.

[0159] 6. Providing and confirming proposals

[0160] The device provides the generated treatment recommendations to the user and displays them with visual and interactive content, making it easier for patients to understand the recommendations.

[0161] Examples:

[0162] The device will display details of drug therapy, side effects, and time to recovery to help patients understand.

[0163] 7. Sharing information with family and friends

[0164] A sharing feature is provided that allows users to share their treatment suggestions with family and friends and get their feedback.

[0165] Examples:

[0166] The user emails the proposal to family members and asks for their opinions.

[0167] 8. Collaboration with medical professionals

[0168] The server shares treatment suggestions with medical professionals and provides a discussion function, allowing for expert input.

[0169] Examples:

[0170] Healthcare professionals use the chat feature to provide feedback to patients.

[0171] 9. Deciding and recording the final treatment plan

[0172] The user, family, friends, and medical professionals decide on the final treatment plan, which the server records and notifies all parties of the relevant information.

[0173] Examples:

[0174] The user confirms the final treatment plan by clicking the "Decide" button, and the server notifies all parties involved by email.

[0175] Examples of prompt statements

[0176] For example, we can input the following prompts into a generative AI model to suggest treatment options:

[0177] Patient information: 42-year-old male with high blood pressure and diabetes. Current symptoms are slight fever and dizziness. Treatment desired: Drug therapy.

[0178] Conditions: Based on current medical guidelines, three treatment options are proposed, one of which focuses on minimizing side effects.

[0179] The above is an embodiment of the present invention. This system allows patients to gain a deeper understanding of their own condition and to select the most appropriate treatment quickly and accurately.

[0180] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0181] Step 1:

[0182] Users input their age, gender, medical history, current symptoms, and desired treatment plan into the terminal, using an interface that involves clicking text boxes and options.

[0183] Input: Patient's age, gender, medical history, current symptoms, treatment preference

[0184] Output: Information entered by the user

[0185] Specific working example:

[0186] The user inputs that he is a 42-year-old male suffering from high blood pressure and diabetes, describes slight fever and dizziness as his current symptoms, and selects drug therapy as his desired treatment.

[0187] Step 2:

[0188] The terminal sends the entered data to the server, which receives the data and performs normalization, duplication checks, and inconsistency checks.

[0189] Input: Patient information received from the user

[0190] Output: Preprocessed and cleaned data

[0191] Specific working example:

[0192] The server normalizes the entered age data, such as "42 years old," and checks for duplicate information and inconsistencies. For example, it deletes duplicate medical history information and corrects incorrect data formats.

[0193] Step 3:

[0194] The server analyzes the latest medical guidelines and related papers to generate treatment protocols, which includes searching guideline databases and related papers to extract optimal treatments.

[0195] Input: Preprocessed and cleaned data

[0196] Output: Generated treatment procedure

[0197] Specific working example:

[0198] The server searches the guideline database for the latest treatments for "hypertension" and "diabetes" and generates a treatment protocol appropriate for the patient.

[0199] Step 4:

[0200] The server uses the generative AI model to simulate treatment options based on treatment protocols and patient information, and the results are evaluated for suitability and predicted outcomes for each treatment option.

[0201] Input: Treatment protocol and patient information

[0202] Output: Simulated treatment options and their fitness

[0203] Specific working example:

[0204] The generative AI model simulates treatment options based on the information of "42-year-old male, high blood pressure, diabetes, slight fever, dizziness" and evaluates their suitability. For example, drug therapy A is evaluated as having a suitability of 90%, and drug therapy B is evaluated as having a suitability of 70%.

[0205] Step 5:

[0206] The server takes into account the patient's preferences (e.g., medication) and risk tolerance and generates a customized treatment proposal, including details of the treatment, expected effects, side effects, and treatment duration.

[0207] Input: Simulated treatment options and patient preferences

[0208] Output: Customized treatment proposals

[0209] Specific working example:

[0210] The server generates a treatment plan with fewer side effects, focusing on drug therapy, and proposes its details. For example, it lists drug therapy A with fewer side effects and its treatment schedule in the proposal.

[0211] Step 6:

[0212] The device provides the generated treatment recommendations to the user and displays them with visual and interactive content, making it easier for patients to understand the recommendations.

[0213] Input: Customized Treatment Proposal

[0214] Output: Visual and interactive treatment proposals provided to the user

[0215] Specific working example:

[0216] The device displays "details of drug therapy, side effects, and time to recovery," and provides interactive graphs and videos to help users understand.

[0217] Step 7:

[0218] A sharing feature is provided that allows users to share their treatment suggestions with family and friends and get their feedback.

[0219] Input: Treatment proposal

[0220] Output: Treatment suggestions and feedback shared with family and friends

[0221] Specific working example:

[0222] The user emails the proposal to family members and asks for their opinions.

[0223] Step 8:

[0224] The server shares treatment suggestions with medical professionals and provides a discussion function, allowing for expert input.

[0225] Input: Treatment proposals and discussion details

[0226] Output: Opinions and feedback from healthcare professionals

[0227] Specific working example:

[0228] Healthcare professionals use the chat feature to provide feedback to patients, for example by providing additional information about side effects of medication.

[0229] Step 9:

[0230] The user, family, friends, and medical professionals decide on the final treatment plan, which the server records and notifies all parties of the relevant information.

[0231] Input: Final treatment plan decision

[0232] Output: Recorded final treatment plan and notification to relevant parties

[0233] Specific working example:

[0234] The user confirms the final treatment plan by clicking the "Decide" button, and the server notifies all parties involved by email.

[0235] (Application example 1)

[0236] 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."

[0237] In conventional healthcare systems, treatment recommendations are based solely on a patient's medical history and symptoms, and comprehensive treatment plans, including dietary therapy, are not adequately proposed. Furthermore, there are insufficient means for communicating the proposed treatment plan with family members and healthcare professionals and obtaining feedback, making it difficult to provide optimal treatment and meal plans to patients. To solve this problem, a comprehensive treatment and meal plan recommendation system that takes into account a patient's medical history, symptoms, and dietary restrictions is needed.

[0238] 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.

[0239] In this invention, the server includes a meal plan proposal unit that proposes a meal plan based on a patient's input of their medical history, symptoms, and dietary restrictions, an input unit for the patient to input their age, sex, medical history, current symptoms, and desired treatment plan, a data preparation unit that receives and preprocesses the input patient information, a guideline analysis unit that analyzes the latest medical guidelines and related medical papers and generates a treatment protocol, a generative AI model unit that simulates treatment options using the generated treatment protocol and patient information, a proposal generation unit that generates a customized treatment proposal taking into account the patient's preferences and risk tolerance, a provision unit that provides the generated proposal to the patient, a display unit that allows the patient to confirm the proposal content and display interactive content, a sharing unit that shares information with the patient's family and friends and receives feedback, a collaboration unit that provides information and discussions with medical professionals, and a recording unit that determines and records the final treatment plan. This enables the optimal treatment and meal plan to be quickly and accurately provided taking into account the patient's medical history, symptoms, and dietary restrictions.

[0240] "Patient" refers to a person receiving medical examination or treatment.

[0241] "Age" refers to the number of years that have passed since the date of birth.

[0242] "Sex" refers to biological characteristics as male or female.

[0243] "Medical history" refers to records and information about illnesses you have had and treatments you have received.

[0244] "Symptoms" refer to specific phenomena or sensations that occur in association with illness or poor health.

[0245] A "treatment policy" refers to a specific treatment direction or plan to improve a patient's condition.

[0246] "Input means" refers to a device or interface that allows a patient to input their age, sex, medical history, current symptoms, and desired treatment course into the system.

[0247] "Data preparation means" refers to the functions and devices that receive input patient information and perform preprocessing.

[0248] "Guideline analysis means" refers to a device or algorithm for analyzing the latest medical guidelines and related medical papers and generating treatment protocols.

[0249] "Generative AI model means" refers to an artificial intelligence model for simulating treatment options using the generated treatment protocol and patient information.

[0250] "Proposal generation means" refers to functionality or devices for generating customized treatment proposals taking into account the patient's preferences and risk tolerance.

[0251] "Meal plan suggestion means" refers to a function or device for inputting a patient's medical history, symptoms, and dietary restrictions and proposing a meal plan based on that information.

[0252] "Providing means" refers to a function or device for providing the generated suggestions to the patient.

[0253] "Display means" refers to a device or interface that allows the patient to review the suggestions and display interactive content.

[0254] "Sharing tools" refers to features and devices that allow patients to share information with their family and friends and receive feedback.

[0255] "Collaboration means" refers to functions and devices for providing information and holding discussions with medical professionals.

[0256] "Recording means" refers to the functions and devices for determining and recording the final treatment plan.

[0257] "Dietary restriction" refers to restrictions or limitations on diet to accommodate a specific health condition or illness.

[0258] The present invention is a system that allows patients to input their medical history, symptoms, treatment preferences, and dietary restrictions, and then uses the latest medical guidelines and generative AI models to propose optimal treatment and diet plans. Specific embodiments for implementing this system are described below.

[0259] Overall system overview

[0260] The server receives patient information, organizes data, analyzes guidelines, and simulates treatment and dietary options using a generative AI model, generating treatment and dietary recommendations. The terminal is used by patients to input information and confirm the proposed treatment and dietary options. Users include the patients themselves, their families, and healthcare professionals.

[0261] Program processing flow

[0262] Entering patient information

[0263] The user uses the terminal to input their age, gender, medical history, current symptoms, treatment preferences, and dietary restrictions, which includes an input form and an option-clicking interface.

[0264] Data preparation and preprocessing

[0265] The terminal sends the input data to the server, which then preprocesses the received data, specifically normalizing the data and checking for duplicates and inconsistencies.

[0266] Guidelines and literature analysis

[0267] The server analyzes the latest medical guidelines and relevant medical literature to generate a treatment protocol that lists the treatment and dietary options that best suit the patient's symptoms and dietary restrictions.

[0268] AI-powered simulation of treatment and dietary options

[0269] Based on patient information and treatment protocols, the server uses a generative AI model to simulate treatment and dietary options, evaluating each option's suitability and predicted outcomes.

[0270] Generating customized treatment and dietary suggestions

[0271] The server takes into account the patient's preferences and risk tolerance and generates customized treatment and dietary recommendations, including details of the treatment and diet, expected effects, side effects, and duration of treatment.

[0272] Providing and confirming proposals

[0273] The device then provides the generated treatment and dietary recommendations to the user, who can then review and deepen their understanding of the recommendations through interactive screens and videos.

[0274] Share information with family and friends

[0275] Users can share their treatment and dietary suggestions with family and friends, allowing them to receive feedback from others.

[0276] Collaboration with medical professionals

[0277] The server provides a discussion function for sharing treatment and dietary suggestions with healthcare professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[0278] Deciding and recording the final treatment plan

[0279] The user, family, friends, and healthcare professionals decide on the final treatment and diet plan, and the server records this decision and notifies all parties with relevant information.

[0280] Hardware and software used

[0281] The following hardware and software are used to implement this system.

[0282] Frontend: Interface using React

[0283] Backend: API server using Flask

[0284] Generative AI model: Generate treatment and dietary options based on patient information

[0285] Specific examples

[0286] For example, if a 42-year-old male patient with high blood pressure and diabetes needs to go on a low-carb diet, the process would go like this:

[0287] 1. The patient enters his / her age (42 years old), gender (male), medical history (high blood pressure, diabetes), current symptoms, treatment requests, and dietary restrictions (low-carbohydrate diet) into the terminal.

[0288] 2. The terminal sends the entered information to the server.

[0289] 3. The server pre-processes the data, normalizing it and checking for inconsistencies.

[0290] 4. The server analyzes the latest medical guidelines and related papers to generate the optimal treatment protocol for the patient.

[0291] 5. The server uses the generative AI model to simulate treatment and dietary options and evaluate the suitability of each option.

[0292] 6. The server takes into account the patient's preferences and risk tolerance, generates customized treatment and dietary suggestions, and sends the details to the device.

[0293] 7. The device will visually display treatment and dietary suggestions, allowing patients to understand the suggestions.

[0294] 8. The patient shares information with family members and gets their opinions.

[0295] 9. Provide a means for the server to collaborate with medical professionals for discussion and to incorporate expert opinions.

[0296] 10. The patient, family, friends, and healthcare professionals decide on the final treatment and diet plan, which is recorded by the server.

[0297] Example prompt sentence:

[0298] "What is the best meal plan for a 45-year-old male with diabetes who needs to follow a low-carb diet?"

[0299] The above is an embodiment of the present invention, which allows patients to deeply understand their own medical condition and dietary restrictions, and to quickly and accurately select optimal treatment and diet.

[0300] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0301] Step 1:

[0302] The user uses the device to input their age, gender, medical history, current symptoms, treatment preferences, and dietary restrictions. The input data is collected through a form on the device. The input information is sent to the server in JSON format.

[0303] Step 2:

[0304] The server preprocesses the patient information received from the terminal using a data preparation method. Specifically, it normalizes the data (for example, standardizing date formats and trimming strings), checks for duplication, and checks for inconsistencies. Once preprocessing is complete, the data is passed to the next processing step.

[0305] Step 3:

[0306] The server uses the guideline analysis means to analyze the latest medical guidelines and related medical papers. Based on the analysis results, it generates a treatment protocol for each patient. This generated treatment protocol is used in the next step.

[0307] Step 4:

[0308] The server uses the generated treatment protocol and preprocessed patient information to apply the generative AI model to simulate treatment and dietary options. Specifically, the AI ​​evaluates and recommends the optimal treatment and dietary plan based on the patient's symptoms and conditions. The output data from the simulation is passed to the next step.

[0309] Step 5:

[0310] The server generates a customized treatment and diet proposal based on the generated treatment and diet options, taking into account the patient's wishes and risk tolerance. This proposal includes details of the treatment and diet, expected effects, side effects, treatment duration, etc. The generated proposal is sent to the terminal.

[0311] Step 6:

[0312] The device displays the treatment and dietary recommendations received from the server. The user can review and deepen their understanding of the recommendations through interactive screens and videos. The display of the recommendations includes visual elements and navigation functions.

[0313] Step 7:

[0314] The user uses the share button on the device to share the treatment and dietary suggestions with family and friends. The device generates a sharing link or file and sends it to the family and friends. The user receives feedback from the family and friends.

[0315] Step 8:

[0316] The server provides a collaborative means to share treatment and dietary suggestions with healthcare professionals, provide discussion features for expert input, including chat and video call features, and receive feedback from healthcare professionals.

[0317] Step 9:

[0318] The user, family, friends, and healthcare professionals decide on the final treatment and diet plan. The server records this decision and notifies all parties of the relevant information. The recorded data is stored for future reference and tracking.

[0319] The above are the specific processing steps of the program for the system that realizes the application example.

[0320] 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.

[0321] This invention is a system in which patients input their medical history, symptoms, and treatment preferences, and then uses the latest medical guidelines and generative AI models to suggest optimal treatment options based on that information. Furthermore, by combining it with an emotion engine that recognizes the patient's emotions, more personalized treatment suggestions are possible. Specific forms for implementing this system are described below.

[0322] Overall system overview

[0323] The server receives patient information, organizes data, analyzes guidelines, simulates treatment options using an AI model, analyzes emotions using an emotion engine, and generates treatment suggestions. The terminal is used by patients to input information and check suggested treatment options. Users include patients themselves, their families, and medical professionals.

[0324] Program processing flow

[0325] Entering patient information

[0326] Users input their age, gender, medical history, current symptoms, and treatment preferences via a terminal, which includes an input form and an interface for clicking options.

[0327] Data preparation and preprocessing

[0328] The terminal sends the input data to the server, which then preprocesses the received data, specifically normalizing the data and checking for duplicates and inconsistencies.

[0329] Guidelines and literature analysis

[0330] The server analyzes the latest medical guidelines and relevant medical literature to generate a treatment protocol, which lists the best treatment options for the patient's condition.

[0331] AI-powered simulation of treatment options

[0332] The server uses a generative AI model to simulate treatment options based on patient information and treatment protocols, evaluating each option's suitability and predicted outcomes.

[0333] Emotion analysis using an emotion engine

[0334] While the patient is entering information, the device uses a camera and microphone to analyze the patient's emotional state in real time. This information is processed by an emotion engine, which generates emotional data based on the patient's facial expressions, tone of voice, and choice of words.

[0335] Generate customized treatment recommendations

[0336] The server takes into account the patient's preferences and risk tolerance, as well as the emotional data obtained from the emotion engine, to generate a customized treatment proposal, including details of the treatment, expected effects, side effects, and treatment duration.

[0337] Providing and confirming proposals

[0338] The device then provides the generated treatment proposal to the user, who can then review and deepen their understanding of the proposal through interactive screens and videos.

[0339] Share information with family and friends

[0340] Users can share their treatment suggestions with family and friends, allowing them to receive feedback from others.

[0341] Collaboration with medical professionals

[0342] The server provides a discussion function for sharing treatment suggestions with medical professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[0343] Deciding and recording the final treatment plan

[0344] The user, family, friends, and medical professionals decide on the final treatment plan, and the server records this decision and notifies all parties with relevant information.

[0345] Specific examples

[0346] For example, in the case of a 42-year-old male patient with hypertension and diabetes, the process would proceed as follows:

[0347] 1. The patient enters his / her age (42 years old), gender (male), medical history (high blood pressure, diabetes), current symptoms, and treatment preferences into the terminal.

[0348] 2. The terminal sends the entered information to the server.

[0349] 3. The server pre-processes the data, normalizing it and checking for inconsistencies.

[0350] 4. The server analyzes the latest medical guidelines and related papers to generate the optimal treatment protocol for the patient.

[0351] 5. The server uses the generative AI model to simulate treatment options and evaluate the suitability of each option.

[0352] 6. The device analyzes the patient's emotional state in real time through a camera and microphone and sends that information to the emotion engine.

[0353] 7. The server generates a customized treatment proposal based on the emotional data, preferences, and risk tolerance, and sends the details to the device.

[0354] 8. The device visually displays treatment suggestions to help patients understand the suggestions.

[0355] 9. The patient shares information with family members and gets their opinions.

[0356] 10. Provide a means for the server to collaborate and hold discussions with medical professionals, thereby incorporating expert opinions.

[0357] 11. The patient, family, friends, and healthcare professionals decide on the final treatment plan, which is recorded by the server.

[0358] The above is an embodiment of the present invention, which allows patients to deeply understand their own medical condition and quickly and accurately select the optimal treatment that reflects their emotional state.

[0359] The processing flow will be explained below.

[0360] Step 1:

[0361] The user inputs his / her age, sex, medical history, current symptoms, and desired treatment course via the terminal.

[0362] Specifically, the user enters data into an input form and presses a send button, thereby inputting the data.

[0363] Step 2:

[0364] The terminal transmits the input data to the server.

[0365] The terminal encrypts the data to be transmitted and sends it to the server over a secure connection.

[0366] Step 3:

[0367] The server stores the received data in a database.

[0368] The database stores patient information in a suitable format.

[0369] Step 4:

[0370] The server performs pre-processing of the data, including normalizing the data, checking for duplicates, and checking for inconsistencies.

[0371] The server converts the received data into a unified format, removes duplicate data, and checks for inconsistencies.

[0372] Step 5:

[0373] The server analyzes the latest medical guidelines and related medical literature and generates treatment protocols.

[0374] Relevant guidelines and papers will be searched from the database, and treatment protocols will be created using analytical algorithms.

[0375] Step 6:

[0376] The server uses a generative AI model to simulate treatment options based on patient information and the generated treatment protocol.

[0377] Simulations will assess the suitability and predicted outcomes of each treatment option.

[0378] Step 7:

[0379] The terminal uses an emotion engine to analyze the patient's emotional state in real time as information is being entered.

[0380] The device uses a camera and microphone to analyze facial expressions, tone of voice, and language usage to generate emotional data.

[0381] Step 8:

[0382] The terminal transmits the analyzed emotion data to the server.

[0383] The device encrypts the emotion data and transmits it to the server over a secure connection.

[0384] Step 9:

[0385] The server receives the affective data and generates a treatment recommendation after taking into account the patient's preferences and risk tolerance.

[0386] Include emotional data to generate more personalized treatment recommendations.

[0387] Step 10:

[0388] The server transmits the generated treatment proposal to the terminal.

[0389] The suggestions are formatted into an interactive format and sent to the device.

[0390] Step 11:

[0391] The terminal visually displays treatment suggestions.

[0392] Interactive images and instructional videos are used to detail treatment options.

[0393] Step 12:

[0394] Users share their treatment suggestions with family and friends.

[0395] Use the share feature to send information via email or message.

[0396] Step 13:

[0397] The device again sends feedback from family and friends to the server.

[0398] The received feedback is sent to the server and stored in a database.

[0399] Step 14:

[0400] The server shares information with medical professionals, including the patient's primary care physician, and provides an environment for discussion.

[0401] Proposals are shared and discussion functions are provided through an interface for medical professionals.

[0402] Step 15:

[0403] The user, family, and healthcare professionals share and discuss information to determine the final treatment plan.

[0404] Discussions are held in real time using the chat and video conferencing functions provided by the server.

[0405] Step 16:

[0406] The server determines and records the final treatment plan.

[0407] The confirmed treatment plan will be recorded in the database and notified to all parties.

[0408] These are the specific processing steps of the system, which allow patients to receive support in making optimal medical choices.

[0409] Example 2

[0410] 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."

