System

Abstract

To provide a system.SOLUTION: A system includes: means for inputting symptom information from a user; means for transmitting the inputted symptom information to a server; means for analyzing the symptom information using a prompt sentence, which is created from the symptom information by the server receiving the symptom information, and a generative AI model; means for estimating a disease on the basis of the symptom information using the generative AI model; means for proposing an appropriate coping method on the basis of an estimation result; and means for displaying the proposed coping method to 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] Challenges include meeting medical needs in areas where there is a shortage of medical institutions, individualizing health promotion and diet, and taking preventive measures for mental health. [Means for solving the problem]

[0005] A generative AI will be used to carry out a primary diagnosis, identify symptoms, and determine whether they can be treated with over-the-counter medication or whether a visit to a nearby hospital is necessary. In addition, to support health promotion and dieting, a personalized training menu will be created, and diet recipes using ingredients found in the home refrigerator will be devised. In addition, AI counseling will be provided as preventive support for mental health. [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] The present invention is a system that uses generative AI to perform a primary diagnosis. Specifically, it receives symptom information from the user as input and identifies the symptoms based on that information. Based on the results, it determines whether the symptom can be treated with over-the-counter medication or whether a visit to a nearby hospital is necessary. For example, if a user inputs "I have a headache," the generative AI uses that information to infer possible illnesses that could be causing the headache and suggests appropriate treatment methods.

[0029] "Example 2"

[0030] The present invention is a system that devise personalized training menus and diet recipes using ingredients in a home refrigerator as a means of health promotion and diet support. Specifically, the system receives inputs of the user's physical information, lifestyle, and information on ingredients in the refrigerator, and generates individual training menus and diet recipes based on that information. For example, if a user inputs "height 170 cm, weight 70 kg, and there is chicken breast and broccoli in the refrigerator," the generative AI will use that information to suggest an appropriate exercise menu and diet recipes using chicken breast and broccoli.

[0031] "Example 3"

[0032] The present invention is a system that provides AI counseling for worries as preventive support for mental health. Specifically, the system receives worry information from the user as input and generates appropriate advice based on that information. For example, if the user inputs "I haven't been able to sleep lately because of work stress," the generative AI provides advice on stress management and sleep improvement based on that information.

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

[0034] "Example 1"

[0035] Step 1: Receive symptom information from the user as input.

[0036] Step 2: The generative AI uses the information to identify symptoms.

[0037] Step 3: Based on the results, determine whether the condition can be treated with over-the-counter medication or whether treatment at a nearby hospital is required.

[0038] "Example 2"

[0039] Step 1: Receive the user's physical information, lifestyle, and food information in the refrigerator as input.

[0040] Step 2: Use that information to generate personalized training menus and diet recipes.

[0041] "Example 3"

[0042] Step 1: Receive problem information from the user as input.

[0043] Step 2: Generate appropriate advice based on that information.

[0044] Example 1

[0045] 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".

[0046] In modern society, it is important for users to quickly and accurately understand their own health condition and obtain appropriate treatment. However, there are many cases where it is difficult to access medical institutions, or people hesitate to visit a medical institution because their symptoms are minor. In such situations, it is difficult for users to find appropriate treatment, and health risks may increase. Therefore, there is a demand for a system that allows users to easily input their own symptoms and obtain appropriate treatment.

[0047] 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.

[0048] In this invention, the server includes a means for inputting symptom information from a user, a means for transmitting the input symptom information to the server, a means for the server to receive the symptom information and analyze it using a generative AI model, a means for the generative AI model to infer a disease based on the symptom information, a means for the server to suggest an appropriate countermeasure based on the inference result, and a means for displaying the suggested countermeasure to the user. This allows the user to easily input their own symptoms and quickly and accurately obtain an appropriate countermeasure.

[0049] "User" refers to an individual who utilizes the system to input their own symptom information and obtain appropriate treatment.

[0050] "Symptom information" refers to data that specifically describes the user's own health condition or any ailments that he or she is experiencing.

[0051] "Server" refers to a computer system that receives symptom information sent by a user, analyzes it using a generative AI model, and proposes appropriate countermeasures.

[0052] A "generative AI model" refers to an artificial intelligence model that analyzes input data and generates appropriate inferences and countermeasures.

[0053] "Analysis" refers to the process in which the generative AI model infers the disease based on symptom information and derives appropriate treatment.

[0054] "Inference results" refers to predictions of possible diseases or health conditions obtained as a result of the generative AI model analyzing symptom information.

[0055] "Countermeasures" refer to specific actions or treatments that the generative AI model suggests to the user based on the inference results.

[0056] "Display" refers to the process in which the terminal visually presents the solution information sent from the server to the user.

[0057] This invention is a system in which a user inputs his / her own symptom information, analyzes the information using a generative AI model, and proposes an appropriate countermeasure. A specific embodiment of this system will be described below.

[0058] A user inputs information about his / her symptoms using a device such as a smartphone or a PC. For example, the user inputs "I have a headache." This input information is sent from the device to the server. The device uses an Internet connection to send the data using the HTTPS protocol.

[0059] The server receives the symptom information sent from the terminal. The received data is temporarily stored in a database in the server. Next, the server uses a generative AI model to analyze the received symptom information. For example, OpenAI's (registered trademark) GPT-4 (registered trademark) is used as this generative AI model. The server sends a prompt sentence such as "If the user inputs 'I have a headache,' please estimate the illnesses that may be causing the headache and suggest appropriate measures to deal with it" to the generative AI model.

[0060] The generative AI model analyzes the symptom information based on the prompt text and infers possible illnesses. For example, based on the information "I have a headache," it considers the possibility of a migraine, tension headache, etc. The generative AI model returns the inference results to the server.

[0061] The server proposes appropriate countermeasures based on the inferences obtained from the generative AI model. For example, it generates countermeasures such as "Use over-the-counter headache medicine." It also provides information on nearby hospitals if necessary. The server transmits the generated countermeasure information to the terminal.

[0062] The terminal displays the information on how to deal with the problem sent from the server to the user. The user then takes appropriate action based on the displayed information. For example, the user may purchase and use an over-the-counter headache medicine.

[0063] As a concrete example, when a user inputs "I have a headache," the following prompt sentence uses a generative AI model to predict possible illnesses that may be causing the headache and suggests appropriate measures to deal with the situation.

[0064] Example prompt:

[0065] “If a user types in ‘I have a headache,’ use a generative AI model to infer what illnesses may be causing the headache and suggest appropriate treatment.”

[0066] In this way, the user can easily input his / her symptoms and obtain appropriate measures quickly and accurately.

[0067] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0068] Step 1:

[0069] Subject: User

[0070] The user inputs their symptom information into the device. For example, they open a smartphone app and type "I have a headache" into the text box.

[0071] Input: User's symptoms (e.g. "I have a headache")

[0072] Output: Symptom information entered into the terminal

[0073] Step 2:

[0074] Subject: Terminal

[0075] The device then transmits the entered symptom information to the server, using an Internet connection to transmit the data using the HTTPS protocol.

[0076] Input: Symptom information entered into the terminal

[0077] Output: Symptom information sent to the server

[0078] Step 3:

[0079] Subject: Server

[0080] The server receives the symptom information sent from the terminal and temporarily stores the received data in a database within the server.

[0081] Input: Symptom information sent from the device

[0082] Output: Symptom information stored in the database

[0083] Step 4:

[0084] Subject: Server

[0085] The server inputs the saved symptom information into the generative AI model, generates a prompt sentence, and sends it. The prompt sentence used is something like, "If the user inputs 'I have a headache,' please estimate the illnesses that may be causing the headache and suggest appropriate measures."

[0086] Input: Symptom information stored in the database

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

[0088] Step 5:

[0089] Subject: Generative AI model

[0090] The generative AI model analyzes symptom information based on the prompt text and predicts possible illnesses. For example, based on the information "I have a headache," it considers the possibility of a migraine, tension headache, etc.

[0091] Input: The prompt sent to the generative AI model

[0092] Output: Estimated disease probability

[0093] Step 6:

[0094] Subject: Server

[0095] The server then proposes appropriate measures based on the inferences obtained from the generative AI model. For example, it generates measures such as "use over-the-counter headache medicine." It also provides information on nearby hospitals if necessary.

[0096] Input: Inference results obtained from a generative AI model

[0097] Output: Generated solutions

[0098] Step 7:

[0099] Subject: Server

[0100] The server transmits the generated solution information to the device. At this time, the server transmits the data via an Internet connection.

[0101] Input: Generated solution

[0102] Output: Actions sent to the device

[0103] Step 8:

[0104] Subject: Terminal

[0105] The terminal displays the information on how to deal with the problem sent from the server to the user. The user then takes appropriate action based on the displayed information. For example, the user may purchase and use an over-the-counter headache medicine.

[0106] Input: Action sent by server

[0107] Output: Actions shown to the user

[0108] (Application example 1)

[0109] 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."

[0110] In modern society, it is a difficult task for users to select appropriate meals based on their own health condition and symptoms. In particular, when using food delivery services, it is required to choose meals that take health into consideration, but there is a lack of information for users to choose meals that are suitable for their own symptoms. For this reason, support is needed to help users use food delivery services while maintaining their health.

[0111] 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.

[0112] In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the symptoms can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for suggesting an appropriate diet based on the user's health condition and symptoms, and a means for generating a prompt sentence using a generative AI model and suggesting an appropriate diet, thereby enabling the user to select an appropriate diet based on their own health condition and symptoms.

[0113] "Primary diagnosis" is the process of making an initial diagnosis based on symptom information provided by the user.

[0114] "Generative AI" is an artificial intelligence technology that generates appropriate responses and suggestions based on information input by the user.

[0115] A "means for identifying symptoms" is a method for analyzing the symptom information entered by the user and identifying the cause of the symptoms and any associated diseases.

[0116] "Means for determining whether symptoms can be treated with over-the-counter drugs, etc." refers to a method for determining whether the user's symptoms can be treated with over-the-counter drugs or whether medical treatment at a medical institution is required.

[0117] The "means for determining whether medical treatment at a nearby hospital is necessary" refers to a method for determining whether the user's symptoms are serious and, if necessary, recommending medical treatment at a nearby medical institution.

[0118] A "means for suggesting appropriate meals based on a user's health condition and symptoms" is a method for selecting and suggesting appropriate meals based on health condition and symptom information input by a user.

[0119] "Means for generating prompt sentences using a generative AI model and suggesting appropriate meals" refers to a method for using a generative AI model to create prompt sentences based on the user's symptoms and then suggesting appropriate meals based on those prompt sentences.

[0120] A system for implementing the present invention is for suggesting an appropriate diet to a user based on the user's health condition or symptoms. The system includes a means for generating prompt sentences using a generative AI model to suggest an appropriate diet to the user.

[0121] System Configuration

[0122] The system consists of the following main components:

[0123] 1. User device: A device such as a smartphone or tablet where the user enters symptom information.

[0124] 2. Server: A server for hosting the generative AI model and processing input from users.

[0125] 3. Generative AI model: An AI model that generates prompts based on user input and suggests appropriate meals.

[0126] Program Processing

[0127] When a user inputs their current symptoms using a device such as a smartphone, the information is sent to a server, which uses a generative AI model to generate a prompt based on the input symptom information. This prompt is intended to suggest an appropriate meal.

[0128] The generated prompts are then processed again by the server to generate appropriate meal suggestions, which are then sent to the user's device and displayed to the user.

[0129] Hardware and Software Used

[0130] Hardware: Smartphones, tablets, servers

[0131] Software: Generative AI models (e.g., OpenAI API), database management systems, communication protocols (e.g., HTTP / HTTPS)

[0132] Examples

[0133] For example, if the user types "I have a stomach ache," the following prompt text is generated:

[0134] User entered 'My stomach hurts'. Please suggest suitable meals.

[0135] Based on this prompt, the generative AI model generates advice such as, "We recommend that you eat foods that are easy on the stomach, such as porridge or soup." This advice is sent to the user's device and displayed to the user.

[0136] In this way, users can choose appropriate meals based on their health conditions and symptoms.

[0137] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0138] Step 1:

[0139] The user inputs his / her current symptoms using a device such as a smartphone. The input symptom information is sent to the server in text format. The input data is the user's symptom information (e.g., "My stomach hurts").

[0140] Step 2:

[0141] The server analyzes the received symptom information and generates a prompt to send to the generative AI model. Specifically, the server generates a prompt based on the symptom information, such as: "The user inputs 'My stomach hurts.' Please suggest an appropriate meal." This prompt is generated.

[0142] Step 3:

[0143] The server sends the generated prompt to the generative AI model, which analyzes the prompt and generates appropriate meal suggestions. The input data is the prompt, and the output data is the meal suggestions (e.g., "We recommend that you eat something easy on your stomach, such as porridge or soup").

[0144] Step 4:

[0145] The server sends the meal suggestions received from the generative AI model to the user device. The user device displays the received meal suggestions. The input data are the meal suggestions, and the output data are the meal suggestions displayed to the user.

[0146] Step 5:

[0147] The user reviews the displayed meal suggestions and selects the appropriate meal. This allows the user to select the appropriate meal based on their health condition and symptoms. The input data is the displayed meal suggestions and the output data is the user's selected meal.

[0148] Example 2

[0149] 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."

[0150] In modern society, there is a demand for personalized health promotion and diet support that is tailored to each individual's health condition and lifestyle. However, conventional systems have had difficulty providing training menus and diet recipes that comprehensively take into account the user's physical information, lifestyle, and information about ingredients in the refrigerator. In addition, they are also inadequate in providing appropriate diagnoses and countermeasures based on this information.

[0151] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the condition can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for receiving the user's physical information, lifestyle, and refrigerator food information as input, and generating individual training menus and diet recipes, and a means for presenting the generated training menus and diet recipes to the user. This makes it possible to provide optimal training menus and diet recipes according to the user's individual health condition and lifestyle.

[0152] The "generative AI means for performing primary diagnosis" is an artificial intelligence technology for evaluating the initial health condition based on information input by the user and proposing appropriate measures.

[0153] "Symptom assessment tools" are techniques for analyzing symptoms reported by a user and determining the cause and severity of the symptoms.

[0154] The "means for determining whether the symptom can be treated with over-the-counter medicine, etc., or whether medical treatment at a nearby hospital is required" is a technology for determining whether the user's symptoms can be treated with over-the-counter medicine, or whether medical treatment at a medical institution is required.

[0155] "Means for receiving a user's physical information, lifestyle, and information on ingredients in the refrigerator as input, and generating individual training menus and diet recipes" refers to technology for creating individually optimized exercise programs and meal plans based on the physical data, lifestyle habits, and information on ingredients in the refrigerator provided by the user.

[0156] The "means for presenting the generated training menu and diet recipes to the user" refers to a technique for displaying the exercise program and meal plan generated by the server to the user in an easy-to-understand manner.

[0157] This invention is a system for the purpose of improving the health of users and supporting their diets, and generates individual training menus and diet recipes based on the user's physical information, lifestyle, and food information in the refrigerator. A specific embodiment of this system will be described below.

[0158] 1. Program Generation

[0159] The server generates a program aimed at improving the user's health and supporting dieting. This program receives the user's physical information, lifestyle, and information on ingredients in the refrigerator as input, and generates individual training menus and diet recipes. One example of the generative AI model used is GPT-4.

[0160] 2. What the program does

[0161] The server processes the data in the following steps:

[0162] Data Entry:

[0163] A user uses a dedicated application or a web interface to input his / her physical information (e.g., height, weight), lifestyle (e.g., exercise frequency, food preferences), and food information in the refrigerator. For example, a user inputs, "I am 170 cm tall, I weigh 70 kg, and there are chicken breasts and broccoli in the refrigerator."

[0164] Data Analysis:

[0165] The server analyzes the input data and generates optimal training menus and diet recipes based on the user's health status and goals. This analysis uses a generative AI model (e.g. GPT-4).

[0166] Generate a training menu:

[0167] The server generates an individual training menu based on the user's physical information and lifestyle. For example, if a user wants to exercise three times a week, the server will suggest a menu that combines aerobic exercise and strength training.

[0168] Diet recipe generation:

[0169] The server generates diet recipes suitable for the user based on the information about ingredients in the refrigerator. For example, if there is chicken breast and broccoli in the refrigerator, the server will suggest low-calorie recipes using these ingredients.

[0170] Presenting the results:

[0171] The server transmits the generated training menu and diet recipes to the terminal and presents them to the user. The user can plan their daily training and meals based on the displayed information.

[0172] 3. Examples and prompts

[0173] As a concrete example, the following prompt sentence is input into the generative AI model.

[0174] Example prompt:

[0175] User Information:

[0176] Height: 170cm

[0177] Weight: 70kg

[0178] Lifestyle: Exercise 3 times a week

[0179] Ingredients in the fridge: Chicken breast, broccoli

[0180] Based on this information, please suggest an appropriate training menu and diet recipes using chicken breast and broccoli.

[0181] An example of the result produced:

[0182] Training Menu:

[0183] Aerobic exercise 3 times a week (e.g. 30 minutes of jogging)

[0184] Strength training twice a week (e.g. squats, push-ups)

[0185] Diet Recipes:

[0186] Low calorie stir-fry with chicken breast and broccoli

[0187] Ingredients: 200g chicken breast, 100g broccoli, 1 tablespoon olive oil, a little salt, a little pepper

[0188] Directions: Cut chicken breast into bite-sized pieces and fry in olive oil. Add broccoli and fry further, then season with salt and pepper.

[0189] In this way, the server generates an individual training menu and diet recipes based on the user's input information and provides them to the user.

[0190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0191] Step 1:

[0192] The user enters the information.

[0193] Using a dedicated application or web interface, users input their physical information (e.g., height 170 cm, weight 70 kg), lifestyle (e.g., exercise three times a week), and information about ingredients in their refrigerator (e.g., chicken breast, broccoli). The input information is then stored on the device.

[0194] Step 2:

[0195] The terminal transmits the input information to the server.

[0196] The device encrypts the information entered by the user and sends it to the server. The data sent includes the user's physical information, lifestyle, and food information in the refrigerator. The server stores the received data for analysis.

[0197] Step 3:

[0198] The server parses the data.

[0199] The server analyzes the received user information. A generative AI model (e.g. GPT-4) is used for the analysis. Specifically, the server evaluates the user's health condition and goals based on the user's physical information and lifestyle. For example, it calculates the user's BMI and determines the appropriate amount of exercise. The input data is processed based on the user's health condition and goals.

[0200] Step 4:

[0201] The server generates a training menu.

[0202] The server generates the optimal training menu for the user based on the analysis results. For example, if the user wants to exercise three times a week, the server will suggest a menu that combines aerobic exercise (e.g., 30 minutes of jogging) and strength training (e.g., squats and push-ups). The generated training menu is customized based on the user's health condition and goals.

[0203] Step 5:

[0204] The server generates diet recipes.

[0205] The server generates diet recipes suitable for the user based on the ingredient information in the refrigerator. For example, if there is chicken breast and broccoli in the refrigerator, the server will suggest low-calorie recipes using these ingredients (e.g., stir-fried chicken breast and broccoli). The generated recipes are customized based on the ingredient information and health goals of the user.

[0206] Step 6:

[0207] The server sends the result to the terminal.

[0208] The server encrypts the generated training menu and diet recipes and transmits them to the terminal. The transmitted data includes the exercise program and meal plan optimized for the user. The terminal stores the received data.

[0209] Step 7:

[0210] The terminal displays the results to the user.

[0211] The terminal displays the training menu and diet recipes received from the server to the user. The user can plan their daily training and meals based on the displayed information. Specifically, the user can check the details of the exercise program and recipes on the application interface.

[0212] (Application example 2)

[0213] 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."

[0214] In modern society, health promotion and diet support are important issues, but it is difficult to provide training menus and diet recipes that are suitable for each individual user. In addition, there is a lack of specific methods for users to effectively use ingredients at home and eat healthy meals. Furthermore, there is a demand for a means to quickly obtain the necessary ingredients and dishes based on the generated recipe. To solve these issues, personalized suggestions based on the user's physical information, lifestyle, and information on ingredients in the refrigerator are necessary.

[0215] 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.

[0216] In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the condition can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for receiving the user's physical information, lifestyle, and information on ingredients in the refrigerator as input, and generating individual training menus and diet recipes, and a means for proposing options for delivering the necessary ingredients and dishes based on the generated recipes. This allows the user to receive training menus and diet recipes that match their own health condition and lifestyle, and further allows the necessary ingredients and dishes to be delivered quickly.

[0217] The "generative AI means for performing primary diagnosis" is an artificial intelligence technology that assesses the initial health condition based on information input by the user and suggests appropriate actions.

[0218] "Symptom assessment tools" are technologies used to analyze symptoms reported by users and determine the cause and severity of those symptoms.

[0219] "Means for determining whether the symptom can be treated with over-the-counter medication, etc., or whether medical treatment at a nearby hospital is required" refers to technology for determining whether the user's symptoms can be treated with over-the-counter medication, or whether medical treatment at a medical institution is required.

[0220] "A means for receiving a user's physical information, lifestyle, and information about ingredients in the refrigerator as input, and generating individual training menus and diet recipes" refers to technology for generating individually optimized exercise programs and meal recipes based on the user's physical characteristics, lifestyle, and information about ingredients in the refrigerator.

[0221] "Means for proposing delivery options for necessary ingredients and dishes based on the generated recipe" refers to a technology that suggests a delivery service for providing the user with ingredients and dishes necessary for the generated diet recipe.

[0222] The system for implementing this invention creates individual training menus and diet recipes based on the user's physical information, lifestyle, and information about ingredients in the refrigerator, and also provides a service of delivering ingredients and meals based on those recipes.

[0223] Hardware and Software Used

[0224] Hardware: Smartphones, servers

[0225] Software: Mobile applications (iOS / ANDROID®), server-side applications, generative AI models (e.g. GPT-4), databases (e.g. MySQL®)

[0226] Program processing explanation

[0227] 1. Enter your user information

[0228] Through a smartphone app, users input their physical information (height, weight, age, etc.), lifestyle (exercise frequency, food preferences, etc.), and information about the ingredients in their refrigerator. This information is collected through the application interface.

[0229] 2. Data transmission and storage

[0230] The entered data is sent from the smartphone to a server and stored in a database, using a relational database such as MySQL.

[0231] 3. Menu generation using generative AI models

[0232] The server-side application inputs the stored data into a generative AI model (e.g., GPT-4) to generate individual training menus and diet recipes. The generative AI model then suggests optimal menus based on the user's physical information, lifestyle, and the ingredients in the refrigerator.

[0233] 4. Propose delivery options

[0234] Based on the generated recipe, the app will suggest the required ingredients and delivery options for the dish to the user, who can then select the delivery option through the app.

[0235] 5. Executing the delivery

[0236] When a user selects delivery, an order is sent to a partner food delivery service and the ingredients and food are delivered to the user's home.

[0237] Examples

[0238] The user enters the following information into the app:

[0239] Height: 170cm

[0240] Weight: 70kg

[0241] Ingredients in the fridge: Chicken breast, broccoli

[0242] Example prompt for a generative AI model:

[0243] User's physical information: Height 170cm, Weight 70kg

[0244] Lifestyle: Exercise 3 times a week

[0245] Ingredients in the fridge: Chicken breast, broccoli

[0246] Based on this information, generate training menus and diet recipes that are suitable for the user.

[0247] Produces the following result:

[0248] Training menu: 30 minutes of aerobic exercise (jogging), 15 minutes of strength training (push-ups, squats)

[0249] Diet recipe: Healthy chicken breast and broccoli salad

[0250] In this way, users can receive a personalized health promotion plan and even have the ingredients and dishes they need delivered to them.

