System

Abstract

A system is provided.SOLUTION: A system comprising: means for obtaining health state information of a user; means for obtaining meal-related information of the user; means for generating an optimized meal plan for the user based on the obtained health state information and the meal-related information; means for suggesting the generated meal plan to the user; means for generating a specific recipe based on a menu selected by the user from the suggested meal plan; and means for providing the generated recipe 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] In preventing dementia in the elderly, it is important to individually propose appropriate meal plans, but it is difficult for elderly people to understand and follow what kind of diet is optimal for them. Furthermore, individual optimization of health status and eating habits requires the collection and analysis of a large amount of data, and a means to easily perform this is needed. The objective of this invention is to provide a system that supports dementia prevention by enabling elderly people to easily select and follow the optimal diet according to their own health status. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for acquiring a user's health condition information, a means for acquiring information related to the user's diet, a means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, a means for proposing the generated meal plan to the user, a means for generating specific recipes based on menus selected by the user from the proposed meal plan, and a means for providing the generated recipes to the user. The system further includes a wearable device that collects the user's daily activity information and optimizes the meal plan based on the collected daily activity information. The system also includes a means for acquiring nutritional information for meals by analyzing images of the meals consumed by the user, thereby creating a system that supports dietary selection that is effective in preventing dementia in the elderly.

[0006] "Health Information" refers to data related to a user's physical and mental health, including, but not limited to, heart rate, number of steps taken, sleep status, body weight, and past medical history.

[0007] "Diet-related information" refers to data about the food consumed by the user, including the names of ingredients, amounts, nutrient content, cooking methods, etc.

[0008] "Meal Plan" refers to a plan of multiple meal menus suggested to a user based on the user's health status information and diet-related information.

[0009] "Recipe" refers to a document or data that details the steps and ingredients required to specifically prepare a selected meal menu.

[0010] A "wearable device" refers to a device that is carried or worn by the user and collects data such as health status information and daily activity information.

[0011] "Image analysis algorithm" refers to a set of computational steps used to analyze image data to identify, classify, or evaluate its content.

[0012] "Nutrient information" refers to information on the nutritional components contained in food, such as protein, lipids, carbohydrates, vitamins, and minerals.

[0013] "Lifestyle activity information" refers to the user's qualitative and quantitative activity data, specifically including the amount of activity during the day, rest time, type and intensity of exercise, etc. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] 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.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] 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.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] 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.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] This invention is a system for supporting dementia prevention in the elderly, providing optimized meal plans and specific recipes based on the user's health status and diet-related information. This system is operated by combining a server, a terminal, and a wearable device.

[0036] System Overview

[0037] The system has the following features:

[0038] 1. Obtaining user health status information

[0039] 2. Obtain information related to the user's diet

[0040] 3. Generate a personalized meal plan for the user based on their health and diet-related information

[0041] 4. Providing the generated meal plan to the user

[0042] 5. Generate specific recipes based on the menu the user selects from the meal plan.

[0043] 6. Provide the generated recipe to the user

[0044] Embodiment

[0045] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server stores the received information in a database.

[0046] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server, which uses image analysis algorithms to extract the meal contents and nutritional information and stores it in a database.

[0047] 3. The user wears a wearable device to collect health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device then sends this data to a server, which analyzes it and evaluates the user's health status.

[0048] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm to generate an optimized meal plan for the user.

[0049] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[0050] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[0051] Specific examples

[0052] Day 1 flow

[0053] 1. At 8:00 a.m., a user has fruit and yogurt for breakfast. Before that, the user takes a photo of the meal using the device's camera, and the device sends the image to the server.

[0054] 2. The server analyzes the image, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and stores it in the database.

[0055] 3. The wearable device collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to the server, which then evaluates the user's health status based on this information.

[0056] 4. Based on the data from that day, the server generates a meal plan for the next day and sends it to the device: "Breakfast: oatmeal and fruit, lunch: salmon and salad, dinner: chicken and vegetable soup."

[0057] 5. The next morning, the user checks the plan and selects the breakfast menu "Oatmeal and Fruit." The server generates a specific recipe (how to cook the oatmeal, the type and amount of fruit required) and sends it to the device. The device displays this recipe to the user, and the user prepares breakfast.

[0058] According to the above-described embodiments, the present invention can provide an optimized meal plan that takes into account the individual health condition and dietary preferences of the user, and can assist in selecting meals that are effective in preventing dementia in the elderly.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] A user registers with the system by entering basic information (such as name, age, gender, medical history, etc.) into a terminal, which then sends it to the server.

[0062] Step 2:

[0063] The server stores the received user basic information in a database.

[0064] Step 3:

[0065] The user takes a photo of their usual meal using the device's camera.

[0066] Step 4:

[0067] The device uploads the captured image of the meal to the server.

[0068] Step 5:

[0069] The server uses image analysis algorithms to extract meal content and nutritional information.

[0070] Step 6:

[0071] The server stores the extracted dietary and nutrient information in a database.

[0072] Step 7:

[0073] Users wear a wearable device to collect health information such as the number of steps taken each day, heart rate, and sleep status.

[0074] Step 8:

[0075] The wearable device sends the collected health information to a server.

[0076] Step 9:

[0077] The health status information received by the server is stored in a database and analyzed.

[0078] Step 10:

[0079] The server provides health and diet-related information stored in a database to an AI algorithm, which generates an optimized meal plan for the user.

[0080] Step 11:

[0081] The server sends the generated meal plan to the terminal.

[0082] Step 12:

[0083] The device will notify and display the received meal plan to the user.

[0084] Step 13:

[0085] The user selects their preferred menu from the suggested meal plans.

[0086] Step 14:

[0087] The terminal transmits the menu information selected by the user to the server.

[0088] Step 15:

[0089] The server uses generative AI to create a specific recipe based on the selected menu.

[0090] Step 16:

[0091] The server sends the generated recipe to the device.

[0092] Step 17:

[0093] The device displays the received recipe to the user, who then performs the actual cooking while looking at it.

[0094] Step 18:

[0095] After the user has finished eating, they report "meal completed" on the terminal.

[0096] Step 19:

[0097] The terminal sends a completion report to the server.

[0098] Step 20:

[0099] The server analyzes the effectiveness of the meal plan based on the completion report and reflects this in the next proposal.

[0100] Example 1

[0101] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0102] To prevent dementia and manage the health of elderly people, it is necessary to provide optimal meal plans that comprehensively consider each individual's health condition and dietary content. However, conventional systems have limited means of efficiently acquiring and analyzing a user's health condition and dietary information, and have not been able to adequately generate optimal meal plans and provide specific recipes. Furthermore, it has been difficult to generate individually optimized meal plans in real time based on detailed analysis of daily activity information and dietary content.

[0103] 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.

[0104] In this invention, the server includes means for acquiring user health condition information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, means for analyzing the health condition information using a specific health condition evaluation algorithm, means for analyzing dietary content using image analysis technology, and means for generating an optimized meal plan using an AI model. This makes it possible to analyze the user's health condition and dietary content in detail and provide an individually optimized meal plan and specific recipes.

[0105] "Means for obtaining user health status information" refers to the functionality of a device or software that records a user's physical condition and medical history and can update it as needed.

[0106] "Means for obtaining information related to the user's diet" refers to the functionality of a device or software that records and stores detailed information about the dietary content and nutritional information of the user's meals.

[0107] "Means for generating a meal plan optimized for a user based on the acquired health status information and diet-related information" refers to an algorithm or program for creating the most appropriate meal plan based on the user's collected health status information and diet information.

[0108] The "means for proposing the generated meal plan to the user" is an interface or application for informing the user of the contents of the generated meal plan and visually displaying it.

[0109] "Means for generating specific recipes based on the menu selected by the user from the suggested meal plan" refers to an algorithm or program that provides specific cooking instructions and ingredients based on the meal plan selected by the user.

[0110] The "means for providing the generated recipe to the user" refers to an interface or application for notifying the user of the generated cooking recipe and visually displaying it.

[0111] "Means for analyzing health status information using a specific health status assessment algorithm" refers to an algorithm or program for analyzing the collected health status information and assessing the user's overall health status.

[0112] "Means for analyzing meal contents using image analysis technology" refers to image analysis technology or software for analyzing photographed images of meals and extracting the meal contents and nutritional components.

[0113] "Means for generating optimized meal plans using AI models" refers to algorithms or programs that use artificial intelligence to create optimal meal plans for users based on collected data.

[0114] A "wearable device that collects a user's daily activity information" is a device or apparatus that collects a user's daily physical activity and biometric information and transmits this information to a server.

[0115] The "means for obtaining nutrient information" refers to a means for identifying the nutritional components of the food consumed by the user and recording this as data.

[0116] This invention is a system to support dementia prevention in the elderly, and provides optimized meal plans and specific recipes based on the user's health status and diet-related information. This system is operated by combining a server, a terminal, and a wearable device.

[0117] System Overview

[0118] The system has the following features:

[0119] 1. Obtaining user health status information

[0120] 2. Obtain information related to the user's diet

[0121] 3. Generate a personalized meal plan for the user based on their health and diet-related information

[0122] 4. Providing the generated meal plan to the user

[0123] 5. Generate specific recipes based on the menu the user selects from the meal plan.

[0124] 6. Provide the generated recipe to the user

[0125] Embodiment

[0126] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server stores the received information in a database.

[0127] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server. The server uses an image analysis algorithm (e.g., Google® Cloud Vision API) to extract the meal contents and nutritional information and store it in a database.

[0128] 3. The user wears a wearable device (e.g., Fitbit) and collects health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device sends this data to a server, which analyzes it and evaluates the user's health status (e.g., using AWS (registered trademark) Lambda or TENSORFLOW (registered trademark)).

[0129] 4. The server provides the health and diet-related information stored in the database to an AI algorithm (e.g., OpenAI® generative AI model) to generate an optimized meal plan for the user.

[0130] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[0131] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[0132] Specific examples

[0133] Day 1 flow

[0134] 1. At 8:00 a.m., a user has fruit and yogurt for breakfast. Before that, the user takes a photo of the meal using the device's camera, and the device sends the image to the server.

[0135] 2. The server analyzes the image using the Google Cloud Vision API, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and saves it in the database.

[0136] 3. A wearable device (e.g., Fitbit) collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to a server. The server uses AWS Lambda and TensorFlow to evaluate the user's health status based on this information.

[0137] 4. Based on the data from that day, the server generates a meal plan for the next day and sends it to the device: "Breakfast: oatmeal and fruit, lunch: fish and salad, dinner: meat and vegetable soup."

[0138] 5. The next morning, the user checks the plan and selects the breakfast menu "Oatmeal and Fruit." The server uses a generative AI model (e.g., OpenAI's generative AI model) to generate a specific recipe (how to cook oatmeal, the type and amount of fruit required) and sends it to the device. The device displays this recipe to the user, who then prepares breakfast.

[0139] Prompt Sentence Examples

[0140] Here are some example prompts to input to the AI ​​generator:

[0141] Data entry: Name: Taro Tanaka Age: 68 Gender: Male Breakfast: 100g yogurt, 1 banana, 50g blueberries Health data: Steps: 8000, Heart rate: 70 bpm, Sleep time: 7 hours

[0142] Output: Generate an optimal meal plan for tomorrow.

[0143] In this way, users are provided with optimal meal plans and specific recipes to support their daily health management.

[0144] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0145] Step 1:

[0146] The user registers with the system by entering basic information such as name, age, gender, and medical history into the terminal, which then sends the entered data to the server.

[0147] Specific operation: When a user enters the required information into the input form on the device and presses the "Submit" button, the data is sent to the server using HTTPS. The server then stores the received data in a database.

[0148] Input: Name, age, gender, medical history

[0149] Output: User basic information stored in the database

[0150] Step 2:

[0151] The user takes a photo of their meal using the device's camera, and the device uploads the captured image data to the server.

[0152] Specific operation: The user takes a photo of the meal and taps the send image button. The device temporarily stores the image data and uploads it to the API endpoint using the communication module.

[0153] Input: Food image

[0154] Output: Food image data sent to the server

[0155] Step 3:

[0156] The server analyzes the image of the meal and uses the Google Cloud Vision API to extract information about the meal and its nutritional content.

[0157] Specific operation: The server sends the received image data to the Google Cloud Vision API and writes the returned analysis results to the database.

[0158] Input: Food image data sent to the server

[0159] Output: Dietary and nutritional information stored in a database

[0160] Step 4:

[0161] The user wears a wearable device to collect health information, such as the number of steps taken, heart rate, and sleep status, and sends this data to a server.

[0162] Specific operation: The wearable device sends health status information to the terminal via Bluetooth, which receives it and uploads it to the server.

[0163] Input: Step count, heart rate, and sleep status data obtained from a wearable device

[0164] Output: Health status information sent to the server

[0165] Step 5:

[0166] The server analyzes and evaluates the health status information. The server uses AWS Lambda and TensorFlow to analyze the received data and evaluate the user's health status.

[0167] Specific operation: The server uses AWS Lambda to perform data analysis and writes the results to the database.

[0168] Input: Health status information sent to the server

[0169] Output: Analysis results and assessed health status information stored in a database

[0170] Step 6:

[0171] The server generates a meal plan, which provides the health and diet-related information stored in the database to an AI algorithm to generate an optimized meal plan for the user.

[0172] What it does: The server retrieves the necessary data from the database, sends it to the generative AI model to create a meal plan, and then sends the plan in JSON format to the device.

[0173] Input: Health and dietary information stored in a database

[0174] Output: Generated meal plan

[0175] Step 7:

[0176] The device notifies the user of the meal plan. The device displays the meal plan received from the server to the user, and the user selects the desired menu from the plan.

[0177] What happens: The device notifies the user of the meal plan via push notification, and the user makes a selection in the app.

[0178] Input: Meal plan sent from server

[0179] Output: The meal plan displayed to the user and the user's selections

[0180] Step 8:

[0181] The server generates a recipe, using a generation AI based on the menu selected by the user to generate a specific recipe.

[0182] Specific operation: The server passes the selected menu to the generation AI as a prompt sentence, and sends the returned recipe data to the terminal.

[0183] Input: The menu selected by the user

[0184] Output: The generated recipe

[0185] Step 9:

[0186] The terminal displays the recipe to the user. The terminal displays the specific recipe data received from the server to the user.

[0187] Specific operation: The device displays the received recipe data on the app's UI, and the user confirms it.

[0188] Input: Specific recipe data sent from the server

[0189] Output: The recipe displayed to the user

[0190] (Application example 1)

[0191] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0192] To effectively support dementia prevention in the elderly, it is important to provide optimal meal plans tailored to each individual's health condition and dietary preferences. However, conventional systems have difficulty generating meal plans that fully reflect the user's health condition and diet-related information. Furthermore, they lack the functionality to track the degree to which the generated meal plans and recipes are actually being followed and to reflect this in future recommendations. To address these issues, a system is needed that can generate accurate, personalized meal plans and track their implementation.

[0193] 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.

[0194] In this invention, the server includes means for acquiring user health status information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health status information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, means for ordering meals from a food delivery service, and means for recording the user's diet history in a database and reflecting this in future suggestions. This allows for the provision of optimal meal plans for individual users and enables continuous health management through the management and tracking of dietary history.

[0195] "Means for acquiring user health status information" refers to a method for collecting health data such as the user's heart rate, number of steps, and sleep time from a wearable device or the like and providing it to a server.

[0196] The "means of obtaining information related to the user's diet" refers to a method of extracting dietary content and nutritional information by analyzing photos of the meals taken by the user and storing the information in a database.

[0197] "Means for generating a meal plan optimized for a user based on the acquired health status information and diet-related information" refers to a method that uses an AI algorithm based on collected data to create a meal plan that is best suited to the user's health status and dietary preferences.

[0198] "Means for proposing the generated meal plan to the user" refers to a method for sending the meal plan generated by the server to the user's terminal and notifying and displaying it to the user.

[0199] "Means for generating specific recipes based on a menu selected by a user from a suggested meal plan" refers to a method that uses a generative AI model to generate detailed cooking instructions and information on the ingredients required based on a menu selected by the user.

[0200] "Means for providing the generated recipe to the user" refers to a method for sending the generated recipe to the user's terminal so that the user can view and use it.

[0201] "Means for ordering meals from a food delivery service" refers to a method of arranging for meals to be delivered through a partner online ordering service based on the generated meal plan.

[0202] "Means of recording the user's dietary history in a database and reflecting it in future suggestions" refers to a method of storing the history of the meals the user actually ate in a database and reflecting it in future meal plan suggestions.

[0203] This invention is a system that effectively supports dementia prevention in the elderly, providing optimized meal plans and specific recipes based on the user's health status and diet-related information. This system is operated by combining a server, a terminal, and a wearable device.

[0204] System Overview

[0205] The system has the following features:

[0206] 1. Wearable devices (e.g., Apple Watch, Fitbit) are used to obtain information about the user's health status. These devices collect data such as the number of steps taken each day, heart rate, and sleep status, and send it to a server.

[0207] 2. To obtain information related to the user's diet, the device camera takes a photo of the meal and sends the image to the server, which then uses an image analysis algorithm (e.g., Amazon Rekognition, Google Cloud Vision) to extract information about the meal and its nutrients, which are then stored in a database.

[0208] 3. The server uses AI algorithms (e.g., TensorFlow, PyTorch) to generate a meal plan optimized for the user based on the acquired health status information and diet-related information, which analyzes the collected data and generates a meal plan that is optimal for the user's health status and dietary preferences.

[0209] 4. As a means of proposing the generated meal plan to the user, the server sends the generated plan to the terminal, which notifies and displays it to the user.

[0210] 5. The server uses a generative AI model to generate specific recipes based on the menus selected by the user from the suggested meal plans, generating specific cooking instructions and information on ingredients needed, which are then sent to the device and displayed to the user.

[0211] 6. As a means of providing the generated recipe to the user, the terminal displays the generated recipe so that the user can view and use it.

[0212] 7. As a means of ordering meals from a food delivery service, the server uses the API of the partner online ordering service (e.g., Uber Eats, DoorDash) to deliver meals based on the generated meal plan.

[0213] 8. As a means of recording the user's dietary history in a database and reflecting it in future suggestions, the server will store the history of the meals the user actually ate in a database and reflect this in future meal plan suggestions.

[0214] Specific examples

[0215] 1. The user takes a photo of their breakfast and their device sends the image to the server, which then sends a prompt to the image analysis service asking them to identify the type and amount of food in the image and return nutritional information.

[0216] 2. The server uses an AI algorithm to generate a meal plan and sends it to the device with the prompt, "Generate the optimal meal plan for this user based on the entered health information and dietary preferences."

[0217] 3. The user selects a menu from the proposed plan, and the server sends a prompt to the generative AI model to "generate a specific recipe based on the selected menu."

[0218] 4. The recipe is generated and displayed on the device, where the user can review the recipe, gather the necessary ingredients, and prepare the meal.

[0219] 5. The food order is automatically placed through an online ordering service and delivered to the user.

[0220] 6. The user's meal history is recorded in a database and reflected in future meal plans.

[0221] According to the above-described embodiments, the present invention can provide an optimized meal plan that takes into account the individual health condition and dietary preferences of each user, and can assist in selecting meals that are effective in preventing dementia in the elderly.

[0222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0223] Step 1:

[0224] User takes a photo of their meal

[0225] The user takes a photo of the meal using the device's camera. The device then sends the image to the server. The input is the photo of the meal taken by the user, and the output is the transfer of image data to the server.

[0226] Step 2:

[0227] The server performs image analysis

[0228] The server passes the received image data to an image analysis algorithm. The image analysis service (e.g., Amazon Rekognition, Google Cloud Vision) processes the data according to the prompt, "Please identify the type and quantity of food from this image and return nutritional information." The input is the image data of the meal, and the output is the analyzed type of food and nutritional information.

[0229] Step 3:

[0230] Collecting health status information from wearable devices

[0231] A user wears a wearable device (e.g., Apple Watch, Fitbit) that collects daily steps, heart rate, sleep status, etc. and sends the data to a server. The input is the health status data collected from the wearable device, and the output is the data sent to the server.

[0232] Step 4:

[0233] The server integrates and analyzes the data

[0234] The server integrates the image analysis results with the health status information sent from the wearable device, and then uses AI algorithms (e.g., TensorFlow, PyTorch) to generate a meal plan optimized for the user. The input is the diet-related information and health status information, and the output is the optimal meal plan for the user.

[0235] Example prompt: "Generate the optimal meal plan for this user based on their health information and dietary preferences."

[0236] Step 5:

[0237] The server sends the generated meal plan to the device.

[0238] The generated meal plan is sent from the server to the terminal, which notifies and displays this information to the user. The input is the generated meal plan, and the output is the transmission to the terminal and the notification to the user.

[0239] Step 6:

[0240] User checks meal plan and selects menu

[0241] The user can view the proposed meal plans on the device and select their preferred menu from them. The input is the meal plan display screen on the device, and the output is the selected menu.

[0242] Step 7:

[0243] The server generates a specific recipe

[0244] When a user selects a menu, the server generates a specific recipe based on that menu. It sends a prompt to the generative AI model saying, "Please generate a specific recipe based on the selected menu." The input is the menu selected by the user, and the output is the generated specific recipe.

[0245] Step 8:

[0246] The server sends the generated recipe to the device.

