system

Abstract

We provide the system. [Solution] A means of receiving and storing individual medical history information, A means for generating medical proposals using generative artificial intelligence based on the received information, A means for providing additional information regarding the generated medical proposal and for generating a response to an inquiry, A means of managing citizens' health information and providing optimal treatment plans through integration with mobile devices, A means of providing the latest medical and facility information in smart cities, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present invention is to solve the difficulty and inefficiency of individualized diagnosis and treatment in infertility treatment. Conventionally, infertility treatment is not unified among medical institutions, and there is a problem that it is difficult to provide an optimal treatment method for each patient. Especially as insurance application progresses, there is an increasing need for a system that provides an efficient and individualized treatment proposal in order for more patients to receive appropriate support.

Means for Solving the Problems

[0005] This invention provides a system that receives and stores individual treatment history information and generates personalized treatment suggestions using generative artificial intelligence based on this information. It also supports more effective treatment by providing additional information regarding the generated treatment suggestions and generating responses to patient questions. This system includes means for updating and improving treatment suggestions based on patient feedback and providing an optimized treatment plan.

[0006] "Individual treatment history information" refers to detailed data about past infertility treatments recorded for each patient.

[0007] "Generative artificial intelligence" refers to a form of artificial intelligence that can automatically generate new treatment suggestions based on input information.

[0008] "Treatment suggestions" refer to recommendations that outline appropriate methods and means for a patient's infertility treatment.

[0009] "Means of providing additional information and generating responses to questions" refers to the function of preparing answers to questions from patients and providing supplementary information to aid understanding.

[0010] "Feedback" refers to the opinions, evaluations, and requests for improvement that patients provide regarding the treatment suggestions they receive.

[0011] "Updating and improving" refers to the process of modifying and optimizing existing treatment proposals based on feedback received and new information. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

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

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

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

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

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

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system that realizes the personalization of infertility treatment, and uses generative artificial intelligence to provide optimal treatment suggestions to patients. This system consists of three main elements: a server, a terminal, and a user.

[0034] First, the user accesses the system using a terminal and enters individual treatment history information. This includes detailed information about past treatments, health status, age, gender, etc. The information entered from the terminal is sent to the server through a secure process.

[0035] The server stores this received information in a database and prepares it for processing by generative artificial intelligence. The generative AI uses this information to generate optimal treatment suggestions for each user. These suggestions are detailed and include treatment methods, medications to be used, schedules, and expected outcomes.

[0036] Once a proposal is generated, the server sends it back to the terminal and presents it to the user. The user can review the proposal through the terminal and ask further questions or request additional information.

[0037] The server utilizes generative artificial intelligence again based on user feedback and questions to provide additional information or update suggestions. For example, if a user asks for details about a specific treatment method, the server researches relevant and up-to-date medical information and provides supplementary information to the user through the terminal.

[0038] By repeating this process, users can receive the fertility treatment plan best suited to them, and this system allows them to enjoy personalized, advanced medical services. This makes the most of treatment experience and new medical knowledge to provide the most effective treatment for patients of all ages.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users access the system using their devices and log in to a new or existing account. Upon initial login, users enter basic information such as their treatment history, current health status, age, and gender. This information is necessary to optimize treatment for each user.

[0042] Step 2:

[0043] The terminal encrypts the entered user information and sends it to the server. The server stores the received information in a structured format in its database and verifies that no information is missing.

[0044] Step 3:

[0045] The server analyzes stored user information and activates generative artificial intelligence. This AI uses the user's treatment history and health information to generate personalized treatment suggestions. These suggestions include recommended tests and treatments, as well as expected outcomes.

[0046] Step 4:

[0047] The generated treatment suggestions are sent from the server to the terminal and presented to the user. The user reviews the suggestions and enters questions about anything they don't understand or would like more detailed information about.

[0048] Step 5:

[0049] The server receives the question sent from the terminal, and generative artificial intelligence processes it again. The AI ​​generates a detailed answer to the question, and provides additional information as needed, referencing the latest treatment information and relevant data.

[0050] Step 6:

[0051] The server sends the generated response and additional information to the terminal and provides it to the user. The user reviews the response and sends feedback or further requests regarding the treatment suggestion to the server.

[0052] Step 7:

[0053] The server re-evaluates treatment suggestions based on user feedback and updates them using generative artificial intelligence. These updated suggestions are then presented to the user again, and the process is repeated.

[0054] In this way, by dynamically updating treatment suggestions based on user feedback, we aim to provide personalized and optimal treatment plans.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] The problem that this invention aims to solve is to provide a means for efficiently generating optimized medical recommendations based on individual medical history data and responding to users. Furthermore, by receiving feedback on the generated medical recommendations and continuously improving subsequent recommendations, the invention aims to enhance the personalization of medical services and realize highly accurate medical support.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for acquiring individual medical history data and storing it in a storage device, means for creating medical suggestions using a generation AI model based on the acquired data, and means for providing supplementary information related to the created medical suggestions and generating responses to inquiries. This not only enables suggestions optimized for individual medical histories, but also allows for further improvement of suggestions based on feedback, thereby improving the quality of medical care.

[0060] "Individual medical history data" refers to a collection of detailed medical information recorded for each patient, including past treatments, health status, medication history, age, and gender.

[0061] A "storage device" is a device that stores data and allows that data to be retrieved quickly when needed, and includes databases and cloud storage.

[0062] A "generative AI model" is a computational model that utilizes artificial intelligence technology to analyze patterns and relationships based on input data and generate new information and suggestions.

[0063] A "medical recommendation" is a set of recommendations based on individual patient data, including appropriate treatment methods, medications to be used, treatment schedules, and expected treatment outcomes.

[0064] "Supplementary information" refers to detailed information added to make the medical recommendations provided easier to understand and implement. Examples include the latest medical research data and information showing the specific effects of medications.

[0065] "Feedback" refers to the user's response, opinion, or suggestion for improvement after receiving a medical suggestion, and serves as a basis for improving future suggestions.

[0066] A "secure communication protocol" is a communication standard that encrypts the transmission and reception of data to maintain confidentiality and integrity, and includes, for example, HTTPS and TLS.

[0067] This invention is a system that realizes the personalization of infertility treatment, and uses a generative AI model to provide patients with the most suitable treatment suggestions. This system consists of three elements: a server, a terminal, and a user.

[0068] First, the user accesses the system via a terminal and enters individual medical history data. This information includes past treatments, health status, age, and gender. The terminal sends this information to the server using a secure communication protocol (e.g., HTTPS). The server stores the received information in its storage device.

[0069] Next, the server uses a generative AI model to create medical recommendations tailored to the user based on the stored data. This AI model employs multiple algorithms to generate the optimal treatment plan based on past treatment history and medical knowledge. In this process, the treatment recommendations can be customized by inputting prompts into the model, such as, "Please generate the optimal infertility treatment plan for a female patient in her 30s. In the past, clomiphene was used, but it had strong side effects and was ineffective. Please suggest any other treatment options using other medications."

[0070] The generated medical suggestions are sent from the server to the terminal and presented to the user. The user reviews them and, if necessary, sends feedback to the server via the terminal. The server uses this feedback to refine the suggestions using the generating AI model again. This ensures that the most suitable treatment plan for each individual patient is continuously provided.

[0071] This system incorporates the latest medical information and realizes advanced technological solutions to provide personalized medical services.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] Users input individual medical history data using a terminal. Specifically, the information provided by the user includes past treatments, health status, age, and gender. This input data is collected through the terminal's input form. The terminal verifies the integrity of the data and transmits it to the server using a secure communication protocol.

[0075] Step 2:

[0076] The server receives medical history data transmitted from the terminal and stores it in storage. The received data is organized in a database and maintained as a profile for each patient. The server checks for data redundancy and normalizes the data as needed. This process ensures the integrity and consistency of the information.

[0077] Step 3:

[0078] The server activates a generative AI model based on data stored in its memory to create medical recommendations. In this process, the AI ​​model analyzes the input data and determines the optimal treatment option based on similar past cases. Specifically, a prompt such as "Generate the optimal infertility treatment plan for a female patient in her 30s" is input, and the model outputs a new treatment recommendation.

[0079] Step 4:

[0080] The server sends the generated medical proposal to the terminal and presents it to the user. The transmitted proposal is visually displayed on the terminal's user interface and provided in a format that is easy for the user to understand. Based on this, the user reviews the proposal and decides whether to accept it or not.

[0081] Step 5:

[0082] Users send feedback to the server via their device. Here, they can enter questions about the suggestions or requests for additional information. Once this input reaches the server, the server re-enters the feedback into the generating AI model and updates the suggestions as needed.

[0083] Step 6:

[0084] Based on user feedback, the server again utilizes the AI ​​model to improve medical recommendations. During this process, it adjusts the recommendations to provide the most suitable treatment plan for the user, referencing the latest medical information. As a result, new recommendations are generated and sent back to the device.

[0085] Through this series of processing steps, users can continuously receive individually optimized medical services.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] When providing optimal medical recommendations to individual users, existing systems fail to fully utilize each resident's individual medical history, and maintaining the timeliness and appropriateness of medical information in smart cities presents challenges. In addition, there is a need for mechanisms that enable users to better utilize and continuously improve medical recommendations.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes means for receiving and storing individual medical history information, means for generating medical recommendations using generative artificial intelligence based on the received information, and means for coordinating with a mobile device for managing citizens' health information and providing optimal treatment plans. This enables real-time optimal medical recommendations for individual residents and improves medical services in smart cities.

[0091] "Individual medical history information" refers to information that includes each resident's unique past treatment history and health status.

[0092] "Generative artificial intelligence" is an artificial intelligence technology that can generate optimal medical recommendations based on input data.

[0093] A "medical proposal" is a proposal that includes recommendations for treatment methods, schedules, and other medical services based on residents' health information.

[0094] A "mobile device" is a portable electronic device used for managing medical information and receiving medical recommendations.

[0095] A "smart city" is a futuristic city that utilizes information and communication technology to optimize urban management and resident services.

[0096] This invention is a system that provides individually customized medical recommendations and supports the healthcare of residents in smart cities. This system mainly consists of a server, terminals, and users.

[0097] The server is built on a cloud computing environment (e.g., AWS®, Azure®) and receives individual medical history information, storing it in a database. Generative artificial intelligence analyzes this data and generates optimal medical recommendations. The generative AI model utilizes GPT-4®, which suggests treatment methods, schedules, and other medical services based on the input data.

[0098] The terminal is a mobile device (smartphone, tablet) owned by the user, through which the user can input their health status and history and receive generated medical suggestions. The terminal communicates with the server via a communication network and exchanges information in real time.

[0099] Users can use the application to review medical suggestions and submit their feedback to the system. This feedback is re-evaluated by the server's AI-generated suggestions and used to further improve the suggestions. For example, if a user has previously experienced a lack of success with a particular treatment, new alternative treatments will be suggested based on that history. Examples of specific prompts for generated medical suggestions are as follows:

[0100] "Based on the user's age, gender, and past treatment history, generate the most effective fertility treatment plan for this user. The information should include treatment methods, expected outcomes, medications used, and a schedule."

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] Users input individual health information and past treatment history through their terminals. This data is formatted on the terminal and sent to the server using a secure protocol. Inputs include the user's age, gender, medical history, and current health status. Outputs are transferred to the server.

[0104] Step 2:

[0105] The server stores the received data in the database. In this step, data is imported and saved, and the saved data is indexed and organized to improve searchability. The input is medical history information sent by the user, and the output is the history information stored in the database.

[0106] Step 3:

[0107] The server uses a generative AI model to generate optimal medical recommendations based on information in the database. Here, the AI ​​model analyzes the user's historical data and forms personalized treatment recommendations by referencing existing medical databases and the latest medical information. The inputs are user information and medical reference materials from the database, and the output is a medical recommendation for the user.

[0108] Step 4:

[0109] The generated medical suggestions are sent from the server to the terminal and notified to the user. The user can view the suggestions through the terminal's application, reviewing the content and displaying detailed information. The input is the medical suggestions from the server, and the output is the information provided to the user in a viewable format.

