System

Abstract

To provide a system.SOLUTION: A system includes: means for receiving information input from a user; means for determining symptom based on the input information, using a generative AI; means for recognizing the emotion of the user using an emotion engine; and means for selecting, by the generative AI, a medical institution by combining the symptom with the information indicating the emotion of the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] The medical field is highly specialized, and it is difficult for ordinary people to determine which department to visit based on their symptoms or what scale of medical institution they should go to. In addition, the characteristics of medical institutions are difficult to understand for amateurs, so there is a possibility that they will choose an institution that does not match their symptoms. [Means for solving the problem]

[0005] It uses a generative AI that receives input from users and judges symptoms based on that information. This AI selects the appropriate medical institution's size and expertise based on the severity and type of symptoms. This allows users to select the medical institution that best suits their symptoms. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16]FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. [Figure 19] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system according to the embodiment 2 of the second embodiment in the case where an emotion engine is combined. [Figure 20] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when combined with an emotion engine. [Figure 21] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the embodiment 3 of the form example 3 in the case where an emotion engine is combined. [Figure 22] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (e.g., voice 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 voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out 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 the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] In one embodiment of the present invention, a system is provided that includes an interface for receiving input information from a user. The interface may be realized, for example, as a form on a website or a smartphone application. The user inputs details of his or her symptoms and physical condition.

[0029] "Example 2"

[0030] Next, the generative AI analyzes the information input by the user and determines the severity and type of symptoms. This AI uses machine learning algorithms and natural language processing techniques to accurately identify symptoms from user input. For example, if a user inputs "I have a bad headache," the AI ​​will determine that this is a severe headache.

[0031] "Example 3"

[0032] Finally, the generative AI selects an appropriate medical institution based on the symptoms it has determined. This selection is made taking into account the size and specialty of the medical institution, as well as the severity and type of the user's symptoms. For example, in the case of a severe headache, the AI ​​will recommend a large medical institution with a neurosurgery department.

[0033] The process flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The user enters details of their symptoms and physical condition into a form on a website or smartphone application. For example, they may describe a specific symptom such as "I have a severe headache."

[0036] Step 2: The generative AI analyzes the information input by the user. This AI uses machine learning algorithms and natural language processing techniques to accurately identify symptoms from the user's input.

[0037] Step 3: The generative AI determines the severity and type of the symptom. For example, if a user types, "I have a bad headache," the AI ​​will determine that this is a severe headache.

[0038] Step 4: An appropriate medical institution is selected based on the symptoms determined by the generative AI. This selection is made taking into consideration factors such as the size and specialty of the medical institution, as well as the severity and type of the user's symptoms. Step 5: Information about the medical institution selected by the AI ​​is presented to the user. For example, in the case of a severe headache, the AI ​​will recommend a large medical institution with a neurosurgery department, and provide the user with information such as the name, location, and contact information of that institution.

[0039] Example 1

[0040] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0041] In conventional medical systems, even if users input their own symptoms and physical condition, it was difficult to properly analyze the information and provide advice and diagnosis results quickly and accurately. In addition, there was a lack of functionality to select appropriate medical institutions based on the severity and type of symptoms, which caused problems such as users taking a long time to find the appropriate medical institution.

[0042] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0043] In this invention, the server includes a means for receiving input information from a user, a means for transmitting the input information to the server, a means for the server to receive the input information and store it in a database, a means for the server to analyze the stored data and generate a prompt sentence to be input to the generative AI model, a means for the generative AI model to generate advice or a diagnosis result based on the prompt sentence, a means for the server to transmit the generated result to the user's terminal, and a means for the user to confirm the result. This allows the user to obtain quick and accurate advice or a diagnosis result based on his or her own symptoms and physical condition. In addition, since an appropriate medical institution can be selected based on the severity and type of symptoms, the user can quickly find an appropriate medical institution.

[0044] The "means for receiving input information from a user" refers to a means for providing an interface for a user to input their own symptoms and physical condition.

[0045] The "means for transmitting the input information to the server" refers to a communication means for transmitting the information input by the user to the server.

[0046] "Means by which the server receives input information and stores it in a database" refers to means by which the server receives information sent by a user and stores the information in a database.

[0047] "Means for the server to analyze the stored data and generate prompt sentences to input into the generative AI model" refers to means for the server to analyze information stored in the database and generate prompt sentences to input into the generative AI model.

[0048] "Means by which a generative AI model generates advice or a diagnostic result based on a prompt sentence" refers to means by which a generative AI model generates appropriate advice or a diagnostic result based on a prompt sentence.

[0049] "Means by which the server transmits the generated results to the user's terminal" refers to the means by which the server transmits the results obtained from the generative AI model to the user's terminal.

[0050] "Means for the user to check the results" refers to the means by which the user can check the generated advice and diagnosis results through the terminal.

[0051] The present invention is a system for receiving input information from a user and providing appropriate advice and diagnostic results based on the information. A specific embodiment of this system will be described below.

[0052] First, users access a form on a website or smartphone application and enter details about their symptoms and physical condition, such as "I have a headache" or "I have a fever."

[0053] The device then sends the information entered by the user to the server, using the HTTPS protocol to transmit the data securely.

[0054] The server receives the input information sent from the terminal and stores it in a database such as MySQL (registered trademark) or PostgreSQL. The stored data is used for later analysis.

[0055] The server analyzes the stored data and generates prompts based on the user's symptoms and physical condition, such as "The user has a headache. What can be done to alleviate this?"

[0056] Next, the server sends the generated prompt sentence to a generative AI model (e.g., GPT-4 (registered trademark)). The generative AI model generates appropriate advice or diagnostic results based on the prompt sentence. For example, it generates a result such as "If you have a headache, it is recommended that you drink plenty of fluids and get plenty of rest."

[0057] The server then transmits the results received from the generative AI model to the user's device, again using the HTTPS protocol.

[0058] Finally, the user can check the results displayed on the device, allowing them to receive appropriate advice and diagnosis results for their symptoms and physical condition.

[0059] As a concrete example, if a user enters "I have a headache," the following prompt sentence is generated:

[0060] "The user is having a headache. What can be done about it?"

[0061] In this way, the system provides appropriate advice and diagnostic results based on the user's input. The hardware used includes servers, terminals, and databases, while the software includes websites, smartphone applications, and generative AI models (e.g., GPT-4).

[0062] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0063] Step 1:

[0064] The user accesses the interface and inputs symptoms and physical condition.

[0065] Specifically, the user enters information such as "I have a headache" or "I have a fever" into a form on a website or smartphone application.

[0066] Input: Information about the user's symptoms and health condition

[0067] Output: Input information is saved to the device

[0068] Step 2:

[0069] The terminal transmits the input information to the server.

[0070] Specifically, the terminal uses the HTTPS protocol to transmit the information entered by the user to the server.

[0071] Input: Information entered by the user

[0072] Output: The server receives the input information.

[0073] Step 3:

[0074] The server receives the input information and stores it in a database.

[0075] Specifically, the server stores the received information in a database such as MySQL or PostgreSQL.

[0076] Input: Input information sent from the device

[0077] Output: Information stored in the database

[0078] Step 4:

[0079] The server analyzes the stored data and generates prompt sentences to input into the generative AI model.

[0080] Specifically, the server analyzes the information stored in the database and generates a prompt such as, "The user has a headache. What can be done to remedy this?"

[0081] Input: Information stored in a database

[0082] Output: Generated prompt statement

[0083] Step 5:

[0084] The server sends the generated prompt to a generative AI model (e.g., GPT-4).

[0085] Specifically, the server sends the generated prompt text to GPT-4.

[0086] Input: Generated prompt text

[0087] Output: The prompt sent to the generative AI model

[0088] Step 6:

[0089] A generative AI model generates advice and diagnostic results based on the prompt.

[0090] Specifically, GPT-4 generates advice and diagnostic results such as, "If you have a headache, it is recommended that you drink plenty of fluids and get plenty of rest."

[0091] Input: Prompt statement

[0092] Output: The advice and diagnostics generated.

[0093] Step 7:

[0094] The server transmits the generated results to the user's terminal.

[0095] Specifically, the server sends the results received from the generative AI model to the user's device using the HTTPS protocol.

[0096] Input: Generated advice and diagnostic results

[0097] Output: The results sent to the user's terminal.

[0098] Step 8:

[0099] The user checks the results.

[0100] As a specific operation, the user checks the advice and diagnosis results displayed on the terminal.

[0101] Input: The result sent to the terminal

[0102] Output: Advice and diagnostic results confirmed by the user

[0103] (Application example 1)

[0104] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0105] Conventional food delivery services were unable to suggest appropriate meal menus based on the user's physical condition or symptoms. This made it difficult for users to choose meals that suited their own health condition, and they were unable to adequately manage their health. Furthermore, suggesting appropriate meal menus required specialized knowledge, which was a high hurdle for general users.

[0106] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0107] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for proposing an appropriate meal menu based on the symptoms, and a means for ordering in cooperation with a food delivery service based on the proposed meal menu. This allows the user to easily propose and order an appropriate meal menu based on their own physical condition and symptoms.

[0108] The "means for receiving input information from a user" refers to a means for providing an interface for a user to input their own physical condition and symptoms.

[0109] A "generative AI means" is a means that uses artificial intelligence technology to determine symptoms based on information input by the user and make appropriate suggestions.

[0110] The "means for determining symptoms" refers to a means for analyzing information input by the user and identifying the user's physical condition and symptoms based on that information.

[0111] A "means for suggesting an appropriate meal menu" is a means for presenting an optimal meal menu to a user based on symptoms determined by a generative AI means.

[0112] The "means for ordering in cooperation with a food delivery service" refers to a means for actually ordering food in cooperation with a food delivery service based on the proposed meal menu.

[0113] A system for implementing the present invention includes a means for receiving input information from a user, a generative AI means, a means for suggesting an appropriate meal menu, and a means for placing an order in cooperation with a food delivery service.

[0114] System Configuration

[0115] 1. How to receive user input:

[0116] Users enter their physical condition and symptoms using a smartphone application or a website form.

[0117] Example: A user types, "I have a bit of a cold and my throat hurts."

[0118] 2. Generative AI means:

[0119] Using the OpenAI (registered trademark) API, the system analyzes the user's input information and generates an appropriate meal menu.

[0120] Example: Prompt: "User's symptoms: cold and sore throat. Please suggest suitable meals."

[0121] 3. How to suggest appropriate meals:

[0122] The meal menu generated by the generative AI means is presented to the user.

[0123] Example: Show the generated menu "Hot chicken soup and orange juice" to the user.

[0124] 4. How to order through food delivery services:

[0125] Based on the suggested meal menu, an order is placed using the food delivery service's API.

[0126] Example: Send a request to a food delivery service API to order "hot chicken soup and orange juice."

[0127] Hardware and software used

[0128] Hardware: Smartphones, servers

[0129] Software: Python (registered trademark), OpenAI API, food delivery service API

[0130] Data processing and calculation

[0131] 1. Getting user input:

[0132] Users enter their physical condition and symptoms through a smartphone application or a website form.

[0133] Example: A user types, "I have a bit of a cold and my throat hurts."

[0134] 2. Using generative AI models:

[0135] Using OpenAI's API, an appropriate meal menu is generated based on user input information.

[0136] Example: Prompt: "User's symptoms: cold and sore throat. Please suggest suitable meals."

[0137] 3. Partnering with food delivery services:

[0138] The generated menu is sent to the food delivery service's API and an order is placed.

[0139] Example: Send a request to a food delivery service API to order "hot chicken soup and orange juice."

[0140] Examples

[0141] If a user types in "I have a cold and a sore throat," the generative AI model suggests "hot chicken soup and orange juice." Based on this suggestion, the API of a food delivery service is used to order "hot chicken soup and orange juice" to the user's address.

[0142] Examples of prompt statements

[0143] User Symptoms: Cold and sore throat

[0144] Please suggest a suitable meal plan.

[0145] In this way, it becomes possible to easily suggest and order meals that suit the user's physical condition.

[0146] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0147] Step 1:

[0148] Users enter their physical condition and symptoms through a smartphone application or a website form.

[0149] Input: User's physical condition and symptoms (e.g. "I have a cold and my throat hurts")

[0150] Output: User input information

[0151] Step 2:

[0152] The terminal transmits the user's input information to the server.

[0153] Input: User input information

[0154] Output: User input sent to the server

[0155] Step 3:

[0156] Based on the user's input information received by the server, a generative AI model is used to determine symptoms and generate an appropriate meal menu.

[0157] Input: User input information

[0158] Data processing: Input prompts into the generative AI model to generate an appropriate meal menu

[0159] Example prompt: "User's symptoms: cold and sore throat. Please suggest suitable meals."

[0160] Output: The generated meal menu (e.g. "Hot chicken soup and orange juice")

[0161] Step 4:

[0162] The server transmits the generated meal menu to the user's terminal.

[0163] Input: Generated meal menu

[0164] Output: The meal menu sent to the user's device.

[0165] Step 5:

[0166] The user reviews the proposed meal menu and confirms the order.

[0167] Input: Generated meal menu

[0168] Output: User's order confirmation information

[0169] Step 6:

[0170] The terminal transmits the user's order confirmation information to the server.

[0171] Input: User's order confirmation information

[0172] Output: Order confirmation information sent to the server

[0173] Step 7:

[0174] The server places an order using the food delivery service's API based on the order confirmation information.

[0175] Input: User's order confirmation information

[0176] Data processing: Send an order request to the food delivery service API

[0177] Output: An order request sent to a food delivery service.

[0178] Step 8:

[0179] A food delivery service receives the order and delivers the meal to the user's address.

[0180] Input: An order request sent to a food delivery service

[0181] Output: Meals delivered to the user's address.

[0182] Example 2

[0183] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0184] In conventional medical systems, it was difficult for users to accurately assess their own symptoms and select an appropriate medical institution. In addition, medical institutions were often not selected appropriately based on the severity and type of symptoms, and users were sometimes unable to receive appropriate medical care. This increased the risk of users' health conditions worsening.

[0185] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0186] In this invention, the server includes a means for receiving input information from a user, a means for transmitting the input information to the server, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and a means for returning the determination result to the user, thereby enabling the user to accurately determine his / her own symptoms and select an appropriate medical institution.

[0187] "User" refers to an individual who utilizes the system to input information about their symptoms.

[0188] "Input information" refers to text data regarding symptoms that a user provides to the system.

[0189] "Server" refers to a computer system that receives input information sent by a user and analyzes it using generative AI means.

[0190] "Generative AI methods" refers to artificial intelligence technologies that use machine learning algorithms and natural language processing techniques to determine symptoms from information input by the user.

[0191] "Symptom" means any physical or mental abnormality or discomfort experienced by a User.

[0192] "Severity" refers to an indicator of the seriousness or urgency of symptoms.

[0193] "Medical institution" refers to a facility that provides appropriate medical care and treatment for the user's symptoms.

[0194] "Scale" refers to the size of the medical institution and the range of medical services it can provide.

[0195] "Expertise" refers to the specialized knowledge and skills that a medical institution has regarding specific symptoms or diseases.

[0196] "Return" refers to the server sending back the results determined by the generative AI means to the user's device.

[0197] This invention is a system that allows users to input information about their symptoms and select an appropriate medical institution based on that information. The system receives the user's input information, uses a generative AI model to determine the severity and type of symptoms, and returns the results to the user.

[0198] Hardware and Software Used

[0199] Hardware: Servers, terminals (user devices)

[0200] Software: Generative AI models, Natural Language Processing (NLP) libraries, Machine Learning algorithms

[0201] Program Processing

[0202] 1. Receiving user input information

[0203] A user uses a terminal to input information about a symptom, for example, "I have a bad headache."

[0204] The terminal receives the information entered by the user.

[0205] 2. Sending input information

[0206] The terminal sends the received user input information to the server, using the HTTPS protocol to transmit the data securely.

[0207] 3. Analysis of information

[0208] The server passes the received user input information to the generative AI model.

[0209] Generative AI models use natural language processing (NLP) libraries to parse user input, tokenizing the text and extracting meaning.

[0210] For example, the text "I have a severe headache" is analyzed to extract the symptom "headache" and the severity level "severe."

[0211] 4. Determining the severity and type of symptoms

[0212] The generative AI model uses machine learning algorithms to determine the severity and type of symptoms from the analyzed information.

[0213] For example, if you say "I have a bad headache," it will determine that you have a severe headache. This determination is made using past data and pre-trained models.

[0214] 5. Returning the results

[0215] The server sends the results of the generative AI model's judgment back to the terminal.

[0216] The device will display the result to the user, for example "severe headache."

[0217] Examples

[0218] A user types "I have a bad headache" into a terminal.

[0219] The terminal transmits this information to the server.

[0220] The server passes this information to the generative AI model.

[0221] The generative AI model uses NLP libraries to parse the text "I have a bad headache" and then uses machine learning algorithms to determine whether it is a severe headache.

[0222] The server returns the result of this decision to the terminal.

[0223] The device tells the user "Severe Headache."

[0224] Examples of prompt statements

[0225] "Analyze the symptom information entered by the user and determine the severity and type of the symptom. For example, if the user enters 'I have a bad headache,' determine that it is a severe headache."

[0226] In this way, the system analyzes the user's input information and makes appropriate decisions.

[0227] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0228] Step 1:

[0229] Receiving user input information

[0230] A user uses a terminal to input information about a symptom, for example, "I have a bad headache."

[0231] Input: Text information entered by the user (e.g. "I have a bad headache")

[0232] The terminal receives the information entered by the user.

[0233] Output: Received text information (e.g. "I have a bad headache")

[0234] Step 2:

[0235] Sending input information

[0236] The terminal transmits the received user input information to the server, using the HTTPS protocol to transmit the data securely.

[0237] Input: Received text information (e.g. "I have a bad headache")

[0238] Output: Text information sent to the server (e.g. "I have a bad headache")

[0239] Step 3:

[0240] Analysis of information

[0241] The server passes the received user input information to the generative AI model.

[0242] Input: Text information sent to the server (e.g. "I have a bad headache")

[0243] Generative AI models use natural language processing (NLP) libraries to parse user input, specifically tokenizing the text and extracting meaning.

[0244] Specific behavior: Use an NLP library (e.g. spaCy or NLTK) to parse the text "I have a bad headache" and extract the symptom "headache" and the severity "severe".

[0245] Output: Extracted keywords (e.g. "headache", "severe")

[0246] Step 4:

[0247] Determining the severity and type of symptoms

[0248] The generative AI model uses machine learning algorithms to determine the severity and type of symptoms from the analyzed information.

[0249] Input: Extracted keywords (e.g. "headache", "severe")

[0250] What it does: Uses machine learning algorithms (e.g. random forests or neural networks) to identify a severe headache based on the keyword "severe."

[0251] Output: Judgment result (e.g. "severe headache")

[0252] Step 5:

[0253] Returning the results

[0254] The server sends the results of the generative AI model's judgment back to the terminal.

[0255] Input: Judgment result (e.g. "severe headache")

[0256] The device will then display the results to the user, for example showing "severe headache" on the app screen.

[0257] Specific behavior: Performs UI update processing to display the results on the device screen.

[0258] Output: The result displayed to the user (e.g. "Severe headache")

[0259] (Application example 2)

[0260] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0261] Conventional health monitoring systems have difficulty accurately determining the severity and type of a user's symptoms, which can delay appropriate responses. In addition, they lack the ability to quickly issue alerts based on the severity of symptoms, which can delay responses in emergencies. This creates a need for real-time monitoring of users' health conditions and the ability to quickly respond when abnormalities are detected.

[0262] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0263] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and a means for determining the severity of the symptoms and issuing an alert based on the severity. This makes it possible to monitor the user's health condition in real time and quickly respond when an abnormality is detected.

[0264] "Means for receiving input information from the user" refers to an interface that allows the user to input their own symptoms and health condition, and is a function that receives information through devices such as smartphones and smart glasses.

[0265] "Generative AI means" refers to artificial intelligence functions that use machine learning algorithms and natural language processing techniques to analyze information input by users and determine the severity and type of symptoms.

[0266] The "means of determining symptoms" refers to a function that uses generative AI means to analyze symptoms from information input by the user and identify their severity and type.

[0267] "Means for selecting an appropriate medical institution" is a function that suggests the most suitable medical institution to the user based on the diagnosed symptoms.

[0268] The "means for determining the severity of symptoms" refers to a function that uses generative AI means to evaluate the severity of symptoms from information input by the user and determines an appropriate response based on the results.

[0269] "Means of issuing an alert" is a function that issues a warning to users and relevant parties when symptoms are severe, encouraging them to take prompt action.

[0270] As an embodiment of the present invention, a security health monitor system will be described as an example. The purpose of this system is to monitor the health status of a user in real time and to respond quickly when an abnormality is detected.

[0271] System Configuration

[0272] The system consists of the following main components:

[0273] 1. User terminal: A device such as a smartphone or smart glasses that provides the means to receive input information from the user.

[0274] 2. Server: A central processing unit containing generative AI means to analyse user input and determine the severity and type of symptoms.

[0275] 3. Alert system: Provide a means to issue alerts based on the severity of symptoms.

[0276] Program Processing

[0277] User Device

[0278] The user device provides an interface for users to input their symptoms and health conditions. For example, it receives text input such as "I have a bad headache" through a smartphone application.

[0279] server

[0280] The server performs the following process.

[0281] 1. Receiving input information: Receives input information sent from the user terminal.

[0282] 2. Analysis using generative AI: The received input information is analyzed using a generative AI model (for example, the DistilBERT model using the Transformers library) to determine the severity and type of symptoms.

[0283] 3. Determination of severity: The severity of symptoms is evaluated based on the analysis results. For example, if the analysis result is "NEGATIVE," it is judged to be severe, and if it is "POSITIVE," it is judged to be mild.

[0284] 4. Alerting: If symptoms are severe, an alert system will be used to warn users and relevant parties and encourage rapid response.

[0285] Alert System

[0286] The alert system issues warnings to users and related parties based on instructions from the server. For example, it uses the smartphone's notification function to display a message such as "Severe headache detected. Please seek medical attention immediately."

[0287] Hardware and Software Used

[0288] Hardware: Smartphones, smart glasses

[0289] Software: Python, Transformers library

[0290] Examples

[0291] When a user types "I have a bad headache" into a smartphone application, the system determines this to be a severe headache and issues an alert.

[0292] "I have a terrible headache"

[0293] "I feel a bit sick"

[0294] "My chest hurts"

[0295] In this way, the security health monitor system is able to monitor the user's health status in real time and respond quickly if an abnormality is detected.