[0247] The generated recipe is sent from the server to the terminal, and the terminal displays the recipe to the user. The input is the generated recipe, and the output is sent to the terminal and displayed to the user.

[0248] Step 9:

[0249] Order food online

[0250] The server orders meals based on the generated meal plan using the API of a partner online ordering service. The input is the generated meal plan, and the output is order data sent to the online ordering service.

[0251] Step 10:

[0252] Food history database storage

[0253] The server stores the user's actual dietary history in a database and reflects it in creating future meal plans. The input is the user's dietary history data, and the output is the data stored in the database.

[0254] Through these steps, the system takes into account the user's individual health condition and dietary preferences, provides an optimized meal plan with specific recipes, and assists in carrying out the plan through a food delivery service.

[0255] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0256] This invention is a system for supporting dementia prevention in the elderly, providing an optimized meal plan and specific recipes based on the user's health status information and diet-related information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system aims to adjust the meal plan according to the user's psychological state, thereby achieving more effective dementia prevention. This system is operated by combining a server, a terminal, a wearable device, and the emotion engine.

[0257] System Overview

[0258] The system has the following features:

[0259] 1. Obtaining user health status information

[0260] 2. Obtain information related to the user's diet

[0261] 3. Generate a personalized meal plan for the user based on their health and diet-related information

[0262] 4. Providing the generated meal plan to the user

[0263] 5. Generate specific recipes based on the menu the user selects from the meal plan.

[0264] 6. Provide the generated recipe to the user

[0265] 7. Includes an emotion engine that recognizes user emotions

[0266] 8. Adjust your meal plan based on emotions identified by the emotion engine

[0267] Embodiment

[0268] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server stores the received information in a database.

[0269] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server, which uses image analysis algorithms to extract the meal contents and nutritional information and stores it in a database.

[0270] 3. The user wears a wearable device to collect health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device then sends this data to a server, which analyzes it and evaluates the user's health status.

[0271] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm to generate an optimized meal plan for the user.

[0272] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[0273] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[0274] 7. The emotion engine built into the system analyzes voice and facial expressions to recognize the user's emotions. When the user uses the device, emotion data is collected in real time through the camera and microphone.

[0275] 8. The server analyzes the emotion data recognized by the emotion engine and adjusts the meal plan accordingly. For example, if the user is feeling stressed, it will suggest a plan that includes foods that have a relaxing effect.

[0276] 9. The server sends the adjusted plan to the device and notifies the user.

[0277] Specific examples

[0278] Day 1 flow

[0279] 1. At 8:00 a.m., a user has fruit and yogurt for breakfast. Before that, the user takes a photo of the meal using the device's camera, and the device sends the image to the server.

[0280] 2. The server analyzes the image, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and stores it in the database.

[0281] 3. The wearable device collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to the server, which then evaluates the user's health status based on this information.

[0282] 4. Based on the data from that day, the server generates a meal plan for the next day and sends it to the device: "Breakfast: oatmeal and fruit, lunch: salmon and salad, dinner: chicken and vegetable soup."

[0283] 5. The next morning, the user checks the plan and selects the breakfast menu "Oatmeal and Fruit." The server generates a specific recipe (how to cook the oatmeal, the type and amount of fruit required) and sends it to the device. The device displays this recipe to the user, and the user prepares breakfast.

[0284] 6. While preparing breakfast, the emotion engine analyzes the user's facial expressions and recognizes that they are feeling stressed.

[0285] 7. The server analyzes the emotional data and generates a new meal plan including foods that have stress-relieving effects, and suggests it to the user.

[0286] Through the above-described embodiments, the present invention can provide an optimized meal plan that takes into account not only the user's individual health condition and dietary preferences, but also their emotional state, and can assist the elderly in choosing meals that are effective in preventing dementia.

[0287] The processing flow will be explained below.

[0288] Step 1:

[0289] A user registers with the system by entering basic information (such as name, age, gender, and medical history) into the terminal, which then sends it to the server.

[0290] Step 2:

[0291] The server stores the received user basic information in a database.

[0292] Step 3:

[0293] The user takes photos of their daily meals using the device's camera.

[0294] Step 4:

[0295] The device takes a photo of the meal and uploads it to the server.

[0296] Step 5:

[0297] The server uses image analysis algorithms to extract food content and nutritional information, identifying ingredients such as yogurt, bananas, and blueberries.

[0298] Step 6:

[0299] The server stores the extracted dietary and nutrient information in a database.

[0300] Step 7:

[0301] Users wear a wearable device to collect health information such as the number of steps taken each day, heart rate, and sleep status.

[0302] Step 8:

[0303] The wearable device sends the collected health information to a server.

[0304] Step 9:

[0305] The server stores the received health information in a database and performs analysis, such as recording the user's daily steps, heart rate, and sleep time, to evaluate their health.

[0306] Step 10:

[0307] The server provides health and diet-related information stored in a database to an AI algorithm, which then generates an optimized meal plan for the user.

[0308] Step 11:

[0309] The server sends the generated meal plan to the terminal.

[0310] Step 12:

[0311] The device notifies the user of the received meal plan and displays it, and the user can select their preferred menu from the proposed meal plan.

[0312] Step 13:

[0313] The user selects their preferred menu from the suggested meal plans.

[0314] Step 14:

[0315] The terminal transmits the menu information selected by the user to the server.

[0316] Step 15:

[0317] The server uses generative AI to create a specific recipe based on the selected menu.

[0318] Step 16:

[0319] The server sends the generated recipe to the device.

[0320] Step 17:

[0321] The device displays the received recipe to the user.

[0322] Step 18:

[0323] The user actually cooks the food while looking at the recipe.

[0324] Step 19:

[0325] The system's built-in emotion engine uses the camera to analyze the user's facial expressions, collecting emotional data as the user uses the device.

[0326] Step 20:

[0327] The emotion engine analyzes the user's voice and facial expressions to identify emotions, such as stress or satisfaction.

[0328] Step 20:

[0329] The server analyzes the emotional data recognized by the emotion engine and adjusts the meal plan as needed. For example, if the user is feeling stressed, it will re-suggest a plan that includes foods that have a relaxing effect.

[0330] Step 21:

[0331] The server sends the adjusted plan to the terminal, which notifies the user.

[0332] Step 22:

[0333] After the user has eaten, they report "meal completed" on the terminal.

[0334] Step 23:

[0335] The terminal sends a completion report to the server.

[0336] Step 24:

[0337] The server analyzes the effectiveness of the meal plan based on the completion report and reflects this in the next proposal.

[0338] Example 2

[0339] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0340] Conventional dementia prevention support systems for the elderly primarily provide plans based on the user's health condition and dietary information, but do not optimize the meal plan by taking into account the user's emotions and psychological state. This creates a problem that makes it difficult to effectively prevent dementia. The present invention aims to solve this problem by providing an optimized meal plan that comprehensively takes into account the user's health condition, dietary information, and emotional state.

[0341] 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.

[0342] In this invention, the server includes means for acquiring user health condition information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, and means for recognizing the user's emotions and adjusting the meal plan based thereon, thereby making it possible to provide a meal plan that takes into account not only the user's health condition and diet information but also their emotional state.

[0343] "User's health condition information" is data that indicates the user's physical condition, and specifically includes information such as the number of steps taken, heart rate, and sleep status.

[0344] "Information related to the user's diet" refers to data related to the food and drinks consumed by the user, including, specifically, dietary content and nutritional information.

[0345] "Means for generating meal plans" refers to an algorithm or program that creates an optimized meal menu for a user based on acquired health and diet-related information.

[0346] "Means for suggesting the generated meal plan to the user" means a device or software capable of informing and displaying the generated meal plan to the user.

[0347] "Means for generating specific recipes" refers to a generative AI model or algorithm that provides specific cooking instructions and ingredient quantities based on a user's menu selections.

[0348] "Means for providing the generated recipe to the user" refers to a device or software that has the function of notifying and displaying the generated recipe to the user.

[0349] "Means for recognizing user emotions and adjusting meal plans accordingly" refers to an emotion engine or algorithm that analyzes a user's emotional data (voice, facial expressions) and adjusts an optimized meal plan based on the results.

[0350] A "wearable device" refers to an electronic device that can be worn by a user to collect health status information and daily activity information.

[0351] This invention is a system for supporting dementia prevention in the elderly, providing an optimized meal plan and specific recipes based on the user's health status information and diet-related information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system aims to adjust the meal plan according to the user's psychological state, thereby achieving more effective dementia prevention. This system is operated by combining a server, a terminal, a wearable device, and the emotion engine.

[0352] Hardware and software used

[0353] The system uses the following hardware and software:

[0354] Server: The main device for collecting and analyzing user information. The server stores the database and runs AI algorithms (e.g., TensorFlow or PyTorch).

[0355] Device: The device where a user enters meal information and views the meal plans and recipes provided. This could be a smartphone or tablet.

[0356] Wearable devices: Devices that collect health information such as the number of steps taken, heart rate, and sleep status of the user. Examples include fitness bands and smartwatches.

[0357] Emotion engine: Software for analyzing user emotions, such as those used for speech recognition and facial expression analysis (e.g., Amazon Rekognition and Microsoft® Azure® Face API).

[0358] Detailed System Description

[0359] 1. When a user uses the system for the first time, they enter basic information (such as name, age, gender, medical history, etc.) into the terminal, which then sends this information to the server. The server then stores the received information in a database.

[0360] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server. The server uses image analysis algorithms (e.g., OpenCV or TensorFlow) to extract information about the meal and its nutrients, and stores it in a database.

[0361] 3. The wearable device worn by the user collects health information such as the number of steps taken, heart rate, and sleep status, and sends this information to a server. The server analyzes this information and evaluates the user's health condition.

[0362] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm, which generates an optimized meal plan for the user using machine learning models based on TensorFlow and PyTorch.

[0363] 5. The server sends the generated meal plan to the device, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[0364] 6. Based on the selected menu, the server creates a specific recipe using a generative AI model (e.g., GPT-3 (registered trademark) or ChatGPT (registered trademark)). The created recipe is sent to the device and displayed to the user.

[0365] 7. The emotion engine built into the system analyzes voice and facial expressions to recognize the user's emotions. Emotion data is collected in real time using the device's camera and microphone.

[0366] 8. The server adjusts the meal plan as needed based on the analysis results of the emotion engine. For example, if the user is feeling stressed, it will suggest a meal plan that includes ingredients that have a relaxing effect.

[0367] 9. The server sends the adjusted plan to the device and notifies the user.

[0368] Example flow

[0369] The specific flow for the first day is shown below.

[0370] 1. At 8:00 AM, before eating fruit and yogurt for breakfast, the user takes a photo of the meal using the device's camera.

[0371] 2. The device sends the image to the server.

[0372] 3. The server analyzes the image, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and saves this in the database.

[0373] 4. The wearable device collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to the server.

[0374] 5. The server then evaluates the user's health based on this and generates a meal plan for the next day.

[0375] 6. The server sends the following plan to the device: "Breakfast: oatmeal and fruit, lunch: salmon and salad, dinner: chicken and vegetable soup."

[0376] 7. The next morning, the user reviews the plan and selects the breakfast menu "Oatmeal and Fruit."

[0377] 8. The server generates a specific recipe (how to cook oatmeal, the type and amount of fruit required) and sends it to the device.

[0378] 9. The device displays this recipe to the user, who then prepares breakfast.

[0379] 10. While preparing breakfast, the emotion engine analyzes the user's facial expressions and recognizes that they are feeling stressed.

[0380] 11. The server analyzes the emotional data, generates a new meal plan including foods that have stress-relieving effects, and suggests it to the user.

[0381] Prompt Sentence Examples

[0382] Below is an example of a prompt to ask the generative AI model for a specific recipe.

[0383] Input: What's a healthy breakfast recipe that uses 100g of oatmeal, half an apple, and 50g of blueberries?

[0384] Cook the oatmeal in water or milk, then top with sliced ​​apples and blueberries. Finish with a little honey for a delicious finish.

[0385] By using this prompt, users can easily obtain specific recipes, which can provide users with meal plans that take into account their health condition, food preferences, and emotional state, and help elderly people make effective dietary choices to prevent dementia.

[0386] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0387] Step 1:

[0388] When a user uses the system for the first time, they enter their basic information (name, age, gender, medical history, etc.) into the terminal. The terminal sends this information to the server, which then stores the received information in a database. The input here is the user's basic information, and the output is the user's basic information stored in the database.

[0389] Step 2:

[0390] The user takes a photo of their usual meal using the device's camera. The device then uploads the image to the server. The server then uses an image analysis algorithm (using OpenCV or TensorFlow) to extract the meal contents and nutritional information from the image and stores it in a database. The input here is the meal image, and the output is the meal contents and nutritional information stored in the database.

[0391] Step 3:

[0392] A wearable device worn by a user collects health information such as the number of steps taken each day, heart rate, and sleep status. The wearable device sends this data to a server, which analyzes it and evaluates the user's health status. The input here is the health information collected from the wearable device, and the output is an evaluation of the user's health status as an analysis result.

[0393] Step 4:

[0394] The server provides the health and diet-related information stored in the database to an AI algorithm (using TensorFlow and PyTorch) to generate an optimized meal plan for the user. Here, the input is the health and diet-related information, and the output is the optimized meal plan.

[0395] Step 5:

[0396] The server sends the generated meal plan to the terminal. The terminal notifies and displays this meal plan to the user. The user selects their preferred menu from the proposed meal plan. The input here is the optimized meal plan, and the output is the meal plan that is notified and displayed to the user.

[0397] Step 6:

[0398] The server creates a specific recipe using a generative AI model (such as GPT-3 or ChatGPT) based on the menu selected by the user. The server then sends the generated recipe to the device, which then displays it to the user. The input here is the selected menu, and the output is the specific recipe provided to the user.

[0399] Step 7:

[0400] The emotion engine built into the system recognizes and analyzes the user's emotions through the device's camera and microphone. The user's voice and facial expression data are collected and input into the emotion engine, where the input is the user's voice and facial expression data, and the output is analyzed emotional data.

[0401] Step 8:

[0402] The server uses the emotion data analyzed by the emotion engine to tailor the meal plan, especially if the user is feeling stressed, to include foods with a relaxing effect. The input here is the analyzed emotion data, and the output is the tailored meal plan.

[0403] Step 9:

[0404] The server sends the adjusted plan to the terminal, which notifies and displays it to the user. The input here is the adjusted meal plan, and the output is the adjusted meal plan notified and displayed to the user.

[0405] (Application example 2)

[0406] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0407] Preventing dementia in the elderly requires a comprehensive approach that considers not only their health status and dietary content, but also their daily emotional state. However, conventional systems do not take emotional state into account, making it difficult to provide optimized meal plans. Additionally, there are issues with the effort required for elderly people to actually prepare meals and understanding what ingredients to use. Furthermore, there is a lack of coordination with delivery services that actually provide the proposed meal plans.

[0408] 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.

[0409] In this invention, the server includes means for acquiring user health condition information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, means for recognizing the user's emotional state, means for adjusting the meal plan based on the recognized emotional state, and means for arranging delivery of meals based on the adjusted meal plan. This makes it possible to provide an optimized meal plan that comprehensively takes into account the user's health condition, diet history, and emotional state, and to deliver actual meals based on the plan.

[0410] The "means for acquiring user's health condition information" refers to a device or system for acquiring physiological data such as the user's heart rate, number of steps, sleep time, blood pressure, etc.

[0411] "Means for obtaining information related to the user's diet" refers to a device or system for obtaining information such as the foods and ingredients consumed by the user, the amount and frequency of intake, and nutrients.

[0412] "Means for generating a user-optimized meal plan" refers to a system or algorithm for planning and creating a meal plan that is most suitable for an individual user based on the user's health status information and diet-related information.

[0413] "Means for suggesting the generated meal plan to the user" means means for informing or displaying the generated meal plan to the user, which is primarily provided through an application or web platform.

[0414] A "means for generating specific recipes" is a system or algorithm for automatically generating recipes that specify cooking steps and required ingredients for food based on a meal plan selected by a user.

[0415] "Means for providing the generated recipe to the user" refers to means for notifying or displaying the specific generated recipe to the user, and is primarily provided through an application or web platform.

[0416] "Means for recognizing the user's emotional state" refers to a system or algorithm that analyzes and recognizes the user's emotions from their facial expressions, voice, words, etc.

[0417] A "means for adjusting a meal plan based on a recognized emotional state" is a system or algorithm for modifying or replanning a pre-generated meal plan based on a recognized emotional state of a user.

[0418] A "meal delivery arrangement" is a system or service that arranges for the delivery of appropriate meals to a user based on a coordinated meal plan.

[0419] This invention is a system for supporting dementia prevention in the elderly, providing an optimized meal plan and specific recipes based on the user's health status information and diet-related information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system aims to adjust the meal plan according to the user's psychological state, thereby achieving more effective dementia prevention. This system is operated by combining a server, a terminal, a wearable device, and the emotion engine.

[0420] System Overview

[0421] The system has the following features:

[0422] 1. The user registers in the system.

[0423] 2. The user takes a photo of their usual meal using the device's camera.

[0424] 3. Wearable devices collect information about the user's health status.

[0425] 4. The server generates an optimized meal plan based on the health status information and diet-related information.

[0426] 5. The server sends the generated meal plan to the device and suggests it to the user.

[0427] 6. The server generates a specific recipe based on the menu selected by the user.

[0428] 7. The emotion engine built into the system recognizes the user's emotions.

[0429] 8. The server analyzes the emotional data recognized by the emotion engine and adjusts the meal plan.

[0430] 9. Arrange for delivery for servers to serve meals based on coordinated plans.

[0431] Embodiment

[0432] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server then stores the received information in a database.

[0433] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server, which uses image analysis algorithms to extract the meal contents and nutritional information and stores it in a database.

[0434] 3. The user wears a wearable device to collect health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device then sends this data to a server, which analyzes it and evaluates the user's health status.

[0435] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm to generate an optimized meal plan for the user.

[0436] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[0437] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[0438] 7. The emotion engine built into the system analyzes voice and facial expressions to recognize the user's emotions. When the user uses the device, emotion data is collected in real time through the camera and microphone.

[0439] 8. The server analyzes the emotion data recognized by the emotion engine and adjusts the meal plan accordingly. For example, if the user is feeling stressed, it will suggest a plan that includes foods that have a relaxing effect.

[0440] 9. The server arranges delivery of meals based on the tailored plan. For example, based on the meal plan selected by the user, the server may coordinate with nearby delivery services to deliver the required meals.

[0441] Hardware and software used

[0442] Devices: Smartphones, tablets, etc.

[0443] Server: A server that supports cloud-based databases and AI algorithms.

[0444] Wearable devices: Smartwatches, fitness trackers, etc.

[0445] Emotion engine: Software that performs facial expression analysis and speech recognition, such as Microsoft Azure's Emotion API or Google Cloud's Speech-to-Text API.

[0446] Generative AI: AI models for creating meal plans and recipes, such as OpenAI's GPT model.

[0447] Specific examples

[0448] Example 1

[0449] 1. A 70-year-old male user registers in the system and enters his basic information.

[0450] 2. The user takes a photo of the yogurt and fruit they ate for breakfast on their device and uploads it to the server.

[0451] 3. The wearable device collects the user's steps, heart rate, and sleep time and sends them to the server.

[0452] 4. The server analyzes this data, creates a meal plan for the next day, and sends it to the user's device.

[0453] 5. The user selects "oatmeal and fruit" from the suggested meal plan, and the server generates a specific recipe.

[0454] 6. The emotion engine recognizes the user's stress while preparing breakfast.

[0455] 7. The server analyzes the emotional data, generates a new meal plan, and suggests it to the user.

[0456] 8. The server arranges for a meal delivery service based on the optimized meal plan and delivers it to the user.

[0457] Prompt Sentence Examples

[0458] "Analyze the user's emotional state and provide the optimal meal plan."

[0459] "Place delivery orders based on your meal plan."

[0460] Through these processes, the present invention comprehensively considers the user's health condition, dietary history, and emotional state to propose an optimal meal plan and deliver appropriate meals based on that plan, thereby supporting elderly people in choosing meals that are effective in preventing dementia.

[0461] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0462] Step 1:

[0463] A user registers in the system.

[0464] Input: User's basic information (name, age, gender, medical history).

[0465] Processing: The terminal inputs the user's basic information and sends it to the server, which stores the received information in a database.

[0466] Output: The user's basic information is saved in the database and a registration completion notification is displayed on the terminal.

[0467] Step 2:

[0468] Users take photos of their usual meals using the device's camera.

[0469] Input: Food images.

[0470] Processing: The device takes a photo of the meal and uploads the image data to a server, which uses image analysis algorithms to extract meal content and nutritional information.

[0471] Output: The analyzed dietary content and nutritional information is stored in a database.

[0472] Step 3:

[0473] The wearable device collects information about the user's health status.

[0474] Input: User health data (steps, heart rate, sleep duration).

[0475] Processing: The wearable device collects daily health data and sends it to the server, which analyzes the data and evaluates the user's health.

[0476] Output: The user's health status assessment is stored in a database.

[0477] Step 4:

[0478] The server generates an optimized meal plan based on the health and diet-related information.

[0479] Input: Health status information, diet-related information.