[0110] Step 5:

[0111] When a user provides feedback on a suggestion, they send it to the server via their device. The server receives the feedback and reuses the generated AI model to improve the suggestion or add new information. This step forms part of a feedback loop, enabling continuous suggestion improvement. The input is the user's feedback, and the output is the improved medical suggestion.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention is a system for providing personalized infertility treatment suggestions that take into account the user's emotions, and is realized by combining generative artificial intelligence and an emotion engine. This system mainly consists of three elements: the user, the terminal, and the server.

[0114] First, the user logs into the system using a terminal and enters their individual treatment history information. This includes information about their health status and past treatment history, which will be used to make subsequent suggestions. The terminal securely transmits this information to the server, which then prepares to store and process the information.

[0115] Generative artificial intelligence generates optimal treatment suggestions for the user based on stored information. These suggestions include details such as treatment methods, schedules, medications to be used, and expected outcomes. The server then sends the suggestions to the user's device for review.

[0116] When a user reviews a treatment suggestion via their device, the emotion engine analyzes their emotions from their facial expressions, input patterns, and voice. For example, if a user feels stressed or anxious about the suggestion, the emotion engine will recognize that emotion.

[0117] The server uses information from the emotion engine to adjust treatment suggestions and additional information. Depending on the user's emotions, it can change how information is presented and provide additional relevant support. For example, if the user is feeling anxious, it can provide reassurance by suggesting relaxation techniques and showing success stories.

[0118] Furthermore, user feedback and questions are collected, and generative artificial intelligence is used to update the suggested treatments. Through this entire process, the system repeatedly provides optimized treatment plans based on the user's feedback and emotional state.

[0119] This will allow users to receive personalized, advanced medical services that take their feelings into consideration, and is expected to maximize the effectiveness of infertility treatment.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] Users access the system through their device and log in to a new or existing account. After logging in, they enter basic medical history information such as their health status, past treatment history, age, and gender into the device.

[0123] Step 2:

[0124] The terminal transmits the entered treatment history information to the server via secure communication. The server stores the received information in a database and prepares the data in a format that can be parsed.

[0125] Step 3:

[0126] The server uses generative artificial intelligence to generate treatment suggestions based on stored information. These suggestions include detailed information on recommended treatments, necessary tests, and expected outcomes.

[0127] Step 4:

[0128] The server sends the generated treatment suggestion to the terminal and presents it to the user. The user then reviews the suggestion on the terminal.

[0129] Step 5:

[0130] The emotion engine is activated when the user reviews the suggested content via their device and enters their response. The emotion engine analyzes the user's facial expressions, input speed, voice tone, etc., to identify their current emotional state.

[0131] Step 6:

[0132] The server uses the results of the emotion engine analysis to adjust treatment suggestions and additional advice. If the user shows signs of anxiety, the server provides updated information to the device, including reassuring information and success stories from other patients.

[0133] Step 7:

[0134] Users view updated information and suggestions on their devices and enter further questions or feedback. This feedback is then sent from the device to the server.

[0135] Step 8:

[0136] The server uses generative artificial intelligence again, based on user feedback, to refine its suggestions. The suggestions are updated accordingly, generating a more personalized treatment plan.

[0137] Step 9:

[0138] The server sends the improved suggestion back to the terminal, and the process is repeated until a suggestion that satisfies the user is obtained. This continuous feedback loop allows the user to receive the most appropriate treatment that takes their feelings into consideration.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] In infertility treatment, it is difficult to provide optimized treatment proposals that take into account each patient's emotions and past treatment history. Traditional systems have struggled to provide treatment plans that adequately reflect the patient's psychological state and feedback, which has sometimes led to anxiety and stress.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes means for collecting and storing individual treatment history information, means for creating medical suggestions using generative artificial intelligence, and means for analyzing the user's facial expressions, voice, and input patterns to determine their emotions. This makes it possible to provide optimal treatment suggestions tailored to the individual needs and emotional state of each patient.

[0144] "Individual treatment history information" refers to information that specifically details each user's health condition and past treatment history.

[0145] "Generative artificial intelligence" is an artificial intelligence technology that generates appropriate suggestions and responses based on user input data.

[0146] "Medical suggestions" refer to information that proposes appropriate treatment methods and schedules based on the individual user's condition and emotions.

[0147] A "terminal" is an electronic device used by users to input information or review suggestions.

[0148] "Means of determining emotions" refer to technologies and methods for identifying emotions by analyzing a user's facial expressions, voice, input patterns, etc.

[0149] This invention is a system that provides personalized treatment suggestions while taking into account the user's emotions. The user first logs into the system using a terminal and inputs their individual health status and past treatment history. The entered data is securely transmitted from the terminal to the server. SSL / TLS protocol is used for data transmission to ensure data security. The server stores the received information using a relational database system (e.g., MySQL® or PostgreSQL).

[0150] The server drives a generative AI model (e.g., using a general-purpose generative language model) based on stored information to generate optimal medical recommendations. Prompts play a crucial role in this process. A specific example of a prompt might be: "Generate optimal treatment recommendations considering the user's past infertility treatment history. Based on health information and the user's emotions, please include specific methods and schedules in the recommendations."

[0151] The generated suggestions are sent from the server to the user's device for review. When the user reviews the suggestions, an emotion engine is activated on the device to analyze facial expressions, voice, and input patterns. The emotion engine uses common facial recognition and voice analysis technologies (e.g., Emotion API or a similar API).

[0152] The server adjusts medical recommendations based on data provided by the emotion engine. For example, if a user expresses anxiety, the server provides additional information, including relaxation techniques and success stories, to alleviate the user's psychological burden. The feedback is then sent back to the generative AI, contributing to updating the recommendations. Through this process, it becomes possible to provide medical services optimized for the user.

[0153] This system is expected to support more effective infertility treatment by providing advanced treatment suggestions tailored to the individual needs and emotions of each patient.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] Users log in to the system using a terminal and enter individual data such as their health status and past treatment history. This input data is collected using text format and checkboxes. Once the user completes the input, the terminal sends this information to the server. The input data is typically sent in JSON format.

[0157] Step 2:

[0158] The server verifies individual treatment history information received from terminals and securely stores it in a database. A relational database system is used, and the information is stored in association with individual users. After confirming the integrity of the input data, the information is ready to be passed to the generating AI model.

[0159] Step 3:

[0160] The server takes the stored information as input and passes it to a generative artificial intelligence to generate prompts. Following these prompts, the generative AI model creates medical recommendations. Specifically, it outputs suggested treatment methods, a list of medications, and a schedule, taking into account the patient's past history and current health condition. This output is returned to the server in text format.

[0161] Step 4:

[0162] The server sends the output from the generated AI model to the user's terminal and displays treatment suggestions. The terminal displays the suggestions in a text area for the user to review. At the same time, the user is provided with an interface to input feedback on these suggestions.

[0163] Step 5:

[0164] When a user reviews a treatment suggestion, the device uses an emotion engine to analyze the user's facial expressions, voice, and input patterns. This identifies the user's emotional state and sends it to the server as log data. This input is data obtained from the emotion engine's API.

[0165] Step 6:

[0166] The server adjusts the content and presentation of treatment suggestions based on data generated by the emotion engine. Specifically, if the user indicates anxiety, it provides additional support information and success stories, and modifies the format of the information output. These adjusted suggestions are then sent back to the user's device.

[0167] Step 7:

[0168] The server inputs user feedback into the AI ​​model, updating the suggestions. This results in optimized output based on the feedback. This output is then saved back into the database for use in future treatment suggestions. Through this entire process, the system can provide the most suitable treatment suggestions for each individual user.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0171] The current medical plan proposal system lacks individualization based on the user's emotions, and therefore fails to adequately alleviate the psychological burden of receiving medical services. In particular, when a user feels anxiety or stress regarding a proposal, the system does not adequately respond to those emotions. There is a need for a system that effectively utilizes user feedback and emotional information to adapt proposals.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes means for receiving and storing individual treatment history information, means for generating treatment suggestions using generative artificial intelligence based on the received information, means for providing additional information regarding the generated treatment suggestions and generating responses to questions, means for analyzing the user's emotions and adjusting the treatment suggestions based on those emotions, and means for providing relaxation methods and past success stories using the results of the emotion analysis. This makes it possible to provide personalized medical plans that take into account the user's emotions and reduce the psychological burden of medical services.

[0174] "Individual treatment history information" refers to a collection of information about a specific user's past health status and treatment history.

[0175] "Generative artificial intelligence" is a program that learns from large amounts of data and generates optimal decisions and suggestions based on specific conditions.

[0176] A "treatment proposal" refers to a plan that includes the optimal treatment method, schedule, and medications to be used, based on the user's health condition and treatment history.

[0177] "Additional information" refers to explanations, reference materials, and supplementary data provided to facilitate further understanding of the basic treatment proposal.

[0178] "Emotional analysis" is the process of identifying and evaluating a user's psychological state based on their facial expressions, voice, input patterns, etc.

[0179] "Relaxation methods" refer to specific activities and techniques used to reduce stress and anxiety in users.

[0180] "Past success stories" refer to information provided as a reference, specifically cases where similar treatments or care methods have been implemented in the past and proven effective.

[0181] This invention is based on a system that combines generative artificial intelligence with an engine for analyzing emotions. This system mainly consists of three elements: a server, a terminal, and a user. First, the user logs into the system using a terminal and enters individual treatment history information. This information includes health status and past treatment history. The terminal securely transmits this information to the server, which then prepares to store and process the information.

[0182] Based on stored information, the server uses generative artificial intelligence to generate optimal treatment suggestions for the user. These suggestions include details such as specific treatment methods, schedules, medications to be used, and expected outcomes. Once a suggestion is generated, the server sends it to the user's device for review.

[0183] When a user reviews a treatment suggestion via the device, the device uses its built-in emotion engine to analyze the user's emotions from their facial expressions, input patterns, and voice. This analysis allows the device to understand how the user is feeling about the suggestion. For example, if the user is feeling stressed or anxious about the suggestion, the emotion engine will recognize this.

[0184] The server uses the information provided by the emotion engine to adjust treatment suggestions and additional information. Depending on the user's emotions, it can change how information is presented or provide additional relevant support. For example, if the user is feeling anxious, the server may offer relaxation techniques or show past success stories to provide reassurance.

[0185] For example, if a user is feeling lonely, the device might suggest "video calls with friends or family that have been effective in the past." Furthermore, an example of a prompt for the generating AI model could be: "The user is currently feeling sad. What information is available in their past health history? What kind of care plan would be appropriate?"

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The user logs into the system via their device. During this process, the user enters their individual treatment history information, which the device collects. This information includes past health status and treatment history. The device securely encrypts this information and transmits it to the server.

[0189] Step 2:

[0190] The server receives treatment history information sent from the terminal and stores it in a database. Based on the received information, generative artificial intelligence is activated and begins processing to generate the optimal treatment suggestion. The AI ​​analyzes past data to generate an individualized treatment plan. The generated suggestion includes specific treatment methods, schedules, and medications to be used.

[0191] Step 3:

[0192] The generated treatment suggestions are sent from the server to the user's device. The device displays this information to the user and prompts them to confirm. The user can review the suggestions and, if necessary, enter questions and send them to the server. During this process, the device analyzes the user's facial expressions and voice using an emotion engine and generates emotional data.

[0193] Step 4:

[0194] The server receives questions and emotional data from the user. Based on the emotional data provided by the emotion engine, the generative artificial intelligence adjusts the treatment suggestions. For example, if anxiety is detected, the server uses the generative AI model to suggest relaxation methods or present success stories to provide reassurance. This adjusted information is then sent back to the terminal.

[0195] Step 5:

[0196] Users receive a tailored treatment plan and review newly provided relaxation methods and success stories. If necessary, they can input feedback via their device and send it to the server. The server collects this feedback and uses it to further improve the suggestions, aiming to continuously provide personalized and optimal treatment plans.

[0197] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0198] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0199] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0203] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0204] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0205] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0206] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0207] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0208] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0209] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0210] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0211] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0212] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0213] This invention is a system that realizes the personalization of infertility treatment, and uses generative artificial intelligence to provide optimal treatment suggestions to patients. This system consists of three main elements: a server, a terminal, and a user.