[0251] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0252] Step 1:

[0253] The user starts the smartphone app and inputs physical information (height, weight, age, etc.), lifestyle information (exercise frequency, food preferences, etc.), and information about ingredients in the refrigerator. The input information is collected through the application interface.

[0254] Input: Physical information, lifestyle, and fridge ingredients

[0255] Output: User input data

[0256] Step 2:

[0257] The terminal sends the collected user input data to the server, which stores the received data in a database using a relational database such as MySQL.

[0258] Input: User-entered data

[0259] Output: User data stored in the database.

[0260] Step 3:

[0261] The server inputs the saved user data into a generative AI model (e.g., GPT-4) to generate individual training menus and diet recipes. The generative AI model suggests optimal menus based on the user's physical information, lifestyle, and information about ingredients in the refrigerator.

[0262] Input: User data stored in the database

[0263] Output: Training menu and diet recipes

[0264] Step 4:

[0265] The server sends the generated training menu and diet recipes to the user's smartphone, where the user can check the proposed menu and recipes through the application.

[0266] Input: Training menu and diet recipes

[0267] Output: Menu and recipes displayed on the user's smartphone

[0268] Step 5:

[0269] The user selects the required ingredients and meal delivery options based on the generated recipe, and the application sends the user's selection to the server.

[0270] Input: User's delivery option selection

[0271] Output: Delivery option selection data sent to the server

[0272] Step 6:

[0273] Based on the user's selection, the server sends the order to a partner food delivery service, which delivers the required ingredients and food to the user's home.

[0274] Input: Delivery option selection data

[0275] Output: Order data sent to the food delivery service

[0276] In this way, users can receive a personalized health plan and even have the ingredients and meals they need delivered.

[0277] Example 3

[0278] 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."

[0279] In modern society, mental health issues are becoming more serious, and there is a need to provide prompt and appropriate advice for each individual's concerns. However, conventional systems have the problem of being unable to accurately analyze a user's concerns and provide appropriate advice. In addition, there was a lack of technology to effectively analyze the information entered by the user and generate personalized advice.

[0280] 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 generative artificial intelligence means, means for analyzing input information using natural language processing technology, means for generating a prompt sentence based on the analysis result, means for inputting the prompt sentence to the generative artificial intelligence model and generating advice, and means for providing the generated advice to the user. This makes it possible to accurately analyze the user's concern information and quickly provide appropriate advice.

[0281] "Generative artificial intelligence means" refers to artificial intelligence technology for generating appropriate advice based on input information from a user.

[0282] "Natural language processing technology" refers to technology for analyzing text information entered by a user and understanding its content and emotional state.

[0283] A "prompt sentence" is an instruction sentence to be input into a generative artificial intelligence model, and refers to a sentence generated based on the user's concern information.

[0284] "Generative artificial intelligence model" refers to an artificial intelligence model for generating appropriate advice based on a prompt sentence.

[0285] The "means for generating advice" refers to a process for generating specific advice for a user's concern information using a generative artificial intelligence model.

[0286] The "means for providing the generated advice to the user" refers to an interface or communication means for transmitting the generated advice to the user.

[0287] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0288] The present invention relates to an AI counseling system for preventing and supporting mental health problems for users. A specific embodiment of this system will be described below.

[0289] System Configuration

[0290] This system includes a terminal into which a user inputs problem information, a server that analyzes the input information and generates advice, and communication means for providing the generated advice to the user.

[0291] Hardware and Software Used

[0292] Device: A user device such as a smartphone, tablet, or computer.

[0293] Server: A server computer with high computing power.

[0294] Software: web browsers, mobile applications, generative artificial intelligence models (e.g., OpenAI's GPT-4), natural language processing techniques (e.g., Google's BERT).

[0295] Data processing and calculation

[0296] 1. User inputs problem information:

[0297] A user inputs information about their worries into a text box using a web browser or mobile application on their smartphone or computer. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[0298] 2. The device sends the entered worry information to the server:

[0299] The device sends the text entered by the user to the server as an HTTP POST request, which includes the user's concerns.

[0300] 3. The server receives the concern information and analyzes it using natural language processing technology:

[0301] The server parses the received request and extracts the concern information, then uses Google's BERT model to analyze the text for content and sentiment.

[0302] 4. The server generates a prompt based on the parsed result:

[0303] The server generates a prompt based on the analysis results, for example, "The user has recently been feeling unable to sleep due to work stress. Please provide specific advice on how to deal with stress and improve sleep in this situation."

[0304] 5. The server inputs the prompt sentence into the generative AI model and generates advice:

[0305] The server inputs the prompt sentence into a generative AI model (e.g., OpenAI's GPT-4) and generates advice. The generative AI model generates appropriate advice based on the prompt sentence.

[0306] 6. The server sends the generated advice to the device:

[0307] The server sends the generated advice to the terminal as an HTTP response. The response includes the generated advice.

[0308] 7. The terminal displays the advice to the user:

[0309] The device displays the received advice to the user. The user sees the advice through the smartphone app: "If you can't sleep because of work stress, try the following measures: 1. Make it a habit to go to bed at a fixed time every day. 2. Create a routine to relax before going to bed (e.g., take a warm bath and do some light stretching). 3. Take short breaks during the day to reduce stress. 4. If necessary, consider consulting a specialist."

[0310] In this way, the user can receive specific and practical advice. This system effectively supports the user's mental health prevention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0311] Step 1:

[0312] The user inputs information about the worries. The user inputs information about the worries into a text box using a web browser or mobile application on a smartphone or PC. For example, the user might input, "Recently, I've been unable to sleep due to work stress." The input information is then sent to the server in the next step.

[0313] Step 2:

[0314] The terminal sends the inputted worry information to the server. The terminal sends the text inputted by the user to the server as an HTTP POST request. This request contains the user's worry information. The input is the user's worry information, and the output is the data to be sent to the server.

[0315] Step 3:

[0316] The server receives the concern information and analyzes it using natural language processing technology. The server analyzes the received request and extracts the concern information. It then uses natural language processing technology (e.g., Google's BERT model) to analyze the content and sentiment of the text. The input is the user's concern information, and the output is the analysis result.

[0317] Step 4:

[0318] The server generates a prompt sentence based on the analysis result.The server generates a prompt sentence based on the analysis result.For example, a prompt sentence such as "The user has recently been feeling that he is unable to sleep due to work stress. Please provide specific advice on how to deal with stress and improve sleep in this situation" is generated.The input is the analysis result, and the output is the prompt sentence.

[0319] Step 5:

[0320] The server inputs a prompt sentence into the generative AI model and generates advice. The server inputs a prompt sentence into the generative AI model (e.g., OpenAI's GPT-4) and generates advice. The generative AI model generates appropriate advice based on the prompt sentence. The input is the prompt sentence, and the output is the generated advice.

[0321] Step 6:

[0322] The server sends the generated advice to the terminal. The server sends the generated advice to the terminal as an HTTP response. This response includes the generated advice. The input is the generated advice, and the output is the data to be sent to the terminal.

[0323] Step 7:

[0324] The device displays the advice to the user. The device displays the received advice to the user. The user confirms the advice through the smartphone app: "If you can't sleep because of work stress, try the following measures: 1. Make a habit of going to bed at a certain time every day. 2. Create a routine to relax before going to bed (e.g. taking a warm bath and doing light stretching). 3. Take short breaks during the day to reduce stress. 4. If necessary, consider consulting a specialist." The input is the generated advice, and the output is the display to the user.

[0325] (Application example 3)

[0326] 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."

[0327] In modern society, mental health problems are becoming more serious, and the number of people suffering from stress and anxiety is increasing. However, receiving counseling from a professional takes time and money, so many people are unable to receive appropriate support. In addition, there is a lack of effective systems for providing mental health support in brick-and-mortar stores. This creates a demand for an environment where users can easily receive mental health support.

[0328] 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.

[0329] In this invention, the server includes generative AI means for performing a primary diagnosis, means for identifying symptoms, means for determining whether the problem can be treated with over-the-counter medicine or whether a consultation at a nearby hospital is necessary, interface means for inputting the user's concerns, means for generating advice using a generative AI model, means for displaying advice, and means for saving and viewing past consultation history. This allows users to easily receive mental health support via their smartphones.

[0330] A "generative AI means for performing initial diagnosis" is an artificial intelligence technology for performing an initial diagnosis based on information provided by a user.

[0331] "Means for identifying symptoms" refers to technology that analyzes information entered by the user and identifies specific symptoms.

[0332] The "means for determining whether the condition can be treated with over-the-counter medication, etc., or whether treatment at a nearby hospital is required" refers to technology for determining, based on the user's symptoms, whether the condition can be treated with over-the-counter medication, or whether treatment at a medical institution is required.

[0333] The "interface means for inputting user's concerns" is a user interface that allows the user to input his or her own concerns or problems.

[0334] An "advice generation means using a generative AI model" is an artificial intelligence model for generating appropriate advice based on concern information input by a user.

[0335] The "means for displaying advice" refers to a technique for visually displaying the generated advice to the user.

[0336] "Means for saving and viewing past consultation history" refers to technology that allows users to save the details of past consultations and view them later.

[0337] The system for carrying out the present invention is designed to enable a user to receive mental health support using a smartphone. A specific embodiment of this system will be described below.

[0338] Hardware and Software Used

[0339] Hardware: Smartphone

[0340] Software: Python, OpenAI API

[0341] System Configuration

[0342] 1. Generative AI means for performing a first-stage diagnosis:

[0343] The server uses a generative AI to perform an initial diagnosis based on the information provided by the user. This AI analyzes the user's input data and generates an initial diagnosis result.

[0344] 2. How to identify symptoms:

[0345] The server analyzes the information entered by the user and runs algorithms to identify specific symptoms. The algorithms identify symptoms based on the data entered by the user and suggest appropriate measures to address the issue.

[0346] 3. How to determine if over-the-counter medications are sufficient or if you need to see a doctor at a nearby hospital:

[0347] Based on the user's symptoms, the server determines whether the problem can be treated with over-the-counter medication or whether medical treatment is required. This decision is made by generative AI.

[0348] 4. Interface for inputting user concerns:

[0349] Users input their concerns and problems through a smartphone application. The interface is designed to make it easy for users to input information.

[0350] 5. How generative AI models generate advice:

[0351] The server uses a generative AI model to generate appropriate advice based on the worries input by the user. For example, if a user inputs, "I haven't been able to sleep lately because of work stress," the generative AI model will provide advice such as, "To reduce stress, try taking some time each day to relax. For example, you may want to try deep breathing or meditation. It is also effective to refrain from using your smartphone or computer before going to bed."

[0352] 6. Ways to display advice:

[0353] The server visually displays the generated advice to the user via the smartphone screen.

[0354] 7. How to store and view past consultation history:

[0355] The server stores the contents of past consultations by the user and allows them to be viewed later. This function allows users to refer to their past consultation history and receive ongoing support.

[0356] Examples of prompt statements

[0357] If the user types "I can't sleep lately because of work stress," the following is an example of a prompt:

[0358] User's problem: I can't sleep lately because of work stress.

[0359] Please provide appropriate advice.

[0360] In this way, users can easily receive mental health support via their smartphone.

[0361] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0362] Step 1:

[0363] The user starts the smartphone application and inputs their concerns.

[0364] Input: User's worries (e.g. "I can't sleep lately because of work stress")

[0365] Output: The user's concerns are sent to the application.

[0366] Specific actions: The user opens the application on their smartphone, enters their concerns into the text box, and presses the send button.

[0367] Step 2:

[0368] The terminal transmits the user's worry information to the server.

[0369] Input: User's worries

[0370] Output: User's worries sent to the server

[0371] Specific operation: The smartphone application transmits the worry information entered by the user to a server via the Internet.

[0372] Step 3:

[0373] The server inputs the received worry information into the generative AI model.

[0374] Input: User's worries sent to the server

[0375] Output: Worry information input to the generative AI model

[0376] Specific operation: The server converts the received user's concern information into an appropriate format to pass it to the generative AI model.

[0377] Step 4:

[0378] The generative AI model generates advice based on concern information.

[0379] Input: Worry information input to the generative AI model

[0380] Output: Generated advice

[0381] Specific operation: The generative AI model analyzes the user's worries and generates appropriate advice, such as, "To reduce stress, try taking some time each day to relax. For example, you may want to try deep breathing or meditation. It is also effective to refrain from using smartphones and computers before going to bed."

[0382] Step 5:

[0383] The server transmits the generated advice to the terminal.

[0384] Input: Generated advice

[0385] Output: Advice sent to terminal

[0386] Specific operation: The server sends advice obtained from the generative AI model to the user's smartphone.

[0387] Step 6:

[0388] The terminal displays the received advice to the user.

[0389] Input: Advice sent to terminal

[0390] Output: The advice displayed to the user

[0391] Specific operation: The smartphone application displays the advice received from the server on the screen, allowing the user to view it and take appropriate measures.

[0392] Step 7:

[0393] The server stores the user's consultation history.

[0394] Input: User's concerns and generated advice

[0395] Output: Saved consultation history

[0396] Specific operation: The server stores the problem information entered by the user and the generated advice in a database so that the user can view it later.

[0397] 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.

[0398] "Example 1"

[0399] As one embodiment of the present invention, a system including an emotion engine that recognizes a user's emotions is provided. The system analyzes emotions from the user's facial expressions, tone of voice, text input, etc., and provides the results to a generative AI. The generative AI takes this emotion information into account to make a more appropriate primary diagnosis. For example, if the user is feeling anxious, the generative AI can use that information to respond to the user's emotions, such as by providing a more detailed explanation to the user.

[0400] "Example 2"

[0401] In addition, a system that devise personalized training menus and diet recipes to support health promotion and dieting is also provided. This system uses an emotion engine that recognizes the user's emotions and provides training menus and diet recipes according to the user's emotional state. For example, if the user is feeling stressed, it can suggest training menus and meals that are effective in relieving stress.

[0402] "Example 3"

[0403] Furthermore, a system including an AI counseling means is also provided as preventive support for mental health. This system uses an emotion engine that recognizes the user's emotions and provides counseling according to the user's emotional state. For example, if the user is feeling sad, advice and support corresponding to that emotion can be provided.

[0404] The process flow of each embodiment will be described below.

[0405] "Example 1"

[0406] Step 1: Receive input from the user (facial expression, tone of voice, text, etc.).

[0407] Step 2: The emotion engine analyzes the user's emotions.

[0408] Step 3: Provide the analysis results to the generative AI.

[0409] Step 4: The generative AI makes a primary diagnosis, taking into account the emotional information.

[0410] "Example 2"

[0411] Step 1: Receive information about the user's body, lifestyle, contents of the refrigerator, etc.

[0412] Step 2: The emotion engine analyzes the user's emotions.

[0413] Step 3: Generate personalized training menus and diet recipes based on the analysis results and user information.

[0414] "Example 3"

[0415] Step 1: Receive concerns or problems from users.

[0416] Step 2: The emotion engine analyzes the user's emotions.

[0417] Step 3: Based on the analysis results, the AI ​​counseling tool provides appropriate advice and assistance.

[0418] Example 1

[0419] 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".

[0420] Conventional primary diagnostic systems have limited means for users to properly diagnose their own symptoms, and in particular do not take emotional information into account when diagnosing, making it difficult to provide appropriate support to users. In addition, the reliability and accuracy of diagnostic results is low, making it difficult for users to select appropriate measures.

[0421] 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.

[0422] In this invention, the server includes a means for a user to input symptom information, a means for a terminal to transmit input information to the server, a means for the server to receive symptom information, a means for the server to create and input a prompt sentence to the generative AI model, a means for the generative AI model to analyze symptoms and generate a diagnosis result, a means for the server to receive the diagnosis result and respond to the user, and a means for the user to confirm the diagnosis result. This makes it possible to perform a more accurate primary diagnosis based on the user's symptom information and emotion information and to propose an appropriate countermeasure.

[0423] The "means for the user to input symptom information" refers to a device or software that provides an interface for the user to input his / her symptoms in text form.

[0424] The "means for the terminal to transmit input information to the server" refers to a device or software having a communication function for transmitting symptom information input by the user to the server.

[0425] The "means for the server to receive symptom information" refers to a device or software for receiving and processing symptom information transmitted from a terminal.

[0426] "Means for the server to create and input a prompt sentence to the generative AI model" refers to a device or software that creates a prompt sentence to be input to the generative AI model based on received symptom information, and inputs the prompt sentence to the generative AI model.

[0427] "Means for the generative AI model to analyze symptoms and generate a diagnostic result" refers to a device or software that enables the generative AI model to analyze symptoms based on a prompt sentence and generate a diagnostic result.

[0428] "Means for the server to receive the diagnostic result and respond to the user" refers to a device or software for responding to the user with the diagnostic result received from the generative AI model.

[0429] The "means for a user to check the diagnostic results" refers to a device or software that provides an interface for a user to check the diagnostic results through a terminal.

[0430] This invention is a system that uses generative AI to perform a primary diagnosis. Specifically, it receives symptom information from the user as input and identifies the symptoms based on that information. Based on the results, it determines whether the problem can be treated with over-the-counter medicine or whether a visit to a nearby hospital is necessary.

[0431] The system uses the following hardware and software:

[0432] A device for users to input symptom information (e.g., smartphone, computer)

[0433] A server for receiving and processing information sent from the terminal

[0434] A generative AI model (e.g., OpenAI's GPT-4) to analyze symptom information and generate diagnostic results

[0435] The user enters their symptoms in text format into an input form on the device. For example, they might enter "I have a headache." The device then sends this input information to the server. The server creates a prompt sentence to be input into the generative AI model based on the received symptom information. For example, it generates a prompt sentence that reads, "The user is complaining of a headache. Please infer the cause of the headache and suggest an appropriate remedy."

[0436] The generative AI model analyzes symptoms based on the prompt text and generates a diagnosis. For example, it may infer that the cause of the headache is migraine or tension headache, and suggest appropriate measures to deal with the headache (e.g., use of over-the-counter medication, or medical treatment at a hospital). The server sends the diagnosis result received from the generative AI model to the terminal to respond to the user. The user checks the diagnosis result displayed on the terminal screen and, if necessary, purchases over-the-counter medication or receives medical treatment at a hospital.

[0437] As a concrete example, consider the case where a user types "I have a headache." The server receives this information and feeds the following prompt to the generative AI model:

[0438] "The user is complaining of a headache. Please deduce the cause of the headache and suggest an appropriate remedy."

[0439] The generative AI model analyzes the prompt text and generates a diagnosis result of "It is likely a migraine. It can be treated with over-the-counter medication, but if symptoms persist, we recommend that you see a doctor at a hospital." The server returns this diagnosis result to the user, who can check it via their device.

[0440] In this way, the system can perform a primary diagnosis based on the user's symptom information and suggest appropriate measures to address the problem.

[0441] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0442] Step 1:

[0443] The user enters the symptom information.

[0444] The user opens a browser on their smartphone or computer, accesses the system's web application, enters "I have a headache" in the input form, and clicks the submit button.

[0445] Input: Symptom information entered by the user (e.g., "I have a headache")

[0446] Output: Symptom information sent to the terminal

[0447] Step 2:

[0448] The terminal transmits the input information to the server.

[0449] The terminal transmits the text data entered by the user, such as "I have a headache," to the server using the HTTPS protocol.

[0450] Input: Symptom information entered by the user (e.g., "I have a headache")

[0451] Output: Symptom information sent to the server

[0452] Step 3:

[0453] A server receives the symptom information.

[0454] The server receives the HTTPS request and extracts the text data "I have a headache" from the request body. This data is temporarily stored in a database.

[0455] Input: Symptom information sent from the device (e.g., "I have a headache")

[0456] Output: Symptom information stored in the database

[0457] Step 4:

[0458] The server creates and inputs a prompt sentence to the generative AI model.

[0459] The server creates a prompt sentence to be input to the generative AI model based on the received symptom information. For example, it generates a prompt sentence such as, "The user is complaining of a headache. Please infer the cause of the headache and suggest an appropriate remedy."

[0460] Input: Symptom information stored in a database (e.g., "I have a headache")

[0461] Output: The prompt that is fed into the generative AI model

[0462] Step 5:

[0463] A generative AI model analyzes symptoms and generates a diagnosis.

[0464] A generative AI model (e.g., OpenAI's GPT-4) analyzes symptoms based on the prompt received from the server. As a result of the analysis, it infers possible illnesses that may be causing the headache (e.g., migraine, tension headache) and generates appropriate treatments (e.g., over-the-counter medication, medical treatment at a hospital).

[0465] Input: A prompt sentence entered into the generative AI model (e.g., "The user is complaining of a headache. Please infer the cause of the headache and suggest an appropriate remedy.")

[0466] Output: The generated diagnosis (e.g., "You are likely suffering from a migraine. This can be managed with over-the-counter medication, but if symptoms persist, we recommend that you see a doctor.")

[0467] Step 6:

[0468] The server receives the diagnosis and responds to the user.

[0469] The server sends the diagnosis results received from the generative AI model to the device in order to respond to the user, for example, sending a message such as "You are likely suffering from a migraine. This can be treated with over-the-counter medication, but if symptoms persist, we recommend that you see a doctor."

[0470] Input: A diagnosis received from a generative AI model (e.g., "This is most likely a migraine headache. Over-the-counter medications can help, but if symptoms persist, we recommend a doctor's appointment.")

[0471] Output: Diagnostic results sent to the user

[0472] Step 7:

[0473] The user checks the diagnosis results.

[0474] The user checks the diagnosis results displayed on the terminal screen and, if necessary, purchases over-the-counter medicine or receives medical treatment at a hospital.

[0475] Input: Diagnosis results displayed on the device (e.g. "You are likely suffering from a migraine. This can be treated with over-the-counter medication, but if symptoms persist, we recommend you see a doctor.")

[0476] Output: User actions (e.g., buying over-the-counter medicine, visiting a doctor)

[0477] (Application example 1)

[0478] 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."

[0479] Conventional primary diagnostic systems can make a diagnosis based on the user's symptom information, but they cannot take into account the user's emotional state, making it difficult to reduce the user's anxiety and stress. In addition, when purchasing over-the-counter medicine or making a hospital appointment based on the diagnosis results, separate procedures are required, which is troublesome for the user.

[0480] 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 generative AI means for performing a primary diagnosis, means for identifying symptoms, means for determining whether the problem can be treated with over-the-counter medicine or the like or whether treatment at a nearby hospital is necessary, emotion engine means for recognizing the user's emotions, electronic payment means, and means for providing the user with an appropriate response based on the emotion information. This enables a primary diagnosis that takes into account the user's emotional state, and allows the user to smoothly purchase over-the-counter medicine or make a hospital appointment based on the diagnosis result.

[0481] The "generative AI means for performing primary diagnosis" is an artificial intelligence technology that estimates possible illnesses and suggests appropriate treatment methods based on symptom information entered by the user.

[0482] "Means of identifying symptoms" refers to technology that analyzes symptom information provided by the user and identifies the cause of the symptoms and related diseases.

[0483] "Means of determining whether the disease can be treated with over-the-counter medication, etc., or whether treatment at a nearby hospital is required" refers to technology for determining whether the disease predicted by the generative AI can be treated with over-the-counter medication, or whether treatment at a medical institution is required.

[0484] The "emotion engine means for recognizing user emotions" is a technology that analyzes emotions from a user's facial expressions, tone of voice, text input, etc., and provides the results to generative AI.