[0480] Processing: The server feeds this data into an AI algorithm to generate an optimized meal plan for the user.

[0481] Output: The generated meal plan is stored in a database and sent to the device.

[0482] Step 5:

[0483] The server sends the generated meal plan to the device and suggests it to the user.

[0484] Input: The generated meal plan.

[0485] Processing: The server sends the meal plan to the terminal, which notifies and displays it to the user.

[0486] Output: A meal plan suggestion notification is displayed to the user.

[0487] Step 6:

[0488] The user selects their preferred menu from the suggested meal plans.

[0489] Input: The menu selected by the user.

[0490] Processing: The device accepts the user's selection and sends it to the server, which uses a generation AI to generate a specific recipe based on the menu selection.

[0491] Output: The generated concrete recipe is sent to the terminal and displayed to the user.

[0492] Step 7:

[0493] The emotion engine recognizes the user's emotions.

[0494] Input: User's facial and voice data.

[0495] Processing: While the user is using the device, emotional data is collected in real time through the camera and microphone. The emotion engine analyzes this data to recognize the user's emotional state.

[0496] Output: The recognized emotional state data is sent to the server.

[0497] Step 8:

[0498] The server analyzes the emotional data recognized by the emotion engine and adjusts the meal plan.

[0499] Input: Recognized emotional state data.

[0500] Processing: The server analyzes the emotional data and generates a new meal plan that includes foods that have a relaxing effect if the user is feeling stressed.

[0501] Output: The adjusted meal plan is stored in the database and sent to the device.

[0502] Step 9:

[0503] The server arranges delivery to provide meals based on the optimized meal plan.

[0504] Enter: a tailored meal plan.

[0505] Processing: The server coordinates with the delivery service to arrange for the required meal to be delivered.

[0506] Output: A notification that delivery has been arranged will be displayed on the terminal.

[0507] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0508] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0509] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0510] [Second embodiment]

[0511] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0512] 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.

[0513] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0514] 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.

[0515] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0516] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0517] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0518] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0523] This invention is a system for supporting dementia prevention in the elderly, providing optimized meal plans and specific recipes based on the user's health status and diet-related information. This system is operated by combining a server, a terminal, and a wearable device.

[0524] System Overview

[0525] The system has the following features:

[0526] 1. Obtaining user health status information

[0527] 2. Obtain information related to the user's diet

[0528] 3. Generate a personalized meal plan for the user based on their health and diet-related information

[0529] 4. Providing the generated meal plan to the user

[0530] 5. Generate specific recipes based on the menu the user selects from the meal plan.

[0531] 6. Provide the generated recipe to the user

[0532] Embodiment

[0533] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server stores the received information in a database.

[0534] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server, which uses image analysis algorithms to extract the meal contents and nutritional information and stores it in a database.

[0535] 3. The user wears a wearable device to collect health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device then sends this data to a server, which analyzes it and evaluates the user's health status.

[0536] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm to generate an optimized meal plan for the user.

[0537] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[0538] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[0539] Specific examples

[0540] Day 1 flow

[0541] 1. At 8:00 a.m., a user has fruit and yogurt for breakfast. Before that, the user takes a photo of the meal using the device's camera, and the device sends the image to the server.

[0542] 2. The server analyzes the image, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and stores it in the database.

[0543] 3. The wearable device collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to the server, which then evaluates the user's health status based on this information.

[0544] 4. Based on the data from that day, the server generates a meal plan for the next day and sends it to the device: "Breakfast: oatmeal and fruit, lunch: salmon and salad, dinner: chicken and vegetable soup."

[0545] 5. The next morning, the user checks the plan and selects the breakfast menu "Oatmeal and Fruit." The server generates a specific recipe (how to cook the oatmeal, the type and amount of fruit required) and sends it to the device. The device displays this recipe to the user, and the user prepares breakfast.

[0546] According to the above-described embodiments, the present invention can provide an optimized meal plan that takes into account the individual health condition and dietary preferences of the user, and can assist in selecting meals that are effective in preventing dementia in the elderly.

[0547] The processing flow will be explained below.

[0548] Step 1:

[0549] A user registers with the system by entering basic information (such as name, age, gender, medical history, etc.) into a terminal, which then sends it to the server.

[0550] Step 2:

[0551] The server stores the received user basic information in a database.

[0552] Step 3:

[0553] The user takes a photo of their usual meal using the device's camera.

[0554] Step 4:

[0555] The device uploads the captured image of the meal to the server.

[0556] Step 5:

[0557] The server uses image analysis algorithms to extract meal content and nutritional information.

[0558] Step 6:

[0559] The server stores the extracted dietary and nutrient information in a database.

[0560] Step 7:

[0561] Users wear a wearable device to collect health information such as the number of steps taken each day, heart rate, and sleep status.

[0562] Step 8:

[0563] The wearable device sends the collected health information to a server.

[0564] Step 9:

[0565] The health status information received by the server is stored in a database and analyzed.

[0566] Step 10:

[0567] The server provides health and diet-related information stored in a database to an AI algorithm, which generates an optimized meal plan for the user.

[0568] Step 11:

[0569] The server sends the generated meal plan to the terminal.

[0570] Step 12:

[0571] The device will notify and display the received meal plan to the user.

[0572] Step 13:

[0573] The user selects their preferred menu from the suggested meal plans.

[0574] Step 14:

[0575] The terminal transmits the menu information selected by the user to the server.

[0576] Step 15:

[0577] The server uses generative AI to create a specific recipe based on the selected menu.

[0578] Step 16:

[0579] The server sends the generated recipe to the device.

[0580] Step 17:

[0581] The device displays the received recipe to the user, who then performs the actual cooking while looking at it.

[0582] Step 18:

[0583] After the user has finished eating, they report "meal completed" on the terminal.

[0584] Step 19:

[0585] The terminal sends a completion report to the server.

[0586] Step 20:

[0587] The server analyzes the effectiveness of the meal plan based on the completion report and reflects this in the next proposal.

[0588] Example 1

[0589] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0590] To prevent dementia and manage the health of elderly people, it is necessary to provide optimal meal plans that comprehensively consider each individual's health condition and dietary content. However, conventional systems have limited means of efficiently acquiring and analyzing a user's health condition and dietary information, and have not been able to adequately generate optimal meal plans and provide specific recipes. Furthermore, it has been difficult to generate individually optimized meal plans in real time based on detailed analysis of daily activity information and dietary content.

[0591] 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.

[0592] In this invention, the server includes means for acquiring user health condition information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, means for analyzing the health condition information using a specific health condition evaluation algorithm, means for analyzing dietary content using image analysis technology, and means for generating an optimized meal plan using an AI model. This makes it possible to analyze the user's health condition and dietary content in detail and provide an individually optimized meal plan and specific recipes.

[0593] "Means for obtaining user health status information" refers to the functionality of a device or software that records a user's physical condition and medical history and can update it as needed.

[0594] "Means for obtaining information related to the user's diet" refers to the functionality of a device or software that records and stores detailed information about the dietary content and nutritional information of the user's meals.

[0595] "Means for generating a meal plan optimized for a user based on the acquired health status information and diet-related information" refers to an algorithm or program for creating the most appropriate meal plan based on the user's collected health status information and diet information.

[0596] The "means for proposing the generated meal plan to the user" is an interface or application for informing the user of the contents of the generated meal plan and visually displaying it.

[0597] "Means for generating specific recipes based on the menu selected by the user from the suggested meal plan" refers to an algorithm or program that provides specific cooking instructions and ingredients based on the meal plan selected by the user.

[0598] The "means for providing the generated recipe to the user" refers to an interface or application for notifying the user of the generated cooking recipe and visually displaying it.

[0599] "Means for analyzing health status information using a specific health status assessment algorithm" refers to an algorithm or program for analyzing the collected health status information and assessing the user's overall health status.

[0600] "Means for analyzing meal contents using image analysis technology" refers to image analysis technology or software for analyzing photographed images of meals and extracting the meal contents and nutritional components.

[0601] "Means for generating optimized meal plans using AI models" refers to algorithms or programs that use artificial intelligence to create optimal meal plans for users based on collected data.

[0602] A "wearable device that collects a user's daily activity information" is a device or apparatus that collects a user's daily physical activity and biometric information and transmits this information to a server.

[0603] The "means for obtaining nutrient information" refers to a means for identifying the nutritional components of the food consumed by the user and recording this as data.

[0604] This invention is a system to support dementia prevention in the elderly, and provides optimized meal plans and specific recipes based on the user's health status and diet-related information. This system is operated by combining a server, a terminal, and a wearable device.

[0605] System Overview

[0606] The system has the following features:

[0607] 1. Obtaining user health status information

[0608] 2. Obtain information related to the user's diet

[0609] 3. Generate a personalized meal plan for the user based on their health and diet-related information

[0610] 4. Providing the generated meal plan to the user

[0611] 5. Generate specific recipes based on the menu the user selects from the meal plan.

[0612] 6. Provide the generated recipe to the user

[0613] Embodiment

[0614] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server stores the received information in a database.

[0615] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server. The server uses an image analysis algorithm (e.g., Google Cloud Vision API) to extract the meal contents and nutritional information and store it in a database.

[0616] 3. A user wears a wearable device (e.g., Fitbit) and collects health information such as daily steps, heart rate, sleep status, etc. The wearable device sends this data to a server, which analyzes it and evaluates the user's health status (e.g., using AWS Lambda or TensorFlow).

[0617] 4. The server provides the health and diet-related information stored in the database to an AI algorithm (e.g., OpenAI's generative AI model) to generate an optimized meal plan for the user.

[0618] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[0619] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[0620] Specific examples

[0621] Day 1 flow

[0622] 1. At 8:00 a.m., a user has fruit and yogurt for breakfast. Before that, the user takes a photo of the meal using the device's camera, and the device sends the image to the server.

[0623] 2. The server analyzes the image using the Google Cloud Vision API, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and saves it in the database.

[0624] 3. A wearable device (e.g., Fitbit) collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to a server. The server uses AWS Lambda and TensorFlow to evaluate the user's health status based on this information.

[0625] 4. Based on the data from that day, the server generates a meal plan for the next day and sends it to the device: "Breakfast: oatmeal and fruit, lunch: fish and salad, dinner: meat and vegetable soup."

[0626] 5. The next morning, the user checks the plan and selects the breakfast menu "Oatmeal and Fruit." The server uses a generative AI model (e.g., OpenAI's generative AI model) to generate a specific recipe (how to cook oatmeal, the type and amount of fruit required) and sends it to the device. The device displays this recipe to the user, who then prepares breakfast.

[0627] Prompt Sentence Examples

[0628] Here are some example prompts to input to the AI ​​generator:

[0629] Data entry: Name: Taro Tanaka Age: 68 Gender: Male Breakfast: 100g yogurt, 1 banana, 50g blueberries Health data: Steps: 8000, Heart rate: 70 bpm, Sleep time: 7 hours

[0630] Output: Generate an optimal meal plan for tomorrow.

[0631] In this way, users are provided with optimal meal plans and specific recipes to support their daily health management.

[0632] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0633] Step 1:

[0634] The user registers with the system by entering basic information such as name, age, gender, and medical history into the terminal, which then sends the entered data to the server.

[0635] Specific operation: When a user enters the required information into the input form on the device and presses the "Submit" button, the data is sent to the server using HTTPS. The server then stores the received data in a database.

[0636] Input: Name, age, gender, medical history

[0637] Output: User basic information stored in the database

[0638] Step 2:

[0639] The user takes a photo of their meal using the device's camera, and the device uploads the captured image data to the server.

[0640] Specific operation: The user takes a photo of the meal and taps the send image button. The device temporarily stores the image data and uploads it to the API endpoint using the communication module.

[0641] Input: Food image

[0642] Output: Food image data sent to the server

[0643] Step 3:

[0644] The server analyzes the image of the meal and uses the Google Cloud Vision API to extract information about the meal and its nutritional content.

[0645] Specific operation: The server sends the received image data to the Google Cloud Vision API and writes the returned analysis results to the database.

[0646] Input: Food image data sent to the server

[0647] Output: Dietary and nutritional information stored in a database

[0648] Step 4:

[0649] The user wears a wearable device to collect health information, such as the number of steps taken, heart rate, and sleep status, and sends this data to a server.

[0650] Specific operation: The wearable device sends health status information to the terminal via Bluetooth, which receives it and uploads it to the server.

[0651] Input: Step count, heart rate, and sleep status data obtained from a wearable device

[0652] Output: Health status information sent to the server

[0653] Step 5:

[0654] The server analyzes and evaluates the health status information. The server uses AWS Lambda and TensorFlow to analyze the received data and evaluate the user's health status.

[0655] Specific operation: The server uses AWS Lambda to perform data analysis and writes the results to the database.

[0656] Input: Health status information sent to the server

[0657] Output: Analysis results and assessed health status information stored in a database

[0658] Step 6:

[0659] The server generates a meal plan, which provides the health and diet-related information stored in the database to an AI algorithm to generate an optimized meal plan for the user.

[0660] What it does: The server retrieves the necessary data from the database, sends it to the generative AI model to create a meal plan, and then sends the plan in JSON format to the device.

[0661] Input: Health and dietary information stored in a database

[0662] Output: Generated meal plan

[0663] Step 7:

[0664] The device notifies the user of the meal plan. The device displays the meal plan received from the server to the user, and the user selects the desired menu from the plan.

[0665] What happens: The device notifies the user of the meal plan via push notification, and the user makes a selection in the app.

[0666] Input: Meal plan sent from server

[0667] Output: The meal plan displayed to the user and the user's selections

[0668] Step 8:

[0669] The server generates a recipe, using a generation AI based on the menu selected by the user to generate a specific recipe.

[0670] Specific operation: The server passes the selected menu to the generation AI as a prompt sentence, and sends the returned recipe data to the terminal.

[0671] Input: The menu selected by the user

[0672] Output: The generated recipe

[0673] Step 9:

[0674] The terminal displays the recipe to the user. The terminal displays the specific recipe data received from the server to the user.

[0675] Specific operation: The device displays the received recipe data on the app's UI, and the user confirms it.

[0676] Input: Specific recipe data sent from the server

[0677] Output: The recipe displayed to the user

[0678] (Application example 1)

[0679] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0680] To effectively support dementia prevention in the elderly, it is important to provide optimal meal plans tailored to each individual's health condition and dietary preferences. However, conventional systems have difficulty generating meal plans that fully reflect the user's health condition and diet-related information. Furthermore, they lack the functionality to track the degree to which the generated meal plans and recipes are actually being followed and to reflect this in future recommendations. To address these issues, a system is needed that can generate accurate, personalized meal plans and track their implementation.

[0681] 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.

[0682] In this invention, the server includes means for acquiring user health status information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health status information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, means for ordering meals from a food delivery service, and means for recording the user's diet history in a database and reflecting this in future suggestions. This allows for the provision of optimal meal plans for individual users and enables continuous health management through the management and tracking of dietary history.

[0683] "Means for acquiring user health status information" refers to a method for collecting health data such as the user's heart rate, number of steps, and sleep time from a wearable device or the like and providing it to a server.

[0684] The "means of obtaining information related to the user's diet" refers to a method of extracting dietary content and nutritional information by analyzing photos of the meals taken by the user and storing the information in a database.

[0685] "Means for generating a meal plan optimized for a user based on the acquired health status information and diet-related information" refers to a method that uses an AI algorithm based on collected data to create a meal plan that is best suited to the user's health status and dietary preferences.

[0686] "Means for proposing the generated meal plan to the user" refers to a method for sending the meal plan generated by the server to the user's terminal and notifying and displaying it to the user.

[0687] "Means for generating specific recipes based on a menu selected by a user from a suggested meal plan" refers to a method that uses a generative AI model to generate detailed cooking instructions and information on the ingredients required based on a menu selected by the user.

[0688] "Means for providing the generated recipe to the user" refers to a method for sending the generated recipe to the user's terminal so that the user can view and use it.

[0689] "Means for ordering meals from a food delivery service" refers to a method of arranging for meals to be delivered through a partner online ordering service based on the generated meal plan.

[0690] "Means of recording the user's dietary history in a database and reflecting it in future suggestions" refers to a method of storing the history of the meals the user actually ate in a database and reflecting it in future meal plan suggestions.

[0691] This invention is a system that effectively supports dementia prevention in the elderly, providing optimized meal plans and specific recipes based on the user's health status and diet-related information. This system is operated by combining a server, a terminal, and a wearable device.

[0692] System Overview

[0693] The system has the following features:

[0694] 1. Wearable devices (e.g., Apple Watch, Fitbit) are used to obtain information about the user's health status. These devices collect data such as the number of steps taken each day, heart rate, and sleep status, and send it to a server.

[0695] 2. To obtain information related to the user's diet, the device camera takes a photo of the meal and sends the image to the server, which then uses an image analysis algorithm (e.g., Amazon Rekognition, Google Cloud Vision) to extract information about the meal and its nutrients, which are then stored in a database.

[0696] 3. The server uses AI algorithms (e.g., TensorFlow, PyTorch) to generate a meal plan optimized for the user based on the acquired health status information and diet-related information, which analyzes the collected data and generates a meal plan that is optimal for the user's health status and dietary preferences.

[0697] 4. As a means of proposing the generated meal plan to the user, the server sends the generated plan to the terminal, which notifies and displays it to the user.

[0698] 5. The server uses a generative AI model to generate specific recipes based on the menus selected by the user from the suggested meal plans, generating specific cooking instructions and information on ingredients needed, which are then sent to the device and displayed to the user.

[0699] 6. As a means of providing the generated recipe to the user, the terminal displays the generated recipe so that the user can view and use it.

[0700] 7. As a means of ordering meals from a food delivery service, the server uses the API of the partner online ordering service (e.g., Uber Eats, DoorDash) to deliver meals based on the generated meal plan.

[0701] 8. As a means of recording the user's dietary history in a database and reflecting it in future suggestions, the server will store the history of the meals the user actually ate in a database and reflect this in future meal plan suggestions.

[0702] Specific examples

[0703] 1. The user takes a photo of their breakfast and their device sends the image to the server, which then sends a prompt to the image analysis service asking them to identify the type and amount of food in the image and return nutritional information.

[0704] 2. The server uses an AI algorithm to generate a meal plan and sends it to the device with the prompt, "Generate the optimal meal plan for this user based on the entered health information and dietary preferences."

[0705] 3. The user selects a menu from the proposed plan, and the server sends a prompt to the generative AI model to "generate a specific recipe based on the selected menu."

[0706] 4. The recipe is generated and displayed on the device, where the user can review the recipe, gather the necessary ingredients, and prepare the meal.

[0707] 5. The food order is automatically placed through an online ordering service and delivered to the user.

[0708] 6. The user's meal history is recorded in a database and reflected in future meal plans.

[0709] According to the above-described embodiments, the present invention can provide an optimized meal plan that takes into account the individual health condition and dietary preferences of each user, and can assist in selecting meals that are effective in preventing dementia in the elderly.

[0710] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0711] Step 1:

[0712] User takes a photo of their meal

[0713] The user takes a photo of the meal using the device's camera. The device then sends the image to the server. The input is the photo of the meal taken by the user, and the output is the transfer of image data to the server.

[0714] Step 2:

[0715] The server performs image analysis

[0716] The server passes the received image data to an image analysis algorithm. The image analysis service (e.g., Amazon Rekognition, Google Cloud Vision) processes the data according to the prompt, "Please identify the type and quantity of food from this image and return nutritional information." The input is the image data of the meal, and the output is the analyzed type of food and nutritional information.

[0717] Step 3:

[0718] Collecting health status information from wearable devices

[0719] A user wears a wearable device (e.g., Apple Watch, Fitbit) that collects daily steps, heart rate, sleep status, etc. and sends the data to a server. The input is the health status data collected from the wearable device, and the output is the data sent to the server.

[0720] Step 4:

[0721] The server integrates and analyzes the data

[0722] The server integrates the image analysis results with the health status information sent from the wearable device, and then uses AI algorithms (e.g., TensorFlow, PyTorch) to generate a meal plan optimized for the user. The input is the diet-related information and health status information, and the output is the optimal meal plan for the user.

[0723] Example prompt: "Generate the optimal meal plan for this user based on their health information and dietary preferences."

[0724] Step 5:

[0725] The server sends the generated meal plan to the device.

[0726] The generated meal plan is sent from the server to the terminal, which notifies and displays this information to the user. The input is the generated meal plan, and the output is the transmission to the terminal and the notification to the user.

[0727] Step 6:

[0728] User checks meal plan and selects menu

[0729] The user can view the proposed meal plans on the device and select their preferred menu from them. The input is the meal plan display screen on the device, and the output is the selected menu.

[0730] Step 7:

[0731] The server generates a specific recipe

[0732] When a user selects a menu, the server generates a specific recipe based on that menu. It sends a prompt to the generative AI model saying, "Please generate a specific recipe based on the selected menu." The input is the menu selected by the user, and the output is the generated specific recipe.

[0733] Step 8:

[0734] The server sends the generated recipe to the device.

[0735] The generated recipe is sent from the server to the terminal, and the terminal displays the recipe to the user. The input is the generated recipe, and the output is sent to the terminal and displayed to the user.

[0736] Step 9:

[0737] Order food online

[0738] The server orders meals based on the generated meal plan using the API of a partner online ordering service. The input is the generated meal plan, and the output is order data sent to the online ordering service.