[0214] First, the user accesses the system using a terminal and enters individual treatment history information. This includes detailed information about past treatments, health status, age, gender, etc. The information entered from the terminal is sent to the server through a secure process.

[0215] The server stores this received information in a database and prepares it for processing by generative artificial intelligence. The generative AI uses this information to generate optimal treatment suggestions for each user. These suggestions are detailed and include treatment methods, medications to be used, schedules, and expected outcomes.

[0216] Once a proposal is generated, the server sends it back to the terminal and presents it to the user. The user can review the proposal through the terminal and ask further questions or request additional information.

[0217] The server utilizes generative artificial intelligence again based on user feedback and questions to provide additional information or update suggestions. For example, if a user asks for details about a specific treatment method, the server researches relevant and up-to-date medical information and provides supplementary information to the user through the terminal.

[0218] By repeating this process, users can receive the fertility treatment plan best suited to them, and this system allows them to enjoy personalized, advanced medical services. This makes the most of treatment experience and new medical knowledge to provide the most effective treatment for patients of all ages.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] Users access the system using their devices and log in to a new or existing account. Upon initial login, users enter basic information such as their treatment history, current health status, age, and gender. This information is necessary to optimize treatment for each user.

[0222] Step 2:

[0223] The terminal encrypts the entered user information and sends it to the server. The server stores the received information in a structured format in its database and verifies that no information is missing.

[0224] Step 3:

[0225] The server analyzes stored user information and activates generative artificial intelligence. This AI uses the user's treatment history and health information to generate personalized treatment suggestions. These suggestions include recommended tests and treatments, as well as expected outcomes.

[0226] Step 4:

[0227] The generated treatment suggestions are sent from the server to the terminal and presented to the user. The user reviews the suggestions and enters questions about anything they don't understand or would like more detailed information about.

[0228] Step 5:

[0229] The server receives the question sent from the terminal, and generative artificial intelligence processes it again. The AI ​​generates a detailed answer to the question, and provides additional information as needed, referencing the latest treatment information and relevant data.

[0230] Step 6:

[0231] The server sends the generated response and additional information to the terminal and provides it to the user. The user reviews the response and sends feedback or further requests regarding the treatment suggestion to the server.

[0232] Step 7:

[0233] The server re-evaluates treatment suggestions based on user feedback and updates them using generative artificial intelligence. These updated suggestions are then presented to the user again, and the process is repeated.

[0234] In this way, by dynamically updating treatment suggestions based on user feedback, we aim to provide personalized and optimal treatment plans.

[0235] (Example 1)

[0236] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0237] The problem that this invention aims to solve is to provide a means for efficiently generating optimized medical recommendations based on individual medical history data and responding to users. Furthermore, by receiving feedback on the generated medical recommendations and continuously improving subsequent recommendations, the invention aims to enhance the personalization of medical services and realize highly accurate medical support.

[0238] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0239] In this invention, the server includes means for acquiring individual medical history data and storing it in a storage device, means for creating medical suggestions using a generation AI model based on the acquired data, and means for providing supplementary information related to the created medical suggestions and generating responses to inquiries. This not only enables suggestions optimized for individual medical histories, but also allows for further improvement of suggestions based on feedback, thereby improving the quality of medical care.

[0240] "Individual medical history data" refers to a collection of detailed medical information recorded for each patient, including past treatments, health status, medication history, age, and gender.

[0241] A "storage device" is a device that stores data and allows that data to be retrieved quickly when needed, and includes databases and cloud storage.

[0242] A "generative AI model" is a computational model that utilizes artificial intelligence technology to analyze patterns and relationships based on input data and generate new information and suggestions.

[0243] A "medical recommendation" is a set of recommendations based on individual patient data, including appropriate treatment methods, medications to be used, treatment schedules, and expected treatment outcomes.

[0244] "Supplementary information" refers to detailed information added to make the medical recommendations provided easier to understand and implement. Examples include the latest medical research data and information showing the specific effects of medications.

[0245] "Feedback" refers to the user's response, opinion, or suggestion for improvement after receiving a medical suggestion, and serves as a basis for improving future suggestions.

[0246] A "secure communication protocol" is a communication standard that encrypts the transmission and reception of data to maintain confidentiality and integrity, and includes, for example, HTTPS and TLS.

[0247] This invention is a system that realizes the personalization of infertility treatment, and uses a generative AI model to provide patients with the most suitable treatment suggestions. This system consists of three elements: a server, a terminal, and a user.

[0248] First, the user accesses the system via a terminal and enters individual medical history data. This information includes past treatments, health status, age, and gender. The terminal sends this information to the server using a secure communication protocol (e.g., HTTPS). The server stores the received information in its storage device.

[0249] Next, the server uses a generative AI model to create medical recommendations tailored to the user based on the stored data. This AI model employs multiple algorithms to generate the optimal treatment plan based on past treatment history and medical knowledge. In this process, the treatment recommendations can be customized by inputting prompts into the model, such as, "Please generate the optimal infertility treatment plan for a female patient in her 30s. In the past, clomiphene was used, but it had strong side effects and was ineffective. Please suggest any other treatment options using other medications."

[0250] The generated medical suggestions are sent from the server to the terminal and presented to the user. The user reviews them and, if necessary, sends feedback to the server via the terminal. The server uses this feedback to refine the suggestions using the generating AI model again. This ensures that the most suitable treatment plan for each individual patient is continuously provided.

[0251] This system incorporates the latest medical information and realizes advanced technological solutions to provide personalized medical services.

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] Users input individual medical history data using a terminal. Specifically, the information provided by the user includes past treatments, health status, age, and gender. This input data is collected through the terminal's input form. The terminal verifies the integrity of the data and transmits it to the server using a secure communication protocol.

[0255] Step 2:

[0256] The server receives medical history data transmitted from the terminal and stores it in storage. The received data is organized in a database and maintained as a profile for each patient. The server checks for data redundancy and normalizes the data as needed. This process ensures the integrity and consistency of the information.

[0257] Step 3:

[0258] The server activates a generative AI model based on data stored in its memory to create medical recommendations. In this process, the AI ​​model analyzes the input data and determines the optimal treatment option based on similar past cases. Specifically, a prompt such as "Generate the optimal infertility treatment plan for a female patient in her 30s" is input, and the model outputs a new treatment recommendation.

[0259] Step 4:

[0260] The server sends the generated medical proposal to the terminal and presents it to the user. The transmitted proposal is visually displayed on the terminal's user interface and provided in a format that is easy for the user to understand. Based on this, the user reviews the proposal and decides whether to accept it or not.

[0261] Step 5:

[0262] Users send feedback to the server via their device. Here, they can enter questions about the suggestions or requests for additional information. Once this input reaches the server, the server re-enters the feedback into the generating AI model and updates the suggestions as needed.

[0263] Step 6:

[0264] Based on user feedback, the server again utilizes the AI ​​model to improve medical recommendations. During this process, it adjusts the recommendations to provide the most suitable treatment plan for the user, referencing the latest medical information. As a result, new recommendations are generated and sent back to the device.

[0265] Through this series of processing steps, users can continuously receive individually optimized medical services.

[0266] (Application Example 1)

[0267] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0268] When providing optimal medical recommendations to individual users, existing systems fail to fully utilize each resident's individual medical history, and maintaining the timeliness and appropriateness of medical information in smart cities presents challenges. In addition, there is a need for mechanisms that enable users to better utilize and continuously improve medical recommendations.

[0269] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0270] In this invention, the server includes means for receiving and storing individual medical history information, means for generating medical recommendations using generative artificial intelligence based on the received information, and means for coordinating with a mobile device for managing citizens' health information and providing optimal treatment plans. This enables real-time optimal medical recommendations for individual residents and improves medical services in smart cities.

[0271] "Individual medical history information" refers to information that includes each resident's unique past treatment history and health status.

[0272] "Generative artificial intelligence" is an artificial intelligence technology that can generate optimal medical recommendations based on input data.

[0273] A "medical proposal" is a proposal that includes recommendations for treatment methods, schedules, and other medical services based on residents' health information.

[0274] A "mobile device" is a portable electronic device used for managing medical information and receiving medical recommendations.

[0275] A "smart city" is a futuristic city that utilizes information and communication technology to optimize urban management and resident services.

[0276] This invention is a system that provides individually customized medical recommendations and supports the healthcare of residents in smart cities. This system mainly consists of a server, terminals, and users.

[0277] The server is built on a cloud computing environment (e.g., AWS, Azure) and receives individual medical history information, storing it in a database. Generative artificial intelligence analyzes this data and generates optimal medical recommendations. The generative AI model utilizes GPT-4, which suggests treatment methods, schedules, and other medical services based on the input data.

[0278] The terminal is a mobile device (smartphone, tablet) owned by the user, through which the user can input their health status and history and receive generated medical suggestions. The terminal communicates with the server via a communication network and exchanges information in real time.

[0279] Users can use the application to review medical suggestions and submit their feedback to the system. This feedback is re-evaluated by the server's AI-generated suggestions and used to further improve the suggestions. For example, if a user has previously experienced a lack of success with a particular treatment, new alternative treatments will be suggested based on that history. Examples of specific prompts for generated medical suggestions are as follows:

[0280] "Based on the user's age, gender, and past treatment history, generate the most effective infertility treatment plan for this user. The information includes the treatment method, expected treatment results, medications to be used, and schedule."

[0281] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0282] Step 1:

[0283] The user inputs their individual health information and past treatment history through the terminal. These data are formatted within the terminal and transmitted to the server using a secure protocol. The inputs include the user's age, gender, medical history, current health status, etc. As output, the data is transferred to the server.

[0284] Step 2:

[0285] The server stores the received data in the database. In this step, the data is imported and saved, and sorting is done to improve the indexing and retrievability of the saved data. The input is the medical history information sent from the user, and the output is the history information saved in the database.

[0286] Step 3:

[0287] Based on the information in the database, the server uses the generative AI model to generate an optimal medical proposal. Here, the AI model analyzes the user's history data and forms an individualized treatment proposal while referring to existing medical databases and the latest medical information. The inputs are the user information and medical reference materials in the database, and the output is the medical proposal for the user.

[0288] Step 4:

[0289] The generated medical suggestions are sent from the server to the terminal and notified to the user. The user can view the suggestions through the terminal's application, reviewing the content and displaying detailed information. The input is the medical suggestions from the server, and the output is the information provided to the user in a viewable format.

[0290] Step 5:

[0291] When a user provides feedback on a suggestion, they send it to the server via their device. The server receives the feedback and reuses the generated AI model to improve the suggestion or add new information. This step forms part of a feedback loop, enabling continuous suggestion improvement. The input is the user's feedback, and the output is the improved medical suggestion.

[0292] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0293] This invention is a system for providing personalized infertility treatment suggestions that take into account the user's emotions, and is realized by combining generative artificial intelligence and an emotion engine. This system mainly consists of three elements: the user, the terminal, and the server.

[0294] First, the user logs into the system using a terminal and enters their individual treatment history information. This includes information about their health status and past treatment history, which will be used to make subsequent suggestions. The terminal securely transmits this information to the server, which then prepares to store and process the information.

[0295] Generative artificial intelligence generates optimal treatment suggestions for the user based on stored information. These suggestions include details such as treatment methods, schedules, medications to be used, and expected outcomes. The server then sends the suggestions to the user's device for review.

[0296] When a user reviews a treatment suggestion via their device, the emotion engine analyzes their emotions from their facial expressions, input patterns, and voice. For example, if a user feels stressed or anxious about the suggestion, the emotion engine will recognize that emotion.

[0297] The server uses information from the emotion engine to adjust treatment suggestions and additional information. Depending on the user's emotions, it can change how information is presented and provide additional relevant support. For example, if the user is feeling anxious, it can provide reassurance by suggesting relaxation techniques and showing success stories.

[0298] Furthermore, user feedback and questions are collected, and generative artificial intelligence is used to update the suggested treatments. Through this entire process, the system repeatedly provides optimized treatment plans based on the user's feedback and emotional state.

[0299] This will allow users to receive personalized, advanced medical services that take their feelings into consideration, and is expected to maximize the effectiveness of infertility treatment.