[0411] Modern medicine requires the ability to quickly and accurately recommend optimal treatments tailored to each patient's individual symptoms and wishes. However, manually collecting and analyzing information and determining treatment plans is time-consuming and prone to errors. Furthermore, determining treatment plans without considering the patient's emotional state can reduce patient satisfaction and reduce treatment effectiveness. Furthermore, information sharing and collaboration are essential for patients, their families, friends, and healthcare professionals to work together to determine the optimal treatment plan. Therefore, a system that can resolve these issues and provide faster and more accurate treatment recommendations is needed.

[0412] 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 data preparation means for receiving input patient information and performing preprocessing; a guideline analysis means for analyzing the latest medical guidelines and related medical papers to generate a treatment protocol; a generation AI model means for simulating treatment options using the generated treatment protocol and patient information; and a proposal generation means for generating a customized treatment proposal by taking into account the patient's wishes, risk tolerance, and emotional data. This enables rapid and accurate generation of individually optimized treatment proposals based on detailed patient information and the latest medical guidelines. Furthermore, customized proposals based on emotional data can improve patient satisfaction and treatment effectiveness. Furthermore, by sharing information with family and friends and collaborating with medical professionals, a more comprehensive and reliable treatment plan can be determined.

[0413] "Patient" refers to an individual with a medical history or condition who uses the system.

[0414] "Input means" refers to a device or interface that allows a patient to input information such as age, sex, medical history, current symptoms, and desired treatment course.

[0415] "Data preparation means" refers to devices or programs that receive input patient information and perform preprocessing (data normalization, duplication checks, inconsistency checks, etc.).

[0416] "Guideline analysis means" refers to a device or program for analyzing the latest medical guidelines and related medical papers and generating treatment protocols.

[0417] "Generative AI model means" refers to an artificial intelligence model for simulating treatment options using the generated treatment protocol and patient information.

[0418] "Proposal Generator" refers to a device or program for generating a customized treatment proposal taking into account the patient's preferences and risk tolerance, as well as emotional data.

[0419] "Providing means" refers to a device or system for providing the generated treatment proposal to the patient.

[0420] "Display means" refers to a display or user interface that allows the patient to confirm the suggestions and display interactive content.

[0421] An "emotion engine" refers to a device or program that uses a camera or microphone to analyze a patient's emotional state in real time and generate emotional data.

[0422] "Sharing means" refers to functions and devices that allow patients to share treatment suggestion information with their family and friends and receive feedback.

[0423] "Collaboration methods" refer to online chat functions and data sharing systems for providing information to medical professionals and for exchanging opinions and holding discussions.

[0424] "Recording means" refers to a database or recording device for determining the final treatment plan and recording that information.

[0425] This invention is a system in which patients input their medical history, symptoms, and treatment preferences, and then uses the latest medical guidelines and generative AI models to suggest optimal treatment options based on that information. Furthermore, by combining it with an emotion engine that recognizes the patient's emotions, more personalized treatment suggestions are possible. Specific forms for implementing this system are described below.

[0426] Overall system overview

[0427] The server receives patient information, organizes data, analyzes guidelines, simulates treatment options using an AI model, analyzes emotions using an emotion engine, and generates treatment suggestions. The terminal is used by patients to input information and check suggested treatment options. Users include patients themselves, their families, and medical professionals.

[0428] Entering patient information

[0429] The user inputs their age, gender, medical history, current symptoms, and desired treatment course via a terminal, which includes an input form and an option-click interface. The input data is then sent to the server by the terminal.

[0430] Data preparation and preprocessing

[0431] The server preprocesses the received data, specifically normalizing it, checking for duplicates, checking for inconsistencies, etc. This preprocessing ensures data consistency and accuracy.

[0432] Guidelines and literature analysis

[0433] The server analyzes the latest medical guidelines and related medical papers to generate treatment protocols, using natural language processing (NLP) technology to extract relevant information from large amounts of medical text data.

[0434] AI-powered simulation of treatment options

[0435] The server uses a generative AI model to simulate treatment options based on patient information and treatment protocols. A prompt is input to the AI ​​model to evaluate the suitability and predicted outcomes of multiple treatment options. For example, a simulation is performed based on the prompt: "42-year-old male with a history of high blood pressure and diabetes. Current symptoms include mild headaches and fatigue. He wishes to undergo a new treatment and minimize side effects. Please suggest the optimal treatment plan."

[0436] Emotion analysis using an emotion engine

[0437] While the patient is entering information, the device uses a camera and microphone to analyze the patient's emotional state in real time. This information is processed by an emotion engine, which generates emotional data based on the patient's facial expressions, tone of voice, and choice of words.

[0438] Generate customized treatment recommendations

[0439] The server generates a customized treatment proposal based on the patient's preferences, risk tolerance, and emotional data, including details of the treatment, expected effects, side effects, and duration of treatment.

[0440] Providing and confirming proposals

[0441] The device then provides the generated treatment proposal to the user, who can then review and deepen their understanding of the proposal through interactive screens and videos.

[0442] Share information with family and friends

[0443] Users can share their treatment suggestions with family and friends, allowing them to receive feedback from others.

[0444] Collaboration with medical professionals

[0445] The server provides a discussion function for sharing treatment suggestions with medical professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[0446] Deciding and recording the final treatment plan

[0447] The user, family, friends, and medical professionals decide on the final treatment plan, and the server records this decision and notifies all parties with relevant information.

[0448] The above is a specific embodiment for carrying out the present invention, which allows patients to deeply understand their own medical condition and quickly and accurately select the optimal treatment that reflects their emotional state.

[0449] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0450] Step 1:

[0451] The user enters their age, gender, medical history, current symptoms, and desired treatment plan into the terminal. Specifically, the user enters information into the input form displayed on the terminal and clicks the "Submit" button. Input is performed using text fields and selection options. The input in this step is data about the patient's personal information and symptoms, and the output is temporary data stored on the terminal.

[0452] Step 2:

[0453] The terminal sends the input data to the server. Specifically, the data is encrypted and sent using the HTTPS protocol. The server receives the data. The input to this step is the patient information stored on the terminal, and the output is the data received by the server.

[0454] Step 3:

[0455] The server pre-processes the data it receives. Specific operations include normalizing the data (e.g., standardizing date formats), checking for duplicates (e.g., deleting duplicate entries), and checking for inconsistencies (e.g., checking for unnatural data values). This pre-processing ensures data consistency and accuracy. The input to this step is the raw data sent earlier, and the output is the pre-processed, clean data.

[0456] Step 4:

[0457] The server analyzes the latest medical guidelines and related medical papers. Specifically, it uses natural language processing (NLP) technology to extract relevant information from large amounts of medical text data and generate treatment protocols. The input for this step is the latest medical guidelines and patient data, and the output is the optimal treatment protocol for the patient.

[0458] Step 5:

[0459] The server uses a generative AI model to simulate treatment options based on the generated treatment protocol and patient information. Specifically, it inputs prompts to the AI ​​model and evaluates the suitability and predicted outcomes of multiple treatment options. The inputs for this step are the treatment protocol and prompts, and the output is multiple treatment options and their evaluation results.

[0460] Step 6:

[0461] While the patient is entering information, the device uses a camera and microphone to analyze the patient's emotional state in real time. Specifically, facial expression data captured by the device's camera and voice data collected by the microphone are sent to the emotion engine for analysis. The input for this step is raw data obtained from the camera and microphone, and the output is emotional data analyzed by the emotion engine.

[0462] Step 7:

[0463] The server generates a customized treatment proposal based on the patient's preferences, risk tolerance, and emotional data. Specifically, it integrates the AI ​​model's simulation results with the emotional data to create a comprehensive treatment plan that includes details of the treatment, expected effects, side effects, and treatment duration. The input for this step is treatment options and emotional data, and the output is a customized treatment proposal.

[0464] Step 8:

[0465] The device provides the generated treatment proposal to the user. Specifically, the device visually displays the treatment proposal on its display and allows the user to easily review the proposal through interactive elements (e.g., tab switching, detailed information display, video explanation, etc.). The input of this step is the customized treatment proposal, and the output is a display screen that the user can view.

[0466] Step 9:

[0467] The user can share the treatment proposal with family and friends by using the sharing function on their device. Specific actions include sending the proposal via email or generating a QR code to scan. The input for this step is the customized treatment proposal, and the output is the shared proposal.

[0468] Step 10:

[0469] The server shares the treatment proposal with healthcare professionals and provides a discussion function to incorporate their expert opinions. Specific operations include providing information and collecting opinions through an online chat function and a data sharing folder. The input of this step is the customized treatment proposal and healthcare professional feedback, and the output is a revised treatment proposal.

[0470] Step 11:

[0471] The user, family, friends, and healthcare professionals decide on a final treatment plan. The server records this decision and notifies all relevant parties of the relevant information. Specific operations include saving the finalized treatment plan digitally and notifying relevant parties via push notifications or email. The input of this step is the finalized treatment plan, and the output is the recorded treatment plan and notification content.

[0472] (Application example 2)

[0473] 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."

[0474] Conventional medical systems are limited in their functionality, not only in inputting a patient's medical history and symptoms, but also in proposing optimal treatment options based on the latest medical guidelines. Furthermore, because they do not take into account the patient's emotional state, there is a lack of personalization, making it difficult to find the optimal treatment. Furthermore, there is no clear guidance on payment methods after a treatment plan is decided, which places a burden on patients.

[0475] 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: an input means for the patient to input their age, gender, medical history, current symptoms, and desired treatment plan; a data preparation means for receiving and preprocessing the input patient information; a guideline analysis means for analyzing the latest medical guidelines and related medical papers and generating a treatment protocol; a generative AI model means for simulating treatment options using the generated treatment protocol and patient information; an emotion analysis means for analyzing the patient's emotional state in real time; a proposal generation means for generating a customized treatment proposal taking into account the patient's wishes, risk tolerance, and emotional data; a provision means for providing the generated proposal to the patient; a display means for allowing the patient to confirm the proposal content and display interactive content; a sharing means for sharing information with the patient's family and friends and receiving feedback; a collaboration means for providing information and discussions with medical professionals; and a recording means for determining and recording a final treatment plan. This enables patients to quickly and accurately obtain optimal treatment options that comprehensively consider their medical condition and emotional state.

[0476] The "input means" is a means for a patient to input information such as age, sex, medical history, current symptoms, and desired treatment course.

[0477] The "data preparation means" is a means for receiving input patient information and performing preprocessing.

[0478] The "guideline analysis means" is a means for analyzing the latest medical guidelines and related medical papers and generating treatment protocols.

[0479] The "generative AI model means" is a means for simulating treatment options using the generated treatment protocol and patient information.

[0480] The "emotion analysis means" is a means for analyzing the emotional state of a patient in real time.

[0481] A "proposal generator" is a means for generating a customized treatment proposal that takes into account the patient's preferences, risk tolerance and emotional data.

[0482] The "means for providing" is a means for providing the generated suggestion to the patient.

[0483] The "display means" is a means for the patient to confirm the proposed content and display interactive content.

[0484] "Sharing tools" are ways to share information with the patient's family and friends and receive feedback.

[0485] "Collaboration methods" are means for providing information and holding discussions with medical professionals.

[0486] "Recording means" is a means for determining and recording the final treatment plan.

[0487] This invention is a system that uses the latest medical guidelines and generative AI models to suggest optimal treatment options by allowing patients to input their medical history, symptoms, and treatment preferences. Furthermore, by combining it with an emotion engine that recognizes the patient's emotions, more personalized treatment suggestions are possible.

[0488] Overall system overview

[0489] The server receives patient information, organizes data, analyzes guidelines, simulates treatment options using a generative AI model, analyzes emotions using an emotion engine, and generates treatment proposals. The terminals are used by patients to input information and check proposed treatment options. These terminals include smartphones. Users include patients themselves, their families, and healthcare professionals.

[0490] Hardware and software used

[0491] The hardware uses a smartphone (iOS / Android device) and performs emotion analysis using the front camera and microphone, while a cloud server is used to analyze medical guidelines and run generative AI models.

[0492] The following software is used:

[0493] Data preparation measures: Preprocessing patient information

[0494] Guideline analysis tools: Analysis of the latest medical guidelines and related medical papers

[0495] Generative AI modeling tools: simulating treatment options

[0496] Emotion analysis tool: Analyze the current emotional state of the patient in real time

[0497] Proposal generation method: Generate treatment proposals taking into account the patient's preferences, risk tolerance, and emotional data

[0498] Operating procedure

[0499] 1. Patients use their smartphones to enter their age, gender, medical history, current symptoms, and treatment preferences.

[0500] 2. The terminal sends the entered data to the server.

[0501] 3. The server performs preprocessing such as data normalization and inconsistency checking.

[0502] 4. The server analyzes the latest medical guidelines and related papers and generates a treatment protocol.

[0503] 5. Use generative AI models to simulate treatment options and evaluate the suitability of each option.

[0504] 6. The device analyzes the patient's emotional state in real time through a camera and microphone and sends the data to the emotion engine.

[0505] 7. The server generates customized treatment recommendations taking into account the patient's preferences, risk tolerance, and emotional data.

[0506] 8. Provide patients with treatment suggestions through video and interactive content.

[0507] Specific examples

[0508] For example, consider a 42-year-old male patient with high blood pressure and diabetes. The patient enters his or her personal information on a smartphone, and the emotion analysis engine analyzes his or her emotional state. The system then recommends optimal treatment options based on the patient's emotional data. These recommendations include expected effects, side effects, treatment duration, and specific payment options.

[0509] Prompt Sentence Examples

[0510] Examples of prompts for generative AI models include:

[0511] "Prompt to generative AI model:

[0512] Patient information: 42-year-old male, medical history: hypertension, diabetes, current symptoms: fatigue, desired treatment: drug therapy

[0513] Please recommend the best treatment options for this patient based on the latest medical guidelines."

[0514] Through this platform, patients will be able to easily access treatment options that take into account their medical condition and emotional state in an integrated manner.

[0515] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0516] Step 1:

[0517] Patients use their smartphones to input their age, gender, medical history, current symptoms, and treatment preferences. This information is collected through the patient information input means. The input data includes age, gender, medical history, current symptoms, and treatment preferences. The input data is sent from the smartphone to the server.

[0518] Step 2:

[0519] The terminal sends the received patient information to the server. The server receives the patient information and performs preprocessing on the data. This preprocessing includes data normalization, duplication checks, and inconsistency checks. The input here is raw data from the patient, and the output is preprocessed data.

[0520] Step 3:

[0521] The server analyzes the latest medical guidelines and related papers based on the preprocessed data and generates a treatment protocol. Using guideline analysis tools, it analyzes medical literature and guidelines to list treatment options. The input is the preprocessed patient data, and the output is the generated treatment protocol.

[0522] Step 4:

[0523] The server uses a generative AI model to simulate treatment options based on treatment protocols and patient information. The simulation process evaluates the suitability and predicted outcomes of each treatment option. The inputs are treatment protocols and patient information, and the output is the simulation results.

[0524] Step 5:

[0525] While the terminal is inputting patient information, it uses a camera and microphone to analyze the patient's emotional state in real time. Using emotion analysis means, emotion data is generated from the patient's facial expressions, tone of voice, and choice of words. This emotion data is later sent to the server. The input is video and audio data, and the output is emotion analysis data.

[0526] Step 6:

[0527] The server generates a customized treatment proposal based on the emotion data, patient information, and treatment protocol. Using the proposal generation means, the optimal treatment option is generated taking into account the patient's wishes, risk tolerance, and emotion data. The input is the treatment protocol, patient information, and emotion data, and the output is a customized treatment proposal.

[0528] Step 7:

[0529] The server provides the generated treatment proposal to the smartphone. The patient can confirm the proposal through the provision means and understand the details through interactive content. The input is the customized treatment proposal, and the output is the treatment proposal confirmed by the patient.

[0530] Step 8:

[0531] After the patient confirms the proposal, they share the information with their family and friends. Using the sharing tool, the patient shares the treatment proposal and receives feedback. The input is the treatment proposal and the shared information, and the output is the feedback.

[0532] Step 9:

[0533] After receiving feedback from patients, their families, and friends, the server uses collaborative methods to provide information and hold discussions with healthcare professionals. Expert opinions are incorporated to refine the final treatment plan. The input is the feedback and shared information, and the output is the treatment plan with added expert opinions.

[0534] Step 10:

[0535] The patient, family, friends, and healthcare professionals decide on the final treatment plan, which is then recorded by the server. Using a recording method, the final treatment plan is formally recorded and notified to the relevant parties. The input is the final treatment proposal and discussion results, and the output is the recorded treatment plan.

[0536] 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.

[0537] 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.

[0538] 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.

[0539] [Second embodiment]

[0540] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0541] 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.

[0542] 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).

[0543] 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.

[0544] 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.

[0545] 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).

[0546] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0547] 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.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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."

[0552] The present invention is a system that allows patients to input their medical history, symptoms, and treatment preferences, and then uses the latest medical guidelines and generative AI models to suggest optimal treatment options. Specific embodiments for implementing this system are described below.

[0553] Overall system overview

[0554] The server receives patient information, organizes the data, analyzes guidelines, and simulates treatment options using AI models to generate treatment proposals. The terminals are used by patients to input information and check the proposed treatment options. Users include patients themselves, their families, and medical professionals.

[0555] Program processing flow

[0556] Entering patient information

[0557] The user uses the terminal to input their age, gender, medical history, current symptoms, and desired treatment course, which includes an input form and an interface for clicking options.

[0558] Data preparation and preprocessing

[0559] The terminal sends the input data to the server, which then preprocesses the received data, specifically normalizing the data and checking for duplicates and inconsistencies.

[0560] Guidelines and literature analysis

[0561] The server analyzes the latest medical guidelines and relevant medical literature to generate a treatment protocol, which lists the best treatment options for the patient's condition.

[0562] AI-powered simulation of treatment options

[0563] The server uses a generative AI model to simulate treatment options based on patient information and treatment protocols, evaluating each option's suitability and predicted outcomes.

[0564] Generate customized treatment recommendations

[0565] The server takes into account the patient's preferences and risk tolerance and generates a customized treatment proposal, including details of the treatment, expected effects, side effects, and duration of treatment.

[0566] Providing and confirming proposals

[0567] The device then provides the generated treatment proposal to the user, who can then review and deepen their understanding of the proposal through interactive screens and videos.

[0568] Share information with family and friends

[0569] Users can share their treatment suggestions with family and friends, allowing them to receive feedback from others.

[0570] Collaboration with medical professionals

[0571] The server provides a discussion function for sharing treatment suggestions with medical professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[0572] Deciding and recording the final treatment plan

[0573] The user, family, friends, and medical professionals decide on the final treatment plan, and the server records this decision and notifies all parties with relevant information.

[0574] Specific examples

[0575] For example, in the case of a 42-year-old male patient with hypertension and diabetes, the process would proceed as follows:

[0576] 1. The patient enters his / her age (42 years old), gender (male), medical history (high blood pressure, diabetes), current symptoms, and treatment preferences into the terminal.

[0577] 2. The terminal sends the entered information to the server.

[0578] 3. The server pre-processes the data, normalizing it and checking for inconsistencies.

[0579] 4. The server analyzes the latest medical guidelines and related papers to generate the optimal treatment protocol for the patient.

[0580] 5. The server uses the generative AI model to simulate treatment options and evaluate the suitability of each option.

[0581] 6. The server takes into account the patient's preferences and risk tolerance, generates a customized treatment proposal, and sends the details to the device.

[0582] 7. The device visually displays treatment suggestions to help patients understand them.

[0583] 8. The patient shares information with family members and gets their opinions.

[0584] 9. Provide a means for the server to collaborate with medical professionals for discussion and to incorporate expert opinions.

[0585] 10. The patient, family, friends, and healthcare professionals decide on the final treatment plan, which is recorded by the server.

[0586] The above is an embodiment of the present invention, which allows patients to gain a deeper understanding of their own condition and to select the most appropriate treatment quickly and accurately.

[0587] The processing flow will be explained below.

[0588] Step 1:

[0589] The user inputs his / her age, sex, medical history, current symptoms, and desired treatment course via the terminal.

[0590] Specifically, the data is entered by filling in the necessary information in the input form and pressing the send button.

[0591] Step 2:

[0592] The terminal transmits the input data to the server.

[0593] The data is encrypted and transmitted over a secure connection.

[0594] Step 3:

[0595] The server receives the transmitted data and stores it in a data database.

[0596] Checks the received data and inserts it into the appropriate tables in the database.

[0597] Step 4:

[0598] The server performs pre-processing, which includes data normalization, duplicate checks, and consistency checks.

[0599] Standardize data formats, remove duplicate records, and check for outliers and inconsistencies.

[0600] Step 5:

[0601] The server analyzes the latest medical guidelines and related medical literature and generates treatment protocols.

[0602] Database queries are performed to extract relevant guidelines and papers, and analytical algorithms are used to create treatment protocols.

[0603] Step 6:

[0604] The server uses a generative AI model to simulate treatment options.

[0605] The generative AI model is run using patient information and treatment protocols as input to simulate multiple treatment options.

[0606] Step 7:

[0607] The server generates a customized treatment proposal based on the simulation results, taking into account the patient's preferences and risk tolerance.

[0608] The simulation results are analyzed to select the treatment plan that best suits the patient's wishes and risk profile.

[0609] Step 8:

[0610] The server transmits the generated treatment proposal to the terminal.

[0611] The suggestions are formatted into an interactive format and sent to the patient's device.

[0612] Step 9:

[0613] The terminal visually displays the received treatment suggestions.

[0614] Interactive images and instructional videos are used to detail treatment options.

[0615] Step 10:

[0616] Users share their treatment suggestions with family and friends.

[0617] Use the share feature to send information via email or message.

[0618] Step 11:

[0619] The device again sends feedback from family and friends to the server.

[0620] The received feedback is sent to the server and stored in a database.