[0739] Step 10:

[0740] Food history database storage

[0741] The server stores the user's actual dietary history in a database and reflects it in creating future meal plans. The input is the user's dietary history data, and the output is the data stored in the database.

[0742] Through these steps, the system takes into account the user's individual health condition and dietary preferences, provides an optimized meal plan with specific recipes, and assists in carrying out the plan through a food delivery service.

[0743] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0744] This invention is a system for supporting dementia prevention in the elderly, providing an optimized meal plan and specific recipes based on the user's health status information and diet-related information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system aims to adjust the meal plan according to the user's psychological state, thereby achieving more effective dementia prevention. This system is operated by combining a server, a terminal, a wearable device, and the emotion engine.

[0745] System Overview

[0746] The system has the following features:

[0747] 1. Obtaining user health status information

[0748] 2. Obtain information related to the user's diet

[0749] 3. Generate a personalized meal plan for the user based on their health and diet-related information

[0750] 4. Providing the generated meal plan to the user

[0751] 5. Generate specific recipes based on the menu the user selects from the meal plan.

[0752] 6. Provide the generated recipe to the user

[0753] 7. Includes an emotion engine that recognizes user emotions

[0754] 8. Adjust your meal plan based on emotions identified by the emotion engine

[0755] Embodiment

[0756] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server stores the received information in a database.

[0757] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server, which uses image analysis algorithms to extract the meal contents and nutritional information and stores it in a database.

[0758] 3. The user wears a wearable device to collect health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device then sends this data to a server, which analyzes it and evaluates the user's health status.

[0759] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm to generate an optimized meal plan for the user.

[0760] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[0761] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[0762] 7. The emotion engine built into the system analyzes voice and facial expressions to recognize the user's emotions. When the user uses the device, emotion data is collected in real time through the camera and microphone.

[0763] 8. The server analyzes the emotion data recognized by the emotion engine and adjusts the meal plan accordingly. For example, if the user is feeling stressed, it will suggest a plan that includes foods that have a relaxing effect.

[0764] 9. The server sends the adjusted plan to the device and notifies the user.

[0765] Specific examples

[0766] Day 1 flow

[0767] 1. At 8:00 a.m., a user has fruit and yogurt for breakfast. Before that, the user takes a photo of the meal using the device's camera, and the device sends the image to the server.

[0768] 2. The server analyzes the image, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and stores it in the database.

[0769] 3. The wearable device collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to the server, which then evaluates the user's health status based on this information.

[0770] 4. Based on the data from that day, the server generates a meal plan for the next day and sends it to the device: "Breakfast: oatmeal and fruit, lunch: salmon and salad, dinner: chicken and vegetable soup."

[0771] 5. The next morning, the user checks the plan and selects the breakfast menu "Oatmeal and Fruit." The server generates a specific recipe (how to cook the oatmeal, the type and amount of fruit required) and sends it to the device. The device displays this recipe to the user, and the user prepares breakfast.

[0772] 6. While preparing breakfast, the emotion engine analyzes the user's facial expressions and recognizes that they are feeling stressed.

[0773] 7. The server analyzes the emotional data and generates a new meal plan including foods that have stress-relieving effects, and suggests it to the user.

[0774] Through the above-described embodiments, the present invention can provide an optimized meal plan that takes into account not only the user's individual health condition and dietary preferences, but also their emotional state, and can assist the elderly in choosing meals that are effective in preventing dementia.

[0775] The processing flow will be explained below.

[0776] Step 1:

[0777] A user registers with the system by entering basic information (such as name, age, gender, and medical history) into the terminal, which then sends it to the server.

[0778] Step 2:

[0779] The server stores the received user basic information in a database.

[0780] Step 3:

[0781] The user takes photos of their daily meals using the device's camera.

[0782] Step 4:

[0783] The device takes a photo of the meal and uploads it to the server.

[0784] Step 5:

[0785] The server uses image analysis algorithms to extract food content and nutritional information, identifying ingredients such as yogurt, bananas, and blueberries.

[0786] Step 6:

[0787] The server stores the extracted dietary and nutrient information in a database.

[0788] Step 7:

[0789] Users wear a wearable device to collect health information such as the number of steps taken each day, heart rate, and sleep status.

[0790] Step 8:

[0791] The wearable device sends the collected health information to a server.

[0792] Step 9:

[0793] The server stores the received health information in a database and performs analysis, such as recording the user's daily steps, heart rate, and sleep time, to evaluate their health.

[0794] Step 10:

[0795] The server provides health and diet-related information stored in a database to an AI algorithm, which then generates an optimized meal plan for the user.

[0796] Step 11:

[0797] The server sends the generated meal plan to the terminal.

[0798] Step 12:

[0799] The device notifies the user of the received meal plan and displays it, and the user can select their preferred menu from the proposed meal plan.

[0800] Step 13:

[0801] The user selects their preferred menu from the suggested meal plans.

[0802] Step 14:

[0803] The terminal transmits the menu information selected by the user to the server.

[0804] Step 15:

[0805] The server uses generative AI to create a specific recipe based on the selected menu.

[0806] Step 16:

[0807] The server sends the generated recipe to the device.

[0808] Step 17:

[0809] The device displays the received recipe to the user.

[0810] Step 18:

[0811] The user actually cooks the food while looking at the recipe.

[0812] Step 19:

[0813] The system's built-in emotion engine uses the camera to analyze the user's facial expressions, collecting emotional data as the user uses the device.

[0814] Step 20:

[0815] The emotion engine analyzes the user's voice and facial expressions to identify emotions, such as stress or satisfaction.

[0816] Step 20:

[0817] The server analyzes the emotional data recognized by the emotion engine and adjusts the meal plan as needed. For example, if the user is feeling stressed, it will re-suggest a plan that includes foods that have a relaxing effect.

[0818] Step 21:

[0819] The server sends the adjusted plan to the terminal, which notifies the user.

[0820] Step 22:

[0821] After the user has eaten, they report "meal completed" on the terminal.

[0822] Step 23:

[0823] The terminal sends a completion report to the server.

[0824] Step 24:

[0825] The server analyzes the effectiveness of the meal plan based on the completion report and reflects this in the next proposal.

[0826] Example 2

[0827] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0828] Conventional dementia prevention support systems for the elderly primarily provide plans based on the user's health condition and dietary information, but do not optimize the meal plan by taking into account the user's emotions and psychological state. This creates a problem that makes it difficult to effectively prevent dementia. The present invention aims to solve this problem by providing an optimized meal plan that comprehensively takes into account the user's health condition, dietary information, and emotional state.

[0829] 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.

[0830] In this invention, the server includes means for acquiring user health condition information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, and means for recognizing the user's emotions and adjusting the meal plan based thereon, thereby making it possible to provide a meal plan that takes into account not only the user's health condition and diet information but also their emotional state.

[0831] "User's health condition information" is data that indicates the user's physical condition, and specifically includes information such as the number of steps taken, heart rate, and sleep status.

[0832] "Information related to the user's diet" refers to data related to the food and drinks consumed by the user, including, specifically, dietary content and nutritional information.

[0833] "Means for generating meal plans" refers to an algorithm or program that creates an optimized meal menu for a user based on acquired health and diet-related information.

[0834] "Means for suggesting the generated meal plan to the user" means a device or software capable of informing and displaying the generated meal plan to the user.

[0835] "Means for generating specific recipes" refers to a generative AI model or algorithm that provides specific cooking instructions and ingredient quantities based on a user's menu selections.

[0836] "Means for providing the generated recipe to the user" refers to a device or software that has the function of notifying and displaying the generated recipe to the user.

[0837] "Means for recognizing user emotions and adjusting meal plans accordingly" refers to an emotion engine or algorithm that analyzes a user's emotional data (voice, facial expressions) and adjusts an optimized meal plan based on the results.

[0838] A "wearable device" refers to an electronic device that can be worn by a user to collect health status information and daily activity information.

[0839] This invention is a system for supporting dementia prevention in the elderly, providing an optimized meal plan and specific recipes based on the user's health status information and diet-related information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system aims to adjust the meal plan according to the user's psychological state, thereby achieving more effective dementia prevention. This system is operated by combining a server, a terminal, a wearable device, and the emotion engine.

[0840] Hardware and software used

[0841] The system uses the following hardware and software:

[0842] Server: The main device for collecting and analyzing user information. The server stores the database and runs AI algorithms (e.g., TensorFlow or PyTorch).

[0843] Device: The device where a user enters meal information and views the meal plans and recipes provided. This could be a smartphone or tablet.

[0844] Wearable devices: Devices that collect health information such as the number of steps taken, heart rate, and sleep status of the user. Examples include fitness bands and smartwatches.

[0845] Emotion engine: Software for analyzing user emotions, such as those used for speech recognition and facial expression analysis (e.g., Amazon Rekognition and Microsoft Azure Face API).

[0846] Detailed System Description

[0847] 1. When a user uses the system for the first time, they enter basic information (such as name, age, gender, medical history, etc.) into the terminal, which then sends this information to the server. The server then stores the received information in a database.

[0848] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server. The server uses image analysis algorithms (e.g., OpenCV or TensorFlow) to extract information about the meal and its nutrients, and stores it in a database.

[0849] 3. The wearable device worn by the user collects health information such as the number of steps taken, heart rate, and sleep status, and sends this information to a server. The server analyzes this information and evaluates the user's health condition.

[0850] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm, which generates an optimized meal plan for the user using machine learning models based on TensorFlow and PyTorch.

[0851] 5. The server sends the generated meal plan to the device, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[0852] 6. Based on the selected menu, the server uses a generative AI model (e.g., GPT-3 or ChatGPT) to create a specific recipe. The generated recipe is sent to the device and displayed to the user.

[0853] 7. The emotion engine built into the system analyzes voice and facial expressions to recognize the user's emotions. Emotion data is collected in real time using the device's camera and microphone.

[0854] 8. The server adjusts the meal plan as needed based on the analysis results of the emotion engine. For example, if the user is feeling stressed, it will suggest a meal plan that includes ingredients that have a relaxing effect.

[0855] 9. The server sends the adjusted plan to the device and notifies the user.

[0856] Example flow

[0857] The specific flow for the first day is shown below.

[0858] 1. At 8:00 AM, before eating fruit and yogurt for breakfast, the user takes a photo of the meal using the device's camera.

[0859] 2. The device sends the image to the server.

[0860] 3. The server analyzes the image, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and saves this in the database.

[0861] 4. The wearable device collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to the server.

[0862] 5. The server then evaluates the user's health based on this and generates a meal plan for the next day.

[0863] 6. The server sends the following plan to the device: "Breakfast: oatmeal and fruit, lunch: salmon and salad, dinner: chicken and vegetable soup."

[0864] 7. The next morning, the user reviews the plan and selects the breakfast menu "Oatmeal and Fruit."

[0865] 8. The server generates a specific recipe (how to cook oatmeal, the type and amount of fruit required) and sends it to the device.

[0866] 9. The device displays this recipe to the user, who then prepares breakfast.

[0867] 10. While preparing breakfast, the emotion engine analyzes the user's facial expressions and recognizes that they are feeling stressed.

[0868] 11. The server analyzes the emotional data, generates a new meal plan including foods that have stress-relieving effects, and suggests it to the user.

[0869] Prompt Sentence Examples

[0870] Below is an example of a prompt to ask the generative AI model for a specific recipe.

[0871] Input: What's a healthy breakfast recipe that uses 100g of oatmeal, half an apple, and 50g of blueberries?

[0872] Cook the oatmeal in water or milk, then top with sliced ​​apples and blueberries. Finish with a little honey for a delicious finish.

[0873] By using this prompt, users can easily obtain specific recipes, which can provide users with meal plans that take into account their health condition, food preferences, and emotional state, and help elderly people make effective dietary choices to prevent dementia.

[0874] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0875] Step 1:

[0876] When a user uses the system for the first time, they enter their basic information (name, age, gender, medical history, etc.) into the terminal. The terminal sends this information to the server, which then stores the received information in a database. The input here is the user's basic information, and the output is the user's basic information stored in the database.

[0877] Step 2:

[0878] The user takes a photo of their usual meal using the device's camera. The device then uploads the image to the server. The server then uses an image analysis algorithm (using OpenCV or TensorFlow) to extract the meal contents and nutritional information from the image and stores it in a database. The input here is the meal image, and the output is the meal contents and nutritional information stored in the database.

[0879] Step 3:

[0880] A wearable device worn by a user collects health information such as the number of steps taken each day, heart rate, and sleep status. The wearable device sends this data to a server, which analyzes it and evaluates the user's health status. The input here is the health information collected from the wearable device, and the output is an evaluation of the user's health status as an analysis result.

[0881] Step 4:

[0882] The server provides the health and diet-related information stored in the database to an AI algorithm (using TensorFlow and PyTorch) to generate an optimized meal plan for the user. Here, the input is the health and diet-related information, and the output is the optimized meal plan.

[0883] Step 5:

[0884] The server sends the generated meal plan to the terminal. The terminal notifies and displays this meal plan to the user. The user selects their preferred menu from the proposed meal plan. The input here is the optimized meal plan, and the output is the meal plan that is notified and displayed to the user.

[0885] Step 6:

[0886] The server creates a specific recipe using a generative AI model (such as GPT-3 or ChatGPT) based on the menu selected by the user. The server then sends the generated recipe to the device, which then displays it to the user. The input here is the selected menu, and the output is the specific recipe provided to the user.

[0887] Step 7:

[0888] The emotion engine built into the system recognizes and analyzes the user's emotions through the device's camera and microphone. The user's voice and facial expression data are collected and input into the emotion engine, where the input is the user's voice and facial expression data, and the output is analyzed emotional data.

[0889] Step 8:

[0890] The server uses the emotion data analyzed by the emotion engine to tailor the meal plan, especially if the user is feeling stressed, to include foods with a relaxing effect. The input here is the analyzed emotion data, and the output is the tailored meal plan.

[0891] Step 9:

[0892] The server sends the adjusted plan to the terminal, which notifies and displays it to the user. The input here is the adjusted meal plan, and the output is the adjusted meal plan notified and displayed to the user.

[0893] (Application example 2)

[0894] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0895] Preventing dementia in the elderly requires a comprehensive approach that considers not only their health status and dietary content, but also their daily emotional state. However, conventional systems do not take emotional state into account, making it difficult to provide optimized meal plans. Additionally, there are issues with the effort required for elderly people to actually prepare meals and understanding what ingredients to use. Furthermore, there is a lack of coordination with delivery services that actually provide the proposed meal plans.

[0896] 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.

[0897] In this invention, the server includes means for acquiring user health condition information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, means for recognizing the user's emotional state, means for adjusting the meal plan based on the recognized emotional state, and means for arranging delivery of meals based on the adjusted meal plan. This makes it possible to provide an optimized meal plan that comprehensively takes into account the user's health condition, diet history, and emotional state, and to deliver actual meals based on the plan.

[0898] The "means for acquiring user's health condition information" refers to a device or system for acquiring physiological data such as the user's heart rate, number of steps, sleep time, blood pressure, etc.

[0899] "Means for obtaining information related to the user's diet" refers to a device or system for obtaining information such as the foods and ingredients consumed by the user, the amount and frequency of intake, and nutrients.

[0900] "Means for generating a user-optimized meal plan" refers to a system or algorithm for planning and creating a meal plan that is most suitable for an individual user based on the user's health status information and diet-related information.

[0901] "Means for suggesting the generated meal plan to the user" means means for informing or displaying the generated meal plan to the user, which is primarily provided through an application or web platform.

[0902] A "means for generating specific recipes" is a system or algorithm for automatically generating recipes that specify cooking steps and required ingredients for food based on a meal plan selected by a user.

[0903] "Means for providing the generated recipe to the user" refers to means for notifying or displaying the specific generated recipe to the user, and is primarily provided through an application or web platform.

[0904] "Means for recognizing the user's emotional state" refers to a system or algorithm that analyzes and recognizes the user's emotions from their facial expressions, voice, words, etc.

[0905] A "means for adjusting a meal plan based on a recognized emotional state" is a system or algorithm for modifying or replanning a pre-generated meal plan based on a recognized emotional state of a user.

[0906] A "meal delivery arrangement" is a system or service that arranges for the delivery of appropriate meals to a user based on a coordinated meal plan.

[0907] This invention is a system for supporting dementia prevention in the elderly, providing an optimized meal plan and specific recipes based on the user's health status information and diet-related information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system aims to adjust the meal plan according to the user's psychological state, thereby achieving more effective dementia prevention. This system is operated by combining a server, a terminal, a wearable device, and the emotion engine.

[0908] System Overview

[0909] The system has the following features:

[0910] 1. The user registers in the system.

[0911] 2. The user takes a photo of their usual meal using the device's camera.

[0912] 3. Wearable devices collect information about the user's health status.

[0913] 4. The server generates an optimized meal plan based on the health status information and diet-related information.

[0914] 5. The server sends the generated meal plan to the device and suggests it to the user.

[0915] 6. The server generates a specific recipe based on the menu selected by the user.

[0916] 7. The emotion engine built into the system recognizes the user's emotions.

[0917] 8. The server analyzes the emotional data recognized by the emotion engine and adjusts the meal plan.

[0918] 9. Arrange for delivery for servers to serve meals based on coordinated plans.

[0919] Embodiment

[0920] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server then stores the received information in a database.

[0921] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server, which uses image analysis algorithms to extract the meal contents and nutritional information and stores it in a database.

[0922] 3. The user wears a wearable device to collect health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device then sends this data to a server, which analyzes it and evaluates the user's health status.

[0923] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm to generate an optimized meal plan for the user.

[0924] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[0925] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[0926] 7. The emotion engine built into the system analyzes voice and facial expressions to recognize the user's emotions. When the user uses the device, emotion data is collected in real time through the camera and microphone.

[0927] 8. The server analyzes the emotion data recognized by the emotion engine and adjusts the meal plan accordingly. For example, if the user is feeling stressed, it will suggest a plan that includes foods that have a relaxing effect.

[0928] 9. The server arranges delivery of meals based on the tailored plan. For example, based on the meal plan selected by the user, the server may coordinate with nearby delivery services to deliver the required meals.

[0929] Hardware and software used

[0930] Devices: Smartphones, tablets, etc.

[0931] Server: A server that supports cloud-based databases and AI algorithms.

[0932] Wearable devices: Smartwatches, fitness trackers, etc.

[0933] Emotion engine: Software that performs facial expression analysis and speech recognition, such as Microsoft Azure's Emotion API or Google Cloud's Speech-to-Text API.

[0934] Generative AI: AI models for creating meal plans and recipes, such as OpenAI's GPT model.

[0935] Specific examples

[0936] Example 1

[0937] 1. A 70-year-old male user registers in the system and enters his basic information.

[0938] 2. The user takes a photo of the yogurt and fruit they ate for breakfast on their device and uploads it to the server.

[0939] 3. The wearable device collects the user's steps, heart rate, and sleep time and sends them to the server.

[0940] 4. The server analyzes this data, creates a meal plan for the next day, and sends it to the user's device.

[0941] 5. The user selects "oatmeal and fruit" from the suggested meal plan, and the server generates a specific recipe.

[0942] 6. The emotion engine recognizes the user's stress while preparing breakfast.

[0943] 7. The server analyzes the emotional data, generates a new meal plan, and suggests it to the user.

[0944] 8. The server arranges for a meal delivery service based on the optimized meal plan and delivers it to the user.

[0945] Prompt Sentence Examples

[0946] "Analyze the user's emotional state and provide the optimal meal plan."

[0947] "Place delivery orders based on your meal plan."

[0948] Through these processes, the present invention comprehensively considers the user's health condition, dietary history, and emotional state to propose an optimal meal plan and deliver appropriate meals based on that plan, thereby supporting elderly people in choosing meals that are effective in preventing dementia.

[0949] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0950] Step 1:

[0951] A user registers in the system.

[0952] Input: User's basic information (name, age, gender, medical history).

[0953] Processing: The terminal inputs the user's basic information and sends it to the server, which stores the received information in a database.

[0954] Output: The user's basic information is saved in the database and a registration completion notification is displayed on the terminal.

[0955] Step 2:

[0956] Users take photos of their usual meals using the device's camera.

[0957] Input: Food images.

[0958] Processing: The device takes a photo of the meal and uploads the image data to a server, which uses image analysis algorithms to extract meal content and nutritional information.

[0959] Output: The analyzed dietary content and nutritional information is stored in a database.

[0960] Step 3:

[0961] The wearable device collects information about the user's health status.

[0962] Input: User health data (steps, heart rate, sleep duration).

[0963] Processing: The wearable device collects daily health data and sends it to the server, which analyzes the data and evaluates the user's health.

[0964] Output: The user's health status assessment is stored in a database.

[0965] Step 4:

[0966] The server generates an optimized meal plan based on the health and diet-related information.

[0967] Input: Health status information, diet-related information.

[0968] Processing: The server feeds this data into an AI algorithm to generate an optimized meal plan for the user.

[0969] Output: The generated meal plan is stored in a database and sent to the device.

[0970] Step 5:

[0971] The server sends the generated meal plan to the device and suggests it to the user.

[0972] Input: The generated meal plan.

[0973] Processing: The server sends the meal plan to the terminal, which notifies and displays it to the user.