[0485] An "electronic payment method" is a technology that enables users to complete payments using credit cards, electronic money, etc. when purchasing over-the-counter medicines or making hospital appointments.

[0486] "Means for providing appropriate responses to users based on emotional information" refers to technology that takes into account the user's emotional state and provides advice and explanations to reduce anxiety and stress.

[0487] The following system configuration is used as an embodiment of the present invention: The system includes a user terminal, a server, and an emotion recognition engine.

[0488] 1. System Programming

[0489] The system includes programs that implement the following main functions:

[0490] Generative AI tools for performing primary diagnosis

[0491] Ways to spot symptoms

[0492] A method to determine whether the condition can be treated with over-the-counter medicines or whether a medical examination at a nearby hospital is required

[0493] Emotion engine means to recognize user emotions

[0494] Electronic Payment Instruments

[0495] A means of providing appropriate responses to users based on emotional information

[0496] 2. Program Processing Explanation

[0497] Hardware and software used:

[0498] Hardware: Smartphones, cameras

[0499] Software: OpenAI API, OpenCV, TextBlob, PaymentGateway

[0500] Data processing and calculation:

[0501] 1. Emotion recognition:

[0502] Images of the user taken with the camera on the user's device are read using OpenCV and analyzed using an emotion recognition engine.

[0503] The analysis results are provided to a generative AI to understand the user's emotional state.

[0504] 2. Symptom diagnosis:

[0505] The symptom information entered by the user into the device is sent to the server, and the generative AI generates a diagnosis result using the OpenAI API.

[0506] The diagnostic results are sent back to the user's terminal and displayed to the user.

[0507] 3. Electronic Payments:

[0508] Payment Gateway is used to process payments when users purchase over-the-counter medications or make doctor appointments.

[0509] The payment result is notified to the user terminal.

[0510] 3. Specific Examples

[0511] Examples:

[0512] If a user enters "I have a headache" and an anxious expression is recognized from the image captured by the camera, the generative AI will diagnose, "It is likely that you have a tension headache. Please purchase over-the-counter painkillers or seek medical attention at a nearby hospital," and offer the option to purchase over-the-counter medication via electronic payment.

[0513] Example prompt:

[0514] User Symptoms: Headache

[0515] Based on these symptoms, diagnose possible illnesses and suggest appropriate treatment.

[0516] The above is an embodiment of the present invention. This system enables a primary diagnosis that takes into account the user's emotional state, and allows smooth purchase of over-the-counter medicines and appointment with a doctor based on the diagnosis results.

[0517] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0518] Step 1:

[0519] A user inputs symptom information into a terminal. The user provides his / her symptoms to the terminal using text input or voice input. The input symptom information is transmitted from the terminal to a server.

[0520] Step 2:

[0521] The server receives the symptom information and performs a primary diagnosis using the generative AI model. The server uses the OpenAI API to analyze the input symptom information and estimate possible illnesses. The generated diagnosis result is sent back from the server to the device.

[0522] Step 3:

[0523] A user takes a picture of himself / herself using the device's camera. The device sends the captured image to an emotion recognition engine to analyze the user's emotional state. The analysis results are then sent from the device to the server.

[0524] Step 4:

[0525] The server receives the emotion recognition results and provides them to the generative AI model. The server takes the emotion information into account to generate an appropriate response for the user. For example, if the user has an anxious expression, the generative AI model will provide a more polite explanation.

[0526] Step 5:

[0527] Based on the diagnosis and emotion information, the server determines whether the problem can be treated with over-the-counter medicine or whether a visit to a nearby hospital is necessary. The server integrates the diagnosis and emotion information and suggests appropriate measures to the user.

[0528] Step 6:

[0529] A user uses the terminal to purchase over-the-counter medicines or make doctor's appointments. The terminal processes the payment using an electronic payment method based on the user's selection. The payment information is sent from the terminal to a server, which notifies the user of the payment result.

[0530] Step 7:

[0531] The server provides the user with a final feedback that integrates the diagnosis result, the emotion information, and the payment result. The user can receive this information through the terminal and take appropriate measures.

[0532] The above are the specific processing steps for carrying out the present invention. Through the specific operations performed in each step, the user can receive a primary diagnosis that takes into account the emotional state, and can smoothly purchase over-the-counter medicines or make hospital appointments based on the diagnosis results.

[0533] Example 2

[0534] 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."

[0535] Conventional health promotion and diet support systems have had difficulty providing personalized training menus and diet recipes that take into account the user's individual physical information, lifestyle, and food information in the refrigerator. They also have been inadequate in providing appropriate training menus and diet recipes that correspond to the user's emotional state. This makes it difficult for users to effectively promote health or diet, and makes it difficult to maintain continuous health management.

[0536] 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.

[0537] In this invention, the server includes a generating AI means, a user information input means, a data transmission means, a data analysis means, a training menu generating means, a diet recipe generating means, a result providing means, and an emotion recognition means. This makes it possible to provide a personalized training menu and diet recipes based on the user's individual physical information, lifestyle, and refrigerator ingredient information. It is also possible to provide an appropriate training menu and diet recipes according to the user's emotional state.

[0538] "Generative AI means" refers to artificial intelligence technology that generates training menus and diet recipes based on information input by the user.

[0539] The "user information input means" is an interface that allows the user to input information about his / her own physical condition, lifestyle, ingredients in the refrigerator, and the like.

[0540] "Data transmission means" refers to a communication means for transmitting information input by the user to the server.

[0541] The "data analysis means" is a means for analyzing the user information received by the server and generating an appropriate training menu and diet recipes.

[0542] The "training menu generating means" is a means for generating an individual training menu based on the user's physical information and lifestyle.

[0543] The "diet recipe generating means" is a means for generating recipes suitable for dieting based on information on ingredients in the user's refrigerator.

[0544] The "result providing means" is a means for providing the generated training menu and diet recipes to the user.

[0545] The "emotion recognition means" is a means for recognizing the user's emotional state and providing a training menu or diet recipe according to that state.

[0546] This invention is a system for the purpose of improving the health of users and supporting their diet, and provides personalized training menus and diet recipes using a generative AI model. A specific embodiment of this system will be described below.

[0547] System Configuration

[0548] This system is composed of three main elements: a server, a terminal, and a user. The server includes a generating AI means, a data analysis means, a training menu generating means, a diet recipe generating means, a result providing means, and an emotion recognition means. The terminal includes a user information input means and a data transmission means.

[0549] Hardware and Software Used

[0550] Server: Use high-performance cloud servers to provide the computational resources to run generative AI models (e.g. GPT-4).

[0551] Terminal: A user device such as a smartphone or tablet is used to enter user information through a dedicated application or web interface.

[0552] Generative AI model: Uses advanced natural language processing models such as GPT-4 to generate training menus and diet recipes based on user information.

[0553] Data Flow and Processing

[0554] 1. Enter your user information:

[0555] The user uses the terminal to input his / her physical information (height, weight, etc.), lifestyle (frequency of exercise, type of work, etc.), and information on ingredients in the refrigerator.

[0556] Specifically, the user accesses a dedicated application or web interface and enters the required information into a form.

[0557] 2. Transmission of data:

[0558] The terminal transmits the input user information to the server.

[0559] Specifically, the terminal transmits data to the server via the Internet. At this time, the data is encrypted before being transmitted.

[0560] 3. Data Analysis:

[0561] The server uses a generative AI model (e.g., GPT-4) to analyze the received user information.

[0562] The analysis includes generating a training menu based on the user's physical information and lifestyle, as well as generating diet recipes based on information about ingredients in the refrigerator.

[0563] 4. Create a training menu:

[0564] The server uses a generative AI model to generate a training menu based on the user's physical information and lifestyle.

[0565] For example, if the user is feeling stressed, the emotion recognition means recognizes the user's emotional state and suggests a training menu that is effective for relieving stress.

[0566] 5. Diet recipe generation:

[0567] The server uses a generative AI model to generate diet recipes based on the ingredients in the refrigerator.

[0568] For example, we suggest a low-calorie recipe using chicken breast and broccoli.

[0569] 6. Delivery of results:

[0570] The server transmits the generated training menu and diet recipes to the terminal and provides them to the user.

[0571] Specifically, the server sends the generated information to the terminal, which displays it to the user, who then views the results through an application or web interface.

[0572] Examples and prompts

[0573] As a concrete example, the following prompt sentence is input into the generative AI model.

[0574] Example prompt:

[0575] User Information:

[0576] Height: 170cm

[0577] Weight: 70kg

[0578] Lifestyle: Sedentary job, light exercise twice a week

[0579] Emotional state: Stressed

[0580] Ingredients in the fridge:

[0581] Chicken breast

[0582] broccoli

[0583] Produces:

[0584] Training menu based on the user's physical information and lifestyle

[0585] Diet recipes using ingredients from your fridge

[0586] Based on this prompt, the generative AI model will output the following:

[0587] Example output:

[0588] Training Menu:

[0589] Stress relief yoga session (30 mins)

[0590] Light jogging (20 minutes)

[0591] Diet Recipes:

[0592] Steamed chicken breast and broccoli

[0593] Ingredients: 200g chicken breast, 100g broccoli, salt, pepper, olive oil

[0594] Directions: Steam the chicken breast and broccoli in a steamer for 15 minutes, then season with salt, pepper and olive oil.

[0595] In this way, the server provides the user with a personalized training menu and diet recipes.

[0596] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0597] Step 1:

[0598] Entering user information

[0599] The user uses the terminal to input his / her physical information (height, weight, etc.), lifestyle (frequency of exercise, type of work, etc.), and information on ingredients in the refrigerator.

[0600] Specifically, the user accesses a dedicated application or web interface and enters the required information into a form.

[0601] Input: Height, weight, lifestyle, and food in your fridge

[0602] Output: User information data entered

[0603] Step 2:

[0604] Sending Data

[0605] The terminal transmits the input user information to the server.

[0606] Specifically, the terminal transmits data to the server via the Internet. At this time, the data is encrypted before being transmitted.

[0607] Input: User information data

[0608] Output: User information data sent to the server

[0609] Step 3:

[0610] Analyzing data

[0611] The server uses a generative AI model (e.g., GPT-4) to analyze the received user information.

[0612] Specifically, the server retrieves user information stored in the database and inputs it as a prompt sentence into the generative AI model.

[0613] Input: User information data stored on the server

[0614] Output: Analysis result data

[0615] Step 4:

[0616] Creation of training menu

[0617] The server uses a generative AI model to generate a training menu based on the user's physical information and lifestyle.

[0618] Specifically, the generative AI model analyzes the prompt text and generates an appropriate training menu. For example, based on the information "height 170 cm, weight 70 kg, light exercise twice a week," it suggests yoga or light jogging.

[0619] Input: Analysis result data

[0620] Output: Training menu

[0621] Step 5:

[0622] Diet recipe generation

[0623] The server uses a generative AI model to generate diet recipes based on the ingredients in the refrigerator.

[0624] Specifically, the generative AI model will suggest low-calorie, nutritionally balanced recipes based on the ingredient information "chicken breast and broccoli." For example, it will generate "steamed chicken breast and broccoli dish."

[0625] Input: Analysis result data

[0626] Output: Diet recipes

[0627] Step 6:

[0628] Providing results

[0629] The server transmits the generated training menu and diet recipes to the terminal and provides them to the user.

[0630] Specifically, the server sends the generated information to the terminal, which displays it to the user, who then views the results through an application or web interface.

[0631] Input: Training menu, diet recipes

[0632] Output: Training menu and diet recipes provided to the user

[0633] (Application example 2)

[0634] 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."

[0635] In modern society, there is a demand for personalized health promotion and diet support that is tailored to each individual's health condition and lifestyle. However, conventional systems have difficulty proposing training menus and diet recipes that take into account the user's physical information and emotional state, and lack the functionality to suggest healthy products in real time while shopping in a physical store. This makes it difficult for users to efficiently manage their health.

[0636] 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.

[0637] In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the condition can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for receiving the user's physical information, lifestyle, and refrigerator food information as inputs and generating individual training menus and diet recipes, a means for recognizing the user's emotional state and providing training menus and diet recipes according to the emotional state, and a means for suggesting necessary food ingredients and healthy products in real time while shopping at a physical store. This enables personalized health promotion and diet support based on the user's physical information and emotional state, and makes it possible to suggest healthy products in real time even while shopping at a physical store.

[0638] The "generative AI means for performing primary diagnosis" is an artificial intelligence technology for evaluating the initial health condition based on information input by the user and proposing appropriate measures.

[0639] "Symptom assessment tools" are technologies that analyze symptoms reported by users and determine the cause and severity of those symptoms.

[0640] The "means for determining whether the condition can be treated with over-the-counter medication, etc., or whether treatment at a nearby hospital is required" refers to technology for determining, based on the user's symptoms, whether the condition can be treated with over-the-counter medication, or whether treatment at a medical institution is required.

[0641] "A means of receiving a user's physical information, lifestyle, and information about ingredients in the refrigerator as input, and generating individual training menus and diet recipes" refers to technology for creating individually optimized exercise programs and meal menus based on the user's physical characteristics, lifestyle, and information about ingredients in the refrigerator.

[0642] "Means of recognizing the user's emotional state and providing training menus and diet recipes according to that emotional state" refers to technology that analyzes the user's emotions and suggests exercise and meal plans appropriate to that emotional state.

[0643] "A means for suggesting necessary ingredients and healthy products in real time while shopping in a physical store" refers to technology that suggests healthy products and necessary ingredients in real time while a user is shopping in a physical store.

[0644] A system for carrying out this invention proposes personalized training menus and diet recipes based on the user's health condition and lifestyle. The system includes generative AI means for performing a primary diagnosis, means for identifying symptoms, means for determining whether the condition can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, means for receiving the user's physical information, lifestyle, and information on ingredients in the refrigerator as input and generating individual training menus and diet recipes, means for recognizing the user's emotional state and providing training menus and diet recipes according to the emotional state, and means for suggesting necessary ingredients and healthy products in real time while shopping at a physical store.

[0645] Hardware and Software Used

[0646] Hardware: Smartphone cameras, refrigerator sensors, brick-and-mortar point-of-sale systems

[0647] Software: OpenCV (emotion recognition), Keras (emotion recognition model), OpenAI API (generative AI model)

[0648] Data processing and calculation

[0649] The server receives the user's physical information (height, weight, allergy information, etc.) and lifestyle (exercise habits, food preferences, etc.) as input. In addition, it obtains food ingredient information from the refrigerator's sensors and recognizes the user's emotional state using the smartphone's camera. Based on this data, a generative AI model is used to generate personalized training menus and diet recipes.

[0650] Examples

[0651] For example, if a user is 170cm tall, weighs 70kg, has no allergies, has an active lifestyle, and has chicken breast and broccoli in the fridge, and is feeling stressed, the system will input the following prompt sentence into the generative AI model:

[0652] Examples of prompt statements

[0653] User info: {'height': 170, 'weight': 70, 'allergies': [], 'lifestyle': 'active', 'fridge_contents': ['chicken breast', 'broccoli']}, Emotion: stressed. Generate personalized training menu and diet recipe.

[0654] Based on the prompt, the generative AI model can suggest suitable training menus and diet recipes for the user. In addition, when the user is shopping in a physical store, the model can suggest healthy products and necessary ingredients in real time through a smartphone application.

[0655] In this way, personalized health promotion and diet support based on the user's physical information and emotional state becomes possible, and healthy products can be suggested in real time while shopping in a physical store.

[0656] The flow of the identification process in the application example 2 will be described with reference to FIG.

[0657] Step 1:

[0658] The user starts the application on their smartphone and inputs their physical information (height, weight, allergy information, etc.) and lifestyle information (exercise habits, food preferences, etc.), which is then sent to the server.

[0659] Step 2:

[0660] The server obtains ingredient information from the refrigerator's sensor. The sensor in the refrigerator detects the current ingredient information and sends the data to the server. In this way, the ingredient information in the user's refrigerator is aggregated in the server.

[0661] Step 3:

[0662] A user takes a picture of his / her face using the smartphone camera. The device analyzes the user's facial expressions using OpenCV and recognizes the user's emotional state. The recognized emotional state is sent to the server.

[0663] Step 4:

[0664] The server inputs prompt sentences into the generative AI model based on the user's physical information, lifestyle, food information in the refrigerator, and emotional state. Examples of prompt sentences that are generated include the following:

[0665] User info: {'height': 170, 'weight': 70, 'allergies': [], 'lifestyle': 'active', 'fridge_contents': ['chicken breast', 'broccoli']}, Emotion: stressed. Generate personalized training menu and diet recipe.

[0666] By inputting this prompt into a generative AI model, a personalized training menu and diet recipes are generated.

[0667] Step 5:

[0668] The generative AI model generates training menus and diet recipes based on the prompt text, and the generated menus and recipes are sent to the user's smartphone via the server.

[0669] Step 6:

[0670] When a user is shopping at a brick-and-mortar store, the smartphone application will link with the store's POS system and suggest healthy products and necessary ingredients in real time based on the user's location information and purchase history, allowing the user to select healthy products.

[0671] Step 7:

[0672] Users can effectively improve their health and diet by carrying out the suggested training menus and diet recipes and purchasing the suggested products at physical stores.

[0673] Example 3

[0674] 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."

[0675] Mental health issues are becoming more serious in modern society, with an increasing number of people suffering from stress and anxiety in particular. In response to this, there is a demand for systems that can provide appropriate counseling and advice. However, conventional systems have the problem of being unable to accurately grasp the user's emotional state and provide appropriate advice accordingly.

[0676] 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.

[0677] In this invention, the server includes a means for a user to input worry information, a means for transmitting the input information to the server, a means for the server to pass information to the generative AI model, a means for the generative AI model to generate advice, a means for the server to receive the generated advice, a means for the server to analyze the user's emotions with an emotion engine, a means for integrating the advice and the emotion analysis result, a means for transmitting the integration result to the terminal, and a means for the terminal to display the advice to the user. This makes it possible to provide appropriate advice according to the user's emotional state.

[0678] "User" refers to an individual who utilizes the system to input problem information and receive advice.

[0679] "Terminal" refers to a device on which a user inputs problem information and displays advice from the server. Specifically, this includes smartphones and personal computers.

[0680] "Server" refers to a central processing unit that receives input information from users, generates advice using generative AI models and emotion engines, and provides it to users.

[0681] "Generative AI model" refers to an artificial intelligence model that analyzes user concerns and generates appropriate advice. Specifically, it includes models that use natural language processing technology.

[0682] An "emotion engine" refers to software that analyzes the user's emotional state from information input and provides appropriate advice based on the results.

[0683] "Advice" refers to advice or suggestions that the generative AI model generates based on the user's concerns and takes into account the analysis results of the emotion engine.

[0684] "Problem information" refers to personal problems or issues, such as stress or anxiety, that users input into the system.

[0685] "Integrated result" refers to the final advice that combines advice from the generative AI model and the results of emotion analysis from the emotion engine.

[0686] The present invention is a system for supporting the mental health of a user, which operates by combining a generative AI model and an emotion engine. A specific embodiment of the system will be described below.

[0687] First, the user uses a device (such as a smartphone or computer) to input information about their worries. For example, they might input, "Recently, I've been unable to sleep due to work stress." This input information is then sent from the device to the server. Communication is securely carried out using the HTTPS protocol.

[0688] The server passes the received worry information to a generative AI model. For example, a model using natural language processing technology (e.g. GPT-4) is used as the generative AI model. The generative AI model analyzes the input worry information and generates appropriate advice. For example, it generates advice such as "To reduce work stress, it is important to set aside time to relax."

[0689] The generated advice is returned to the server. The server then uses an emotion engine to analyze the user's emotions. As the emotion engine, for example, software using emotion analysis technology (e.g., Watson (registered trademark) Tone Analyzer) is used. Emotions are extracted from the text entered by the user, and an emotional state such as "sad" or "stressed" is identified.

[0690] The server integrates the generated advice with the emotion analysis results. For example, if the user feels "sad", it adds additional advice corresponding to that emotion. Finally, the server sends the integrated advice to the terminal. This communication is also performed using the HTTPS protocol.

[0691] The terminal displays the advice received from the server to the user, who can then confirm and act on the displayed advice.

[0692] As a concrete example, if a user types, "I can't sleep lately because of work stress," a generative AI model might offer the following advice:

[0693] "In order to reduce work-related stress, it is important to set aside time to relax. For example, it is a good idea to get into the habit of taking deep breaths every night before going to bed. It is also effective to take time for moderate exercise or a hobby."

[0694] Example prompt:

[0695] "If a user has recently been feeling like they've been having trouble sleeping because of work stress, what advice can you give them?"

[0696] This system utilizes a generative AI model and an emotion engine to support the user's mental health. Specific hardware requires a server and a user's terminal, and software uses a generative AI model (e.g., GPT-4) and an emotion engine (e.g., Watson Tone Analyzer). The flow of the specific process in the third embodiment will be described with reference to FIG. 21.

[0697] Step 1:

[0698] The user inputs the problem information.

[0699] The user uses the terminal to input information about the worries. For example, the user inputs, "Recently, I have been unable to sleep due to work stress." This input information is stored in the terminal as text data.

[0700] Step 2:

[0701] The terminal transmits the input information to the server.

[0702] The terminal transmits the user-entered worry information to the server. This communication is securely performed using the HTTPS protocol. The input is the user's worry information, and the output is the text data transmitted to the server.

[0703] Step 3:

[0704] The server passes information to the generative AI model.

[0705] The server passes the received worry information to the generative AI model. The input is the text data sent to the server, and the output is the text data passed to the generative AI model.

[0706] Step 4:

[0707] A generative AI model generates advice.

[0708] The generative AI model analyzes the input worry information and generates appropriate advice. For example, it generates advice such as, "To reduce work-related stress, it is important to set aside time to relax." The input is the text data passed to the generative AI model, and the output is the generated text data of the advice.

[0709] Step 5:

[0710] The server receives the generated advice.

[0711] The server receives advice generated from the generative AI model. The input is the text data of the generated advice, and the output is the text data of the advice stored on the server.

[0712] Step 6:

[0713] The server analyzes the user's emotions using an emotion engine.

[0714] The server uses an emotion engine to analyze the user's emotions. The emotion engine extracts emotions from the text entered by the user and identifies the user's emotional state, such as "sad" or "stressed." The input is text data of the user's worries, and the output is data of the emotion analysis results.

[0715] Step 7:

[0716] The server integrates the advice with the sentiment analysis results.

[0717] The server integrates the generated advice with the emotion analysis results. For example, if the user feels "sad," it adds additional advice corresponding to that emotion. The input is the text data of the generated advice and the emotion analysis results, and the output is the text data of the integrated advice.

[0718] Step 8:

[0719] The server transmits the integrated result to the terminal.

[0720] The server sends the integrated advice to the terminal. This communication is also performed using the HTTPS protocol. The input is the text data of the integrated advice, and the output is the text data of the advice sent to the terminal.

[0721] Step 9:

[0722] The terminal displays the advice to the user.

[0723] The terminal displays the advice received from the server to the user. The user can confirm and execute the displayed advice. The input is the text data of the advice sent to the terminal, and the output is the advice displayed to the user.

[0724] (Application example 3)

[0725] 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."

[0726] Mental health issues are becoming more serious in modern society, and there is a demand for ways to quickly and appropriately resolve the worries and stress users are experiencing, especially in brick-and-mortar stores. However, with conventional systems, it is difficult to accurately recognize the emotional state of the user and provide appropriate counseling. In addition, there is a lack of information for brick-and-mortar store staff to respond based on the user's emotional state, making it difficult to improve the user experience.