[0621] Step 12:

[0622] The server provides information to medical professionals, including the patient's primary care physician, and provides an environment for discussion.

[0623] Proposals are shared and discussion functions are provided through an interface for medical professionals.

[0624] Step 13:

[0625] The user, family, and healthcare professionals share and discuss information to determine the final treatment plan.

[0626] Discussions are held in real time using the chat and video conferencing functions provided by the server.

[0627] Step 14:

[0628] The server determines and records the final treatment plan.

[0629] The confirmed treatment plan will be recorded in the database and notified to all parties.

[0630] These are the specific processing steps of the system, which allow patients to receive support in making optimal medical choices.

[0631] Example 1

[0632] 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."

[0633] Conventional medical systems have difficulty instantly generating effective and appropriate treatment proposals based on the medical history and symptoms entered by the patient themselves. They also face challenges in providing customized treatment proposals that reflect the patient's wishes and insufficient collaboration with family and medical professionals. As a result, it has been difficult for patients to gain a deep understanding of their condition and quickly select the optimal treatment.

[0634] 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.

[0635] In this invention, the server includes: an input means for a patient to input their age, gender, medical history, current symptoms, and desired treatment plan; a data preparation means for receiving the input patient information and performing preprocessing such as normalization and inconsistency checks; a guideline analysis means for analyzing the latest medical guidelines and related literature and generating a treatment protocol; a generative AI model means for simulating treatment options using the generated treatment protocol and patient information; a proposal generation means for generating a customized treatment proposal taking into account the patient's wishes and risk tolerance; a provision means for providing the generated proposal to the patient and displaying visual and interactive content; a sharing means for sharing information with the patient's family and friends and receiving feedback; a collaboration means for providing information and discussions with medical professionals; and a recording means for determining and recording a final treatment plan. This enables patients to deeply understand their condition and quickly and accurately select the optimal treatment.

[0636] The "input means" is a means for a patient to input information such as age, sex, medical history, current symptoms, and desired treatment course.

[0637] The "data preparation means" is a means for receiving input patient information and performing pre-processing such as normalization and inconsistency checks.

[0638] The "guideline analysis means" is a means for analyzing the latest medical guidelines and related literature and generating treatment protocols.

[0639] The "generative AI model means" is a means for simulating treatment options using the generated treatment protocol and patient information.

[0640] A "proposal generator" is a means for generating a customized treatment proposal, taking into account the patient's preferences and risk tolerance.

[0641] The "delivery means" is a means for providing the generated suggestions to the patient and displaying visual and interactive content.

[0642] "Sharing tools" are ways to share information with the patient's family and friends and receive feedback.

[0643] "Collaboration methods" are means for providing information and holding discussions with medical professionals.

[0644] "Recording means" is a means for determining and recording the final treatment plan.

[0645] MODE FOR CARRYING OUT THE INVENTION

[0646] This system utilizes the latest medical guidelines and generative AI models to suggest optimal treatment options based on patient-entered information. A specific implementation of the entire system is described below.

[0647] System Overview

[0648] The server receives patient information, organizes the data, analyzes guidelines, and simulates treatment options using a generative AI model to generate treatment proposals. The terminal is used by patients to input information and check the proposed treatment options. Users include patients themselves, their families, and medical professionals.

[0649] Hardware and software used

[0650] Hardware: Servers, devices (PCs, tablets, smartphones)

[0651] Software: Database management system, generative AI model, interactive interface

[0652] Processing flow

[0653] 1. Enter patient information

[0654] The user inputs their age, gender, medical history, current symptoms, and desired treatment plan via a terminal, using an interface where they click on text boxes and options.

[0655] Examples:

[0656] The user inputs that he is a 42-year-old male suffering from high blood pressure and diabetes, and describes his current symptoms as slight fever and dizziness. He also inputs that he wishes to receive drug therapy as his treatment preference.

[0657] 2. Data preparation and preprocessing

[0658] The terminal sends the entered data to the server, which receives the data and performs normalization, duplication checks, and inconsistency checks.

[0659] Examples:

[0660] The server normalizes the entered age data of "42 years old" and checks for duplicate information about "high blood pressure" and "diabetes."

[0661] 3. Analysis of guidelines and literature

[0662] The server analyzes the latest medical guidelines and related literature to generate the optimal treatment plan for the patient's condition. This analysis includes searching guideline databases and related papers to extract the optimal treatment plan.

[0663] Examples:

[0664] The server searches the guideline database for the latest treatments for "high blood pressure" and "diabetes" and generates a treatment protocol appropriate for the patient.

[0665] 4. AI-powered simulation of treatment options

[0666] The server uses the generative AI model to simulate treatment options based on treatment protocols and patient information, and the results are evaluated for suitability and predicted outcomes for each treatment option.

[0667] Examples:

[0668] The generative AI model simulates treatment options based on the information of "42-year-old male, high blood pressure, diabetes, slight fever, dizziness" and evaluates suitability.

[0669] 5. Generating customized treatment suggestions

[0670] The server generates a customized treatment proposal that takes into account the patient's preferences (e.g., drug therapy) and risk tolerance. The proposal includes details of the treatment, expected effects, side effects, and treatment duration.

[0671] Examples:

[0672] The server generates treatment plans with minimal side effects, focusing on drug therapy, and proposes details.

[0673] 6. Providing and confirming proposals

[0674] The device provides the generated treatment recommendations to the user and displays them with visual and interactive content, making it easier for patients to understand the recommendations.

[0675] Examples:

[0676] The device will display details of drug therapy, side effects, and time to recovery to help patients understand.

[0677] 7. Sharing information with family and friends

[0678] A sharing feature is provided that allows users to share their treatment suggestions with family and friends and get their feedback.

[0679] Examples:

[0680] The user emails the proposal to family members and asks for their opinions.

[0681] 8. Collaboration with medical professionals

[0682] The server shares treatment suggestions with medical professionals and provides a discussion function, allowing for expert input.

[0683] Examples:

[0684] Healthcare professionals use the chat feature to provide feedback to patients.

[0685] 9. Deciding and recording the final treatment plan

[0686] The user, family, friends, and medical professionals decide on the final treatment plan, which the server records and notifies all parties of the relevant information.

[0687] Examples:

[0688] The user confirms the final treatment plan by clicking the "Decide" button, and the server notifies all parties involved by email.

[0689] Examples of prompt statements

[0690] For example, we can input the following prompts into a generative AI model to suggest treatment options:

[0691] Patient information: 42-year-old male with high blood pressure and diabetes. Current symptoms are slight fever and dizziness. Treatment desired: Drug therapy.

[0692] Conditions: Based on current medical guidelines, three treatment options are proposed, one of which focuses on minimizing side effects.

[0693] The above is an embodiment of the present invention. This system allows patients to gain a deeper understanding of their own condition and to select the most appropriate treatment quickly and accurately.

[0694] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0695] Step 1:

[0696] Users input their age, gender, medical history, current symptoms, and desired treatment plan into the terminal, using an interface that involves clicking text boxes and options.

[0697] Input: Patient's age, gender, medical history, current symptoms, treatment preference

[0698] Output: Information entered by the user

[0699] Specific working example:

[0700] The user inputs that he is a 42-year-old male suffering from high blood pressure and diabetes, describes slight fever and dizziness as his current symptoms, and selects drug therapy as his desired treatment.

[0701] Step 2:

[0702] The terminal sends the entered data to the server, which receives the data and performs normalization, duplication checks, and inconsistency checks.

[0703] Input: Patient information received from the user

[0704] Output: Preprocessed and cleaned data

[0705] Specific working example:

[0706] The server normalizes the entered age data, such as "42 years old," and checks for duplicate information and inconsistencies. For example, it deletes duplicate medical history information and corrects incorrect data formats.

[0707] Step 3:

[0708] The server analyzes the latest medical guidelines and related papers to generate treatment protocols, which includes searching guideline databases and related papers to extract optimal treatments.

[0709] Input: Preprocessed and cleaned data

[0710] Output: Generated treatment procedure

[0711] Specific working example:

[0712] The server searches the guideline database for the latest treatments for "hypertension" and "diabetes" and generates a treatment protocol appropriate for the patient.

[0713] Step 4:

[0714] The server uses the generative AI model to simulate treatment options based on treatment protocols and patient information, and the results are evaluated for suitability and predicted outcomes for each treatment option.

[0715] Input: Treatment protocol and patient information

[0716] Output: Simulated treatment options and their fitness

[0717] Specific working example:

[0718] The generative AI model simulates treatment options based on the information of "42-year-old male, high blood pressure, diabetes, slight fever, dizziness" and evaluates their suitability. For example, drug therapy A is evaluated as having a suitability of 90%, and drug therapy B is evaluated as having a suitability of 70%.

[0719] Step 5:

[0720] The server takes into account the patient's preferences (e.g., medication) and risk tolerance and generates a customized treatment proposal, including details of the treatment, expected effects, side effects, and treatment duration.

[0721] Input: Simulated treatment options and patient preferences

[0722] Output: Customized treatment proposals

[0723] Specific working example:

[0724] The server generates a treatment plan with fewer side effects, focusing on drug therapy, and proposes its details. For example, it lists drug therapy A with fewer side effects and its treatment schedule in the proposal.

[0725] Step 6:

[0726] The device provides the generated treatment recommendations to the user and displays them with visual and interactive content, making it easier for patients to understand the recommendations.

[0727] Input: Customized Treatment Proposal

[0728] Output: Visual and interactive treatment proposals provided to the user

[0729] Specific working example:

[0730] The device displays "details of drug therapy, side effects, and time to recovery," and provides interactive graphs and videos to help users understand.

[0731] Step 7:

[0732] A sharing feature is provided that allows users to share their treatment suggestions with family and friends and get their feedback.

[0733] Input: Treatment proposal

[0734] Output: Treatment suggestions and feedback shared with family and friends

[0735] Specific working example:

[0736] The user emails the proposal to family members and asks for their opinions.

[0737] Step 8:

[0738] The server shares treatment suggestions with medical professionals and provides a discussion function, allowing for expert input.

[0739] Input: Treatment proposals and discussion details

[0740] Output: Opinions and feedback from healthcare professionals

[0741] Specific working example:

[0742] Healthcare professionals use the chat feature to provide feedback to patients, for example by providing additional information about side effects of medication.

[0743] Step 9:

[0744] The user, family, friends, and medical professionals decide on the final treatment plan, which the server records and notifies all parties of the relevant information.

[0745] Input: Final treatment plan decision

[0746] Output: Recorded final treatment plan and notification to relevant parties

[0747] Specific working example:

[0748] The user confirms the final treatment plan by clicking the "Decide" button, and the server notifies all parties involved by email.

[0749] (Application example 1)

[0750] 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."

[0751] In conventional healthcare systems, treatment recommendations are based solely on a patient's medical history and symptoms, and comprehensive treatment plans, including dietary therapy, are not adequately proposed. Furthermore, there are insufficient means for communicating the proposed treatment plan with family members and healthcare professionals and obtaining feedback, making it difficult to provide optimal treatment and meal plans to patients. To solve this problem, a comprehensive treatment and meal plan recommendation system that takes into account a patient's medical history, symptoms, and dietary restrictions is needed.

[0752] 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.

[0753] In this invention, the server includes a meal plan proposal unit that proposes a meal plan based on a patient's input of their medical history, symptoms, and dietary restrictions, an input unit for the patient to input their age, sex, medical history, current symptoms, and desired treatment plan, a data preparation unit that receives and preprocesses the input patient information, a guideline analysis unit that analyzes the latest medical guidelines and related medical papers and generates a treatment protocol, a generative AI model unit that simulates treatment options using the generated treatment protocol and patient information, a proposal generation unit that generates a customized treatment proposal taking into account the patient's preferences and risk tolerance, a provision unit that provides the generated proposal to the patient, a display unit that allows the patient to confirm the proposal content and display interactive content, a sharing unit that shares information with the patient's family and friends and receives feedback, a collaboration unit that provides information and discussions with medical professionals, and a recording unit that determines and records the final treatment plan. This enables the optimal treatment and meal plan to be quickly and accurately provided taking into account the patient's medical history, symptoms, and dietary restrictions.

[0754] "Patient" refers to a person receiving medical examination or treatment.

[0755] "Age" refers to the number of years that have passed since the date of birth.

[0756] "Sex" refers to biological characteristics as male or female.

[0757] "Medical history" refers to records and information about illnesses you have had and treatments you have received.

[0758] "Symptoms" refer to specific phenomena or sensations that occur in association with illness or poor health.

[0759] A "treatment policy" refers to a specific treatment direction or plan to improve a patient's condition.

[0760] "Input means" refers to a device or interface that allows a patient to input their age, sex, medical history, current symptoms, and desired treatment course into the system.

[0761] "Data preparation means" refers to the functions and devices that receive input patient information and perform preprocessing.

[0762] "Guideline analysis means" refers to a device or algorithm for analyzing the latest medical guidelines and related medical papers and generating treatment protocols.

[0763] "Generative AI model means" refers to an artificial intelligence model for simulating treatment options using the generated treatment protocol and patient information.

[0764] "Proposal generation means" refers to functionality or devices for generating customized treatment proposals taking into account the patient's preferences and risk tolerance.

[0765] "Meal plan suggestion means" refers to a function or device for inputting a patient's medical history, symptoms, and dietary restrictions and proposing a meal plan based on that information.

[0766] "Providing means" refers to a function or device for providing the generated suggestions to the patient.

[0767] "Display means" refers to a device or interface that allows the patient to review the suggestions and display interactive content.

[0768] "Sharing tools" refers to features and devices that allow patients to share information with their family and friends and receive feedback.

[0769] "Collaboration means" refers to functions and devices for providing information and holding discussions with medical professionals.

[0770] "Recording means" refers to the functions and devices for determining and recording the final treatment plan.

[0771] "Dietary restriction" refers to restrictions or limitations on diet to accommodate a specific health condition or illness.

[0772] The present invention is a system that allows patients to input their medical history, symptoms, treatment preferences, and dietary restrictions, and then uses the latest medical guidelines and generative AI models to propose optimal treatment and diet plans. Specific embodiments for implementing this system are described below.

[0773] Overall system overview

[0774] The server receives patient information, organizes data, analyzes guidelines, and simulates treatment and dietary options using a generative AI model, generating treatment and dietary recommendations. The terminal is used by patients to input information and confirm the proposed treatment and dietary options. Users include the patients themselves, their families, and healthcare professionals.

[0775] Program processing flow

[0776] Entering patient information

[0777] The user uses the terminal to input their age, gender, medical history, current symptoms, treatment preferences, and dietary restrictions, which includes an input form and an option-clicking interface.

[0778] Data preparation and preprocessing

[0779] The terminal sends the input data to the server, which then preprocesses the received data, specifically normalizing the data and checking for duplicates and inconsistencies.

[0780] Guidelines and literature analysis

[0781] The server analyzes the latest medical guidelines and relevant medical literature to generate a treatment protocol that lists the treatment and dietary options that best suit the patient's symptoms and dietary restrictions.

[0782] AI-powered simulation of treatment and dietary options

[0783] Based on patient information and treatment protocols, the server uses a generative AI model to simulate treatment and dietary options, evaluating each option's suitability and predicted outcomes.

[0784] Generating customized treatment and dietary suggestions

[0785] The server takes into account the patient's preferences and risk tolerance and generates customized treatment and dietary recommendations, including details of the treatment and diet, expected effects, side effects, and duration of treatment.

[0786] Providing and confirming proposals

[0787] The device then provides the generated treatment and dietary recommendations to the user, who can then review and deepen their understanding of the recommendations through interactive screens and videos.

[0788] Share information with family and friends

[0789] Users can share their treatment and dietary suggestions with family and friends, allowing them to receive feedback from others.

[0790] Collaboration with medical professionals

[0791] The server provides a discussion function for sharing treatment and dietary suggestions with healthcare professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[0792] Deciding and recording the final treatment plan

[0793] The user, family, friends, and healthcare professionals decide on the final treatment and diet plan, and the server records this decision and notifies all parties with relevant information.

[0794] Hardware and software used

[0795] The following hardware and software are used to implement this system.

[0796] Frontend: Interface using React

[0797] Backend: API server using Flask

[0798] Generative AI model: Generate treatment and dietary options based on patient information

[0799] Specific examples

[0800] For example, if a 42-year-old male patient with high blood pressure and diabetes needs to go on a low-carb diet, the process would go like this:

[0801] 1. The patient enters his / her age (42 years old), gender (male), medical history (high blood pressure, diabetes), current symptoms, treatment requests, and dietary restrictions (low-carbohydrate diet) into the terminal.

[0802] 2. The terminal sends the entered information to the server.

[0803] 3. The server pre-processes the data, normalizing it and checking for inconsistencies.

[0804] 4. The server analyzes the latest medical guidelines and related papers to generate the optimal treatment protocol for the patient.

[0805] 5. The server uses the generative AI model to simulate treatment and dietary options and evaluate the suitability of each option.

[0806] 6. The server takes into account the patient's preferences and risk tolerance, generates customized treatment and dietary suggestions, and sends the details to the device.

[0807] 7. The device will visually display treatment and dietary suggestions, allowing patients to understand the suggestions.

[0808] 8. The patient shares information with family members and gets their opinions.

[0809] 9. Provide a means for the server to collaborate with medical professionals for discussion and to incorporate expert opinions.

[0810] 10. The patient, family, friends, and healthcare professionals decide on the final treatment and diet plan, which is recorded by the server.

[0811] Example prompt sentence:

[0812] "What is the best meal plan for a 45-year-old male with diabetes who needs to follow a low-carb diet?"

[0813] The above is an embodiment of the present invention, which allows patients to deeply understand their own medical condition and dietary restrictions, and to quickly and accurately select optimal treatment and diet.

[0814] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0815] Step 1:

[0816] The user uses the device to input their age, gender, medical history, current symptoms, treatment preferences, and dietary restrictions. The input data is collected through a form on the device. The input information is sent to the server in JSON format.

[0817] Step 2:

[0818] The server preprocesses the patient information received from the terminal using a data preparation method. Specifically, it normalizes the data (for example, standardizing date formats and trimming strings), checks for duplication, and checks for inconsistencies. Once preprocessing is complete, the data is passed to the next processing step.

[0819] Step 3:

[0820] The server uses the guideline analysis means to analyze the latest medical guidelines and related medical papers. Based on the analysis results, it generates a treatment protocol for each patient. This generated treatment protocol is used in the next step.

[0821] Step 4:

[0822] The server uses the generated treatment protocol and preprocessed patient information to apply the generative AI model to simulate treatment and dietary options. Specifically, the AI ​​evaluates and recommends the optimal treatment and dietary plan based on the patient's symptoms and conditions. The output data from the simulation is passed to the next step.

[0823] Step 5:

[0824] The server generates a customized treatment and diet proposal based on the generated treatment and diet options, taking into account the patient's wishes and risk tolerance. This proposal includes details of the treatment and diet, expected effects, side effects, treatment duration, etc. The generated proposal is sent to the terminal.

[0825] Step 6:

[0826] The device displays the treatment and dietary recommendations received from the server. The user can review and deepen their understanding of the recommendations through interactive screens and videos. The display of the recommendations includes visual elements and navigation functions.

[0827] Step 7:

[0828] The user uses the share button on the device to share the treatment and dietary suggestions with family and friends. The device generates a sharing link or file and sends it to the family and friends. The user receives feedback from the family and friends.

[0829] Step 8:

[0830] The server provides a collaborative means to share treatment and dietary suggestions with healthcare professionals, provide discussion features for expert input, including chat and video call features, and receive feedback from healthcare professionals.

[0831] Step 9:

[0832] The user, family, friends, and healthcare professionals decide on the final treatment and diet plan. The server records this decision and notifies all parties of the relevant information. The recorded data is stored for future reference and tracking.

[0833] The above are the specific processing steps of the program for the system that realizes the application example.

[0834] 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.

[0835] This invention is a system in which patients input their medical history, symptoms, and treatment preferences, and then uses the latest medical guidelines and generative AI models to suggest optimal treatment options based on that information. Furthermore, by combining it with an emotion engine that recognizes the patient's emotions, more personalized treatment suggestions are possible. Specific forms for implementing this system are described below.

[0836] Overall system overview

[0837] The server receives patient information, organizes data, analyzes guidelines, simulates treatment options using an AI model, analyzes emotions using an emotion engine, and generates treatment suggestions. The terminal is used by patients to input information and check suggested treatment options. Users include patients themselves, their families, and medical professionals.

[0838] Program processing flow

[0839] Entering patient information

[0840] Users input their age, gender, medical history, current symptoms, and treatment preferences via a terminal, which includes an input form and an interface for clicking options.

[0841] Data preparation and preprocessing

[0842] The terminal sends the input data to the server, which then preprocesses the received data, specifically normalizing the data and checking for duplicates and inconsistencies.

[0843] Guidelines and literature analysis

[0844] The server analyzes the latest medical guidelines and relevant medical literature to generate a treatment protocol, which lists the best treatment options for the patient's condition.

[0845] AI-powered simulation of treatment options

[0846] The server uses a generative AI model to simulate treatment options based on patient information and treatment protocols, evaluating each option's suitability and predicted outcomes.

[0847] Emotion analysis using an emotion engine

[0848] While the patient is entering information, the device uses a camera and microphone to analyze the patient's emotional state in real time. This information is processed by an emotion engine, which generates emotional data based on the patient's facial expressions, tone of voice, and choice of words.

[0849] Generate customized treatment recommendations

[0850] The server takes into account the patient's preferences and risk tolerance, as well as the emotional data obtained from the emotion engine, to generate a customized treatment proposal, including details of the treatment, expected effects, side effects, and treatment duration.