[0974] Output: A meal plan suggestion notification is displayed to the user.

[0975] Step 6:

[0976] The user selects their preferred menu from the suggested meal plans.

[0977] Input: The menu selected by the user.

[0978] Processing: The device accepts the user's selection and sends it to the server, which uses a generation AI to generate a specific recipe based on the menu selection.

[0979] Output: The generated concrete recipe is sent to the terminal and displayed to the user.

[0980] Step 7:

[0981] The emotion engine recognizes the user's emotions.

[0982] Input: User's facial and voice data.

[0983] Processing: While the user is using the device, emotional data is collected in real time through the camera and microphone. The emotion engine analyzes this data to recognize the user's emotional state.

[0984] Output: The recognized emotional state data is sent to the server.

[0985] Step 8:

[0986] The server analyzes the emotional data recognized by the emotion engine and adjusts the meal plan.

[0987] Input: Recognized emotional state data.

[0988] Processing: The server analyzes the emotional data and generates a new meal plan that includes foods that have a relaxing effect if the user is feeling stressed.

[0989] Output: The adjusted meal plan is stored in the database and sent to the device.

[0990] Step 9:

[0991] The server arranges delivery to provide meals based on the optimized meal plan.

[0992] Enter: a tailored meal plan.

[0993] Processing: The server coordinates with the delivery service to arrange for the required meal to be delivered.

[0994] Output: A notification that delivery has been arranged will be displayed on the terminal.

[0995] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0996] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0997] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0998] [Third embodiment]

[0999] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1000] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1001] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1002] 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.

[1003] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1004] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1005] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1006] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1007] 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.

[1008] 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.

[1009] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1010] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1011] This invention is a system for supporting dementia prevention in the elderly, providing optimized meal plans and specific recipes based on the user's health status and diet-related information. This system is operated by combining a server, a terminal, and a wearable device.

[1012] System Overview

[1013] The system has the following features:

[1014] 1. Obtaining user health status information

[1015] 2. Obtain information related to the user's diet

[1016] 3. Generate a personalized meal plan for the user based on their health and diet-related information

[1017] 4. Providing the generated meal plan to the user

[1018] 5. Generate specific recipes based on the menu the user selects from the meal plan.

[1019] 6. Provide the generated recipe to the user

[1020] Embodiment

[1021] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server stores the received information in a database.

[1022] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server, which uses image analysis algorithms to extract the meal contents and nutritional information and stores it in a database.

[1023] 3. The user wears a wearable device to collect health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device then sends this data to a server, which analyzes it and evaluates the user's health status.

[1024] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm to generate an optimized meal plan for the user.

[1025] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[1026] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[1027] Specific examples

[1028] Day 1 flow

[1029] 1. At 8:00 a.m., a user has fruit and yogurt for breakfast. Before that, the user takes a photo of the meal using the device's camera, and the device sends the image to the server.

[1030] 2. The server analyzes the image, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and stores it in the database.

[1031] 3. The wearable device collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to the server, which then evaluates the user's health status based on this information.

[1032] 4. Based on the data from that day, the server generates a meal plan for the next day and sends it to the device: "Breakfast: oatmeal and fruit, lunch: salmon and salad, dinner: chicken and vegetable soup."

[1033] 5. The next morning, the user checks the plan and selects the breakfast menu "Oatmeal and Fruit." The server generates a specific recipe (how to cook the oatmeal, the type and amount of fruit required) and sends it to the device. The device displays this recipe to the user, and the user prepares breakfast.

[1034] According to the above-described embodiments, the present invention can provide an optimized meal plan that takes into account the individual health condition and dietary preferences of the user, and can assist in selecting meals that are effective in preventing dementia in the elderly.

[1035] The processing flow will be explained below.

[1036] Step 1:

[1037] A user registers with the system by entering basic information (such as name, age, gender, medical history, etc.) into a terminal, which then sends it to the server.

[1038] Step 2:

[1039] The server stores the received user basic information in a database.

[1040] Step 3:

[1041] The user takes a photo of their usual meal using the device's camera.

[1042] Step 4:

[1043] The device uploads the captured image of the meal to the server.

[1044] Step 5:

[1045] The server uses image analysis algorithms to extract meal content and nutritional information.

[1046] Step 6:

[1047] The server stores the extracted dietary and nutrient information in a database.

[1048] Step 7:

[1049] Users wear a wearable device to collect health information such as the number of steps taken each day, heart rate, and sleep status.

[1050] Step 8:

[1051] The wearable device sends the collected health information to a server.

[1052] Step 9:

[1053] The health status information received by the server is stored in a database and analyzed.

[1054] Step 10:

[1055] The server provides health and diet-related information stored in a database to an AI algorithm, which generates an optimized meal plan for the user.

[1056] Step 11:

[1057] The server sends the generated meal plan to the terminal.

[1058] Step 12:

[1059] The device will notify and display the received meal plan to the user.

[1060] Step 13:

[1061] The user selects their preferred menu from the suggested meal plans.

[1062] Step 14:

[1063] The terminal transmits the menu information selected by the user to the server.

[1064] Step 15:

[1065] The server uses generative AI to create a specific recipe based on the selected menu.

[1066] Step 16:

[1067] The server sends the generated recipe to the device.

[1068] Step 17:

[1069] The device displays the received recipe to the user, who then performs the actual cooking while looking at it.

[1070] Step 18:

[1071] After the user has finished eating, they report "meal completed" on the terminal.

[1072] Step 19:

[1073] The terminal sends a completion report to the server.

[1074] Step 20:

[1075] The server analyzes the effectiveness of the meal plan based on the completion report and reflects this in the next proposal.

[1076] Example 1

[1077] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1078] To prevent dementia and manage the health of elderly people, it is necessary to provide optimal meal plans that comprehensively consider each individual's health condition and dietary content. However, conventional systems have limited means of efficiently acquiring and analyzing a user's health condition and dietary information, and have not been able to adequately generate optimal meal plans and provide specific recipes. Furthermore, it has been difficult to generate individually optimized meal plans in real time based on detailed analysis of daily activity information and dietary content.

[1079] 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.

[1080] In this invention, the server includes means for acquiring user health condition information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, means for analyzing the health condition information using a specific health condition evaluation algorithm, means for analyzing dietary content using image analysis technology, and means for generating an optimized meal plan using an AI model. This makes it possible to analyze the user's health condition and dietary content in detail and provide an individually optimized meal plan and specific recipes.

[1081] "Means for obtaining user health status information" refers to the functionality of a device or software that records a user's physical condition and medical history and can update it as needed.

[1082] "Means for obtaining information related to the user's diet" refers to the functionality of a device or software that records and stores detailed information about the dietary content and nutritional information of the user's meals.

[1083] "Means for generating a meal plan optimized for a user based on the acquired health status information and diet-related information" refers to an algorithm or program for creating the most appropriate meal plan based on the user's collected health status information and diet information.

[1084] The "means for proposing the generated meal plan to the user" is an interface or application for informing the user of the contents of the generated meal plan and visually displaying it.

[1085] "Means for generating specific recipes based on the menu selected by the user from the suggested meal plan" refers to an algorithm or program that provides specific cooking instructions and ingredients based on the meal plan selected by the user.

[1086] The "means for providing the generated recipe to the user" refers to an interface or application for notifying the user of the generated cooking recipe and visually displaying it.

[1087] "Means for analyzing health status information using a specific health status assessment algorithm" refers to an algorithm or program for analyzing the collected health status information and assessing the user's overall health status.

[1088] "Means for analyzing meal contents using image analysis technology" refers to image analysis technology or software for analyzing photographed images of meals and extracting the meal contents and nutritional components.

[1089] "Means for generating optimized meal plans using AI models" refers to algorithms or programs that use artificial intelligence to create optimal meal plans for users based on collected data.

[1090] A "wearable device that collects a user's daily activity information" is a device or apparatus that collects a user's daily physical activity and biometric information and transmits this information to a server.

[1091] The "means for obtaining nutrient information" refers to a means for identifying the nutritional components of the food consumed by the user and recording this as data.

[1092] This invention is a system to support dementia prevention in the elderly, and provides optimized meal plans and specific recipes based on the user's health status and diet-related information. This system is operated by combining a server, a terminal, and a wearable device.

[1093] System Overview

[1094] The system has the following features:

[1095] 1. Obtaining user health status information

[1096] 2. Obtain information related to the user's diet

[1097] 3. Generate a personalized meal plan for the user based on their health and diet-related information

[1098] 4. Providing the generated meal plan to the user

[1099] 5. Generate specific recipes based on the menu the user selects from the meal plan.

[1100] 6. Provide the generated recipe to the user

[1101] Embodiment

[1102] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server stores the received information in a database.

[1103] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server. The server uses an image analysis algorithm (e.g., Google Cloud Vision API) to extract the meal contents and nutritional information and store it in a database.

[1104] 3. A user wears a wearable device (e.g., Fitbit) and collects health information such as daily steps, heart rate, sleep status, etc. The wearable device sends this data to a server, which analyzes it and evaluates the user's health status (e.g., using AWS Lambda or TensorFlow).

[1105] 4. The server provides the health and diet-related information stored in the database to an AI algorithm (e.g., OpenAI's generative AI model) to generate an optimized meal plan for the user.

[1106] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[1107] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[1108] Specific examples

[1109] Day 1 flow

[1110] 1. At 8:00 a.m., a user has fruit and yogurt for breakfast. Before that, the user takes a photo of the meal using the device's camera, and the device sends the image to the server.

[1111] 2. The server analyzes the image using the Google Cloud Vision API, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and saves it in the database.

[1112] 3. A wearable device (e.g., Fitbit) collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to a server. The server uses AWS Lambda and TensorFlow to evaluate the user's health status based on this information.

[1113] 4. Based on the data from that day, the server generates a meal plan for the next day and sends it to the device: "Breakfast: oatmeal and fruit, lunch: fish and salad, dinner: meat and vegetable soup."

[1114] 5. The next morning, the user checks the plan and selects the breakfast menu "Oatmeal and Fruit." The server uses a generative AI model (e.g., OpenAI's generative AI model) to generate a specific recipe (how to cook oatmeal, the type and amount of fruit required) and sends it to the device. The device displays this recipe to the user, who then prepares breakfast.

[1115] Prompt Sentence Examples

[1116] Here are some example prompts to input to the AI ​​generator:

[1117] Data entry: Name: Taro Tanaka Age: 68 Gender: Male Breakfast: 100g yogurt, 1 banana, 50g blueberries Health data: Steps: 8000, Heart rate: 70 bpm, Sleep time: 7 hours

[1118] Output: Generate an optimal meal plan for tomorrow.

[1119] In this way, users are provided with optimal meal plans and specific recipes to support their daily health management.

[1120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1121] Step 1:

[1122] The user registers with the system by entering basic information such as name, age, gender, and medical history into the terminal, which then sends the entered data to the server.

[1123] Specific operation: When a user enters the required information into the input form on the device and presses the "Submit" button, the data is sent to the server using HTTPS. The server then stores the received data in a database.

[1124] Input: Name, age, gender, medical history

[1125] Output: User basic information stored in the database

[1126] Step 2:

[1127] The user takes a photo of their meal using the device's camera, and the device uploads the captured image data to the server.

[1128] Specific operation: The user takes a photo of the meal and taps the send image button. The device temporarily stores the image data and uploads it to the API endpoint using the communication module.

[1129] Input: Food image

[1130] Output: Food image data sent to the server

[1131] Step 3:

[1132] The server analyzes the image of the meal and uses the Google Cloud Vision API to extract information about the meal and its nutritional content.

[1133] Specific operation: The server sends the received image data to the Google Cloud Vision API and writes the returned analysis results to the database.

[1134] Input: Food image data sent to the server

[1135] Output: Dietary and nutritional information stored in a database

[1136] Step 4:

[1137] The user wears a wearable device to collect health information, such as the number of steps taken, heart rate, and sleep status, and sends this data to a server.

[1138] Specific operation: The wearable device sends health status information to the terminal via Bluetooth, which receives it and uploads it to the server.

[1139] Input: Step count, heart rate, and sleep status data obtained from a wearable device

[1140] Output: Health status information sent to the server

[1141] Step 5:

[1142] The server analyzes and evaluates the health status information. The server uses AWS Lambda and TensorFlow to analyze the received data and evaluate the user's health status.

[1143] Specific operation: The server uses AWS Lambda to perform data analysis and writes the results to the database.

[1144] Input: Health status information sent to the server

[1145] Output: Analysis results and assessed health status information stored in a database

[1146] Step 6:

[1147] The server generates a meal plan, which provides the health and diet-related information stored in the database to an AI algorithm to generate an optimized meal plan for the user.

[1148] What it does: The server retrieves the necessary data from the database, sends it to the generative AI model to create a meal plan, and then sends the plan in JSON format to the device.

[1149] Input: Health and dietary information stored in a database

[1150] Output: Generated meal plan

[1151] Step 7:

[1152] The device notifies the user of the meal plan. The device displays the meal plan received from the server to the user, and the user selects the desired menu from the plan.

[1153] What happens: The device notifies the user of the meal plan via push notification, and the user makes a selection in the app.

[1154] Input: Meal plan sent from server

[1155] Output: The meal plan displayed to the user and the user's selections

[1156] Step 8:

[1157] The server generates a recipe, using a generation AI based on the menu selected by the user to generate a specific recipe.

[1158] Specific operation: The server passes the selected menu to the generation AI as a prompt sentence, and sends the returned recipe data to the terminal.

[1159] Input: The menu selected by the user

[1160] Output: The generated recipe

[1161] Step 9:

[1162] The terminal displays the recipe to the user. The terminal displays the specific recipe data received from the server to the user.

[1163] Specific operation: The device displays the received recipe data on the app's UI, and the user confirms it.

[1164] Input: Specific recipe data sent from the server

[1165] Output: The recipe displayed to the user

[1166] (Application example 1)

[1167] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1168] To effectively support dementia prevention in the elderly, it is important to provide optimal meal plans tailored to each individual's health condition and dietary preferences. However, conventional systems have difficulty generating meal plans that fully reflect the user's health condition and diet-related information. Furthermore, they lack the functionality to track the degree to which the generated meal plans and recipes are actually being followed and to reflect this in future recommendations. To address these issues, a system is needed that can generate accurate, personalized meal plans and track their implementation.

[1169] 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.

[1170] In this invention, the server includes means for acquiring user health status information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health status information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, means for ordering meals from a food delivery service, and means for recording the user's diet history in a database and reflecting this in future suggestions. This allows for the provision of optimal meal plans for individual users and enables continuous health management through the management and tracking of dietary history.

[1171] "Means for acquiring user health status information" refers to a method for collecting health data such as the user's heart rate, number of steps, and sleep time from a wearable device or the like and providing it to a server.

[1172] The "means of obtaining information related to the user's diet" refers to a method of extracting dietary content and nutritional information by analyzing photos of the meals taken by the user and storing the information in a database.

[1173] "Means for generating a meal plan optimized for a user based on the acquired health status information and diet-related information" refers to a method that uses an AI algorithm based on collected data to create a meal plan that is best suited to the user's health status and dietary preferences.

[1174] "Means for proposing the generated meal plan to the user" refers to a method for sending the meal plan generated by the server to the user's terminal and notifying and displaying it to the user.

[1175] "Means for generating specific recipes based on a menu selected by a user from a suggested meal plan" refers to a method that uses a generative AI model to generate detailed cooking instructions and information on the ingredients required based on a menu selected by the user.

[1176] "Means for providing the generated recipe to the user" refers to a method for sending the generated recipe to the user's terminal so that the user can view and use it.

[1177] "Means for ordering meals from a food delivery service" refers to a method of arranging for meals to be delivered through a partner online ordering service based on the generated meal plan.

[1178] "Means of recording the user's dietary history in a database and reflecting it in future suggestions" refers to a method of storing the history of the meals the user actually ate in a database and reflecting it in future meal plan suggestions.

[1179] This invention is a system that effectively supports dementia prevention in the elderly, providing optimized meal plans and specific recipes based on the user's health status and diet-related information. This system is operated by combining a server, a terminal, and a wearable device.

[1180] System Overview

[1181] The system has the following features:

[1182] 1. Wearable devices (e.g., Apple Watch, Fitbit) are used to obtain information about the user's health status. These devices collect data such as the number of steps taken each day, heart rate, and sleep status, and send it to a server.

[1183] 2. To obtain information related to the user's diet, the device camera takes a photo of the meal and sends the image to the server, which then uses an image analysis algorithm (e.g., Amazon Rekognition, Google Cloud Vision) to extract information about the meal and its nutrients, which are then stored in a database.

[1184] 3. The server uses AI algorithms (e.g., TensorFlow, PyTorch) to generate a meal plan optimized for the user based on the acquired health status information and diet-related information, which analyzes the collected data and generates a meal plan that is optimal for the user's health status and dietary preferences.

[1185] 4. As a means of proposing the generated meal plan to the user, the server sends the generated plan to the terminal, which notifies and displays it to the user.

[1186] 5. The server uses a generative AI model to generate specific recipes based on the menus selected by the user from the suggested meal plans, generating specific cooking instructions and information on ingredients needed, which are then sent to the device and displayed to the user.

[1187] 6. As a means of providing the generated recipe to the user, the terminal displays the generated recipe so that the user can view and use it.

[1188] 7. As a means of ordering meals from a food delivery service, the server uses the API of the partner online ordering service (e.g., Uber Eats, DoorDash) to deliver meals based on the generated meal plan.

[1189] 8. As a means of recording the user's dietary history in a database and reflecting it in future suggestions, the server will store the history of the meals the user actually ate in a database and reflect this in future meal plan suggestions.

[1190] Specific examples

[1191] 1. The user takes a photo of their breakfast and their device sends the image to the server, which then sends a prompt to the image analysis service asking them to identify the type and amount of food in the image and return nutritional information.

[1192] 2. The server uses an AI algorithm to generate a meal plan and sends it to the device with the prompt, "Generate the optimal meal plan for this user based on the entered health information and dietary preferences."

[1193] 3. The user selects a menu from the proposed plan, and the server sends a prompt to the generative AI model to "generate a specific recipe based on the selected menu."

[1194] 4. The recipe is generated and displayed on the device, where the user can review the recipe, gather the necessary ingredients, and prepare the meal.

[1195] 5. The food order is automatically placed through an online ordering service and delivered to the user.

[1196] 6. The user's meal history is recorded in a database and reflected in future meal plans.

[1197] According to the above-described embodiments, the present invention can provide an optimized meal plan that takes into account the individual health condition and dietary preferences of each user, and can assist in selecting meals that are effective in preventing dementia in the elderly.

[1198] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1199] Step 1:

[1200] User takes a photo of their meal

[1201] The user takes a photo of the meal using the device's camera. The device then sends the image to the server. The input is the photo of the meal taken by the user, and the output is the transfer of image data to the server.

[1202] Step 2:

[1203] The server performs image analysis

[1204] The server passes the received image data to an image analysis algorithm. The image analysis service (e.g., Amazon Rekognition, Google Cloud Vision) processes the data according to the prompt, "Please identify the type and quantity of food from this image and return nutritional information." The input is the image data of the meal, and the output is the analyzed type of food and nutritional information.

[1205] Step 3:

[1206] Collecting health status information from wearable devices

[1207] A user wears a wearable device (e.g., Apple Watch, Fitbit) that collects daily steps, heart rate, sleep status, etc. and sends the data to a server. The input is the health status data collected from the wearable device, and the output is the data sent to the server.

[1208] Step 4:

[1209] The server integrates and analyzes the data

[1210] The server integrates the image analysis results with the health status information sent from the wearable device, and then uses AI algorithms (e.g., TensorFlow, PyTorch) to generate a meal plan optimized for the user. The input is the diet-related information and health status information, and the output is the optimal meal plan for the user.

[1211] Example prompt: "Generate the optimal meal plan for this user based on their health information and dietary preferences."

[1212] Step 5:

[1213] The server sends the generated meal plan to the device.

[1214] The generated meal plan is sent from the server to the terminal, which notifies and displays this information to the user. The input is the generated meal plan, and the output is the transmission to the terminal and the notification to the user.

[1215] Step 6:

[1216] User checks meal plan and selects menu

[1217] The user can view the proposed meal plans on the device and select their preferred menu from them. The input is the meal plan display screen on the device, and the output is the selected menu.

[1218] Step 7:

[1219] The server generates a specific recipe

[1220] When a user selects a menu, the server generates a specific recipe based on that menu. It sends a prompt to the generative AI model saying, "Please generate a specific recipe based on the selected menu." The input is the menu selected by the user, and the output is the generated specific recipe.

[1221] Step 8:

[1222] The server sends the generated recipe to the device.

[1223] The generated recipe is sent from the server to the terminal, and the terminal displays the recipe to the user. The input is the generated recipe, and the output is sent to the terminal and displayed to the user.

[1224] Step 9:

[1225] Order food online

[1226] The server orders meals based on the generated meal plan using the API of a partner online ordering service. The input is the generated meal plan, and the output is order data sent to the online ordering service.

[1227] Step 10:

[1228] Food history database storage

[1229] The server stores the user's actual dietary history in a database and reflects it in creating future meal plans. The input is the user's dietary history data, and the output is the data stored in the database.