[0727] 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.

[0728] In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the problem can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for receiving a user's trouble information as an input and generating appropriate advice based on the information, a means for providing counseling according to the user's emotional state using an emotion engine that recognizes the user's emotions, and a means for inputting the user's trouble information at a physical store and providing the analysis results to the physical store staff. This allows the provision of appropriate counseling and advice according to the user's troubles and emotional state, and enables the physical store staff to respond based on the user's emotional state.

[0729] "Generative AI means for conducting initial diagnosis" refers to artificial intelligence technology for conducting initial diagnosis based on information input by the user.

[0730] "Means of determining symptoms" refers to technology for accurately determining symptoms from information entered by the user.

[0731] "Means of determining whether the condition can be treated with over-the-counter medication, etc., or whether treatment at a nearby hospital is required" refers to technology for determining, based on the user's symptoms, whether the condition can be treated with over-the-counter medication, or whether treatment at a medical institution is required.

[0732] "A means for receiving a user's concern information as input and generating appropriate advice based on that information" refers to technology for analyzing the concern information entered by the user and providing appropriate advice based on that information.

[0733] "A means for providing counseling according to the user's emotional state by using an emotion engine that recognizes the user's emotions" refers to technology for analyzing a user's emotions and providing counseling according to that emotional state.

[0734] "A means for inputting a user's concern information at a physical store and providing the analysis results to staff at the physical store" refers to technology for analyzing the concern information input by a user at a physical store and providing the results to staff.

[0735] The system for implementing the present invention allows a user to input information about a problem at a brick-and-mortar store, generates appropriate advice based on the information, and provides counseling according to the user's emotional state. A specific embodiment of this system will be described below.

[0736] 1. System Programming

[0737] The system consists of the following main components:

[0738] Generative AI means: Analyzes the concern information entered by the user and generates appropriate advice.

[0739] Emotion engine: Recognizes emotions from user input and provides counseling according to the user's emotional state.

[0740] Support for store staff: The analysis results are provided to store staff to assist them in responding based on the user's emotional state.

[0741] 2. Hardware and software used

[0742] Hardware: Smartphones, servers

[0743] Software: Python, OpenAI API, TextBlob

[0744] 3. Data processing and calculation

[0745] Input and analysis of user concerns

[0746] A user inputs worry information through a smartphone app. This information is sent to a server and analyzed by the generative AI means. The generative AI means uses OpenAI's generative AI model (e.g., GPT-3 (registered trademark)) to generate appropriate advice for the user's worries.

[0747] Emotion recognition

[0748] The emotion engine uses TextBlob to analyze the emotion of the user's input text. The emotion engine recognizes the positive, negative, and neutral emotional state of the text and provides counseling accordingly.

[0749] Providing information to store staff

[0750] The analysis results are provided to store staff, who can provide better service by receiving suggestions on how to respond based on the user's emotional state.

[0751] 4. Specific Examples

[0752] For example, a user visits a cafe and enters into a smartphone app, "I haven't been able to sleep lately because of work stress." This information is sent to the server, and the generative AI generates advice such as, "To combat stress, try some relaxation techniques. For example, deep breathing and meditation are effective." At the same time, the emotion engine analyzes the emotion as "negative," and provides information to the store staff such as, "The user is feeling stressed, so please provide a relaxing environment."

[0753] 5. Examples of prompts

[0754] User's problem: I can't sleep lately because of work stress.

[0755] Please provide appropriate advice.

[0756] In this way, appropriate counseling and advice can be provided based on the user's concerns and emotional state, enabling store staff to respond based on the user's emotional state.

[0757] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0758] Step 1:

[0759] The user launches the smartphone app and enters information about their concerns.

[0760] Input: User's worries (e.g. "I can't sleep lately because of work stress")

[0761] Output: The user's worries are sent to the server.

[0762] Specific operation: When a user enters a concern into the input field of the app and presses the send button, the concern information is sent to the server.

[0763] Step 2:

[0764] The server passes the received worry information to a generative AI means, which generates appropriate advice.

[0765] Input: User's worries

[0766] Output: Advice generated by generative AI means (e.g., "To combat stress, try relaxation techniques. For example, deep breathing and meditation can be effective.")

[0767] Specific operation: The server calls the OpenAI API and passes the user's worries as a prompt. The generative AI model generates appropriate advice and returns it to the server.

[0768] Step 3:

[0769] The server returns the received advice to the user.

[0770] Input: Advice generated by a generative AI tool

[0771] Output: Advice displayed on the user's smartphone app

[0772] Specific operation: The server sends the generated advice to the user's smartphone app, and the app displays the advice.

[0773] Step 4:

[0774] The server passes the received worry information to an emotion engine and analyzes the emotional state.

[0775] Input: User's worries

[0776] Output: Emotion state parsed by the emotion engine (e.g. "negative")

[0777] Specific operation: The server uses TextBlob to analyze the emotions in the user's worry information and determine the user's emotional state.

[0778] Step 5:

[0779] The server provides the analyzed emotional state to staff at the brick-and-mortar store.

[0780] Input: Parsed emotional state

[0781] Output: Emotional state information provided to brick-and-mortar store staff

[0782] Specific operation: The server sends the analyzed emotional state to a terminal used by staff at the physical store, and the staff responds to the user based on that information.

[0783] Step 6:

[0784] Store staff respond based on the user's emotional state.

[0785] Input: Emotional state information

[0786] Output: An appropriate response to the user

[0787] Specific actions: Based on the emotional state information provided by the server, the staff at the brick-and-mortar store will take measures such as providing a relaxing environment for the user.

[0788] In this way, appropriate counseling and advice can be provided according to the user's concerns and emotional state, enabling store staff to respond based on the user's emotional state.

[0789] 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.

[0790] 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.

[0791] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0792] 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.

[0793] [Second embodiment]

[0794] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0795] 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.

[0796] 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).

[0797] 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.

[0798] 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.

[0799] 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).

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0806] "Example 1"

[0807] The present invention is a system that uses generative AI to perform a primary diagnosis. Specifically, it receives symptom information from the user as input and identifies the symptoms based on that information. Based on the results, it determines whether the symptom can be treated with over-the-counter medication or whether a visit to a nearby hospital is necessary. For example, if a user inputs "I have a headache," the generative AI uses that information to infer possible illnesses that could be causing the headache and suggests appropriate treatment methods.

[0808] "Example 2"

[0809] The present invention is a system that devise personalized training menus and diet recipes using ingredients in a home refrigerator as a means of health promotion and diet support. Specifically, the system receives inputs of the user's physical information, lifestyle, and information on ingredients in the refrigerator, and generates individual training menus and diet recipes based on that information. For example, if a user inputs "height 170 cm, weight 70 kg, and there is chicken breast and broccoli in the refrigerator," the generative AI will use that information to suggest an appropriate exercise menu and diet recipes using chicken breast and broccoli.

[0810] "Example 3"

[0811] The present invention is a system that provides AI counseling for worries as preventive support for mental health. Specifically, the system receives worry information from the user as input and generates appropriate advice based on that information. For example, if the user inputs "I haven't been able to sleep lately because of work stress," the generative AI provides advice on stress management and sleep improvement based on that information.

[0812] The process flow of each embodiment will be described below.

[0813] "Example 1"

[0814] Step 1: Receive symptom information from the user as input.

[0815] Step 2: The generative AI uses the information to identify symptoms.

[0816] Step 3: Based on the results, determine whether the condition can be treated with over-the-counter medication or whether treatment at a nearby hospital is required.

[0817] "Example 2"

[0818] Step 1: Receive the user's physical information, lifestyle, and food information in the refrigerator as input.

[0819] Step 2: Use that information to generate personalized training menus and diet recipes.

[0820] "Example 3"

[0821] Step 1: Receive problem information from the user as input.

[0822] Step 2: Generate appropriate advice based on that information.

[0823] Example 1

[0824] 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".

[0825] In modern society, it is important for users to quickly and accurately understand their own health condition and obtain appropriate treatment. However, there are many cases where it is difficult to access medical institutions, or people hesitate to visit a medical institution because their symptoms are minor. In such situations, it is difficult for users to find appropriate treatment, and health risks may increase. Therefore, there is a demand for a system that allows users to easily input their own symptoms and obtain appropriate treatment.

[0826] 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.

[0827] In this invention, the server includes a means for inputting symptom information from a user, a means for transmitting the input symptom information to the server, a means for the server to receive the symptom information and analyze it using a generative AI model, a means for the generative AI model to infer a disease based on the symptom information, a means for the server to suggest an appropriate countermeasure based on the inference result, and a means for displaying the suggested countermeasure to the user. This allows the user to easily input their own symptoms and quickly and accurately obtain an appropriate countermeasure.

[0828] "User" refers to an individual who utilizes the system to input their own symptom information and obtain appropriate treatment.

[0829] "Symptom information" refers to data that specifically describes the user's own health condition or any ailments that he or she is experiencing.

[0830] "Server" refers to a computer system that receives symptom information sent by a user, analyzes it using a generative AI model, and proposes appropriate countermeasures.

[0831] A "generative AI model" refers to an artificial intelligence model that analyzes input data and generates appropriate inferences and countermeasures.

[0832] "Analysis" refers to the process in which the generative AI model infers the disease based on symptom information and derives appropriate treatment.

[0833] "Inference results" refers to predictions of possible diseases or health conditions obtained as a result of the generative AI model analyzing symptom information.

[0834] "Countermeasures" refer to specific actions or treatments that the generative AI model suggests to the user based on the inference results.

[0835] "Display" refers to the process in which the terminal visually presents the solution information sent from the server to the user.

[0836] This invention is a system in which a user inputs his / her own symptom information, analyzes the information using a generative AI model, and proposes an appropriate countermeasure. A specific embodiment of this system will be described below.

[0837] A user inputs information about his / her symptoms using a device such as a smartphone or a PC. For example, the user inputs "I have a headache." This input information is sent from the device to the server. The device uses an Internet connection to send the data using the HTTPS protocol.

[0838] The server receives the symptom information sent from the device. The received data is temporarily stored in a database in the server. The server then uses a generative AI model to analyze the received symptom information. For example, OpenAI's GPT-4 is used as this generative AI model. The server sends a prompt sentence such as "If the user enters 'I have a headache,' please estimate the illnesses that may be causing the headache and suggest appropriate measures to deal with it" to the generative AI model.

[0839] The generative AI model analyzes the symptom information based on the prompt text and infers possible illnesses. For example, based on the information "I have a headache," it considers the possibility of a migraine, tension headache, etc. The generative AI model returns the inference results to the server.

[0840] The server proposes appropriate countermeasures based on the inferences obtained from the generative AI model. For example, it generates countermeasures such as "Use over-the-counter headache medicine." It also provides information on nearby hospitals if necessary. The server transmits the generated countermeasure information to the terminal.

[0841] The terminal displays the information on how to deal with the problem sent from the server to the user. The user then takes appropriate action based on the displayed information. For example, the user may purchase and use an over-the-counter headache medicine.

[0842] As a concrete example, when a user inputs "I have a headache," the following prompt sentence uses a generative AI model to predict possible illnesses that may be causing the headache and suggests appropriate measures to deal with the situation.

[0843] Example prompt:

[0844] “If a user types in ‘I have a headache,’ use a generative AI model to infer what illnesses may be causing the headache and suggest appropriate treatment.”

[0845] In this way, the user can easily input his / her symptoms and obtain appropriate measures quickly and accurately.

[0846] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0847] Step 1:

[0848] Subject: User

[0849] The user inputs their symptom information into the device. For example, they open a smartphone app and type "I have a headache" into the text box.

[0850] Input: User's symptoms (e.g. "I have a headache")

[0851] Output: Symptom information entered into the terminal

[0852] Step 2:

[0853] Subject: Terminal

[0854] The device then transmits the entered symptom information to the server, using an Internet connection to transmit the data using the HTTPS protocol.

[0855] Input: Symptom information entered into the terminal

[0856] Output: Symptom information sent to the server

[0857] Step 3:

[0858] Subject: Server

[0859] The server receives the symptom information sent from the terminal and temporarily stores the received data in a database within the server.

[0860] Input: Symptom information sent from the device

[0861] Output: Symptom information stored in the database

[0862] Step 4:

[0863] Subject: Server

[0864] The server inputs the saved symptom information into the generative AI model, generates a prompt sentence, and sends it. The prompt sentence used is something like, "If the user inputs 'I have a headache,' please estimate the illnesses that may be causing the headache and suggest appropriate measures."

[0865] Input: Symptom information stored in the database

[0866] Output: The prompt sent to the generative AI model

[0867] Step 5:

[0868] Subject: Generative AI model

[0869] The generative AI model analyzes symptom information based on the prompt text and predicts possible illnesses. For example, based on the information "I have a headache," it considers the possibility of a migraine, tension headache, etc.

[0870] Input: The prompt sent to the generative AI model

[0871] Output: Estimated disease probability

[0872] Step 6:

[0873] Subject: Server

[0874] The server then proposes appropriate measures based on the inferences obtained from the generative AI model. For example, it generates measures such as "use over-the-counter headache medicine." It also provides information on nearby hospitals if necessary.

[0875] Input: Inference results obtained from a generative AI model

[0876] Output: Generated solutions

[0877] Step 7:

[0878] Subject: Server

[0879] The server transmits the generated solution information to the device. At this time, the server transmits the data via an Internet connection.

[0880] Input: Generated solution

[0881] Output: Actions sent to the device

[0882] Step 8:

[0883] Subject: Terminal

[0884] The terminal displays the information on how to deal with the problem sent from the server to the user. The user then takes appropriate action based on the displayed information. For example, the user may purchase and use an over-the-counter headache medicine.

[0885] Input: Action sent by server

[0886] Output: Actions shown to the user

[0887] (Application example 1)

[0888] 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".

[0889] In modern society, it is a difficult task for users to select appropriate meals based on their own health condition and symptoms. In particular, when using food delivery services, it is required to choose meals that take health into consideration, but there is a lack of information for users to choose meals that are suitable for their own symptoms. For this reason, support is needed to help users use food delivery services while maintaining their health.

[0890] 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.

[0891] In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the symptoms can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for suggesting an appropriate diet based on the user's health condition and symptoms, and a means for generating a prompt sentence using a generative AI model and suggesting an appropriate diet, thereby enabling the user to select an appropriate diet based on their own health condition and symptoms.

[0892] "Primary diagnosis" is the process of making an initial diagnosis based on symptom information provided by the user.

[0893] "Generative AI" is an artificial intelligence technology that generates appropriate responses and suggestions based on information input by the user.

[0894] A "means for identifying symptoms" is a method for analyzing the symptom information entered by the user and identifying the cause of the symptoms and any associated diseases.

[0895] "Means for determining whether symptoms can be treated with over-the-counter drugs, etc." refers to a method for determining whether the user's symptoms can be treated with over-the-counter drugs or whether medical treatment at a medical institution is required.

[0896] The "means for determining whether medical treatment at a nearby hospital is necessary" refers to a method for determining whether the user's symptoms are serious and, if necessary, recommending medical treatment at a nearby medical institution.

[0897] A "means for suggesting appropriate meals based on a user's health condition and symptoms" is a method for selecting and suggesting appropriate meals based on health condition and symptom information input by a user.

[0898] "Means for generating prompt sentences using a generative AI model and suggesting appropriate meals" refers to a method for using a generative AI model to create prompt sentences based on the user's symptoms and then suggesting appropriate meals based on those prompt sentences.

[0899] A system for implementing the present invention is for suggesting an appropriate diet to a user based on the user's health condition or symptoms. The system includes a means for generating prompt sentences using a generative AI model to suggest an appropriate diet to the user.

[0900] System Configuration

[0901] The system consists of the following main components:

[0902] 1. User device: A device such as a smartphone or tablet where the user enters symptom information.

[0903] 2. Server: A server for hosting the generative AI model and processing input from users.

[0904] 3. Generative AI model: An AI model that generates prompts based on user input and suggests appropriate meals.

[0905] Program Processing

[0906] When a user inputs their current symptoms using a device such as a smartphone, the information is sent to a server, which uses a generative AI model to generate a prompt based on the input symptom information. This prompt is intended to suggest an appropriate meal.

[0907] The generated prompts are then processed again by the server to generate appropriate meal suggestions, which are then sent to the user's device and displayed to the user.

[0908] Hardware and Software Used

[0909] Hardware: Smartphones, tablets, servers

[0910] Software: Generative AI models (e.g., OpenAI API), database management systems, communication protocols (e.g., HTTP / HTTPS)

[0911] Examples

[0912] For example, if the user types "I have a stomach ache," the following prompt text is generated:

[0913] User entered 'My stomach hurts'. Please suggest suitable meals.

[0914] Based on this prompt, the generative AI model generates advice such as, "We recommend that you eat foods that are easy on the stomach, such as porridge or soup." This advice is sent to the user's device and displayed to the user.

[0915] In this way, users can choose appropriate meals based on their health conditions and symptoms.

[0916] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0917] Step 1:

[0918] The user inputs his / her current symptoms using a device such as a smartphone. The input symptom information is sent to the server in text format. The input data is the user's symptom information (e.g., "My stomach hurts").

[0919] Step 2:

[0920] The server analyzes the received symptom information and generates a prompt to send to the generative AI model. Specifically, the server generates a prompt based on the symptom information, such as: "The user inputs 'My stomach hurts.' Please suggest an appropriate meal." This prompt is generated.

[0921] Step 3:

[0922] The server sends the generated prompt to the generative AI model, which analyzes the prompt and generates appropriate meal suggestions. The input data is the prompt, and the output data is the meal suggestions (e.g., "We recommend that you eat something easy on your stomach, such as porridge or soup").

[0923] Step 4:

[0924] The server sends the meal suggestions received from the generative AI model to the user device. The user device displays the received meal suggestions. The input data are the meal suggestions, and the output data are the meal suggestions displayed to the user.

[0925] Step 5:

[0926] The user reviews the displayed meal suggestions and selects the appropriate meal. This allows the user to select the appropriate meal based on their health condition and symptoms. The input data is the displayed meal suggestions and the output data is the user's selected meal.

[0927] Example 2

[0928] 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".

[0929] In modern society, there is a demand for personalized health promotion and diet support that is tailored to each individual's health condition and lifestyle. However, conventional systems have had difficulty providing training menus and diet recipes that comprehensively take into account the user's physical information, lifestyle, and information about ingredients in the refrigerator. In addition, they are also inadequate in providing appropriate diagnoses and countermeasures based on this information.

[0930] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the condition can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for receiving the user's physical information, lifestyle, and refrigerator food information as input, and generating individual training menus and diet recipes, and a means for presenting the generated training menus and diet recipes to the user. This makes it possible to provide optimal training menus and diet recipes according to the user's individual health condition and lifestyle.

[0931] The "generative AI means for performing primary diagnosis" is an artificial intelligence technology for evaluating the initial health condition based on information input by the user and proposing appropriate measures.

[0932] "Symptom assessment tools" are techniques for analyzing symptoms reported by a user and determining the cause and severity of the symptoms.

[0933] The "means for determining whether the symptom can be treated with over-the-counter medicine, etc., or whether medical treatment at a nearby hospital is required" is a technology for determining whether the user's symptoms can be treated with over-the-counter medicine, or whether medical treatment at a medical institution is required.

[0934] "Means for receiving a user's physical information, lifestyle, and information on ingredients in the refrigerator as input, and generating individual training menus and diet recipes" refers to technology for creating individually optimized exercise programs and meal plans based on the physical data, lifestyle habits, and information on ingredients in the refrigerator provided by the user.

[0935] The "means for presenting the generated training menu and diet recipes to the user" refers to a technique for displaying the exercise program and meal plan generated by the server to the user in an easy-to-understand manner.

[0936] This invention is a system for the purpose of improving the health of users and supporting their diets, and generates individual training menus and diet recipes based on the user's physical information, lifestyle, and food information in the refrigerator. A specific embodiment of this system will be described below.

[0937] 1. Program Generation

[0938] The server generates a program aimed at improving the user's health and supporting dieting. This program receives the user's physical information, lifestyle, and information on ingredients in the refrigerator as input, and generates individual training menus and diet recipes. One example of the generative AI model used is GPT-4.

[0939] 2. What the program does

[0940] The server processes the data in the following steps:

[0941] Data Entry:

[0942] A user uses a dedicated application or a web interface to input his / her physical information (e.g., height, weight), lifestyle (e.g., exercise frequency, food preferences), and food information in the refrigerator. For example, a user inputs, "I am 170 cm tall, I weigh 70 kg, and there are chicken breasts and broccoli in the refrigerator."

[0943] Data Analysis:

[0944] The server analyzes the input data and generates optimal training menus and diet recipes based on the user's health status and goals. This analysis uses a generative AI model (e.g. GPT-4).

[0945] Generate a training menu:

[0946] The server generates an individual training menu based on the user's physical information and lifestyle. For example, if a user wants to exercise three times a week, the server will suggest a menu that combines aerobic exercise and strength training.

[0947] Diet recipe generation:

[0948] The server generates diet recipes suitable for the user based on the information about ingredients in the refrigerator. For example, if there is chicken breast and broccoli in the refrigerator, the server will suggest low-calorie recipes using these ingredients.

[0949] Presenting the results:

[0950] The server transmits the generated training menu and diet recipes to the terminal and presents them to the user. The user can plan their daily training and meals based on the displayed information.

[0951] 3. Examples and prompts

[0952] As a concrete example, the following prompt sentence is input into the generative AI model.

[0953] Example prompt:

[0954] User Information:

[0955] Height: 170cm

[0956] Weight: 70kg

[0957] Lifestyle: Exercise 3 times a week

[0958] Ingredients in the fridge: Chicken breast, broccoli

[0959] Based on this information, please suggest an appropriate training menu and diet recipes using chicken breast and broccoli.

[0960] An example of the result produced:

[0961] Training Menu:

[0962] Aerobic exercise 3 times a week (e.g. 30 minutes of jogging)

[0963] Strength training twice a week (e.g. squats, push-ups)

[0964] Diet Recipes:

[0965] Low calorie stir-fry with chicken breast and broccoli

[0966] Ingredients: 200g chicken breast, 100g broccoli, 1 tablespoon olive oil, a little salt, a little pepper

[0967] Directions: Cut chicken breast into bite-sized pieces and fry in olive oil. Add broccoli and fry further, then season with salt and pepper.

[0968] In this way, the server generates an individual training menu and diet recipes based on the user's input information and provides them to the user.

[0969] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0970] Step 1:

[0971] The user enters the information.

[0972] Using a dedicated application or web interface, users input their physical information (e.g., height 170 cm, weight 70 kg), lifestyle (e.g., exercise three times a week), and information about ingredients in their refrigerator (e.g., chicken breast, broccoli). The input information is then stored on the device.

[0973] Step 2:

[0974] The terminal transmits the input information to the server.

[0975] The device encrypts the information entered by the user and sends it to the server. The data sent includes the user's physical information, lifestyle, and food information in the refrigerator. The server stores the received data for analysis.

[0976] Step 3:

[0977] The server parses the data.

[0978] The server analyzes the received user information. A generative AI model (e.g. GPT-4) is used for the analysis. Specifically, the server evaluates the user's health condition and goals based on the user's physical information and lifestyle. For example, it calculates the user's BMI and determines the appropriate amount of exercise. The input data is processed based on the user's health condition and goals.

[0979] Step 4:

[0980] The server generates a training menu.