[0851] Providing and confirming proposals

[0852] The device then provides the generated treatment proposal to the user, who can then review and deepen their understanding of the proposal through interactive screens and videos.

[0853] Share information with family and friends

[0854] Users can share their treatment suggestions with family and friends, allowing them to receive feedback from others.

[0855] Collaboration with medical professionals

[0856] The server provides a discussion function for sharing treatment suggestions with medical professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[0857] Deciding and recording the final treatment plan

[0858] The user, family, friends, and medical professionals decide on the final treatment plan, and the server records this decision and notifies all parties with relevant information.

[0859] Specific examples

[0860] For example, in the case of a 42-year-old male patient with hypertension and diabetes, the process would proceed as follows:

[0861] 1. The patient enters his / her age (42 years old), gender (male), medical history (high blood pressure, diabetes), current symptoms, and treatment preferences into the terminal.

[0862] 2. The terminal sends the entered information to the server.

[0863] 3. The server pre-processes the data, normalizing it and checking for inconsistencies.

[0864] 4. The server analyzes the latest medical guidelines and related papers to generate the optimal treatment protocol for the patient.

[0865] 5. The server uses the generative AI model to simulate treatment options and evaluate the suitability of each option.

[0866] 6. The device analyzes the patient's emotional state in real time through a camera and microphone and sends that information to the emotion engine.

[0867] 7. The server generates a customized treatment proposal based on the emotional data, preferences, and risk tolerance, and sends the details to the device.

[0868] 8. The device visually displays treatment suggestions to help patients understand the suggestions.

[0869] 9. The patient shares information with family members and gets their opinions.

[0870] 10. Provide a means for the server to collaborate and hold discussions with medical professionals, thereby incorporating expert opinions.

[0871] 11. The patient, family, friends, and healthcare professionals decide on the final treatment plan, which is recorded by the server.

[0872] The above is an embodiment of the present invention, which allows patients to deeply understand their own medical condition and quickly and accurately select the optimal treatment that reflects their emotional state.

[0873] The processing flow will be explained below.

[0874] Step 1:

[0875] The user inputs his / her age, sex, medical history, current symptoms, and desired treatment course via the terminal.

[0876] Specifically, the user enters data into an input form and presses a send button, thereby inputting the data.

[0877] Step 2:

[0878] The terminal transmits the input data to the server.

[0879] The terminal encrypts the data to be transmitted and sends it to the server over a secure connection.

[0880] Step 3:

[0881] The server stores the received data in a database.

[0882] The database stores patient information in a suitable format.

[0883] Step 4:

[0884] The server performs pre-processing of the data, including normalizing the data, checking for duplicates, and checking for inconsistencies.

[0885] The server converts the received data into a unified format, removes duplicate data, and checks for inconsistencies.

[0886] Step 5:

[0887] The server analyzes the latest medical guidelines and related medical literature and generates treatment protocols.

[0888] Relevant guidelines and papers will be searched from the database, and treatment protocols will be created using analytical algorithms.

[0889] Step 6:

[0890] The server uses a generative AI model to simulate treatment options based on patient information and the generated treatment protocol.

[0891] Simulations will assess the suitability and predicted outcomes of each treatment option.

[0892] Step 7:

[0893] The terminal uses an emotion engine to analyze the patient's emotional state in real time as information is being entered.

[0894] The device uses a camera and microphone to analyze facial expressions, tone of voice, and language usage to generate emotional data.

[0895] Step 8:

[0896] The terminal transmits the analyzed emotion data to the server.

[0897] The device encrypts the emotion data and transmits it to the server over a secure connection.

[0898] Step 9:

[0899] The server receives the affective data and generates a treatment recommendation after taking into account the patient's preferences and risk tolerance.

[0900] Include emotional data to generate more personalized treatment recommendations.

[0901] Step 10:

[0902] The server transmits the generated treatment proposal to the terminal.

[0903] The suggestions are formatted into an interactive format and sent to the device.

[0904] Step 11:

[0905] The terminal visually displays treatment suggestions.

[0906] Interactive images and instructional videos are used to detail treatment options.

[0907] Step 12:

[0908] Users share their treatment suggestions with family and friends.

[0909] Use the share feature to send information via email or message.

[0910] Step 13:

[0911] The device again sends feedback from family and friends to the server.

[0912] The received feedback is sent to the server and stored in a database.

[0913] Step 14:

[0914] The server shares information with medical professionals, including the patient's primary care physician, and provides an environment for discussion.

[0915] Proposals are shared and discussion functions are provided through an interface for medical professionals.

[0916] Step 15:

[0917] The user, family, and healthcare professionals share and discuss information to determine the final treatment plan.

[0918] Discussions are held in real time using the chat and video conferencing functions provided by the server.

[0919] Step 16:

[0920] The server determines and records the final treatment plan.

[0921] The confirmed treatment plan will be recorded in the database and notified to all parties.

[0922] These are the specific processing steps of the system, which allow patients to receive support in making optimal medical choices.

[0923] Example 2

[0924] 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."

[0925] Modern medicine requires the ability to quickly and accurately recommend optimal treatments tailored to each patient's individual symptoms and wishes. However, manually collecting and analyzing information and determining treatment plans is time-consuming and prone to errors. Furthermore, determining treatment plans without considering the patient's emotional state can reduce patient satisfaction and reduce treatment effectiveness. Furthermore, information sharing and collaboration are essential for patients, their families, friends, and healthcare professionals to work together to determine the optimal treatment plan. Therefore, a system that can resolve these issues and provide faster and more accurate treatment recommendations is needed.

[0926] 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 data preparation means for receiving input patient information and performing preprocessing; a guideline analysis means for analyzing the latest medical guidelines and related medical papers to generate a treatment protocol; a generation AI model means for simulating treatment options using the generated treatment protocol and patient information; and a proposal generation means for generating a customized treatment proposal by taking into account the patient's wishes, risk tolerance, and emotional data. This enables rapid and accurate generation of individually optimized treatment proposals based on detailed patient information and the latest medical guidelines. Furthermore, customized proposals based on emotional data can improve patient satisfaction and treatment effectiveness. Furthermore, by sharing information with family and friends and collaborating with medical professionals, a more comprehensive and reliable treatment plan can be determined.

[0927] "Patient" refers to an individual with a medical history or condition who uses the system.

[0928] "Input means" refers to a device or interface that allows a patient to input information such as age, sex, medical history, current symptoms, and desired treatment course.

[0929] "Data preparation means" refers to devices or programs that receive input patient information and perform preprocessing (data normalization, duplication checks, inconsistency checks, etc.).

[0930] "Guideline analysis means" refers to a device or program for analyzing the latest medical guidelines and related medical papers and generating treatment protocols.

[0931] "Generative AI model means" refers to an artificial intelligence model for simulating treatment options using the generated treatment protocol and patient information.

[0932] "Proposal Generator" refers to a device or program for generating a customized treatment proposal taking into account the patient's preferences and risk tolerance, as well as emotional data.

[0933] "Providing means" refers to a device or system for providing the generated treatment proposal to the patient.

[0934] "Display means" refers to a display or user interface that allows the patient to confirm the suggestions and display interactive content.

[0935] An "emotion engine" refers to a device or program that uses a camera or microphone to analyze a patient's emotional state in real time and generate emotional data.

[0936] "Sharing means" refers to functions and devices that allow patients to share treatment suggestion information with their family and friends and receive feedback.

[0937] "Collaboration methods" refer to online chat functions and data sharing systems for providing information to medical professionals and for exchanging opinions and holding discussions.

[0938] "Recording means" refers to a database or recording device for determining the final treatment plan and recording that information.

[0939] This invention is a system in which patients input their medical history, symptoms, and treatment preferences, and then uses the latest medical guidelines and generative AI models to suggest optimal treatment options based on that information. Furthermore, by combining it with an emotion engine that recognizes the patient's emotions, more personalized treatment suggestions are possible. Specific forms for implementing this system are described below.

[0940] Overall system overview

[0941] The server receives patient information, organizes data, analyzes guidelines, simulates treatment options using an AI model, analyzes emotions using an emotion engine, and generates treatment suggestions. The terminal is used by patients to input information and check suggested treatment options. Users include patients themselves, their families, and medical professionals.

[0942] Entering patient information

[0943] The user inputs their age, gender, medical history, current symptoms, and desired treatment course via a terminal, which includes an input form and an option-click interface. The input data is then sent to the server by the terminal.

[0944] Data preparation and preprocessing

[0945] The server preprocesses the received data, specifically normalizing it, checking for duplicates, checking for inconsistencies, etc. This preprocessing ensures data consistency and accuracy.

[0946] Guidelines and literature analysis

[0947] The server analyzes the latest medical guidelines and related medical papers to generate treatment protocols, using natural language processing (NLP) technology to extract relevant information from large amounts of medical text data.

[0948] AI-powered simulation of treatment options

[0949] The server uses a generative AI model to simulate treatment options based on patient information and treatment protocols. A prompt is input to the AI ​​model to evaluate the suitability and predicted outcomes of multiple treatment options. For example, a simulation is performed based on the prompt: "42-year-old male with a history of high blood pressure and diabetes. Current symptoms include mild headaches and fatigue. He wishes to undergo a new treatment and minimize side effects. Please suggest the optimal treatment plan."

[0950] Emotion analysis using an emotion engine

[0951] While the patient is entering information, the device uses a camera and microphone to analyze the patient's emotional state in real time. This information is processed by an emotion engine, which generates emotional data based on the patient's facial expressions, tone of voice, and choice of words.

[0952] Generate customized treatment recommendations

[0953] The server generates a customized treatment proposal based on the patient's preferences, risk tolerance, and emotional data, including details of the treatment, expected effects, side effects, and duration of treatment.

[0954] Providing and confirming proposals

[0955] The device then provides the generated treatment proposal to the user, who can then review and deepen their understanding of the proposal through interactive screens and videos.

[0956] Share information with family and friends

[0957] Users can share their treatment suggestions with family and friends, allowing them to receive feedback from others.

[0958] Collaboration with medical professionals

[0959] The server provides a discussion function for sharing treatment suggestions with medical professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[0960] Deciding and recording the final treatment plan

[0961] The user, family, friends, and medical professionals decide on the final treatment plan, and the server records this decision and notifies all parties with relevant information.

[0962] The above is a specific embodiment for carrying out the present invention, which allows patients to deeply understand their own medical condition and quickly and accurately select the optimal treatment that reflects their emotional state.

[0963] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0964] Step 1:

[0965] The user enters their age, gender, medical history, current symptoms, and desired treatment plan into the terminal. Specifically, the user enters information into the input form displayed on the terminal and clicks the "Submit" button. Input is performed using text fields and selection options. The input in this step is data about the patient's personal information and symptoms, and the output is temporary data stored on the terminal.

[0966] Step 2:

[0967] The terminal sends the input data to the server. Specifically, the data is encrypted and sent using the HTTPS protocol. The server receives the data. The input to this step is the patient information stored on the terminal, and the output is the data received by the server.

[0968] Step 3:

[0969] The server pre-processes the data it receives. Specific operations include normalizing the data (e.g., standardizing date formats), checking for duplicates (e.g., deleting duplicate entries), and checking for inconsistencies (e.g., checking for unnatural data values). This pre-processing ensures data consistency and accuracy. The input to this step is the raw data sent earlier, and the output is the pre-processed, clean data.

[0970] Step 4:

[0971] The server analyzes the latest medical guidelines and related medical papers. Specifically, it uses natural language processing (NLP) technology to extract relevant information from large amounts of medical text data and generate treatment protocols. The input for this step is the latest medical guidelines and patient data, and the output is the optimal treatment protocol for the patient.

[0972] Step 5:

[0973] The server uses a generative AI model to simulate treatment options based on the generated treatment protocol and patient information. Specifically, it inputs prompts to the AI ​​model and evaluates the suitability and predicted outcomes of multiple treatment options. The inputs for this step are the treatment protocol and prompts, and the output is multiple treatment options and their evaluation results.

[0974] Step 6:

[0975] While the patient is entering information, the device uses a camera and microphone to analyze the patient's emotional state in real time. Specifically, facial expression data captured by the device's camera and voice data collected by the microphone are sent to the emotion engine for analysis. The input for this step is raw data obtained from the camera and microphone, and the output is emotional data analyzed by the emotion engine.

[0976] Step 7:

[0977] The server generates a customized treatment proposal based on the patient's preferences, risk tolerance, and emotional data. Specifically, it integrates the AI ​​model's simulation results with the emotional data to create a comprehensive treatment plan that includes details of the treatment, expected effects, side effects, and treatment duration. The input for this step is treatment options and emotional data, and the output is a customized treatment proposal.

[0978] Step 8:

[0979] The device provides the generated treatment proposal to the user. Specifically, the device visually displays the treatment proposal on its display and allows the user to easily review the proposal through interactive elements (e.g., tab switching, detailed information display, video explanation, etc.). The input of this step is the customized treatment proposal, and the output is a display screen that the user can view.

[0980] Step 9:

[0981] The user can share the treatment proposal with family and friends by using the sharing function on their device. Specific actions include sending the proposal via email or generating a QR code to scan. The input for this step is the customized treatment proposal, and the output is the shared proposal.

[0982] Step 10:

[0983] The server shares the treatment proposal with healthcare professionals and provides a discussion function to incorporate their expert opinions. Specific operations include providing information and collecting opinions through an online chat function and a data sharing folder. The input of this step is the customized treatment proposal and healthcare professional feedback, and the output is a revised treatment proposal.

[0984] Step 11:

[0985] The user, family, friends, and healthcare professionals decide on a final treatment plan. The server records this decision and notifies all relevant parties of the relevant information. Specific operations include saving the finalized treatment plan digitally and notifying relevant parties via push notifications or email. The input of this step is the finalized treatment plan, and the output is the recorded treatment plan and notification content.

[0986] (Application example 2)

[0987] 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."

[0988] Conventional medical systems are limited in their functionality, not only in inputting a patient's medical history and symptoms, but also in proposing optimal treatment options based on the latest medical guidelines. Furthermore, because they do not take into account the patient's emotional state, there is a lack of personalization, making it difficult to find the optimal treatment. Furthermore, there is no clear guidance on payment methods after a treatment plan is decided, which places a burden on patients.

[0989] 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: an input means for the patient to input their age, gender, medical history, current symptoms, and desired treatment plan; a data preparation means for receiving and preprocessing the input patient information; a guideline analysis means for analyzing the latest medical guidelines and related medical papers and generating a treatment protocol; a generative AI model means for simulating treatment options using the generated treatment protocol and patient information; an emotion analysis means for analyzing the patient's emotional state in real time; a proposal generation means for generating a customized treatment proposal taking into account the patient's wishes, risk tolerance, and emotional data; a provision means for providing the generated proposal to the patient; a display means for allowing the patient to confirm the proposal content and display interactive content; a sharing means for sharing information with the patient's family and friends and receiving feedback; a collaboration means for providing information and discussions with medical professionals; and a recording means for determining and recording a final treatment plan. This enables patients to quickly and accurately obtain optimal treatment options that comprehensively consider their medical condition and emotional state.

[0990] The "input means" is a means for a patient to input information such as age, sex, medical history, current symptoms, and desired treatment course.

[0991] The "data preparation means" is a means for receiving input patient information and performing preprocessing.

[0992] The "guideline analysis means" is a means for analyzing the latest medical guidelines and related medical papers and generating treatment protocols.

[0993] The "generative AI model means" is a means for simulating treatment options using the generated treatment protocol and patient information.

[0994] The "emotion analysis means" is a means for analyzing the emotional state of a patient in real time.

[0995] A "proposal generator" is a means for generating a customized treatment proposal that takes into account the patient's preferences, risk tolerance and emotional data.

[0996] The "means for providing" is a means for providing the generated suggestion to the patient.

[0997] The "display means" is a means for the patient to confirm the proposed content and display interactive content.

[0998] "Sharing tools" are ways to share information with the patient's family and friends and receive feedback.

[0999] "Collaboration methods" are means for providing information and holding discussions with medical professionals.

[1000] "Recording means" is a means for determining and recording the final treatment plan.

[1001] This invention is a system that uses the latest medical guidelines and generative AI models to suggest optimal treatment options by allowing patients to input their medical history, symptoms, and treatment preferences. Furthermore, by combining it with an emotion engine that recognizes the patient's emotions, more personalized treatment suggestions are possible.

[1002] Overall system overview

[1003] The server receives patient information, organizes data, analyzes guidelines, simulates treatment options using a generative AI model, analyzes emotions using an emotion engine, and generates treatment proposals. The terminals are used by patients to input information and check proposed treatment options. These terminals include smartphones. Users include patients themselves, their families, and healthcare professionals.

[1004] Hardware and software used

[1005] The hardware uses a smartphone (iOS / Android device) and performs emotion analysis using the front camera and microphone, while a cloud server is used to analyze medical guidelines and run generative AI models.

[1006] The following software is used:

[1007] Data preparation measures: Preprocessing patient information

[1008] Guideline analysis tools: Analysis of the latest medical guidelines and related medical papers

[1009] Generative AI modeling tools: simulating treatment options

[1010] Emotion analysis tool: Analyze the current emotional state of the patient in real time

[1011] Proposal generation method: Generate treatment proposals taking into account the patient's preferences, risk tolerance, and emotional data

[1012] Operating procedure

[1013] 1. Patients use their smartphones to enter their age, gender, medical history, current symptoms, and treatment preferences.

[1014] 2. The terminal sends the entered data to the server.

[1015] 3. The server performs preprocessing such as data normalization and inconsistency checking.

[1016] 4. The server analyzes the latest medical guidelines and related papers and generates a treatment protocol.

[1017] 5. Use generative AI models to simulate treatment options and evaluate the suitability of each option.

[1018] 6. The device analyzes the patient's emotional state in real time through a camera and microphone and sends the data to the emotion engine.

[1019] 7. The server generates customized treatment recommendations taking into account the patient's preferences, risk tolerance, and emotional data.

[1020] 8. Provide patients with treatment suggestions through video and interactive content.

[1021] Specific examples

[1022] For example, consider a 42-year-old male patient with high blood pressure and diabetes. The patient enters his or her personal information on a smartphone, and the emotion analysis engine analyzes his or her emotional state. The system then recommends optimal treatment options based on the patient's emotional data. These recommendations include expected effects, side effects, treatment duration, and specific payment options.

[1023] Prompt Sentence Examples

[1024] Examples of prompts for generative AI models include:

[1025] "Prompt to generative AI model:

[1026] Patient information: 42-year-old male, medical history: hypertension, diabetes, current symptoms: fatigue, desired treatment: drug therapy

[1027] Please recommend the best treatment options for this patient based on the latest medical guidelines."

[1028] Through this platform, patients will be able to easily access treatment options that take into account their medical condition and emotional state in an integrated manner.

[1029] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1030] Step 1:

[1031] Patients use their smartphones to input their age, gender, medical history, current symptoms, and treatment preferences. This information is collected through the patient information input means. The input data includes age, gender, medical history, current symptoms, and treatment preferences. The input data is sent from the smartphone to the server.

[1032] Step 2:

[1033] The terminal sends the received patient information to the server. The server receives the patient information and performs preprocessing on the data. This preprocessing includes data normalization, duplication checks, and inconsistency checks. The input here is raw data from the patient, and the output is preprocessed data.

[1034] Step 3:

[1035] The server analyzes the latest medical guidelines and related papers based on the preprocessed data and generates a treatment protocol. Using guideline analysis tools, it analyzes medical literature and guidelines to list treatment options. The input is the preprocessed patient data, and the output is the generated treatment protocol.

[1036] Step 4:

[1037] The server uses a generative AI model to simulate treatment options based on treatment protocols and patient information. The simulation process evaluates the suitability and predicted outcomes of each treatment option. The inputs are treatment protocols and patient information, and the output is the simulation results.

[1038] Step 5:

[1039] While the terminal is inputting patient information, it uses a camera and microphone to analyze the patient's emotional state in real time. Using emotion analysis means, emotion data is generated from the patient's facial expressions, tone of voice, and choice of words. This emotion data is later sent to the server. The input is video and audio data, and the output is emotion analysis data.

[1040] Step 6:

[1041] The server generates a customized treatment proposal based on the emotion data, patient information, and treatment protocol. Using the proposal generation means, the optimal treatment option is generated taking into account the patient's wishes, risk tolerance, and emotion data. The input is the treatment protocol, patient information, and emotion data, and the output is a customized treatment proposal.

[1042] Step 7:

[1043] The server provides the generated treatment proposal to the smartphone. The patient can confirm the proposal through the provision means and understand the details through interactive content. The input is the customized treatment proposal, and the output is the treatment proposal confirmed by the patient.

[1044] Step 8:

[1045] After the patient confirms the proposal, they share the information with their family and friends. Using the sharing tool, the patient shares the treatment proposal and receives feedback. The input is the treatment proposal and the shared information, and the output is the feedback.

[1046] Step 9:

[1047] After receiving feedback from patients, their families, and friends, the server uses collaborative methods to provide information and hold discussions with healthcare professionals. Expert opinions are incorporated to refine the final treatment plan. The input is the feedback and shared information, and the output is the treatment plan with added expert opinions.

[1048] Step 10:

[1049] The patient, family, friends, and healthcare professionals decide on the final treatment plan, which is then recorded by the server. Using a recording method, the final treatment plan is formally recorded and notified to the relevant parties. The input is the final treatment proposal and discussion results, and the output is the recorded treatment plan.

[1050] 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.

[1051] 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.

[1052] 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.

[1053] [Third embodiment]

[1054] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1055] 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.

[1056] 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).

[1057] 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.

[1058] 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.

[1059] 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).

[1060] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1061] 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.

[1062] 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.