[1230] Through these steps, the system takes into account the user's individual health condition and dietary preferences, provides an optimized meal plan with specific recipes, and assists in carrying out the plan through a food delivery service.

[1231] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1232] This invention is a system for supporting dementia prevention in the elderly, providing an optimized meal plan and specific recipes based on the user's health status information and diet-related information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system aims to adjust the meal plan according to the user's psychological state, thereby achieving more effective dementia prevention. This system is operated by combining a server, a terminal, a wearable device, and the emotion engine.

[1233] System Overview

[1234] The system has the following features:

[1235] 1. Obtaining user health status information

[1236] 2. Obtain information related to the user's diet

[1237] 3. Generate a personalized meal plan for the user based on their health and diet-related information

[1238] 4. Providing the generated meal plan to the user

[1239] 5. Generate specific recipes based on the menu the user selects from the meal plan.

[1240] 6. Provide the generated recipe to the user

[1241] 7. Includes an emotion engine that recognizes user emotions

[1242] 8. Adjust your meal plan based on emotions identified by the emotion engine

[1243] Embodiment

[1244] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server stores the received information in a database.

[1245] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server, which uses image analysis algorithms to extract the meal contents and nutritional information and stores it in a database.

[1246] 3. The user wears a wearable device to collect health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device then sends this data to a server, which analyzes it and evaluates the user's health status.

[1247] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm to generate an optimized meal plan for the user.

[1248] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[1249] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[1250] 7. The emotion engine built into the system analyzes voice and facial expressions to recognize the user's emotions. When the user uses the device, emotion data is collected in real time through the camera and microphone.

[1251] 8. The server analyzes the emotion data recognized by the emotion engine and adjusts the meal plan accordingly. For example, if the user is feeling stressed, it will suggest a plan that includes foods that have a relaxing effect.

[1252] 9. The server sends the adjusted plan to the device and notifies the user.

[1253] Specific examples

[1254] Day 1 flow

[1255] 1. At 8:00 a.m., a user has fruit and yogurt for breakfast. Before that, the user takes a photo of the meal using the device's camera, and the device sends the image to the server.

[1256] 2. The server analyzes the image, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and stores it in the database.

[1257] 3. The wearable device collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to the server, which then evaluates the user's health status based on this information.

[1258] 4. Based on the data from that day, the server generates a meal plan for the next day and sends it to the device: "Breakfast: oatmeal and fruit, lunch: salmon and salad, dinner: chicken and vegetable soup."

[1259] 5. The next morning, the user checks the plan and selects the breakfast menu "Oatmeal and Fruit." The server generates a specific recipe (how to cook the oatmeal, the type and amount of fruit required) and sends it to the device. The device displays this recipe to the user, and the user prepares breakfast.

[1260] 6. While preparing breakfast, the emotion engine analyzes the user's facial expressions and recognizes that they are feeling stressed.

[1261] 7. The server analyzes the emotional data and generates a new meal plan including foods that have stress-relieving effects, and suggests it to the user.

[1262] Through the above-described embodiments, the present invention can provide an optimized meal plan that takes into account not only the user's individual health condition and dietary preferences, but also their emotional state, and can assist the elderly in choosing meals that are effective in preventing dementia.

[1263] The processing flow will be explained below.

[1264] Step 1:

[1265] A user registers with the system by entering basic information (such as name, age, gender, and medical history) into the terminal, which then sends it to the server.

[1266] Step 2:

[1267] The server stores the received user basic information in a database.

[1268] Step 3:

[1269] The user takes photos of their daily meals using the device's camera.

[1270] Step 4:

[1271] The device takes a photo of the meal and uploads it to the server.

[1272] Step 5:

[1273] The server uses image analysis algorithms to extract food content and nutritional information, identifying ingredients such as yogurt, bananas, and blueberries.

[1274] Step 6:

[1275] The server stores the extracted dietary and nutrient information in a database.

[1276] Step 7:

[1277] Users wear a wearable device to collect health information such as the number of steps taken each day, heart rate, and sleep status.

[1278] Step 8:

[1279] The wearable device sends the collected health information to a server.

[1280] Step 9:

[1281] The server stores the received health information in a database and performs analysis, such as recording the user's daily steps, heart rate, and sleep time, to evaluate their health.

[1282] Step 10:

[1283] The server provides health and diet-related information stored in a database to an AI algorithm, which then generates an optimized meal plan for the user.

[1284] Step 11:

[1285] The server sends the generated meal plan to the terminal.

[1286] Step 12:

[1287] The device notifies the user of the received meal plan and displays it, and the user can select their preferred menu from the proposed meal plan.

[1288] Step 13:

[1289] The user selects their preferred menu from the suggested meal plans.

[1290] Step 14:

[1291] The terminal transmits the menu information selected by the user to the server.

[1292] Step 15:

[1293] The server uses generative AI to create a specific recipe based on the selected menu.

[1294] Step 16:

[1295] The server sends the generated recipe to the device.

[1296] Step 17:

[1297] The device displays the received recipe to the user.

[1298] Step 18:

[1299] The user actually cooks the food while looking at the recipe.

[1300] Step 19:

[1301] The system's built-in emotion engine uses the camera to analyze the user's facial expressions, collecting emotional data as the user uses the device.

[1302] Step 20:

[1303] The emotion engine analyzes the user's voice and facial expressions to identify emotions, such as stress or satisfaction.

[1304] Step 20:

[1305] The server analyzes the emotional data recognized by the emotion engine and adjusts the meal plan as needed. For example, if the user is feeling stressed, it will re-suggest a plan that includes foods that have a relaxing effect.

[1306] Step 21:

[1307] The server sends the adjusted plan to the terminal, which notifies the user.

[1308] Step 22:

[1309] After the user has eaten, they report "meal completed" on the terminal.

[1310] Step 23:

[1311] The terminal sends a completion report to the server.

[1312] Step 24:

[1313] The server analyzes the effectiveness of the meal plan based on the completion report and reflects this in the next proposal.

[1314] Example 2

[1315] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1316] Conventional dementia prevention support systems for the elderly primarily provide plans based on the user's health condition and dietary information, but do not optimize the meal plan by taking into account the user's emotions and psychological state. This creates a problem that makes it difficult to effectively prevent dementia. The present invention aims to solve this problem by providing an optimized meal plan that comprehensively takes into account the user's health condition, dietary information, and emotional state.

[1317] 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.

[1318] In this invention, the server includes means for acquiring user health condition information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, and means for recognizing the user's emotions and adjusting the meal plan based thereon, thereby making it possible to provide a meal plan that takes into account not only the user's health condition and diet information but also their emotional state.

[1319] "User's health condition information" is data that indicates the user's physical condition, and specifically includes information such as the number of steps taken, heart rate, and sleep status.

[1320] "Information related to the user's diet" refers to data related to the food and drinks consumed by the user, including, specifically, dietary content and nutritional information.

[1321] "Means for generating meal plans" refers to an algorithm or program that creates an optimized meal menu for a user based on acquired health and diet-related information.

[1322] "Means for suggesting the generated meal plan to the user" means a device or software capable of informing and displaying the generated meal plan to the user.

[1323] "Means for generating specific recipes" refers to a generative AI model or algorithm that provides specific cooking instructions and ingredient quantities based on a user's menu selections.

[1324] "Means for providing the generated recipe to the user" refers to a device or software that has the function of notifying and displaying the generated recipe to the user.

[1325] "Means for recognizing user emotions and adjusting meal plans accordingly" refers to an emotion engine or algorithm that analyzes a user's emotional data (voice, facial expressions) and adjusts an optimized meal plan based on the results.

[1326] A "wearable device" refers to an electronic device that can be worn by a user to collect health status information and daily activity information.

[1327] This invention is a system for supporting dementia prevention in the elderly, providing an optimized meal plan and specific recipes based on the user's health status information and diet-related information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system aims to adjust the meal plan according to the user's psychological state, thereby achieving more effective dementia prevention. This system is operated by combining a server, a terminal, a wearable device, and the emotion engine.

[1328] Hardware and software used

[1329] The system uses the following hardware and software:

[1330] Server: The main device for collecting and analyzing user information. The server stores the database and runs AI algorithms (e.g., TensorFlow or PyTorch).

[1331] Device: The device where a user enters meal information and views the meal plans and recipes provided. This could be a smartphone or tablet.

[1332] Wearable devices: Devices that collect health information such as the number of steps taken, heart rate, and sleep status of the user. Examples include fitness bands and smartwatches.

[1333] Emotion engine: Software for analyzing user emotions, such as those used for speech recognition and facial expression analysis (e.g., Amazon Rekognition and Microsoft Azure Face API).

[1334] Detailed System Description

[1335] 1. When a user uses the system for the first time, they enter basic information (such as name, age, gender, medical history, etc.) into the terminal, which then sends this information to the server. The server then stores the received information in a database.

[1336] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server. The server uses image analysis algorithms (e.g., OpenCV or TensorFlow) to extract information about the meal and its nutrients, and stores it in a database.

[1337] 3. The wearable device worn by the user collects health information such as the number of steps taken, heart rate, and sleep status, and sends this information to a server. The server analyzes this information and evaluates the user's health condition.

[1338] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm, which generates an optimized meal plan for the user using machine learning models based on TensorFlow and PyTorch.

[1339] 5. The server sends the generated meal plan to the device, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[1340] 6. Based on the selected menu, the server uses a generative AI model (e.g., GPT-3 or ChatGPT) to create a specific recipe. The generated recipe is sent to the device and displayed to the user.

[1341] 7. The emotion engine built into the system analyzes voice and facial expressions to recognize the user's emotions. Emotion data is collected in real time using the device's camera and microphone.

[1342] 8. The server adjusts the meal plan as needed based on the analysis results of the emotion engine. For example, if the user is feeling stressed, it will suggest a meal plan that includes ingredients that have a relaxing effect.

[1343] 9. The server sends the adjusted plan to the device and notifies the user.

[1344] Example flow

[1345] The specific flow for the first day is shown below.

[1346] 1. At 8:00 AM, before eating fruit and yogurt for breakfast, the user takes a photo of the meal using the device's camera.

[1347] 2. The device sends the image to the server.

[1348] 3. The server analyzes the image, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and saves this in the database.

[1349] 4. The wearable device collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to the server.

[1350] 5. The server then evaluates the user's health based on this and generates a meal plan for the next day.

[1351] 6. The server sends the following plan to the device: "Breakfast: oatmeal and fruit, lunch: salmon and salad, dinner: chicken and vegetable soup."

[1352] 7. The next morning, the user reviews the plan and selects the breakfast menu "Oatmeal and Fruit."

[1353] 8. The server generates a specific recipe (how to cook oatmeal, the type and amount of fruit required) and sends it to the device.

[1354] 9. The device displays this recipe to the user, who then prepares breakfast.

[1355] 10. While preparing breakfast, the emotion engine analyzes the user's facial expressions and recognizes that they are feeling stressed.

[1356] 11. The server analyzes the emotional data, generates a new meal plan including foods that have stress-relieving effects, and suggests it to the user.

[1357] Prompt Sentence Examples

[1358] Below is an example of a prompt to ask the generative AI model for a specific recipe.

[1359] Input: What's a healthy breakfast recipe that uses 100g of oatmeal, half an apple, and 50g of blueberries?

[1360] Cook the oatmeal in water or milk, then top with sliced ​​apples and blueberries. Finish with a little honey for a delicious finish.

[1361] By using this prompt, users can easily obtain specific recipes, which can provide users with meal plans that take into account their health condition, food preferences, and emotional state, and help elderly people make effective dietary choices to prevent dementia.

[1362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1363] Step 1:

[1364] When a user uses the system for the first time, they enter their basic information (name, age, gender, medical history, etc.) into the terminal. The terminal sends this information to the server, which then stores the received information in a database. The input here is the user's basic information, and the output is the user's basic information stored in the database.

[1365] Step 2:

[1366] The user takes a photo of their usual meal using the device's camera. The device then uploads the image to the server. The server then uses an image analysis algorithm (using OpenCV or TensorFlow) to extract the meal contents and nutritional information from the image and stores it in a database. The input here is the meal image, and the output is the meal contents and nutritional information stored in the database.

[1367] Step 3:

[1368] A wearable device worn by a user collects health information such as the number of steps taken each day, heart rate, and sleep status. The wearable device sends this data to a server, which analyzes it and evaluates the user's health status. The input here is the health information collected from the wearable device, and the output is an evaluation of the user's health status as an analysis result.

[1369] Step 4:

[1370] The server provides the health and diet-related information stored in the database to an AI algorithm (using TensorFlow and PyTorch) to generate an optimized meal plan for the user. Here, the input is the health and diet-related information, and the output is the optimized meal plan.

[1371] Step 5:

[1372] The server sends the generated meal plan to the terminal. The terminal notifies and displays this meal plan to the user. The user selects their preferred menu from the proposed meal plan. The input here is the optimized meal plan, and the output is the meal plan that is notified and displayed to the user.

[1373] Step 6:

[1374] The server creates a specific recipe using a generative AI model (such as GPT-3 or ChatGPT) based on the menu selected by the user. The server then sends the generated recipe to the device, which then displays it to the user. The input here is the selected menu, and the output is the specific recipe provided to the user.

[1375] Step 7:

[1376] The emotion engine built into the system recognizes and analyzes the user's emotions through the device's camera and microphone. The user's voice and facial expression data are collected and input into the emotion engine, where the input is the user's voice and facial expression data, and the output is analyzed emotional data.

[1377] Step 8:

[1378] The server uses the emotion data analyzed by the emotion engine to tailor the meal plan, especially if the user is feeling stressed, to include foods with a relaxing effect. The input here is the analyzed emotion data, and the output is the tailored meal plan.

[1379] Step 9:

[1380] The server sends the adjusted plan to the terminal, which notifies and displays it to the user. The input here is the adjusted meal plan, and the output is the adjusted meal plan notified and displayed to the user.

[1381] (Application example 2)

[1382] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1383] Preventing dementia in the elderly requires a comprehensive approach that considers not only their health status and dietary content, but also their daily emotional state. However, conventional systems do not take emotional state into account, making it difficult to provide optimized meal plans. Additionally, there are issues with the effort required for elderly people to actually prepare meals and understanding what ingredients to use. Furthermore, there is a lack of coordination with delivery services that actually provide the proposed meal plans.

[1384] 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.

[1385] In this invention, the server includes means for acquiring user health condition information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, means for recognizing the user's emotional state, means for adjusting the meal plan based on the recognized emotional state, and means for arranging delivery of meals based on the adjusted meal plan. This makes it possible to provide an optimized meal plan that comprehensively takes into account the user's health condition, diet history, and emotional state, and to deliver actual meals based on the plan.

[1386] The "means for acquiring user's health condition information" refers to a device or system for acquiring physiological data such as the user's heart rate, number of steps, sleep time, blood pressure, etc.

[1387] "Means for obtaining information related to the user's diet" refers to a device or system for obtaining information such as the foods and ingredients consumed by the user, the amount and frequency of intake, and nutrients.

[1388] "Means for generating a user-optimized meal plan" refers to a system or algorithm for planning and creating a meal plan that is most suitable for an individual user based on the user's health status information and diet-related information.

[1389] "Means for suggesting the generated meal plan to the user" means means for informing or displaying the generated meal plan to the user, which is primarily provided through an application or web platform.

[1390] A "means for generating specific recipes" is a system or algorithm for automatically generating recipes that specify cooking steps and required ingredients for food based on a meal plan selected by a user.

[1391] "Means for providing the generated recipe to the user" refers to means for notifying or displaying the specific generated recipe to the user, and is primarily provided through an application or web platform.

[1392] "Means for recognizing the user's emotional state" refers to a system or algorithm that analyzes and recognizes the user's emotions from their facial expressions, voice, words, etc.

[1393] A "means for adjusting a meal plan based on a recognized emotional state" is a system or algorithm for modifying or replanning a pre-generated meal plan based on a recognized emotional state of a user.

[1394] A "meal delivery arrangement" is a system or service that arranges for the delivery of appropriate meals to a user based on a coordinated meal plan.

[1395] This invention is a system for supporting dementia prevention in the elderly, providing an optimized meal plan and specific recipes based on the user's health status information and diet-related information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system aims to adjust the meal plan according to the user's psychological state, thereby achieving more effective dementia prevention. This system is operated by combining a server, a terminal, a wearable device, and the emotion engine.

[1396] System Overview

[1397] The system has the following features:

[1398] 1. The user registers in the system.

[1399] 2. The user takes a photo of their usual meal using the device's camera.

[1400] 3. Wearable devices collect information about the user's health status.

[1401] 4. The server generates an optimized meal plan based on the health status information and diet-related information.

[1402] 5. The server sends the generated meal plan to the device and suggests it to the user.

[1403] 6. The server generates a specific recipe based on the menu selected by the user.

[1404] 7. The emotion engine built into the system recognizes the user's emotions.

[1405] 8. The server analyzes the emotional data recognized by the emotion engine and adjusts the meal plan.

[1406] 9. Arrange for delivery for servers to serve meals based on coordinated plans.

[1407] Embodiment

[1408] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server then stores the received information in a database.

[1409] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server, which uses image analysis algorithms to extract the meal contents and nutritional information and stores it in a database.

[1410] 3. The user wears a wearable device to collect health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device then sends this data to a server, which analyzes it and evaluates the user's health status.

[1411] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm to generate an optimized meal plan for the user.

[1412] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[1413] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[1414] 7. The emotion engine built into the system analyzes voice and facial expressions to recognize the user's emotions. When the user uses the device, emotion data is collected in real time through the camera and microphone.

[1415] 8. The server analyzes the emotion data recognized by the emotion engine and adjusts the meal plan accordingly. For example, if the user is feeling stressed, it will suggest a plan that includes foods that have a relaxing effect.

[1416] 9. The server arranges delivery of meals based on the tailored plan. For example, based on the meal plan selected by the user, the server may coordinate with nearby delivery services to deliver the required meals.

[1417] Hardware and software used

[1418] Devices: Smartphones, tablets, etc.

[1419] Server: A server that supports cloud-based databases and AI algorithms.

[1420] Wearable devices: Smartwatches, fitness trackers, etc.

[1421] Emotion engine: Software that performs facial expression analysis and speech recognition, such as Microsoft Azure's Emotion API or Google Cloud's Speech-to-Text API.

[1422] Generative AI: AI models for creating meal plans and recipes, such as OpenAI's GPT model.

[1423] Specific examples

[1424] Example 1

[1425] 1. A 70-year-old male user registers in the system and enters his basic information.

[1426] 2. The user takes a photo of the yogurt and fruit they ate for breakfast on their device and uploads it to the server.

[1427] 3. The wearable device collects the user's steps, heart rate, and sleep time and sends them to the server.

[1428] 4. The server analyzes this data, creates a meal plan for the next day, and sends it to the user's device.

[1429] 5. The user selects "oatmeal and fruit" from the suggested meal plan, and the server generates a specific recipe.

[1430] 6. The emotion engine recognizes the user's stress while preparing breakfast.

[1431] 7. The server analyzes the emotional data, generates a new meal plan, and suggests it to the user.

[1432] 8. The server arranges for a meal delivery service based on the optimized meal plan and delivers it to the user.

[1433] Prompt Sentence Examples

[1434] "Analyze the user's emotional state and provide the optimal meal plan."

[1435] "Place delivery orders based on your meal plan."

[1436] Through these processes, the present invention comprehensively considers the user's health condition, dietary history, and emotional state to propose an optimal meal plan and deliver appropriate meals based on that plan, thereby supporting elderly people in choosing meals that are effective in preventing dementia.

[1437] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1438] Step 1:

[1439] A user registers in the system.

[1440] Input: User's basic information (name, age, gender, medical history).

[1441] Processing: The terminal inputs the user's basic information and sends it to the server, which stores the received information in a database.

[1442] Output: The user's basic information is saved in the database and a registration completion notification is displayed on the terminal.

[1443] Step 2:

[1444] Users take photos of their usual meals using the device's camera.

[1445] Input: Food images.

[1446] Processing: The device takes a photo of the meal and uploads the image data to a server, which uses image analysis algorithms to extract meal content and nutritional information.

[1447] Output: The analyzed dietary content and nutritional information is stored in a database.

[1448] Step 3:

[1449] The wearable device collects information about the user's health status.

[1450] Input: User health data (steps, heart rate, sleep duration).

[1451] Processing: The wearable device collects daily health data and sends it to the server, which analyzes the data and evaluates the user's health.

[1452] Output: The user's health status assessment is stored in a database.

[1453] Step 4:

[1454] The server generates an optimized meal plan based on the health and diet-related information.

[1455] Input: Health status information, diet-related information.

[1456] Processing: The server feeds this data into an AI algorithm to generate an optimized meal plan for the user.

[1457] Output: The generated meal plan is stored in a database and sent to the device.

[1458] Step 5:

[1459] The server sends the generated meal plan to the device and suggests it to the user.

[1460] Input: The generated meal plan.

[1461] Processing: The server sends the meal plan to the terminal, which notifies and displays it to the user.

[1462] Output: A meal plan suggestion notification is displayed to the user.

[1463] Step 6:

[1464] The user selects their preferred menu from the suggested meal plans.

[1465] Input: The menu selected by the user.

[1466] Processing: The device accepts the user's selection and sends it to the server, which uses a generation AI to generate a specific recipe based on the menu selection.