[0300] The following describes the processing flow.

[0301] Step 1:

[0302] Users access the system through their device and log in to a new or existing account. After logging in, they enter basic medical history information such as their health status, past treatment history, age, and gender into the device.

[0303] Step 2:

[0304] The terminal transmits the entered treatment history information to the server via secure communication. The server stores the received information in a database and prepares the data in a format that can be parsed.

[0305] Step 3:

[0306] The server generates a treatment proposal using generative artificial intelligence based on the stored information. This proposal includes in detail the recommended treatment methods, necessary examinations, and expected results.

[0307] Step 4:

[0308] The server sends the generated treatment proposal to the terminal and presents it to the user. The user checks the content of the proposal on the terminal.

[0309] Step 5:

[0310] When the user makes an input while checking the content of the proposal via the terminal, the emotion engine is activated. The emotion engine analyzes the user's expression, input speed, voice tone, etc., and identifies the current emotional state.

[0311] Step 6:

[0312] The server uses the analysis results of the emotion engine to adjust the treatment proposal and provide additional advice. If the user shows anxiety, the server provides updated information to the terminal, including information that gives a sense of security and success stories of other patients.

[0313] Step 7:

[0314] [[ID=,ID=35]]The user checks the updated information and proposals on the terminal and enters further questions or feedback. These feedbacks are sent from the terminal to the server.

[0315] Step 8:

[0316] The server re-utilizes generative artificial intelligence based on the feedback from the user to improve the proposal. The proposal is updated accordingly, and a more personalized treatment plan is generated.

[0317] Step 9:

[0318] The server sends the improved suggestion back to the terminal, and the process is repeated until a suggestion that satisfies the user is obtained. This continuous feedback loop allows the user to receive the most appropriate treatment that takes their feelings into consideration.

[0319] (Example 2)

[0320] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0321] In infertility treatment, it is difficult to provide optimized treatment proposals that take into account each patient's emotions and past treatment history. Traditional systems have struggled to provide treatment plans that adequately reflect the patient's psychological state and feedback, which has sometimes led to anxiety and stress.

[0322] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0323] In this invention, the server includes means for collecting and storing individual treatment history information, means for creating medical suggestions using generative artificial intelligence, and means for analyzing the user's facial expressions, voice, and input patterns to determine their emotions. This makes it possible to provide optimal treatment suggestions tailored to the individual needs and emotional state of each patient.

[0324] "Individual treatment history information" refers to information that specifically details each user's health condition and past treatment history.

[0325] "Generative artificial intelligence" is an artificial intelligence technology that generates appropriate suggestions and responses based on user input data.

[0326] "Medical suggestions" refer to information that proposes appropriate treatment methods and schedules based on the individual user's condition and emotions.

[0327] A "terminal" is an electronic device used by users to input information or review suggestions.

[0328] "Means of determining emotions" refer to technologies and methods for identifying emotions by analyzing a user's facial expressions, voice, input patterns, etc.

[0329] This invention is a system that provides personalized treatment suggestions while taking into account the user's emotions. The user first logs into the system using a terminal and inputs their individual health status and past treatment history. The entered data is securely transmitted from the terminal to the server. SSL / TLS protocol is used for data transmission to ensure data security. The server stores the received information using a relational database system (e.g., MySQL or PostgreSQL).

[0330] The server drives a generative AI model (e.g., using a general-purpose generative language model) based on stored information to generate optimal medical recommendations. Prompts play a crucial role in this process. A specific example of a prompt might be: "Generate optimal treatment recommendations considering the user's past infertility treatment history. Based on health information and the user's emotions, please include specific methods and schedules in the recommendations."

[0331] The generated suggestions are sent from the server to the user's device for review. When the user reviews the suggestions, an emotion engine is activated on the device to analyze facial expressions, voice, and input patterns. The emotion engine uses common facial recognition and voice analysis technologies (e.g., Emotion API or a similar API).

[0332] The server adjusts medical recommendations based on data provided by the emotion engine. For example, if a user expresses anxiety, the server provides additional information, including relaxation techniques and success stories, to alleviate the user's psychological burden. The feedback is then sent back to the generative AI, contributing to updating the recommendations. Through this process, it becomes possible to provide medical services optimized for the user.

[0333] This system is expected to support more effective infertility treatment by providing advanced treatment suggestions tailored to the individual needs and emotions of each patient.

[0334] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0335] Step 1:

[0336] Users log in to the system using a terminal and enter individual data such as their health status and past treatment history. This input data is collected using text format and checkboxes. Once the user completes the input, the terminal sends this information to the server. The input data is typically sent in JSON format.

[0337] Step 2:

[0338] The server verifies individual treatment history information received from terminals and securely stores it in a database. A relational database system is used, and the information is stored in association with individual users. After confirming the integrity of the input data, the information is ready to be passed to the generating AI model.

[0339] Step 3:

[0340] The server takes the stored information as input and passes it to a generative artificial intelligence to generate prompts. Following these prompts, the generative AI model creates medical recommendations. Specifically, it outputs suggested treatment methods, a list of medications, and a schedule, taking into account the patient's past history and current health condition. This output is returned to the server in text format.

[0341] Step 4:

[0342] The server sends the output from the generated AI model to the user's terminal and displays treatment suggestions. The terminal displays the suggestions in a text area for the user to review. At the same time, the user is provided with an interface to input feedback on these suggestions.

[0343] Step 5:

[0344] When a user reviews a treatment suggestion, the device uses an emotion engine to analyze the user's facial expressions, voice, and input patterns. This identifies the user's emotional state and sends it to the server as log data. This input is data obtained from the emotion engine's API.

[0345] Step 6:

[0346] The server adjusts the content and presentation of treatment suggestions based on data generated by the emotion engine. Specifically, if the user indicates anxiety, it provides additional support information and success stories, and modifies the format of the information output. These adjusted suggestions are then sent back to the user's device.

[0347] Step 7:

[0348] The server inputs user feedback into the AI ​​model, updating the suggestions. This results in optimized output based on the feedback. This output is then saved back into the database for use in future treatment suggestions. Through this entire process, the system can provide the most suitable treatment suggestions for each individual user.

[0349] (Application Example 2)

[0350] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0351] The current medical plan proposal system lacks individualization based on the user's emotions, and therefore fails to adequately alleviate the psychological burden of receiving medical services. In particular, when a user feels anxiety or stress regarding a proposal, the system does not adequately respond to those emotions. There is a need for a system that effectively utilizes user feedback and emotional information to adapt proposals.

[0352] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0353] In this invention, the server includes means for receiving and storing individual treatment history information, means for generating treatment suggestions using generative artificial intelligence based on the received information, means for providing additional information regarding the generated treatment suggestions and generating responses to questions, means for analyzing the user's emotions and adjusting the treatment suggestions based on those emotions, and means for providing relaxation methods and past success stories using the results of the emotion analysis. This makes it possible to provide personalized medical plans that take into account the user's emotions and reduce the psychological burden of medical services.

[0354] "Individual treatment history information" refers to a collection of information about a specific user's past health status and treatment history.

[0355] "Generative artificial intelligence" is a program that learns from large amounts of data and generates optimal decisions and suggestions based on specific conditions.

[0356] A "treatment proposal" refers to a plan that includes the optimal treatment method, schedule, and medications to be used, based on the user's health condition and treatment history.

[0357] "Additional information" refers to explanations, reference materials, and supplementary data provided to facilitate further understanding of the basic treatment proposal.

[0358] "Emotional analysis" is the process of identifying and evaluating a user's psychological state based on their facial expressions, voice, input patterns, etc.

[0359] "Relaxation methods" refer to specific activities and techniques used to reduce stress and anxiety in users.

[0360] "Past success stories" refer to information provided as a reference, specifically cases where similar treatments or care methods have been implemented in the past and proven effective.

[0361] This invention is based on a system that combines generative artificial intelligence with an engine for analyzing emotions. This system mainly consists of three elements: a server, a terminal, and a user. First, the user logs into the system using a terminal and enters individual treatment history information. This information includes health status and past treatment history. The terminal securely transmits this information to the server, which then prepares to store and process the information.

[0362] Based on stored information, the server uses generative artificial intelligence to generate optimal treatment suggestions for the user. These suggestions include details such as specific treatment methods, schedules, medications to be used, and expected outcomes. Once a suggestion is generated, the server sends it to the user's device for review.

[0363] When a user reviews a treatment suggestion via the device, the device uses its built-in emotion engine to analyze the user's emotions from their facial expressions, input patterns, and voice. This analysis allows the device to understand how the user is feeling about the suggestion. For example, if the user is feeling stressed or anxious about the suggestion, the emotion engine will recognize this.

[0364] The server uses the information provided by the emotion engine to adjust treatment suggestions and additional information. Depending on the user's emotions, it can change how information is presented or provide additional relevant support. For example, if the user is feeling anxious, the server may offer relaxation techniques or show past success stories to provide reassurance.

[0365] For example, if a user is feeling lonely, the device might suggest "video calls with friends or family that have been effective in the past." Furthermore, an example of a prompt for the generating AI model could be: "The user is currently feeling sad. What information is available in their past health history? What kind of care plan would be appropriate?"

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The user logs into the system via their device. During this process, the user enters their individual treatment history information, which the device collects. This information includes past health status and treatment history. The device securely encrypts this information and transmits it to the server.

[0369] Step 2:

[0370] The server receives treatment history information sent from the terminal and stores it in a database. Based on the received information, generative artificial intelligence is activated and begins processing to generate the optimal treatment suggestion. The AI ​​analyzes past data to generate an individualized treatment plan. The generated suggestion includes specific treatment methods, schedules, and medications to be used.

[0371] Step 3:

[0372] The generated treatment suggestions are sent from the server to the user's device. The device displays this information to the user and prompts them to confirm. The user can review the suggestions and, if necessary, enter questions and send them to the server. During this process, the device analyzes the user's facial expressions and voice using an emotion engine and generates emotional data.

[0373] Step 4:

[0374] The server receives questions and emotional data from the user. Based on the emotional data provided by the emotion engine, the generative artificial intelligence adjusts the treatment suggestions. For example, if anxiety is detected, the server uses the generative AI model to suggest relaxation methods or present success stories to provide reassurance. This adjusted information is then sent back to the terminal.

[0375] Step 5:

[0376] Users receive a tailored treatment plan and review newly provided relaxation methods and success stories. If necessary, they can input feedback via their device and send it to the server. The server collects this feedback and uses it to further improve the suggestions, aiming to continuously provide personalized and optimal treatment plans.

[0377] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0378] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0379] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0383] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0384] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0385] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0386] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0387] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0388] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0389] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0390] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0391] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0392] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0393] This invention is a system that realizes the personalization of infertility treatment, and uses generative artificial intelligence to provide optimal treatment suggestions to patients. This system consists of three main elements: a server, a terminal, and a user.

[0394] First, the user accesses the system using a terminal and enters individual treatment history information. This includes detailed information about past treatments, health status, age, gender, etc. The information entered from the terminal is sent to the server through a secure process.

[0395] The server stores this received information in a database and prepares it for processing by generative artificial intelligence. The generative AI uses this information to generate optimal treatment suggestions for each user. These suggestions are detailed and include treatment methods, medications to be used, schedules, and expected outcomes.

[0396] Once a proposal is generated, the server sends it back to the terminal and presents it to the user. The user can review the proposal through the terminal and ask further questions or request additional information.

[0397] The server utilizes generative artificial intelligence again based on user feedback and questions to provide additional information or update suggestions. For example, if a user asks for details about a specific treatment method, the server researches relevant and up-to-date medical information and provides supplementary information to the user through the terminal.

[0398] By repeating this process, users can receive the fertility treatment plan best suited to them, and this system allows them to enjoy personalized, advanced medical services. This makes the most of treatment experience and new medical knowledge to provide the most effective treatment for patients of all ages.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] Users access the system using their devices and log in to a new or existing account. Upon initial login, users enter basic information such as their treatment history, current health status, age, and gender. This information is necessary to optimize treatment for each user.

[0402] Step 2:

[0403] The terminal encrypts the entered user information and sends it to the server. The server stores the received information in a structured format in its database and verifies that no information is missing.