[1063] 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.

[1064] 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.

[1065] 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."

[1066] The present invention is a system that allows patients to input their medical history, symptoms, and treatment preferences, and then uses the latest medical guidelines and generative AI models to suggest optimal treatment options. Specific embodiments for implementing this system are described below.

[1067] Overall system overview

[1068] The server receives patient information, organizes the data, analyzes guidelines, and simulates treatment options using AI models to generate treatment proposals. The terminals are used by patients to input information and check the proposed treatment options. Users include patients themselves, their families, and medical professionals.

[1069] Program processing flow

[1070] Entering patient information

[1071] The user uses the terminal to input their age, gender, medical history, current symptoms, and desired treatment course, which includes an input form and an interface for clicking options.

[1072] Data preparation and preprocessing

[1073] The terminal sends the input data to the server, which then preprocesses the received data, specifically normalizing the data and checking for duplicates and inconsistencies.

[1074] Guidelines and literature analysis

[1075] The server analyzes the latest medical guidelines and relevant medical literature to generate a treatment protocol, which lists the best treatment options for the patient's condition.

[1076] AI-powered simulation of treatment options

[1077] The server uses a generative AI model to simulate treatment options based on patient information and treatment protocols, evaluating each option's suitability and predicted outcomes.

[1078] Generate customized treatment recommendations

[1079] The server takes into account the patient's preferences and risk tolerance and generates a customized treatment proposal, including details of the treatment, expected effects, side effects, and duration of treatment.

[1080] Providing and confirming proposals

[1081] The device then provides the generated treatment proposal to the user, who can then review and deepen their understanding of the proposal through interactive screens and videos.

[1082] Share information with family and friends

[1083] Users can share their treatment suggestions with family and friends, allowing them to receive feedback from others.

[1084] Collaboration with medical professionals

[1085] The server provides a discussion function for sharing treatment suggestions with medical professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[1086] Deciding and recording the final treatment plan

[1087] The user, family, friends, and medical professionals decide on the final treatment plan, and the server records this decision and notifies all parties with relevant information.

[1088] Specific examples

[1089] For example, in the case of a 42-year-old male patient with hypertension and diabetes, the process would proceed as follows:

[1090] 1. The patient enters his / her age (42 years old), gender (male), medical history (high blood pressure, diabetes), current symptoms, and treatment preferences into the terminal.

[1091] 2. The terminal sends the entered information to the server.

[1092] 3. The server pre-processes the data, normalizing it and checking for inconsistencies.

[1093] 4. The server analyzes the latest medical guidelines and related papers to generate the optimal treatment protocol for the patient.

[1094] 5. The server uses the generative AI model to simulate treatment options and evaluate the suitability of each option.

[1095] 6. The server takes into account the patient's preferences and risk tolerance, generates a customized treatment proposal, and sends the details to the device.

[1096] 7. The device visually displays treatment suggestions to help patients understand them.

[1097] 8. The patient shares information with family members and gets their opinions.

[1098] 9. Provide a means for the server to collaborate with medical professionals for discussion and to incorporate expert opinions.

[1099] 10. The patient, family, friends, and healthcare professionals decide on the final treatment plan, which is recorded by the server.

[1100] The above is an embodiment of the present invention, which allows patients to gain a deeper understanding of their own condition and to select the most appropriate treatment quickly and accurately.

[1101] The processing flow will be explained below.

[1102] Step 1:

[1103] The user inputs his / her age, sex, medical history, current symptoms, and desired treatment course via the terminal.

[1104] Specifically, the data is entered by filling in the necessary information in the input form and pressing the send button.

[1105] Step 2:

[1106] The terminal transmits the input data to the server.

[1107] The data is encrypted and transmitted over a secure connection.

[1108] Step 3:

[1109] The server receives the transmitted data and stores it in a data database.

[1110] Checks the received data and inserts it into the appropriate tables in the database.

[1111] Step 4:

[1112] The server performs pre-processing, which includes data normalization, duplicate checks, and consistency checks.

[1113] Standardize data formats, remove duplicate records, and check for outliers and inconsistencies.

[1114] Step 5:

[1115] The server analyzes the latest medical guidelines and related medical literature and generates treatment protocols.

[1116] Database queries are performed to extract relevant guidelines and papers, and analytical algorithms are used to create treatment protocols.

[1117] Step 6:

[1118] The server uses a generative AI model to simulate treatment options.

[1119] The generative AI model is run using patient information and treatment protocols as input to simulate multiple treatment options.

[1120] Step 7:

[1121] The server generates a customized treatment proposal based on the simulation results, taking into account the patient's preferences and risk tolerance.

[1122] The simulation results are analyzed to select the treatment plan that best suits the patient's wishes and risk profile.

[1123] Step 8:

[1124] The server transmits the generated treatment proposal to the terminal.

[1125] The suggestions are formatted into an interactive format and sent to the patient's device.

[1126] Step 9:

[1127] The terminal visually displays the received treatment suggestions.

[1128] Interactive images and instructional videos are used to detail treatment options.

[1129] Step 10:

[1130] Users share their treatment suggestions with family and friends.

[1131] Use the share feature to send information via email or message.

[1132] Step 11:

[1133] The device again sends feedback from family and friends to the server.

[1134] The received feedback is sent to the server and stored in a database.

[1135] Step 12:

[1136] The server provides information to medical professionals, including the patient's primary care physician, and provides an environment for discussion.

[1137] Proposals are shared and discussion functions are provided through an interface for medical professionals.

[1138] Step 13:

[1139] The user, family, and healthcare professionals share and discuss information to determine the final treatment plan.

[1140] Discussions are held in real time using the chat and video conferencing functions provided by the server.

[1141] Step 14:

[1142] The server determines and records the final treatment plan.

[1143] The confirmed treatment plan will be recorded in the database and notified to all parties.

[1144] These are the specific processing steps of the system, which allow patients to receive support in making optimal medical choices.

[1145] Example 1

[1146] 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."

[1147] Conventional medical systems have difficulty instantly generating effective and appropriate treatment proposals based on the medical history and symptoms entered by the patient themselves. They also face challenges in providing customized treatment proposals that reflect the patient's wishes and insufficient collaboration with family and medical professionals. As a result, it has been difficult for patients to gain a deep understanding of their condition and quickly select the optimal treatment.

[1148] 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.

[1149] In this invention, the server includes: an input means for a patient to input their age, gender, medical history, current symptoms, and desired treatment plan; a data preparation means for receiving the input patient information and performing preprocessing such as normalization and inconsistency checks; a guideline analysis means for analyzing the latest medical guidelines and related literature and generating a treatment protocol; a generative AI model means for simulating treatment options using the generated treatment protocol and patient information; a proposal generation means for generating a customized treatment proposal taking into account the patient's wishes and risk tolerance; a provision means for providing the generated proposal to the patient and displaying visual and interactive content; a sharing means for sharing information with the patient's family and friends and receiving feedback; a collaboration means for providing information and discussions with medical professionals; and a recording means for determining and recording a final treatment plan. This enables patients to deeply understand their condition and quickly and accurately select the optimal treatment.

[1150] The "input means" is a means for a patient to input information such as age, sex, medical history, current symptoms, and desired treatment course.

[1151] The "data preparation means" is a means for receiving input patient information and performing pre-processing such as normalization and inconsistency checks.

[1152] The "guideline analysis means" is a means for analyzing the latest medical guidelines and related literature and generating treatment protocols.

[1153] The "generative AI model means" is a means for simulating treatment options using the generated treatment protocol and patient information.

[1154] A "proposal generator" is a means for generating a customized treatment proposal, taking into account the patient's preferences and risk tolerance.

[1155] The "delivery means" is a means for providing the generated suggestions to the patient and displaying visual and interactive content.

[1156] "Sharing tools" are ways to share information with the patient's family and friends and receive feedback.

[1157] "Collaboration methods" are means for providing information and holding discussions with medical professionals.

[1158] "Recording means" is a means for determining and recording the final treatment plan.

[1159] MODE FOR CARRYING OUT THE INVENTION

[1160] This system utilizes the latest medical guidelines and generative AI models to suggest optimal treatment options based on patient-entered information. A specific implementation of the entire system is described below.

[1161] System Overview

[1162] The server receives patient information, organizes the data, analyzes guidelines, and simulates treatment options using a generative AI model to generate treatment proposals. The terminal is used by patients to input information and check the proposed treatment options. Users include patients themselves, their families, and medical professionals.

[1163] Hardware and software used

[1164] Hardware: Servers, devices (PCs, tablets, smartphones)

[1165] Software: Database management system, generative AI model, interactive interface

[1166] Processing flow

[1167] 1. Enter patient information

[1168] The user inputs their age, gender, medical history, current symptoms, and desired treatment plan via a terminal, using an interface where they click on text boxes and options.

[1169] Examples:

[1170] The user inputs that he is a 42-year-old male suffering from high blood pressure and diabetes, and describes his current symptoms as slight fever and dizziness. He also inputs that he wishes to receive drug therapy as his treatment preference.

[1171] 2. Data preparation and preprocessing

[1172] The terminal sends the entered data to the server, which receives the data and performs normalization, duplication checks, and inconsistency checks.

[1173] Examples:

[1174] The server normalizes the entered age data of "42 years old" and checks for duplicate information about "high blood pressure" and "diabetes."

[1175] 3. Analysis of guidelines and literature

[1176] The server analyzes the latest medical guidelines and related literature to generate the optimal treatment plan for the patient's condition. This analysis includes searching guideline databases and related papers to extract the optimal treatment plan.

[1177] Examples:

[1178] The server searches the guideline database for the latest treatments for "high blood pressure" and "diabetes" and generates a treatment protocol appropriate for the patient.

[1179] 4. AI-powered simulation of treatment options

[1180] The server uses the generative AI model to simulate treatment options based on treatment protocols and patient information, and the results are evaluated for suitability and predicted outcomes for each treatment option.

[1181] Examples:

[1182] The generative AI model simulates treatment options based on the information of "42-year-old male, high blood pressure, diabetes, slight fever, dizziness" and evaluates suitability.

[1183] 5. Generating customized treatment suggestions

[1184] The server generates a customized treatment proposal that takes into account the patient's preferences (e.g., drug therapy) and risk tolerance. The proposal includes details of the treatment, expected effects, side effects, and treatment duration.

[1185] Examples:

[1186] The server generates treatment plans with minimal side effects, focusing on drug therapy, and proposes details.

[1187] 6. Providing and confirming proposals

[1188] The device provides the generated treatment recommendations to the user and displays them with visual and interactive content, making it easier for patients to understand the recommendations.

[1189] Examples:

[1190] The device will display details of drug therapy, side effects, and time to recovery to help patients understand.

[1191] 7. Sharing information with family and friends

[1192] A sharing feature is provided that allows users to share their treatment suggestions with family and friends and get their feedback.

[1193] Examples:

[1194] The user emails the proposal to family members and asks for their opinions.

[1195] 8. Collaboration with medical professionals

[1196] The server shares treatment suggestions with medical professionals and provides a discussion function, allowing for expert input.

[1197] Examples:

[1198] Healthcare professionals use the chat feature to provide feedback to patients.

[1199] 9. Deciding and recording the final treatment plan

[1200] The user, family, friends, and medical professionals decide on the final treatment plan, which the server records and notifies all parties of the relevant information.

[1201] Examples:

[1202] The user confirms the final treatment plan by clicking the "Decide" button, and the server notifies all parties involved by email.

[1203] Examples of prompt statements

[1204] For example, we can input the following prompts into a generative AI model to suggest treatment options:

[1205] Patient information: 42-year-old male with high blood pressure and diabetes. Current symptoms are slight fever and dizziness. Treatment desired: Drug therapy.

[1206] Conditions: Based on current medical guidelines, three treatment options are proposed, one of which focuses on minimizing side effects.

[1207] The above is an embodiment of the present invention. This system allows patients to gain a deeper understanding of their own condition and to select the most appropriate treatment quickly and accurately.

[1208] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1209] Step 1:

[1210] Users input their age, gender, medical history, current symptoms, and desired treatment plan into the terminal, using an interface that involves clicking text boxes and options.

[1211] Input: Patient's age, gender, medical history, current symptoms, treatment preference

[1212] Output: Information entered by the user

[1213] Specific working example:

[1214] The user inputs that he is a 42-year-old male suffering from high blood pressure and diabetes, describes slight fever and dizziness as his current symptoms, and selects drug therapy as his desired treatment.

[1215] Step 2:

[1216] The terminal sends the entered data to the server, which receives the data and performs normalization, duplication checks, and inconsistency checks.

[1217] Input: Patient information received from the user

[1218] Output: Preprocessed and cleaned data

[1219] Specific working example:

[1220] The server normalizes the entered age data, such as "42 years old," and checks for duplicate information and inconsistencies. For example, it deletes duplicate medical history information and corrects incorrect data formats.

[1221] Step 3:

[1222] The server analyzes the latest medical guidelines and related papers to generate treatment protocols, which includes searching guideline databases and related papers to extract optimal treatments.

[1223] Input: Preprocessed and cleaned data

[1224] Output: Generated treatment procedure

[1225] Specific working example:

[1226] The server searches the guideline database for the latest treatments for "hypertension" and "diabetes" and generates a treatment protocol appropriate for the patient.

[1227] Step 4:

[1228] The server uses the generative AI model to simulate treatment options based on treatment protocols and patient information, and the results are evaluated for suitability and predicted outcomes for each treatment option.

[1229] Input: Treatment protocol and patient information

[1230] Output: Simulated treatment options and their fitness

[1231] Specific working example:

[1232] The generative AI model simulates treatment options based on the information of "42-year-old male, high blood pressure, diabetes, slight fever, dizziness" and evaluates their suitability. For example, drug therapy A is evaluated as having a suitability of 90%, and drug therapy B is evaluated as having a suitability of 70%.

[1233] Step 5:

[1234] The server takes into account the patient's preferences (e.g., medication) and risk tolerance and generates a customized treatment proposal, including details of the treatment, expected effects, side effects, and treatment duration.

[1235] Input: Simulated treatment options and patient preferences

[1236] Output: Customized treatment proposals

[1237] Specific working example:

[1238] The server generates a treatment plan with fewer side effects, focusing on drug therapy, and proposes its details. For example, it lists drug therapy A with fewer side effects and its treatment schedule in the proposal.

[1239] Step 6:

[1240] The device provides the generated treatment recommendations to the user and displays them with visual and interactive content, making it easier for patients to understand the recommendations.

[1241] Input: Customized Treatment Proposal

[1242] Output: Visual and interactive treatment proposals provided to the user

[1243] Specific working example:

[1244] The device displays "details of drug therapy, side effects, and time to recovery," and provides interactive graphs and videos to help users understand.

[1245] Step 7:

[1246] A sharing feature is provided that allows users to share their treatment suggestions with family and friends and get their feedback.

[1247] Input: Treatment proposal

[1248] Output: Treatment suggestions and feedback shared with family and friends

[1249] Specific working example:

[1250] The user emails the proposal to family members and asks for their opinions.

[1251] Step 8:

[1252] The server shares treatment suggestions with medical professionals and provides a discussion function, allowing for expert input.

[1253] Input: Treatment proposals and discussion details

[1254] Output: Opinions and feedback from healthcare professionals

[1255] Specific working example:

[1256] Healthcare professionals use the chat feature to provide feedback to patients, for example by providing additional information about side effects of medication.

[1257] Step 9:

[1258] The user, family, friends, and medical professionals decide on the final treatment plan, which the server records and notifies all parties of the relevant information.

[1259] Input: Final treatment plan decision

[1260] Output: Recorded final treatment plan and notification to relevant parties

[1261] Specific working example:

[1262] The user confirms the final treatment plan by clicking the "Decide" button, and the server notifies all parties involved by email.

[1263] (Application example 1)

[1264] 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."

[1265] In conventional healthcare systems, treatment recommendations are based solely on a patient's medical history and symptoms, and comprehensive treatment plans, including dietary therapy, are not adequately proposed. Furthermore, there are insufficient means for communicating the proposed treatment plan with family members and healthcare professionals and obtaining feedback, making it difficult to provide optimal treatment and meal plans to patients. To solve this problem, a comprehensive treatment and meal plan recommendation system that takes into account a patient's medical history, symptoms, and dietary restrictions is needed.

[1266] 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.

[1267] In this invention, the server includes a meal plan proposal unit that proposes a meal plan based on a patient's input of their medical history, symptoms, and dietary restrictions, an input unit for the patient to input their age, sex, medical history, current symptoms, and desired treatment plan, a data preparation unit that receives and preprocesses the input patient information, a guideline analysis unit that analyzes the latest medical guidelines and related medical papers and generates a treatment protocol, a generative AI model unit that simulates treatment options using the generated treatment protocol and patient information, a proposal generation unit that generates a customized treatment proposal taking into account the patient's preferences and risk tolerance, a provision unit that provides the generated proposal to the patient, a display unit that allows the patient to confirm the proposal content and display interactive content, a sharing unit that shares information with the patient's family and friends and receives feedback, a collaboration unit that provides information and discussions with medical professionals, and a recording unit that determines and records the final treatment plan. This enables the optimal treatment and meal plan to be quickly and accurately provided taking into account the patient's medical history, symptoms, and dietary restrictions.

[1268] "Patient" refers to a person receiving medical examination or treatment.

[1269] "Age" refers to the number of years that have passed since the date of birth.

[1270] "Sex" refers to biological characteristics as male or female.

[1271] "Medical history" refers to records and information about illnesses you have had and treatments you have received.

[1272] "Symptoms" refer to specific phenomena or sensations that occur in association with illness or poor health.

[1273] A "treatment policy" refers to a specific treatment direction or plan to improve a patient's condition.

[1274] "Input means" refers to a device or interface that allows a patient to input their age, sex, medical history, current symptoms, and desired treatment course into the system.

[1275] "Data preparation means" refers to the functions and devices that receive input patient information and perform preprocessing.

[1276] "Guideline analysis means" refers to a device or algorithm for analyzing the latest medical guidelines and related medical papers and generating treatment protocols.

[1277] "Generative AI model means" refers to an artificial intelligence model for simulating treatment options using the generated treatment protocol and patient information.

[1278] "Proposal generation means" refers to functionality or devices for generating customized treatment proposals taking into account the patient's preferences and risk tolerance.

[1279] "Meal plan suggestion means" refers to a function or device for inputting a patient's medical history, symptoms, and dietary restrictions and proposing a meal plan based on that information.

[1280] "Providing means" refers to a function or device for providing the generated suggestions to the patient.

[1281] "Display means" refers to a device or interface that allows the patient to review the suggestions and display interactive content.

[1282] "Sharing tools" refers to features and devices that allow patients to share information with their family and friends and receive feedback.

[1283] "Collaboration means" refers to functions and devices for providing information and holding discussions with medical professionals.

[1284] "Recording means" refers to the functions and devices for determining and recording the final treatment plan.

[1285] "Dietary restriction" refers to restrictions or limitations on diet to accommodate a specific health condition or illness.

[1286] The present invention is a system that allows patients to input their medical history, symptoms, treatment preferences, and dietary restrictions, and then uses the latest medical guidelines and generative AI models to propose optimal treatment and diet plans. Specific embodiments for implementing this system are described below.

[1287] Overall system overview

[1288] The server receives patient information, organizes data, analyzes guidelines, and simulates treatment and dietary options using a generative AI model, generating treatment and dietary recommendations. The terminal is used by patients to input information and confirm the proposed treatment and dietary options. Users include the patients themselves, their families, and healthcare professionals.

[1289] Program processing flow

[1290] Entering patient information

[1291] The user uses the terminal to input their age, gender, medical history, current symptoms, treatment preferences, and dietary restrictions, which includes an input form and an option-clicking interface.

[1292] Data preparation and preprocessing

[1293] The terminal sends the input data to the server, which then preprocesses the received data, specifically normalizing the data and checking for duplicates and inconsistencies.

[1294] Guidelines and literature analysis

[1295] The server analyzes the latest medical guidelines and relevant medical literature to generate a treatment protocol that lists the treatment and dietary options that best suit the patient's symptoms and dietary restrictions.

[1296] AI-powered simulation of treatment and dietary options

[1297] Based on patient information and treatment protocols, the server uses a generative AI model to simulate treatment and dietary options, evaluating each option's suitability and predicted outcomes.

[1298] Generating customized treatment and dietary suggestions

[1299] The server takes into account the patient's preferences and risk tolerance and generates customized treatment and dietary recommendations, including details of the treatment and diet, expected effects, side effects, and duration of treatment.

[1300] Providing and confirming proposals

[1301] The device then provides the generated treatment and dietary recommendations to the user, who can then review and deepen their understanding of the recommendations through interactive screens and videos.

[1302] Share information with family and friends

[1303] Users can share their treatment and dietary suggestions with family and friends, allowing them to receive feedback from others.

[1304] Collaboration with medical professionals

[1305] The server provides a discussion function for sharing treatment and dietary suggestions with healthcare professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[1306] Deciding and recording the final treatment plan

[1307] The user, family, friends, and healthcare professionals decide on the final treatment and diet plan, and the server records this decision and notifies all parties with relevant information.

[1308] Hardware and software used

[1309] The following hardware and software are used to implement this system.

[1310] Frontend: Interface using React

[1311] Backend: API server using Flask

[1312] Generative AI model: Generate treatment and dietary options based on patient information

[1313] Specific examples

[1314] For example, if a 42-year-old male patient with high blood pressure and diabetes needs to go on a low-carb diet, the process would go like this:

[1315] 1. The patient enters his / her age (42 years old), gender (male), medical history (high blood pressure, diabetes), current symptoms, treatment requests, and dietary restrictions (low-carbohydrate diet) into the terminal.