[0296] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0297] Step 1:

[0298] The user launches the application on their smartphone and enters their symptoms and health condition.

[0299] Input: The user enters some text information, such as "I have a bad headache."

[0300] Specific action: The user enters symptoms into the application's text box and presses the submit button.

[0301] Step 2:

[0302] The terminal sends the user's input information to the server.

[0303] Input: Text information entered by the user.

[0304] Output: Text information sent to the server.

[0305] Specific operation: The smartphone application sends the entered text information to the server as an HTTP request.

[0306] Step 3:

[0307] The input information received by the server is passed to the generative AI model for analysis.

[0308] Input: Text information sent from the device.

[0309] Output: Analysis results (symptom severity and type).

[0310] Specific operation: The server analyzes the received text information using the Transformers library to determine the severity and type of symptoms.

[0311] Step 4:

[0312] The server determines the severity of the symptoms based on the analysis results.

[0313] Input: Analysis results of the generative AI model.

[0314] Output: Symptom severity (e.g. severe, mild).

[0315] Specific operation: The server evaluates the analysis results, determining that the problem is severe if the result is "NEGATIVE" or mild if the result is "POSITIVE".

[0316] Step 5:

[0317] The server decides whether to raise an alert based on the severity of the symptoms.

[0318] Input: Symptom severity.

[0319] Output: Instructions to send an alert.

[0320] Specific Actions: If a server is deemed critical, instructions are sent to issue an alert to the alert system.

[0321] Step 6:

[0322] The alert system will warn users and relevant parties.

[0323] Input: Alert sending instruction from the server.

[0324] Output: A warning message for the user or interested parties.

[0325] Specific action: Using the smartphone's notification function, a message such as "Severe headache has been detected. Please seek medical attention immediately" is displayed.

[0326] In this way, the security health monitor system is able to monitor the user's health status in real time and respond quickly if an abnormality is detected.

[0327] Example 3

[0328] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0329] Conventional medical institution selection systems make it difficult for users to quickly find the medical institution best suited to their symptoms. In addition, the accuracy of selecting an appropriate medical institution based on the severity and type of symptoms is low, and there is a problem that it takes a long time for users to receive appropriate treatment.

[0330] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for receiving input information from a user, a means for transmitting the input information to the server, a means for generating a prompt sentence based on the input information and transmitting it to the generative AI model, a means for the generative AI model to analyze symptoms based on the prompt sentence and select an appropriate medical institution, and a means for returning the selection result to the user. This enables the user to quickly find a medical institution that is best suited to his or her symptoms.

[0331] "User" refers to an individual who uses the system to input their symptoms and find an appropriate medical institution.

[0332] "Input information" refers to data entered by the user in text format describing their symptoms.

[0333] "Server" refers to a computer system that receives input information from a user, sends prompts to the generative AI model, and sends output from the generative AI model back to the user.

[0334] A "prompt sentence" refers to an instruction sentence that is generated based on user input information and sent to a generative AI model.

[0335] A "generative AI model" refers to an artificial intelligence model that analyzes a user's symptoms based on a prompt text and selects an appropriate medical institution.

[0336] "Symptom Severity" refers to a user-entered scale for rating the severity of a symptom.

[0337] "Size of medical institution" refers to the size of the medical institution and the level of its facilities.

[0338] "Symptom type" refers to the classification of the specific symptoms reported by the user.

[0339] "Expertise of a medical institution" refers to the specialized knowledge and skills that a medical institution has for specific symptoms or diseases.

[0340] "Selection results" refers to information about medical institutions recommended to users as a result of analysis by the generative AI model.

[0341] This invention is a system that allows users to input their symptoms and find an appropriate medical institution. The system receives the user's input information, analyzes the symptoms using a generative AI model based on the input, and selects an appropriate medical institution.

[0342] Hardware and Software Used

[0343] Hardware: Servers, terminals (user devices)

[0344] Software: Generative AI models (e.g. GPT-4), database management systems (e.g. MySQL), healthcare information systems

[0345] Program Processing

[0346] 1. User enters symptoms:

[0347] The user inputs symptoms in text format using their own device (such as a smartphone or computer). For example, the user inputs "I have a headache." This input is done through a dedicated application or web form.

[0348] 2. The device sends the input data to the server:

[0349] The device sends the symptom data entered by the user to the server as an HTTP request. For example, the device uses a "POST" request to send the input data to a specific endpoint on the server.

[0350] 3. The server sends a prompt to the generative AI model:

[0351] The server sends a prompt to the generative AI model based on the received symptom data. Specifically, the server analyzes the symptom data and generates an appropriate prompt. For example, it generates a prompt such as "The user is complaining of a headache. Please select an appropriate medical institution" and sends it to the generative AI model.

[0352] 4. Generative AI model analyzes symptoms and selects appropriate medical institution:

[0353] The generative AI model receives the prompt and analyzes the user's symptoms. Based on the analysis results, it selects an appropriate medical institution. In this case, the generative AI model refers to a database of medical institutions and takes into account the size and specialty of the medical institution, as well as the severity and type of the user's symptoms. For example, in the case of a severe headache, the generative AI model will recommend a large medical institution with a neurosurgery department.

[0354] 5. The server sends the selection back to the user:

[0355] The server sends the information about medical institutions obtained from the generative AI model back to the user's device. Specifically, the server receives the output of the generative AI model, converts it into a format that is easy for the user to understand, and sends it to the user. For example, the server generates a message saying, "We recommend large medical institutions with neurosurgery capabilities" and sends it to the user's device.

[0356] Examples

[0357] User input: The user types "I have a headache" into the terminal.

[0358] Prompt for generative AI model: "The user is complaining of a headache. Please select the appropriate medical institution."

[0359] Output of the generative AI model: “This suggests a severe headache and would recommend a larger medical institution with a neurosurgery department.”

[0360] In this way, the user can quickly find the medical institution that is best suited to his / her symptoms. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0361] Step 1:

[0362] The user enters the symptoms.

[0363] A user inputs symptoms in text format using their own device (such as a smartphone or PC). For example, the user inputs "I have a headache." This input is done through a dedicated application or a web form. The input data is saved in text format on the device.

[0364] Step 2:

[0365] The terminal transmits the input data to the server.

[0366] The terminal sends the symptom data entered by the user to the server. The data is sent as an HTTP request. For example, the terminal uses a "POST" request to send the input data to a specific endpoint on the server. The input data is sent to the server in text format.

[0367] Step 3:

[0368] The server sends prompts to the generative AI model.

[0369] The server sends a prompt to the generative AI model based on the received symptom data. Specifically, the server analyzes the symptom data and generates an appropriate prompt sentence. For example, it generates a prompt sentence such as "The user is complaining of a headache. Please select an appropriate medical institution" and sends it to the generative AI model. The prompt sentence is sent to the generative AI model in text format.

[0370] Step 4:

[0371] The generative AI model analyzes symptoms and selects the appropriate medical institution.

[0372] The generative AI model receives the prompt and analyzes the user's symptoms. Based on the analysis results, it selects an appropriate medical institution. In this case, the generative AI model refers to a database of medical institutions and takes into account the size and specialty of the medical institution, as well as the severity and type of the user's symptoms. For example, in the case of a severe headache, the generative AI model will recommend a large medical institution with a neurosurgery department. The analysis results are returned to the server in text format.

[0373] Step 5:

[0374] The server sends the selection back to the user.

[0375] The server returns the medical institution information obtained from the generative AI model to the user's device. Specifically, the server receives the output of the generative AI model, converts it into a format that is easy for the user to understand, and sends it to the user. For example, the server generates a message saying, "Large medical institutions with neurosurgery departments are recommended," and sends it to the user's device. The selection result is displayed in text format on the user's device.

[0376] (Application example 3)

[0377] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0378] In modern society, it is important for users to find a prompt and appropriate medical institution when they feel unwell. However, it is not easy to select the most appropriate medical institution depending on the severity and type of symptoms. In addition, while a prompt response is required in an emergency, contacting the appropriate medical institution may be delayed. To solve these problems, a system is needed that can accurately assess the user's symptoms and quickly recommend an appropriate medical institution.

[0379] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0380] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and a means for automatically contacting the nearest emergency medical institution in an emergency based on the selected medical institution. This allows the user to quickly find an appropriate medical institution when feeling unwell. In addition, by automatically contacting the nearest emergency medical institution in an emergency, a quick response is possible.

[0381] "Means for receiving input information from a user" refers to a device or software that provides an interface for a user to input information about their symptoms or physical condition.

[0382] A "generative AI means" is a system that uses artificial intelligence technology to analyze information input by the user and determine symptoms based on that information.

[0383] The "means for selecting an appropriate medical institution" refers to an algorithm or process for recommending the most suitable medical institution to the user based on the symptoms determined by the generative AI means.

[0384] "Means of automatically contacting the nearest emergency medical institution in the event of an emergency" refers to a communications system that automatically contacts the nearest emergency medical institution based on the user's current location information if the user's symptoms are determined to be severe.

[0385] The following system configuration will be described as an embodiment of the present invention.

[0386] System Configuration

[0387] The system includes a means for receiving input information from a user, a generative AI means, a means for selecting an appropriate medical institution, and a means for automatically contacting the nearest emergency medical institution in the event of an emergency.

[0388] A means of receiving input information from the user

[0389] Users input information about their symptoms and physical condition through a smartphone app. The interface supports text and voice input and is designed to make it easy for users to provide information.

[0390] Generative AI means

[0391] The server receives input information from the user and analyzes it using a generative AI model. This analysis determines the user's symptoms and identifies their severity and type. The generative AI model uses the OpenAI API.

[0392] How to choose the right medical facility

[0393] Based on the symptoms determined by the generative AI means, the server selects an appropriate medical institution. This selection is made taking into account the size and specialty of the medical institution, the user's current location, etc. For example, in the case of a severe headache, a large medical institution with a neurosurgery department will be recommended.

[0394] A means to automatically contact the nearest emergency medical facility in the event of an emergency

[0395] If the user's symptoms are deemed severe, the server will automatically contact the nearest emergency medical facility based on the user's current location, using the Requests library to send HTTP requests.

[0396] Examples

[0397] If a user experiences a "severe headache," they input their symptoms into a smartphone app. The generative AI model analyzes this information and recommends "large medical institutions with neurosurgery capabilities." In addition, in the event of an emergency, the app automatically contacts the nearest emergency medical institution.

[0398] Examples of prompt statements

[0399] "User symptoms: Severe headache.\nPlease recommend appropriate medical help."

[0400] In this way, a system can be realized that helps users quickly access appropriate medical institutions.

[0401] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0402] Step 1:

[0403] The user launches the smartphone app and enters information about their symptoms and physical condition.

[0404] Input: User's symptoms (e.g., severe headache)

[0405] Output: The entered symptom information is sent to the server.

[0406] Step 2:

[0407] A server receives the symptom information sent by the user.

[0408] Input: Symptom information submitted by the user

[0409] Output: The received symptom information is passed to the generative AI model.

[0410] Step 3:

[0411] The server uses a generative AI model to analyze the received symptom information and determine the user's symptoms.

[0412] Input: Received symptom information

[0413] Output: Parsed symptom information (e.g., severe headache)

[0414] Step 4:

[0415] The server selects an appropriate medical institution based on the analyzed symptom information.

[0416] Input: Parsed symptom information

[0417] Output: Recommended medical institution information (e.g., a large medical institution with neurosurgery)

[0418] Step 5:

[0419] The server sends the recommended medical institution information to the user's smartphone app.

[0420] Input: Recommended medical institution information

[0421] Output: Medical institution information displayed on the user's smartphone app

[0422] Step 6:

[0423] If the user's symptoms are determined to be severe, the server will automatically contact the nearest emergency medical facility based on the user's current location information.

[0424] Input: User's current location information, severe symptoms information

[0425] Output: Emergency call sent to the nearest emergency medical facility

[0426] Step 7:

[0427] The server notifies the user's smartphone app of the results of the emergency contact.

[0428] Input: Emergency message transmission result

[0429] Output: A notification displayed on the user's smartphone app (e.g., emergency medical services have been contacted)

[0430] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0431] "Example 1"

[0432] As one embodiment of the present invention, a system is provided that includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and an emotion engine for recognizing the user's emotions. In this system, when a user inputs details of his or her symptoms and physical condition, the generative AI analyzes the information and determines the severity and type of symptoms. Furthermore, the emotion engine analyzes the user's emotions and feeds back the results to the generative AI. The generative AI combines the symptom information and emotion information to select an appropriate medical institution. Specifically, if a user inputs "I have a severe headache" and the emotion engine detects the user's anxiety or fear, the generative AI will recommend a large medical institution with a neurosurgery department, taking into account the severe headache and high anxiety level.

[0433] "Example 2"

[0434] As one embodiment of the present invention, a system is provided that includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and an emotion engine for recognizing the user's emotions. In this system, when a user inputs details of his or her symptoms and physical condition, the generative AI analyzes the information and determines the severity and type of symptoms. Furthermore, the emotion engine analyzes the user's emotions and feeds back the results to the generative AI. The generative AI combines the symptom information and emotion information to select an appropriate medical institution. Specifically, if a user inputs "I have a severe headache" and the emotion engine detects the user's anxiety or fear, the generative AI will recommend a large medical institution with a neurosurgery department, taking into account the severe headache and high anxiety level.

[0435] "Example 3"

[0436] As one embodiment of the present invention, a system is provided that includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and an emotion engine for recognizing the user's emotions. In this system, when a user inputs details of his or her symptoms and physical condition, the generative AI analyzes the information and determines the severity and type of symptoms. Furthermore, the emotion engine analyzes the user's emotions and feeds back the results to the generative AI. The generative AI combines the symptom information and emotion information to select an appropriate medical institution. Specifically, if a user inputs "I have a severe headache" and the emotion engine detects the user's anxiety or fear, the generative AI will recommend a large medical institution with a neurosurgery department, taking into account the severe headache and high anxiety level.

[0437] The process flow of each embodiment will be described below.

[0438] "Example 1"

[0439] Step 1: The user enters their symptoms and physical condition into the system. For example, they enter information like "I have a bad headache."

[0440] Step 2: The generative AI analyzes the input from the user and determines the severity and type of symptoms. In this example, the AI ​​determines that the user is experiencing a severe headache based on the input "I have a bad headache."

[0441] Step 3: The emotion engine analyzes the user's emotions. In this example, the emotion engine determines that the user is feeling anxious or scared based on the expressions and language used when providing the input information.

[0442] Step 4: The generative AI combines the symptom information with the emotion information to select the appropriate medical institution. In this example, the AI ​​takes into account the severe headaches and high anxiety levels and recommends a large medical institution with neurosurgery.

[0443] Example 1

[0444] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0445] In conventional medical systems, even if a user inputs their symptoms, it can be difficult to select an appropriate medical institution based on that information alone. In addition, because the user's emotional state is not taken into account, the system may not be able to select an appropriate medical institution. This can lead to a risk that the user will not be able to visit an appropriate medical institution and their symptoms may worsen.

[0446] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0447] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, an emotion analysis means for recognizing the emotions of the user, a means for feeding back the results of the emotion analysis means to the generative AI means, and a means for the generative AI means to integrate and analyze the symptom information and the emotion information. This makes it possible to comprehensively analyze the user's symptoms and emotions and select the most appropriate medical institution.

[0448] The "means for receiving input information from a user" refers to a means for providing an interface for a user to input their own symptoms and physical condition.

[0449] A "generative AI means" is a means that uses artificial intelligence technology to analyze information input by the user and determine the severity and type of symptoms.

[0450] The "means for selecting an appropriate medical institution" refers to a means for selecting the medical institution most suitable for the user based on the symptoms determined by the generative AI means.

[0451] The "emotion analysis means" refers to a means for recognizing and analyzing emotions from information input by a user.

[0452] The "means of providing feedback" refers to the means of providing the results of the emotion analysis means to the generative AI means.

[0453] An "integrative analysis means" is a means by which a generative AI means combines symptom information and emotional information for analysis.

[0454] This invention is a system that provides an interface for users to input their symptoms and physical condition. Users input information through a form on a website or a smartphone application. This interface is designed to allow users to input information easily.

[0455] The server sends the input information received from the user to the generative AI. The generative AI analyzes this information and determines the severity and type of symptoms. For example, if the user inputs "I have a bad headache," the generative AI will use that information to assess the severity of the headache. Natural language processing (NLP) technology is used in generative AI.

[0456] In addition, the server also transmits the user's input information to the emotion analysis means. The emotion analysis means analyzes emotions from the user's input and feeds the results back to the generative AI. For example, it detects anxiety or fear from the user's input. The emotion analysis means uses text analysis technology.

[0457] The generative AI integrates and analyzes symptom information and emotional information. For example, it analyzes the combination of severe headache and high anxiety level. Based on this integrated information, the generative AI selects an appropriate medical institution. For example, taking into account severe headache and high anxiety level, it recommends a large medical institution with neurosurgery.

[0458] The server sends information about the medical institution selected by the generative AI to the terminal. The terminal displays the information about the medical institution recommended to the user. This allows the user to receive treatment at a prompt and appropriate medical institution.

[0459] As a concrete example, consider the case where a user uses a smartphone application to input "I have a severe headache." The emotion analysis means detects anxiety from the user's input. Taking into account the severe headache and high anxiety level, the generative AI recommends a large medical institution with a neurosurgery department. The server sends this information to the terminal, which then displays a message to the user saying, "Please visit the neurosurgery department of a nearby university hospital."

[0460] An example of a prompt is as follows:

[0461] "If a user types 'I have a bad headache' through a smartphone application and emotion analysis measures detect anxiety, what kind of medical institution will the generative AI recommend?"

[0462] The system aims to provide prompt and appropriate medical services by comprehensively analyzing the user's symptoms and emotions and selecting the most appropriate medical institution.

[0463] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0464] Step 1:

[0465] A user enters details about his / her symptoms and physical condition through a form on a website or smartphone application. For example, the user enters "I have a severe headache." The entered information is sent from the device to a server. The input data is in text format and includes detailed information about the user's symptoms and physical condition.

[0466] Step 2:

[0467] The server sends the input information received from the user to the generative AI. The generative AI uses natural language processing (NLP) technology to analyze the input information and determine the severity and type of symptoms. For example, it analyzes the information "I have a severe headache" and evaluates the severity of the headache. The analysis results are output as data on the severity and type of symptoms.

[0468] Step 3:

[0469] The server also transmits the user's input information to the emotion analysis means. The emotion analysis means uses text analysis techniques to analyze emotions from the user's input. For example, it detects anxiety or fear from the user's input. The analysis results are output as data on the user's emotional state.

[0470] Step 4:

[0471] The generative AI receives feedback from the emotion analysis tool and integrates the symptom information with the emotion information for analysis (for example, combining a severe headache with high anxiety levels). The integrated information is output as data for selecting the appropriate medical institution.

[0472] Step 5:

[0473] The generative AI selects an appropriate medical institution based on the integrated information. For example, it recommends a large medical institution with a neurosurgery department considering the severe headache and high anxiety level. The information of the selected medical institution is sent to the server.

[0474] Step 6:

[0475] The server sends information about the medical institution selected by the generative AI to the terminal. The terminal displays the information about the medical institution recommended to the user. For example, it displays a message saying, "Please visit the neurosurgery department of a nearby university hospital." This allows the user to visit an appropriate medical institution quickly.

[0476] (Application example 1)

[0477] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0478] Conventional medical institution selection systems can select an appropriate medical institution based on the user's symptoms, but they cannot take the user's emotional state into account, which means that they cannot provide optimal support in emergencies or for users with high anxiety. In addition, selecting a medical institution without considering the user's emotional state may reduce the user's satisfaction and sense of security.

[0479] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, an emotion engine for recognizing the user's emotions, a means for feeding back emotion information analyzed by the emotion engine to the generative AI means, and a means for the generative AI means to combine symptom information and emotion information to select an appropriate medical institution. This makes it possible to select an optimal medical institution that comprehensively considers the user's symptoms and emotional state.

[0480] "Means for receiving input information from the user" refers to an interface through which the user can input their own symptoms and physical condition, and can be realized through forms on a website or smartphone application, etc.

[0481] "Generative AI means" is an artificial intelligence technology that analyzes information input by users and determines the severity and type of symptoms.

[0482] The "means for selecting an appropriate medical institution" is a function for recommending the most suitable medical institution to the user based on the severity and type of symptoms determined by the generative AI means.

[0483] An "emotion engine" is a technology that analyzes emotions from user input information and feeds the results back to generative AI means.

[0484] The "means for feeding back emotion information to the generative AI means" is a function for providing emotion information analyzed by the emotion engine to the generative AI means and for combining and analyzing symptom information and emotion information.

[0485] "Means for selecting an appropriate medical institution by combining symptom information and emotional information" refers to a function in which the generative AI means comprehensively considers symptom information and emotional information to select the medical institution that is most suitable for the user.

[0486] A system for implementing this invention includes a means for receiving input information from a user, a generative AI means, a means for selecting an appropriate medical institution, an emotion engine, a means for feeding back emotion information to the generative AI means, and a means for selecting an appropriate medical institution by combining symptom information and emotion information.

[0487] System Programming

[0488] The system is implemented as a smartphone application, providing an interface for users to input their symptoms and physical condition, and analyzing them using generative AI methods and an emotion engine.

[0489] Processing Description

[0490] The server provides a form on the smartphone application to receive input information from the user. Through this form, the user inputs details of their symptoms and physical condition. The input information is then sent to the server.

[0491] The server uses generative AI to analyze the input information from the user and determine the severity and type of symptoms. This analysis uses artificial intelligence technology using Python. Specifically, a symptom analysis library is used.

[0492] Next, the server uses an emotion engine to analyze emotions from the user's input information. An emotion recognition library is used for this analysis. The emotion information analyzed by the emotion engine is fed back to the generative AI means.

[0493] The generative AI means combines and analyzes symptom information and emotion information to select an appropriate medical institution. For example, if a user inputs "severe headache" and the emotion engine detects "anxiety," the generative AI means will take into account the severe headache and high anxiety level and recommend a large medical institution with neurosurgery.

[0494] Examples

[0495] As a specific example, if a user inputs "I have a bad headache" and the emotion engine detects "anxiety," the following prompt sentence will be input to the generative AI model.

[0496] Example prompt:

[0497] User Symptoms: Severe headaches

[0498] User Sentiment: Anxiety

[0499] Recommended medical institution: Large medical institution (Neurosurgery)

[0500] Recommended action: Urgent action required. Go to nearest major medical facility.

[0501] In this way, it becomes possible to select the most appropriate medical institution that takes into consideration the user's symptoms and emotional state comprehensively.