[0981] The server generates the optimal training menu for the user based on the analysis results. For example, if the user wants to exercise three times a week, the server will suggest a menu that combines aerobic exercise (e.g., 30 minutes of jogging) and strength training (e.g., squats and push-ups). The generated training menu is customized based on the user's health condition and goals.

[0982] Step 5:

[0983] The server generates diet recipes.

[0984] The server generates diet recipes suitable for the user based on the ingredient information in the refrigerator. For example, if there is chicken breast and broccoli in the refrigerator, the server will suggest low-calorie recipes using these ingredients (e.g., stir-fried chicken breast and broccoli). The generated recipes are customized based on the ingredient information and health goals of the user.

[0985] Step 6:

[0986] The server sends the result to the terminal.

[0987] The server encrypts the generated training menu and diet recipes and transmits them to the terminal. The transmitted data includes the exercise program and meal plan optimized for the user. The terminal stores the received data.

[0988] Step 7:

[0989] The terminal displays the results to the user.

[0990] The terminal displays the training menu and diet recipes received from the server to the user. The user can plan their daily training and meals based on the displayed information. Specifically, the user can check the details of the exercise program and recipes on the application interface.

[0991] (Application example 2)

[0992] 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".

[0993] In modern society, health promotion and diet support are important issues, but it is difficult to provide training menus and diet recipes that are suitable for each individual user. In addition, there is a lack of specific methods for users to effectively use ingredients at home and eat healthy meals. Furthermore, there is a demand for a means to quickly obtain the necessary ingredients and dishes based on the generated recipe. To solve these issues, personalized suggestions based on the user's physical information, lifestyle, and information on ingredients in the refrigerator are necessary.

[0994] 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.

[0995] In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the condition can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for receiving the user's physical information, lifestyle, and information on ingredients in the refrigerator as input, and generating individual training menus and diet recipes, and a means for proposing options for delivering the necessary ingredients and dishes based on the generated recipes. This allows the user to receive training menus and diet recipes that match their own health condition and lifestyle, and further allows the necessary ingredients and dishes to be delivered quickly.

[0996] The "generative AI means for performing primary diagnosis" is an artificial intelligence technology that assesses the initial health condition based on information input by the user and suggests appropriate actions.

[0997] "Symptom assessment tools" are technologies used to analyze symptoms reported by users and determine the cause and severity of those symptoms.

[0998] "Means for determining whether the symptom can be treated with over-the-counter medication, etc., or whether medical treatment at a nearby hospital is required" refers to technology for determining whether the user's symptoms can be treated with over-the-counter medication, or whether medical treatment at a medical institution is required.

[0999] "A means for receiving a user's physical information, lifestyle, and information about ingredients in the refrigerator as input, and generating individual training menus and diet recipes" refers to technology for generating individually optimized exercise programs and meal recipes based on the user's physical characteristics, lifestyle, and information about ingredients in the refrigerator.

[1000] "Means for proposing delivery options for necessary ingredients and dishes based on the generated recipe" refers to a technology that suggests a delivery service for providing the user with ingredients and dishes necessary for the generated diet recipe.

[1001] The system for implementing this invention creates individual training menus and diet recipes based on the user's physical information, lifestyle, and information about ingredients in the refrigerator, and also provides a service of delivering ingredients and meals based on those recipes.

[1002] Hardware and Software Used

[1003] Hardware: Smartphones, servers

[1004] Software: Mobile applications (iOS / Android), server-side applications, generative AI models (e.g. GPT-4), databases (e.g. MySQL)

[1005] Program processing explanation

[1006] 1. Enter your user information

[1007] Through a smartphone app, users input their physical information (height, weight, age, etc.), lifestyle (exercise frequency, food preferences, etc.), and information about the ingredients in their refrigerator. This information is collected through the application interface.

[1008] 2. Data transmission and storage

[1009] The entered data is sent from the smartphone to a server and stored in a database, using a relational database such as MySQL.

[1010] 3. Menu generation using generative AI models

[1011] The server-side application inputs the stored data into a generative AI model (e.g., GPT-4) to generate individual training menus and diet recipes. The generative AI model then suggests optimal menus based on the user's physical information, lifestyle, and the ingredients in the refrigerator.

[1012] 4. Propose delivery options

[1013] Based on the generated recipe, the app will suggest the required ingredients and delivery options for the dish to the user, who can then select the delivery option through the app.

[1014] 5. Executing the delivery

[1015] When a user selects delivery, an order is sent to a partner food delivery service and the ingredients and food are delivered to the user's home.

[1016] Examples

[1017] The user enters the following information into the app:

[1018] Height: 170cm

[1019] Weight: 70kg

[1020] Ingredients in the fridge: Chicken breast, broccoli

[1021] Example prompt for a generative AI model:

[1022] User's physical information: Height 170cm, Weight 70kg

[1023] Lifestyle: Exercise 3 times a week

[1024] Ingredients in the fridge: Chicken breast, broccoli

[1025] Based on this information, generate training menus and diet recipes that are suitable for the user.

[1026] Produces the following result:

[1027] Training menu: 30 minutes of aerobic exercise (jogging), 15 minutes of strength training (push-ups, squats)

[1028] Diet recipe: Healthy chicken breast and broccoli salad

[1029] In this way, users can receive a personalized health promotion plan and even have the ingredients and dishes they need delivered to them.

[1030] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1031] Step 1:

[1032] The user starts the smartphone app and inputs physical information (height, weight, age, etc.), lifestyle information (exercise frequency, food preferences, etc.), and information about ingredients in the refrigerator. The input information is collected through the application interface.

[1033] Input: Physical information, lifestyle, and fridge ingredients

[1034] Output: User input data

[1035] Step 2:

[1036] The terminal sends the collected user input data to the server, which stores the received data in a database using a relational database such as MySQL.

[1037] Input: User-entered data

[1038] Output: User data stored in the database.

[1039] Step 3:

[1040] The server inputs the saved user data into a generative AI model (e.g., GPT-4) to generate individual training menus and diet recipes. The generative AI model suggests optimal menus based on the user's physical information, lifestyle, and information about ingredients in the refrigerator.

[1041] Input: User data stored in the database

[1042] Output: Training menu and diet recipes

[1043] Step 4:

[1044] The server sends the generated training menu and diet recipes to the user's smartphone, where the user can check the proposed menu and recipes through the application.

[1045] Input: Training menu and diet recipes

[1046] Output: Menu and recipes displayed on the user's smartphone

[1047] Step 5:

[1048] The user selects the required ingredients and meal delivery options based on the generated recipe, and the application sends the user's selection to the server.

[1049] Input: User's delivery option selection

[1050] Output: Delivery option selection data sent to the server

[1051] Step 6:

[1052] Based on the user's selection, the server sends the order to a partner food delivery service, which delivers the required ingredients and food to the user's home.

[1053] Input: Delivery option selection data

[1054] Output: Order data sent to the food delivery service

[1055] In this way, users can receive a personalized health plan and even have the ingredients and meals they need delivered.

[1056] Example 3

[1057] 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".

[1058] In modern society, mental health issues are becoming more serious, and there is a need to provide prompt and appropriate advice for each individual's concerns. However, conventional systems have the problem of being unable to accurately analyze a user's concerns and provide appropriate advice. In addition, there was a lack of technology to effectively analyze the information entered by the user and generate personalized advice.

[1059] 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 generative artificial intelligence means, means for analyzing input information using natural language processing technology, means for generating a prompt sentence based on the analysis result, means for inputting the prompt sentence to the generative artificial intelligence model and generating advice, and means for providing the generated advice to the user. This makes it possible to accurately analyze the user's concern information and quickly provide appropriate advice.

[1060] "Generative artificial intelligence means" refers to artificial intelligence technology for generating appropriate advice based on input information from a user.

[1061] "Natural language processing technology" refers to technology for analyzing text information entered by a user and understanding its content and emotional state.

[1062] A "prompt sentence" is an instruction sentence to be input into a generative artificial intelligence model, and refers to a sentence generated based on the user's concern information.

[1063] "Generative artificial intelligence model" refers to an artificial intelligence model for generating appropriate advice based on a prompt sentence.

[1064] The "means for generating advice" refers to a process for generating specific advice for a user's concern information using a generative artificial intelligence model.

[1065] The "means for providing the generated advice to the user" refers to an interface or communication means for transmitting the generated advice to the user.

[1066] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1067] The present invention relates to an AI counseling system for preventing and supporting mental health problems for users. A specific embodiment of this system will be described below.

[1068] System Configuration

[1069] This system includes a terminal into which a user inputs problem information, a server that analyzes the input information and generates advice, and communication means for providing the generated advice to the user.

[1070] Hardware and Software Used

[1071] Device: A user device such as a smartphone, tablet, or computer.

[1072] Server: A server computer with high computing power.

[1073] Software: Web browsers, mobile applications, generative artificial intelligence models (e.g. OpenAI's GPT-4), natural language processing techniques (e.g. Google's BERT).

[1074] Data processing and calculation

[1075] 1. User inputs problem information:

[1076] A user inputs information about their worries into a text box using a web browser or mobile application on their smartphone or computer. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[1077] 2. The device sends the entered worry information to the server:

[1078] The device sends the text entered by the user to the server as an HTTP POST request, which includes the user's concerns.

[1079] 3. The server receives the concern information and analyzes it using natural language processing technology:

[1080] The server parses the received request and extracts the concern information, then uses Google's BERT model to analyze the text for content and sentiment.

[1081] 4. The server generates a prompt based on the parsed result:

[1082] The server generates a prompt based on the analysis results, for example, "The user has recently been feeling unable to sleep due to work stress. Please provide specific advice on how to deal with stress and improve sleep in this situation."

[1083] 5. The server inputs the prompt sentence into the generative AI model and generates advice:

[1084] The server inputs the prompt sentence into a generative AI model (e.g., OpenAI's GPT-4) and generates advice. The generative AI model generates appropriate advice based on the prompt sentence.

[1085] 6. The server sends the generated advice to the device:

[1086] The server sends the generated advice to the terminal as an HTTP response. The response includes the generated advice.

[1087] 7. The terminal displays the advice to the user:

[1088] The device displays the received advice to the user. The user sees the advice through the smartphone app: "If you can't sleep because of work stress, try the following measures: 1. Make it a habit to go to bed at a fixed time every day. 2. Create a routine to relax before going to bed (e.g., take a warm bath and do some light stretching). 3. Take short breaks during the day to reduce stress. 4. If necessary, consider consulting a specialist."

[1089] In this way, the user can receive specific and practical advice. This system effectively supports the user's mental health prevention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1090] Step 1:

[1091] The user inputs information about the worries. The user inputs information about the worries into a text box using a web browser or mobile application on a smartphone or PC. For example, the user might input, "Recently, I've been unable to sleep due to work stress." The input information is then sent to the server in the next step.

[1092] Step 2:

[1093] The terminal sends the inputted worry information to the server. The terminal sends the text inputted by the user to the server as an HTTP POST request. This request contains the user's worry information. The input is the user's worry information, and the output is the data to be sent to the server.

[1094] Step 3:

[1095] The server receives the concern information and analyzes it using natural language processing technology. The server analyzes the received request and extracts the concern information. It then uses natural language processing technology (e.g., Google's BERT model) to analyze the content and sentiment of the text. The input is the user's concern information, and the output is the analysis result.

[1096] Step 4:

[1097] The server generates a prompt sentence based on the analysis result.The server generates a prompt sentence based on the analysis result.For example, a prompt sentence such as "The user has recently been feeling that he is unable to sleep due to work stress. Please provide specific advice on how to deal with stress and improve sleep in this situation" is generated.The input is the analysis result, and the output is the prompt sentence.

[1098] Step 5:

[1099] The server inputs a prompt sentence into the generative AI model and generates advice. The server inputs a prompt sentence into the generative AI model (e.g., OpenAI's GPT-4) and generates advice. The generative AI model generates appropriate advice based on the prompt sentence. The input is the prompt sentence, and the output is the generated advice.

[1100] Step 6:

[1101] The server sends the generated advice to the terminal. The server sends the generated advice to the terminal as an HTTP response. This response includes the generated advice. The input is the generated advice, and the output is the data to be sent to the terminal.

[1102] Step 7:

[1103] The device displays the advice to the user. The device displays the received advice to the user. The user confirms the advice through the smartphone app: "If you can't sleep because of work stress, try the following measures: 1. Make a habit of going to bed at a certain time every day. 2. Create a routine to relax before going to bed (e.g. taking a warm bath and doing light stretching). 3. Take short breaks during the day to reduce stress. 4. If necessary, consider consulting a specialist." The input is the generated advice, and the output is the display to the user.

[1104] (Application example 3)

[1105] 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".

[1106] In modern society, mental health problems are becoming more serious, and the number of people suffering from stress and anxiety is increasing. However, receiving counseling from a professional takes time and money, so many people are unable to receive appropriate support. In addition, there is a lack of effective systems for providing mental health support in brick-and-mortar stores. This creates a demand for an environment where users can easily receive mental health support.

[1107] 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.

[1108] In this invention, the server includes generative AI means for performing a primary diagnosis, means for identifying symptoms, means for determining whether the problem can be treated with over-the-counter medicine or whether a consultation at a nearby hospital is necessary, interface means for inputting the user's concerns, means for generating advice using a generative AI model, means for displaying advice, and means for saving and viewing past consultation history. This allows users to easily receive mental health support via their smartphones.

[1109] A "generative AI means for performing initial diagnosis" is an artificial intelligence technology for performing an initial diagnosis based on information provided by a user.

[1110] "Means for identifying symptoms" refers to technology that analyzes information entered by the user and identifies specific symptoms.

[1111] The "means for determining whether the condition can be treated with over-the-counter medication, etc., or whether treatment at a nearby hospital is required" refers to technology for determining, based on the user's symptoms, whether the condition can be treated with over-the-counter medication, or whether treatment at a medical institution is required.

[1112] The "interface means for inputting user's concerns" is a user interface that allows the user to input his or her own concerns or problems.

[1113] An "advice generation means using a generative AI model" is an artificial intelligence model for generating appropriate advice based on concern information input by a user.

[1114] The "means for displaying advice" refers to a technique for visually displaying the generated advice to the user.

[1115] "Means for saving and viewing past consultation history" refers to technology that allows users to save the details of past consultations and view them later.

[1116] The system for carrying out the present invention is designed to enable a user to receive mental health support using a smartphone. A specific embodiment of this system will be described below.

[1117] Hardware and Software Used

[1118] Hardware: Smartphone

[1119] Software: Python, OpenAI API

[1120] System Configuration

[1121] 1. Generative AI means for performing a first-stage diagnosis:

[1122] The server uses a generative AI to perform an initial diagnosis based on the information provided by the user. This AI analyzes the user's input data and generates an initial diagnosis result.

[1123] 2. How to identify symptoms:

[1124] The server analyzes the information entered by the user and runs algorithms to identify specific symptoms. The algorithms identify symptoms based on the data entered by the user and suggest appropriate measures to address the issue.

[1125] 3. How to determine if over-the-counter medications are sufficient or if you need to see a doctor at a nearby hospital:

[1126] Based on the user's symptoms, the server determines whether the problem can be treated with over-the-counter medication or whether medical treatment is required. This decision is made by generative AI.

[1127] 4. Interface for inputting user concerns:

[1128] Users input their concerns and problems through a smartphone application. The interface is designed to make it easy for users to input information.

[1129] 5. How generative AI models generate advice:

[1130] The server uses a generative AI model to generate appropriate advice based on the worries input by the user. For example, if a user inputs, "I haven't been able to sleep lately because of work stress," the generative AI model will provide advice such as, "To reduce stress, try taking some time each day to relax. For example, you may want to try deep breathing or meditation. It is also effective to refrain from using your smartphone or computer before going to bed."

[1131] 6. Ways to display advice:

[1132] The server visually displays the generated advice to the user via the smartphone screen.

[1133] 7. How to store and view past consultation history:

[1134] The server stores the contents of past consultations by the user and allows them to be viewed later. This function allows users to refer to their past consultation history and receive ongoing support.

[1135] Examples of prompt statements

[1136] If the user types "I can't sleep lately because of work stress," the following is an example of a prompt:

[1137] User's problem: I can't sleep lately because of work stress.

[1138] Please provide appropriate advice.

[1139] In this way, users can easily receive mental health support via their smartphone.

[1140] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1141] Step 1:

[1142] The user starts the smartphone application and inputs their concerns.

[1143] Input: User's worries (e.g. "I can't sleep lately because of work stress")

[1144] Output: The user's concerns are sent to the application.

[1145] Specific actions: The user opens the application on their smartphone, enters their concerns into the text box, and presses the send button.

[1146] Step 2:

[1147] The terminal transmits the user's worry information to the server.

[1148] Input: User's worries

[1149] Output: User's worries sent to the server

[1150] Specific operation: The smartphone application transmits the worry information entered by the user to a server via the Internet.

[1151] Step 3:

[1152] The server inputs the received worry information into the generative AI model.

[1153] Input: User's worries sent to the server

[1154] Output: Worry information input to the generative AI model

[1155] Specific operation: The server converts the received user's concern information into an appropriate format to pass it to the generative AI model.

[1156] Step 4:

[1157] The generative AI model generates advice based on concern information.

[1158] Input: Worry information input to the generative AI model

[1159] Output: Generated advice

[1160] Specific operation: The generative AI model analyzes the user's worries and generates appropriate advice, such as, "To reduce stress, try taking some time each day to relax. For example, you may want to try deep breathing or meditation. It is also effective to refrain from using smartphones and computers before going to bed."

[1161] Step 5:

[1162] The server transmits the generated advice to the terminal.

[1163] Input: Generated advice

[1164] Output: Advice sent to terminal

[1165] Specific operation: The server sends advice obtained from the generative AI model to the user's smartphone.

[1166] Step 6:

[1167] The terminal displays the received advice to the user.

[1168] Input: Advice sent to terminal

[1169] Output: The advice displayed to the user

[1170] Specific operation: The smartphone application displays the advice received from the server on the screen, allowing the user to view it and take appropriate measures.

[1171] Step 7:

[1172] The server stores the user's consultation history.

[1173] Input: User's concerns and generated advice

[1174] Output: Saved consultation history

[1175] Specific operation: The server stores the problem information entered by the user and the generated advice in a database so that the user can view it later.

[1176] 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.

[1177] "Example 1"

[1178] As one embodiment of the present invention, a system including an emotion engine that recognizes a user's emotions is provided. The system analyzes emotions from the user's facial expressions, tone of voice, text input, etc., and provides the results to a generative AI. The generative AI takes this emotion information into account to make a more appropriate primary diagnosis. For example, if the user is feeling anxious, the generative AI can use that information to respond to the user's emotions, such as by providing a more detailed explanation to the user.

[1179] "Example 2"

[1180] In addition, a system that devise personalized training menus and diet recipes to support health promotion and dieting is also provided. This system uses an emotion engine that recognizes the user's emotions and provides training menus and diet recipes according to the user's emotional state. For example, if the user is feeling stressed, it can suggest training menus and meals that are effective in relieving stress.

[1181] "Example 3"

[1182] Furthermore, a system including an AI counseling means is also provided as preventive support for mental health. This system uses an emotion engine that recognizes the user's emotions and provides counseling according to the user's emotional state. For example, if the user is feeling sad, advice and support corresponding to that emotion can be provided.

[1183] The process flow of each embodiment will be described below.

[1184] "Example 1"

[1185] Step 1: Receive input from the user (facial expression, tone of voice, text, etc.).

[1186] Step 2: The emotion engine analyzes the user's emotions.

[1187] Step 3: Provide the analysis results to the generative AI.

[1188] Step 4: The generative AI makes a primary diagnosis, taking into account the emotional information.

[1189] "Example 2"

[1190] Step 1: Receive information about the user's body, lifestyle, contents of the refrigerator, etc.

[1191] Step 2: The emotion engine analyzes the user's emotions.

[1192] Step 3: Generate personalized training menus and diet recipes based on the analysis results and user information.

[1193] "Example 3"

[1194] Step 1: Receive concerns or problems from users.

[1195] Step 2: The emotion engine analyzes the user's emotions.

[1196] Step 3: Based on the analysis results, the AI ​​counseling tool provides appropriate advice and assistance.

[1197] Example 1

[1198] 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".

[1199] Conventional primary diagnostic systems have limited means for users to properly diagnose their own symptoms, and in particular do not take emotional information into account when diagnosing, making it difficult to provide appropriate support to users. In addition, the reliability and accuracy of diagnostic results is low, making it difficult for users to select appropriate measures.

[1200] 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.

[1201] In this invention, the server includes a means for a user to input symptom information, a means for a terminal to transmit input information to the server, a means for the server to receive symptom information, a means for the server to create and input a prompt sentence to the generative AI model, a means for the generative AI model to analyze symptoms and generate a diagnosis result, a means for the server to receive the diagnosis result and respond to the user, and a means for the user to confirm the diagnosis result. This makes it possible to perform a more accurate primary diagnosis based on the user's symptom information and emotion information and to propose an appropriate countermeasure.

[1202] The "means for the user to input symptom information" refers to a device or software that provides an interface for the user to input his / her symptoms in text form.

[1203] The "means for the terminal to transmit input information to the server" refers to a device or software having a communication function for transmitting symptom information input by the user to the server.

[1204] The "means for the server to receive symptom information" refers to a device or software for receiving and processing symptom information transmitted from a terminal.

[1205] "Means for the server to create and input a prompt sentence to the generative AI model" refers to a device or software that creates a prompt sentence to be input to the generative AI model based on received symptom information, and inputs the prompt sentence to the generative AI model.

[1206] "Means for the generative AI model to analyze symptoms and generate a diagnostic result" refers to a device or software that enables the generative AI model to analyze symptoms based on a prompt sentence and generate a diagnostic result.

[1207] "Means for the server to receive the diagnostic result and respond to the user" refers to a device or software for responding to the user with the diagnostic result received from the generative AI model.

[1208] The "means for a user to check the diagnostic results" refers to a device or software that provides an interface for a user to check the diagnostic results through a terminal.

[1209] This invention is a system that uses generative AI to perform a primary diagnosis. Specifically, it receives symptom information from the user as input and identifies the symptoms based on that information. Based on the results, it determines whether the problem can be treated with over-the-counter medicine or whether a visit to a nearby hospital is necessary.

[1210] The system uses the following hardware and software:

[1211] A device for users to input symptom information (e.g., smartphone, computer)

[1212] A server for receiving and processing information sent from the terminal

[1213] A generative AI model (e.g., OpenAI's GPT-4) to analyze symptom information and generate diagnostic results

[1214] The user enters their symptoms in text format into an input form on the device. For example, they might enter "I have a headache." The device then sends this input information to the server. The server creates a prompt sentence to be input into the generative AI model based on the received symptom information. For example, it generates a prompt sentence that reads, "The user is complaining of a headache. Please infer the cause of the headache and suggest an appropriate remedy."

[1215] The generative AI model analyzes symptoms based on the prompt text and generates a diagnosis. For example, it may infer that the cause of the headache is migraine or tension headache, and suggest appropriate measures to deal with the headache (e.g., use of over-the-counter medication, or medical treatment at a hospital). The server sends the diagnosis result received from the generative AI model to the terminal to respond to the user. The user checks the diagnosis result displayed on the terminal screen and, if necessary, purchases over-the-counter medication or receives medical treatment at a hospital.

[1216] As a concrete example, consider the case where a user types "I have a headache." The server receives this information and feeds the following prompt to the generative AI model:

[1217] "The user is complaining of a headache. Please deduce the cause of the headache and suggest an appropriate remedy."