[1316] 2. The terminal sends the entered information to the server.

[1317] 3. The server pre-processes the data, normalizing it and checking for inconsistencies.

[1318] 4. The server analyzes the latest medical guidelines and related papers to generate the optimal treatment protocol for the patient.

[1319] 5. The server uses the generative AI model to simulate treatment and dietary options and evaluate the suitability of each option.

[1320] 6. The server takes into account the patient's preferences and risk tolerance, generates customized treatment and dietary suggestions, and sends the details to the device.

[1321] 7. The device will visually display treatment and dietary suggestions, allowing patients to understand the suggestions.

[1322] 8. The patient shares information with family members and gets their opinions.

[1323] 9. Provide a means for the server to collaborate with medical professionals for discussion and to incorporate expert opinions.

[1324] 10. The patient, family, friends, and healthcare professionals decide on the final treatment and diet plan, which is recorded by the server.

[1325] Example prompt sentence:

[1326] "What is the best meal plan for a 45-year-old male with diabetes who needs to follow a low-carb diet?"

[1327] The above is an embodiment of the present invention, which allows patients to deeply understand their own medical condition and dietary restrictions, and to quickly and accurately select optimal treatment and diet.

[1328] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1329] Step 1:

[1330] The user uses the device to input their age, gender, medical history, current symptoms, treatment preferences, and dietary restrictions. The input data is collected through a form on the device. The input information is sent to the server in JSON format.

[1331] Step 2:

[1332] The server preprocesses the patient information received from the terminal using a data preparation method. Specifically, it normalizes the data (for example, standardizing date formats and trimming strings), checks for duplication, and checks for inconsistencies. Once preprocessing is complete, the data is passed to the next processing step.

[1333] Step 3:

[1334] The server uses the guideline analysis means to analyze the latest medical guidelines and related medical papers. Based on the analysis results, it generates a treatment protocol for each patient. This generated treatment protocol is used in the next step.

[1335] Step 4:

[1336] The server uses the generated treatment protocol and preprocessed patient information to apply the generative AI model to simulate treatment and dietary options. Specifically, the AI ​​evaluates and recommends the optimal treatment and dietary plan based on the patient's symptoms and conditions. The output data from the simulation is passed to the next step.

[1337] Step 5:

[1338] The server generates a customized treatment and diet proposal based on the generated treatment and diet options, taking into account the patient's wishes and risk tolerance. This proposal includes details of the treatment and diet, expected effects, side effects, treatment duration, etc. The generated proposal is sent to the terminal.

[1339] Step 6:

[1340] The device displays the treatment and dietary recommendations received from the server. The user can review and deepen their understanding of the recommendations through interactive screens and videos. The display of the recommendations includes visual elements and navigation functions.

[1341] Step 7:

[1342] The user uses the share button on the device to share the treatment and dietary suggestions with family and friends. The device generates a sharing link or file and sends it to the family and friends. The user receives feedback from the family and friends.

[1343] Step 8:

[1344] The server provides a collaborative means to share treatment and dietary suggestions with healthcare professionals, provide discussion features for expert input, including chat and video call features, and receive feedback from healthcare professionals.

[1345] Step 9:

[1346] The user, family, friends, and healthcare professionals decide on the final treatment and diet plan. The server records this decision and notifies all parties of the relevant information. The recorded data is stored for future reference and tracking.

[1347] The above are the specific processing steps of the program for the system that realizes the application example.

[1348] 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.

[1349] This invention is a system in which patients input their medical history, symptoms, and treatment preferences, and then uses the latest medical guidelines and generative AI models to suggest optimal treatment options based on that information. Furthermore, by combining it with an emotion engine that recognizes the patient's emotions, more personalized treatment suggestions are possible. Specific forms for implementing this system are described below.

[1350] Overall system overview

[1351] The server receives patient information, organizes data, analyzes guidelines, simulates treatment options using an AI model, analyzes emotions using an emotion engine, and generates treatment suggestions. The terminal is used by patients to input information and check suggested treatment options. Users include patients themselves, their families, and medical professionals.

[1352] Program processing flow

[1353] Entering patient information

[1354] Users input their age, gender, medical history, current symptoms, and treatment preferences via a terminal, which includes an input form and an interface for clicking options.

[1355] Data preparation and preprocessing

[1356] The terminal sends the input data to the server, which then preprocesses the received data, specifically normalizing the data and checking for duplicates and inconsistencies.

[1357] Guidelines and literature analysis

[1358] The server analyzes the latest medical guidelines and relevant medical literature to generate a treatment protocol, which lists the best treatment options for the patient's condition.

[1359] AI-powered simulation of treatment options

[1360] The server uses a generative AI model to simulate treatment options based on patient information and treatment protocols, evaluating each option's suitability and predicted outcomes.

[1361] Emotion analysis using an emotion engine

[1362] While the patient is entering information, the device uses a camera and microphone to analyze the patient's emotional state in real time. This information is processed by an emotion engine, which generates emotional data based on the patient's facial expressions, tone of voice, and choice of words.

[1363] Generate customized treatment recommendations

[1364] The server takes into account the patient's preferences and risk tolerance, as well as the emotional data obtained from the emotion engine, to generate a customized treatment proposal, including details of the treatment, expected effects, side effects, and treatment duration.

[1365] Providing and confirming proposals

[1366] The device then provides the generated treatment proposal to the user, who can then review and deepen their understanding of the proposal through interactive screens and videos.

[1367] Share information with family and friends

[1368] Users can share their treatment suggestions with family and friends, allowing them to receive feedback from others.

[1369] Collaboration with medical professionals

[1370] The server provides a discussion function for sharing treatment suggestions with medical professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[1371] Deciding and recording the final treatment plan

[1372] The user, family, friends, and medical professionals decide on the final treatment plan, and the server records this decision and notifies all parties with relevant information.

[1373] Specific examples

[1374] For example, in the case of a 42-year-old male patient with hypertension and diabetes, the process would proceed as follows:

[1375] 1. The patient enters his / her age (42 years old), gender (male), medical history (high blood pressure, diabetes), current symptoms, and treatment preferences into the terminal.

[1376] 2. The terminal sends the entered information to the server.

[1377] 3. The server pre-processes the data, normalizing it and checking for inconsistencies.

[1378] 4. The server analyzes the latest medical guidelines and related papers to generate the optimal treatment protocol for the patient.

[1379] 5. The server uses the generative AI model to simulate treatment options and evaluate the suitability of each option.

[1380] 6. The device analyzes the patient's emotional state in real time through a camera and microphone and sends that information to the emotion engine.

[1381] 7. The server generates a customized treatment proposal based on the emotional data, preferences, and risk tolerance, and sends the details to the device.

[1382] 8. The device visually displays treatment suggestions to help patients understand the suggestions.

[1383] 9. The patient shares information with family members and gets their opinions.

[1384] 10. Provide a means for the server to collaborate and hold discussions with medical professionals, thereby incorporating expert opinions.

[1385] 11. The patient, family, friends, and healthcare professionals decide on the final treatment plan, which is recorded by the server.

[1386] The above is an embodiment of the present invention, which allows patients to deeply understand their own medical condition and quickly and accurately select the optimal treatment that reflects their emotional state.

[1387] The processing flow will be explained below.

[1388] Step 1:

[1389] The user inputs his / her age, sex, medical history, current symptoms, and desired treatment course via the terminal.

[1390] Specifically, the user enters data into an input form and presses a send button, thereby inputting the data.

[1391] Step 2:

[1392] The terminal transmits the input data to the server.

[1393] The terminal encrypts the data to be transmitted and sends it to the server over a secure connection.

[1394] Step 3:

[1395] The server stores the received data in a database.

[1396] The database stores patient information in a suitable format.

[1397] Step 4:

[1398] The server performs pre-processing of the data, including normalizing the data, checking for duplicates, and checking for inconsistencies.

[1399] The server converts the received data into a unified format, removes duplicate data, and checks for inconsistencies.

[1400] Step 5:

[1401] The server analyzes the latest medical guidelines and related medical literature and generates treatment protocols.

[1402] Relevant guidelines and papers will be searched from the database, and treatment protocols will be created using analytical algorithms.

[1403] Step 6:

[1404] The server uses a generative AI model to simulate treatment options based on patient information and the generated treatment protocol.

[1405] Simulations will assess the suitability and predicted outcomes of each treatment option.

[1406] Step 7:

[1407] The terminal uses an emotion engine to analyze the patient's emotional state in real time as information is being entered.

[1408] The device uses a camera and microphone to analyze facial expressions, tone of voice, and language usage to generate emotional data.

[1409] Step 8:

[1410] The terminal transmits the analyzed emotion data to the server.

[1411] The device encrypts the emotion data and transmits it to the server over a secure connection.

[1412] Step 9:

[1413] The server receives the affective data and generates a treatment recommendation after taking into account the patient's preferences and risk tolerance.

[1414] Include emotional data to generate more personalized treatment recommendations.

[1415] Step 10:

[1416] The server transmits the generated treatment proposal to the terminal.

[1417] The suggestions are formatted into an interactive format and sent to the device.

[1418] Step 11:

[1419] The terminal visually displays treatment suggestions.

[1420] Interactive images and instructional videos are used to detail treatment options.

[1421] Step 12:

[1422] Users share their treatment suggestions with family and friends.

[1423] Use the share feature to send information via email or message.

[1424] Step 13:

[1425] The device again sends feedback from family and friends to the server.

[1426] The received feedback is sent to the server and stored in a database.

[1427] Step 14:

[1428] The server shares information with medical professionals, including the patient's primary care physician, and provides an environment for discussion.

[1429] Proposals are shared and discussion functions are provided through an interface for medical professionals.

[1430] Step 15:

[1431] The user, family, and healthcare professionals share and discuss information to determine the final treatment plan.

[1432] Discussions are held in real time using the chat and video conferencing functions provided by the server.

[1433] Step 16:

[1434] The server determines and records the final treatment plan.

[1435] The confirmed treatment plan will be recorded in the database and notified to all parties.

[1436] These are the specific processing steps of the system, which allow patients to receive support in making optimal medical choices.

[1437] Example 2

[1438] 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."

[1439] Modern medicine requires the ability to quickly and accurately recommend optimal treatments tailored to each patient's individual symptoms and wishes. However, manually collecting and analyzing information and determining treatment plans is time-consuming and prone to errors. Furthermore, determining treatment plans without considering the patient's emotional state can reduce patient satisfaction and reduce treatment effectiveness. Furthermore, information sharing and collaboration are essential for patients, their families, friends, and healthcare professionals to work together to determine the optimal treatment plan. Therefore, a system that can resolve these issues and provide faster and more accurate treatment recommendations is needed.

[1440] 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 data preparation means for receiving input patient information and performing preprocessing; a guideline analysis means for analyzing the latest medical guidelines and related medical papers to generate a treatment protocol; a generation AI model means for simulating treatment options using the generated treatment protocol and patient information; and a proposal generation means for generating a customized treatment proposal by taking into account the patient's wishes, risk tolerance, and emotional data. This enables rapid and accurate generation of individually optimized treatment proposals based on detailed patient information and the latest medical guidelines. Furthermore, customized proposals based on emotional data can improve patient satisfaction and treatment effectiveness. Furthermore, by sharing information with family and friends and collaborating with medical professionals, a more comprehensive and reliable treatment plan can be determined.

[1441] "Patient" refers to an individual with a medical history or condition who uses the system.

[1442] "Input means" refers to a device or interface that allows a patient to input information such as age, sex, medical history, current symptoms, and desired treatment course.

[1443] "Data preparation means" refers to devices or programs that receive input patient information and perform preprocessing (data normalization, duplication checks, inconsistency checks, etc.).

[1444] "Guideline analysis means" refers to a device or program for analyzing the latest medical guidelines and related medical papers and generating treatment protocols.

[1445] "Generative AI model means" refers to an artificial intelligence model for simulating treatment options using the generated treatment protocol and patient information.

[1446] "Proposal Generator" refers to a device or program for generating a customized treatment proposal taking into account the patient's preferences and risk tolerance, as well as emotional data.

[1447] "Providing means" refers to a device or system for providing the generated treatment proposal to the patient.

[1448] "Display means" refers to a display or user interface that allows the patient to confirm the suggestions and display interactive content.

[1449] An "emotion engine" refers to a device or program that uses a camera or microphone to analyze a patient's emotional state in real time and generate emotional data.

[1450] "Sharing means" refers to functions and devices that allow patients to share treatment suggestion information with their family and friends and receive feedback.

[1451] "Collaboration methods" refer to online chat functions and data sharing systems for providing information to medical professionals and for exchanging opinions and holding discussions.

[1452] "Recording means" refers to a database or recording device for determining the final treatment plan and recording that information.

[1453] This invention is a system in which patients input their medical history, symptoms, and treatment preferences, and then uses the latest medical guidelines and generative AI models to suggest optimal treatment options based on that information. Furthermore, by combining it with an emotion engine that recognizes the patient's emotions, more personalized treatment suggestions are possible. Specific forms for implementing this system are described below.

[1454] Overall system overview

[1455] The server receives patient information, organizes data, analyzes guidelines, simulates treatment options using an AI model, analyzes emotions using an emotion engine, and generates treatment suggestions. The terminal is used by patients to input information and check suggested treatment options. Users include patients themselves, their families, and medical professionals.

[1456] Entering patient information

[1457] The user inputs their age, gender, medical history, current symptoms, and desired treatment course via a terminal, which includes an input form and an option-click interface. The input data is then sent to the server by the terminal.

[1458] Data preparation and preprocessing

[1459] The server preprocesses the received data, specifically normalizing it, checking for duplicates, checking for inconsistencies, etc. This preprocessing ensures data consistency and accuracy.

[1460] Guidelines and literature analysis

[1461] The server analyzes the latest medical guidelines and related medical papers to generate treatment protocols, using natural language processing (NLP) technology to extract relevant information from large amounts of medical text data.

[1462] AI-powered simulation of treatment options

[1463] The server uses a generative AI model to simulate treatment options based on patient information and treatment protocols. A prompt is input to the AI ​​model to evaluate the suitability and predicted outcomes of multiple treatment options. For example, a simulation is performed based on the prompt: "42-year-old male with a history of high blood pressure and diabetes. Current symptoms include mild headaches and fatigue. He wishes to undergo a new treatment and minimize side effects. Please suggest the optimal treatment plan."

[1464] Emotion analysis using an emotion engine

[1465] While the patient is entering information, the device uses a camera and microphone to analyze the patient's emotional state in real time. This information is processed by an emotion engine, which generates emotional data based on the patient's facial expressions, tone of voice, and choice of words.

[1466] Generate customized treatment recommendations

[1467] The server generates a customized treatment proposal based on the patient's preferences, risk tolerance, and emotional data, including details of the treatment, expected effects, side effects, and duration of treatment.

[1468] Providing and confirming proposals

[1469] The device then provides the generated treatment proposal to the user, who can then review and deepen their understanding of the proposal through interactive screens and videos.

[1470] Share information with family and friends

[1471] Users can share their treatment suggestions with family and friends, allowing them to receive feedback from others.

[1472] Collaboration with medical professionals

[1473] The server provides a discussion function for sharing treatment suggestions with medical professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[1474] Deciding and recording the final treatment plan

[1475] The user, family, friends, and medical professionals decide on the final treatment plan, and the server records this decision and notifies all parties with relevant information.

[1476] The above is a specific embodiment for carrying out the present invention, which allows patients to deeply understand their own medical condition and quickly and accurately select the optimal treatment that reflects their emotional state.

[1477] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1478] Step 1:

[1479] The user enters their age, gender, medical history, current symptoms, and desired treatment plan into the terminal. Specifically, the user enters information into the input form displayed on the terminal and clicks the "Submit" button. Input is performed using text fields and selection options. The input in this step is data about the patient's personal information and symptoms, and the output is temporary data stored on the terminal.

[1480] Step 2:

[1481] The terminal sends the input data to the server. Specifically, the data is encrypted and sent using the HTTPS protocol. The server receives the data. The input to this step is the patient information stored on the terminal, and the output is the data received by the server.

[1482] Step 3:

[1483] The server pre-processes the data it receives. Specific operations include normalizing the data (e.g., standardizing date formats), checking for duplicates (e.g., deleting duplicate entries), and checking for inconsistencies (e.g., checking for unnatural data values). This pre-processing ensures data consistency and accuracy. The input to this step is the raw data sent earlier, and the output is the pre-processed, clean data.

[1484] Step 4:

[1485] The server analyzes the latest medical guidelines and related medical papers. Specifically, it uses natural language processing (NLP) technology to extract relevant information from large amounts of medical text data and generate treatment protocols. The input for this step is the latest medical guidelines and patient data, and the output is the optimal treatment protocol for the patient.

[1486] Step 5:

[1487] The server uses a generative AI model to simulate treatment options based on the generated treatment protocol and patient information. Specifically, it inputs prompts to the AI ​​model and evaluates the suitability and predicted outcomes of multiple treatment options. The inputs for this step are the treatment protocol and prompts, and the output is multiple treatment options and their evaluation results.

[1488] Step 6:

[1489] While the patient is entering information, the device uses a camera and microphone to analyze the patient's emotional state in real time. Specifically, facial expression data captured by the device's camera and voice data collected by the microphone are sent to the emotion engine for analysis. The input for this step is raw data obtained from the camera and microphone, and the output is emotional data analyzed by the emotion engine.

[1490] Step 7:

[1491] The server generates a customized treatment proposal based on the patient's preferences, risk tolerance, and emotional data. Specifically, it integrates the AI ​​model's simulation results with the emotional data to create a comprehensive treatment plan that includes details of the treatment, expected effects, side effects, and treatment duration. The input for this step is treatment options and emotional data, and the output is a customized treatment proposal.

[1492] Step 8:

[1493] The device provides the generated treatment proposal to the user. Specifically, the device visually displays the treatment proposal on its display and allows the user to easily review the proposal through interactive elements (e.g., tab switching, detailed information display, video explanation, etc.). The input of this step is the customized treatment proposal, and the output is a display screen that the user can view.

[1494] Step 9:

[1495] The user can share the treatment proposal with family and friends by using the sharing function on their device. Specific actions include sending the proposal via email or generating a QR code to scan. The input for this step is the customized treatment proposal, and the output is the shared proposal.

[1496] Step 10:

[1497] The server shares the treatment proposal with healthcare professionals and provides a discussion function to incorporate their expert opinions. Specific operations include providing information and collecting opinions through an online chat function and a data sharing folder. The input of this step is the customized treatment proposal and healthcare professional feedback, and the output is a revised treatment proposal.

[1498] Step 11:

[1499] The user, family, friends, and healthcare professionals decide on a final treatment plan. The server records this decision and notifies all relevant parties of the relevant information. Specific operations include saving the finalized treatment plan digitally and notifying relevant parties via push notifications or email. The input of this step is the finalized treatment plan, and the output is the recorded treatment plan and notification content.

[1500] (Application example 2)

[1501] 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."

[1502] Conventional medical systems are limited in their functionality, not only in inputting a patient's medical history and symptoms, but also in proposing optimal treatment options based on the latest medical guidelines. Furthermore, because they do not take into account the patient's emotional state, there is a lack of personalization, making it difficult to find the optimal treatment. Furthermore, there is no clear guidance on payment methods after a treatment plan is decided, which places a burden on patients.

[1503] 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: an input means for the patient to input their age, gender, medical history, current symptoms, and desired treatment plan; a data preparation means for receiving and preprocessing the input patient information; a guideline analysis means for analyzing the latest medical guidelines and related medical papers and generating a treatment protocol; a generative AI model means for simulating treatment options using the generated treatment protocol and patient information; an emotion analysis means for analyzing the patient's emotional state in real time; a proposal generation means for generating a customized treatment proposal taking into account the patient's wishes, risk tolerance, and emotional data; a provision means for providing the generated proposal to the patient; a display means for allowing the patient to confirm the proposal content and display interactive content; a sharing means for sharing information with the patient's family and friends and receiving feedback; a collaboration means for providing information and discussions with medical professionals; and a recording means for determining and recording a final treatment plan. This enables patients to quickly and accurately obtain optimal treatment options that comprehensively consider their medical condition and emotional state.

[1504] The "input means" is a means for a patient to input information such as age, sex, medical history, current symptoms, and desired treatment course.

[1505] The "data preparation means" is a means for receiving input patient information and performing preprocessing.

[1506] The "guideline analysis means" is a means for analyzing the latest medical guidelines and related medical papers and generating treatment protocols.

[1507] The "generative AI model means" is a means for simulating treatment options using the generated treatment protocol and patient information.

[1508] The "emotion analysis means" is a means for analyzing the emotional state of a patient in real time.

[1509] A "proposal generator" is a means for generating a customized treatment proposal that takes into account the patient's preferences, risk tolerance and emotional data.

[1510] The "means for providing" is a means for providing the generated suggestion to the patient.

[1511] The "display means" is a means for the patient to confirm the proposed content and display interactive content.

[1512] "Sharing tools" are ways to share information with the patient's family and friends and receive feedback.

[1513] "Collaboration methods" are means for providing information and holding discussions with medical professionals.

[1514] "Recording means" is a means for determining and recording the final treatment plan.

[1515] This invention is a system that uses the latest medical guidelines and generative AI models to suggest optimal treatment options by allowing patients to input their medical history, symptoms, and treatment preferences. Furthermore, by combining it with an emotion engine that recognizes the patient's emotions, more personalized treatment suggestions are possible.

[1516] Overall system overview

[1517] The server receives patient information, organizes data, analyzes guidelines, simulates treatment options using a generative AI model, analyzes emotions using an emotion engine, and generates treatment proposals. The terminals are used by patients to input information and check proposed treatment options. These terminals include smartphones. Users include patients themselves, their families, and healthcare professionals.