[0404] Step 3:

[0405] The server analyzes stored user information and activates generative artificial intelligence. This AI uses the user's treatment history and health information to generate personalized treatment suggestions. These suggestions include recommended tests and treatments, as well as expected outcomes.

[0406] Step 4:

[0407] The generated treatment suggestions are sent from the server to the terminal and presented to the user. The user reviews the suggestions and enters questions about anything they don't understand or would like more detailed information about.

[0408] Step 5:

[0409] The server receives the question sent from the terminal, and generative artificial intelligence processes it again. The AI ​​generates a detailed answer to the question, and provides additional information as needed, referencing the latest treatment information and relevant data.

[0410] Step 6:

[0411] The server sends the generated response and additional information to the terminal and provides it to the user. The user reviews the response and sends feedback or further requests regarding the treatment suggestion to the server.

[0412] Step 7:

[0413] The server re-evaluates treatment suggestions based on user feedback and updates them using generative artificial intelligence. These updated suggestions are then presented to the user again, and the process is repeated.

[0414] In this way, by dynamically updating treatment suggestions based on user feedback, we aim to provide personalized and optimal treatment plans.

[0415] (Example 1)

[0416] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0417] The problem that this invention aims to solve is to provide a means for efficiently generating optimized medical recommendations based on individual medical history data and responding to users. Furthermore, by receiving feedback on the generated medical recommendations and continuously improving subsequent recommendations, the invention aims to enhance the personalization of medical services and realize highly accurate medical support.

[0418] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0419] In this invention, the server includes means for acquiring individual medical history data and storing it in a storage device, means for creating medical suggestions using a generation AI model based on the acquired data, and means for providing supplementary information related to the created medical suggestions and generating responses to inquiries. This not only enables suggestions optimized for individual medical histories, but also allows for further improvement of suggestions based on feedback, thereby improving the quality of medical care.

[0420] "Individual medical history data" refers to a collection of detailed medical information recorded for each patient, including past treatments, health status, medication history, age, and gender.

[0421] A "storage device" is a device that stores data and allows that data to be retrieved quickly when needed, and includes databases and cloud storage.

[0422] A "generative AI model" is a computational model that utilizes artificial intelligence technology to analyze patterns and relationships based on input data and generate new information and suggestions.

[0423] A "medical recommendation" is a set of recommendations based on individual patient data, including appropriate treatment methods, medications to be used, treatment schedules, and expected treatment outcomes.

[0424] "Supplementary information" refers to detailed information added to make the medical recommendations provided easier to understand and implement. Examples include the latest medical research data and information showing the specific effects of medications.

[0425] "Feedback" refers to the user's response, opinion, or suggestion for improvement after receiving a medical suggestion, and serves as a basis for improving future suggestions.

[0426] A "secure communication protocol" is a communication standard that encrypts the transmission and reception of data to maintain confidentiality and integrity, and includes, for example, HTTPS and TLS.

[0427] This invention is a system that realizes the personalization of infertility treatment, and uses a generative AI model to provide patients with the most suitable treatment suggestions. This system consists of three elements: a server, a terminal, and a user.

[0428] First, the user accesses the system via a terminal and enters individual medical history data. This information includes past treatments, health status, age, and gender. The terminal sends this information to the server using a secure communication protocol (e.g., HTTPS). The server stores the received information in its storage device.

[0429] Next, the server uses a generative AI model to create medical recommendations tailored to the user based on the stored data. This AI model employs multiple algorithms to generate the optimal treatment plan based on past treatment history and medical knowledge. In this process, the treatment recommendations can be customized by inputting prompts into the model, such as, "Please generate the optimal infertility treatment plan for a female patient in her 30s. In the past, clomiphene was used, but it had strong side effects and was ineffective. Please suggest any other treatment options using other medications."

[0430] The generated medical suggestions are sent from the server to the terminal and presented to the user. The user reviews them and, if necessary, sends feedback to the server via the terminal. The server uses this feedback to refine the suggestions using the generating AI model again. This ensures that the most suitable treatment plan for each individual patient is continuously provided.

[0431] This system incorporates the latest medical information and realizes advanced technological solutions to provide personalized medical services.

[0432] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0433] Step 1:

[0434] Users input individual medical history data using a terminal. Specifically, the information provided by the user includes past treatments, health status, age, and gender. This input data is collected through the terminal's input form. The terminal verifies the integrity of the data and transmits it to the server using a secure communication protocol.

[0435] Step 2:

[0436] The server receives medical history data transmitted from the terminal and stores it in storage. The received data is organized in a database and maintained as a profile for each patient. The server checks for data redundancy and normalizes the data as needed. This process ensures the integrity and consistency of the information.

[0437] Step 3:

[0438] The server activates a generative AI model based on data stored in its memory to create medical recommendations. In this process, the AI ​​model analyzes the input data and determines the optimal treatment option based on similar past cases. Specifically, a prompt such as "Generate the optimal infertility treatment plan for a female patient in her 30s" is input, and the model outputs a new treatment recommendation.

[0439] Step 4:

[0440] The server sends the generated medical proposal to the terminal and presents it to the user. The transmitted proposal is visually displayed on the terminal's user interface and provided in a format that is easy for the user to understand. Based on this, the user reviews the proposal and decides whether to accept it or not.

[0441] Step 5:

[0442] Users send feedback to the server via their device. Here, they can enter questions about the suggestions or requests for additional information. Once this input reaches the server, the server re-enters the feedback into the generating AI model and updates the suggestions as needed.

[0443] Step 6:

[0444] Based on user feedback, the server again utilizes the AI ​​model to improve medical recommendations. During this process, it adjusts the recommendations to provide the most suitable treatment plan for the user, referencing the latest medical information. As a result, new recommendations are generated and sent back to the device.

[0445] Through this series of processing steps, users can continuously receive individually optimized medical services.

[0446] (Application Example 1)

[0447] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0448] When providing optimal medical recommendations to individual users, existing systems fail to fully utilize each resident's individual medical history, and maintaining the timeliness and appropriateness of medical information in smart cities presents challenges. In addition, there is a need for mechanisms that enable users to better utilize and continuously improve medical recommendations.

[0449] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0450] In this invention, the server includes means for receiving and storing individual medical history information, means for generating medical recommendations using generative artificial intelligence based on the received information, and means for coordinating with a mobile device for managing citizens' health information and providing optimal treatment plans. This enables real-time optimal medical recommendations for individual residents and improves medical services in smart cities.

[0451] "Individual medical history information" refers to information that includes each resident's unique past treatment history and health status.

[0452] "Generative artificial intelligence" is an artificial intelligence technology that can generate optimal medical recommendations based on input data.

[0453] A "medical proposal" is a proposal that includes recommendations for treatment methods, schedules, and other medical services based on residents' health information.

[0454] A "mobile device" is a portable electronic device used for managing medical information and receiving medical recommendations.

[0455] A "smart city" is a futuristic city that utilizes information and communication technology to optimize urban management and resident services.

[0456] This invention is a system that provides individually customized medical recommendations and supports the healthcare of residents in smart cities. This system mainly consists of a server, terminals, and users.

[0457] The server is built on a cloud computing environment (e.g., AWS, Azure) and receives individual medical history information, storing it in a database. Generative artificial intelligence analyzes this data and generates optimal medical recommendations. The generative AI model utilizes GPT-4, which suggests treatment methods, schedules, and other medical services based on the input data.

[0458] The terminal is a mobile device (smartphone, tablet) owned by the user, through which the user can input their health status and history and receive generated medical suggestions. The terminal communicates with the server via a communication network and exchanges information in real time.

[0459] Users can use the application to review medical suggestions and submit their feedback to the system. This feedback is re-evaluated by the server's AI-generated suggestions and used to further improve the suggestions. For example, if a user has previously experienced a lack of success with a particular treatment, new alternative treatments will be suggested based on that history. Examples of specific prompts for generated medical suggestions are as follows:

[0460] "Based on the user's age, gender, and past treatment history, generate the most effective fertility treatment plan for this user. The information should include treatment methods, expected outcomes, medications used, and a schedule."

[0461] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0462] Step 1:

[0463] Users input individual health information and past treatment history through their terminals. This data is formatted on the terminal and sent to the server using a secure protocol. Inputs include the user's age, gender, medical history, and current health status. Outputs are transferred to the server.

[0464] Step 2:

[0465] The server stores the received data in the database. In this step, data is imported and saved, and the saved data is indexed and organized to improve searchability. The input is medical history information sent by the user, and the output is the history information stored in the database.

[0466] Step 3:

[0467] The server uses a generative AI model to generate optimal medical recommendations based on information in the database. Here, the AI ​​model analyzes the user's historical data and forms personalized treatment recommendations by referencing existing medical databases and the latest medical information. The inputs are user information and medical reference materials from the database, and the output is a medical recommendation for the user.

[0468] Step 4:

[0469] The generated medical suggestions are sent from the server to the terminal and notified to the user. The user can view the suggestions through the terminal's application, reviewing the content and displaying detailed information. The input is the medical suggestions from the server, and the output is the information provided to the user in a viewable format.

[0470] Step 5:

[0471] When a user provides feedback on a suggestion, they send it to the server via their device. The server receives the feedback and reuses the generated AI model to improve the suggestion or add new information. This step forms part of a feedback loop, enabling continuous suggestion improvement. The input is the user's feedback, and the output is the improved medical suggestion.

[0472] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0473] This invention is a system for providing personalized infertility treatment suggestions that take into account the user's emotions, and is realized by combining generative artificial intelligence and an emotion engine. This system mainly consists of three elements: the user, the terminal, and the server.

[0474] First, the user logs into the system using a terminal and enters their individual treatment history information. This includes information about their health status and past treatment history, which will be used to make subsequent recommendations. The terminal securely transmits this information to the server, which then prepares to store and process the information.

[0475] Generative artificial intelligence generates optimal treatment suggestions for the user based on stored information. These suggestions include details such as treatment methods, schedules, medications to be used, and expected outcomes. The server then sends the suggestions to the user's device for review.

[0476] When a user reviews a treatment suggestion via their device, the emotion engine analyzes their emotions from their facial expressions, input patterns, and voice. For example, if a user feels stressed or anxious about the suggestion, the emotion engine will recognize that emotion.

[0477] The server uses information from the emotion engine to adjust treatment suggestions and additional information. Depending on the user's emotions, it can change how information is presented and provide additional relevant support. For example, if the user is feeling anxious, it can provide reassurance by suggesting relaxation techniques and showing success stories.

[0478] Furthermore, user feedback and questions are collected, and generative artificial intelligence is used to update the suggested treatments. Through this entire process, the system repeatedly provides optimized treatment plans based on user feedback and emotional state.

[0479] This will allow users to receive personalized, advanced medical services that take their feelings into consideration, and is expected to maximize the effectiveness of infertility treatment.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] Users access the system through their device and log in to a new or existing account. After logging in, they enter basic medical history information such as their health status, past treatment history, age, and gender into the device.

[0483] Step 2:

[0484] The terminal transmits the entered treatment history information to the server via secure communication. The server stores the received information in a database and prepares the data in a format that can be parsed.

[0485] Step 3:

[0486] The server uses generative artificial intelligence to generate treatment suggestions based on stored information. These suggestions include detailed information on recommended treatments, necessary tests, and expected outcomes.

[0487] Step 4:

[0488] The server sends the generated treatment suggestion to the terminal and presents it to the user. The user then reviews the suggestion on the terminal.

[0489] Step 5:

[0490] The emotion engine is activated when the user reviews the suggested content via their device and enters their response. The emotion engine analyzes the user's facial expressions, input speed, voice tone, etc., to identify their current emotional state.

[0491] Step 6:

[0492] The server uses the results of the emotion engine analysis to adjust treatment suggestions and additional advice. If the user shows signs of anxiety, the server provides updated information to the device, including reassuring information and success stories from other patients.

[0493] Step 7:

[0494] Users view updated information and suggestions on their devices and enter further questions or feedback. This feedback is then sent from the device to the server.