[0502] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0503] Step 1:

[0504] The user starts the smartphone application and inputs their symptoms and physical condition. The input information is sent to the server through a form in the smartphone application. The input information includes specific symptoms, such as "I have a severe headache."

[0505] Step 2:

[0506] The server receives input information sent by the user. The received information is passed to the generative AI means, which analyzes the severity and type of symptoms. A symptom analysis library is used for this analysis. Based on the input information, the generative AI means determines the severity (e.g. high, medium, low) and type (e.g. headache, stomachache, etc.) of the symptom.

[0507] Step 3:

[0508] The server uses an emotion engine to analyze the user's emotions based on the analysis results. The emotion engine uses an emotion recognition library. It extracts emotions (e.g. anxiety, fear, relief, etc.) from the user's input information and feeds the results back to the generative AI means.

[0509] Step 4:

[0510] The server uses a generative AI to combine and analyze symptom information and emotion information to select an appropriate medical institution. For example, taking into account severe headaches and high anxiety levels, a large medical institution with a neurosurgery department is recommended. Based on the results of this analysis, information on recommended medical institutions is generated.

[0511] Step 5:

[0512] The server notifies the user of the generated information on recommended medical institutions. The notification function of the smartphone application is used for the notification. The user can check the information on the recommended medical institutions and contact the medical institutions if necessary.

[0513] Step 6:

[0514] The user contacts the recommended medical institution and undergoes a medical examination. The results of the examination and details of treatment can be input by the user into the application, and the information can be used for future analysis.

[0515] Example 2

[0516] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0517] Conventional medical support systems can select an appropriate medical institution based on the user's symptoms, but do not consider the user's emotional state when selecting a medical institution. As a result, they are unable to select a medical institution that appropriately reflects the user's feelings, such as anxiety or fear, and are therefore unable to provide sufficient satisfaction or peace of mind to the user.

[0518] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0519] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, an emotion analysis means for recognizing the emotions of the user, a means for feeding back the results of the emotion analysis means to the generative AI means, and a means for the generative AI means to integrate the symptom information and the emotion information to select an appropriate medical institution. This makes it possible to select a medical institution that takes into account not only the user's symptoms but also their emotional state.

[0520] "Means for receiving input information from a user" refers to a device or software that provides an interface for a user to input their own symptoms and physical condition and has the function of transmitting that information to the system.

[0521] A "generative AI means" is an artificial intelligence system that uses machine learning algorithms and natural language processing techniques to analyze information input by users and determine the severity and type of symptoms.

[0522] A "means for selecting an appropriate medical institution" is a device or software that has the function of selecting the medical institution most suitable for the user based on symptom information determined by the generative AI means.

[0523] An "emotion analysis means" is a device or software that has the function of analyzing emotions from the user's input information, voice, facial expressions, etc., and detecting emotional states such as anxiety or fear.

[0524] The "feedback means" refers to a device or software having a function of returning the emotional information obtained by the emotion analysis means to the generative AI means and integrating the symptom information and the emotional information.

[0525] "Means for integrating symptom information and emotional information to select an appropriate medical institution" refers to a device or software that has the function of a generative AI means for combining and analyzing symptom information and emotional information to select the medical institution that is most suitable for the user.

[0526] This invention is a system that receives input information from the user, judges the severity and type of symptoms based on that information, and selects an appropriate medical institution. Furthermore, it aims to increase the user's sense of security by analyzing the user's emotional state, integrating that information, and reflecting it in the selection of a medical institution.

[0527] Hardware and Software Used

[0528] Hardware

[0529] Terminal: A device that a user uses to input information, such as a smartphone, tablet, or computer.

[0530] Server: A central processing unit for analyzing input information and selecting appropriate medical institutions.

[0531] software

[0532] Generative AI model: Analyzes user input using machine learning algorithms and natural language processing techniques. Specifically, an AI model implemented in Python is used.

[0533] Sentiment analysis engine: Software for analyzing user sentiment. For example, IBM Watson® sentiment analysis API is used.

[0534] Database: A database for storing information about medical institutions. MySQL, PostgreSQL, etc. are used.

[0535] Program Processing

[0536] Receiving input from the user

[0537] The user uses the device to input details about their symptoms and physical condition. For example, the user might input, "I have a bad headache."

[0538] Sending input information

[0539] The terminal encrypts the entered information using SSL / TLS and sends it to the server.

[0540] Symptom analysis using generative AI

[0541] The server uses a generative AI model implemented in Python to parse the input "I have a bad headache." Based on the pre-trained dataset, the model determines that it is a severe headache.

[0542] Emotion analysis using emotion engine

[0543] The server uses an emotion engine (e.g., IBM Watson's emotion analysis API) to detect anxiety or fear from the user's input.

[0544] Integrating symptoms and emotions

[0545] The server combines "severe headache" from the generative AI and "high anxiety level" from the emotion engine.

[0546] Choosing the right medical institution

[0547] Based on the integrated information, the server searches the database for and selects large medical institutions with neurosurgery departments.

[0548] Sending recommendations

[0549] The server sends information about the selected medical institution to the terminal, and the terminal displays to the user, "We recommend large medical institutions with neurosurgery departments."

[0550] Examples

[0551] The user types "I have a bad headache" into the device. The emotion engine detects the user's anxiety and fear and feeds the results back to the generative AI. Taking into account the severe headache and high anxiety level, the generative AI recommends a large medical institution with a neurosurgery department.

[0552] Examples of prompt statements

[0553] "If a user types in 'I have a bad headache,' and the emotion engine detects high anxiety levels, what kind of medical care will the generative AI recommend?"

[0554] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0555] Step 1:

[0556] The user uses the terminal to input details of his / her symptoms and physical condition. For example, the user inputs "I have a bad headache." The input information is obtained through the input interface of the terminal. The input information is in text format and includes the user's symptoms and feelings.

[0557] Step 2:

[0558] The terminal encrypts the information entered by the user using SSL / TLS and sends it to the server. The input information is sent to the server as encrypted data, which ensures the security of the data.

[0559] Step 3:

[0560] The server passes the received input information to the generative AI model. The generative AI model uses machine learning algorithms and natural language processing techniques to analyze the user's input information. For example, it analyzes the input "I have a bad headache" and determines that it is a severe headache. The input is text data, and the output is a number or category indicating the severity of the symptoms.

[0561] Step 4:

[0562] The server also passes the input information to an emotion analysis engine. The emotion analysis engine analyzes emotions from the user's input, voice, facial expressions, etc. For example, it can detect anxiety or fear from the user's input. The input is text data, and the output is a number or category indicating the type of emotion and its intensity.

[0563] Step 5:

[0564] The server integrates the symptom information from the generative AI and the emotion information from the emotion analysis engine, for example combining a severe headache with high anxiety levels. The input is the symptom information and the emotion information, and the output is data showing the integrated information.

[0565] Step 6:

[0566] The server selects an appropriate medical institution based on the integrated information. For example, it searches the database for large medical institutions with neurosurgery capabilities, taking into account the severe headache and high anxiety level. The input is the integrated information, and the output is the information of the selected medical institution.

[0567] Step 7:

[0568] The server sends information about the selected medical institution to the terminal. The terminal displays the recommended results to the user. For example, it displays "Large medical institutions with neurosurgery departments are recommended." The input is the information about the selected medical institution, and the output is the recommended results displayed to the user.

[0569] (Application example 2)

[0570] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0571] Conventional food delivery systems do not suggest meals that take into account the user's health condition or emotions, making it difficult for users to choose meals that suit their physical condition and emotions. In addition, in order to choose an appropriate meal, users need to accurately understand their own symptoms and emotions and select meals based on them, which is a burden for users.

[0572] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0573] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, an emotion engine for recognizing the user's emotions, a means for feeding back the results of the emotion engine to the generative AI means, and a means for the generative AI means to combine symptom information and emotion information to suggest an appropriate diet. This makes it possible to suggest an appropriate diet based on the user's health condition and emotions.

[0574] "Means for receiving input information from a user" refers to a device or software that provides an interface for a user to input their symptoms or feelings.

[0575] A "generative AI means" is a system that uses machine learning algorithms and natural language processing techniques to analyze information input by users and determine the severity and type of symptoms.

[0576] The "means for selecting an appropriate medical institution" is a system for recommending the most suitable medical institution to the user based on the severity and type of symptoms determined by the generative AI means.

[0577] An "emotion engine" is a technology that analyzes emotions from user input information and feeds the results back to generative AI means.

[0578] The "means for suggesting appropriate meals" is a system in which a generative AI means combines symptom information and emotional information to suggest optimal meals to the user.

[0579] In order to implement this invention, it is necessary to build a system that receives input information from a user, analyzes it using a generative AI means and an emotion engine, and suggests appropriate meals. This system includes a user terminal, a server, a generative AI model, an emotion engine, and a database.

[0580] System Configuration

[0581] 1. User Device

[0582] The user terminal is a device such as a smartphone or tablet that provides an interface for users to input their symptoms and emotions. Users send the input information through the application.

[0583] 2. Server

[0584] The server receives input information from users and analyzes it using generative AI methods and emotion engines. The server has the following main functions:

[0585] Receiving user input information

[0586] Symptom analysis by generative AI means

[0587] Emotion analysis using emotion engine

[0588] Combining symptom and emotional information to provide appropriate dietary suggestions

[0589] 3. Generative AI Models

[0590] The generative AI model uses machine learning algorithms and natural language processing techniques to analyze user input and determine the severity and type of symptoms. For example, it uses the OpenAI API to analyze user input.

[0591] 4. Emotion Engine

[0592] The emotion engine analyzes emotions from user input information and feeds the results back to the generative AI means. The emotion engine uses technologies such as emotion recognition libraries.

[0593] 5. Database

[0594] The database stores information on symptoms, emotions, and the corresponding appropriate meals. Based on the analysis results of the generative AI means and the emotion engine, the system suggests appropriate meals from the database.

[0595] Processing flow

[0596] 1. Receiving User Input

[0597] The user enters symptoms such as "I have a stomachache" and sends them to the server via the application.

[0598] 2. Symptom Analysis

[0599] The server uses a generative AI model to analyze the information entered by the user and determine the severity and type of symptoms.

[0600] 3. Emotion Analysis

[0601] The emotion engine analyzes emotions from user input information and feeds the results back to the generative AI means. For example, it detects stress from user input.

[0602] 4. Appropriate dietary suggestions

[0603] The generative AI system combines symptom information and emotion information to suggest appropriate meals from a database. The suggested meals are displayed on the user's device.

[0604] Examples

[0605] If the user types "I have a stomach ache" and the emotion engine detects "stress," the generative AI model will generate the following prompt:

[0606] Example prompt:

[0607] User Symptoms: Stomach ache

[0608] User Emotion: Stress

[0609] Please suggest suitable meals.

[0610] By inputting this prompt into a generative AI model, it will suggest meals that are easy to digest and help reduce stress, such as "porridge, warm soup, and chamomile tea."

[0611] The flow of the identification process in the application example 2 will be described with reference to FIG.

[0612] Step 1:

[0613] Users input their symptoms and feelings.

[0614] Input: The user inputs a symptom, such as "I have a stomach ache," into the application.

[0615] Output: The entered information is sent from the user terminal to the server.

[0616] Specific actions: A user uses an application on a smartphone or tablet to enter symptoms into a text box and press the send button.

[0617] Step 2:

[0618] The server receives input information from the user.

[0619] Input: Symptom information sent from the user device.

[0620] Output: The received information is passed to an analysis module in the server.

[0621] Specific operation: The server receives an HTTP request and passes the input information to the analysis module.

[0622] Step 3:

[0623] The generative AI model analyzes the information entered by the user and determines the severity and type of symptoms.

[0624] Input: The user's symptoms information received by the server.

[0625] Output: The analysis results include the severity and type of symptoms.

[0626] Specific operation: The server uses OpenAI's API to send the user's input information to the generative AI model and receive the analysis results.

[0627] Step 4:

[0628] The emotion engine analyzes emotions from user input information.

[0629] Input: The user's symptoms information received by the server.

[0630] Output: The analysis result is the user's emotional information.

[0631] Specific operation: The server uses the emotion engine to send the user's input information to the emotion engine and receive the emotion analysis result.

[0632] Step 5:

[0633] A generative AI model combines symptom information with emotional information to suggest appropriate dietary recommendations.

[0634] Input: Symptom severity and type, user emotional information.

[0635] Output:Suggestions for appropriate meals.

[0636] Specific operation: The server generates and sends the following prompt to the generative AI model:

[0637] User Symptoms: Stomach ache

[0638] User Emotion: Stress

[0639] Please suggest suitable meals.

[0640] It receives suggestions returned by the generative AI model and retrieves appropriate meal information from the database.

[0641] Step 6:

[0642] The server sends the suggested meals to the user's device.

[0643] Input: Appropriate meal suggestions derived from a generative AI model.

[0644] Output: Meal suggestions displayed on the user's device.

[0645] Specific operation: The server generates an HTTP response and sends the suggested meal information to the user terminal.

[0646] Step 7:

[0647] The user device displays the suggested meals.

[0648] Input: Meal recommendation information sent from the server.

[0649] Output: Meal suggestions are displayed for the user to review.

[0650] Specific operation: The user terminal displays the received information on the application UI so that the user can view it.

[0651] Example 3

[0652] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0653] Conventional medical institution selection systems can select an appropriate medical institution based on the user's symptoms, but because they cannot take the user's emotional state into account, it is difficult to select an appropriate medical institution that will reduce the user's anxiety and fear. In addition, when selecting the size and specialty of a medical institution based on the severity and type of symptoms, the user's emotional state cannot be reflected, making it difficult to select the medical institution that is most suitable for the user.

[0654] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0655] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, an emotion engine means for recognizing the emotion of the user, and a means for feeding back the result of the emotion engine means to the generative AI means. This makes it possible to select an optimal medical institution by taking into consideration not only the symptoms of the user but also the emotional state of the user.

[0656] "Means for receiving input information from a user" refers to a device or software that provides an interface for a user to input their own symptoms and physical condition and has the function of transmitting that information to the system.

[0657] "Generative AI means" refers to an artificial intelligence model that analyzes input information received from a user and determines the severity and type of symptoms.

[0658] A "means for selecting an appropriate medical institution" is a device or software that has the function of selecting the medical institution that is most suitable for the user based on the symptoms determined by the generative AI means.

[0659] The "emotion engine means" refers to software or a device for recognizing emotions from the user's input information, facial expressions, tone of voice, etc., and analyzing the results.

[0660] The "feedback means" refers to a device or software having a function of passing the emotional information obtained by the emotion engine means to the generative AI means and reflecting it in assessing symptoms and selecting a medical institution.

[0661] This invention is a system that receives input information from a user, judges symptoms based on that information, and selects an appropriate medical institution. This system also takes into account the user's emotional state, making it possible to select a more appropriate medical institution.

[0662] Hardware and Software Used

[0663] Hardware: Servers, user devices (smartphones, PCs, etc.)

[0664] Software: Generative AI model, emotion engine

[0665] System Configuration

[0666] 1. Receiving user input:

[0667] Users use the device to enter detailed information about their symptoms and physical condition.

[0668] Example: A user opens an app on their smartphone and types, "I have a bad headache."

[0669] 2. Symptom assessment:

[0670] The server receives the input information sent by the user.

[0671] The server uses a generative AI model to analyze the user's input.

[0672] The generative AI model determines the severity and type of symptoms from the input information.

[0673] Example: The server passes the information "I have a bad headache" to a generative AI model, which determines that it is a severe headache.

[0674] 3. Sentiment Analysis:

[0675] The server uses an emotion engine to analyze the user's emotions.

[0676] The emotion engine recognizes emotions from user input, facial expressions, tone of voice, etc.

[0677] Example: The server uses an emotion engine to detect anxiety or fear in user input.

[0678] 4. Choice of medical institution:

[0679] The server uses a generative AI model to combine symptom information with emotional information to select an appropriate medical institution.

[0680] The selection will be based on the size and expertise of the medical institution, as well as the severity and type of the user's symptoms.

[0681] Example: A server recommends a large medical institution with neurosurgery, given their severe headaches and high anxiety levels.

[0682] 5. Displaying recommended results:

[0683] The server sends information about the selected medical institution to the user's terminal.

[0684] The device displays information about recommended medical institutions to the user.

[0685] Example: A message appears on the user's smartphone saying, "We recommend large medical institutions with neurosurgery capabilities."

[0686] Examples

[0687] As a concrete example, the following prompt sentence is input into the generative AI model.

[0688] Example prompt:

[0689] "If a user types in 'I have a bad headache,' and the emotion engine detects anxiety or fear, what medical care would you recommend?"

[0690] In response to this prompt, the generative AI model responds as follows:

[0691] Example response:

[0692] "Since the user's symptoms are severe headaches and the emotion engine detects high anxiety levels, we recommend a large medical institution with neurosurgery."

[0693] The above is an embodiment of the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0694] Step 1:

[0695] Receiving user input

[0696] Users use the device to enter detailed information about their symptoms and physical condition.

[0697] Input: A user opens an app on their smartphone and types, "I have a bad headache."

[0698] Specific action: The user enters symptoms in the text box and presses the submit button.

[0699] Output: The input information is sent to the server.

[0700] Step 2:

[0701] Symptom assessment

[0702] The server receives the input information sent by the user.

[0703] Input: Information entered by the user (e.g. "I have a bad headache").

[0704] Specific operation: The server passes input information to the generative AI model.

[0705] Data processing / calculation: The generative AI model analyzes the input information and determines the severity and type of symptoms.

[0706] Output: Symptom severity or type (e.g., Severe headache).

[0707] Step 3:

[0708] Sentiment Analysis

[0709] The server uses an emotion engine to analyze the user's emotions.

[0710] Input: User input and associated data (e.g. text, facial expressions, tone of voice).

[0711] What happens: The server passes the input information to the emotion engine and performs emotion analysis.

[0712] Data processing / calculation: The emotion engine recognizes the user's emotions and generates analysis results.

[0713] Output: Emotion analysis (e.g. anxiety or fear).

[0714] Step 4:

[0715] Selecting a medical institution

[0716] The server uses a generative AI model to combine symptom information with emotional information to select an appropriate medical institution.

[0717] Input: Symptom information (e.g., severe headache) and emotion information (e.g., anxiety or fear).

[0718] Specific operation: The server passes symptom and emotion information to the generative AI model and selects a medical institution.

[0719] Data processing / calculation: A generative AI model analyzes symptom and emotion information to select the most appropriate medical institution.

[0720] Output: Recommended medical institution information (e.g., a large medical institution with a neurosurgery department).

[0721] Step 5:

[0722] Displaying recommended results

[0723] The server sends information about the selected medical institution to the user's terminal.

[0724] Input: Recommended healthcare provider information.

[0725] Specific operation: The server sends medical institution information to the user's device.

[0726] Output: Information about the medical institution displayed on the user's device (e.g. "We recommend large medical institutions with neurosurgery capabilities").

[0727] (Application example 3)

[0728] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0729] Conventional medical institution selection systems can select an appropriate medical institution based on the user's symptoms, but because they cannot take into account the user's emotional state, it is difficult to select the optimal medical institution to reduce the user's anxiety and fear. There is also a problem that there is a lack of systems that can quickly recommend appropriate medical institutions in the event of an emergency.

[0730] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0731] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and an emotion engine for recognizing the user's emotions. This makes it possible to comprehensively analyze the user's symptoms and emotions and quickly recommend the most appropriate medical institution.

[0732] "Means for receiving input information from the user" refers to an interface that allows the user to input their symptoms and feelings.

[0733] A "generative AI means" is an artificial intelligence algorithm that analyzes information input by the user and determines the severity and type of symptoms.

[0734] The "means for selecting an appropriate medical institution" is an algorithm for recommending the most appropriate medical institution based on the symptoms determined by the generative AI means.

[0735] An "emotion engine" is software that analyzes emotions from user input information and feeds the results back to generative AI means.

[0736] "Symptom severity" is a scale entered by the user to rate the severity of the symptom.

[0737] "Size of a medical institution" refers to the size of the medical institution's facilities, number of staff, etc.

[0738] "Symptom type" refers to the specific classification of a symptom entered by the user.

[0739] "The expertise of a medical institution" refers to the specialized knowledge and skills that a medical institution has in a particular medical field.

[0740] An "application installed on a smartphone" is software that runs on a smartphone and allows the user to input symptoms and feelings.

[0741] The system for carrying out the present invention includes a means for receiving input information from a user, a generative AI means, a means for selecting an appropriate medical institution, and an emotion engine. These means are implemented as applications installed on a smartphone.

[0742] System Configuration

[0743] The system is configured as follows:

[0744] 1. A means of receiving input information from the user

[0745] Users input their symptoms and feelings through a smartphone application, and the input interface supports text input and voice input.

[0746] 2. Generative AI means

[0747] The generative AI means analyzes the information input by the user and determines the severity and type of symptoms. Natural language processing technology and machine learning algorithms are used for this purpose. Specifically, natural language processing libraries using Python (e.g., NLTK and spaCy) and machine learning libraries (e.g., TENSORFLOW (registered trademark) and PyTorch) are used.

[0748] 3. How to choose the right medical institution

[0749] Based on the symptoms determined by the generative AI means, an appropriate medical institution is selected. This selection is made taking into account the size and specialization of the medical institution. For example, in the case of a severe headache, a large medical institution with a neurosurgery department is recommended.

[0750] 4. Emotion Engine

[0751] The emotion engine analyzes emotions from user input information and feeds the results back to the generative AI means. An emotion recognition library is used for emotion analysis.

[0752] Processing flow

[0753] 1. Getting User Input

[0754] A user types in "I have a bad headache" and the emotion engine detects "anxiety."

[0755] 2. Symptom Analysis

[0756] A generative AI means analyzes the input "I have a bad headache" and determines that it is a severe headache.

[0757] 3. Emotion Analysis

[0758] The emotion engine detects "anxiety" from user input and feeds the results back to the generative AI means.

[0759] 4. Medical institution recommendations

[0760] A generative AI tool combines symptom severity and emotional information to recommend large medical institutions with neurosurgery departments.

[0761] Examples

[0762] The user opens the application on their smartphone and enters the following:

[0763] Enter your symptoms: Severe headache

[0764] Enter your emotion: Anxiety

[0765] Based on this input, the system can analyze the user's symptoms and emotions and quickly recommend the most suitable medical institution.

[0766] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0767] Step 1:

[0768] The user opens the application on their smartphone and enters their symptoms and feelings.

[0769] Input: User enters symptom as "severe headache" and emotion as "anxiety."

[0770] Output: The input symptom and emotion data.