[1467] Output: The generated concrete recipe is sent to the terminal and displayed to the user.

[1468] Step 7:

[1469] The emotion engine recognizes the user's emotions.

[1470] Input: User's facial and voice data.

[1471] Processing: While the user is using the device, emotional data is collected in real time through the camera and microphone. The emotion engine analyzes this data to recognize the user's emotional state.

[1472] Output: The recognized emotional state data is sent to the server.

[1473] Step 8:

[1474] The server analyzes the emotional data recognized by the emotion engine and adjusts the meal plan.

[1475] Input: Recognized emotional state data.

[1476] Processing: The server analyzes the emotional data and generates a new meal plan that includes foods that have a relaxing effect if the user is feeling stressed.

[1477] Output: The adjusted meal plan is stored in the database and sent to the device.

[1478] Step 9:

[1479] The server arranges delivery to provide meals based on the optimized meal plan.

[1480] Enter: a tailored meal plan.

[1481] Processing: The server coordinates with the delivery service to arrange for the required meal to be delivered.

[1482] Output: A notification that delivery has been arranged will be displayed on the terminal.

[1483] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1484] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1485] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1486] [Fourth embodiment]

[1487] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1488] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1489] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1490] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1491] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1492] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1493] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1494] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1495] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1496] 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.

[1497] 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.

[1498] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1499] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1500] This invention is a system for supporting dementia prevention in the elderly, providing optimized meal plans and specific recipes based on the user's health status and diet-related information. This system is operated by combining a server, a terminal, and a wearable device.

[1501] System Overview

[1502] The system has the following features:

[1503] 1. Obtaining user health status information

[1504] 2. Obtain information related to the user's diet

[1505] 3. Generate a personalized meal plan for the user based on their health and diet-related information

[1506] 4. Providing the generated meal plan to the user

[1507] 5. Generate specific recipes based on the menu the user selects from the meal plan.

[1508] 6. Provide the generated recipe to the user

[1509] Embodiment

[1510] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server stores the received information in a database.

[1511] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server, which uses image analysis algorithms to extract the meal contents and nutritional information and stores it in a database.

[1512] 3. The user wears a wearable device to collect health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device then sends this data to a server, which analyzes it and evaluates the user's health status.

[1513] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm to generate an optimized meal plan for the user.

[1514] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[1515] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[1516] Specific examples

[1517] Day 1 flow

[1518] 1. At 8:00 a.m., a user has fruit and yogurt for breakfast. Before that, the user takes a photo of the meal using the device's camera, and the device sends the image to the server.

[1519] 2. The server analyzes the image, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and stores it in the database.

[1520] 3. The wearable device collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to the server, which then evaluates the user's health status based on this information.

[1521] 4. Based on the data from that day, the server generates a meal plan for the next day and sends it to the device: "Breakfast: oatmeal and fruit, lunch: salmon and salad, dinner: chicken and vegetable soup."

[1522] 5. The next morning, the user checks the plan and selects the breakfast menu "Oatmeal and Fruit." The server generates a specific recipe (how to cook the oatmeal, the type and amount of fruit required) and sends it to the device. The device displays this recipe to the user, and the user prepares breakfast.

[1523] According to the above-described embodiments, the present invention can provide an optimized meal plan that takes into account the individual health condition and dietary preferences of the user, and can assist in selecting meals that are effective in preventing dementia in the elderly.

[1524] The processing flow will be explained below.

[1525] Step 1:

[1526] A user registers with the system by entering basic information (such as name, age, gender, medical history, etc.) into a terminal, which then sends it to the server.

[1527] Step 2:

[1528] The server stores the received user basic information in a database.

[1529] Step 3:

[1530] The user takes a photo of their usual meal using the device's camera.

[1531] Step 4:

[1532] The device uploads the captured image of the meal to the server.

[1533] Step 5:

[1534] The server uses image analysis algorithms to extract meal content and nutritional information.

[1535] Step 6:

[1536] The server stores the extracted dietary and nutrient information in a database.

[1537] Step 7:

[1538] Users wear a wearable device to collect health information such as the number of steps taken each day, heart rate, and sleep status.

[1539] Step 8:

[1540] The wearable device sends the collected health information to a server.

[1541] Step 9:

[1542] The health status information received by the server is stored in a database and analyzed.

[1543] Step 10:

[1544] The server provides health and diet-related information stored in a database to an AI algorithm, which generates an optimized meal plan for the user.

[1545] Step 11:

[1546] The server sends the generated meal plan to the terminal.

[1547] Step 12:

[1548] The device will notify and display the received meal plan to the user.

[1549] Step 13:

[1550] The user selects their preferred menu from the suggested meal plans.

[1551] Step 14:

[1552] The terminal transmits the menu information selected by the user to the server.

[1553] Step 15:

[1554] The server uses generative AI to create a specific recipe based on the selected menu.

[1555] Step 16:

[1556] The server sends the generated recipe to the device.

[1557] Step 17:

[1558] The device displays the received recipe to the user, who then performs the actual cooking while looking at it.

[1559] Step 18:

[1560] After the user has finished eating, they report "meal completed" on the terminal.

[1561] Step 19:

[1562] The terminal sends a completion report to the server.

[1563] Step 20:

[1564] The server analyzes the effectiveness of the meal plan based on the completion report and reflects this in the next proposal.

[1565] Example 1

[1566] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1567] To prevent dementia and manage the health of elderly people, it is necessary to provide optimal meal plans that comprehensively consider each individual's health condition and dietary content. However, conventional systems have limited means of efficiently acquiring and analyzing a user's health condition and dietary information, and have not been able to adequately generate optimal meal plans and provide specific recipes. Furthermore, it has been difficult to generate individually optimized meal plans in real time based on detailed analysis of daily activity information and dietary content.

[1568] 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.

[1569] In this invention, the server includes means for acquiring user health condition information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, means for analyzing the health condition information using a specific health condition evaluation algorithm, means for analyzing dietary content using image analysis technology, and means for generating an optimized meal plan using an AI model. This makes it possible to analyze the user's health condition and dietary content in detail and provide an individually optimized meal plan and specific recipes.

[1570] "Means for obtaining user health status information" refers to the functionality of a device or software that records a user's physical condition and medical history and can update it as needed.

[1571] "Means for obtaining information related to the user's diet" refers to the functionality of a device or software that records and stores detailed information about the dietary content and nutritional information of the user's meals.

[1572] "Means for generating a meal plan optimized for a user based on the acquired health status information and diet-related information" refers to an algorithm or program for creating the most appropriate meal plan based on the user's collected health status information and diet information.

[1573] The "means for proposing the generated meal plan to the user" is an interface or application for informing the user of the contents of the generated meal plan and visually displaying it.

[1574] "Means for generating specific recipes based on the menu selected by the user from the suggested meal plan" refers to an algorithm or program that provides specific cooking instructions and ingredients based on the meal plan selected by the user.

[1575] The "means for providing the generated recipe to the user" refers to an interface or application for notifying the user of the generated cooking recipe and visually displaying it.

[1576] "Means for analyzing health status information using a specific health status assessment algorithm" refers to an algorithm or program for analyzing the collected health status information and assessing the user's overall health status.

[1577] "Means for analyzing meal contents using image analysis technology" refers to image analysis technology or software for analyzing photographed images of meals and extracting the meal contents and nutritional components.

[1578] "Means for generating optimized meal plans using AI models" refers to algorithms or programs that use artificial intelligence to create optimal meal plans for users based on collected data.

[1579] A "wearable device that collects a user's daily activity information" is a device or apparatus that collects a user's daily physical activity and biometric information and transmits this information to a server.

[1580] The "means for obtaining nutrient information" refers to a means for identifying the nutritional components of the food consumed by the user and recording this as data.

[1581] This invention is a system to support dementia prevention in the elderly, and provides optimized meal plans and specific recipes based on the user's health status and diet-related information. This system is operated by combining a server, a terminal, and a wearable device.

[1582] System Overview

[1583] The system has the following features:

[1584] 1. Obtaining user health status information

[1585] 2. Obtain information related to the user's diet

[1586] 3. Generate a personalized meal plan for the user based on their health and diet-related information

[1587] 4. Providing the generated meal plan to the user

[1588] 5. Generate specific recipes based on the menu the user selects from the meal plan.

[1589] 6. Provide the generated recipe to the user

[1590] Embodiment

[1591] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server stores the received information in a database.

[1592] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server. The server uses an image analysis algorithm (e.g., Google Cloud Vision API) to extract the meal contents and nutritional information and store it in a database.

[1593] 3. A user wears a wearable device (e.g., Fitbit) and collects health information such as daily steps, heart rate, sleep status, etc. The wearable device sends this data to a server, which analyzes it and evaluates the user's health status (e.g., using AWS Lambda or TensorFlow).

[1594] 4. The server provides the health and diet-related information stored in the database to an AI algorithm (e.g., OpenAI's generative AI model) to generate an optimized meal plan for the user.

[1595] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[1596] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[1597] Specific examples

[1598] Day 1 flow

[1599] 1. At 8:00 a.m., a user has fruit and yogurt for breakfast. Before that, the user takes a photo of the meal using the device's camera, and the device sends the image to the server.

[1600] 2. The server analyzes the image using the Google Cloud Vision API, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and saves it in the database.

[1601] 3. A wearable device (e.g., Fitbit) collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to a server. The server uses AWS Lambda and TensorFlow to evaluate the user's health status based on this information.

[1602] 4. Based on the data from that day, the server generates a meal plan for the next day and sends it to the device: "Breakfast: oatmeal and fruit, lunch: fish and salad, dinner: meat and vegetable soup."

[1603] 5. The next morning, the user checks the plan and selects the breakfast menu "Oatmeal and Fruit." The server uses a generative AI model (e.g., OpenAI's generative AI model) to generate a specific recipe (how to cook oatmeal, the type and amount of fruit required) and sends it to the device. The device displays this recipe to the user, who then prepares breakfast.

[1604] Prompt Sentence Examples

[1605] Here are some example prompts to input to the AI ​​generator:

[1606] Data entry: Name: Taro Tanaka Age: 68 Gender: Male Breakfast: 100g yogurt, 1 banana, 50g blueberries Health data: Steps: 8000, Heart rate: 70 bpm, Sleep time: 7 hours

[1607] Output: Generate an optimal meal plan for tomorrow.

[1608] In this way, users are provided with optimal meal plans and specific recipes to support their daily health management.

[1609] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1610] Step 1:

[1611] The user registers with the system by entering basic information such as name, age, gender, and medical history into the terminal, which then sends the entered data to the server.

[1612] Specific operation: When a user enters the required information into the input form on the device and presses the "Submit" button, the data is sent to the server using HTTPS. The server then stores the received data in a database.

[1613] Input: Name, age, gender, medical history

[1614] Output: User basic information stored in the database

[1615] Step 2:

[1616] The user takes a photo of their meal using the device's camera, and the device uploads the captured image data to the server.

[1617] Specific operation: The user takes a photo of the meal and taps the send image button. The device temporarily stores the image data and uploads it to the API endpoint using the communication module.

[1618] Input: Food image

[1619] Output: Food image data sent to the server

[1620] Step 3:

[1621] The server analyzes the image of the meal and uses the Google Cloud Vision API to extract information about the meal and its nutritional content.

[1622] Specific operation: The server sends the received image data to the Google Cloud Vision API and writes the returned analysis results to the database.

[1623] Input: Food image data sent to the server

[1624] Output: Dietary and nutritional information stored in a database

[1625] Step 4:

[1626] The user wears a wearable device to collect health information, such as the number of steps taken, heart rate, and sleep status, and sends this data to a server.

[1627] Specific operation: The wearable device sends health status information to the terminal via Bluetooth, which receives it and uploads it to the server.

[1628] Input: Step count, heart rate, and sleep status data obtained from a wearable device

[1629] Output: Health status information sent to the server

[1630] Step 5:

[1631] The server analyzes and evaluates the health status information. The server uses AWS Lambda and TensorFlow to analyze the received data and evaluate the user's health status.

[1632] Specific operation: The server uses AWS Lambda to perform data analysis and writes the results to the database.

[1633] Input: Health status information sent to the server

[1634] Output: Analysis results and assessed health status information stored in a database

[1635] Step 6:

[1636] The server generates a meal plan, which provides the health and diet-related information stored in the database to an AI algorithm to generate an optimized meal plan for the user.

[1637] What it does: The server retrieves the necessary data from the database, sends it to the generative AI model to create a meal plan, and then sends the plan in JSON format to the device.

[1638] Input: Health and dietary information stored in a database

[1639] Output: Generated meal plan

[1640] Step 7:

[1641] The device notifies the user of the meal plan. The device displays the meal plan received from the server to the user, and the user selects the desired menu from the plan.

[1642] What happens: The device notifies the user of the meal plan via push notification, and the user makes a selection in the app.

[1643] Input: Meal plan sent from server

[1644] Output: The meal plan displayed to the user and the user's selections

[1645] Step 8:

[1646] The server generates a recipe, using a generation AI based on the menu selected by the user to generate a specific recipe.

[1647] Specific operation: The server passes the selected menu to the generation AI as a prompt sentence, and sends the returned recipe data to the terminal.

[1648] Input: The menu selected by the user

[1649] Output: The generated recipe

[1650] Step 9:

[1651] The terminal displays the recipe to the user. The terminal displays the specific recipe data received from the server to the user.

[1652] Specific operation: The device displays the received recipe data on the app's UI, and the user confirms it.

[1653] Input: Specific recipe data sent from the server

[1654] Output: The recipe displayed to the user

[1655] (Application example 1)

[1656] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1657] To effectively support dementia prevention in the elderly, it is important to provide optimal meal plans tailored to each individual's health condition and dietary preferences. However, conventional systems have difficulty generating meal plans that fully reflect the user's health condition and diet-related information. Furthermore, they lack the functionality to track the degree to which the generated meal plans and recipes are actually being followed and to reflect this in future recommendations. To address these issues, a system is needed that can generate accurate, personalized meal plans and track their implementation.

[1658] 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.

[1659] In this invention, the server includes means for acquiring user health status information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health status information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, means for ordering meals from a food delivery service, and means for recording the user's diet history in a database and reflecting this in future suggestions. This allows for the provision of optimal meal plans for individual users and enables continuous health management through the management and tracking of dietary history.

[1660] "Means for acquiring user health status information" refers to a method for collecting health data such as the user's heart rate, number of steps, and sleep time from a wearable device or the like and providing it to a server.

[1661] The "means of obtaining information related to the user's diet" refers to a method of extracting dietary content and nutritional information by analyzing photos of the meals taken by the user and storing the information in a database.

[1662] "Means for generating a meal plan optimized for a user based on the acquired health status information and diet-related information" refers to a method that uses an AI algorithm based on collected data to create a meal plan that is best suited to the user's health status and dietary preferences.

[1663] "Means for proposing the generated meal plan to the user" refers to a method for sending the meal plan generated by the server to the user's terminal and notifying and displaying it to the user.

[1664] "Means for generating specific recipes based on a menu selected by a user from a suggested meal plan" refers to a method that uses a generative AI model to generate detailed cooking instructions and information on the ingredients required based on a menu selected by the user.

[1665] "Means for providing the generated recipe to the user" refers to a method for sending the generated recipe to the user's terminal so that the user can view and use it.

[1666] "Means for ordering meals from a food delivery service" refers to a method of arranging for meals to be delivered through a partner online ordering service based on the generated meal plan.

[1667] "Means of recording the user's dietary history in a database and reflecting it in future suggestions" refers to a method of storing the history of the meals the user actually ate in a database and reflecting it in future meal plan suggestions.

[1668] This invention is a system that effectively supports dementia prevention in the elderly, providing optimized meal plans and specific recipes based on the user's health status and diet-related information. This system is operated by combining a server, a terminal, and a wearable device.

[1669] System Overview

[1670] The system has the following features:

[1671] 1. Wearable devices (e.g., Apple Watch, Fitbit) are used to obtain information about the user's health status. These devices collect data such as the number of steps taken each day, heart rate, and sleep status, and send it to a server.

[1672] 2. To obtain information related to the user's diet, the device camera takes a photo of the meal and sends the image to the server, which then uses an image analysis algorithm (e.g., Amazon Rekognition, Google Cloud Vision) to extract information about the meal and its nutrients, which are then stored in a database.

[1673] 3. The server uses AI algorithms (e.g., TensorFlow, PyTorch) to generate a meal plan optimized for the user based on the acquired health status information and diet-related information, which analyzes the collected data and generates a meal plan that is optimal for the user's health status and dietary preferences.

[1674] 4. As a means of proposing the generated meal plan to the user, the server sends the generated plan to the terminal, which notifies and displays it to the user.

[1675] 5. The server uses a generative AI model to generate specific recipes based on the menus selected by the user from the suggested meal plans, generating specific cooking instructions and information on ingredients needed, which are then sent to the device and displayed to the user.

[1676] 6. As a means of providing the generated recipe to the user, the terminal displays the generated recipe so that the user can view and use it.

[1677] 7. As a means of ordering meals from a food delivery service, the server uses the API of the partner online ordering service (e.g., Uber Eats, DoorDash) to deliver meals based on the generated meal plan.

[1678] 8. As a means of recording the user's dietary history in a database and reflecting it in future suggestions, the server will store the history of the meals the user actually ate in a database and reflect this in future meal plan suggestions.

[1679] Specific examples

[1680] 1. The user takes a photo of their breakfast and their device sends the image to the server, which then sends a prompt to the image analysis service asking them to identify the type and amount of food in the image and return nutritional information.

[1681] 2. The server uses an AI algorithm to generate a meal plan and sends it to the device with the prompt, "Generate the optimal meal plan for this user based on the entered health information and dietary preferences."

[1682] 3. The user selects a menu from the proposed plan, and the server sends a prompt to the generative AI model to "generate a specific recipe based on the selected menu."

[1683] 4. The recipe is generated and displayed on the device, where the user can review the recipe, gather the necessary ingredients, and prepare the meal.

[1684] 5. The food order is automatically placed through an online ordering service and delivered to the user.

[1685] 6. The user's meal history is recorded in a database and reflected in future meal plans.

[1686] According to the above-described embodiments, the present invention can provide an optimized meal plan that takes into account the individual health condition and dietary preferences of each user, and can assist in selecting meals that are effective in preventing dementia in the elderly.

[1687] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1688] Step 1:

[1689] User takes a photo of their meal

[1690] The user takes a photo of the meal using the device's camera. The device then sends the image to the server. The input is the photo of the meal taken by the user, and the output is the transfer of image data to the server.

[1691] Step 2:

[1692] The server performs image analysis

[1693] The server passes the received image data to an image analysis algorithm. The image analysis service (e.g., Amazon Rekognition, Google Cloud Vision) processes the data according to the prompt, "Please identify the type and quantity of food from this image and return nutritional information." The input is the image data of the meal, and the output is the analyzed type of food and nutritional information.

[1694] Step 3:

[1695] Collecting health status information from wearable devices

[1696] A user wears a wearable device (e.g., Apple Watch, Fitbit) that collects daily steps, heart rate, sleep status, etc. and sends the data to a server. The input is the health status data collected from the wearable device, and the output is the data sent to the server.

[1697] Step 4:

[1698] The server integrates and analyzes the data

[1699] The server integrates the image analysis results with the health status information sent from the wearable device, and then uses AI algorithms (e.g., TensorFlow, PyTorch) to generate a meal plan optimized for the user. The input is the diet-related information and health status information, and the output is the optimal meal plan for the user.

[1700] Example prompt: "Generate the optimal meal plan for this user based on their health information and dietary preferences."

[1701] Step 5:

[1702] The server sends the generated meal plan to the device.

[1703] The generated meal plan is sent from the server to the terminal, which notifies and displays this information to the user. The input is the generated meal plan, and the output is the transmission to the terminal and the notification to the user.

[1704] Step 6:

[1705] User checks meal plan and selects menu

[1706] The user can view the proposed meal plans on the device and select their preferred menu from them. The input is the meal plan display screen on the device, and the output is the selected menu.

[1707] Step 7:

[1708] The server generates a specific recipe

[1709] When a user selects a menu, the server generates a specific recipe based on that menu. It sends a prompt to the generative AI model saying, "Please generate a specific recipe based on the selected menu." The input is the menu selected by the user, and the output is the generated specific recipe.

[1710] Step 8:

[1711] The server sends the generated recipe to the device.

[1712] The generated recipe is sent from the server to the terminal, and the terminal displays the recipe to the user. The input is the generated recipe, and the output is sent to the terminal and displayed to the user.

[1713] Step 9:

[1714] Order food online

[1715] The server orders meals based on the generated meal plan using the API of a partner online ordering service. The input is the generated meal plan, and the output is order data sent to the online ordering service.

[1716] Step 10:

[1717] Food history database storage

[1718] The server stores the user's actual dietary history in a database and reflects it in creating future meal plans. The input is the user's dietary history data, and the output is the data stored in the database.

[1719] Through these steps, the system takes into account the user's individual health condition and dietary preferences, provides an optimized meal plan with specific recipes, and assists in carrying out the plan through a food delivery service.

[1720] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1721] This invention is a system for supporting dementia prevention in the elderly, providing an optimized meal plan and specific recipes based on the user's health status information and diet-related information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system aims to adjust the meal plan according to the user's psychological state, thereby achieving more effective dementia prevention. This system is operated by combining a server, a terminal, a wearable device, and the emotion engine.