[1218] The generative AI model analyzes the prompt text and generates a diagnosis result of "It is likely a migraine. It can be treated with over-the-counter medication, but if symptoms persist, we recommend that you see a doctor at a hospital." The server returns this diagnosis result to the user, who can check it via their device.

[1219] In this way, the system can perform a primary diagnosis based on the user's symptom information and suggest appropriate measures to address the problem.

[1220] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1221] Step 1:

[1222] The user enters the symptom information.

[1223] The user opens a browser on their smartphone or computer, accesses the system's web application, enters "I have a headache" in the input form, and clicks the submit button.

[1224] Input: Symptom information entered by the user (e.g., "I have a headache")

[1225] Output: Symptom information sent to the terminal

[1226] Step 2:

[1227] The terminal transmits the input information to the server.

[1228] The terminal transmits the text data entered by the user, such as "I have a headache," to the server using the HTTPS protocol.

[1229] Input: Symptom information entered by the user (e.g., "I have a headache")

[1230] Output: Symptom information sent to the server

[1231] Step 3:

[1232] A server receives the symptom information.

[1233] The server receives the HTTPS request and extracts the text data "I have a headache" from the request body. This data is temporarily stored in a database.

[1234] Input: Symptom information sent from the device (e.g., "I have a headache")

[1235] Output: Symptom information stored in the database

[1236] Step 4:

[1237] The server creates and inputs a prompt sentence to the generative AI model.

[1238] The server creates a prompt sentence to be input to the generative AI model based on the received symptom information. For example, it generates a prompt sentence such as, "The user is complaining of a headache. Please infer the cause of the headache and suggest an appropriate remedy."

[1239] Input: Symptom information stored in a database (e.g., "I have a headache")

[1240] Output: The prompt that is fed into the generative AI model

[1241] Step 5:

[1242] A generative AI model analyzes symptoms and generates a diagnosis.

[1243] A generative AI model (e.g., OpenAI's GPT-4) analyzes symptoms based on the prompt received from the server. As a result of the analysis, it infers possible illnesses that may be causing the headache (e.g., migraine, tension headache) and generates appropriate treatments (e.g., over-the-counter medication, medical treatment at a hospital).

[1244] Input: A prompt sentence entered into the generative AI model (e.g., "The user is complaining of a headache. Please infer the cause of the headache and suggest an appropriate remedy.")

[1245] Output: The generated diagnosis (e.g., "You are likely suffering from a migraine. This can be managed with over-the-counter medication, but if symptoms persist, we recommend that you see a doctor.")

[1246] Step 6:

[1247] The server receives the diagnosis and responds to the user.

[1248] The server sends the diagnosis results received from the generative AI model to the device in order to respond to the user, for example, sending a message such as "You are likely suffering from a migraine. This can be treated with over-the-counter medication, but if symptoms persist, we recommend that you see a doctor."

[1249] Input: A diagnosis received from a generative AI model (e.g., "This is most likely a migraine headache. Over-the-counter medications can help, but if symptoms persist, we recommend a doctor's appointment.")

[1250] Output: Diagnostic results sent to the user

[1251] Step 7:

[1252] The user checks the diagnosis results.

[1253] The user checks the diagnosis results displayed on the terminal screen and, if necessary, purchases over-the-counter medicine or receives medical treatment at a hospital.

[1254] Input: Diagnosis results displayed on the device (e.g. "You are likely suffering from a migraine. This can be treated with over-the-counter medication, but if symptoms persist, we recommend you see a doctor.")

[1255] Output: User actions (e.g., buying over-the-counter medicine, visiting a doctor)

[1256] (Application example 1)

[1257] 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".

[1258] Conventional primary diagnostic systems can make a diagnosis based on the user's symptom information, but they cannot take into account the user's emotional state, making it difficult to reduce the user's anxiety and stress. In addition, when purchasing over-the-counter medicine or making a hospital appointment based on the diagnosis results, separate procedures are required, which is troublesome for the user.

[1259] 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 generative AI means for performing a primary diagnosis, means for identifying symptoms, means for determining whether the problem can be treated with over-the-counter medicine or the like or whether treatment at a nearby hospital is necessary, emotion engine means for recognizing the user's emotions, electronic payment means, and means for providing the user with an appropriate response based on the emotion information. This enables a primary diagnosis that takes into account the user's emotional state, and allows the user to smoothly purchase over-the-counter medicine or make a hospital appointment based on the diagnosis result.

[1260] The "generative AI means for performing primary diagnosis" is an artificial intelligence technology that estimates possible illnesses and suggests appropriate treatment methods based on symptom information entered by the user.

[1261] "Means of identifying symptoms" refers to technology that analyzes symptom information provided by the user and identifies the cause of the symptoms and related diseases.

[1262] "Means of determining whether the disease can be treated with over-the-counter medication, etc., or whether treatment at a nearby hospital is required" refers to technology for determining whether the disease predicted by the generative AI can be treated with over-the-counter medication, or whether treatment at a medical institution is required.

[1263] The "emotion engine means for recognizing user emotions" is a technology that analyzes emotions from a user's facial expressions, tone of voice, text input, etc., and provides the results to generative AI.

[1264] An "electronic payment method" is a technology that enables users to complete payments using credit cards, electronic money, etc. when purchasing over-the-counter medicines or making hospital appointments.

[1265] "Means for providing appropriate responses to users based on emotional information" refers to technology that takes into account the user's emotional state and provides advice and explanations to reduce anxiety and stress.

[1266] The following system configuration is used as an embodiment of the present invention: The system includes a user terminal, a server, and an emotion recognition engine.

[1267] 1. System Programming

[1268] The system includes programs that implement the following main functions:

[1269] Generative AI tools for performing primary diagnosis

[1270] Ways to spot symptoms

[1271] A method to determine whether the condition can be treated with over-the-counter medicines or whether a medical examination at a nearby hospital is required

[1272] Emotion engine means to recognize user emotions

[1273] Electronic Payment Instruments

[1274] A means of providing appropriate responses to users based on emotional information

[1275] 2. Program Processing Explanation

[1276] Hardware and software used:

[1277] Hardware: Smartphones, cameras

[1278] Software: OpenAI API, OpenCV, TextBlob, PaymentGateway

[1279] Data processing and calculation:

[1280] 1. Emotion recognition:

[1281] Images of the user taken with the camera on the user's device are read using OpenCV and analyzed using an emotion recognition engine.

[1282] The analysis results are provided to a generative AI to understand the user's emotional state.

[1283] 2. Symptom diagnosis:

[1284] The symptom information entered by the user into the device is sent to the server, and the generative AI generates a diagnosis result using the OpenAI API.

[1285] The diagnostic results are sent back to the user's terminal and displayed to the user.

[1286] 3. Electronic Payments:

[1287] Payment Gateway is used to process payments when users purchase over-the-counter medications or make doctor appointments.

[1288] The payment result is notified to the user terminal.

[1289] 3. Specific Examples

[1290] Examples:

[1291] If a user enters "I have a headache" and an anxious expression is recognized from the image captured by the camera, the generative AI will diagnose, "It is likely that you have a tension headache. Please purchase over-the-counter painkillers or seek medical attention at a nearby hospital," and offer the option to purchase over-the-counter medication via electronic payment.

[1292] Example prompt:

[1293] User Symptoms: Headache

[1294] Based on these symptoms, diagnose possible illnesses and suggest appropriate treatment.

[1295] The above is an embodiment of the present invention. This system enables a primary diagnosis that takes into account the user's emotional state, and allows smooth purchase of over-the-counter medicines and appointment with a doctor based on the diagnosis results.

[1296] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1297] Step 1:

[1298] A user inputs symptom information into a terminal. The user provides his / her symptoms to the terminal using text input or voice input. The input symptom information is transmitted from the terminal to a server.

[1299] Step 2:

[1300] The server receives the symptom information and performs a primary diagnosis using the generative AI model. The server uses the OpenAI API to analyze the input symptom information and estimate possible illnesses. The generated diagnosis result is sent back from the server to the device.

[1301] Step 3:

[1302] A user takes a picture of himself / herself using the device's camera. The device sends the captured image to an emotion recognition engine to analyze the user's emotional state. The analysis results are then sent from the device to the server.

[1303] Step 4:

[1304] The server receives the emotion recognition results and provides them to the generative AI model. The server takes the emotion information into account to generate an appropriate response for the user. For example, if the user has an anxious expression, the generative AI model will provide a more polite explanation.

[1305] Step 5:

[1306] Based on the diagnosis and emotion information, the server determines whether the problem can be treated with over-the-counter medicine or whether a visit to a nearby hospital is necessary. The server integrates the diagnosis and emotion information and suggests appropriate measures to the user.

[1307] Step 6:

[1308] A user uses the terminal to purchase over-the-counter medicines or make doctor's appointments. The terminal processes the payment using an electronic payment method based on the user's selection. The payment information is sent from the terminal to a server, which notifies the user of the payment result.

[1309] Step 7:

[1310] The server provides the user with a final feedback that integrates the diagnosis result, the emotion information, and the payment result. The user can receive this information through the terminal and take appropriate measures.

[1311] The above are the specific processing steps for carrying out the present invention. Through the specific operations performed in each step, the user can receive a primary diagnosis that takes into account the emotional state, and can smoothly purchase over-the-counter medicines or make hospital appointments based on the diagnosis results.

[1312] Example 2

[1313] 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".

[1314] Conventional health promotion and diet support systems have had difficulty providing personalized training menus and diet recipes that take into account the user's individual physical information, lifestyle, and food information in the refrigerator. They also have been inadequate in providing appropriate training menus and diet recipes that correspond to the user's emotional state. This makes it difficult for users to effectively promote health or diet, and makes it difficult to maintain continuous health management.

[1315] 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.

[1316] In this invention, the server includes a generating AI means, a user information input means, a data transmission means, a data analysis means, a training menu generating means, a diet recipe generating means, a result providing means, and an emotion recognition means. This makes it possible to provide a personalized training menu and diet recipes based on the user's individual physical information, lifestyle, and refrigerator ingredient information. It is also possible to provide an appropriate training menu and diet recipes according to the user's emotional state.

[1317] "Generative AI means" refers to artificial intelligence technology that generates training menus and diet recipes based on information input by the user.

[1318] The "user information input means" is an interface that allows the user to input information about his / her own physical condition, lifestyle, ingredients in the refrigerator, and the like.

[1319] "Data transmission means" refers to a communication means for transmitting information input by the user to the server.

[1320] The "data analysis means" is a means for analyzing the user information received by the server and generating an appropriate training menu and diet recipes.

[1321] The "training menu generating means" is a means for generating an individual training menu based on the user's physical information and lifestyle.

[1322] The "diet recipe generating means" is a means for generating recipes suitable for dieting based on information on ingredients in the user's refrigerator.

[1323] The "result providing means" is a means for providing the generated training menu and diet recipes to the user.

[1324] The "emotion recognition means" is a means for recognizing the user's emotional state and providing a training menu or diet recipe according to that state.

[1325] This invention is a system for the purpose of improving the health of users and supporting their diet, and provides personalized training menus and diet recipes using a generative AI model. A specific embodiment of this system will be described below.

[1326] System Configuration

[1327] This system is composed of three main elements: a server, a terminal, and a user. The server includes a generating AI means, a data analysis means, a training menu generating means, a diet recipe generating means, a result providing means, and an emotion recognition means. The terminal includes a user information input means and a data transmission means.

[1328] Hardware and Software Used

[1329] Server: Use high-performance cloud servers to provide the computational resources to run generative AI models (e.g. GPT-4).

[1330] Terminal: A user device such as a smartphone or tablet is used to enter user information through a dedicated application or web interface.

[1331] Generative AI model: Uses advanced natural language processing models such as GPT-4 to generate training menus and diet recipes based on user information.

[1332] Data Flow and Processing

[1333] 1. Enter your user information:

[1334] The user uses the terminal to input his / her physical information (height, weight, etc.), lifestyle (frequency of exercise, type of work, etc.), and information on ingredients in the refrigerator.

[1335] Specifically, the user accesses a dedicated application or web interface and enters the required information into a form.

[1336] 2. Transmission of data:

[1337] The terminal transmits the input user information to the server.

[1338] Specifically, the terminal transmits data to the server via the Internet. At this time, the data is encrypted before being transmitted.

[1339] 3. Data Analysis:

[1340] The server uses a generative AI model (e.g., GPT-4) to analyze the received user information.

[1341] The analysis includes generating a training menu based on the user's physical information and lifestyle, as well as generating diet recipes based on information about ingredients in the refrigerator.

[1342] 4. Create a training menu:

[1343] The server uses a generative AI model to generate a training menu based on the user's physical information and lifestyle.

[1344] For example, if the user is feeling stressed, the emotion recognition means recognizes the user's emotional state and suggests a training menu that is effective for relieving stress.

[1345] 5. Diet recipe generation:

[1346] The server uses a generative AI model to generate diet recipes based on the ingredients in the refrigerator.

[1347] For example, we suggest a low-calorie recipe using chicken breast and broccoli.

[1348] 6. Delivery of results:

[1349] The server transmits the generated training menu and diet recipes to the terminal and provides them to the user.

[1350] Specifically, the server sends the generated information to the terminal, which displays it to the user, who then views the results through an application or web interface.

[1351] Examples and prompts

[1352] As a concrete example, the following prompt sentence is input into the generative AI model.

[1353] Example prompt:

[1354] User Information:

[1355] Height: 170cm

[1356] Weight: 70kg

[1357] Lifestyle: Sedentary job, light exercise twice a week

[1358] Emotional state: Stressed

[1359] Ingredients in the fridge:

[1360] Chicken breast

[1361] broccoli

[1362] Produces:

[1363] Training menu based on the user's physical information and lifestyle

[1364] Diet recipes using ingredients from your fridge

[1365] Based on this prompt, the generative AI model will output the following:

[1366] Example output:

[1367] Training Menu:

[1368] Stress relief yoga session (30 mins)

[1369] Light jogging (20 minutes)

[1370] Diet Recipes:

[1371] Steamed chicken breast and broccoli

[1372] Ingredients: 200g chicken breast, 100g broccoli, salt, pepper, olive oil

[1373] Directions: Steam the chicken breast and broccoli in a steamer for 15 minutes, then season with salt, pepper and olive oil.

[1374] In this way, the server provides the user with a personalized training menu and diet recipes.

[1375] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1376] Step 1:

[1377] Entering user information

[1378] The user uses the terminal to input his / her physical information (height, weight, etc.), lifestyle (frequency of exercise, type of work, etc.), and information on ingredients in the refrigerator.

[1379] Specifically, the user accesses a dedicated application or web interface and enters the required information into a form.

[1380] Input: Height, weight, lifestyle, and food in your fridge

[1381] Output: User information data entered

[1382] Step 2:

[1383] Sending Data

[1384] The terminal transmits the input user information to the server.

[1385] Specifically, the terminal transmits data to the server via the Internet. At this time, the data is encrypted before being transmitted.

[1386] Input: User information data

[1387] Output: User information data sent to the server

[1388] Step 3:

[1389] Analyzing data

[1390] The server uses a generative AI model (e.g., GPT-4) to analyze the received user information.

[1391] Specifically, the server retrieves user information stored in the database and inputs it as a prompt sentence into the generative AI model.

[1392] Input: User information data stored on the server

[1393] Output: Analysis result data

[1394] Step 4:

[1395] Creation of training menu

[1396] The server uses a generative AI model to generate a training menu based on the user's physical information and lifestyle.

[1397] Specifically, the generative AI model analyzes the prompt text and generates an appropriate training menu. For example, based on the information "height 170 cm, weight 70 kg, light exercise twice a week," it suggests yoga or light jogging.

[1398] Input: Analysis result data

[1399] Output: Training menu

[1400] Step 5:

[1401] Diet recipe generation

[1402] The server uses a generative AI model to generate diet recipes based on the ingredients in the refrigerator.

[1403] Specifically, the generative AI model will suggest low-calorie, nutritionally balanced recipes based on the ingredient information "chicken breast and broccoli." For example, it will generate "steamed chicken breast and broccoli dish."

[1404] Input: Analysis result data

[1405] Output: Diet recipes

[1406] Step 6:

[1407] Providing results

[1408] The server transmits the generated training menu and diet recipes to the terminal and provides them to the user.

[1409] Specifically, the server sends the generated information to the terminal, which displays it to the user, who then views the results through an application or web interface.

[1410] Input: Training menu, diet recipes

[1411] Output: Training menu and diet recipes provided to the user

[1412] (Application example 2)

[1413] 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".

[1414] In modern society, there is a demand for personalized health promotion and diet support that is tailored to each individual's health condition and lifestyle. However, conventional systems have difficulty proposing training menus and diet recipes that take into account the user's physical information and emotional state, and lack the functionality to suggest healthy products in real time while shopping in a physical store. This makes it difficult for users to efficiently manage their health.

[1415] 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.

[1416] In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the condition can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for receiving the user's physical information, lifestyle, and refrigerator food information as inputs and generating individual training menus and diet recipes, a means for recognizing the user's emotional state and providing training menus and diet recipes according to the emotional state, and a means for suggesting necessary food ingredients and healthy products in real time while shopping at a physical store. This enables personalized health promotion and diet support based on the user's physical information and emotional state, and makes it possible to suggest healthy products in real time even while shopping at a physical store.

[1417] The "generative AI means for performing primary diagnosis" is an artificial intelligence technology for evaluating the initial health condition based on information input by the user and proposing appropriate measures.

[1418] "Symptom assessment tools" are technologies that analyze symptoms reported by users and determine the cause and severity of those symptoms.

[1419] The "means for determining whether the condition can be treated with over-the-counter medication, etc., or whether treatment at a nearby hospital is required" refers to technology for determining, based on the user's symptoms, whether the condition can be treated with over-the-counter medication, or whether treatment at a medical institution is required.

[1420] "A means of receiving a user's physical information, lifestyle, and information about ingredients in the refrigerator as input, and generating individual training menus and diet recipes" refers to technology for creating individually optimized exercise programs and meal menus based on the user's physical characteristics, lifestyle, and information about ingredients in the refrigerator.

[1421] "Means of recognizing the user's emotional state and providing training menus and diet recipes according to that emotional state" refers to technology that analyzes the user's emotions and suggests exercise and meal plans appropriate to that emotional state.

[1422] "A means for suggesting necessary ingredients and healthy products in real time while shopping in a physical store" refers to technology that suggests healthy products and necessary ingredients in real time while a user is shopping in a physical store.

[1423] A system for carrying out this invention proposes personalized training menus and diet recipes based on the user's health condition and lifestyle. The system includes generative AI means for performing a primary diagnosis, means for identifying symptoms, means for determining whether the condition can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, means for receiving the user's physical information, lifestyle, and information on ingredients in the refrigerator as input and generating individual training menus and diet recipes, means for recognizing the user's emotional state and providing training menus and diet recipes according to the emotional state, and means for suggesting necessary ingredients and healthy products in real time while shopping at a physical store.

[1424] Hardware and Software Used

[1425] Hardware: Smartphone cameras, refrigerator sensors, brick-and-mortar point-of-sale systems

[1426] Software: OpenCV (emotion recognition), Keras (emotion recognition model), OpenAI API (generative AI model)

[1427] Data processing and calculation

[1428] The server receives the user's physical information (height, weight, allergy information, etc.) and lifestyle (exercise habits, food preferences, etc.) as input. In addition, it obtains food ingredient information from the refrigerator's sensors and recognizes the user's emotional state using the smartphone's camera. Based on this data, a generative AI model is used to generate personalized training menus and diet recipes.

[1429] Examples

[1430] For example, if a user is 170cm tall, weighs 70kg, has no allergies, has an active lifestyle, and has chicken breast and broccoli in the fridge, and is feeling stressed, the system will input the following prompt sentence into the generative AI model:

[1431] Examples of prompt statements

[1432] User info: {'height': 170, 'weight': 70, 'allergies': [], 'lifestyle': 'active', 'fridge_contents': ['chicken breast', 'broccoli']}, Emotion: stressed. Generate personalized training menu and diet recipe.

[1433] Based on the prompt, the generative AI model can suggest suitable training menus and diet recipes for the user. In addition, when the user is shopping in a physical store, the model can suggest healthy products and necessary ingredients in real time through a smartphone application.

[1434] In this way, personalized health promotion and diet support based on the user's physical information and emotional state becomes possible, and healthy products can be suggested in real time while shopping in a physical store.

[1435] The flow of the identification process in the application example 2 will be described with reference to FIG.

[1436] Step 1:

[1437] The user starts the application on their smartphone and inputs their physical information (height, weight, allergy information, etc.) and lifestyle information (exercise habits, food preferences, etc.), which is then sent to the server.

[1438] Step 2:

[1439] The server obtains ingredient information from the refrigerator's sensor. The sensor in the refrigerator detects the current ingredient information and sends the data to the server. In this way, the ingredient information in the user's refrigerator is aggregated in the server.

[1440] Step 3:

[1441] A user takes a picture of his / her face using the smartphone camera. The device analyzes the user's facial expressions using OpenCV and recognizes the user's emotional state. The recognized emotional state is sent to the server.

[1442] Step 4:

[1443] The server inputs prompt sentences into the generative AI model based on the user's physical information, lifestyle, food information in the refrigerator, and emotional state. Examples of prompt sentences that are generated include the following:

[1444] User info: {'height': 170, 'weight': 70, 'allergies': [], 'lifestyle': 'active', 'fridge_contents': ['chicken breast', 'broccoli']}, Emotion: stressed. Generate personalized training menu and diet recipe.

[1445] By inputting this prompt into a generative AI model, a personalized training menu and diet recipes are generated.

[1446] Step 5:

[1447] The generative AI model generates training menus and diet recipes based on the prompt text, and the generated menus and recipes are sent to the user's smartphone via the server.

[1448] Step 6:

[1449] When a user is shopping at a brick-and-mortar store, the smartphone application will link with the store's POS system and suggest healthy products and necessary ingredients in real time based on the user's location information and purchase history, allowing the user to select healthy products.

[1450] Step 7:

[1451] Users can effectively improve their health and diet by carrying out the suggested training menus and diet recipes and purchasing the suggested products at physical stores.

[1452] Example 3

[1453] 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".

[1454] Mental health issues are becoming more serious in modern society, with an increasing number of people suffering from stress and anxiety in particular. In response to this, there is a demand for systems that can provide appropriate counseling and advice. However, conventional systems have the problem of being unable to accurately grasp the user's emotional state and provide appropriate advice accordingly.

[1455] 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.

[1456] In this invention, the server includes a means for a user to input worry information, a means for transmitting the input information to the server, a means for the server to pass information to the generative AI model, a means for the generative AI model to generate advice, a means for the server to receive the generated advice, a means for the server to analyze the user's emotions with an emotion engine, a means for integrating the advice and the emotion analysis result, a means for transmitting the integration result to the terminal, and a means for the terminal to display the advice to the user. This makes it possible to provide appropriate advice according to the user's emotional state.

[1457] "User" refers to an individual who utilizes the system to input problem information and receive advice.

[1458] "Terminal" refers to a device on which a user inputs problem information and displays advice from the server. Specifically, this includes smartphones and personal computers.

[1459] "Server" refers to a central processing unit that receives input information from users, generates advice using generative AI models and emotion engines, and provides it to users.

[1460] "Generative AI model" refers to an artificial intelligence model that analyzes user concerns and generates appropriate advice. Specifically, it includes models that use natural language processing technology.

[1461] An "emotion engine" refers to software that analyzes the user's emotional state from information input and provides appropriate advice based on the results.