[0771] Specific behavior: The user enters symptoms and emotions into the application's text input field and presses the submit button.

[0772] Step 2:

[0773] The terminal receives input information from the user and transmits it to the generative AI means.

[0774] Input: User-entered symptom and emotion data.

[0775] Output: Symptom and emotion data sent to the generative AI means.

[0776] Specific behavior: The application makes an API request to send user input data to the server.

[0777] Step 3:

[0778] The server uses generative AI tools to analyze the user's symptoms and determine their severity and type.

[0779] Input: Symptom data entered by the user.

[0780] Output: Symptom severity and type data.

[0781] What it does: The server uses Python natural language processing libraries (e.g. NLTK or spaCy) to analyze the symptom text and determine its severity and type.

[0782] Step 4:

[0783] The server uses an emotion engine to analyze the user's emotions and feeds the results back to the generative AI means.

[0784] Input: Emotion data entered by the user.

[0785] Output: The result data of sentiment analysis.

[0786] Specific operation: The server uses an emotion recognition library to analyze the emotion text and identify the type of emotion.

[0787] Step 5:

[0788] The server uses generative AI tools to combine information on the severity and type of symptoms, as well as emotions, to select an appropriate medical institution.

[0789] Input: Symptom severity and type data, sentiment analysis results data.

[0790] Output: Recommended healthcare provider data.

[0791] Specific operation: The server uses generative AI means to comprehensively analyze the symptom and emotion data, and executes an algorithm to select the most suitable medical institution.

[0792] Step 6:

[0793] The server transmits information about recommended medical institutions to the terminal.

[0794] Input: Recommended healthcare provider data.

[0795] Output: The medical institution information sent to the device.

[0796] Specific operation: The server generates an API response containing information about recommended medical institutions and sends it to the terminal.

[0797] Step 7:

[0798] The terminal displays information about recommended medical institutions to the user.

[0799] Input: Medical institution information received from the server.

[0800] Output: The clinic information displayed to the user.

[0801] Specific behavior: The application displays the medical institution information received from the server in the user interface.

[0802] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0803] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0804] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0805] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0806] [Second embodiment]

[0807] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0808] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0809] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0810] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0811] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0812] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0813] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0814] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0815] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0816] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0817] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0818] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0819] "Example 1"

[0820] In one embodiment of the present invention, a system is provided that includes an interface for receiving input information from a user. The interface may be realized, for example, as a form on a website or a smartphone application. The user inputs details of his or her symptoms and physical condition.

[0821] "Example 2"

[0822] Next, the generative AI analyzes the information input by the user and determines the severity and type of symptoms. This AI uses machine learning algorithms and natural language processing techniques to accurately identify symptoms from user input. For example, if a user inputs "I have a bad headache," the AI ​​will determine that this is a severe headache.

[0823] "Example 3"

[0824] Finally, the generative AI selects an appropriate medical institution based on the symptoms it has determined. This selection is made taking into account the size and specialty of the medical institution, as well as the severity and type of the user's symptoms. For example, in the case of a severe headache, the AI ​​will recommend a large medical institution with a neurosurgery department.

[0825] The process flow of each embodiment will be described below.

[0826] "Example 1"

[0827] Step 1: The user enters details of their symptoms and physical condition into a form on a website or smartphone application. For example, they may describe a specific symptom such as "I have a severe headache."

[0828] Step 2: The generative AI analyzes the information input by the user. This AI uses machine learning algorithms and natural language processing techniques to accurately identify symptoms from the user's input.

[0829] Step 3: The generative AI determines the severity and type of the symptom. For example, if a user types, "I have a bad headache," the AI ​​will determine that this is a severe headache.

[0830] Step 4: An appropriate medical institution is selected based on the symptoms determined by the generative AI. This selection is made taking into consideration factors such as the size and specialty of the medical institution, as well as the severity and type of the user's symptoms. Step 5: Information about the medical institution selected by the AI ​​is presented to the user. For example, in the case of a severe headache, the AI ​​will recommend a large medical institution with a neurosurgery department, and provide the user with information such as the name, location, and contact information of that institution.

[0831] Example 1

[0832] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0833] In conventional medical systems, even if users input their own symptoms and physical condition, it was difficult to properly analyze the information and provide advice and diagnosis results quickly and accurately. In addition, there was a lack of functionality to select appropriate medical institutions based on the severity and type of symptoms, which caused problems such as users taking a long time to find the appropriate medical institution.

[0834] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0835] In this invention, the server includes a means for receiving input information from a user, a means for transmitting the input information to the server, a means for the server to receive the input information and store it in a database, a means for the server to analyze the stored data and generate a prompt sentence to be input to the generative AI model, a means for the generative AI model to generate advice or a diagnosis result based on the prompt sentence, a means for the server to transmit the generated result to the user's terminal, and a means for the user to confirm the result. This allows the user to obtain quick and accurate advice or a diagnosis result based on his or her own symptoms and physical condition. In addition, since an appropriate medical institution can be selected based on the severity and type of symptoms, the user can quickly find an appropriate medical institution.

[0836] The "means for receiving input information from a user" refers to a means for providing an interface for a user to input their own symptoms and physical condition.

[0837] The "means for transmitting the input information to the server" refers to a communication means for transmitting the information input by the user to the server.

[0838] "Means by which the server receives input information and stores it in a database" refers to means by which the server receives information sent by a user and stores the information in a database.

[0839] "Means for the server to analyze the stored data and generate prompt sentences to input into the generative AI model" refers to means for the server to analyze information stored in the database and generate prompt sentences to input into the generative AI model.

[0840] "Means by which a generative AI model generates advice or a diagnostic result based on a prompt sentence" refers to means by which a generative AI model generates appropriate advice or a diagnostic result based on a prompt sentence.

[0841] "Means by which the server transmits the generated results to the user's terminal" refers to the means by which the server transmits the results obtained from the generative AI model to the user's terminal.

[0842] "Means for the user to check the results" refers to the means by which the user can check the generated advice and diagnosis results through the terminal.

[0843] The present invention is a system for receiving input information from a user and providing appropriate advice and diagnostic results based on the information. A specific embodiment of this system will be described below.

[0844] First, users access a form on a website or smartphone application and enter details about their symptoms and physical condition, such as "I have a headache" or "I have a fever."

[0845] The device then sends the information entered by the user to the server, using the HTTPS protocol to transmit the data securely.

[0846] The server receives the input information sent from the terminal and stores it in a database such as MySQL or PostgreSQL. The stored data is used for later analysis.

[0847] The server analyzes the stored data and generates prompts based on the user's symptoms and physical condition, such as "The user has a headache. What can be done to alleviate this?"

[0848] Next, the server sends the generated prompt sentence to a generative AI model (e.g., GPT-4). The generative AI model generates appropriate advice or diagnostic results based on the prompt sentence. For example, it generates a result such as "If you have a headache, it is recommended that you drink plenty of water and take a rest."

[0849] The server then transmits the results received from the generative AI model to the user's device, again using the HTTPS protocol.

[0850] Finally, the user can check the results displayed on the device, allowing them to receive appropriate advice and diagnosis results for their symptoms and physical condition.

[0851] As a concrete example, if a user enters "I have a headache," the following prompt sentence is generated:

[0852] "The user is having a headache. What can be done about it?"

[0853] In this way, the system provides appropriate advice and diagnostic results based on the user's input. The hardware used includes servers, terminals, and databases, while the software includes websites, smartphone applications, and generative AI models (e.g., GPT-4).

[0854] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0855] Step 1:

[0856] The user accesses the interface and inputs symptoms and physical condition.

[0857] Specifically, the user enters information such as "I have a headache" or "I have a fever" into a form on a website or smartphone application.

[0858] Input: Information about the user's symptoms and health condition

[0859] Output: Input information is saved to the device

[0860] Step 2:

[0861] The terminal transmits the input information to the server.

[0862] Specifically, the terminal uses the HTTPS protocol to transmit the information entered by the user to the server.

[0863] Input: Information entered by the user

[0864] Output: The server receives the input information.

[0865] Step 3:

[0866] The server receives the input information and stores it in a database.

[0867] Specifically, the server stores the received information in a database such as MySQL or PostgreSQL.

[0868] Input: Input information sent from the device

[0869] Output: Information stored in the database

[0870] Step 4:

[0871] The server analyzes the stored data and generates prompt sentences to input into the generative AI model.

[0872] Specifically, the server analyzes the information stored in the database and generates a prompt such as, "The user has a headache. What can be done to remedy this?"

[0873] Input: Information stored in a database

[0874] Output: Generated prompt statement

[0875] Step 5:

[0876] The server sends the generated prompt to a generative AI model (e.g., GPT-4).

[0877] Specifically, the server sends the generated prompt text to GPT-4.

[0878] Input: Generated prompt text

[0879] Output: The prompt sent to the generative AI model

[0880] Step 6:

[0881] A generative AI model generates advice and diagnostic results based on the prompt.

[0882] Specifically, GPT-4 generates advice and diagnostic results such as, "If you have a headache, it is recommended that you drink plenty of fluids and get plenty of rest."

[0883] Input: Prompt statement

[0884] Output: The advice and diagnostics generated.

[0885] Step 7:

[0886] The server transmits the generated results to the user's terminal.

[0887] Specifically, the server sends the results received from the generative AI model to the user's device using the HTTPS protocol.

[0888] Input: Generated advice and diagnostic results

[0889] Output: The results sent to the user's terminal.

[0890] Step 8:

[0891] The user checks the results.

[0892] As a specific operation, the user checks the advice and diagnosis results displayed on the terminal.

[0893] Input: The result sent to the terminal

[0894] Output: Advice and diagnostic results confirmed by the user

[0895] (Application example 1)

[0896] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0897] Conventional food delivery services were unable to suggest appropriate meal menus based on the user's physical condition or symptoms. This made it difficult for users to choose meals that suited their own health condition, and they were unable to adequately manage their health. Furthermore, suggesting appropriate meal menus required specialized knowledge, which was a high hurdle for general users.

[0898] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0899] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for proposing an appropriate meal menu based on the symptoms, and a means for ordering in cooperation with a food delivery service based on the proposed meal menu. This allows the user to easily propose and order an appropriate meal menu based on their own physical condition and symptoms.

[0900] The "means for receiving input information from a user" refers to a means for providing an interface for a user to input their own physical condition and symptoms.

[0901] A "generative AI means" is a means that uses artificial intelligence technology to determine symptoms based on information input by the user and make appropriate suggestions.

[0902] The "means for determining symptoms" refers to a means for analyzing information input by the user and identifying the user's physical condition and symptoms based on that information.

[0903] A "means for suggesting an appropriate meal menu" is a means for presenting an optimal meal menu to a user based on symptoms determined by a generative AI means.

[0904] The "means for ordering in cooperation with a food delivery service" refers to a means for actually ordering food in cooperation with a food delivery service based on the proposed meal menu.

[0905] A system for implementing the present invention includes a means for receiving input information from a user, a generative AI means, a means for suggesting an appropriate meal menu, and a means for placing an order in cooperation with a food delivery service.

[0906] System Configuration

[0907] 1. How to receive user input:

[0908] Users enter their physical condition and symptoms using a smartphone application or a website form.

[0909] Example: A user types, "I have a bit of a cold and my throat hurts."

[0910] 2. Generative AI means:

[0911] Using OpenAI's API, it analyzes user input and generates an appropriate meal menu.

[0912] Example: Prompt: "User's symptoms: cold and sore throat. Please suggest suitable meals."

[0913] 3. How to suggest appropriate meals:

[0914] The meal menu generated by the generative AI means is presented to the user.

[0915] Example: Show the generated menu "Hot chicken soup and orange juice" to the user.

[0916] 4. How to order through food delivery services:

[0917] Based on the suggested meal menu, an order is placed using the food delivery service's API.

[0918] Example: Send a request to a food delivery service API to order "hot chicken soup and orange juice."

[0919] Hardware and software used

[0920] Hardware: Smartphones, servers

[0921] Software: Python, OpenAI API, Food Delivery Service API

[0922] Data processing and calculation

[0923] 1. Getting user input:

[0924] Users enter their physical condition and symptoms through a smartphone application or a website form.

[0925] Example: A user types, "I have a bit of a cold and my throat hurts."

[0926] 2. Using generative AI models:

[0927] Using OpenAI's API, an appropriate meal menu is generated based on user input information.

[0928] Example: Prompt: "User's symptoms: cold and sore throat. Please suggest suitable meals."

[0929] 3. Partnering with food delivery services:

[0930] The generated menu is sent to the food delivery service's API and an order is placed.

[0931] Example: Send a request to a food delivery service API to order "hot chicken soup and orange juice."

[0932] Examples

[0933] If a user types in "I have a cold and a sore throat," the generative AI model suggests "hot chicken soup and orange juice." Based on this suggestion, the API of a food delivery service is used to order "hot chicken soup and orange juice" to the user's address.

[0934] Examples of prompt statements

[0935] User Symptoms: Cold and sore throat

[0936] Please suggest a suitable meal plan.

[0937] In this way, it becomes possible to easily suggest and order meals that suit the user's physical condition.

[0938] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0939] Step 1:

[0940] Users enter their physical condition and symptoms through a smartphone application or a website form.

[0941] Input: User's physical condition and symptoms (e.g. "I have a cold and my throat hurts")

[0942] Output: User input information

[0943] Step 2:

[0944] The terminal transmits the user's input information to the server.

[0945] Input: User input information

[0946] Output: User input sent to the server

[0947] Step 3:

[0948] Based on the user's input information received by the server, a generative AI model is used to determine symptoms and generate an appropriate meal menu.

[0949] Input: User input information

[0950] Data processing: Input prompts into the generative AI model to generate an appropriate meal menu

[0951] Example prompt: "User's symptoms: cold and sore throat. Please suggest suitable meals."

[0952] Output: The generated meal menu (e.g. "Hot chicken soup and orange juice")

[0953] Step 4:

[0954] The server transmits the generated meal menu to the user's terminal.

[0955] Input: Generated meal menu

[0956] Output: The meal menu sent to the user's device.

[0957] Step 5:

[0958] The user reviews the proposed meal menu and confirms the order.

[0959] Input: Generated meal menu

[0960] Output: User's order confirmation information

[0961] Step 6:

[0962] The terminal transmits the user's order confirmation information to the server.

[0963] Input: User's order confirmation information

[0964] Output: Order confirmation information sent to the server

[0965] Step 7:

[0966] The server places an order using the food delivery service's API based on the order confirmation information.

[0967] Input: User's order confirmation information

[0968] Data processing: Send an order request to the food delivery service API

[0969] Output: An order request sent to a food delivery service.

[0970] Step 8:

[0971] A food delivery service receives the order and delivers the meal to the user's address.

[0972] Input: An order request sent to a food delivery service

[0973] Output: Meals delivered to the user's address.

[0974] Example 2

[0975] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0976] In conventional medical systems, it was difficult for users to accurately assess their own symptoms and select an appropriate medical institution. In addition, medical institutions were often not selected appropriately based on the severity and type of symptoms, and users were sometimes unable to receive appropriate medical care. This increased the risk of users' health conditions worsening.

[0977] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0978] In this invention, the server includes a means for receiving input information from a user, a means for transmitting the input information to the server, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and a means for returning the determination result to the user, thereby enabling the user to accurately determine his / her own symptoms and select an appropriate medical institution.

[0979] "User" refers to an individual who utilizes the system to input information about their symptoms.

[0980] "Input information" refers to text data regarding symptoms that a user provides to the system.

[0981] "Server" refers to a computer system that receives input information sent by a user and analyzes it using generative AI means.

[0982] "Generative AI methods" refers to artificial intelligence technologies that use machine learning algorithms and natural language processing techniques to determine symptoms from information input by the user.

[0983] "Symptom" means any physical or mental abnormality or discomfort experienced by a User.

[0984] "Severity" refers to an indicator of the seriousness or urgency of symptoms.

[0985] "Medical institution" refers to a facility that provides appropriate medical care and treatment for the user's symptoms.

[0986] "Scale" refers to the size of the medical institution and the range of medical services it can provide.

[0987] "Expertise" refers to the specialized knowledge and skills that a medical institution has regarding specific symptoms or diseases.

[0988] "Return" refers to the server sending back the results determined by the generative AI means to the user's device.

[0989] This invention is a system that allows users to input information about their symptoms and select an appropriate medical institution based on that information. The system receives the user's input information, uses a generative AI model to determine the severity and type of symptoms, and returns the results to the user.

[0990] Hardware and Software Used

[0991] Hardware: Servers, terminals (user devices)

[0992] Software: Generative AI models, Natural Language Processing (NLP) libraries, Machine Learning algorithms

[0993] Program Processing

[0994] 1. Receiving user input information

[0995] A user uses a terminal to input information about a symptom, for example, "I have a bad headache."

[0996] The terminal receives the information entered by the user.

[0997] 2. Sending input information

[0998] The terminal sends the received user input information to the server, using the HTTPS protocol to transmit the data securely.

[0999] 3. Analysis of information

[1000] The server passes the received user input information to the generative AI model.

[1001] Generative AI models use natural language processing (NLP) libraries to parse user input, tokenizing the text and extracting meaning.

[1002] For example, the text "I have a severe headache" is analyzed to extract the symptom "headache" and the severity level "severe."

[1003] 4. Determining the severity and type of symptoms

[1004] The generative AI model uses machine learning algorithms to determine the severity and type of symptoms from the analyzed information.

[1005] For example, if you say "I have a bad headache," it will determine that you have a severe headache. This determination is made using past data and pre-trained models.

[1006] 5. Returning the results

[1007] The server sends the results of the generative AI model's judgment back to the terminal.

[1008] The device will display the result to the user, for example "severe headache."

[1009] Examples

[1010] A user types "I have a bad headache" into a terminal.

[1011] The terminal transmits this information to the server.

[1012] The server passes this information to the generative AI model.

[1013] The generative AI model uses NLP libraries to parse the text "I have a bad headache" and then uses machine learning algorithms to determine whether it is a severe headache.

[1014] The server returns the result of this decision to the terminal.

[1015] The device tells the user "Severe Headache."

[1016] Examples of prompt statements

[1017] "Analyze the symptom information entered by the user and determine the severity and type of the symptom. For example, if the user enters 'I have a bad headache,' determine that it is a severe headache."

[1018] In this way, the system analyzes the user's input information and makes appropriate decisions.

[1019] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1020] Step 1:

[1021] Receiving user input information

[1022] A user uses a terminal to input information about a symptom, for example, "I have a bad headache."

[1023] Input: Text information entered by the user (e.g. "I have a bad headache")

[1024] The terminal receives the information entered by the user.

[1025] Output: Received text information (e.g. "I have a bad headache")

[1026] Step 2:

[1027] Sending input information

[1028] The terminal transmits the received user input information to the server, using the HTTPS protocol to transmit the data securely.

[1029] Input: Received text information (e.g. "I have a bad headache")

[1030] Output: Text information sent to the server (e.g. "I have a bad headache")

[1031] Step 3:

[1032] Analysis of information

[1033] The server passes the received user input information to the generative AI model.

[1034] Input: Text information sent to the server (e.g. "I have a bad headache")

[1035] Generative AI models use natural language processing (NLP) libraries to parse user input, specifically tokenizing the text and extracting meaning.

[1036] Specific behavior: Use an NLP library (e.g. spaCy or NLTK) to parse the text "I have a bad headache" and extract the symptom "headache" and the severity "severe".

[1037] Output: Extracted keywords (e.g. "headache", "severe")

[1038] Step 4:

[1039] Determining the severity and type of symptoms

[1040] The generative AI model uses machine learning algorithms to determine the severity and type of symptoms from the analyzed information.

[1041] Input: Extracted keywords (e.g. "headache", "severe")

[1042] What it does: Uses machine learning algorithms (e.g. random forests or neural networks) to identify a severe headache based on the keyword "severe."

[1043] Output: Judgment result (e.g. "severe headache")

[1044] Step 5:

[1045] Returning the results

[1046] The server sends the results of the generative AI model's judgment back to the terminal.

[1047] Input: Judgment result (e.g. "severe headache")

[1048] The device will then display the results to the user, for example showing "severe headache" on the app screen.

[1049] Specific behavior: Performs UI update processing to display the results on the device screen.

[1050] Output: The result displayed to the user (e.g. "Severe headache")

[1051] (Application example 2)

[1052] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1053] Conventional health monitoring systems have difficulty accurately determining the severity and type of a user's symptoms, which can delay appropriate responses. In addition, they lack the ability to quickly issue alerts based on the severity of symptoms, which can delay responses in emergencies. This creates a need for real-time monitoring of users' health conditions and the ability to quickly respond when abnormalities are detected.

[1054] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1055] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and a means for determining the severity of the symptoms and issuing an alert based on the severity. This makes it possible to monitor the user's health condition in real time and quickly respond when an abnormality is detected.

[1056] "Means for receiving input information from the user" refers to an interface that allows the user to input their own symptoms and health condition, and is a function that receives information through devices such as smartphones and smart glasses.

[1057] "Generative AI means" refers to artificial intelligence functions that use machine learning algorithms and natural language processing techniques to analyze information input by users and determine the severity and type of symptoms.

[1058] The "means of determining symptoms" refers to a function that uses generative AI means to analyze symptoms from information input by the user and identify their severity and type.

[1059] "Means for selecting an appropriate medical institution" is a function that suggests the most suitable medical institution to the user based on the diagnosed symptoms.

[1060] The "means for determining the severity of symptoms" refers to a function that uses generative AI means to evaluate the severity of symptoms from information input by the user and determines an appropriate response based on the results.

[1061] "Means of issuing an alert" is a function that issues a warning to users and relevant parties when symptoms are severe, encouraging them to take prompt action.

[1062] As an embodiment of the present invention, a security health monitor system will be described as an example. The purpose of this system is to monitor the health status of a user in real time and to respond quickly when an abnormality is detected.

[1063] System Configuration

[1064] The system consists of the following main components:

[1065] 1. User terminal: A device such as a smartphone or smart glasses that provides the means to receive input information from the user.

[1066] 2. Server: A central processing unit containing generative AI means to analyse user input and determine the severity and type of symptoms.

[1067] 3. Alert system: Provide a means to issue alerts based on the severity of symptoms.

[1068] Program Processing

[1069] User Device

[1070] The user device provides an interface for users to input their symptoms and health conditions. For example, it receives text input such as "I have a bad headache" through a smartphone application.

[1071] server

[1072] The server performs the following process.

[1073] 1. Receiving input information: Receives input information sent from the user terminal.

[1074] 2. Analysis using generative AI: The received input information is analyzed using a generative AI model (for example, the DistilBERT model using the Transformers library) to determine the severity and type of symptoms.