[1518] Hardware and software used

[1519] The hardware uses a smartphone (iOS / Android device) and performs emotion analysis using the front camera and microphone, while a cloud server is used to analyze medical guidelines and run generative AI models.

[1520] The following software is used:

[1521] Data preparation measures: Preprocessing patient information

[1522] Guideline analysis tools: Analysis of the latest medical guidelines and related medical papers

[1523] Generative AI modeling tools: simulating treatment options

[1524] Emotion analysis tool: Analyze the current emotional state of the patient in real time

[1525] Proposal generation method: Generate treatment proposals taking into account the patient's preferences, risk tolerance, and emotional data

[1526] Operating procedure

[1527] 1. Patients use their smartphones to enter their age, gender, medical history, current symptoms, and treatment preferences.

[1528] 2. The terminal sends the entered data to the server.

[1529] 3. The server performs preprocessing such as data normalization and inconsistency checking.

[1530] 4. The server analyzes the latest medical guidelines and related papers and generates a treatment protocol.

[1531] 5. Use generative AI models to simulate treatment options and evaluate the suitability of each option.

[1532] 6. The device analyzes the patient's emotional state in real time through a camera and microphone and sends the data to the emotion engine.

[1533] 7. The server generates customized treatment recommendations taking into account the patient's preferences, risk tolerance, and emotional data.

[1534] 8. Provide patients with treatment suggestions through video and interactive content.

[1535] Specific examples

[1536] For example, consider a 42-year-old male patient with high blood pressure and diabetes. The patient enters his or her personal information on a smartphone, and the emotion analysis engine analyzes his or her emotional state. The system then recommends optimal treatment options based on the patient's emotional data. These recommendations include expected effects, side effects, treatment duration, and specific payment options.

[1537] Prompt Sentence Examples

[1538] Examples of prompts for generative AI models include:

[1539] "Prompt to generative AI model:

[1540] Patient information: 42-year-old male, medical history: hypertension, diabetes, current symptoms: fatigue, desired treatment: drug therapy

[1541] Please recommend the best treatment options for this patient based on the latest medical guidelines."

[1542] Through this platform, patients will be able to easily access treatment options that take into account their medical condition and emotional state in an integrated manner.

[1543] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1544] Step 1:

[1545] Patients use their smartphones to input their age, gender, medical history, current symptoms, and treatment preferences. This information is collected through the patient information input means. The input data includes age, gender, medical history, current symptoms, and treatment preferences. The input data is sent from the smartphone to the server.

[1546] Step 2:

[1547] The terminal sends the received patient information to the server. The server receives the patient information and performs preprocessing on the data. This preprocessing includes data normalization, duplication checks, and inconsistency checks. The input here is raw data from the patient, and the output is preprocessed data.

[1548] Step 3:

[1549] The server analyzes the latest medical guidelines and related papers based on the preprocessed data and generates a treatment protocol. Using guideline analysis tools, it analyzes medical literature and guidelines to list treatment options. The input is the preprocessed patient data, and the output is the generated treatment protocol.

[1550] Step 4:

[1551] The server uses a generative AI model to simulate treatment options based on treatment protocols and patient information. The simulation process evaluates the suitability and predicted outcomes of each treatment option. The inputs are treatment protocols and patient information, and the output is the simulation results.

[1552] Step 5:

[1553] While the terminal is inputting patient information, it uses a camera and microphone to analyze the patient's emotional state in real time. Using emotion analysis means, emotion data is generated from the patient's facial expressions, tone of voice, and choice of words. This emotion data is later sent to the server. The input is video and audio data, and the output is emotion analysis data.

[1554] Step 6:

[1555] The server generates a customized treatment proposal based on the emotion data, patient information, and treatment protocol. Using the proposal generation means, the optimal treatment option is generated taking into account the patient's wishes, risk tolerance, and emotion data. The input is the treatment protocol, patient information, and emotion data, and the output is a customized treatment proposal.

[1556] Step 7:

[1557] The server provides the generated treatment proposal to the smartphone. The patient can confirm the proposal through the provision means and understand the details through interactive content. The input is the customized treatment proposal, and the output is the treatment proposal confirmed by the patient.

[1558] Step 8:

[1559] After the patient confirms the proposal, they share the information with their family and friends. Using the sharing tool, the patient shares the treatment proposal and receives feedback. The input is the treatment proposal and the shared information, and the output is the feedback.

[1560] Step 9:

[1561] After receiving feedback from patients, their families, and friends, the server uses collaborative methods to provide information and hold discussions with healthcare professionals. Expert opinions are incorporated to refine the final treatment plan. The input is the feedback and shared information, and the output is the treatment plan with added expert opinions.

[1562] Step 10:

[1563] The patient, family, friends, and healthcare professionals decide on the final treatment plan, which is then recorded by the server. Using a recording method, the final treatment plan is formally recorded and notified to the relevant parties. The input is the final treatment proposal and discussion results, and the output is the recorded treatment plan.

[1564] 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.

[1565] 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.

[1566] 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.

[1567] [Fourth embodiment]

[1568] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1569] 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.

[1570] 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).

[1571] 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.

[1572] 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.

[1573] 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).

[1574] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] 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."

[1581] The present invention is a system that allows patients to input their medical history, symptoms, and treatment preferences, and then uses the latest medical guidelines and generative AI models to suggest optimal treatment options. Specific embodiments for implementing this system are described below.

[1582] Overall system overview

[1583] The server receives patient information, organizes the data, analyzes guidelines, and simulates treatment options using AI models to generate treatment proposals. The terminals are used by patients to input information and check the proposed treatment options. Users include patients themselves, their families, and medical professionals.

[1584] Program processing flow

[1585] Entering patient information

[1586] The user uses the terminal to input their age, gender, medical history, current symptoms, and desired treatment course, which includes an input form and an interface for clicking options.

[1587] Data preparation and preprocessing

[1588] The terminal sends the input data to the server, which then preprocesses the received data, specifically normalizing the data and checking for duplicates and inconsistencies.

[1589] Guidelines and literature analysis

[1590] The server analyzes the latest medical guidelines and relevant medical literature to generate a treatment protocol, which lists the best treatment options for the patient's condition.

[1591] AI-powered simulation of treatment options

[1592] The server uses a generative AI model to simulate treatment options based on patient information and treatment protocols, evaluating each option's suitability and predicted outcomes.

[1593] Generate customized treatment recommendations

[1594] The server takes into account the patient's preferences and risk tolerance and generates a customized treatment proposal, including details of the treatment, expected effects, side effects, and duration of treatment.

[1595] Providing and confirming proposals

[1596] The device then provides the generated treatment proposal to the user, who can then review and deepen their understanding of the proposal through interactive screens and videos.

[1597] Share information with family and friends

[1598] Users can share their treatment suggestions with family and friends, allowing them to receive feedback from others.

[1599] Collaboration with medical professionals

[1600] The server provides a discussion function for sharing treatment suggestions with medical professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[1601] Deciding and recording the final treatment plan

[1602] The user, family, friends, and medical professionals decide on the final treatment plan, and the server records this decision and notifies all parties with relevant information.

[1603] Specific examples

[1604] For example, in the case of a 42-year-old male patient with hypertension and diabetes, the process would proceed as follows:

[1605] 1. The patient enters his / her age (42 years old), gender (male), medical history (high blood pressure, diabetes), current symptoms, and treatment preferences into the terminal.

[1606] 2. The terminal sends the entered information to the server.

[1607] 3. The server pre-processes the data, normalizing it and checking for inconsistencies.

[1608] 4. The server analyzes the latest medical guidelines and related papers to generate the optimal treatment protocol for the patient.

[1609] 5. The server uses the generative AI model to simulate treatment options and evaluate the suitability of each option.

[1610] 6. The server takes into account the patient's preferences and risk tolerance, generates a customized treatment proposal, and sends the details to the device.

[1611] 7. The device visually displays treatment suggestions to help patients understand them.

[1612] 8. The patient shares information with family members and gets their opinions.

[1613] 9. Provide a means for the server to collaborate with medical professionals for discussion and to incorporate expert opinions.

[1614] 10. The patient, family, friends, and healthcare professionals decide on the final treatment plan, which is recorded by the server.

[1615] The above is an embodiment of the present invention, which allows patients to gain a deeper understanding of their own condition and to select the most appropriate treatment quickly and accurately.

[1616] The processing flow will be explained below.

[1617] Step 1:

[1618] The user inputs his / her age, sex, medical history, current symptoms, and desired treatment course via the terminal.

[1619] Specifically, the data is entered by filling in the necessary information in the input form and pressing the send button.

[1620] Step 2:

[1621] The terminal transmits the input data to the server.

[1622] The data is encrypted and transmitted over a secure connection.

[1623] Step 3:

[1624] The server receives the transmitted data and stores it in a data database.

[1625] Checks the received data and inserts it into the appropriate tables in the database.

[1626] Step 4:

[1627] The server performs pre-processing, which includes data normalization, duplicate checks, and consistency checks.

[1628] Standardize data formats, remove duplicate records, and check for outliers and inconsistencies.

[1629] Step 5:

[1630] The server analyzes the latest medical guidelines and related medical literature and generates treatment protocols.

[1631] Database queries are performed to extract relevant guidelines and papers, and analytical algorithms are used to create treatment protocols.

[1632] Step 6:

[1633] The server uses a generative AI model to simulate treatment options.

[1634] The generative AI model is run using patient information and treatment protocols as input to simulate multiple treatment options.

[1635] Step 7:

[1636] The server generates a customized treatment proposal based on the simulation results, taking into account the patient's preferences and risk tolerance.

[1637] The simulation results are analyzed to select the treatment plan that best suits the patient's wishes and risk profile.

[1638] Step 8:

[1639] The server transmits the generated treatment proposal to the terminal.

[1640] The suggestions are formatted into an interactive format and sent to the patient's device.

[1641] Step 9:

[1642] The terminal visually displays the received treatment suggestions.

[1643] Interactive images and instructional videos are used to detail treatment options.

[1644] Step 10:

[1645] Users share their treatment suggestions with family and friends.

[1646] Use the share feature to send information via email or message.

[1647] Step 11:

[1648] The device again sends feedback from family and friends to the server.

[1649] The received feedback is sent to the server and stored in a database.

[1650] Step 12:

[1651] The server provides information to medical professionals, including the patient's primary care physician, and provides an environment for discussion.

[1652] Proposals are shared and discussion functions are provided through an interface for medical professionals.

[1653] Step 13:

[1654] The user, family, and healthcare professionals share and discuss information to determine the final treatment plan.

[1655] Discussions are held in real time using the chat and video conferencing functions provided by the server.

[1656] Step 14:

[1657] The server determines and records the final treatment plan.

[1658] The confirmed treatment plan will be recorded in the database and notified to all parties.

[1659] These are the specific processing steps of the system, which allow patients to receive support in making optimal medical choices.

[1660] Example 1

[1661] 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."

[1662] Conventional medical systems have difficulty instantly generating effective and appropriate treatment proposals based on the medical history and symptoms entered by the patient themselves. They also face challenges in providing customized treatment proposals that reflect the patient's wishes and insufficient collaboration with family and medical professionals. As a result, it has been difficult for patients to gain a deep understanding of their condition and quickly select the optimal treatment.

[1663] 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.

[1664] In this invention, the server includes: an input means for a patient to input their age, gender, medical history, current symptoms, and desired treatment plan; a data preparation means for receiving the input patient information and performing preprocessing such as normalization and inconsistency checks; a guideline analysis means for analyzing the latest medical guidelines and related literature and generating a treatment protocol; a generative AI model means for simulating treatment options using the generated treatment protocol and patient information; a proposal generation means for generating a customized treatment proposal taking into account the patient's wishes and risk tolerance; a provision means for providing the generated proposal to the patient and displaying visual and interactive content; a sharing means for sharing information with the patient's family and friends and receiving feedback; a collaboration means for providing information and discussions with medical professionals; and a recording means for determining and recording a final treatment plan. This enables patients to deeply understand their condition and quickly and accurately select the optimal treatment.

[1665] The "input means" is a means for a patient to input information such as age, sex, medical history, current symptoms, and desired treatment course.

[1666] The "data preparation means" is a means for receiving input patient information and performing pre-processing such as normalization and inconsistency checks.

[1667] The "guideline analysis means" is a means for analyzing the latest medical guidelines and related literature and generating treatment protocols.

[1668] The "generative AI model means" is a means for simulating treatment options using the generated treatment protocol and patient information.

[1669] A "proposal generator" is a means for generating a customized treatment proposal, taking into account the patient's preferences and risk tolerance.

[1670] The "delivery means" is a means for providing the generated suggestions to the patient and displaying visual and interactive content.

[1671] "Sharing tools" are ways to share information with the patient's family and friends and receive feedback.

[1672] "Collaboration methods" are means for providing information and holding discussions with medical professionals.

[1673] "Recording means" is a means for determining and recording the final treatment plan.

[1674] MODE FOR CARRYING OUT THE INVENTION

[1675] This system utilizes the latest medical guidelines and generative AI models to suggest optimal treatment options based on patient-entered information. A specific implementation of the entire system is described below.

[1676] System Overview

[1677] The server receives patient information, organizes the data, analyzes guidelines, and simulates treatment options using a generative AI model to generate treatment proposals. The terminal is used by patients to input information and check the proposed treatment options. Users include patients themselves, their families, and medical professionals.

[1678] Hardware and software used

[1679] Hardware: Servers, devices (PCs, tablets, smartphones)

[1680] Software: Database management system, generative AI model, interactive interface

[1681] Processing flow

[1682] 1. Enter patient information

[1683] The user inputs their age, gender, medical history, current symptoms, and desired treatment plan via a terminal, using an interface where they click on text boxes and options.

[1684] Examples:

[1685] The user inputs that he is a 42-year-old male suffering from high blood pressure and diabetes, and describes his current symptoms as slight fever and dizziness. He also inputs that he wishes to receive drug therapy as his treatment preference.

[1686] 2. Data preparation and preprocessing

[1687] The terminal sends the entered data to the server, which receives the data and performs normalization, duplication checks, and inconsistency checks.

[1688] Examples:

[1689] The server normalizes the entered age data of "42 years old" and checks for duplicate information about "high blood pressure" and "diabetes."

[1690] 3. Analysis of guidelines and literature

[1691] The server analyzes the latest medical guidelines and related literature to generate the optimal treatment plan for the patient's condition. This analysis includes searching guideline databases and related papers to extract the optimal treatment plan.

[1692] Examples:

[1693] The server searches the guideline database for the latest treatments for "high blood pressure" and "diabetes" and generates a treatment protocol appropriate for the patient.

[1694] 4. AI-powered simulation of treatment options

[1695] The server uses the generative AI model to simulate treatment options based on treatment protocols and patient information, and the results are evaluated for suitability and predicted outcomes for each treatment option.

[1696] Examples:

[1697] The generative AI model simulates treatment options based on the information of "42-year-old male, high blood pressure, diabetes, slight fever, dizziness" and evaluates suitability.

[1698] 5. Generating customized treatment suggestions

[1699] The server generates a customized treatment proposal that takes into account the patient's preferences (e.g., drug therapy) and risk tolerance. The proposal includes details of the treatment, expected effects, side effects, and treatment duration.

[1700] Examples:

[1701] The server generates treatment plans with minimal side effects, focusing on drug therapy, and proposes details.

[1702] 6. Providing and confirming proposals

[1703] The device provides the generated treatment recommendations to the user and displays them with visual and interactive content, making it easier for patients to understand the recommendations.

[1704] Examples:

[1705] The device will display details of drug therapy, side effects, and time to recovery to help patients understand.

[1706] 7. Sharing information with family and friends

[1707] A sharing feature is provided that allows users to share their treatment suggestions with family and friends and get their feedback.

[1708] Examples:

[1709] The user emails the proposal to family members and asks for their opinions.

[1710] 8. Collaboration with medical professionals

[1711] The server shares treatment suggestions with medical professionals and provides a discussion function, allowing for expert input.

[1712] Examples:

[1713] Healthcare professionals use the chat feature to provide feedback to patients.

[1714] 9. Deciding and recording the final treatment plan

[1715] The user, family, friends, and medical professionals decide on the final treatment plan, which the server records and notifies all parties of the relevant information.

[1716] Examples:

[1717] The user confirms the final treatment plan by clicking the "Decide" button, and the server notifies all parties involved by email.

[1718] Examples of prompt statements

[1719] For example, we can input the following prompts into a generative AI model to suggest treatment options:

[1720] Patient information: 42-year-old male with high blood pressure and diabetes. Current symptoms are slight fever and dizziness. Treatment desired: Drug therapy.

[1721] Conditions: Based on current medical guidelines, three treatment options are proposed, one of which focuses on minimizing side effects.

[1722] The above is an embodiment of the present invention. This system allows patients to gain a deeper understanding of their own condition and to select the most appropriate treatment quickly and accurately.

[1723] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1724] Step 1:

[1725] Users input their age, gender, medical history, current symptoms, and desired treatment plan into the terminal, using an interface that involves clicking text boxes and options.

[1726] Input: Patient's age, gender, medical history, current symptoms, treatment preference

[1727] Output: Information entered by the user

[1728] Specific working example:

[1729] The user inputs that he is a 42-year-old male suffering from high blood pressure and diabetes, describes slight fever and dizziness as his current symptoms, and selects drug therapy as his desired treatment.

[1730] Step 2:

[1731] The terminal sends the entered data to the server, which receives the data and performs normalization, duplication checks, and inconsistency checks.

[1732] Input: Patient information received from the user

[1733] Output: Preprocessed and cleaned data

[1734] Specific working example:

[1735] The server normalizes the entered age data, such as "42 years old," and checks for duplicate information and inconsistencies. For example, it deletes duplicate medical history information and corrects incorrect data formats.

[1736] Step 3:

[1737] The server analyzes the latest medical guidelines and related papers to generate treatment protocols, which includes searching guideline databases and related papers to extract optimal treatments.

[1738] Input: Preprocessed and cleaned data

[1739] Output: Generated treatment procedure

[1740] Specific working example:

[1741] The server searches the guideline database for the latest treatments for "hypertension" and "diabetes" and generates a treatment protocol appropriate for the patient.

[1742] Step 4:

[1743] The server uses the generative AI model to simulate treatment options based on treatment protocols and patient information, and the results are evaluated for suitability and predicted outcomes for each treatment option.

[1744] Input: Treatment protocol and patient information

[1745] Output: Simulated treatment options and their fitness

[1746] Specific working example:

[1747] The generative AI model simulates treatment options based on the information of "42-year-old male, high blood pressure, diabetes, slight fever, dizziness" and evaluates their suitability. For example, drug therapy A is evaluated as having a suitability of 90%, and drug therapy B is evaluated as having a suitability of 70%.

[1748] Step 5:

[1749] The server takes into account the patient's preferences (e.g., medication) and risk tolerance and generates a customized treatment proposal, including details of the treatment, expected effects, side effects, and treatment duration.

[1750] Input: Simulated treatment options and patient preferences

[1751] Output: Customized treatment proposals

[1752] Specific working example:

[1753] The server generates a treatment plan with fewer side effects, focusing on drug therapy, and proposes its details. For example, it lists drug therapy A with fewer side effects and its treatment schedule in the proposal.

[1754] Step 6:

[1755] The device provides the generated treatment recommendations to the user and displays them with visual and interactive content, making it easier for patients to understand the recommendations.

[1756] Input: Customized Treatment Proposal

[1757] Output: Visual and interactive treatment proposals provided to the user

[1758] Specific working example:

[1759] The device displays "details of drug therapy, side effects, and time to recovery," and provides interactive graphs and videos to help users understand.

[1760] Step 7:

[1761] A sharing feature is provided that allows users to share their treatment suggestions with family and friends and get their feedback.

[1762] Input: Treatment proposal

[1763] Output: Treatment suggestions and feedback shared with family and friends

[1764] Specific working example:

[1765] The user emails the proposal to family members and asks for their opinions.

[1766] Step 8:

[1767] The server shares treatment suggestions with medical professionals and provides a discussion function, allowing for expert input.

[1768] Input: Treatment proposals and discussion details

[1769] Output: Opinions and feedback from healthcare professionals

[1770] Specific working example:

[1771] Healthcare professionals use the chat feature to provide feedback to patients, for example by providing additional information about side effects of medication.

[1772] Step 9:

[1773] The user, family, friends, and medical professionals decide on the final treatment plan, which the server records and notifies all parties of the relevant information.

[1774] Input: Final treatment plan decision

[1775] Output: Recorded final treatment plan and notification to relevant parties

[1776] Specific working example:

[1777] The user confirms the final treatment plan by clicking the "Decide" button, and the server notifies all parties involved by email.

[1778] (Application example 1)

[1779] 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."

[1780] In conventional healthcare systems, treatment recommendations are based solely on a patient's medical history and symptoms, and comprehensive treatment plans, including dietary therapy, are not adequately proposed. Furthermore, there are insufficient means for communicating the proposed treatment plan with family members and healthcare professionals and obtaining feedback, making it difficult to provide optimal treatment and meal plans to patients. To solve this problem, a comprehensive treatment and meal plan recommendation system that takes into account a patient's medical history, symptoms, and dietary restrictions is needed.

[1781] 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.