[0495] Step 8:

[0496] The server uses generative artificial intelligence again, based on user feedback, to refine its suggestions. The suggestions are updated accordingly, generating a more personalized treatment plan.

[0497] Step 9:

[0498] The server sends the improved suggestion back to the terminal, and the process is repeated until a suggestion that satisfies the user is obtained. This continuous feedback loop allows the user to receive the most appropriate treatment that takes their feelings into consideration.

[0499] (Example 2)

[0500] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0501] In infertility treatment, it is difficult to provide optimized treatment proposals that take into account each patient's emotions and past treatment history. Traditional systems have struggled to provide treatment plans that adequately reflect the patient's psychological state and feedback, which has sometimes led to anxiety and stress.

[0502] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0503] In this invention, the server includes means for collecting and storing individual treatment history information, means for creating medical suggestions using generative artificial intelligence, and means for analyzing the user's facial expressions, voice, and input patterns to determine their emotions. This makes it possible to provide optimal treatment suggestions tailored to the individual needs and emotional state of each patient.

[0504] "Individual treatment history information" refers to information that specifically details each user's health condition and past treatment history.

[0505] "Generative artificial intelligence" is an artificial intelligence technology that generates appropriate suggestions and responses based on user input data.

[0506] "Medical suggestions" refer to information that proposes appropriate treatment methods and schedules based on the individual user's condition and emotions.

[0507] A "terminal" is an electronic device used by users to input information or review suggestions.

[0508] "Means of determining emotions" refer to technologies and methods for identifying emotions by analyzing a user's facial expressions, voice, input patterns, etc.

[0509] This invention is a system that provides personalized treatment suggestions while taking into account the user's emotions. The user first logs into the system using a terminal and inputs their individual health status and past treatment history. The entered data is securely transmitted from the terminal to the server. SSL / TLS protocol is used for data transmission to ensure data security. The server stores the received information using a relational database system (e.g., MySQL or PostgreSQL).

[0510] The server drives a generative AI model (e.g., using a general-purpose generative language model) based on stored information to generate optimal medical recommendations. Prompts play a crucial role in this process. A specific example of a prompt might be: "Generate optimal treatment recommendations considering the user's past infertility treatment history. Based on health information and the user's emotions, please include specific methods and schedules in the recommendations."

[0511] The generated suggestions are sent from the server to the user's device for review. When the user reviews the suggestions, an emotion engine is activated on the device to analyze facial expressions, voice, and input patterns. The emotion engine uses common facial recognition and voice analysis technologies (e.g., Emotion API or a similar API).

[0512] The server adjusts medical recommendations based on data provided by the emotion engine. For example, if a user expresses anxiety, the server provides additional information, including relaxation techniques and success stories, to alleviate the user's psychological burden. The feedback is then sent back to the generative AI, contributing to updating the recommendations. Through this process, it becomes possible to provide medical services optimized for the user.

[0513] This system is expected to support more effective infertility treatment by providing advanced treatment suggestions tailored to the individual needs and emotions of each patient.

[0514] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0515] Step 1:

[0516] Users log in to the system using a terminal and enter individual data such as their health status and past treatment history. This input data is collected using text format and checkboxes. Once the user completes the input, the terminal sends this information to the server. The input data is typically sent in JSON format.

[0517] Step 2:

[0518] The server verifies individual treatment history information received from terminals and securely stores it in a database. A relational database system is used, and the information is stored in association with individual users. After confirming the integrity of the input data, the information is ready to be passed to the generating AI model.

[0519] Step 3:

[0520] The server takes the stored information as input and passes it to a generative artificial intelligence to generate prompts. Following these prompts, the generative AI model creates medical recommendations. Specifically, it outputs suggested treatment methods, a list of medications, and a schedule, taking into account the patient's past history and current health condition. This output is returned to the server in text format.

[0521] Step 4:

[0522] The server sends the output from the generated AI model to the user's terminal and displays treatment suggestions. The terminal displays the suggestions in a text area for the user to review. At the same time, the user is provided with an interface to input feedback on these suggestions.

[0523] Step 5:

[0524] When a user reviews a treatment suggestion, the device uses an emotion engine to analyze the user's facial expressions, voice, and input patterns. This identifies the user's emotional state and sends it to the server as log data. This input is data obtained from the emotion engine's API.

[0525] Step 6:

[0526] The server adjusts the content and presentation of treatment suggestions based on data generated by the emotion engine. Specifically, if the user indicates anxiety, it provides additional support information and success stories, and modifies the format of the information output. These adjusted suggestions are then sent back to the user's device.

[0527] Step 7:

[0528] The server inputs user feedback into the AI ​​model, updating the suggestions. This results in optimized output based on the feedback. This output is then saved back into the database for use in future treatment suggestions. Through this entire process, the system can provide the most suitable treatment suggestions for each individual user.

[0529] (Application Example 2)

[0530] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0531] The current medical plan proposal system lacks individualization based on the user's emotions, and therefore fails to adequately alleviate the psychological burden of receiving medical services. In particular, when a user feels anxiety or stress regarding a proposal, the system does not adequately respond to those emotions. There is a need for a system that effectively utilizes user feedback and emotional information to adapt proposals.

[0532] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0533] In this invention, the server includes means for receiving and storing individual treatment history information, means for generating treatment suggestions using generative artificial intelligence based on the received information, means for providing additional information regarding the generated treatment suggestions and generating responses to questions, means for analyzing the user's emotions and adjusting the treatment suggestions based on those emotions, and means for providing relaxation methods and past success stories using the results of the emotion analysis. This makes it possible to provide personalized medical plans that take into account the user's emotions and reduce the psychological burden of medical services.

[0534] "Individual treatment history information" refers to a collection of information about a specific user's past health status and treatment history.

[0535] "Generative artificial intelligence" is a program that learns from large amounts of data and generates optimal decisions and suggestions based on specific conditions.

[0536] A "treatment proposal" refers to a plan that includes the optimal treatment method, schedule, and medications to be used, based on the user's health condition and treatment history.

[0537] "Additional information" refers to explanations, reference materials, and supplementary data provided to facilitate further understanding of the basic treatment proposal.

[0538] "Emotional analysis" is the process of identifying and evaluating a user's psychological state based on their facial expressions, voice, input patterns, etc.

[0539] "Relaxation methods" refer to specific activities and techniques used to reduce stress and anxiety in users.

[0540] "Past success stories" refer to information provided as a reference, specifically cases where similar treatments or care methods have been implemented in the past and proven effective.

[0541] This invention is based on a system that combines generative artificial intelligence with an engine for analyzing emotions. This system mainly consists of three elements: a server, a terminal, and a user. First, the user logs into the system using a terminal and enters individual treatment history information. This information includes health status and past treatment history. The terminal securely transmits this information to the server, which then prepares to store and process the information.

[0542] Based on stored information, the server uses generative artificial intelligence to generate optimal treatment suggestions for the user. These suggestions include details such as specific treatment methods, schedules, medications to be used, and expected outcomes. Once a suggestion is generated, the server sends it to the user's device for review.

[0543] When a user reviews a treatment suggestion via the device, the device uses its built-in emotion engine to analyze the user's emotions from their facial expressions, input patterns, and voice. This analysis allows the device to understand how the user is feeling about the suggestion. For example, if the user is feeling stressed or anxious about the suggestion, the emotion engine will recognize this.

[0544] The server uses the information provided by the emotion engine to adjust treatment suggestions and additional information. Depending on the user's emotions, it can change how information is presented or provide additional relevant support. For example, if the user is feeling anxious, the server may offer relaxation techniques or show past success stories to provide reassurance.

[0545] For example, if a user is feeling lonely, the device might suggest "video calls with friends or family that have been effective in the past." Furthermore, an example of a prompt for the generating AI model could be: "The user is currently feeling sad. What information is available in their past health history? What kind of care plan would be appropriate?"

[0546] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0547] Step 1:

[0548] The user logs into the system via their device. During this process, the user enters their individual treatment history information, which the device collects. This information includes past health status and treatment history. The device securely encrypts this information and transmits it to the server.

[0549] Step 2:

[0550] The server receives treatment history information sent from the terminal and stores it in a database. Based on the received information, generative artificial intelligence is activated and begins processing to generate the optimal treatment suggestion. The AI ​​analyzes past data to generate an individualized treatment plan. The generated suggestion includes specific treatment methods, schedules, and medications to be used.

[0551] Step 3:

[0552] The generated treatment suggestions are sent from the server to the user's device. The device displays this information to the user and prompts them to confirm. The user can review the suggestions and, if necessary, enter questions and send them to the server. During this process, the device analyzes the user's facial expressions and voice using an emotion engine and generates emotional data.

[0553] Step 4:

[0554] The server receives questions and emotional data from the user. Based on the emotional data provided by the emotion engine, the generative artificial intelligence adjusts the treatment suggestions. For example, if anxiety is detected, the server uses the generative AI model to suggest relaxation methods or present success stories to provide reassurance. This adjusted information is then sent back to the terminal.

[0555] Step 5:

[0556] Users receive a tailored treatment plan and review newly provided relaxation methods and success stories. If necessary, they can input feedback via their device and send it to the server. The server collects this feedback and uses it to further improve the suggestions, aiming to continuously provide personalized and optimal treatment plans.

[0557] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0558] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0559] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0560] [Fourth Embodiment]

[0561] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0562] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0563] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0564] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0565] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0566] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0567] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0568] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0569] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0570] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0571] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0572] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0573] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0574] This invention is a system that realizes the personalization of infertility treatment, and uses generative artificial intelligence to provide optimal treatment suggestions to patients. This system consists of three main elements: a server, a terminal, and a user.

[0575] First, the user accesses the system using a terminal and enters individual treatment history information. This includes detailed information about past treatments, health status, age, gender, etc. The information entered from the terminal is sent to the server through a secure process.

[0576] The server stores this received information in a database and prepares it for processing by generative artificial intelligence. The generative AI uses this information to generate optimal treatment suggestions for each user. These suggestions are detailed and include treatment methods, medications to be used, schedules, and expected outcomes.

[0577] Once a proposal is generated, the server sends it back to the terminal and presents it to the user. The user can review the proposal through the terminal and ask further questions or request additional information.

[0578] The server utilizes generative artificial intelligence again based on user feedback and questions to provide additional information or update suggestions. For example, if a user asks for details about a specific treatment method, the server researches relevant and up-to-date medical information and provides supplementary information to the user through the terminal.

[0579] By repeating this process, users can receive the fertility treatment plan best suited to them, and this system allows them to enjoy personalized, advanced medical services. This makes the most of treatment experience and new medical knowledge to provide the most effective treatment for patients of all ages.

[0580] The following describes the processing flow.

[0581] Step 1:

[0582] Users access the system using their devices and log in to a new or existing account. Upon initial login, users enter basic information such as their treatment history, current health status, age, and gender. This information is necessary to optimize treatment for each user.

[0583] Step 2:

[0584] The terminal encrypts the entered user information and sends it to the server. The server stores the received information in a structured format in its database and verifies that no information is missing.

[0585] Step 3:

[0586] The server analyzes stored user information and activates generative artificial intelligence. This AI uses the user's treatment history and health information to generate personalized treatment suggestions. These suggestions include recommended tests and treatments, as well as expected outcomes.

[0587] Step 4:

[0588] The generated treatment suggestions are sent from the server to the terminal and presented to the user. The user reviews the suggestions and enters questions about anything they don't understand or would like more detailed information about.

[0589] Step 5:

[0590] The server receives the question sent from the terminal, and generative artificial intelligence processes it again. The AI ​​generates a detailed answer to the question, and provides additional information as needed, referencing the latest treatment information and relevant data.

[0591] Step 6:

[0592] The server sends the generated response and additional information to the terminal and provides it to the user. The user reviews the response and sends feedback or further requests regarding the treatment suggestion to the server.

[0593] Step 7:

[0594] The server re-evaluates treatment suggestions based on user feedback and updates them using generative artificial intelligence. These updated suggestions are then presented to the user again, and the process is repeated.

[0595] In this way, by dynamically updating treatment suggestions based on user feedback, we aim to provide personalized and optimal treatment plans.