[1075] 3. Determination of severity: The severity of symptoms is evaluated based on the analysis results. For example, if the analysis result is "NEGATIVE," it is judged to be severe, and if it is "POSITIVE," it is judged to be mild.

[1076] 4. Alerting: If symptoms are severe, an alert system will be used to warn users and relevant parties and encourage rapid response.

[1077] Alert System

[1078] The alert system issues warnings to users and related parties based on instructions from the server. For example, it uses the smartphone's notification function to display a message such as "Severe headache detected. Please seek medical attention immediately."

[1079] Hardware and Software Used

[1080] Hardware: Smartphones, smart glasses

[1081] Software: Python, Transformers library

[1082] Examples

[1083] When a user types "I have a bad headache" into a smartphone application, the system determines this to be a severe headache and issues an alert.

[1084] "I have a terrible headache"

[1085] "I feel a bit sick"

[1086] "My chest hurts"

[1087] In this way, the security health monitor system is able to monitor the user's health status in real time and respond quickly if an abnormality is detected.

[1088] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1089] Step 1:

[1090] The user launches the application on their smartphone and enters their symptoms and health condition.

[1091] Input: The user enters some text information, such as "I have a bad headache."

[1092] Specific action: The user enters symptoms into the application's text box and presses the submit button.

[1093] Step 2:

[1094] The terminal sends the user's input information to the server.

[1095] Input: Text information entered by the user.

[1096] Output: Text information sent to the server.

[1097] Specific operation: The smartphone application sends the entered text information to the server as an HTTP request.

[1098] Step 3:

[1099] The input information received by the server is passed to the generative AI model for analysis.

[1100] Input: Text information sent from the device.

[1101] Output: Analysis results (symptom severity and type).

[1102] Specific operation: The server analyzes the received text information using the Transformers library to determine the severity and type of symptoms.

[1103] Step 4:

[1104] The server determines the severity of the symptoms based on the analysis results.

[1105] Input: Analysis results of the generative AI model.

[1106] Output: Symptom severity (e.g. severe, mild).

[1107] Specific operation: The server evaluates the analysis results, determining that the problem is severe if the result is "NEGATIVE" or mild if the result is "POSITIVE".

[1108] Step 5:

[1109] The server decides whether to raise an alert based on the severity of the symptoms.

[1110] Input: Symptom severity.

[1111] Output: Instructions to send an alert.

[1112] Specific Actions: If a server is deemed critical, instructions are sent to issue an alert to the alert system.

[1113] Step 6:

[1114] The alert system will warn users and relevant parties.

[1115] Input: Alert sending instruction from the server.

[1116] Output: A warning message for the user or interested parties.

[1117] Specific action: Using the smartphone's notification function, a message such as "Severe headache has been detected. Please seek medical attention immediately" is displayed.

[1118] In this way, the security health monitor system is able to monitor the user's health status in real time and respond quickly if an abnormality is detected.

[1119] Example 3

[1120] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1121] Conventional medical institution selection systems make it difficult for users to quickly find the medical institution best suited to their symptoms. In addition, the accuracy of selecting an appropriate medical institution based on the severity and type of symptoms is low, and there is a problem that it takes a long time for users to receive appropriate treatment.

[1122] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for receiving input information from a user, a means for transmitting the input information to the server, a means for generating a prompt sentence based on the input information and transmitting it to the generative AI model, a means for the generative AI model to analyze symptoms based on the prompt sentence and select an appropriate medical institution, and a means for returning the selection result to the user. This enables the user to quickly find a medical institution that is best suited to his or her symptoms.

[1123] "User" refers to an individual who uses the system to input their symptoms and find an appropriate medical institution.

[1124] "Input information" refers to data entered by the user in text format describing their symptoms.

[1125] "Server" refers to a computer system that receives input information from a user, sends prompts to the generative AI model, and sends output from the generative AI model back to the user.

[1126] A "prompt sentence" refers to an instruction sentence that is generated based on user input information and sent to a generative AI model.

[1127] A "generative AI model" refers to an artificial intelligence model that analyzes a user's symptoms based on a prompt text and selects an appropriate medical institution.

[1128] "Symptom Severity" refers to a user-entered scale for rating the severity of a symptom.

[1129] "Size of medical institution" refers to the size of the medical institution and the level of its facilities.

[1130] "Symptom type" refers to the classification of the specific symptoms reported by the user.

[1131] "Expertise of a medical institution" refers to the specialized knowledge and skills that a medical institution has for specific symptoms or diseases.

[1132] "Selection results" refers to information about medical institutions recommended to users as a result of analysis by the generative AI model.

[1133] This invention is a system that allows users to input their symptoms and find an appropriate medical institution. The system receives the user's input information, analyzes the symptoms using a generative AI model based on the input, and selects an appropriate medical institution.

[1134] Hardware and Software Used

[1135] Hardware: Servers, terminals (user devices)

[1136] Software: Generative AI models (e.g. GPT-4), database management systems (e.g. MySQL), healthcare information systems

[1137] Program Processing

[1138] 1. User enters symptoms:

[1139] The user inputs symptoms in text format using their own device (such as a smartphone or computer). For example, the user inputs "I have a headache." This input is done through a dedicated application or web form.

[1140] 2. The device sends the input data to the server:

[1141] The device sends the symptom data entered by the user to the server as an HTTP request. For example, the device uses a "POST" request to send the input data to a specific endpoint on the server.

[1142] 3. The server sends a prompt to the generative AI model:

[1143] The server sends a prompt to the generative AI model based on the received symptom data. Specifically, the server analyzes the symptom data and generates an appropriate prompt. For example, it generates a prompt such as "The user is complaining of a headache. Please select an appropriate medical institution" and sends it to the generative AI model.

[1144] 4. Generative AI model analyzes symptoms and selects appropriate medical institution:

[1145] The generative AI model receives the prompt and analyzes the user's symptoms. Based on the analysis results, it selects an appropriate medical institution. In this case, the generative AI model refers to a database of medical institutions and takes into account the size and specialty of the medical institution, as well as the severity and type of the user's symptoms. For example, in the case of a severe headache, the generative AI model will recommend a large medical institution with a neurosurgery department.

[1146] 5. The server sends the selection back to the user:

[1147] The server sends the information about medical institutions obtained from the generative AI model back to the user's device. Specifically, the server receives the output of the generative AI model, converts it into a format that is easy for the user to understand, and sends it to the user. For example, the server generates a message saying, "We recommend large medical institutions with neurosurgery capabilities" and sends it to the user's device.

[1148] Examples

[1149] User input: The user types "I have a headache" into the terminal.

[1150] Prompt for generative AI model: "The user is complaining of a headache. Please select the appropriate medical institution."

[1151] Output of the generative AI model: “This suggests a severe headache and would recommend a larger medical institution with a neurosurgery department.”

[1152] In this way, the user can quickly find the medical institution that is best suited to his / her symptoms. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1153] Step 1:

[1154] The user enters the symptoms.

[1155] A user inputs symptoms in text format using their own device (such as a smartphone or PC). For example, the user inputs "I have a headache." This input is done through a dedicated application or a web form. The input data is saved in text format on the device.

[1156] Step 2:

[1157] The terminal transmits the input data to the server.

[1158] The terminal sends the symptom data entered by the user to the server. The data is sent as an HTTP request. For example, the terminal uses a "POST" request to send the input data to a specific endpoint on the server. The input data is sent to the server in text format.

[1159] Step 3:

[1160] The server sends prompts to the generative AI model.

[1161] The server sends a prompt to the generative AI model based on the received symptom data. Specifically, the server analyzes the symptom data and generates an appropriate prompt sentence. For example, it generates a prompt sentence such as "The user is complaining of a headache. Please select an appropriate medical institution" and sends it to the generative AI model. The prompt sentence is sent to the generative AI model in text format.

[1162] Step 4:

[1163] The generative AI model analyzes symptoms and selects the appropriate medical institution.

[1164] The generative AI model receives the prompt and analyzes the user's symptoms. Based on the analysis results, it selects an appropriate medical institution. In this case, the generative AI model refers to a database of medical institutions and takes into account the size and specialty of the medical institution, as well as the severity and type of the user's symptoms. For example, in the case of a severe headache, the generative AI model will recommend a large medical institution with a neurosurgery department. The analysis results are returned to the server in text format.

[1165] Step 5:

[1166] The server sends the selection back to the user.

[1167] The server returns the medical institution information obtained from the generative AI model to the user's device. Specifically, the server receives the output of the generative AI model, converts it into a format that is easy for the user to understand, and sends it to the user. For example, the server generates a message saying, "Large medical institutions with neurosurgery departments are recommended," and sends it to the user's device. The selection result is displayed in text format on the user's device.

[1168] (Application example 3)

[1169] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1170] In modern society, it is important for users to find a prompt and appropriate medical institution when they feel unwell. However, it is not easy to select the most appropriate medical institution depending on the severity and type of symptoms. In addition, while a prompt response is required in an emergency, contacting the appropriate medical institution may be delayed. To solve these problems, a system is needed that can accurately assess the user's symptoms and quickly recommend an appropriate medical institution.

[1171] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1172] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and a means for automatically contacting the nearest emergency medical institution in an emergency based on the selected medical institution. This allows the user to quickly find an appropriate medical institution when feeling unwell. In addition, by automatically contacting the nearest emergency medical institution in an emergency, a quick response is possible.

[1173] "Means for receiving input information from a user" refers to a device or software that provides an interface for a user to input information about their symptoms or physical condition.

[1174] A "generative AI means" is a system that uses artificial intelligence technology to analyze information input by the user and determine symptoms based on that information.

[1175] The "means for selecting an appropriate medical institution" refers to an algorithm or process for recommending the most suitable medical institution to the user based on the symptoms determined by the generative AI means.

[1176] "Means of automatically contacting the nearest emergency medical institution in the event of an emergency" refers to a communications system that automatically contacts the nearest emergency medical institution based on the user's current location information if the user's symptoms are determined to be severe.

[1177] The following system configuration will be described as an embodiment of the present invention.

[1178] System Configuration

[1179] The system includes a means for receiving input information from a user, a generative AI means, a means for selecting an appropriate medical institution, and a means for automatically contacting the nearest emergency medical institution in the event of an emergency.

[1180] A means of receiving input information from the user

[1181] Users input information about their symptoms and physical condition through a smartphone app. The interface supports text and voice input and is designed to make it easy for users to provide information.

[1182] Generative AI means

[1183] The server receives input information from the user and analyzes it using a generative AI model. This analysis determines the user's symptoms and identifies their severity and type. The generative AI model uses the OpenAI API.

[1184] How to choose the right medical facility

[1185] Based on the symptoms determined by the generative AI means, the server selects an appropriate medical institution. This selection is made taking into account the size and specialty of the medical institution, the user's current location, etc. For example, in the case of a severe headache, a large medical institution with a neurosurgery department will be recommended.

[1186] A means to automatically contact the nearest emergency medical facility in the event of an emergency

[1187] If the user's symptoms are deemed severe, the server will automatically contact the nearest emergency medical facility based on the user's current location, using the Requests library to send HTTP requests.

[1188] Examples

[1189] If a user experiences a "severe headache," they input their symptoms into a smartphone app. The generative AI model analyzes this information and recommends "large medical institutions with neurosurgery capabilities." In addition, in the event of an emergency, the app automatically contacts the nearest emergency medical institution.

[1190] Examples of prompt statements

[1191] "User symptoms: Severe headache.\nPlease recommend appropriate medical help."

[1192] In this way, a system can be realized that helps users quickly access appropriate medical institutions.

[1193] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1194] Step 1:

[1195] The user launches the smartphone app and enters information about their symptoms and physical condition.

[1196] Input: User's symptoms (e.g., severe headache)

[1197] Output: The entered symptom information is sent to the server.

[1198] Step 2:

[1199] A server receives the symptom information sent by the user.

[1200] Input: Symptom information submitted by the user

[1201] Output: The received symptom information is passed to the generative AI model.

[1202] Step 3:

[1203] The server uses a generative AI model to analyze the received symptom information and determine the user's symptoms.

[1204] Input: Received symptom information

[1205] Output: Parsed symptom information (e.g., severe headache)

[1206] Step 4:

[1207] The server selects an appropriate medical institution based on the analyzed symptom information.

[1208] Input: Parsed symptom information

[1209] Output: Recommended medical institution information (e.g., a large medical institution with neurosurgery)

[1210] Step 5:

[1211] The server sends the recommended medical institution information to the user's smartphone app.

[1212] Input: Recommended medical institution information

[1213] Output: Medical institution information displayed on the user's smartphone app

[1214] Step 6:

[1215] If the user's symptoms are determined to be severe, the server will automatically contact the nearest emergency medical facility based on the user's current location information.

[1216] Input: User's current location information, severe symptoms information

[1217] Output: Emergency call sent to the nearest emergency medical facility

[1218] Step 7:

[1219] The server notifies the user's smartphone app of the results of the emergency contact.

[1220] Input: Emergency message transmission result

[1221] Output: A notification displayed on the user's smartphone app (e.g., emergency medical services have been contacted)

[1222] In addition, an emotion engine that estimates the emotion of the user may be further combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[1223] "Example 1"

[1224] As one embodiment of the present invention, a system is provided that includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and an emotion engine for recognizing the user's emotions. In this system, when a user inputs details of his or her symptoms and physical condition, the generative AI analyzes the information and determines the severity and type of symptoms. Furthermore, the emotion engine analyzes the user's emotions and feeds back the results to the generative AI. The generative AI combines the symptom information and emotion information to select an appropriate medical institution. Specifically, if a user inputs "I have a severe headache" and the emotion engine detects the user's anxiety or fear, the generative AI will recommend a large medical institution with a neurosurgery department, taking into account the severe headache and high anxiety level.

[1225] "Example 2"

[1226] As one embodiment of the present invention, a system is provided that includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and an emotion engine for recognizing the user's emotions. In this system, when a user inputs details of his or her symptoms and physical condition, the generative AI analyzes the information and determines the severity and type of symptoms. Furthermore, the emotion engine analyzes the user's emotions and feeds back the results to the generative AI. The generative AI combines the symptom information and emotion information to select an appropriate medical institution. Specifically, if a user inputs "I have a severe headache" and the emotion engine detects the user's anxiety or fear, the generative AI will recommend a large medical institution with a neurosurgery department, taking into account the severe headache and high anxiety level.

[1227] "Example 3"

[1228] As one embodiment of the present invention, a system is provided that includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and an emotion engine for recognizing the user's emotions. In this system, when a user inputs details of his or her symptoms and physical condition, the generative AI analyzes the information and determines the severity and type of symptoms. Furthermore, the emotion engine analyzes the user's emotions and feeds back the results to the generative AI. The generative AI combines the symptom information and emotion information to select an appropriate medical institution. Specifically, if a user inputs "I have a severe headache" and the emotion engine detects the user's anxiety or fear, the generative AI will recommend a large medical institution with a neurosurgery department, taking into account the severe headache and high anxiety level.

[1229] The process flow of each embodiment will be described below.

[1230] "Example 1"

[1231] Step 1: The user enters their symptoms and physical condition into the system. For example, they enter information like "I have a bad headache."

[1232] Step 2: The generative AI analyzes the input from the user and determines the severity and type of symptoms. In this example, the AI ​​determines that the user is experiencing a severe headache based on the input "I have a bad headache."

[1233] Step 3: The emotion engine analyzes the user's emotions. In this example, the emotion engine determines that the user is feeling anxious or scared based on the expressions and language used when providing the input information.

[1234] Step 4: The generative AI combines the symptom information with the emotion information to select the appropriate medical institution. In this example, the AI ​​takes into account the severe headaches and high anxiety levels and recommends a large medical institution with neurosurgery.

[1235] Example 1

[1236] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1237] In conventional medical systems, even if a user inputs their symptoms, it can be difficult to select an appropriate medical institution based on that information alone. In addition, because the user's emotional state is not taken into account, the system may not be able to select an appropriate medical institution. This can lead to a risk that the user will not be able to visit an appropriate medical institution and their symptoms may worsen.

[1238] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1239] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, an emotion analysis means for recognizing the emotions of the user, a means for feeding back the results of the emotion analysis means to the generative AI means, and a means for the generative AI means to integrate and analyze the symptom information and the emotion information. This makes it possible to comprehensively analyze the user's symptoms and emotions and select the most appropriate medical institution.

[1240] The "means for receiving input information from a user" refers to a means for providing an interface for a user to input their own symptoms and physical condition.

[1241] A "generative AI means" is a means that uses artificial intelligence technology to analyze information input by the user and determine the severity and type of symptoms.

[1242] The "means for selecting an appropriate medical institution" refers to a means for selecting the medical institution most suitable for the user based on the symptoms determined by the generative AI means.

[1243] The "emotion analysis means" refers to a means for recognizing and analyzing emotions from information input by a user.

[1244] The "means of providing feedback" refers to the means of providing the results of the emotion analysis means to the generative AI means.

[1245] An "integrative analysis means" is a means by which a generative AI means combines symptom information and emotional information for analysis.

[1246] This invention is a system that provides an interface for users to input their symptoms and physical condition. Users input information through a form on a website or a smartphone application. This interface is designed to allow users to input information easily.

[1247] The server sends the input information received from the user to the generative AI. The generative AI analyzes this information and determines the severity and type of symptoms. For example, if the user inputs "I have a bad headache," the generative AI will use that information to assess the severity of the headache. Natural language processing (NLP) technology is used in generative AI.

[1248] In addition, the server also transmits the user's input information to the emotion analysis means. The emotion analysis means analyzes emotions from the user's input and feeds the results back to the generative AI. For example, it detects anxiety or fear from the user's input. The emotion analysis means uses text analysis technology.

[1249] The generative AI integrates and analyzes symptom information and emotional information. For example, it analyzes the combination of severe headache and high anxiety level. Based on this integrated information, the generative AI selects an appropriate medical institution. For example, taking into account severe headache and high anxiety level, it recommends a large medical institution with neurosurgery.

[1250] The server sends information about the medical institution selected by the generative AI to the terminal. The terminal displays the information about the medical institution recommended to the user. This allows the user to receive treatment at a prompt and appropriate medical institution.

[1251] As a concrete example, consider the case where a user uses a smartphone application to input "I have a severe headache." The emotion analysis means detects anxiety from the user's input. Taking into account the severe headache and high anxiety level, the generative AI recommends a large medical institution with a neurosurgery department. The server sends this information to the terminal, which then displays a message to the user saying, "Please visit the neurosurgery department of a nearby university hospital."

[1252] An example of a prompt is as follows:

[1253] "If a user types 'I have a bad headache' through a smartphone application and emotion analysis measures detect anxiety, what kind of medical institution will the generative AI recommend?"

[1254] The system aims to provide prompt and appropriate medical services by comprehensively analyzing the user's symptoms and emotions and selecting the most appropriate medical institution.

[1255] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1256] Step 1:

[1257] A user enters details about his / her symptoms and physical condition through a form on a website or smartphone application. For example, the user enters "I have a severe headache." The entered information is sent from the device to a server. The input data is in text format and includes detailed information about the user's symptoms and physical condition.

[1258] Step 2:

[1259] The server sends the input information received from the user to the generative AI. The generative AI uses natural language processing (NLP) technology to analyze the input information and determine the severity and type of symptoms. For example, it analyzes the information "I have a severe headache" and evaluates the severity of the headache. The analysis results are output as data on the severity and type of symptoms.

[1260] Step 3:

[1261] The server also transmits the user's input information to the emotion analysis means. The emotion analysis means uses text analysis techniques to analyze emotions from the user's input. For example, it detects anxiety or fear from the user's input. The analysis results are output as data on the user's emotional state.

[1262] Step 4:

[1263] The generative AI receives feedback from the emotion analysis tool and integrates the symptom information with the emotion information for analysis (for example, combining a severe headache with high anxiety levels). The integrated information is output as data for selecting the appropriate medical institution.

[1264] Step 5:

[1265] The generative AI selects an appropriate medical institution based on the integrated information. For example, it recommends a large medical institution with a neurosurgery department considering the severe headache and high anxiety level. The information of the selected medical institution is sent to the server.

[1266] Step 6:

[1267] The server sends information about the medical institution selected by the generative AI to the terminal. The terminal displays the information about the medical institution recommended to the user. For example, it displays a message saying, "Please visit the neurosurgery department of a nearby university hospital." This allows the user to visit an appropriate medical institution quickly.

[1268] (Application example 1)

[1269] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1270] Conventional medical institution selection systems can select an appropriate medical institution based on the user's symptoms, but they cannot take the user's emotional state into account, which means that they cannot provide optimal support in emergencies or for users with high anxiety. In addition, selecting a medical institution without considering the user's emotional state may reduce the user's satisfaction and sense of security.

[1271] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, an emotion engine for recognizing the user's emotions, a means for feeding back emotion information analyzed by the emotion engine to the generative AI means, and a means for the generative AI means to combine symptom information and emotion information to select an appropriate medical institution. This makes it possible to select an optimal medical institution that comprehensively considers the user's symptoms and emotional state.

[1272] "Means for receiving input information from the user" refers to an interface through which the user can input their own symptoms and physical condition, and can be realized through forms on a website or smartphone application, etc.

[1273] "Generative AI means" is an artificial intelligence technology that analyzes information input by users and determines the severity and type of symptoms.

[1274] The "means for selecting an appropriate medical institution" is a function for recommending the most suitable medical institution to the user based on the severity and type of symptoms determined by the generative AI means.

[1275] An "emotion engine" is a technology that analyzes emotions from user input information and feeds the results back to generative AI means.

[1276] The "means for feeding back emotion information to the generative AI means" is a function for providing emotion information analyzed by the emotion engine to the generative AI means and for combining and analyzing symptom information and emotion information.

[1277] "Means for selecting an appropriate medical institution by combining symptom information and emotional information" refers to a function in which the generative AI means comprehensively considers symptom information and emotional information to select the medical institution that is most suitable for the user.

[1278] A system for implementing this invention includes a means for receiving input information from a user, a generative AI means, a means for selecting an appropriate medical institution, an emotion engine, a means for feeding back emotion information to the generative AI means, and a means for selecting an appropriate medical institution by combining symptom information and emotion information.

[1279] System Programming

[1280] The system is implemented as a smartphone application, providing an interface for users to input their symptoms and physical condition, and analyzing them using generative AI methods and an emotion engine.

[1281] Processing Description

[1282] The server provides a form on the smartphone application to receive input information from the user. Through this form, the user inputs details of their symptoms and physical condition. The input information is then sent to the server.