[1462] "Advice" refers to advice or suggestions that the generative AI model generates based on the user's concerns and takes into account the analysis results of the emotion engine.

[1463] "Problem information" refers to personal problems or issues, such as stress or anxiety, that users input into the system.

[1464] "Integrated result" refers to the final advice that combines advice from the generative AI model and the results of emotion analysis from the emotion engine.

[1465] The present invention is a system for supporting the mental health of a user, which operates by combining a generative AI model and an emotion engine. A specific embodiment of the system will be described below.

[1466] First, the user uses a device (such as a smartphone or computer) to input information about their worries. For example, they might input, "Recently, I've been unable to sleep due to work stress." This input information is then sent from the device to the server. Communication is securely carried out using the HTTPS protocol.

[1467] The server passes the received worry information to a generative AI model. For example, a model using natural language processing technology (e.g. GPT-4) is used as the generative AI model. The generative AI model analyzes the input worry information and generates appropriate advice. For example, it generates advice such as "To reduce work stress, it is important to set aside time to relax."

[1468] The generated advice is returned to the server. The server then uses an emotion engine to analyze the user's emotions. The emotion engine uses software that uses emotion analysis technology (e.g., Watson Tone Analyzer). Emotions are extracted from the text entered by the user, and an emotional state such as "sad" or "stressed" is identified.

[1469] The server integrates the generated advice with the emotion analysis results. For example, if the user feels "sad", it adds additional advice corresponding to that emotion. Finally, the server sends the integrated advice to the terminal. This communication is also performed using the HTTPS protocol.

[1470] The terminal displays the advice received from the server to the user, who can then confirm and act on the displayed advice.

[1471] As a concrete example, if a user types, "I can't sleep lately because of work stress," a generative AI model might offer the following advice:

[1472] "In order to reduce work-related stress, it is important to set aside time to relax. For example, it is a good idea to get into the habit of taking deep breaths every night before going to bed. It is also effective to take time for moderate exercise or a hobby."

[1473] Example prompt:

[1474] "If a user has recently been feeling like they've been having trouble sleeping because of work stress, what advice can you give them?"

[1475] This system utilizes a generative AI model and an emotion engine to support the user's mental health. Specific hardware requires a server and a user's terminal, and software uses a generative AI model (e.g., GPT-4) and an emotion engine (e.g., Watson Tone Analyzer). The flow of the specific process in the third embodiment will be described with reference to FIG. 21.

[1476] Step 1:

[1477] The user inputs the problem information.

[1478] The user uses the terminal to input information about the worries. For example, the user inputs, "Recently, I have been unable to sleep due to work stress." This input information is stored in the terminal as text data.

[1479] Step 2:

[1480] The terminal transmits the input information to the server.

[1481] The terminal transmits the user-entered worry information to the server. This communication is securely performed using the HTTPS protocol. The input is the user's worry information, and the output is the text data transmitted to the server.

[1482] Step 3:

[1483] The server passes information to the generative AI model.

[1484] The server passes the received worry information to the generative AI model. The input is the text data sent to the server, and the output is the text data passed to the generative AI model.

[1485] Step 4:

[1486] A generative AI model generates advice.

[1487] The generative AI model analyzes the input worry information and generates appropriate advice. For example, it generates advice such as, "To reduce work-related stress, it is important to set aside time to relax." The input is the text data passed to the generative AI model, and the output is the generated text data of the advice.

[1488] Step 5:

[1489] The server receives the generated advice.

[1490] The server receives advice generated from the generative AI model. The input is the text data of the generated advice, and the output is the text data of the advice stored on the server.

[1491] Step 6:

[1492] The server analyzes the user's emotions using an emotion engine.

[1493] The server uses an emotion engine to analyze the user's emotions. The emotion engine extracts emotions from the text entered by the user and identifies the user's emotional state, such as "sad" or "stressed." The input is text data of the user's worries, and the output is data of the emotion analysis results.

[1494] Step 7:

[1495] The server integrates the advice with the sentiment analysis results.

[1496] The server integrates the generated advice with the emotion analysis results. For example, if the user feels "sad," it adds additional advice corresponding to that emotion. The input is the text data of the generated advice and the emotion analysis results, and the output is the text data of the integrated advice.

[1497] Step 8:

[1498] The server transmits the integrated result to the terminal.

[1499] The server sends the integrated advice to the terminal. This communication is also performed using the HTTPS protocol. The input is the text data of the integrated advice, and the output is the text data of the advice sent to the terminal.

[1500] Step 9:

[1501] The terminal displays the advice to the user.

[1502] The terminal displays the advice received from the server to the user. The user can confirm and execute the displayed advice. The input is the text data of the advice sent to the terminal, and the output is the advice displayed to the user.

[1503] (Application example 3)

[1504] 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".

[1505] Mental health issues are becoming more serious in modern society, and there is a demand for ways to quickly and appropriately resolve the worries and stress users are experiencing, especially in brick-and-mortar stores. However, with conventional systems, it is difficult to accurately recognize the emotional state of the user and provide appropriate counseling. In addition, there is a lack of information for brick-and-mortar store staff to respond based on the user's emotional state, making it difficult to improve the user experience.

[1506] 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.

[1507] In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the problem can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for receiving a user's trouble information as an input and generating appropriate advice based on the information, a means for providing counseling according to the user's emotional state using an emotion engine that recognizes the user's emotions, and a means for inputting the user's trouble information at a physical store and providing the analysis results to the physical store staff. This allows the provision of appropriate counseling and advice according to the user's troubles and emotional state, and enables the physical store staff to respond based on the user's emotional state.

[1508] "Generative AI means for conducting initial diagnosis" refers to artificial intelligence technology for conducting initial diagnosis based on information input by the user.

[1509] "Means of determining symptoms" refers to technology for accurately determining symptoms from information entered by the user.

[1510] "Means of determining whether the condition can be treated with over-the-counter medication, etc., or whether treatment at a nearby hospital is required" refers to technology for determining, based on the user's symptoms, whether the condition can be treated with over-the-counter medication, or whether treatment at a medical institution is required.

[1511] "A means for receiving a user's concern information as input and generating appropriate advice based on that information" refers to technology for analyzing the concern information entered by the user and providing appropriate advice based on that information.

[1512] "A means for providing counseling according to the user's emotional state by using an emotion engine that recognizes the user's emotions" refers to technology for analyzing a user's emotions and providing counseling according to that emotional state.

[1513] "A means for inputting a user's concern information at a physical store and providing the analysis results to staff at the physical store" refers to technology for analyzing the concern information input by a user at a physical store and providing the results to staff.

[1514] The system for implementing the present invention allows a user to input information about a problem at a brick-and-mortar store, generates appropriate advice based on the information, and provides counseling according to the user's emotional state. A specific embodiment of this system will be described below.

[1515] 1. System Programming

[1516] The system consists of the following main components:

[1517] Generative AI means: Analyzes the concern information entered by the user and generates appropriate advice.

[1518] Emotion engine: Recognizes emotions from user input and provides counseling according to the user's emotional state.

[1519] Support for store staff: The analysis results are provided to store staff to assist them in responding based on the user's emotional state.

[1520] 2. Hardware and software used

[1521] Hardware: Smartphones, servers

[1522] Software: Python, OpenAI API, TextBlob

[1523] 3. Data processing and calculation

[1524] Input and analysis of user concerns

[1525] A user inputs information about their worries through a smartphone app. This information is sent to a server and analyzed by the generative AI means. The generative AI means uses OpenAI's generative AI model (e.g., GPT-3) to generate appropriate advice for the user's worries.

[1526] Emotion recognition

[1527] The emotion engine uses TextBlob to analyze the emotion of the user's input text. The emotion engine recognizes the positive, negative, and neutral emotional state of the text and provides counseling accordingly.

[1528] Providing information to store staff

[1529] The analysis results are provided to store staff, who can provide better service by receiving suggestions on how to respond based on the user's emotional state.

[1530] 4. Specific Examples

[1531] For example, a user visits a cafe and enters into a smartphone app, "I haven't been able to sleep lately because of work stress." This information is sent to the server, and the generative AI generates advice such as, "To combat stress, try some relaxation techniques. For example, deep breathing and meditation are effective." At the same time, the emotion engine analyzes the emotion as "negative," and provides information to the store staff such as, "The user is feeling stressed, so please provide a relaxing environment."

[1532] 5. Examples of prompts

[1533] User's problem: I can't sleep lately because of work stress.

[1534] Please provide appropriate advice.

[1535] In this way, appropriate counseling and advice can be provided based on the user's concerns and emotional state, enabling store staff to respond based on the user's emotional state.

[1536] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1537] Step 1:

[1538] The user launches the smartphone app and enters information about their concerns.

[1539] Input: User's worries (e.g. "I can't sleep lately because of work stress")

[1540] Output: The user's worries are sent to the server.

[1541] Specific operation: When a user enters a concern into the input field of the app and presses the send button, the concern information is sent to the server.

[1542] Step 2:

[1543] The server passes the received worry information to a generative AI means, which generates appropriate advice.

[1544] Input: User's worries

[1545] Output: Advice generated by generative AI means (e.g., "To combat stress, try relaxation techniques. For example, deep breathing and meditation can be effective.")

[1546] Specific operation: The server calls the OpenAI API and passes the user's worries as a prompt. The generative AI model generates appropriate advice and returns it to the server.

[1547] Step 3:

[1548] The server returns the received advice to the user.

[1549] Input: Advice generated by a generative AI tool

[1550] Output: Advice displayed on the user's smartphone app

[1551] Specific operation: The server sends the generated advice to the user's smartphone app, and the app displays the advice.

[1552] Step 4:

[1553] The server passes the received worry information to an emotion engine and analyzes the emotional state.

[1554] Input: User's worries

[1555] Output: Emotion state parsed by the emotion engine (e.g. "negative")

[1556] Specific operation: The server uses TextBlob to analyze the emotions in the user's worry information and determine the user's emotional state.

[1557] Step 5:

[1558] The server provides the analyzed emotional state to staff at the brick-and-mortar store.

[1559] Input: Parsed emotional state

[1560] Output: Emotional state information provided to brick-and-mortar store staff

[1561] Specific operation: The server sends the analyzed emotional state to a terminal used by staff at the physical store, and the staff responds to the user based on that information.

[1562] Step 6:

[1563] Store staff respond based on the user's emotional state.

[1564] Input: Emotional state information

[1565] Output: An appropriate response to the user

[1566] Specific actions: Based on the emotional state information provided by the server, the staff at the brick-and-mortar store will take measures such as providing a relaxing environment for the user.

[1567] In this way, appropriate counseling and advice can be provided according to the user's concerns and emotional state, enabling store staff to respond based on the user's emotional state.

[1568] 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.

[1569] 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.

[1570] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1571] 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.

[1572] [Third embodiment]

[1573] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1574] 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.

[1575] 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).

[1576] 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.

[1577] 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.

[1578] 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).

[1579] 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.

[1580] 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.

[1581] 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.

[1582] 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.

[1583] 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.

[1584] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1585] "Example 1"

[1586] The present invention is a system that uses generative AI to perform a primary diagnosis. Specifically, it receives symptom information from the user as input and identifies the symptoms based on that information. Based on the results, it determines whether the symptom can be treated with over-the-counter medication or whether a visit to a nearby hospital is necessary. For example, if a user inputs "I have a headache," the generative AI uses that information to infer possible illnesses that could be causing the headache and suggests appropriate treatment methods.

[1587] "Example 2"

[1588] The present invention is a system that devise personalized training menus and diet recipes using ingredients in a home refrigerator as a means of health promotion and diet support. Specifically, the system receives inputs of the user's physical information, lifestyle, and information on ingredients in the refrigerator, and generates individual training menus and diet recipes based on that information. For example, if a user inputs "height 170 cm, weight 70 kg, and there is chicken breast and broccoli in the refrigerator," the generative AI will use that information to suggest an appropriate exercise menu and diet recipes using chicken breast and broccoli.

[1589] "Example 3"

[1590] The present invention is a system that provides AI counseling for worries as preventive support for mental health. Specifically, the system receives worry information from the user as input and generates appropriate advice based on that information. For example, if the user inputs "I haven't been able to sleep lately because of work stress," the generative AI provides advice on stress management and sleep improvement based on that information.

[1591] The process flow of each embodiment will be described below.

[1592] "Example 1"

[1593] Step 1: Receive symptom information from the user as input.

[1594] Step 2: The generative AI uses the information to identify symptoms.

[1595] Step 3: Based on the results, determine whether the condition can be treated with over-the-counter medication or whether treatment at a nearby hospital is required.

[1596] "Example 2"

[1597] Step 1: Receive the user's physical information, lifestyle, and food information in the refrigerator as input.

[1598] Step 2: Use that information to generate personalized training menus and diet recipes.

[1599] "Example 3"

[1600] Step 1: Receive problem information from the user as input.

[1601] Step 2: Generate appropriate advice based on that information.

[1602] Example 1

[1603] 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".

[1604] In modern society, it is important for users to quickly and accurately understand their own health condition and obtain appropriate treatment. However, there are many cases where it is difficult to access medical institutions, or people hesitate to visit a medical institution because their symptoms are minor. In such situations, it is difficult for users to find appropriate treatment, and health risks may increase. Therefore, there is a demand for a system that allows users to easily input their own symptoms and obtain appropriate treatment.

[1605] 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.

[1606] In this invention, the server includes a means for inputting symptom information from a user, a means for transmitting the input symptom information to the server, a means for the server to receive the symptom information and analyze it using a generative AI model, a means for the generative AI model to infer a disease based on the symptom information, a means for the server to suggest an appropriate countermeasure based on the inference result, and a means for displaying the suggested countermeasure to the user. This allows the user to easily input their own symptoms and quickly and accurately obtain an appropriate countermeasure.

[1607] "User" refers to an individual who utilizes the system to input their own symptom information and obtain appropriate treatment.

[1608] "Symptom information" refers to data that specifically describes the user's own health condition or any ailments that he or she is experiencing.

[1609] "Server" refers to a computer system that receives symptom information sent by a user, analyzes it using a generative AI model, and proposes appropriate countermeasures.

[1610] A "generative AI model" refers to an artificial intelligence model that analyzes input data and generates appropriate inferences and countermeasures.

[1611] "Analysis" refers to the process in which the generative AI model infers the disease based on symptom information and derives appropriate treatment.

[1612] "Inference results" refers to predictions of possible diseases or health conditions obtained as a result of the generative AI model analyzing symptom information.

[1613] "Countermeasures" refer to specific actions or treatments that the generative AI model suggests to the user based on the inference results.

[1614] "Display" refers to the process in which the terminal visually presents the solution information sent from the server to the user.

[1615] This invention is a system in which a user inputs his / her own symptom information, analyzes the information using a generative AI model, and proposes an appropriate countermeasure. A specific embodiment of this system will be described below.

[1616] A user inputs information about his / her symptoms using a device such as a smartphone or a PC. For example, the user inputs "I have a headache." This input information is sent from the device to the server. The device uses an Internet connection to send the data using the HTTPS protocol.

[1617] The server receives the symptom information sent from the device. The received data is temporarily stored in a database in the server. The server then uses a generative AI model to analyze the received symptom information. For example, OpenAI's GPT-4 is used as this generative AI model. The server sends a prompt sentence such as "If the user enters 'I have a headache,' please estimate the illnesses that may be causing the headache and suggest appropriate measures to deal with it" to the generative AI model.

[1618] The generative AI model analyzes the symptom information based on the prompt text and infers possible illnesses. For example, based on the information "I have a headache," it considers the possibility of a migraine, tension headache, etc. The generative AI model returns the inference results to the server.

[1619] The server proposes appropriate countermeasures based on the inferences obtained from the generative AI model. For example, it generates countermeasures such as "Use over-the-counter headache medicine." It also provides information on nearby hospitals if necessary. The server transmits the generated countermeasure information to the terminal.

[1620] The terminal displays the information on how to deal with the problem sent from the server to the user. The user then takes appropriate action based on the displayed information. For example, the user may purchase and use an over-the-counter headache medicine.

[1621] As a concrete example, when a user inputs "I have a headache," the following prompt sentence uses a generative AI model to predict possible illnesses that may be causing the headache and suggests appropriate measures to deal with the situation.

[1622] Example prompt:

[1623] “If a user types in ‘I have a headache,’ use a generative AI model to infer what illnesses may be causing the headache and suggest appropriate treatment.”

[1624] In this way, the user can easily input his / her symptoms and obtain appropriate measures quickly and accurately.

[1625] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1626] Step 1:

[1627] Subject: User

[1628] The user inputs their symptom information into the device. For example, they open a smartphone app and type "I have a headache" into the text box.

[1629] Input: User's symptoms (e.g. "I have a headache")

[1630] Output: Symptom information entered into the terminal

[1631] Step 2:

[1632] Subject: Terminal

[1633] The device then transmits the entered symptom information to the server, using an Internet connection to transmit the data using the HTTPS protocol.

[1634] Input: Symptom information entered into the terminal

[1635] Output: Symptom information sent to the server

[1636] Step 3:

[1637] Subject: Server

[1638] The server receives the symptom information sent from the terminal and temporarily stores the received data in a database within the server.

[1639] Input: Symptom information sent from the device

[1640] Output: Symptom information stored in the database

[1641] Step 4:

[1642] Subject: Server

[1643] The server inputs the saved symptom information into the generative AI model, generates a prompt sentence, and sends it. The prompt sentence used is something like, "If the user inputs 'I have a headache,' please estimate the illnesses that may be causing the headache and suggest appropriate measures."

[1644] Input: Symptom information stored in the database

[1645] Output: The prompt sent to the generative AI model

[1646] Step 5:

[1647] Subject: Generative AI model

[1648] The generative AI model analyzes symptom information based on the prompt text and predicts possible illnesses. For example, based on the information "I have a headache," it considers the possibility of a migraine, tension headache, etc.

[1649] Input: The prompt sent to the generative AI model

[1650] Output: Estimated disease probability

[1651] Step 6:

[1652] Subject: Server

[1653] The server then proposes appropriate measures based on the inferences obtained from the generative AI model. For example, it generates measures such as "use over-the-counter headache medicine." It also provides information on nearby hospitals if necessary.

[1654] Input: Inference results obtained from a generative AI model

[1655] Output: Generated solutions

[1656] Step 7:

[1657] Subject: Server

[1658] The server transmits the generated solution information to the device. At this time, the server transmits the data via an Internet connection.

[1659] Input: Generated solution

[1660] Output: Actions sent to the device

[1661] Step 8:

[1662] Subject: Terminal

[1663] The terminal displays the information on how to deal with the problem sent from the server to the user. The user then takes appropriate action based on the displayed information. For example, the user may purchase and use an over-the-counter headache medicine.

[1664] Input: Action sent by server

[1665] Output: Actions shown to the user

[1666] (Application example 1)

[1667] 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".

[1668] In modern society, it is a difficult task for users to select appropriate meals based on their own health condition and symptoms. In particular, when using food delivery services, it is required to choose meals that take health into consideration, but there is a lack of information for users to choose meals that are suitable for their own symptoms. For this reason, support is needed to help users use food delivery services while maintaining their health.

[1669] 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.

[1670] In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the symptoms can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for suggesting an appropriate diet based on the user's health condition and symptoms, and a means for generating a prompt sentence using a generative AI model and suggesting an appropriate diet, thereby enabling the user to select an appropriate diet based on their own health condition and symptoms.

[1671] "Primary diagnosis" is the process of making an initial diagnosis based on symptom information provided by the user.

[1672] "Generative AI" is an artificial intelligence technology that generates appropriate responses and suggestions based on information input by the user.

[1673] A "means for identifying symptoms" is a method for analyzing the symptom information entered by the user and identifying the cause of the symptoms and any associated diseases.

[1674] "Means for determining whether symptoms can be treated with over-the-counter drugs, etc." refers to a method for determining whether the user's symptoms can be treated with over-the-counter drugs or whether medical treatment at a medical institution is required.

[1675] The "means for determining whether medical treatment at a nearby hospital is necessary" refers to a method for determining whether the user's symptoms are serious and, if necessary, recommending medical treatment at a nearby medical institution.

[1676] A "means for suggesting appropriate meals based on a user's health condition and symptoms" is a method for selecting and suggesting appropriate meals based on health condition and symptom information input by a user.

[1677] "Means for generating prompt sentences using a generative AI model and suggesting appropriate meals" refers to a method for using a generative AI model to create prompt sentences based on the user's symptoms and then suggesting appropriate meals based on those prompt sentences.

[1678] A system for implementing the present invention is for suggesting an appropriate diet to a user based on the user's health condition or symptoms. The system includes a means for generating prompt sentences using a generative AI model to suggest an appropriate diet to the user.

[1679] System Configuration

[1680] The system consists of the following main components:

[1681] 1. User device: A device such as a smartphone or tablet where the user enters symptom information.

[1682] 2. Server: A server for hosting the generative AI model and processing input from users.

[1683] 3. Generative AI model: An AI model that generates prompts based on user input and suggests appropriate meals.

[1684] Program Processing

[1685] When a user inputs their current symptoms using a device such as a smartphone, the information is sent to a server, which uses a generative AI model to generate a prompt based on the input symptom information. This prompt is intended to suggest an appropriate meal.

[1686] The generated prompts are then processed again by the server to generate appropriate meal suggestions, which are then sent to the user's device and displayed to the user.

[1687] Hardware and Software Used

[1688] Hardware: Smartphones, tablets, servers

[1689] Software: Generative AI models (e.g., OpenAI API), database management systems, communication protocols (e.g., HTTP / HTTPS)

[1690] Examples

[1691] For example, if the user types "I have a stomach ache," the following prompt text is generated:

[1692] User entered 'My stomach hurts'. Please suggest suitable meals.

[1693] Based on this prompt, the generative AI model generates advice such as, "We recommend that you eat foods that are easy on the stomach, such as porridge or soup." This advice is sent to the user's device and displayed to the user.

[1694] In this way, users can choose appropriate meals based on their health conditions and symptoms.

[1695] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1696] Step 1:

[1697] The user inputs his / her current symptoms using a device such as a smartphone. The input symptom information is sent to the server in text format. The input data is the user's symptom information (e.g., "My stomach hurts").

[1698] Step 2:

[1699] The server analyzes the received symptom information and generates a prompt to send to the generative AI model. Specifically, the server generates a prompt based on the symptom information, such as: "The user inputs 'My stomach hurts.' Please suggest an appropriate meal." This prompt is generated.

[1700] Step 3:

[1701] The server sends the generated prompt to the generative AI model, which analyzes the prompt and generates appropriate meal suggestions. The input data is the prompt, and the output data is the meal suggestions (e.g., "We recommend that you eat something easy on your stomach, such as porridge or soup").

[1702] Step 4:

[1703] The server sends the meal suggestions received from the generative AI model to the user device. The user device displays the received meal suggestions. The input data are the meal suggestions, and the output data are the meal suggestions displayed to the user.

[1704] Step 5:

[1705] The user reviews the displayed meal suggestions and selects the appropriate meal. This allows the user to select the appropriate meal based on their health condition and symptoms. The input data is the displayed meal suggestions and the output data is the user's selected meal.

[1706] Example 2

[1707] 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".

[1708] In modern society, there is a demand for personalized health promotion and diet support that is tailored to each individual's health condition and lifestyle. However, conventional systems have had difficulty providing training menus and diet recipes that comprehensively take into account the user's physical information, lifestyle, and information about ingredients in the refrigerator. In addition, they are also inadequate in providing appropriate diagnoses and countermeasures based on this information.