[0596] (Example 1)

[0597] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0598] The problem that this invention aims to solve is to provide a means for efficiently generating optimized medical recommendations based on individual medical history data and responding to users. Furthermore, by receiving feedback on the generated medical recommendations and continuously improving subsequent recommendations, the invention aims to enhance the personalization of medical services and realize highly accurate medical support.

[0599] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0600] In this invention, the server includes means for acquiring individual medical history data and storing it in a storage device, means for creating medical suggestions using a generation AI model based on the acquired data, and means for providing supplementary information related to the created medical suggestions and generating responses to inquiries. This not only enables suggestions optimized for individual medical histories, but also allows for further improvement of suggestions based on feedback, thereby improving the quality of medical care.

[0601] "Individual medical history data" refers to a collection of detailed medical information recorded for each patient, including past treatments, health status, medication history, age, and gender.

[0602] A "storage device" is a device that stores data and allows that data to be retrieved quickly when needed, and includes databases and cloud storage.

[0603] A "generative AI model" is a computational model that utilizes artificial intelligence technology to analyze patterns and relationships based on input data and generate new information and suggestions.

[0604] A "medical recommendation" is a set of recommendations based on individual patient data, including appropriate treatment methods, medications to be used, treatment schedules, and expected treatment outcomes.

[0605] "Supplementary information" refers to detailed information added to make the medical recommendations provided easier to understand and implement. Examples include the latest medical research data and information showing the specific effects of medications.

[0606] "Feedback" refers to the user's response, opinion, or suggestion for improvement after receiving a medical suggestion, and serves as a basis for improving future suggestions.

[0607] A "secure communication protocol" is a communication standard that encrypts the transmission and reception of data to maintain confidentiality and integrity, and includes, for example, HTTPS and TLS.

[0608] This invention is a system that realizes the personalization of infertility treatment, and uses a generative AI model to provide patients with the most suitable treatment suggestions. This system consists of three elements: a server, a terminal, and a user.

[0609] First, the user accesses the system via a terminal and enters individual medical history data. This information includes past treatments, health status, age, and gender. The terminal sends this information to the server using a secure communication protocol (e.g., HTTPS). The server stores the received information in its storage device.

[0610] Next, the server uses a generative AI model to create medical recommendations tailored to the user based on the stored data. This AI model employs multiple algorithms to generate the optimal treatment plan based on past treatment history and medical knowledge. In this process, the treatment recommendations can be customized by inputting prompts into the model, such as, "Please generate the optimal infertility treatment plan for a female patient in her 30s. In the past, clomiphene was used, but it had strong side effects and was ineffective. Please suggest any other treatment options using other medications."

[0611] The generated medical suggestions are sent from the server to the terminal and presented to the user. The user reviews them and, if necessary, sends feedback to the server via the terminal. The server uses this feedback to refine the suggestions using the generating AI model again. This ensures that the most suitable treatment plan for each individual patient is continuously provided.

[0612] This system incorporates the latest medical information and realizes advanced technological solutions to provide personalized medical services.

[0613] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0614] Step 1:

[0615] Users input individual medical history data using a terminal. Specifically, the information provided by the user includes past treatments, health status, age, and gender. This input data is collected through the terminal's input form. The terminal verifies the integrity of the data and transmits it to the server using a secure communication protocol.

[0616] Step 2:

[0617] The server receives medical history data transmitted from the terminal and stores it in storage. The received data is organized in a database and maintained as a profile for each patient. The server checks for data redundancy and normalizes the data as needed. This process ensures the integrity and consistency of the information.

[0618] Step 3:

[0619] The server activates a generative AI model based on data stored in its memory to create medical recommendations. In this process, the AI ​​model analyzes the input data and determines the optimal treatment option based on similar past cases. Specifically, a prompt such as "Generate the optimal infertility treatment plan for a female patient in her 30s" is input, and the model outputs a new treatment recommendation.

[0620] Step 4:

[0621] The server sends the generated medical proposal to the terminal and presents it to the user. The transmitted proposal is visually displayed on the terminal's user interface and provided in a format that is easy for the user to understand. Based on this, the user reviews the proposal and decides whether to accept it or not.

[0622] Step 5:

[0623] Users send feedback to the server via their device. Here, they can enter questions about the suggestions or requests for additional information. Once this input reaches the server, the server re-enters the feedback into the generating AI model and updates the suggestions as needed.

[0624] Step 6:

[0625] Based on user feedback, the server again utilizes the AI ​​model to improve medical recommendations. During this process, it adjusts the recommendations to provide the most suitable treatment plan for the user, referencing the latest medical information. As a result, new recommendations are generated and sent back to the device.

[0626] Through this series of processing steps, users can continuously receive individually optimized medical services.

[0627] (Application Example 1)

[0628] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0629] When providing optimal medical recommendations to individual users, existing systems fail to fully utilize each resident's individual medical history, and maintaining the timeliness and appropriateness of medical information in smart cities presents challenges. In addition, there is a need for mechanisms that enable users to better utilize and continuously improve medical recommendations.

[0630] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0631] In this invention, the server includes means for receiving and storing individual medical history information, means for generating medical recommendations using generative artificial intelligence based on the received information, and means for coordinating with a mobile device for managing citizens' health information and providing optimal treatment plans. This enables real-time optimal medical recommendations for individual residents and improves medical services in smart cities.

[0632] "Individual medical history information" refers to information that includes each resident's unique past treatment history and health status.

[0633] "Generative artificial intelligence" is an artificial intelligence technology that can generate optimal medical recommendations based on input data.

[0634] A "medical proposal" is a proposal that includes recommendations for treatment methods, schedules, and other medical services based on residents' health information.

[0635] A "mobile device" is a portable electronic device used for managing medical information and receiving medical recommendations.

[0636] A "smart city" is a futuristic city that utilizes information and communication technology to optimize urban management and resident services.

[0637] This invention is a system that provides individually customized medical recommendations and supports the healthcare of residents in smart cities. This system mainly consists of a server, terminals, and users.

[0638] The server is built on a cloud computing environment (e.g., AWS, Azure) and receives individual medical history information, storing it in a database. Generative artificial intelligence analyzes this data and generates optimal medical recommendations. The generative AI model utilizes GPT-4, which suggests treatment methods, schedules, and other medical services based on the input data.

[0639] The terminal is a mobile device (smartphone, tablet) owned by the user, through which the user can input their health status and history and receive generated medical suggestions. The terminal communicates with the server via a communication network and exchanges information in real time.

[0640] Users can use the application to review medical suggestions and submit their feedback to the system. This feedback is re-evaluated by the server's AI-generated suggestions and used to further improve the suggestions. For example, if a user has previously experienced a lack of success with a particular treatment, new alternative treatments will be suggested based on that history. Examples of specific prompts for generated medical suggestions are as follows:

[0641] "Based on the user's age, gender, and past treatment history, generate the most effective fertility treatment plan for this user. The information should include treatment methods, expected outcomes, medications used, and a schedule."

[0642] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0643] Step 1:

[0644] Users input individual health information and past treatment history through their terminals. This data is formatted on the terminal and sent to the server using a secure protocol. Inputs include the user's age, gender, medical history, and current health status. Outputs are transferred to the server.

[0645] Step 2:

[0646] The server stores the received data in the database. In this step, data is imported and saved, and the saved data is indexed and organized to improve searchability. The input is medical history information sent by the user, and the output is the history information stored in the database.

[0647] Step 3:

[0648] The server uses a generative AI model to generate optimal medical recommendations based on information in the database. Here, the AI ​​model analyzes the user's historical data and forms personalized treatment recommendations by referencing existing medical databases and the latest medical information. The inputs are user information and medical reference materials from the database, and the output is a medical recommendation for the user.

[0649] Step 4:

[0650] The generated medical suggestions are sent from the server to the terminal and notified to the user. The user can view the suggestions through the terminal's application, reviewing the content and displaying detailed information. The input is the medical suggestions from the server, and the output is the information provided to the user in a viewable format.

[0651] Step 5:

[0652] When a user provides feedback on a suggestion, they send it to the server via their device. The server receives the feedback and reuses the generated AI model to improve the suggestion or add new information. This step forms part of a feedback loop, enabling continuous suggestion improvement. The input is the user's feedback, and the output is the improved medical suggestion.

[0653] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0654] This invention is a system for providing personalized infertility treatment suggestions that take into account the user's emotions, and is realized by combining generative artificial intelligence and an emotion engine. This system mainly consists of three elements: the user, the terminal, and the server.

[0655] First, the user logs into the system using a terminal and enters their individual treatment history information. This includes information about their health status and past treatment history, which will be used to make subsequent recommendations. The terminal securely transmits this information to the server, which then prepares to store and process the information.

[0656] Generative artificial intelligence generates optimal treatment suggestions for the user based on stored information. These suggestions include details such as treatment methods, schedules, medications to be used, and expected outcomes. The server then sends the suggestions to the user's device for review.

[0657] When a user reviews a treatment suggestion via their device, the emotion engine analyzes their emotions from their facial expressions, input patterns, and voice. For example, if a user feels stressed or anxious about the suggestion, the emotion engine will recognize that emotion.

[0658] The server uses information from the emotion engine to adjust treatment suggestions and additional information. Depending on the user's emotions, it can change how information is presented and provide additional relevant support. For example, if the user is feeling anxious, it can provide reassurance by suggesting relaxation techniques and showing success stories.

[0659] Furthermore, user feedback and questions are collected, and generative artificial intelligence is used to update the suggested treatments. Through this entire process, the system repeatedly provides optimized treatment plans based on user feedback and emotional state.

[0660] This will allow users to receive personalized, advanced medical services that take their feelings into consideration, and is expected to maximize the effectiveness of infertility treatment.

[0661] The following describes the processing flow.

[0662] Step 1:

[0663] Users access the system through their device and log in to a new or existing account. After logging in, they enter basic medical history information such as their health status, past treatment history, age, and gender into the device.

[0664] Step 2:

[0665] The terminal transmits the entered treatment history information to the server via secure communication. The server stores the received information in a database and prepares the data in a format that can be parsed.

[0666] Step 3:

[0667] The server uses generative artificial intelligence to generate treatment suggestions based on stored information. These suggestions include detailed information on recommended treatments, necessary tests, and expected outcomes.

[0668] Step 4:

[0669] The server sends the generated treatment suggestion to the terminal and presents it to the user. The user then reviews the suggestion on the terminal.

[0670] Step 5:

[0671] The emotion engine is activated when the user reviews the suggested content via their device and enters their response. The emotion engine analyzes the user's facial expressions, input speed, voice tone, etc., to identify their current emotional state.

[0672] Step 6:

[0673] The server uses the results of the emotion engine analysis to adjust treatment suggestions and additional advice. If the user shows signs of anxiety, the server provides updated information to the device, including reassuring information and success stories from other patients.

[0674] Step 7:

[0675] Users view updated information and suggestions on their devices and enter further questions or feedback. This feedback is then sent from the device to the server.

[0676] Step 8:

[0677] The server uses generative artificial intelligence again, based on user feedback, to refine its suggestions. The suggestions are updated accordingly, generating a more personalized treatment plan.

[0678] Step 9:

[0679] The server sends the improved suggestion back to the terminal, and the process is repeated until a suggestion that satisfies the user is obtained. This continuous feedback loop allows the user to receive the most appropriate treatment that takes their feelings into consideration.

[0680] (Example 2)

[0681] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0682] In infertility treatment, it is difficult to provide optimized treatment proposals that take into account each patient's emotions and past treatment history. Traditional systems have struggled to provide treatment plans that adequately reflect the patient's psychological state and feedback, which has sometimes led to anxiety and stress.

[0683] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0684] In this invention, the server includes means for collecting and storing individual treatment history information, means for creating medical suggestions using generative artificial intelligence, and means for analyzing the user's facial expressions, voice, and input patterns to determine their emotions. This makes it possible to provide optimal treatment suggestions tailored to the individual needs and emotional state of each patient.

[0685] "Individual treatment history information" refers to information that specifically details each user's health condition and past treatment history.

[0686] "Generative artificial intelligence" is an artificial intelligence technology that generates appropriate suggestions and responses based on user input data.