[1283] The server uses generative AI to analyze the input information from the user and determine the severity and type of symptoms. This analysis uses artificial intelligence technology using Python. Specifically, a symptom analysis library is used.

[1284] Next, the server uses an emotion engine to analyze emotions from the user's input information. An emotion recognition library is used for this analysis. The emotion information analyzed by the emotion engine is fed back to the generative AI means.

[1285] The generative AI means combines and analyzes symptom information and emotion information to select an appropriate medical institution. For example, if a user inputs "severe headache" and the emotion engine detects "anxiety," the generative AI means will take into account the severe headache and high anxiety level and recommend a large medical institution with neurosurgery.

[1286] Examples

[1287] As a specific example, if a user inputs "I have a bad headache" and the emotion engine detects "anxiety," the following prompt sentence will be input to the generative AI model.

[1288] Example prompt:

[1289] User Symptoms: Severe headaches

[1290] User Sentiment: Anxiety

[1291] Recommended medical institution: Large medical institution (Neurosurgery)

[1292] Recommended action: Urgent action required. Go to nearest major medical facility.

[1293] In this way, it becomes possible to select the most appropriate medical institution that takes into consideration the user's symptoms and emotional state comprehensively.

[1294] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1295] Step 1:

[1296] The user starts the smartphone application and inputs their symptoms and physical condition. The input information is sent to the server through a form in the smartphone application. The input information includes specific symptoms, such as "I have a severe headache."

[1297] Step 2:

[1298] The server receives input information sent by the user. The received information is passed to the generative AI means, which analyzes the severity and type of symptoms. A symptom analysis library is used for this analysis. Based on the input information, the generative AI means determines the severity (e.g. high, medium, low) and type (e.g. headache, stomachache, etc.) of the symptom.

[1299] Step 3:

[1300] The server uses an emotion engine to analyze the user's emotions based on the analysis results. The emotion engine uses an emotion recognition library. It extracts emotions (e.g. anxiety, fear, relief, etc.) from the user's input information and feeds the results back to the generative AI means.

[1301] Step 4:

[1302] The server uses a generative AI to combine and analyze symptom information and emotion information to select an appropriate medical institution. For example, taking into account severe headaches and high anxiety levels, a large medical institution with a neurosurgery department is recommended. Based on the results of this analysis, information on recommended medical institutions is generated.

[1303] Step 5:

[1304] The server notifies the user of the generated information on recommended medical institutions. The notification function of the smartphone application is used for the notification. The user can check the information on the recommended medical institutions and contact the medical institutions if necessary.

[1305] Step 6:

[1306] The user contacts the recommended medical institution and undergoes a medical examination. The results of the examination and details of treatment can be input by the user into the application, and the information can be used for future analysis.

[1307] Example 2

[1308] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[1309] Conventional medical support systems can select an appropriate medical institution based on the user's symptoms, but do not consider the user's emotional state when selecting a medical institution. As a result, they are unable to select a medical institution that appropriately reflects the user's feelings, such as anxiety or fear, and are therefore unable to provide sufficient satisfaction or peace of mind to the user.

[1310] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1311] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, an emotion analysis means for recognizing the emotions of the user, a means for feeding back the results of the emotion analysis means to the generative AI means, and a means for the generative AI means to integrate the symptom information and the emotion information to select an appropriate medical institution. This makes it possible to select a medical institution that takes into account not only the user's symptoms but also their emotional state.

[1312] "Means for receiving input information from a user" refers to a device or software that provides an interface for a user to input their own symptoms and physical condition and has the function of transmitting that information to the system.

[1313] A "generative AI means" is an artificial intelligence system that uses machine learning algorithms and natural language processing techniques to analyze information input by users and determine the severity and type of symptoms.

[1314] A "means for selecting an appropriate medical institution" is a device or software that has the function of selecting the medical institution most suitable for the user based on symptom information determined by the generative AI means.

[1315] An "emotion analysis means" is a device or software that has the function of analyzing emotions from the user's input information, voice, facial expressions, etc., and detecting emotional states such as anxiety or fear.

[1316] The "feedback means" refers to a device or software having a function of returning the emotional information obtained by the emotion analysis means to the generative AI means and integrating the symptom information and the emotional information.

[1317] "Means for integrating symptom information and emotional information to select an appropriate medical institution" refers to a device or software that has the function of a generative AI means for combining and analyzing symptom information and emotional information to select the medical institution that is most suitable for the user.

[1318] This invention is a system that receives input information from the user, judges the severity and type of symptoms based on that information, and selects an appropriate medical institution. Furthermore, it aims to increase the user's sense of security by analyzing the user's emotional state, integrating that information, and reflecting it in the selection of a medical institution.

[1319] Hardware and Software Used

[1320] Hardware

[1321] Terminal: A device that a user uses to input information, such as a smartphone, tablet, or computer.

[1322] Server: A central processing unit for analyzing input information and selecting appropriate medical institutions.

[1323] software

[1324] Generative AI model: Analyzes user input using machine learning algorithms and natural language processing techniques. Specifically, an AI model implemented in Python is used.

[1325] Sentiment analysis engine: Software for analyzing user sentiment. For example, IBM Watson's sentiment analysis API is used.

[1326] Database: A database for storing information about medical institutions. MySQL, PostgreSQL, etc. are used.

[1327] Program Processing

[1328] Receiving input from the user

[1329] The user uses the device to input details about their symptoms and physical condition. For example, the user might input, "I have a bad headache."

[1330] Sending input information

[1331] The terminal encrypts the entered information using SSL / TLS and sends it to the server.

[1332] Symptom analysis using generative AI

[1333] The server uses a generative AI model implemented in Python to parse the input "I have a bad headache." Based on the pre-trained dataset, the model determines that it is a severe headache.

[1334] Emotion analysis using emotion engine

[1335] The server uses an emotion engine (e.g., IBM Watson's emotion analysis API) to detect anxiety or fear from the user's input.

[1336] Integrating symptoms and emotions

[1337] The server combines "severe headache" from the generative AI and "high anxiety level" from the emotion engine.

[1338] Choosing the right medical institution

[1339] Based on the integrated information, the server searches the database for and selects large medical institutions with neurosurgery departments.

[1340] Sending recommendations

[1341] The server sends information about the selected medical institution to the terminal, and the terminal displays to the user, "We recommend large medical institutions with neurosurgery departments."

[1342] Examples

[1343] The user types "I have a bad headache" into the device. The emotion engine detects the user's anxiety and fear and feeds the results back to the generative AI. Taking into account the severe headache and high anxiety level, the generative AI recommends a large medical institution with a neurosurgery department.

[1344] Examples of prompt statements

[1345] "If a user types in 'I have a bad headache,' and the emotion engine detects high anxiety levels, what kind of medical care will the generative AI recommend?"

[1346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1347] Step 1:

[1348] The user uses the terminal to input details of his / her symptoms and physical condition. For example, the user inputs "I have a bad headache." The input information is obtained through the input interface of the terminal. The input information is in text format and includes the user's symptoms and feelings.

[1349] Step 2:

[1350] The terminal encrypts the information entered by the user using SSL / TLS and sends it to the server. The input information is sent to the server as encrypted data, which ensures the security of the data.

[1351] Step 3:

[1352] The server passes the received input information to the generative AI model. The generative AI model uses machine learning algorithms and natural language processing techniques to analyze the user's input information. For example, it analyzes the input "I have a bad headache" and determines that it is a severe headache. The input is text data, and the output is a number or category indicating the severity of the symptoms.

[1353] Step 4:

[1354] The server also passes the input information to an emotion analysis engine. The emotion analysis engine analyzes emotions from the user's input, voice, facial expressions, etc. For example, it can detect anxiety or fear from the user's input. The input is text data, and the output is a number or category indicating the type of emotion and its intensity.

[1355] Step 5:

[1356] The server integrates the symptom information from the generative AI and the emotion information from the emotion analysis engine, for example combining a severe headache with high anxiety levels. The input is the symptom information and the emotion information, and the output is data showing the integrated information.

[1357] Step 6:

[1358] The server selects an appropriate medical institution based on the integrated information. For example, it searches the database for large medical institutions with neurosurgery capabilities, taking into account the severe headache and high anxiety level. The input is the integrated information, and the output is the information of the selected medical institution.

[1359] Step 7:

[1360] The server sends information about the selected medical institution to the terminal. The terminal displays the recommended results to the user. For example, it displays "Large medical institutions with neurosurgery departments are recommended." The input is the information about the selected medical institution, and the output is the recommended results displayed to the user.

[1361] (Application example 2)

[1362] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1363] Conventional food delivery systems do not suggest meals that take into account the user's health condition or emotions, making it difficult for users to choose meals that suit their physical condition and emotions. In addition, in order to choose an appropriate meal, users need to accurately understand their own symptoms and emotions and select meals based on them, which is a burden for users.

[1364] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1365] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, an emotion engine for recognizing the user's emotions, a means for feeding back the results of the emotion engine to the generative AI means, and a means for the generative AI means to combine symptom information and emotion information to suggest an appropriate diet. This makes it possible to suggest an appropriate diet based on the user's health condition and emotions.

[1366] "Means for receiving input information from a user" refers to a device or software that provides an interface for a user to input their symptoms or feelings.

[1367] A "generative AI means" is a system that uses machine learning algorithms and natural language processing techniques to analyze information input by users and determine the severity and type of symptoms.

[1368] The "means for selecting an appropriate medical institution" is a system for recommending the most suitable medical institution to the user based on the severity and type of symptoms determined by the generative AI means.

[1369] An "emotion engine" is a technology that analyzes emotions from user input information and feeds the results back to generative AI means.

[1370] The "means for suggesting appropriate meals" is a system in which a generative AI means combines symptom information and emotional information to suggest optimal meals to the user.

[1371] In order to implement this invention, it is necessary to build a system that receives input information from a user, analyzes it using a generative AI means and an emotion engine, and suggests appropriate meals. This system includes a user terminal, a server, a generative AI model, an emotion engine, and a database.

[1372] System Configuration

[1373] 1. User Device

[1374] The user terminal is a device such as a smartphone or tablet that provides an interface for users to input their symptoms and emotions. Users send the input information through the application.

[1375] 2. Server

[1376] The server receives input information from users and analyzes it using generative AI methods and emotion engines. The server has the following main functions:

[1377] Receiving user input information

[1378] Symptom analysis by generative AI means

[1379] Emotion analysis using emotion engine

[1380] Combining symptom and emotional information to provide appropriate dietary suggestions

[1381] 3. Generative AI Models

[1382] The generative AI model uses machine learning algorithms and natural language processing techniques to analyze user input and determine the severity and type of symptoms. For example, it uses the OpenAI API to analyze user input.

[1383] 4. Emotion Engine

[1384] The emotion engine analyzes emotions from user input information and feeds the results back to the generative AI means. The emotion engine uses technologies such as emotion recognition libraries.

[1385] 5. Database

[1386] The database stores information on symptoms, emotions, and the corresponding appropriate meals. Based on the analysis results of the generative AI means and the emotion engine, the system suggests appropriate meals from the database.

[1387] Processing flow

[1388] 1. Receiving User Input

[1389] The user enters symptoms such as "I have a stomachache" and sends them to the server via the application.

[1390] 2. Symptom Analysis

[1391] The server uses a generative AI model to analyze the information entered by the user and determine the severity and type of symptoms.

[1392] 3. Emotion Analysis

[1393] The emotion engine analyzes emotions from user input information and feeds the results back to the generative AI means. For example, it detects stress from user input.

[1394] 4. Appropriate dietary suggestions

[1395] The generative AI system combines symptom information and emotion information to suggest appropriate meals from a database. The suggested meals are displayed on the user's device.

[1396] Examples

[1397] If the user types "I have a stomach ache" and the emotion engine detects "stress," the generative AI model will generate the following prompt:

[1398] Example prompt:

[1399] User Symptoms: Stomach ache

[1400] User Emotion: Stress

[1401] Please suggest suitable meals.

[1402] By inputting this prompt into a generative AI model, it will suggest meals that are easy to digest and help reduce stress, such as "porridge, warm soup, and chamomile tea."

[1403] The flow of the identification process in the application example 2 will be described with reference to FIG.

[1404] Step 1:

[1405] Users input their symptoms and feelings.

[1406] Input: The user inputs a symptom, such as "I have a stomach ache," into the application.

[1407] Output: The entered information is sent from the user terminal to the server.

[1408] Specific actions: A user uses an application on a smartphone or tablet to enter symptoms into a text box and press the send button.

[1409] Step 2:

[1410] The server receives input information from the user.

[1411] Input: Symptom information sent from the user device.

[1412] Output: The received information is passed to an analysis module in the server.

[1413] Specific operation: The server receives an HTTP request and passes the input information to the analysis module.

[1414] Step 3:

[1415] The generative AI model analyzes the information entered by the user and determines the severity and type of symptoms.

[1416] Input: The user's symptoms information received by the server.

[1417] Output: The analysis results include the severity and type of symptoms.

[1418] Specific operation: The server uses OpenAI's API to send the user's input information to the generative AI model and receive the analysis results.

[1419] Step 4:

[1420] The emotion engine analyzes emotions from user input information.

[1421] Input: The user's symptoms information received by the server.

[1422] Output: The analysis result is the user's emotional information.

[1423] Specific operation: The server uses the emotion engine to send the user's input information to the emotion engine and receive the emotion analysis result.

[1424] Step 5:

[1425] A generative AI model combines symptom information with emotional information to suggest appropriate dietary recommendations.

[1426] Input: Symptom severity and type, user emotional information.

[1427] Output:Suggestions for appropriate meals.

[1428] Specific operation: The server generates and sends the following prompt to the generative AI model:

[1429] User Symptoms: Stomach ache

[1430] User Emotion: Stress

[1431] Please suggest suitable meals.

[1432] It receives suggestions returned by the generative AI model and retrieves appropriate meal information from the database.

[1433] Step 6:

[1434] The server sends the suggested meals to the user's device.

[1435] Input: Appropriate meal suggestions derived from a generative AI model.

[1436] Output: Meal suggestions displayed on the user's device.

[1437] Specific operation: The server generates an HTTP response and sends the suggested meal information to the user terminal.

[1438] Step 7:

[1439] The user device displays the suggested meals.

[1440] Input: Meal recommendation information sent from the server.

[1441] Output: Meal suggestions are displayed for the user to review.

[1442] Specific operation: The user terminal displays the received information on the application UI so that the user can view it.

[1443] Example 3

[1444] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1445] Conventional medical institution selection systems can select an appropriate medical institution based on the user's symptoms, but because they cannot take the user's emotional state into account, it is difficult to select an appropriate medical institution that will reduce the user's anxiety and fear. In addition, when selecting the size and specialty of a medical institution based on the severity and type of symptoms, the user's emotional state cannot be reflected, making it difficult to select the medical institution that is most suitable for the user.

[1446] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1447] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, an emotion engine means for recognizing the emotion of the user, and a means for feeding back the result of the emotion engine means to the generative AI means. This makes it possible to select an optimal medical institution by taking into consideration not only the symptoms of the user but also the emotional state of the user.

[1448] "Means for receiving input information from a user" refers to a device or software that provides an interface for a user to input their own symptoms and physical condition and has the function of transmitting that information to the system.

[1449] "Generative AI means" refers to an artificial intelligence model that analyzes input information received from a user and determines the severity and type of symptoms.

[1450] A "means for selecting an appropriate medical institution" is a device or software that has the function of selecting the medical institution that is most suitable for the user based on the symptoms determined by the generative AI means.

[1451] The "emotion engine means" refers to software or a device for recognizing emotions from the user's input information, facial expressions, tone of voice, etc., and analyzing the results.

[1452] The "feedback means" refers to a device or software having a function of passing the emotional information obtained by the emotion engine means to the generative AI means and reflecting it in assessing symptoms and selecting a medical institution.

[1453] This invention is a system that receives input information from a user, judges symptoms based on that information, and selects an appropriate medical institution. This system also takes into account the user's emotional state, making it possible to select a more appropriate medical institution.

[1454] Hardware and Software Used

[1455] Hardware: Servers, user devices (smartphones, PCs, etc.)

[1456] Software: Generative AI model, emotion engine

[1457] System Configuration

[1458] 1. Receiving user input:

[1459] Users use the device to enter detailed information about their symptoms and physical condition.

[1460] Example: A user opens an app on their smartphone and types, "I have a bad headache."

[1461] 2. Symptom assessment:

[1462] The server receives the input information sent by the user.

[1463] The server uses a generative AI model to analyze the user's input.

[1464] The generative AI model determines the severity and type of symptoms from the input information.

[1465] Example: The server passes the information "I have a bad headache" to a generative AI model, which determines that it is a severe headache.

[1466] 3. Sentiment Analysis:

[1467] The server uses an emotion engine to analyze the user's emotions.

[1468] The emotion engine recognizes emotions from user input, facial expressions, tone of voice, etc.

[1469] Example: The server uses an emotion engine to detect anxiety or fear in user input.

[1470] 4. Choice of medical institution:

[1471] The server uses a generative AI model to combine symptom information with emotional information to select an appropriate medical institution.

[1472] The selection will be based on the size and expertise of the medical institution, as well as the severity and type of the user's symptoms.

[1473] Example: A server recommends a large medical institution with neurosurgery, given their severe headaches and high anxiety levels.

[1474] 5. Displaying recommended results:

[1475] The server sends information about the selected medical institution to the user's terminal.

[1476] The device displays information about recommended medical institutions to the user.

[1477] Example: A message appears on the user's smartphone saying, "We recommend large medical institutions with neurosurgery capabilities."

[1478] Examples

[1479] As a concrete example, the following prompt sentence is input into the generative AI model.

[1480] Example prompt:

[1481] "If a user types in 'I have a bad headache,' and the emotion engine detects anxiety or fear, what medical care would you recommend?"

[1482] In response to this prompt, the generative AI model responds as follows:

[1483] Example response:

[1484] "Since the user's symptoms are severe headaches and the emotion engine detects high anxiety levels, we recommend a large medical institution with neurosurgery."

[1485] The above is an embodiment of the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1486] Step 1:

[1487] Receiving user input

[1488] Users use the device to enter detailed information about their symptoms and physical condition.

[1489] Input: A user opens an app on their smartphone and types, "I have a bad headache."

[1490] Specific action: The user enters symptoms in the text box and presses the submit button.

[1491] Output: The input information is sent to the server.

[1492] Step 2:

[1493] Symptom assessment

[1494] The server receives the input information sent by the user.

[1495] Input: Information entered by the user (e.g. "I have a bad headache").

[1496] Specific operation: The server passes input information to the generative AI model.

[1497] Data processing / calculation: The generative AI model analyzes the input information and determines the severity and type of symptoms.

[1498] Output: Symptom severity or type (e.g., Severe headache).

[1499] Step 3:

[1500] Sentiment Analysis

[1501] The server uses an emotion engine to analyze the user's emotions.

[1502] Input: User input and associated data (e.g. text, facial expressions, tone of voice).

[1503] What happens: The server passes the input information to the emotion engine and performs emotion analysis.

[1504] Data processing / calculation: The emotion engine recognizes the user's emotions and generates analysis results.

[1505] Output: Emotion analysis (e.g. anxiety or fear).

[1506] Step 4:

[1507] Selecting a medical institution

[1508] The server uses a generative AI model to combine symptom information with emotional information to select an appropriate medical institution.

[1509] Input: Symptom information (e.g., severe headache) and emotion information (e.g., anxiety or fear).

[1510] Specific operation: The server passes symptom and emotion information to the generative AI model and selects a medical institution.

[1511] Data processing / calculation: A generative AI model analyzes symptom and emotion information to select the most appropriate medical institution.

[1512] Output: Recommended medical institution information (e.g., a large medical institution with a neurosurgery department).

[1513] Step 5:

[1514] Displaying recommended results

[1515] The server sends information about the selected medical institution to the user's terminal.

[1516] Input: Recommended healthcare provider information.

[1517] Specific operation: The server sends medical institution information to the user's device.

[1518] Output: Information about the medical institution displayed on the user's device (e.g. "We recommend large medical institutions with neurosurgery capabilities").

[1519] (Application example 3)

[1520] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1521] Conventional medical institution selection systems can select an appropriate medical institution based on the user's symptoms, but because they cannot take into account the user's emotional state, it is difficult to select the optimal medical institution to reduce the user's anxiety and fear. There is also a problem that there is a lack of systems that can quickly recommend appropriate medical institutions in the event of an emergency.

[1522] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1523] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and an emotion engine for recognizing the user's emotions. This makes it possible to comprehensively analyze the user's symptoms and emotions and quickly recommend the most appropriate medical institution.

[1524] "Means for receiving input information from the user" refers to an interface that allows the user to input their symptoms and feelings.

[1525] A "generative AI means" is an artificial intelligence algorithm that analyzes information input by the user and determines the severity and type of symptoms.

[1526] The "means for selecting an appropriate medical institution" is an algorithm for recommending the most appropriate medical institution based on the symptoms determined by the generative AI means.

[1527] An "emotion engine" is software that analyzes emotions from user input information and feeds the results back to generative AI means.

[1528] "Symptom severity" is a scale entered by the user to rate the severity of the symptom.

[1529] "Size of a medical institution" refers to the size of the medical institution's facilities, number of staff, etc.

[1530] "Symptom type" refers to the specific classification of a symptom entered by the user.

[1531] "The expertise of a medical institution" refers to the specialized knowledge and skills that a medical institution has in a particular medical field.

[1532] An "application installed on a smartphone" is software that runs on a smartphone and allows the user to input symptoms and feelings.

[1533] The system for carrying out the present invention includes a means for receiving input information from a user, a generative AI means, a means for selecting an appropriate medical institution, and an emotion engine. These means are implemented as applications installed on a smartphone.

[1534] System Configuration

[1535] The system is configured as follows:

[1536] 1. A means of receiving input information from the user

[1537] Users input their symptoms and feelings through a smartphone application, and the input interface supports text input and voice input.

[1538] 2. Generative AI means

[1539] The generative AI means analyzes the input information from the user and determines the severity and type of symptoms. Natural language processing technology and machine learning algorithms are used for this. Specifically, natural language processing libraries using Python (e.g., NLTK and spaCy) and machine learning libraries (e.g., TensorFlow and PyTorch) are used.

[1540] 3. How to choose the right medical institution

[1541] Based on the symptoms determined by the generative AI means, an appropriate medical institution is selected. This selection is made taking into account the size and specialization of the medical institution. For example, in the case of a severe headache, a large medical institution with a neurosurgery department is recommended.

[1542] 4. Emotion Engine

[1543] The emotion engine analyzes emotions from user input information and feeds the results back to the generative AI means. An emotion recognition library is used for emotion analysis.