[1709] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the condition can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for receiving the user's physical information, lifestyle, and refrigerator food information as input, and generating individual training menus and diet recipes, and a means for presenting the generated training menus and diet recipes to the user. This makes it possible to provide optimal training menus and diet recipes according to the user's individual health condition and lifestyle.

[1710] The "generative AI means for performing primary diagnosis" is an artificial intelligence technology for evaluating the initial health condition based on information input by the user and proposing appropriate measures.

[1711] "Symptom assessment tools" are techniques for analyzing symptoms reported by a user and determining the cause and severity of the symptoms.

[1712] The "means for determining whether the symptom can be treated with over-the-counter medicine, etc., or whether medical treatment at a nearby hospital is required" is a technology for determining whether the user's symptoms can be treated with over-the-counter medicine, or whether medical treatment at a medical institution is required.

[1713] "Means for receiving a user's physical information, lifestyle, and information on ingredients in the refrigerator as input, and generating individual training menus and diet recipes" refers to technology for creating individually optimized exercise programs and meal plans based on the physical data, lifestyle habits, and information on ingredients in the refrigerator provided by the user.

[1714] The "means for presenting the generated training menu and diet recipes to the user" refers to a technique for displaying the exercise program and meal plan generated by the server to the user in an easy-to-understand manner.

[1715] This invention is a system for the purpose of improving the health of users and supporting their diets, and generates individual training menus and diet recipes based on the user's physical information, lifestyle, and food information in the refrigerator. A specific embodiment of this system will be described below.

[1716] 1. Program Generation

[1717] The server generates a program aimed at improving the user's health and supporting dieting. This program receives the user's physical information, lifestyle, and information on ingredients in the refrigerator as input, and generates individual training menus and diet recipes. One example of the generative AI model used is GPT-4.

[1718] 2. What the program does

[1719] The server processes the data in the following steps:

[1720] Data Entry:

[1721] A user uses a dedicated application or a web interface to input his / her physical information (e.g., height, weight), lifestyle (e.g., exercise frequency, food preferences), and food information in the refrigerator. For example, a user inputs, "I am 170 cm tall, I weigh 70 kg, and there are chicken breasts and broccoli in the refrigerator."

[1722] Data Analysis:

[1723] The server analyzes the input data and generates optimal training menus and diet recipes based on the user's health status and goals. This analysis uses a generative AI model (e.g. GPT-4).

[1724] Generate a training menu:

[1725] The server generates an individual training menu based on the user's physical information and lifestyle. For example, if a user wants to exercise three times a week, the server will suggest a menu that combines aerobic exercise and strength training.

[1726] Diet recipe generation:

[1727] The server generates diet recipes suitable for the user based on the information about ingredients in the refrigerator. For example, if there is chicken breast and broccoli in the refrigerator, the server will suggest low-calorie recipes using these ingredients.

[1728] Presenting the results:

[1729] The server transmits the generated training menu and diet recipes to the terminal and presents them to the user. The user can plan their daily training and meals based on the displayed information.

[1730] 3. Examples and prompts

[1731] As a concrete example, the following prompt sentence is input into the generative AI model.

[1732] Example prompt:

[1733] User Information:

[1734] Height: 170cm

[1735] Weight: 70kg

[1736] Lifestyle: Exercise 3 times a week

[1737] Ingredients in the fridge: Chicken breast, broccoli

[1738] Based on this information, please suggest an appropriate training menu and diet recipes using chicken breast and broccoli.

[1739] An example of the result produced:

[1740] Training Menu:

[1741] Aerobic exercise 3 times a week (e.g. 30 minutes of jogging)

[1742] Strength training twice a week (e.g. squats, push-ups)

[1743] Diet Recipes:

[1744] Low calorie stir-fry with chicken breast and broccoli

[1745] Ingredients: 200g chicken breast, 100g broccoli, 1 tablespoon olive oil, a little salt, a little pepper

[1746] Directions: Cut chicken breast into bite-sized pieces and fry in olive oil. Add broccoli and fry further, then season with salt and pepper.

[1747] In this way, the server generates an individual training menu and diet recipes based on the user's input information and provides them to the user.

[1748] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1749] Step 1:

[1750] The user enters the information.

[1751] Using a dedicated application or web interface, users input their physical information (e.g., height 170 cm, weight 70 kg), lifestyle (e.g., exercise three times a week), and information about ingredients in their refrigerator (e.g., chicken breast, broccoli). The input information is then stored on the device.

[1752] Step 2:

[1753] The terminal transmits the input information to the server.

[1754] The device encrypts the information entered by the user and sends it to the server. The data sent includes the user's physical information, lifestyle, and food information in the refrigerator. The server stores the received data for analysis.

[1755] Step 3:

[1756] The server parses the data.

[1757] The server analyzes the received user information. A generative AI model (e.g. GPT-4) is used for the analysis. Specifically, the server evaluates the user's health condition and goals based on the user's physical information and lifestyle. For example, it calculates the user's BMI and determines the appropriate amount of exercise. The input data is processed based on the user's health condition and goals.

[1758] Step 4:

[1759] The server generates a training menu.

[1760] The server generates the optimal training menu for the user based on the analysis results. For example, if the user wants to exercise three times a week, the server will suggest a menu that combines aerobic exercise (e.g., 30 minutes of jogging) and strength training (e.g., squats and push-ups). The generated training menu is customized based on the user's health condition and goals.

[1761] Step 5:

[1762] The server generates diet recipes.

[1763] The server generates diet recipes suitable for the user based on the ingredient information in the refrigerator. For example, if there is chicken breast and broccoli in the refrigerator, the server will suggest low-calorie recipes using these ingredients (e.g., stir-fried chicken breast and broccoli). The generated recipes are customized based on the ingredient information and health goals of the user.

[1764] Step 6:

[1765] The server sends the result to the terminal.

[1766] The server encrypts the generated training menu and diet recipes and transmits them to the terminal. The transmitted data includes the exercise program and meal plan optimized for the user. The terminal stores the received data.

[1767] Step 7:

[1768] The terminal displays the results to the user.

[1769] The terminal displays the training menu and diet recipes received from the server to the user. The user can plan their daily training and meals based on the displayed information. Specifically, the user can check the details of the exercise program and recipes on the application interface.

[1770] (Application example 2)

[1771] 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".

[1772] In modern society, health promotion and diet support are important issues, but it is difficult to provide training menus and diet recipes that are suitable for each individual user. In addition, there is a lack of specific methods for users to effectively use ingredients at home and eat healthy meals. Furthermore, there is a demand for a means to quickly obtain the necessary ingredients and dishes based on the generated recipe. To solve these issues, personalized suggestions based on the user's physical information, lifestyle, and information on ingredients in the refrigerator are necessary.

[1773] 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.

[1774] In this invention, the server includes a generative AI means for performing a primary diagnosis, a means for identifying symptoms, a means for determining whether the condition can be treated with over-the-counter medicine or whether a medical examination at a nearby hospital is necessary, a means for receiving the user's physical information, lifestyle, and information on ingredients in the refrigerator as input, and generating individual training menus and diet recipes, and a means for proposing options for delivering the necessary ingredients and dishes based on the generated recipes. This allows the user to receive training menus and diet recipes that match their own health condition and lifestyle, and further allows the necessary ingredients and dishes to be delivered quickly.

[1775] The "generative AI means for performing primary diagnosis" is an artificial intelligence technology that assesses the initial health condition based on information input by the user and suggests appropriate actions.

[1776] "Symptom assessment tools" are technologies used to analyze symptoms reported by users and determine the cause and severity of those symptoms.

[1777] "Means for determining whether the symptom can be treated with over-the-counter medication, etc., or whether medical treatment at a nearby hospital is required" refers to technology for determining whether the user's symptoms can be treated with over-the-counter medication, or whether medical treatment at a medical institution is required.

[1778] "A means for receiving a user's physical information, lifestyle, and information about ingredients in the refrigerator as input, and generating individual training menus and diet recipes" refers to technology for generating individually optimized exercise programs and meal recipes based on the user's physical characteristics, lifestyle, and information about ingredients in the refrigerator.

[1779] "Means for proposing delivery options for necessary ingredients and dishes based on the generated recipe" refers to a technology that suggests a delivery service for providing the user with ingredients and dishes necessary for the generated diet recipe.

[1780] The system for implementing this invention creates individual training menus and diet recipes based on the user's physical information, lifestyle, and information about ingredients in the refrigerator, and also provides a service of delivering ingredients and meals based on those recipes.

[1781] Hardware and Software Used

[1782] Hardware: Smartphones, servers

[1783] Software: Mobile applications (iOS / Android), server-side applications, generative AI models (e.g. GPT-4), databases (e.g. MySQL)

[1784] Program processing explanation

[1785] 1. Enter your user information

[1786] Through a smartphone app, users input their physical information (height, weight, age, etc.), lifestyle (exercise frequency, food preferences, etc.), and information about the ingredients in their refrigerator. This information is collected through the application interface.

[1787] 2. Data transmission and storage

[1788] The entered data is sent from the smartphone to a server and stored in a database, using a relational database such as MySQL.

[1789] 3. Menu generation using generative AI models

[1790] The server-side application inputs the stored data into a generative AI model (e.g., GPT-4) to generate individual training menus and diet recipes. The generative AI model then suggests optimal menus based on the user's physical information, lifestyle, and the ingredients in the refrigerator.

[1791] 4. Propose delivery options

[1792] Based on the generated recipe, the app will suggest the required ingredients and delivery options for the dish to the user, who can then select the delivery option through the app.

[1793] 5. Executing the delivery

[1794] When a user selects delivery, an order is sent to a partner food delivery service and the ingredients and food are delivered to the user's home.

[1795] Examples

[1796] The user enters the following information into the app:

[1797] Height: 170cm

[1798] Weight: 70kg

[1799] Ingredients in the fridge: Chicken breast, broccoli

[1800] Example prompt for a generative AI model:

[1801] User's physical information: Height 170cm, Weight 70kg

[1802] Lifestyle: Exercise 3 times a week

[1803] Ingredients in the fridge: Chicken breast, broccoli

[1804] Based on this information, generate training menus and diet recipes that are suitable for the user.

[1805] Produces the following result:

[1806] Training menu: 30 minutes of aerobic exercise (jogging), 15 minutes of strength training (push-ups, squats)

[1807] Diet recipe: Healthy chicken breast and broccoli salad

[1808] In this way, users can receive a personalized health promotion plan and even have the ingredients and dishes they need delivered to them.

[1809] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1810] Step 1:

[1811] The user starts the smartphone app and inputs physical information (height, weight, age, etc.), lifestyle information (exercise frequency, food preferences, etc.), and information about ingredients in the refrigerator. The input information is collected through the application interface.

[1812] Input: Physical information, lifestyle, and fridge ingredients

[1813] Output: User input data

[1814] Step 2:

[1815] The terminal sends the collected user input data to the server, which stores the received data in a database using a relational database such as MySQL.

[1816] Input: User-entered data

[1817] Output: User data stored in the database.

[1818] Step 3:

[1819] The server inputs the saved user data into a generative AI model (e.g., GPT-4) to generate individual training menus and diet recipes. The generative AI model suggests optimal menus based on the user's physical information, lifestyle, and information about ingredients in the refrigerator.

[1820] Input: User data stored in the database

[1821] Output: Training menu and diet recipes

[1822] Step 4:

[1823] The server sends the generated training menu and diet recipes to the user's smartphone, where the user can check the proposed menu and recipes through the application.

[1824] Input: Training menu and diet recipes

[1825] Output: Menu and recipes displayed on the user's smartphone

[1826] Step 5:

[1827] The user selects the required ingredients and meal delivery options based on the generated recipe, and the application sends the user's selection to the server.

[1828] Input: User's delivery option selection

[1829] Output: Delivery option selection data sent to the server

[1830] Step 6:

[1831] Based on the user's selection, the server sends the order to a partner food delivery service, which delivers the required ingredients and food to the user's home.

[1832] Input: Delivery option selection data

[1833] Output: Order data sent to the food delivery service

[1834] In this way, users can receive a personalized health plan and even have the ingredients and meals they need delivered.

[1835] Example 3

[1836] 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".

[1837] In modern society, mental health issues are becoming more serious, and there is a need to provide prompt and appropriate advice for each individual's concerns. However, conventional systems have the problem of being unable to accurately analyze a user's concerns and provide appropriate advice. In addition, there was a lack of technology to effectively analyze the information entered by the user and generate personalized advice.

[1838] 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 generative artificial intelligence means, means for analyzing input information using natural language processing technology, means for generating a prompt sentence based on the analysis result, means for inputting the prompt sentence to the generative artificial intelligence model and generating advice, and means for providing the generated advice to the user. This makes it possible to accurately analyze the user's concern information and quickly provide appropriate advice.

[1839] "Generative artificial intelligence means" refers to artificial intelligence technology for generating appropriate advice based on input information from a user.

[1840] "Natural language processing technology" refers to technology for analyzing text information entered by a user and understanding its content and emotional state.

[1841] A "prompt sentence" is an instruction sentence to be input into a generative artificial intelligence model, and refers to a sentence generated based on the user's concern information.

[1842] "Generative artificial intelligence model" refers to an artificial intelligence model for generating appropriate advice based on a prompt sentence.

[1843] The "means for generating advice" refers to a process for generating specific advice for a user's concern information using a generative artificial intelligence model.

[1844] The "means for providing the generated advice to the user" refers to an interface or communication means for transmitting the generated advice to the user.

[1845] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1846] The present invention relates to an AI counseling system for preventing and supporting mental health problems for users. A specific embodiment of this system will be described below.

[1847] System Configuration

[1848] This system includes a terminal into which a user inputs problem information, a server that analyzes the input information and generates advice, and communication means for providing the generated advice to the user.

[1849] Hardware and Software Used

[1850] Device: A user device such as a smartphone, tablet, or computer.

[1851] Server: A server computer with high computing power.

[1852] Software: Web browsers, mobile applications, generative artificial intelligence models (e.g. OpenAI's GPT-4), natural language processing techniques (e.g. Google's BERT).

[1853] Data processing and calculation

[1854] 1. User enters problem information:

[1855] A user inputs information about their worries into a text box using a web browser or mobile application on their smartphone or computer. For example, they might input, "Recently, I've been unable to sleep because of work stress."

[1856] 2. The device sends the entered worry information to the server:

[1857] The device sends the text entered by the user to the server as an HTTP POST request, which includes the user's concerns.

[1858] 3. The server receives the concern information and analyzes it using natural language processing technology:

[1859] The server parses the received request and extracts the concern information, then uses Google's BERT model to analyze the text for content and sentiment.

[1860] 4. The server generates a prompt based on the parsed result:

[1861] The server generates a prompt based on the analysis results, for example, "The user has recently been feeling unable to sleep due to work stress. Please provide specific advice on how to deal with stress and improve sleep in this situation."

[1862] 5. The server inputs the prompt sentence into the generative AI model and generates advice:

[1863] The server inputs the prompt sentence into a generative AI model (e.g., OpenAI's GPT-4) and generates advice. The generative AI model generates appropriate advice based on the prompt sentence.

[1864] 6. The server sends the generated advice to the device:

[1865] The server sends the generated advice to the terminal as an HTTP response. The response includes the generated advice.

[1866] 7. The terminal displays the advice to the user:

[1867] The device displays the received advice to the user. The user sees the advice through the smartphone app: "If you can't sleep because of work stress, try the following measures: 1. Make it a habit to go to bed at a fixed time every day. 2. Create a routine to relax before going to bed (e.g., take a warm bath and do some light stretching). 3. Take short breaks during the day to reduce stress. 4. If necessary, consider consulting a specialist."

[1868] In this way, the user can receive specific and practical advice. This system effectively supports the user's mental health prevention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1869] Step 1:

[1870] The user inputs information about the worries. The user inputs information about the worries into a text box using a web browser or mobile application on a smartphone or PC. For example, the user might input, "Recently, I've been unable to sleep due to work stress." The input information is then sent to the server in the next step.

[1871] Step 2:

[1872] The terminal sends the inputted worry information to the server. The terminal sends the text inputted by the user to the server as an HTTP POST request. This request contains the user's worry information. The input is the user's worry information, and the output is the data to be sent to the server.

[1873] Step 3:

[1874] The server receives the concern information and analyzes it using natural language processing technology. The server analyzes the received request and extracts the concern information. It then uses natural language processing technology (e.g., Google's BERT model) to analyze the content and sentiment of the text. The input is the user's concern information, and the output is the analysis result.

[1875] Step 4:

[1876] The server generates a prompt sentence based on the analysis result.The server generates a prompt sentence based on the analysis result.For example, a prompt sentence such as "The user has recently been feeling that he is unable to sleep due to work stress. Please provide specific advice on how to deal with stress and improve sleep in this situation" is generated.The input is the analysis result, and the output is the prompt sentence.

[1877] Step 5:

[1878] The server inputs a prompt sentence into the generative AI model and generates advice. The server inputs a prompt sentence into the generative AI model (e.g., OpenAI's GPT-4) and generates advice. The generative AI model generates appropriate advice based on the prompt sentence. The input is the prompt sentence, and the output is the generated advice.

[1879] Step 6:

[1880] The server sends the generated advice to the terminal. The server sends the generated advice to the terminal as an HTTP response. This response includes the generated advice. The input is the generated advice, and the output is the data to be sent to the terminal.

[1881] Step 7:

[1882] The device displays the advice to the user. The device displays the received advice to the user. The user confirms the advice through the smartphone app: "If you can't sleep because of work stress, try the following measures: 1. Make a habit of going to bed at a certain time every day. 2. Create a routine to relax before going to bed (e.g. taking a warm bath and doing light stretching). 3. Take short breaks during the day to reduce stress. 4. If necessary, consider consulting a specialist." The input is the generated advice, and the output is the display to the user.

[1883] (Application example 3)

[1884] 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".

[1885] In modern society, mental health problems are becoming more serious, and the number of people suffering from stress and anxiety is increasing. However, receiving counseling from a professional takes time and money, so many people are unable to receive appropriate support. In addition, there is a lack of effective systems for providing mental health support in brick-and-mortar stores. This creates a demand for an environment where users can easily receive mental health support.

[1886] 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.

[1887] In this invention, the server includes generative AI means for performing a primary diagnosis, means for identifying symptoms, means for determining whether the problem can be treated with over-the-counter medicine or whether a consultation at a nearby hospital is necessary, interface means for inputting the user's concerns, means for generating advice using a generative AI model, means for displaying advice, and means for saving and viewing past consultation history. This allows users to easily receive mental health support via their smartphones.

[1888] A "generative AI means for performing initial diagnosis" is an artificial intelligence technology for performing an initial diagnosis based on information provided by a user.

[1889] "Means for identifying symptoms" refers to technology that analyzes information entered by the user and identifies specific symptoms.

[1890] The "means for determining whether the condition can be treated with over-the-counter medication, etc., or whether treatment at a nearby hospital is required" refers to technology for determining, based on the user's symptoms, whether the condition can be treated with over-the-counter medication, or whether treatment at a medical institution is required.

[1891] The "interface means for inputting user's concerns" is a user interface that allows the user to input his or her own concerns or problems.

[1892] An "advice generation means using a generative AI model" is an artificial intelligence model for generating appropriate advice based on concern information input by a user.

[1893] The "means for displaying advice" refers to a technique for visually displaying the generated advice to the user.

[1894] "Means for saving and viewing past consultation history" refers to technology that allows users to save the details of past consultations and view them later.

[1895] The system for carrying out the present invention is designed to enable a user to receive mental health support using a smartphone. A specific embodiment of this system will be described below.

[1896] Hardware and Software Used

[1897] Hardware: Smartphone

[1898] Software: Python, OpenAI API

[1899] System Configuration

[1900] 1. Generative AI means for performing a first-stage diagnosis:

[1901] The server uses a generative AI to perform an initial diagnosis based on the information provided by the user. This AI analyzes the user's input data and generates an initial diagnosis result.

[1902] 2. How to identify symptoms:

[1903] The server analyzes the information entered by the user and runs algorithms to identify specific symptoms. The algorithms identify symptoms based on the data entered by the user and suggest appropriate measures to address the issue.

[1904] 3. How to determine if over-the-counter medications are sufficient or if you need to see a doctor at a nearby hospital:

[1905] Based on the user's symptoms, the server determines whether the problem can be treated with over-the-counter medication or whether medical treatment is required. This decision is made by generative AI.

[1906] 4. Interface for inputting user concerns:

[1907] Users input their concerns and problems through a smartphone application. The interface is designed to make it easy for users to input information.

[1908] 5. How generative AI models generate advice:

[1909] The server uses a generative AI model to generate appropriate advice based on the worries input by the user. For example, if a user inputs, "I haven't been able to sleep lately because of work stress," the generative AI model will provide advice such as, "To reduce stress, try taking some time each day to relax. For example, you may want to try deep breathing or meditation. It is also effective to refrain from using your smartphone or computer before going to bed."

[1910] 6. Ways to display advice:

[1911] The server visually displays the generated advice to the user via the smartphone screen.

[1912] 7. How to store and view past consultation history:

[1913] The server stores the contents of past consultations by the user and allows them to be viewed later. This function allows users to refer to their past consultation history and receive ongoing support.

[1914] Examples of prompt statements

[1915] If the user types "I can't sleep lately because of work stress," the following is an example of a prompt:

[1916] User's problem: I can't sleep lately because of work stress.

[1917] Please provide appropriate advice.

[1918] In this way, users can easily receive mental health support via their smartphone.

[1919] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1920] Step 1:

[1921] The user starts the smartphone application and inputs their concerns.

[1922] Input: User's worries (e.g. "I can't sleep lately because of work stress")

[1923] Output: The user's concerns are sent to the application.

[1924] Specific actions: The user opens the application on their smartphone, enters their concerns into the text box, and presses the send button.

[1925] Step 2:

[1926] The terminal transmits the user's worry information to the server.

[1927] Input: User's worries

[1928] Output: User's worries sent to the server

[1929] Specific operation: The smartphone application transmits the worry information entered by the user to a server via the Internet.

[1930] Step 3:

[1931] The server inputs the received worry information into the generative AI model.

[1932] Input: User's worries sent to the server

[1933] Output: Worry information input to the generative AI model

[1934] Specific operation: The server converts the received user's concern information into an appropriate format to pass it to the generative AI model.

[1935] Step 4:

[1936] The generative AI model generates advice based on concern information.

[1937] Input: Worry information input to the generative AI model

[1938] Output: Generated advice

[1939] Specific operation: The generative AI model analyzes the user's worries and generates appropriate advice, such as, "To reduce stress, try taking some time each day to relax. For example, you may want to try deep breathing or meditation. It is also effective to refrain from using smartphones and computers before going to bed."

[1940] Step 5:

[1941] The server transmits the generated advice to the terminal.

[1942] Input: Generated advice 【194...

Claims

1. A means for inputting symptom information from a user; means for transmitting the input symptom information to a server; A server receives the symptom information, and analyzes the symptom information using a prompt sentence and a generative AI model created from the symptom information; A means for predicting a disease based on the symptom information using the generative AI model; A method to propose appropriate countermeasures based on the estimation results, and means for displaying a suggested remedy to the user; A system including:

2. A means for inputting the user's physical information, lifestyle, and food ingredient information of the refrigerator; A means for analyzing the input information using a prompt sentence and a generation AI model created from the input information, and generating an individual training menu and diet recipe; The system of claim 1 , comprising:

3. A means for inputting the user's worry information; a means for generating advice using a prompt sentence created from the inputted worry information and a generation AI model; The system of claim 1 , comprising:

Images