[0687] "Medical suggestions" refer to information that proposes appropriate treatment methods and schedules based on the individual user's condition and emotions.

[0688] A "terminal" is an electronic device used by users to input information or review suggestions.

[0689] "Means of determining emotions" refer to technologies and methods for identifying emotions by analyzing a user's facial expressions, voice, input patterns, etc.

[0690] This invention is a system that provides personalized treatment suggestions while taking into account the user's emotions. The user first logs into the system using a terminal and inputs their individual health status and past treatment history. The entered data is securely transmitted from the terminal to the server. SSL / TLS protocol is used for data transmission to ensure data security. The server stores the received information using a relational database system (e.g., MySQL or PostgreSQL).

[0691] The server drives a generative AI model (e.g., using a general-purpose generative language model) based on stored information to generate optimal medical recommendations. Prompts play a crucial role in this process. A specific example of a prompt might be: "Generate optimal treatment recommendations considering the user's past infertility treatment history. Based on health information and the user's emotions, please include specific methods and schedules in the recommendations."

[0692] The generated suggestions are sent from the server to the user's device for review. When the user reviews the suggestions, an emotion engine is activated on the device to analyze facial expressions, voice, and input patterns. The emotion engine uses common facial recognition and voice analysis technologies (e.g., Emotion API or a similar API).

[0693] The server adjusts medical recommendations based on data provided by the emotion engine. For example, if a user expresses anxiety, the server provides additional information, including relaxation techniques and success stories, to alleviate the user's psychological burden. The feedback is then sent back to the generative AI, contributing to updating the recommendations. Through this process, it becomes possible to provide medical services optimized for the user.

[0694] This system is expected to support more effective infertility treatment by providing advanced treatment suggestions tailored to the individual needs and emotions of each patient.

[0695] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0696] Step 1:

[0697] Users log in to the system using a terminal and enter individual data such as their health status and past treatment history. This input data is collected using text format and checkboxes. Once the user completes the input, the terminal sends this information to the server. The input data is typically sent in JSON format.

[0698] Step 2:

[0699] The server verifies individual treatment history information received from terminals and securely stores it in a database. A relational database system is used, and the information is stored in association with individual users. After confirming the integrity of the input data, the information is ready to be passed to the generating AI model.

[0700] Step 3:

[0701] The server takes the stored information as input and passes it to a generative artificial intelligence to generate prompts. Following these prompts, the generative AI model creates medical recommendations. Specifically, it outputs suggested treatment methods, a list of medications, and a schedule, taking into account the patient's past history and current health condition. This output is returned to the server in text format.

[0702] Step 4:

[0703] The server sends the output from the generated AI model to the user's terminal and displays treatment suggestions. The terminal displays the suggestions in a text area for the user to review. At the same time, the user is provided with an interface to input feedback on these suggestions.

[0704] Step 5:

[0705] When a user reviews a treatment suggestion, the device uses an emotion engine to analyze the user's facial expressions, voice, and input patterns. This identifies the user's emotional state and sends it to the server as log data. This input is data obtained from the emotion engine's API.

[0706] Step 6:

[0707] The server adjusts the content and presentation of treatment suggestions based on data generated by the emotion engine. Specifically, if the user indicates anxiety, it provides additional support information and success stories, and modifies the format of the information output. These adjusted suggestions are then sent back to the user's device.

[0708] Step 7:

[0709] The server inputs user feedback into the AI ​​model, updating the suggestions. This results in optimized output based on the feedback. This output is then saved back into the database for use in future treatment suggestions. Through this entire process, the system can provide the most suitable treatment suggestions for each individual user.

[0710] (Application Example 2)

[0711] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] The current medical plan proposal system lacks individualization based on the user's emotions, and therefore fails to adequately alleviate the psychological burden of receiving medical services. In particular, when a user feels anxiety or stress regarding a proposal, the system does not adequately respond to those emotions. There is a need for a system that effectively utilizes user feedback and emotional information to adapt proposals.

[0713] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0714] In this invention, the server includes means for receiving and storing individual treatment history information, means for generating treatment suggestions using generative artificial intelligence based on the received information, means for providing additional information regarding the generated treatment suggestions and generating responses to questions, means for analyzing the user's emotions and adjusting the treatment suggestions based on those emotions, and means for providing relaxation methods and past success stories using the results of the emotion analysis. This makes it possible to provide personalized medical plans that take into account the user's emotions and reduce the psychological burden of medical services.

[0715] "Individual treatment history information" refers to a collection of information about a specific user's past health status and treatment history.

[0716] "Generative artificial intelligence" is a program that learns from large amounts of data and generates optimal decisions and suggestions based on specific conditions.

[0717] A "treatment proposal" refers to a plan that includes the optimal treatment method, schedule, and medications to be used, based on the user's health condition and treatment history.

[0718] "Additional information" refers to explanations, reference materials, and supplementary data provided to facilitate further understanding of the basic treatment proposal.

[0719] "Emotional analysis" is the process of identifying and evaluating a user's psychological state based on their facial expressions, voice, input patterns, etc.

[0720] "Relaxation methods" refer to specific activities and techniques used to reduce stress and anxiety in users.

[0721] "Past success stories" refer to information provided as a reference, specifically cases where similar treatments or care methods have been implemented in the past and proven effective.

[0722] This invention is based on a system that combines generative artificial intelligence with an engine for analyzing emotions. This system mainly consists of three elements: a server, a terminal, and a user. First, the user logs into the system using a terminal and enters individual treatment history information. This information includes health status and past treatment history. The terminal securely transmits this information to the server, which then prepares to store and process the information.

[0723] Based on stored information, the server uses generative artificial intelligence to generate optimal treatment suggestions for the user. These suggestions include details such as specific treatment methods, schedules, medications to be used, and expected outcomes. Once a suggestion is generated, the server sends it to the user's device for review.

[0724] When a user reviews a treatment suggestion via the device, the device uses its built-in emotion engine to analyze the user's emotions from their facial expressions, input patterns, and voice. This analysis allows the device to understand how the user is feeling about the suggestion. For example, if the user is feeling stressed or anxious about the suggestion, the emotion engine will recognize this.

[0725] The server uses the information provided by the emotion engine to adjust treatment suggestions and additional information. Depending on the user's emotions, it can change how information is presented or provide additional relevant support. For example, if the user is feeling anxious, the server may offer relaxation techniques or show past success stories to provide reassurance.

[0726] For example, if a user is feeling lonely, the device might suggest "video calls with friends or family that have been effective in the past." Furthermore, an example of a prompt for the generating AI model could be: "The user is currently feeling sad. What information is available in their past health history? What kind of care plan would be appropriate?"

[0727] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0728] Step 1:

[0729] The user logs into the system via their device. During this process, the user enters their individual treatment history information, which the device collects. This information includes past health status and treatment history. The device securely encrypts this information and transmits it to the server.

[0730] Step 2:

[0731] The server receives treatment history information sent from the terminal and stores it in a database. Based on the received information, generative artificial intelligence is activated and begins processing to generate the optimal treatment suggestion. The AI ​​analyzes past data to generate an individualized treatment plan. The generated suggestion includes specific treatment methods, schedules, and medications to be used.

[0732] Step 3:

[0733] The generated treatment suggestions are sent from the server to the user's device. The device displays this information to the user and prompts them to confirm. The user can review the suggestions and, if necessary, enter questions and send them to the server. During this process, the device analyzes the user's facial expressions and voice using an emotion engine and generates emotional data.

[0734] Step 4:

[0735] The server receives questions and emotional data from the user. Based on the emotional data provided by the emotion engine, the generative artificial intelligence adjusts the treatment suggestions. For example, if anxiety is detected, the server uses the generative AI model to suggest relaxation methods or present success stories to provide reassurance. This adjusted information is then sent back to the terminal.

[0736] Step 5:

[0737] Users receive a tailored treatment plan and review newly provided relaxation methods and success stories. If necessary, they can input feedback via their device and send it to the server. The server collects this feedback and uses it to further improve the suggestions, aiming to continuously provide personalized and optimal treatment plans.

[0738] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0739] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0740] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0741] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0742] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0743] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0744] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0745] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0746] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0747] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0748] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0749] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0750] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0751] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0752] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0753] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0754] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0755] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0756] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0757] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0758] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0759] The following is further disclosed regarding the embodiments described above.

[0760] (Claim 1)

[0761] A means of receiving and storing individual treatment history information,

[0762] A means for generating treatment suggestions using generative artificial intelligence based on the received information,

[0763] A means for providing additional information regarding the generated treatment proposal and for generating responses to questions,

[0764] ...

[0765] A system that includes this.

[0766] (Claim 2)

[0767] The system according to claim 1, which updates and provides optimal treatment suggestions using the generative artificial intelligence described above.

[0768] (Claim 3)

[0769] The system according to claim 1, which receives feedback on the aforementioned treatment proposal and improves the proposal.

[0770] "Example 1"

[0771] (Claim 1)

[0772] A means of acquiring individual medical history data and storing it in a storage device,

[0773] A means of creating medical proposals using a generative AI model based on the acquired data,

[0774] A means for providing supplementary information related to the medical proposal created and for generating responses to inquiries,

[0775] When creating the aforementioned medical proposal, a means for sending and receiving data using a secure communication protocol is provided.

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, which uses a generative AI model to optimize and update medical recommendations based on received feedback.

[0779] (Claim 3)

[0780] The system according to claim 1, which obtains feedback on the aforementioned medical proposal and improves the content of the proposal.

[0781] "Application Example 1"

[0782] (Claim 1)

[0783] A means of receiving and storing individual medical history information,

[0784] A means for generating medical proposals using generative artificial intelligence based on the received information,

[0785] A means for providing additional information regarding the generated medical proposal and for generating a response to an inquiry,

[0786] A means of managing citizens' health information and providing optimal treatment plans through integration with mobile devices,

[0787] A means of providing the latest medical and facility information in smart cities,

[0788] ...

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, which updates and provides optimal medical suggestions using the generative artificial intelligence.

[0792] (Claim 3)

[0793] The system according to claim 1, which receives feedback on the aforementioned medical proposal and improves the proposal.

[0794] "Example 2 of combining an emotion engine"

[0795] (Claim 1)

[0796] A means of collecting and storing individual treatment history information,

[0797] A means for creating medical proposals using generative artificial intelligence based on the information collected above,

[0798] A means of sending the aforementioned proposal to a terminal so that the user can confirm it,

[0799] A means of determining emotions by analyzing the user's facial expressions, voice, and input patterns,

[0800] A means of adjusting and optimizing medical proposals based on the aforementioned emotions,

[0801] A means for generating and providing a response to the aforementioned medical proposal to the user,

[0802] ...

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, which uses the generative artificial intelligence to update and provide optimal medical suggestions according to the user's emotional state.

[0806] (Claim 3)

[0807] The system according to claim 1, which obtains feedback on the aforementioned medical proposal and improves the proposal.

[0808] "Application example 2 when combining with an emotional engine"

[0809] (Claim 1)

[0810] A means of receiving and storing individual treatment history information,

[0811] A means for generating treatment suggestions using generative artificial intelligence based on the received information,

[0812] A means for providing additional information regarding the generated treatment proposal and for generating responses to questions,

[0813] A means of analyzing the user's emotions and adjusting treatment proposals based on those emotions,

[0814] A means of providing relaxation methods and past success stories using the results of emotional analysis,

[0815] ...

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, which updates and provides optimal treatment suggestions using the generative artificial intelligence described above.

[0819] (Claim 3)

[0820] The system according to claim 1, which receives feedback on the aforementioned treatment proposal, improves the proposal, and provides a plan based on emotional analysis. [Explanation of Symbols]

[0821] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving and storing individual medical history information, A means for generating medical proposals using generative artificial intelligence based on the received information, A means for providing additional information regarding the generated medical proposal and for generating a response to an inquiry, A means of managing citizens' health information and providing optimal treatment plans through integration with mobile devices, A means of providing the latest medical and facility information in smart cities, A system that includes this.

2. The system according to claim 1, which updates and provides optimal medical suggestions using the generative artificial intelligence described above.

3. The system according to claim 1, which receives opinions regarding the aforementioned medical proposal and improves the proposal.

Images