[1544] Processing flow

[1545] 1. Getting User Input

[1546] A user types in "I have a bad headache" and the emotion engine detects "anxiety."

[1547] 2. Symptom Analysis

[1548] A generative AI means analyzes the input "I have a bad headache" and determines that it is a severe headache.

[1549] 3. Emotion Analysis

[1550] The emotion engine detects "anxiety" from user input and feeds the results back to the generative AI means.

[1551] 4. Medical institution recommendations

[1552] A generative AI tool combines symptom severity and emotional information to recommend large medical institutions with neurosurgery departments.

[1553] Examples

[1554] The user opens the application on their smartphone and enters the following:

[1555] Enter your symptoms: Severe headache

[1556] Enter your emotion: Anxiety

[1557] Based on this input, the system can analyze the user's symptoms and emotions and quickly recommend the most suitable medical institution.

[1558] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1559] Step 1:

[1560] The user opens the application on their smartphone and enters their symptoms and feelings.

[1561] Input: User enters symptom as "severe headache" and emotion as "anxiety."

[1562] Output: The input symptom and emotion data.

[1563] Specific behavior: The user enters symptoms and emotions into the application's text input field and presses the submit button.

[1564] Step 2:

[1565] The terminal receives input information from the user and transmits it to the generative AI means.

[1566] Input: User-entered symptom and emotion data.

[1567] Output: Symptom and emotion data sent to the generative AI means.

[1568] Specific behavior: The application makes an API request to send user input data to the server.

[1569] Step 3:

[1570] The server uses generative AI tools to analyze the user's symptoms and determine their severity and type.

[1571] Input: Symptom data entered by the user.

[1572] Output: Symptom severity and type data.

[1573] What it does: The server uses Python natural language processing libraries (e.g. NLTK or spaCy) to analyze the symptom text and determine its severity and type.

[1574] Step 4:

[1575] The server uses an emotion engine to analyze the user's emotions and feeds the results back to the generative AI means.

[1576] Input: Emotion data entered by the user.

[1577] Output: The result data of sentiment analysis.

[1578] Specific operation: The server uses an emotion recognition library to analyze the emotion text and identify the type of emotion.

[1579] Step 5:

[1580] The server uses generative AI tools to combine information on the severity and type of symptoms, as well as emotions, to select an appropriate medical institution.

[1581] Input: Symptom severity and type data, sentiment analysis results data.

[1582] Output: Recommended healthcare provider data.

[1583] Specific operation: The server uses generative AI means to comprehensively analyze the symptom and emotion data, and executes an algorithm to select the most suitable medical institution.

[1584] Step 6:

[1585] The server transmits information about recommended medical institutions to the terminal.

[1586] Input: Recommended healthcare provider data.

[1587] Output: The medical institution information sent to the device.

[1588] Specific operation: The server generates an API response containing information about recommended medical institutions and sends it to the terminal.

[1589] Step 7:

[1590] The terminal displays information about recommended medical institutions to the user.

[1591] Input: Medical institution information received from the server.

[1592] Output: The clinic information displayed to the user.

[1593] Specific behavior: The application displays the medical institution information received from the server in the user interface.

[1594] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1595] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1596] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1597] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1598] [Third embodiment]

[1599] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1600] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1601] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[1602] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1603] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[1604] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1605] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1606] Fig. 6 shows an example of main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1607] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1608] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1609] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1610] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1611] "Example 1"

[1612] In one embodiment of the present invention, a system is provided that includes an interface for receiving input information from a user. The interface may be realized, for example, as a form on a website or a smartphone application. The user inputs details of his or her symptoms and physical condition.

[1613] "Example 2"

[1614] Next, the generative AI analyzes the information input by the user and determines the severity and type of symptoms. This AI uses machine learning algorithms and natural language processing techniques to accurately identify symptoms from user input. For example, if a user inputs "I have a bad headache," the AI ​​will determine that this is a severe headache.

[1615] "Example 3"

[1616] Finally, the generative AI selects an appropriate medical institution based on the symptoms it has determined. This selection is made taking into account the size and specialty of the medical institution, as well as the severity and type of the user's symptoms. For example, in the case of a severe headache, the AI ​​will recommend a large medical institution with a neurosurgery department.

[1617] The process flow of each embodiment will be described below.

[1618] "Example 1"

[1619] Step 1: The user enters details of their symptoms and physical condition into a form on a website or smartphone application. For example, they may describe a specific symptom such as "I have a severe headache."

[1620] Step 2: The generative AI analyzes the information input by the user. This AI uses machine learning algorithms and natural language processing techniques to accurately identify symptoms from the user's input.

[1621] Step 3: The generative AI determines the severity and type of the symptom. For example, if a user types, "I have a bad headache," the AI ​​will determine that this is a severe headache.

[1622] Step 4: An appropriate medical institution is selected based on the symptoms determined by the generative AI. This selection is made taking into consideration factors such as the size and specialty of the medical institution, as well as the severity and type of the user's symptoms. Step 5: Information about the medical institution selected by the AI ​​is presented to the user. For example, in the case of a severe headache, the AI ​​will recommend a large medical institution with a neurosurgery department, and provide the user with information such as the name, location, and contact information of that institution.

[1623] Example 1

[1624] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1625] In conventional medical systems, even if users input their own symptoms and physical condition, it was difficult to properly analyze the information and provide advice and diagnosis results quickly and accurately. In addition, there was a lack of functionality to select appropriate medical institutions based on the severity and type of symptoms, which caused problems such as users taking a long time to find the appropriate medical institution.

[1626] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1627] In this invention, the server includes a means for receiving input information from a user, a means for transmitting the input information to the server, a means for the server to receive the input information and store it in a database, a means for the server to analyze the stored data and generate a prompt sentence to be input to the generative AI model, a means for the generative AI model to generate advice or a diagnosis result based on the prompt sentence, a means for the server to transmit the generated result to the user's terminal, and a means for the user to confirm the result. This allows the user to obtain quick and accurate advice or a diagnosis result based on his or her own symptoms and physical condition. In addition, since an appropriate medical institution can be selected based on the severity and type of symptoms, the user can quickly find an appropriate medical institution.

[1628] The "means for receiving input information from a user" refers to a means for providing an interface for a user to input their own symptoms and physical condition.

[1629] The "means for transmitting the input information to the server" refers to a communication means for transmitting the information input by the user to the server.

[1630] "Means by which the server receives input information and stores it in a database" refers to means by which the server receives information sent by a user and stores the information in a database.

[1631] "Means for the server to analyze the stored data and generate prompt sentences to input into the generative AI model" refers to means for the server to analyze information stored in the database and generate prompt sentences to input into the generative AI model.

[1632] "Means by which a generative AI model generates advice or a diagnostic result based on a prompt sentence" refers to means by which a generative AI model generates appropriate advice or a diagnostic result based on a prompt sentence.

[1633] "Means by which the server transmits the generated results to the user's terminal" refers to the means by which the server transmits the results obtained from the generative AI model to the user's terminal.

[1634] "Means for the user to check the results" refers to the means by which the user checks the generated advice and diagnosis results through the terminal.

[1635] The present invention is a system for receiving input information from a user and providing appropriate advice and diagnostic results based on the information. A specific embodiment of this system will be described below.

[1636] First, users access a form on a website or smartphone application and enter details about their symptoms and physical condition, such as "I have a headache" or "I have a fever."

[1637] The device then sends the information entered by the user to the server, using the HTTPS protocol to transmit the data securely.

[1638] The server receives the input information sent from the terminal and stores it in a database such as MySQL or PostgreSQL. The stored data is used for later analysis.

[1639] The server analyzes the stored data and generates prompts based on the user's symptoms and physical condition, such as "The user has a headache. What can be done to alleviate this?"

[1640] Next, the server sends the generated prompt sentence to a generative AI model (e.g., GPT-4). The generative AI model generates appropriate advice or diagnostic results based on the prompt sentence. For example, it generates a result such as "If you have a headache, it is recommended that you drink plenty of water and take a rest."

[1641] The server then transmits the results received from the generative AI model to the user's device, again using the HTTPS protocol.

[1642] Finally, the user can check the results displayed on the device, allowing them to receive appropriate advice and diagnosis results for their symptoms and physical condition.

[1643] As a concrete example, if a user enters "I have a headache," the following prompt sentence is generated:

[1644] "The user is having a headache. What can be done about it?"

[1645] In this way, the system provides appropriate advice and diagnostic results based on the user's input. The hardware used includes servers, terminals, and databases, while the software includes websites, smartphone applications, and generative AI models (e.g., GPT-4).

[1646] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1647] Step 1:

[1648] The user accesses the interface and inputs symptoms and physical condition.

[1649] Specifically, the user enters information such as "I have a headache" or "I have a fever" into a form on a website or smartphone application.

[1650] Input: Information about the user's symptoms and health condition

[1651] Output: Input information is saved to the device

[1652] Step 2:

[1653] The terminal transmits the input information to the server.

[1654] Specifically, the terminal uses the HTTPS protocol to transmit the information entered by the user to the server.

[1655] Input: Information entered by the user

[1656] Output: The server receives the input information.

[1657] Step 3:

[1658] The server receives the input information and stores it in a database.

[1659] Specifically, the server stores the received information in a database such as MySQL or PostgreSQL.

[1660] Input: Input information sent from the device

[1661] Output: Information stored in the database

[1662] Step 4:

[1663] The server analyzes the stored data and generates prompt sentences to input into the generative AI model.

[1664] Specifically, the server analyzes the information stored in the database and generates a prompt such as, "The user has a headache. What can be done to remedy this?"

[1665] Input: Information stored in a database

[1666] Output: Generated prompt statement

[1667] Step 5:

[1668] The server sends the generated prompt to a generative AI model (e.g., GPT-4).

[1669] Specifically, the server sends the generated prompt text to GPT-4.

[1670] Input: Generated prompt text

[1671] Output: The prompt sent to the generative AI model

[1672] Step 6:

[1673] A generative AI model generates advice and diagnostic results based on the prompt.

[1674] Specifically, GPT-4 generates advice and diagnostic results such as, "If you have a headache, it is recommended that you drink plenty of fluids and get plenty of rest."

[1675] Input: Prompt statement

[1676] Output: The advice and diagnostics generated.

[1677] Step 7:

[1678] The server transmits the generated results to the user's terminal.

[1679] Specifically, the server sends the results received from the generative AI model to the user's device using the HTTPS protocol.

[1680] Input: Generated advice and diagnostic results

[1681] Output: The results sent to the user's terminal.

[1682] Step 8:

[1683] The user checks the results.

[1684] As a specific operation, the user checks the advice and diagnosis results displayed on the terminal.

[1685] Input: The result sent to the terminal

[1686] Output: Advice and diagnostic results confirmed by the user

[1687] (Application example 1)

[1688] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1689] Conventional food delivery services were unable to suggest appropriate meal menus based on the user's physical condition or symptoms. This made it difficult for users to choose meals that suited their own health condition, and they were unable to adequately manage their health. Furthermore, suggesting appropriate meal menus required specialized knowledge, which was a high hurdle for general users.

[1690] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1691] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for proposing an appropriate meal menu based on the symptoms, and a means for ordering in cooperation with a food delivery service based on the proposed meal menu. This allows the user to easily propose and order an appropriate meal menu based on their own physical condition and symptoms.

[1692] The "means for receiving input information from a user" refers to a means for providing an interface for a user to input their own physical condition and symptoms.

[1693] A "generative AI means" is a means that uses artificial intelligence technology to determine symptoms based on information input by the user and make appropriate suggestions.

[1694] The "means for determining symptoms" refers to a means for analyzing information input by the user and identifying the user's physical condition and symptoms based on that information.

[1695] A "means for suggesting an appropriate meal menu" is a means for presenting an optimal meal menu to a user based on symptoms determined by a generative AI means.

[1696] The "means for ordering in cooperation with a food delivery service" refers to a means for actually ordering food in cooperation with a food delivery service based on a proposed meal menu.

[1697] A system for implementing the present invention includes a means for receiving input information from a user, a generative AI means, a means for suggesting an appropriate meal menu, and a means for placing an order in cooperation with a food delivery service.

[1698] System Configuration

[1699] 1. How to receive user input:

[1700] Users enter their physical condition and symptoms using a smartphone application or a website form.

[1701] Example: A user types, "I have a bit of a cold and my throat hurts."

[1702] 2. Generative AI means:

[1703] Using OpenAI's API, it analyzes user input and generates an appropriate meal menu.

[1704] Example: Prompt: "User's symptoms: cold and sore throat. Please suggest suitable meals."

[1705] 3. How to suggest appropriate meals:

[1706] The meal menu generated by the generative AI means is presented to the user.

[1707] Example: Show the generated menu "Hot chicken soup and orange juice" to the user.

[1708] 4. How to order through food delivery services:

[1709] Based on the suggested meal menu, an order is placed using the food delivery service's API.

[1710] Example: Send a request to a food delivery service API to order "hot chicken soup and orange juice."

[1711] Hardware and software used

[1712] Hardware: Smartphones, servers

[1713] Software: Python, OpenAI API, Food Delivery Service API

[1714] Data processing and calculation

[1715] 1. Getting user input:

[1716] Users enter their physical condition and symptoms through a smartphone application or a website form.

[1717] Example: A user types, "I have a bit of a cold and my throat hurts."

[1718] 2. Using generative AI models:

[1719] Using OpenAI's API, an appropriate meal menu is generated based on user input information.

[1720] Example: Prompt: "User's symptoms: cold and sore throat. Please suggest suitable meals."

[1721] 3. Partnering with food delivery services:

[1722] The generated menu is sent to the food delivery service's API and an order is placed.

[1723] Example: Send a request to a food delivery service API to order "hot chicken soup and orange juice."

[1724] Examples

[1725] If a user types in "I have a cold and a sore throat," the generative AI model suggests "hot chicken soup and orange juice." Based on this suggestion, the API of a food delivery service is used to order "hot chicken soup and orange juice" to the user's address.

[1726] Examples of prompt statements

[1727] User Symptoms: Cold and sore throat

[1728] Please suggest a suitable meal plan.

[1729] In this way, it becomes possible to easily suggest and order meals that suit the user's physical condition.

[1730] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1731] Step 1:

[1732] Users enter their physical condition and symptoms through a smartphone application or a website form.

[1733] Input: User's physical condition and symptoms (e.g. "I have a cold and my throat hurts")

[1734] Output: User input information

[1735] Step 2:

[1736] The terminal transmits the user's input information to the server.

[1737] Input: User input information

[1738] Output: User input sent to the server

[1739] Step 3:

[1740] Based on the user's input information received by the server, a generative AI model is used to determine symptoms and generate an appropriate meal menu.

[1741] Input: User input information

[1742] Data processing: Input prompts into the generative AI model to generate an appropriate meal menu

[1743] Example prompt: "User's symptoms: cold and sore throat. Please suggest suitable meals."

[1744] Output: The generated meal menu (e.g. "Hot chicken soup and orange juice")

[1745] Step 4:

[1746] The server transmits the generated meal menu to the user's terminal.

[1747] Input: Generated meal menu

[1748] Output: The meal menu sent to the user's device.

[1749] Step 5:

[1750] The user reviews the proposed meal menu and confirms the order.

[1751] Input: Generated meal menu

[1752] Output: User's order confirmation information

[1753] Step 6:

[1754] The terminal transmits the user's order confirmation information to the server.

[1755] Input: User's order confirmation information

[1756] Output: Order confirmation information sent to the server

[1757] Step 7:

[1758] The server places an order using the food delivery service's API based on the order confirmation information.

[1759] Input: User's order confirmation information

[1760] Data processing: Send an order request to the food delivery service API

[1761] Output: An order request sent to a food delivery service.

[1762] Step 8:

[1763] A food delivery service receives the order and delivers the meal to the user's address.

[1764] Input: An order request sent to a food delivery service

[1765] Output: Meals delivered to the user's address.

[1766] Example 2

[1767] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1768] In conventional medical systems, it was difficult for users to accurately assess their own symptoms and select an appropriate medical institution. In addition, medical institutions were often not selected appropriately based on the severity and type of symptoms, and users were sometimes unable to receive appropriate medical care. This increased the risk of users' health conditions worsening.

[1769] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1770] In this invention, the server includes a means for receiving input information from a user, a means for transmitting the input information to the server, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and a means for returning the determination result to the user, thereby enabling the user to accurately determine his / her own symptoms and select an appropriate medical institution.

[1771] "User" refers to an individual who utilizes the system to input information about their symptoms.

[1772] "Input information" refers to text data regarding symptoms that a user provides to the system.

[1773] "Server" refers to a computer system that receives input information sent by a user and analyzes it using generative AI means.

[1774] "Generative AI methods" refers to artificial intelligence technologies that use machine learning algorithms and natural language processing techniques to determine symptoms from information input by the user.

[1775] "Symptom" means any physical or mental abnormality or discomfort experienced by a User.

[1776] "Severity" refers to an indicator of the seriousness or urgency of symptoms.

[1777] "Medical institution" refers to a facility that provides appropriate medical care and treatment for the user's symptoms.

[1778] "Scale" refers to the size of the medical institution and the range of medical services it can provide.

[1779] "Expertise" refers to the specialized knowledge and skills that a medical institution has regarding specific symptoms or diseases.

[1780] "Return" refers to the server sending back the results determined by the generative AI means to the user's device.

[1781] This invention is a system that allows users to input information about their symptoms and select an appropriate medical institution based on that information. The system receives the user's input information, uses a generative AI model to determine the severity and type of symptoms, and returns the results to the user.

[1782] Hardware and Software Used

[1783] Hardware: Servers, terminals (user devices)

[1784] Software: Generative AI models, Natural Language Processing (NLP) libraries, Machine Learning algorithms

[1785] Program Processing

[1786] 1. Receiving user input information

[1787] A user uses a terminal to input information about a symptom, for example, "I have a bad headache."

[1788] The terminal receives the information entered by the user.

[1789] 2. Sending input information

[1790] The terminal sends the received user input information to the server, using the HTTPS protocol to transmit the data securely.

[1791] 3. Analysis of information

[1792] The server passes the received user input information to the generative AI model.

[1793] Generative AI models use natural language processing (NLP) libraries to parse user input, tokenizing the text and extracting meaning.

[1794] For example, the text "I have a severe headache" is analyzed to extract the symptom "headache" and the severity level "severe."

[1795] 4. Determining the severity and type of symptoms

[1796] The generative AI model uses machine learning algorithms to determine the severity and type of symptoms from the analyzed information.

[1797] For example, if you say "I have a bad headache," it will determine that you have a severe headache. This determination is made using past data and pre-trained models.

[1798] 5. Returning the results

[1799] The server sends the results of the generative AI model's judgment back to the terminal.

[1800] The device will display the result to the user, for example "severe headache."

[1801] Examples

[1802] A user types "I have a bad headache" into a terminal.

[1803] The terminal transmits this information to the server.

[1804] The server passes this information to the generative AI model.

[1805] The generative AI model uses NLP libraries to parse the text "I have a bad headache" and then uses machine learning algorithms to determine whether it is a severe headache.

[1806] The server returns the result of this decision to the terminal.

[1807] The device tells the user "Severe Headache."

[1808] Examples of prompt statements

[1809] "Analyze the symptom information entered by the user and determine the severity and type of the symptom. For example, if the user enters 'I have a bad headache,' determine that it is a severe headache."

[1810] In this way, the system analyzes the user's input information and makes appropriate decisions.

[1811] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1812] Step 1:

[1813] Receiving user input information

[1814] A user uses a terminal to input information about a symptom, for example, "I have a bad headache."

[1815] Input: Text information entered by the user (e.g. "I have a bad headache")

[1816] The terminal receives the information entered by the user.

[1817] Output: Received text information (e.g. "I have a bad headache")

[1818] Step 2:

[1819] Sending input information

[1820] The terminal transmits the received user input information to the server, using the HTTPS protocol to transmit the data securely.

[1821] Input: Received text information (e.g. "I have a bad headache")

[1822] Output: Text information sent to the server (e.g. "I have a bad headache")

[1823] Step 3:

[1824] Analysis of information

[1825] The server passes the received user input information to the generative AI model.

[1826] Input: Text information sent to the server (e.g. "I have a bad headache")

[1827] Generative AI models use natural language processing (NLP) libraries to parse user input, specifically tokenizing the text and extracting meaning.

[1828] Specific behavior: Use an NLP library (e.g. spaCy or NLTK) to parse the text "I have a bad headache" and extract the symptom "headache" and the severity "severe".

[1829] Output: Extracted keywords (e.g. "headache", "severe")

[1830] Step 4:

[1831] Determining the severity and type of symptoms

[1832] The generative AI model uses machine learning algorithms to determine the severity and type of symptoms from the analyzed information.

[1833] Input: Extracted keywords (e.g. "headache", "severe")

[1834] What it does: Uses machine learning algorithms (e.g. random forests or neural networks) to identify a severe headache based on the keyword "severe."

[1835] Output: Judgment result (e.g. "severe headache")

[1836] Step 5:

[1837] Returning the results

[1838] The server sends the results of the generative AI model's judgment back to the terminal.

[1839] Input: Judgment result (e.g. "severe headache")

[1840] The device will then display the results to the user, for example showing "severe headache" on the app screen.

[1841] Specific behavior: Performs UI update processing to display the results on the device screen.

[1842] Output: The result displayed to the user (e.g. "Severe headache")

[1843] (Application example 2)

[1844] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1845] Conventional health monitoring systems have difficulty accurately determining the severity and type of a user's symptoms, which can delay appropriate responses. In addition, they lack the ability to quickly issue alerts based on the severity of symptoms, which can delay responses in emergencies. This creates a need for real-time monitoring of users' health conditions and the ability to quickly respond when abnormalities are detected.

[1846] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1847] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and a means for determining the severity of the symptoms and issuing an alert based on the severity. This makes it possible to monitor the user's health condition in real time and quickly respond when an abnormality is detected.

[1848] "Means for receiving input information from the user" refers to an interface that allows the user to input their own symptoms and health condition, and is a function that receives information through devices such as smartphones and smart glasses.

[1849] "Generative AI means" refers to artificial intelligence functions that use machine learning algorithms and natural language processing techniques to analyze information input by users and determine the severity and type of symptoms.

[1850] The "means of determining symptoms" refers to a function that uses generative AI means to analyze symptoms from information input by the user and identify their severity and type.

[1851] "Means for selecting an appropriate medical institution" is a function that suggests the most suitable medical institution to the user based on the diagnosed symptoms.