[1782] In this invention, the server includes a meal plan proposal unit that proposes a meal plan based on a patient's input of their medical history, symptoms, and dietary restrictions, an input unit for the patient to input their age, sex, medical history, current symptoms, and desired treatment plan, a data preparation unit that receives and preprocesses the input patient information, a guideline analysis unit that analyzes the latest medical guidelines and related medical papers and generates a treatment protocol, a generative AI model unit that simulates treatment options using the generated treatment protocol and patient information, a proposal generation unit that generates a customized treatment proposal taking into account the patient's preferences and risk tolerance, a provision unit that provides the generated proposal to the patient, a display unit that allows the patient to confirm the proposal content and display interactive content, a sharing unit that shares information with the patient's family and friends and receives feedback, a collaboration unit that provides information and discussions with medical professionals, and a recording unit that determines and records the final treatment plan. This enables the optimal treatment and meal plan to be quickly and accurately provided taking into account the patient's medical history, symptoms, and dietary restrictions.

[1783] "Patient" refers to a person receiving medical examination or treatment.

[1784] "Age" refers to the number of years that have passed since the date of birth.

[1785] "Sex" refers to biological characteristics as male or female.

[1786] "Medical history" refers to records and information about illnesses you have had and treatments you have received.

[1787] "Symptoms" refer to specific phenomena or sensations that occur in association with illness or poor health.

[1788] A "treatment policy" refers to a specific treatment direction or plan to improve a patient's condition.

[1789] "Input means" refers to a device or interface that allows a patient to input their age, sex, medical history, current symptoms, and desired treatment course into the system.

[1790] "Data preparation means" refers to the functions and devices that receive input patient information and perform preprocessing.

[1791] "Guideline analysis means" refers to a device or algorithm for analyzing the latest medical guidelines and related medical papers and generating treatment protocols.

[1792] "Generative AI model means" refers to an artificial intelligence model for simulating treatment options using the generated treatment protocol and patient information.

[1793] "Proposal generation means" refers to functionality or devices for generating customized treatment proposals taking into account the patient's preferences and risk tolerance.

[1794] "Meal plan suggestion means" refers to a function or device for inputting a patient's medical history, symptoms, and dietary restrictions and proposing a meal plan based on that information.

[1795] "Providing means" refers to a function or device for providing the generated suggestions to the patient.

[1796] "Display means" refers to a device or interface that allows the patient to review the suggestions and display interactive content.

[1797] "Sharing tools" refers to features and devices that allow patients to share information with their family and friends and receive feedback.

[1798] "Collaboration means" refers to functions and devices for providing information and holding discussions with medical professionals.

[1799] "Recording means" refers to the functions and devices for determining and recording the final treatment plan.

[1800] "Dietary restriction" refers to restrictions or limitations on diet to accommodate a specific health condition or illness.

[1801] The present invention is a system that allows patients to input their medical history, symptoms, treatment preferences, and dietary restrictions, and then uses the latest medical guidelines and generative AI models to propose optimal treatment and diet plans. Specific embodiments for implementing this system are described below.

[1802] Overall system overview

[1803] The server receives patient information, organizes data, analyzes guidelines, and simulates treatment and dietary options using a generative AI model, generating treatment and dietary recommendations. The terminal is used by patients to input information and confirm the proposed treatment and dietary options. Users include the patients themselves, their families, and healthcare professionals.

[1804] Program processing flow

[1805] Entering patient information

[1806] The user uses the terminal to input their age, gender, medical history, current symptoms, treatment preferences, and dietary restrictions, which includes an input form and an option-clicking interface.

[1807] Data preparation and preprocessing

[1808] The terminal sends the input data to the server, which then preprocesses the received data, specifically normalizing the data and checking for duplicates and inconsistencies.

[1809] Guidelines and literature analysis

[1810] The server analyzes the latest medical guidelines and relevant medical literature to generate a treatment protocol that lists the treatment and dietary options that best suit the patient's symptoms and dietary restrictions.

[1811] AI-powered simulation of treatment and dietary options

[1812] Based on patient information and treatment protocols, the server uses a generative AI model to simulate treatment and dietary options, evaluating each option's suitability and predicted outcomes.

[1813] Generating customized treatment and dietary suggestions

[1814] The server takes into account the patient's preferences and risk tolerance and generates customized treatment and dietary recommendations, including details of the treatment and diet, expected effects, side effects, and duration of treatment.

[1815] Providing and confirming proposals

[1816] The device then provides the generated treatment and dietary recommendations to the user, who can then review and deepen their understanding of the recommendations through interactive screens and videos.

[1817] Share information with family and friends

[1818] Users can share their treatment and dietary suggestions with family and friends, allowing them to receive feedback from others.

[1819] Collaboration with medical professionals

[1820] The server provides a discussion function for sharing treatment and dietary suggestions with healthcare professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[1821] Deciding and recording the final treatment plan

[1822] The user, family, friends, and healthcare professionals decide on the final treatment and diet plan, and the server records this decision and notifies all parties with relevant information.

[1823] Hardware and software used

[1824] The following hardware and software are used to implement this system.

[1825] Frontend: Interface using React

[1826] Backend: API server using Flask

[1827] Generative AI model: Generate treatment and dietary options based on patient information

[1828] Specific examples

[1829] For example, if a 42-year-old male patient with high blood pressure and diabetes needs to go on a low-carb diet, the process would go like this:

[1830] 1. The patient enters his / her age (42 years old), gender (male), medical history (high blood pressure, diabetes), current symptoms, treatment requests, and dietary restrictions (low-carbohydrate diet) into the terminal.

[1831] 2. The terminal sends the entered information to the server.

[1832] 3. The server pre-processes the data, normalizing it and checking for inconsistencies.

[1833] 4. The server analyzes the latest medical guidelines and related papers to generate the optimal treatment protocol for the patient.

[1834] 5. The server uses the generative AI model to simulate treatment and dietary options and evaluate the suitability of each option.

[1835] 6. The server takes into account the patient's preferences and risk tolerance, generates customized treatment and dietary suggestions, and sends the details to the device.

[1836] 7. The device will visually display treatment and dietary suggestions, allowing patients to understand the suggestions.

[1837] 8. The patient shares information with family members and gets their opinions.

[1838] 9. Provide a means for the server to collaborate with medical professionals for discussion and to incorporate expert opinions.

[1839] 10. The patient, family, friends, and healthcare professionals decide on the final treatment and diet plan, which is recorded by the server.

[1840] Example prompt sentence:

[1841] "What is the best meal plan for a 45-year-old male with diabetes who needs to follow a low-carb diet?"

[1842] The above is an embodiment of the present invention, which allows patients to deeply understand their own medical condition and dietary restrictions, and to quickly and accurately select optimal treatment and diet.

[1843] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1844] Step 1:

[1845] The user uses the device to input their age, gender, medical history, current symptoms, treatment preferences, and dietary restrictions. The input data is collected through a form on the device. The input information is sent to the server in JSON format.

[1846] Step 2:

[1847] The server preprocesses the patient information received from the terminal using a data preparation method. Specifically, it normalizes the data (for example, standardizing date formats and trimming strings), checks for duplication, and checks for inconsistencies. Once preprocessing is complete, the data is passed to the next processing step.

[1848] Step 3:

[1849] The server uses the guideline analysis means to analyze the latest medical guidelines and related medical papers. Based on the analysis results, it generates a treatment protocol for each patient. This generated treatment protocol is used in the next step.

[1850] Step 4:

[1851] The server uses the generated treatment protocol and preprocessed patient information to apply the generative AI model to simulate treatment and dietary options. Specifically, the AI ​​evaluates and recommends the optimal treatment and dietary plan based on the patient's symptoms and conditions. The output data from the simulation is passed to the next step.

[1852] Step 5:

[1853] The server generates a customized treatment and diet proposal based on the generated treatment and diet options, taking into account the patient's wishes and risk tolerance. This proposal includes details of the treatment and diet, expected effects, side effects, treatment duration, etc. The generated proposal is sent to the terminal.

[1854] Step 6:

[1855] The device displays the treatment and dietary recommendations received from the server. The user can review and deepen their understanding of the recommendations through interactive screens and videos. The display of the recommendations includes visual elements and navigation functions.

[1856] Step 7:

[1857] The user uses the share button on the device to share the treatment and dietary suggestions with family and friends. The device generates a sharing link or file and sends it to the family and friends. The user receives feedback from the family and friends.

[1858] Step 8:

[1859] The server provides a collaborative means to share treatment and dietary suggestions with healthcare professionals, provide discussion features for expert input, including chat and video call features, and receive feedback from healthcare professionals.

[1860] Step 9:

[1861] The user, family, friends, and healthcare professionals decide on the final treatment and diet plan. The server records this decision and notifies all parties of the relevant information. The recorded data is stored for future reference and tracking.

[1862] The above are the specific processing steps of the program for the system that realizes the application example.

[1863] 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.

[1864] This invention is a system in which patients input their medical history, symptoms, and treatment preferences, and then uses the latest medical guidelines and generative AI models to suggest optimal treatment options based on that information. Furthermore, by combining it with an emotion engine that recognizes the patient's emotions, more personalized treatment suggestions are possible. Specific forms for implementing this system are described below.

[1865] Overall system overview

[1866] The server receives patient information, organizes data, analyzes guidelines, simulates treatment options using an AI model, analyzes emotions using an emotion engine, and generates treatment suggestions. The terminal is used by patients to input information and check suggested treatment options. Users include patients themselves, their families, and medical professionals.

[1867] Program processing flow

[1868] Entering patient information

[1869] Users input their age, gender, medical history, current symptoms, and treatment preferences via a terminal, which includes an input form and an interface for clicking options.

[1870] Data preparation and preprocessing

[1871] The terminal sends the input data to the server, which then preprocesses the received data, specifically normalizing the data and checking for duplicates and inconsistencies.

[1872] Guidelines and literature analysis

[1873] The server analyzes the latest medical guidelines and relevant medical literature to generate a treatment protocol, which lists the best treatment options for the patient's condition.

[1874] AI-powered simulation of treatment options

[1875] The server uses a generative AI model to simulate treatment options based on patient information and treatment protocols, evaluating each option's suitability and predicted outcomes.

[1876] Emotion analysis using an emotion engine

[1877] While the patient is entering information, the device uses a camera and microphone to analyze the patient's emotional state in real time. This information is processed by an emotion engine, which generates emotional data based on the patient's facial expressions, tone of voice, and choice of words.

[1878] Generate customized treatment recommendations

[1879] The server takes into account the patient's preferences and risk tolerance, as well as the emotional data obtained from the emotion engine, to generate a customized treatment proposal, including details of the treatment, expected effects, side effects, and treatment duration.

[1880] Providing and confirming proposals

[1881] The device then provides the generated treatment proposal to the user, who can then review and deepen their understanding of the proposal through interactive screens and videos.

[1882] Share information with family and friends

[1883] Users can share their treatment suggestions with family and friends, allowing them to receive feedback from others.

[1884] Collaboration with medical professionals

[1885] The server provides a discussion function for sharing treatment suggestions with medical professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[1886] Deciding and recording the final treatment plan

[1887] The user, family, friends, and medical professionals decide on the final treatment plan, and the server records this decision and notifies all parties with relevant information.

[1888] Specific examples

[1889] For example, in the case of a 42-year-old male patient with hypertension and diabetes, the process would proceed as follows:

[1890] 1. The patient enters his / her age (42 years old), gender (male), medical history (high blood pressure, diabetes), current symptoms, and treatment preferences into the terminal.

[1891] 2. The terminal sends the entered information to the server.

[1892] 3. The server pre-processes the data, normalizing it and checking for inconsistencies.

[1893] 4. The server analyzes the latest medical guidelines and related papers to generate the optimal treatment protocol for the patient.

[1894] 5. The server uses the generative AI model to simulate treatment options and evaluate the suitability of each option.

[1895] 6. The device analyzes the patient's emotional state in real time through a camera and microphone and sends that information to the emotion engine.

[1896] 7. The server generates a customized treatment proposal based on the emotional data, preferences, and risk tolerance, and sends the details to the device.

[1897] 8. The device visually displays treatment suggestions to help patients understand the suggestions.

[1898] 9. The patient shares information with family members and gets their opinions.

[1899] 10. Provide a means for the server to collaborate and hold discussions with medical professionals, thereby incorporating expert opinions.

[1900] 11. The patient, family, friends, and healthcare professionals decide on the final treatment plan, which is recorded by the server.

[1901] The above is an embodiment of the present invention, which allows patients to deeply understand their own medical condition and quickly and accurately select the optimal treatment that reflects their emotional state.

[1902] The processing flow will be explained below.

[1903] Step 1:

[1904] The user inputs his / her age, sex, medical history, current symptoms, and desired treatment course via the terminal.

[1905] Specifically, the user enters data into an input form and presses a send button, thereby inputting the data.

[1906] Step 2:

[1907] The terminal transmits the input data to the server.

[1908] The terminal encrypts the data to be transmitted and sends it to the server over a secure connection.

[1909] Step 3:

[1910] The server stores the received data in a database.

[1911] The database stores patient information in a suitable format.

[1912] Step 4:

[1913] The server performs pre-processing of the data, including normalizing the data, checking for duplicates, and checking for inconsistencies.

[1914] The server converts the received data into a unified format, removes duplicate data, and checks for inconsistencies.

[1915] Step 5:

[1916] The server analyzes the latest medical guidelines and related medical literature and generates treatment protocols.

[1917] Relevant guidelines and papers will be searched from the database, and treatment protocols will be created using analytical algorithms.

[1918] Step 6:

[1919] The server uses a generative AI model to simulate treatment options based on patient information and the generated treatment protocol.

[1920] Simulations will assess the suitability and predicted outcomes of each treatment option.

[1921] Step 7:

[1922] The terminal uses an emotion engine to analyze the patient's emotional state in real time as information is being entered.

[1923] The device uses a camera and microphone to analyze facial expressions, tone of voice, and language usage to generate emotional data.

[1924] Step 8:

[1925] The terminal transmits the analyzed emotion data to the server.

[1926] The device encrypts the emotion data and transmits it to the server over a secure connection.

[1927] Step 9:

[1928] The server receives the affective data and generates a treatment recommendation after taking into account the patient's preferences and risk tolerance.

[1929] Include emotional data to generate more personalized treatment recommendations.

[1930] Step 10:

[1931] The server transmits the generated treatment proposal to the terminal.

[1932] The suggestions are formatted into an interactive format and sent to the device.

[1933] Step 11:

[1934] The terminal visually displays treatment suggestions.

[1935] Interactive images and instructional videos are used to detail treatment options.

[1936] Step 12:

[1937] Users share their treatment suggestions with family and friends.

[1938] Use the share feature to send information via email or message.

[1939] Step 13:

[1940] The device again sends feedback from family and friends to the server.

[1941] The received feedback is sent to the server and stored in a database.

[1942] Step 14:

[1943] The server shares information with medical professionals, including the patient's primary care physician, and provides an environment for discussion.

[1944] Proposals are shared and discussion functions are provided through an interface for medical professionals.

[1945] Step 15:

[1946] The user, family, and healthcare professionals share and discuss information to determine the final treatment plan.

[1947] Discussions are held in real time using the chat and video conferencing functions provided by the server.

[1948] Step 16:

[1949] The server determines and records the final treatment plan.

[1950] The confirmed treatment plan will be recorded in the database and notified to all parties.

[1951] These are the specific processing steps of the system, which allow patients to receive support in making optimal medical choices.

[1952] Example 2

[1953] 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."

[1954] Modern medicine requires the ability to quickly and accurately recommend optimal treatments tailored to each patient's individual symptoms and wishes. However, manually collecting and analyzing information and determining treatment plans is time-consuming and prone to errors. Furthermore, determining treatment plans without considering the patient's emotional state can reduce patient satisfaction and reduce treatment effectiveness. Furthermore, information sharing and collaboration are essential for patients, their families, friends, and healthcare professionals to work together to determine the optimal treatment plan. Therefore, a system that can resolve these issues and provide faster and more accurate treatment recommendations is needed.

[1955] 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 data preparation means for receiving input patient information and performing preprocessing; a guideline analysis means for analyzing the latest medical guidelines and related medical papers to generate a treatment protocol; a generation AI model means for simulating treatment options using the generated treatment protocol and patient information; and a proposal generation means for generating a customized treatment proposal by taking into account the patient's wishes, risk tolerance, and emotional data. This enables rapid and accurate generation of individually optimized treatment proposals based on detailed patient information and the latest medical guidelines. Furthermore, customized proposals based on emotional data can improve patient satisfaction and treatment effectiveness. Furthermore, by sharing information with family and friends and collaborating with medical professionals, a more comprehensive and reliable treatment plan can be determined.

[1956] "Patient" refers to an individual with a medical history or condition who uses the system.

[1957] "Input means" refers to a device or interface that allows a patient to input information such as age, sex, medical history, current symptoms, and desired treatment course.

[1958] "Data preparation means" refers to devices or programs that receive input patient information and perform preprocessing (data normalization, duplication checks, inconsistency checks, etc.).

[1959] "Guideline analysis means" refers to a device or program for analyzing the latest medical guidelines and related medical papers and generating treatment protocols.

[1960] "Generative AI model means" refers to an artificial intelligence model for simulating treatment options using the generated treatment protocol and patient information.

[1961] "Proposal Generator" refers to a device or program for generating a customized treatment proposal taking into account the patient's preferences and risk tolerance, as well as emotional data.

[1962] "Providing means" refers to a device or system for providing the generated treatment proposal to the patient.

[1963] "Display means" refers to a display or user interface that allows the patient to confirm the suggestions and display interactive content.

[1964] An "emotion engine" refers to a device or program that uses a camera or microphone to analyze a patient's emotional state in real time and generate emotional data.

[1965] "Sharing means" refers to functions and devices that allow patients to share treatment suggestion information with their family and friends and receive feedback.

[1966] "Collaboration methods" refer to online chat functions and data sharing systems for providing information to medical professionals and for exchanging opinions and holding discussions.

[1967] "Recording means" refers to a database or recording device for determining the final treatment plan and recording that information.

[1968] This invention is a system in which patients input their medical history, symptoms, and treatment preferences, and then uses the latest medical guidelines and generative AI models to suggest optimal treatment options based on that information. Furthermore, by combining it with an emotion engine that recognizes the patient's emotions, more personalized treatment suggestions are possible. Specific forms for implementing this system are described below.

[1969] Overall system overview

[1970] The server receives patient information, organizes data, analyzes guidelines, simulates treatment options using an AI model, analyzes emotions using an emotion engine, and generates treatment suggestions. The terminal is used by patients to input information and check suggested treatment options. Users include patients themselves, their families, and medical professionals.

[1971] Entering patient information

[1972] The user inputs their age, gender, medical history, current symptoms, and desired treatment course via a terminal, which includes an input form and an option-click interface. The input data is then sent to the server by the terminal.

[1973] Data preparation and preprocessing

[1974] The server preprocesses the received data, specifically normalizing it, checking for duplicates, checking for inconsistencies, etc. This preprocessing ensures data consistency and accuracy.

[1975] Guidelines and literature analysis

[1976] The server analyzes the latest medical guidelines and related medical papers to generate treatment protocols, using natural language processing (NLP) technology to extract relevant information from large amounts of medical text data.

[1977] AI-powered simulation of treatment options

[1978] The server uses a generative AI model to simulate treatment options based on patient information and treatment protocols. A prompt is input to the AI ​​model to evaluate the suitability and predicted outcomes of multiple treatment options. For example, a simulation is performed based on the prompt: "42-year-old male with a history of high blood pressure and diabetes. Current symptoms include mild headaches and fatigue. He wishes to undergo a new treatment and minimize side effects. Please suggest the optimal treatment plan."

[1979] Emotion analysis using an emotion engine

[1980] While the patient is entering information, the device uses a camera and microphone to analyze the patient's emotional state in real time. This information is processed by an emotion engine, which generates emotional data based on the patient's facial expressions, tone of voice, and choice of words.

[1981] Generate customized treatment recommendations

[1982] The server generates a customized treatment proposal based on the patient's preferences, risk tolerance, and emotional data, including details of the treatment, expected effects, side effects, and duration of treatment.

[1983] Providing and confirming proposals

[1984] The device then provides the generated treatment proposal to the user, who can then review and deepen their understanding of the proposal through interactive screens and videos.

[1985] Share information with family and friends

[1986] Users can share their treatment suggestions with family and friends, allowing them to receive feedback from others.

[1987] Collaboration with medical professionals

[1988] The server provides a discussion function for sharing treatment suggestions with medical professionals and incorporating their expert opinions, thereby improving the accuracy and reliability of the suggestions.

[1989] Deciding and recording the final treatment plan

[1990] The user, family, friends, and medical professionals decide on the final treatment plan, and the server records this decision and notifies all parties with relevant information.

[1991] The above is a specific embodiment for carrying out the present invention, which allows patients to deeply understand their own medical condition and quickly and accurately select the optimal treatment that reflects their emotional state.

[1992] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1993] Step 1:

[1994] The user enters their age, gender, medical history, current symptoms, and desired treatmen...

Claims

1. an input means for a patient to input his / her age, sex, medical history, current symptoms, and desired treatment course; a data preparation means for receiving and preprocessing input patient information; guideline analysis means for analyzing the latest medical guidelines and related medical papers and generating treatment protocols; an AI model means for simulating treatment options using the generated treatment protocol and patient information; a proposal generation means for generating a customized treatment proposal taking into account the patient's preferences and risk tolerance; a providing means for providing the generated suggestions to the patient; a display means for displaying interactive content and for the patient to confirm the suggestions; A means of sharing information with the patient's family and friends and receiving feedback; A means of collaboration to provide information and hold discussions with healthcare professionals; A system that includes a recording means for determining and recording the final treatment plan.

2. 10. The system of claim 1, further comprising a sharing means for family and friends of the patient to provide feedback.

3. The system according to claim 1, further comprising a collaboration means for holding a discussion with a medical professional.

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