[1852] The "means for determining the severity of symptoms" refers to a function that uses generative AI means to evaluate the severity of symptoms from information input by the user and determines an appropriate response based on the results.

[1853] "Means of issuing an alert" is a function that issues a warning to users and relevant parties when symptoms are severe, encouraging them to take prompt action.

[1854] As an embodiment of the present invention, a security health monitor system will be described as an example. The purpose of this system is to monitor the health status of a user in real time and to respond quickly when an abnormality is detected.

[1855] System Configuration

[1856] The system consists of the following main components:

[1857] 1. User terminal: A device such as a smartphone or smart glasses that provides the means to receive input information from the user.

[1858] 2. Server: A central processing unit containing generative AI means to analyse user input and determine the severity and type of symptoms.

[1859] 3. Alert system: Provide a means to issue alerts based on the severity of symptoms.

[1860] Program Processing

[1861] User Device

[1862] The user device provides an interface for users to input their symptoms and health conditions. For example, it receives text input such as "I have a bad headache" through a smartphone application.

[1863] server

[1864] The server performs the following process.

[1865] 1. Receiving input information: Receives input information sent from the user terminal.

[1866] 2. Analysis using generative AI: The received input information is analyzed using a generative AI model (for example, the DistilBERT model using the Transformers library) to determine the severity and type of symptoms.

[1867] 3. Determination of severity: The severity of symptoms is evaluated based on the analysis results. For example, if the analysis result is "NEGATIVE," it is judged to be severe, and if it is "POSITIVE," it is judged to be mild.

[1868] 4. Alerting: If symptoms are severe, an alert system will be used to warn users and relevant parties and encourage rapid response.

[1869] Alert System

[1870] The alert system issues warnings to users and related parties based on instructions from the server. For example, it uses the smartphone's notification function to display a message such as "Severe headache detected. Please seek medical attention immediately."

[1871] Hardware and Software Used

[1872] Hardware: Smartphones, smart glasses

[1873] Software: Python, Transformers library

[1874] Examples

[1875] When a user types "I have a bad headache" into a smartphone application, the system determines this to be a severe headache and issues an alert.

[1876] "I have a terrible headache"

[1877] "I feel a bit sick"

[1878] "My chest hurts"

[1879] In this way, the security health monitor system is able to monitor the user's health status in real time and respond quickly if an abnormality is detected.

[1880] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1881] Step 1:

[1882] The user launches the application on their smartphone and enters their symptoms and health condition.

[1883] Input: The user enters some text information, such as "I have a bad headache."

[1884] Specific action: The user enters symptoms into the application's text box and presses the submit button.

[1885] Step 2:

[1886] The terminal sends the user's input information to the server.

[1887] Input: Text information entered by the user.

[1888] Output: Text information sent to the server.

[1889] Specific operation: The smartphone application sends the entered text information to the server as an HTTP request.

[1890] Step 3:

[1891] The input information received by the server is passed to the generative AI model for analysis.

[1892] Input: Text information sent from the device.

[1893] Output: Analysis results (symptom severity and type).

[1894] Specific operation: The server analyzes the received text information using the Transformers library to determine the severity and type of symptoms.

[1895] Step 4:

[1896] The server determines the severity of the symptoms based on the analysis results.

[1897] Input: Analysis results of the generative AI model.

[1898] Output: Symptom severity (e.g. severe, mild).

[1899] Specific operation: The server evaluates the analysis results, determining that the problem is severe if the result is "NEGATIVE" or mild if the result is "POSITIVE".

[1900] Step 5:

[1901] The server decides whether to raise an alert based on the severity of the symptoms.

[1902] Input: Symptom severity.

[1903] Output: Instructions to send an alert.

[1904] Specific Actions: If a server is deemed critical, instructions are sent to issue an alert to the alert system.

[1905] Step 6:

[1906] The alert system will warn users and relevant parties.

[1907] Input: Alert sending instruction from the server.

[1908] Output: A warning message for the user or interested parties.

[1909] Specific action: Using the smartphone's notification function, a message such as "Severe headache has been detected. Please seek medical attention immediately" is displayed.

[1910] In this way, the security health monitor system is able to monitor the user's health status in real time and respond quickly if an abnormality is detected.

[1911] Example 3

[1912] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1913] Conventional medical institution selection systems make it difficult for users to quickly find the medical institution best suited to their symptoms. In addition, the accuracy of selecting an appropriate medical institution based on the severity and type of symptoms is low, and there is a problem that it takes a long time for users to receive appropriate treatment.

[1914] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for receiving input information from a user, a means for transmitting the input information to the server, a means for generating a prompt sentence based on the input information and transmitting it to the generative AI model, a means for the generative AI model to analyze symptoms based on the prompt sentence and select an appropriate medical institution, and a means for returning the selection result to the user. This enables the user to quickly find a medical institution that is best suited to his or her symptoms.

[1915] "User" refers to an individual who uses the system to input their symptoms and find an appropriate medical institution.

[1916] "Input information" refers to data entered by the user in text format describing their symptoms.

[1917] "Server" refers to a computer system that receives input information from a user, sends prompts to the generative AI model, and sends output from the generative AI model back to the user.

[1918] A "prompt sentence" refers to an instruction sentence that is generated based on user input information and sent to a generative AI model.

[1919] A "generative AI model" refers to an artificial intelligence model that analyzes a user's symptoms based on a prompt text and selects an appropriate medical institution.

[1920] "Symptom Severity" refers to a user-entered scale for rating the severity of a symptom.

[1921] "Size of medical institution" refers to the size of the medical institution and the level of its facilities.

[1922] "Symptom type" refers to the classification of the specific symptoms reported by the user.

[1923] "Expertise of a medical institution" refers to the specialized knowledge and skills that a medical institution has for specific symptoms or diseases.

[1924] "Selection results" refers to information about medical institutions recommended to users as a result of analysis by the generative AI model.

[1925] This invention is a system that allows users to input their symptoms and find an appropriate medical institution. The system receives the user's input information, analyzes the symptoms using a generative AI model based on the input, and selects an appropriate medical institution.

[1926] Hardware and Software Used

[1927] Hardware: Servers, terminals (user devices)

[1928] Software: Generative AI models (e.g. GPT-4), database management systems (e.g. MySQL), healthcare information systems

[1929] Program Processing

[1930] 1. User enters symptoms:

[1931] The user inputs symptoms in text format using their own device (such as a smartphone or computer). For example, the user inputs "I have a headache." This input is done through a dedicated application or web form.

[1932] 2. The device sends the input data to the server:

[1933] The device sends the symptom data entered by the user to the server as an HTTP request. For example, the device uses a "POST" request to send the input data to a specific endpoint on the server.

[1934] 3. The server sends a prompt to the generative AI model:

[1935] The server sends a prompt to the generative AI model based on the received symptom data. Specifically, the server analyzes the symptom data and generates an appropriate prompt. For example, it generates a prompt such as "The user is complaining of a headache. Please select an appropriate medical institution" and sends it to the generative AI model.

[1936] 4. Generative AI model analyzes symptoms and selects appropriate medical institution:

[1937] The generative AI model receives the prompt and analyzes the user's symptoms. Based on the analysis results, it selects an appropriate medical institution. In this case, the generative AI model refers to a database of medical institutions and takes into account the size and specialty of the medical institution, as well as the severity and type of the user's symptoms. For example, in the case of a severe headache, the generative AI model will recommend a large medical institution with a neurosurgery department.

[1938] 5. The server sends the selection back to the user:

[1939] The server sends the information about medical institutions obtained from the generative AI model back to the user's device. Specifically, the server receives the output of the generative AI model, converts it into a format that is easy for the user to understand, and sends it to the user. For example, the server generates a message saying, "We recommend large medical institutions with neurosurgery capabilities" and sends it to the user's device.

[1940] Examples

[1941] User input: The user types "I have a headache" into the terminal.

[1942] Prompt for generative AI model: "The user is complaining of a headache. Please select the appropriate medical institution."

[1943] Output of the generative AI model: “This suggests a severe headache and would recommend a larger medical institution with a neurosurgery department.”

[1944] In this way, the user can quickly find the medical institution that is best suited to his / her symptoms. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1945] Step 1:

[1946] The user enters the symptoms.

[1947] A user inputs symptoms in text format using their own device (such as a smartphone or PC). For example, the user inputs "I have a headache." This input is done through a dedicated application or a web form. The input data is saved in text format on the device.

[1948] Step 2:

[1949] The terminal transmits the input data to the server.

[1950] The terminal sends the symptom data entered by the user to the server. The data is sent as an HTTP request. For example, the terminal uses a "POST" request to send the input data to a specific endpoint on the server. The input data is sent to the server in text format.

[1951] Step 3:

[1952] The server sends prompts to the generative AI model.

[1953] The server sends a prompt to the generative AI model based on the received symptom data. Specifically, the server analyzes the symptom data and generates an appropriate prompt sentence. For example, it generates a prompt sentence such as "The user is complaining of a headache. Please select an appropriate medical institution" and sends it to the generative AI model. The prompt sentence is sent to the generative AI model in text format.

[1954] Step 4:

[1955] The generative AI model analyzes symptoms and selects the appropriate medical institution.

[1956] The generative AI model receives the prompt and analyzes the user's symptoms. Based on the analysis results, it selects an appropriate medical institution. In this case, the generative AI model refers to a database of medical institutions and takes into account the size and specialty of the medical institution, as well as the severity and type of the user's symptoms. For example, in the case of a severe headache, the generative AI model will recommend a large medical institution with a neurosurgery department. The analysis results are returned to the server in text format.

[1957] Step 5:

[1958] The server sends the selection back to the user.

[1959] The server returns the medical institution information obtained from the generative AI model to the user's device. Specifically, the server receives the output of the generative AI model, converts it into a format that is easy for the user to understand, and sends it to the user. For example, the server generates a message saying, "Large medical institutions with neurosurgery departments are recommended," and sends it to the user's device. The selection result is displayed in text format on the user's device.

[1960] (Application example 3)

[1961] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1962] In modern society, it is important for users to find a prompt and appropriate medical institution when they feel unwell. However, it is not easy to select the most appropriate medical institution depending on the severity and type of symptoms. In addition, while a prompt response is required in an emergency, contacting the appropriate medical institution may be delayed. To solve these problems, a system is needed that can accurately assess the user's symptoms and quickly recommend an appropriate medical institution.

[1963] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1964] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and a means for automatically contacting the nearest emergency medical institution in an emergency based on the selected medical institution. This allows the user to quickly find an appropriate medical institution when feeling unwell. In addition, by automatically contacting the nearest emergency medical institution in an emergency, a quick response is possible.

[1965] "Means for receiving input information from a user" refers to a device or software that provides an interface for a user to input information about their symptoms or physical condition.

[1966] A "generative AI means" is a system that uses artificial intelligence technology to analyze information input by the user and determine symptoms based on that information.

[1967] The "means for selecting an appropriate medical institution" refers to an algorithm or process for recommending the most suitable medical institution to the user based on the symptoms determined by the generative AI means.

[1968] "Means of automatically contacting the nearest emergency medical institution in the event of an emergency" refers to a communications system that automatically contacts the nearest emergency medical institution based on the user's current location information if the user's symptoms are determined to be severe.

[1969] The following system configuration will be described as an embodiment of the present invention.

[1970] System Configuration

[1971] The system includes a means for receiving input information from a user, a generative AI means, a means for selecting an appropriate medical institution, and a means for automatically contacting the nearest emergency medical institution in the event of an emergency.

[1972] A means of receiving input information from the user

[1973] Users input information about their symptoms and physical condition through a smartphone app. The interface supports text and voice input and is designed to make it easy for users to provide information.

[1974] Generative AI means

[1975] The server receives input information from the user and analyzes it using a generative AI model. This analysis determines the user's symptoms and identifies their severity and type. The generative AI model uses the OpenAI API.

[1976] How to choose the right medical facility

[1977] Based on the symptoms determined by the generative AI means, the server selects an appropriate medical institution. This selection is made taking into account the size and specialty of the medical institution, the user's current location, etc. For example, in the case of a severe headache, a large medical institution with a neurosurgery department will be recommended.

[1978] A means to automatically contact the nearest emergency medical facility in the event of an emergency

[1979] If the user's symptoms are deemed severe, the server will automatically contact the nearest emergency medical facility based on the user's current location, using the Requests library to send HTTP requests.

[1980] Examples

[1981] If a user experiences a "severe headache," they input their symptoms into a smartphone app. The generative AI model analyzes this information and recommends "large medical institutions with neurosurgery capabilities." In addition, in the event of an emergency, the app automatically contacts the nearest emergency medical institution.

[1982] Examples of prompt statements

[1983] "User symptoms: Severe headache.\nPlease recommend appropriate medical help."

[1984] In this way, a system can be realized that helps users quickly access appropriate medical institutions.

[1985] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1986] Step 1:

[1987] The user launches the smartphone app and enters information about their symptoms and physical condition.

[1988] Input: User's symptoms (e.g., severe headache)

[1989] Output: The entered symptom information is sent to the server.

[1990] Step 2:

[1991] A server receives the symptom information sent by the user.

[1992] Input: Symptom information submitted by the user

[1993] Output: The received symptom information is passed to the generative AI model.

[1994] Step 3:

[1995] The server uses a generative AI model to analyze the received symptom information and determine the user's symptoms.

[1996] Input: Received symptom information

[1997] Output: Parsed symptom information (e.g., severe headache)

[1998] Step 4:

[1999] The server selects an appropriate medical institution based on the analyzed symptom information.

[2000] Input: Parsed symptom information

[2001] Output: Recommended medical institution information (e.g., a large medical institution with neurosurgery)

[2002] Step 5:

[2003] The server sends the recommended medical institution information to the user's smartphone app.

[2004] Input: Recommended medical institution information

[2005] Output: Medical institution information displayed on the user's smartphone app

[2006] Step 6:

[2007] If the user's symptoms are determined to be severe, the server will automatically contact the nearest emergency medical facility based on the user's current location information.

[2008] Input: User's current location information, severe symptoms information

[2009] Output: Emergency call sent to the nearest emergency medical facility

[2010] Step 7:

[2011] The server notifies the user's smartphone app of the results of the emergency contact.

[2012] Input: Emergency message transmission result

[2013] Output: A notification displayed on the user's smartphone app (e.g., emergency medical services have been contacted)

[2014] In addition, an emotion engine that estimates the emotion of the user may be further combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[2015] "Example 1"

[2016] As one embodiment of the present invention, a system is provided that includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and an emotion engine for recognizing the user's emotions. In this system, when a user inputs details of his or her symptoms and physical condition, the generative AI analyzes the information and determines the severity and type of symptoms. Furthermore, the emotion engine analyzes the user's emotions and feeds back the results to the generative AI. The generative AI combines the symptom information and emotion information to select an appropriate medical institution. Specifically, if a user inputs "I have a severe headache" and the emotion engine detects the user's anxiety or fear, the generative AI will recommend a large medical institution with a neurosurgery department, taking into account the severe headache and high anxiety level.

[2017] "Example 2"

[2018] As one embodiment of the present invention, a system is provided that includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and an emotion engine for recognizing the user's emotions. In this system, when a user inputs details of his or her symptoms and physical condition, the generative AI analyzes the information and determines the severity and type of symptoms. Furthermore, the emotion engine analyzes the user's emotions and feeds back the results to the generative AI. The generative AI combines the symptom information and emotion information to select an appropriate medical institution. Specifically, if a user inputs "I have a severe headache" and the emotion engine detects the user's anxiety or fear, the generative AI will recommend a large medical institution with a neurosurgery department, taking into account the severe headache and high anxiety level.

[2019] "Example 3"

[2020] As one embodiment of the present invention, a system is provided that includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, and an emotion engine for recognizing the user's emotions. In this system, when a user inputs details of his or her symptoms and physical condition, the generative AI analyzes the information and determines the severity and type of symptoms. Furthermore, the emotion engine analyzes the user's emotions and feeds back the results to the generative AI. The generative AI combines the symptom information and emotion information to select an appropriate medical institution. Specifically, if a user inputs "I have a severe headache" and the emotion engine detects the user's anxiety or fear, the generative AI will recommend a large medical institution with a neurosurgery department, taking into account the severe headache and high anxiety level.

[2021] The process flow of each embodiment will be described below.

[2022] "Example 1"

[2023] Step 1: The user enters their symptoms and physical condition into the system. For example, they enter information like "I have a bad headache."

[2024] Step 2: The generative AI analyzes the input from the user and determines the severity and type of symptoms. In this example, the AI ​​determines that the user is experiencing a severe headache based on the input "I have a bad headache."

[2025] Step 3: The emotion engine analyzes the user's emotions. In this example, the emotion engine determines that the user is feeling anxious or scared based on the expressions and language used when providing the input information.

[2026] Step 4: The generative AI combines the symptom information with the emotion information to select the appropriate medical institution. In this example, the AI ​​takes into account the severe headaches and high anxiety levels and recommends a large medical institution with neurosurgery.

[2027] Example 1

[2028] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[2029] In conventional medical systems, even if a user inputs their symptoms, it can be difficult to select an appropriate medical institution based on that information alone. In addition, because the user's emotional state is not taken into account, the system may not be able to select an appropriate medical institution. This can lead to a risk that the user will not be able to visit an appropriate medical institution and their symptoms may worsen.

[2030] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2031] In this invention, the server includes a means for receiving input information from a user, a generative AI means for determining symptoms based on the input information, a means for selecting an appropriate medical institution based on the symptoms, an emotion analysis means for recognizing the emotions of the user, a means for feeding back the results of the emotion analysis means to the generative AI means, and a means for the generative AI means to integrate and analyze the symptom information and the emotion information. This makes it possible to comprehensively analyze the user's symptoms and emotions and select the most appropriate medical institution.

[2032] The "means for receiving input information from a user" refers to a means for providing an interface for a user to input their own symptoms and physical condition.

[2033] A "generative AI means" is a means that uses artificial intelligence technology to analyze information input by the user and determine the severity and type of symptoms.

[2034] The "means for selecting an appropriate medical institution" refers to a means for selecting the medical institution most suitable for the user based on the symptoms determined by the generative AI means.

[2035] The "emotion analysis means" refers to a means for recognizing and analyzing emotions from information input by a user.

[2036] The "means of providing feedback" refers to the means of providing the results of the emotion analysis means to the generative AI means.

[2037] An "integrative analysis means" is a means by which a generative AI means combines symptom information and emotional information for analysis.

[2038] This invention is a system that provides an interface for users to input their symptoms and physical condition. Users input information through a form on a website or a smartphone application. This interface is designed to allow users to input information easily.

[2039] The server sends the input information received from the user to the generative AI. The generative AI analyzes this information and determines the severity and type of symptoms. For example, if the user inputs "I have a bad headache," the generative AI will use that information to assess the severity of the headache. Natural language processing (NLP) technology is used in generative AI.

[2040] In addition, the server also transmits the user's input information to the emotion analysis means. The emotion analysis means analyzes emotions from the user's input and feeds the results back to the generative AI. For example, it detects anxiety or fear from the user's input. The emotion analysis means uses text analysis technology.

[2041] The generative AI integrates and analyzes symptom information and emotional information. For example, it analyzes the combination of severe headache and high anxiety level. Based on this integrated information, the generative AI selects an appropriate medical institution. For example, taking into account severe headache and high anxiety level, it recommends a large medical institution with neurosurgery.

[2042] The server sends information about the medical institution selected by the generative AI to the terminal. The terminal displays the information about the medical institution recommended to the user. This allows the user to receive treatment at a prompt and appropriate medical institution.

[2043] As a concrete example, consider the case where a user uses a smartphone application to input "I have a severe headache." The emotion analysis means detects anxiety from the user's input. Taking into account the severe headache and high anxiety level, the generative AI recommends a large medical institution with a neurosurgery department. The server sends this information to the terminal, which then displays a message to the user saying, "Please visit the neurosurgery department of a nearby university hospital."

[2044] An example of a prompt is as follows:

[2045] "If a user types 'I have a bad headache' through a smartphone application and emotion analysis measures detect anxiety, what kind of medical institution will the generative AI recommend?"

[2046] The system aims to provide prompt and appropriate medical services by comprehensively analyzing the user's symptoms and emotions and selecting the most appropriate medical institution.

[2047] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2048] Step 1:

[2049] A user enters details about his / her symptoms and physical condition through a form on a website or smartphone application. For example, the user enters "I have a severe headache." The entered information is sent from the device to a server. The input data is in text format and includes detailed information about the user's symptoms and physical condition.

[2050] Step 2:

[2051] The server sends the input information received from the user to the generative AI. The generative AI uses natural language processing (NLP) technology to analyze the input information and determine the severity and type of symptoms. For example, it analyzes the information "I have a severe headache" and evaluates the severity of the headache. The analysis results are output as data on the severity and type of symptoms.

[2052] Step 3:

[2053] The server also transmits the user's input information to the emotion analysis means. The emotion analysis means uses text analysis techniques to analyze emotions from the user's input. For example, it detects anxiety or fear from the user's input. The analysis results are output as data on the user's emotional state.

[2054] Step 4:

[2055] The generative AI receives feedback from the emotion analysis tool and integrates the symptom information with the emotion information for analysis (for example, combining a severe headache with high anxiety levels). The integrated information is output as data for selecting the appropriate medical institution.

[2056] Step 5:

[2057] The generative AI selects an appropriate medical institution based on the integrated information. For example, it recommends a large medical institution with a neurosurgery department considering the severe headache and high anxiety level. The information of the selected medical institution is sent to the server.

[2058] Step 6:

[2059] The server sends information about the medical institution selected by the generative AI to the terminal. The terminal displays the information about the medical institution recommended to the user. For example, it displays a message saying, "Please visit the neurosurgery department of a nearby university hospital." This allows the user to visit an appropriate medical institution quickly.

[2060] (Application example 1)

[2061] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[2062] Conventional medical institution selection systems can select an appropriate medical institution based on the user's symptoms, but they cannot take the user's emotional state into account, which means that they cannot provide optimal support in emergencies or for users with high anxiety. In addition, selecting a medical institution without considering the user's emotional state may reduce the user's satisfaction and sense of security.

[2063] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means ...

Claims

1. A means for receiving input information from a user; A means for determining symptoms based on the input information using a generative AI; means for recognizing emotions of the user using an emotion engine; A means for the generative AI to select a medical institution by combining the symptom information and the information indicating the user's emotion; A system including:

2. The determining means determines the severity of the symptom from the input information, The selecting means selects the size of the medical institution based on the severity. The system of claim 1 .

3. The determining means determines the type of the symptom from the input information, The selecting means selects the specialty of the medical institution based on the type of the symptom. The system of claim 1 .

Images