[1722] System Overview

[1723] The system has the following features:

[1724] 1. Obtaining user health status information

[1725] 2. Obtain information related to the user's diet

[1726] 3. Generate a personalized meal plan for the user based on their health and diet-related information

[1727] 4. Providing the generated meal plan to the user

[1728] 5. Generate specific recipes based on the menu the user selects from the meal plan.

[1729] 6. Provide the generated recipe to the user

[1730] 7. Includes an emotion engine that recognizes user emotions

[1731] 8. Adjust your meal plan based on emotions identified by the emotion engine

[1732] Embodiment

[1733] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server stores the received information in a database.

[1734] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server, which uses image analysis algorithms to extract the meal contents and nutritional information and stores it in a database.

[1735] 3. The user wears a wearable device to collect health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device then sends this data to a server, which analyzes it and evaluates the user's health status.

[1736] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm to generate an optimized meal plan for the user.

[1737] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[1738] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[1739] 7. The emotion engine built into the system analyzes voice and facial expressions to recognize the user's emotions. When the user uses the device, emotion data is collected in real time through the camera and microphone.

[1740] 8. The server analyzes the emotion data recognized by the emotion engine and adjusts the meal plan accordingly. For example, if the user is feeling stressed, it will suggest a plan that includes foods that have a relaxing effect.

[1741] 9. The server sends the adjusted plan to the device and notifies the user.

[1742] Specific examples

[1743] Day 1 flow

[1744] 1. At 8:00 a.m., a user has fruit and yogurt for breakfast. Before that, the user takes a photo of the meal using the device's camera, and the device sends the image to the server.

[1745] 2. The server analyzes the image, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and stores it in the database.

[1746] 3. The wearable device collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to the server, which then evaluates the user's health status based on this information.

[1747] 4. Based on the data from that day, the server generates a meal plan for the next day and sends it to the device: "Breakfast: oatmeal and fruit, lunch: salmon and salad, dinner: chicken and vegetable soup."

[1748] 5. The next morning, the user checks the plan and selects the breakfast menu "Oatmeal and Fruit." The server generates a specific recipe (how to cook the oatmeal, the type and amount of fruit required) and sends it to the device. The device displays this recipe to the user, and the user prepares breakfast.

[1749] 6. While preparing breakfast, the emotion engine analyzes the user's facial expressions and recognizes that they are feeling stressed.

[1750] 7. The server analyzes the emotional data and generates a new meal plan including foods that have stress-relieving effects, and suggests it to the user.

[1751] Through the above-described embodiments, the present invention can provide an optimized meal plan that takes into account not only the user's individual health condition and dietary preferences, but also their emotional state, and can assist the elderly in choosing meals that are effective in preventing dementia.

[1752] The processing flow will be explained below.

[1753] Step 1:

[1754] A user registers with the system by entering basic information (such as name, age, gender, and medical history) into the terminal, which then sends it to the server.

[1755] Step 2:

[1756] The server stores the received user basic information in a database.

[1757] Step 3:

[1758] The user takes photos of their daily meals using the device's camera.

[1759] Step 4:

[1760] The device takes a photo of the meal and uploads it to the server.

[1761] Step 5:

[1762] The server uses image analysis algorithms to extract food content and nutritional information, identifying ingredients such as yogurt, bananas, and blueberries.

[1763] Step 6:

[1764] The server stores the extracted dietary and nutrient information in a database.

[1765] Step 7:

[1766] Users wear a wearable device to collect health information such as the number of steps taken each day, heart rate, and sleep status.

[1767] Step 8:

[1768] The wearable device sends the collected health information to a server.

[1769] Step 9:

[1770] The server stores the received health information in a database and performs analysis, such as recording the user's daily steps, heart rate, and sleep time, to evaluate their health.

[1771] Step 10:

[1772] The server provides health and diet-related information stored in a database to an AI algorithm, which then generates an optimized meal plan for the user.

[1773] Step 11:

[1774] The server sends the generated meal plan to the terminal.

[1775] Step 12:

[1776] The device notifies the user of the received meal plan and displays it, and the user can select their preferred menu from the proposed meal plan.

[1777] Step 13:

[1778] The user selects their preferred menu from the suggested meal plans.

[1779] Step 14:

[1780] The terminal transmits the menu information selected by the user to the server.

[1781] Step 15:

[1782] The server uses generative AI to create a specific recipe based on the selected menu.

[1783] Step 16:

[1784] The server sends the generated recipe to the device.

[1785] Step 17:

[1786] The device displays the received recipe to the user.

[1787] Step 18:

[1788] The user actually cooks the food while looking at the recipe.

[1789] Step 19:

[1790] The system's built-in emotion engine uses the camera to analyze the user's facial expressions, collecting emotional data as the user uses the device.

[1791] Step 20:

[1792] The emotion engine analyzes the user's voice and facial expressions to identify emotions, such as stress or satisfaction.

[1793] Step 20:

[1794] The server analyzes the emotional data recognized by the emotion engine and adjusts the meal plan as needed. For example, if the user is feeling stressed, it will re-suggest a plan that includes foods that have a relaxing effect.

[1795] Step 21:

[1796] The server sends the adjusted plan to the terminal, which notifies the user.

[1797] Step 22:

[1798] After the user has eaten, they report "meal completed" on the terminal.

[1799] Step 23:

[1800] The terminal sends a completion report to the server.

[1801] Step 24:

[1802] The server analyzes the effectiveness of the meal plan based on the completion report and reflects this in the next proposal.

[1803] Example 2

[1804] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1805] Conventional dementia prevention support systems for the elderly primarily provide plans based on the user's health condition and dietary information, but do not optimize the meal plan by taking into account the user's emotions and psychological state. This creates a problem that makes it difficult to effectively prevent dementia. The present invention aims to solve this problem by providing an optimized meal plan that comprehensively takes into account the user's health condition, dietary information, and emotional state.

[1806] 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.

[1807] In this invention, the server includes means for acquiring user health condition information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, and means for recognizing the user's emotions and adjusting the meal plan based thereon, thereby making it possible to provide a meal plan that takes into account not only the user's health condition and diet information but also their emotional state.

[1808] "User's health condition information" is data that indicates the user's physical condition, and specifically includes information such as the number of steps taken, heart rate, and sleep status.

[1809] "Information related to the user's diet" refers to data related to the food and drinks consumed by the user, including, specifically, dietary content and nutritional information.

[1810] "Means for generating meal plans" refers to an algorithm or program that creates an optimized meal menu for a user based on acquired health and diet-related information.

[1811] "Means for suggesting the generated meal plan to the user" means a device or software capable of informing and displaying the generated meal plan to the user.

[1812] "Means for generating specific recipes" refers to a generative AI model or algorithm that provides specific cooking instructions and ingredient quantities based on a user's menu selections.

[1813] "Means for providing the generated recipe to the user" refers to a device or software that has the function of notifying and displaying the generated recipe to the user.

[1814] "Means for recognizing user emotions and adjusting meal plans accordingly" refers to an emotion engine or algorithm that analyzes a user's emotional data (voice, facial expressions) and adjusts an optimized meal plan based on the results.

[1815] A "wearable device" refers to an electronic device that can be worn by a user to collect health status information and daily activity information.

[1816] This invention is a system for supporting dementia prevention in the elderly, providing an optimized meal plan and specific recipes based on the user's health status information and diet-related information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system aims to adjust the meal plan according to the user's psychological state, thereby achieving more effective dementia prevention. This system is operated by combining a server, a terminal, a wearable device, and the emotion engine.

[1817] Hardware and software used

[1818] The system uses the following hardware and software:

[1819] Server: The main device for collecting and analyzing user information. The server stores the database and runs AI algorithms (e.g., TensorFlow or PyTorch).

[1820] Device: The device where a user enters meal information and views the meal plans and recipes provided. This could be a smartphone or tablet.

[1821] Wearable devices: Devices that collect health information such as the number of steps taken, heart rate, and sleep status of the user. Examples include fitness bands and smartwatches.

[1822] Emotion engine: Software for analyzing user emotions, such as those used for speech recognition and facial expression analysis (e.g., Amazon Rekognition and Microsoft Azure Face API).

[1823] Detailed System Description

[1824] 1. When a user uses the system for the first time, they enter basic information (such as name, age, gender, medical history, etc.) into the terminal, which then sends this information to the server. The server then stores the received information in a database.

[1825] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server. The server uses image analysis algorithms (e.g., OpenCV or TensorFlow) to extract information about the meal and its nutrients, and stores it in a database.

[1826] 3. The wearable device worn by the user collects health information such as the number of steps taken, heart rate, and sleep status, and sends this information to a server. The server analyzes this information and evaluates the user's health condition.

[1827] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm, which generates an optimized meal plan for the user using machine learning models based on TensorFlow and PyTorch.

[1828] 5. The server sends the generated meal plan to the device, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[1829] 6. Based on the selected menu, the server uses a generative AI model (e.g., GPT-3 or ChatGPT) to create a specific recipe. The generated recipe is sent to the device and displayed to the user.

[1830] 7. The emotion engine built into the system analyzes voice and facial expressions to recognize the user's emotions. Emotion data is collected in real time using the device's camera and microphone.

[1831] 8. The server adjusts the meal plan as needed based on the analysis results of the emotion engine. For example, if the user is feeling stressed, it will suggest a meal plan that includes ingredients that have a relaxing effect.

[1832] 9. The server sends the adjusted plan to the device and notifies the user.

[1833] Example flow

[1834] The specific flow for the first day is shown below.

[1835] 1. At 8:00 AM, before eating fruit and yogurt for breakfast, the user takes a photo of the meal using the device's camera.

[1836] 2. The device sends the image to the server.

[1837] 3. The server analyzes the image, recognizes it as "100g of yogurt, 1 banana, 50g of blueberries," and saves this in the database.

[1838] 4. The wearable device collects the user's daily activity information (number of steps, heart rate, sleep time, etc.) and sends it to the server.

[1839] 5. The server then evaluates the user's health based on this and generates a meal plan for the next day.

[1840] 6. The server sends the following plan to the device: "Breakfast: oatmeal and fruit, lunch: salmon and salad, dinner: chicken and vegetable soup."

[1841] 7. The next morning, the user reviews the plan and selects the breakfast menu "Oatmeal and Fruit."

[1842] 8. The server generates a specific recipe (how to cook oatmeal, the type and amount of fruit required) and sends it to the device.

[1843] 9. The device displays this recipe to the user, who then prepares breakfast.

[1844] 10. While preparing breakfast, the emotion engine analyzes the user's facial expressions and recognizes that they are feeling stressed.

[1845] 11. The server analyzes the emotional data, generates a new meal plan including foods that have stress-relieving effects, and suggests it to the user.

[1846] Prompt Sentence Examples

[1847] Below is an example of a prompt to ask the generative AI model for a specific recipe.

[1848] Input: What's a healthy breakfast recipe that uses 100g of oatmeal, half an apple, and 50g of blueberries?

[1849] Cook the oatmeal in water or milk, then top with sliced ​​apples and blueberries. Finish with a little honey for a delicious finish.

[1850] By using this prompt, users can easily obtain specific recipes, which can provide users with meal plans that take into account their health condition, food preferences, and emotional state, and help elderly people make effective dietary choices to prevent dementia.

[1851] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1852] Step 1:

[1853] When a user uses the system for the first time, they enter their basic information (name, age, gender, medical history, etc.) into the terminal. The terminal sends this information to the server, which then stores the received information in a database. The input here is the user's basic information, and the output is the user's basic information stored in the database.

[1854] Step 2:

[1855] The user takes a photo of their usual meal using the device's camera. The device then uploads the image to the server. The server then uses an image analysis algorithm (using OpenCV or TensorFlow) to extract the meal contents and nutritional information from the image and stores it in a database. The input here is the meal image, and the output is the meal contents and nutritional information stored in the database.

[1856] Step 3:

[1857] A wearable device worn by a user collects health information such as the number of steps taken each day, heart rate, and sleep status. The wearable device sends this data to a server, which analyzes it and evaluates the user's health status. The input here is the health information collected from the wearable device, and the output is an evaluation of the user's health status as an analysis result.

[1858] Step 4:

[1859] The server provides the health and diet-related information stored in the database to an AI algorithm (using TensorFlow and PyTorch) to generate an optimized meal plan for the user. Here, the input is the health and diet-related information, and the output is the optimized meal plan.

[1860] Step 5:

[1861] The server sends the generated meal plan to the terminal. The terminal notifies and displays this meal plan to the user. The user selects their preferred menu from the proposed meal plan. The input here is the optimized meal plan, and the output is the meal plan that is notified and displayed to the user.

[1862] Step 6:

[1863] The server creates a specific recipe using a generative AI model (such as GPT-3 or ChatGPT) based on the menu selected by the user. The server then sends the generated recipe to the device, which then displays it to the user. The input here is the selected menu, and the output is the specific recipe provided to the user.

[1864] Step 7:

[1865] The emotion engine built into the system recognizes and analyzes the user's emotions through the device's camera and microphone. The user's voice and facial expression data are collected and input into the emotion engine, where the input is the user's voice and facial expression data, and the output is analyzed emotional data.

[1866] Step 8:

[1867] The server uses the emotion data analyzed by the emotion engine to tailor the meal plan, especially if the user is feeling stressed, to include foods with a relaxing effect. The input here is the analyzed emotion data, and the output is the tailored meal plan.

[1868] Step 9:

[1869] The server sends the adjusted plan to the terminal, which notifies and displays it to the user. The input here is the adjusted meal plan, and the output is the adjusted meal plan notified and displayed to the user.

[1870] (Application example 2)

[1871] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1872] Preventing dementia in the elderly requires a comprehensive approach that considers not only their health status and dietary content, but also their daily emotional state. However, conventional systems do not take emotional state into account, making it difficult to provide optimized meal plans. Additionally, there are issues with the effort required for elderly people to actually prepare meals and understanding what ingredients to use. Furthermore, there is a lack of coordination with delivery services that actually provide the proposed meal plans.

[1873] 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.

[1874] In this invention, the server includes means for acquiring user health condition information, means for acquiring information related to the user's diet, means for generating an optimized meal plan for the user based on the acquired health condition information and diet-related information, means for proposing the generated meal plan to the user, means for generating specific recipes based on menus selected by the user from the proposed meal plan, means for providing the generated recipes to the user, means for recognizing the user's emotional state, means for adjusting the meal plan based on the recognized emotional state, and means for arranging delivery of meals based on the adjusted meal plan. This makes it possible to provide an optimized meal plan that comprehensively takes into account the user's health condition, diet history, and emotional state, and to deliver actual meals based on the plan.

[1875] The "means for acquiring user's health condition information" refers to a device or system for acquiring physiological data such as the user's heart rate, number of steps, sleep time, blood pressure, etc.

[1876] "Means for obtaining information related to the user's diet" refers to a device or system for obtaining information such as the foods and ingredients consumed by the user, the amount and frequency of intake, and nutrients.

[1877] "Means for generating a user-optimized meal plan" refers to a system or algorithm for planning and creating a meal plan that is most suitable for an individual user based on the user's health status information and diet-related information.

[1878] "Means for suggesting the generated meal plan to the user" means means for informing or displaying the generated meal plan to the user, which is primarily provided through an application or web platform.

[1879] A "means for generating specific recipes" is a system or algorithm for automatically generating recipes that specify cooking steps and required ingredients for food based on a meal plan selected by a user.

[1880] "Means for providing the generated recipe to the user" refers to means for notifying or displaying the specific generated recipe to the user, and is primarily provided through an application or web platform.

[1881] "Means for recognizing the user's emotional state" refers to a system or algorithm that analyzes and recognizes the user's emotions from their facial expressions, voice, words, etc.

[1882] A "means for adjusting a meal plan based on a recognized emotional state" is a system or algorithm for modifying or replanning a pre-generated meal plan based on a recognized emotional state of a user.

[1883] A "meal delivery arrangement" is a system or service that arranges for the delivery of appropriate meals to a user based on a coordinated meal plan.

[1884] This invention is a system for supporting dementia prevention in the elderly, providing an optimized meal plan and specific recipes based on the user's health status information and diet-related information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system aims to adjust the meal plan according to the user's psychological state, thereby achieving more effective dementia prevention. This system is operated by combining a server, a terminal, a wearable device, and the emotion engine.

[1885] System Overview

[1886] The system has the following features:

[1887] 1. The user registers in the system.

[1888] 2. The user takes a photo of their usual meal using the device's camera.

[1889] 3. Wearable devices collect information about the user's health status.

[1890] 4. The server generates an optimized meal plan based on the health status information and diet-related information.

[1891] 5. The server sends the generated meal plan to the device and suggests it to the user.

[1892] 6. The server generates a specific recipe based on the menu selected by the user.

[1893] 7. The emotion engine built into the system recognizes the user's emotions.

[1894] 8. The server analyzes the emotional data recognized by the emotion engine and adjusts the meal plan.

[1895] 9. Arrange for delivery for servers to serve meals based on coordinated plans.

[1896] Embodiment

[1897] 1. The user registers with the system for the first time. During registration, basic information (name, age, gender, medical history, etc.) is entered into the terminal, which then sends it to the server. The server then stores the received information in a database.

[1898] 2. The user takes a photo of their usual meal with the device's camera, and the device uploads the image to the server, which uses image analysis algorithms to extract the meal contents and nutritional information and stores it in a database.

[1899] 3. The user wears a wearable device to collect health information such as the number of steps taken each day, heart rate, sleep status, etc. The wearable device then sends this data to a server, which analyzes it and evaluates the user's health status.

[1900] 4. The server provides the health and diet-related information stored in the database to the AI ​​algorithm to generate an optimized meal plan for the user.

[1901] 5. The server sends the generated meal plan to the terminal, which notifies and displays it to the user. The user selects their preferred menu from the proposed meal plan.

[1902] 6. The server uses a generative AI to create a specific recipe based on the menu selected by the user. The generated recipe is sent to the device and displayed to the user.

[1903] 7. The emotion engine built into the system analyzes voice and facial expressions to recognize the user's emotions. When the user uses the device, emotion data is collected in real time through the camera and microphone.

[1904] 8. The server analyzes the emotion data recognized by the emotion engine and adjusts the meal plan accordingly. For example, if the user is feeling stressed, it will suggest a plan that includes foods that have a relaxing effect.

[1905] 9. The server arranges delivery of meals based on the tailored plan. For example, based on the meal plan selected by the user, the server may coordinate with nearby delivery services to deliver the required meals.

[1906] Hardware and software used

[1907] Devices: Smartphones, tablets, etc.

[1908] Server: A server that supports cloud-based databases and AI algorithms.

[1909] Wearable devices: Smartwatches, fitness trackers, etc.

[1910] Emotion engine: Software that performs facial expression analysis and speech recognition, such as Microsoft Azure's Emotion API or Google Cloud's Speech-to-Text API.

[1911] Generative AI: AI models for creating meal plans and recipes, such as OpenAI's GPT model.

[1912] Specific examples

[1913] Example 1

[1914] 1. A 70-year-old male user registers in the system and enters his basic information.

[1915] 2. The user takes a photo of the yogurt and fruit they ate for breakfast on their device and uploads it to the server.

[1916] 3. The wearable device collects the user's steps, heart rate, and sleep time and sends them to the server.

[1917] 4. The server analyzes this data, creates a meal plan for the next day, and sends it to the user's device.

[1918] 5. The user selects "oatmeal and fruit" from the suggested meal plan, and the server generates a specific recipe.

[1919] 6. The emotion engine recognizes the user's stress while preparing breakfast.

[1920] 7. The server analyzes the emotional data, generates a new meal plan, and suggests it to the user.

[1921] 8. The server arranges for a meal delivery service based on the optimized meal plan and delivers it to the user.

[1922] Prompt Sentence Examples

[1923] "Analyze the user's emotional state and provide the optimal meal plan."

[1924] "Place delivery orders based on your meal plan."

[1925] Through these processes, the present invention comprehensively considers the user's health condition, dietary history, and emotional state to propose an optimal meal plan and deliver appropriate meals based on that plan, thereby supporting elderly people in choosing meals that are effective in preventing dementia.

[1926] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1927] Step 1:

[1928] A user registers in the system.

[1929] Input: User's basic information (name, age, gender, medical history).

[1930] Processing: The terminal inputs the user's basic information and sends it to the server, which stores the received information in a database.

[1931] Output: The user's basic information is saved in the database and a registration completion notification is displayed on the terminal.

[1932] Step 2:

[1933] Users take photos of their usual meals using the device's camera.

[1934] Input: Food images.

[1935] Processing: The device takes a photo of the meal and uploads the image data to a server, which uses image analysis algorithms to extract meal content and nutritional information.

[1936] Output: The analyzed dietary content and nutritional information is stored in a database.

[1937] Step 3:

[1938] The wearable device collects information about the user's health status.

[1939] Input: User health data (steps, heart rate, sleep duration).

[1940] Processing: The wearable device collects daily health data and sends it to the server, which analyzes the data and evaluates the user's health.

[1941] Output: The user's health status assessment is stored in a database.

[1942] Step 4:

[1943] The server generates an optimized meal plan based on the health and diet-related information.

[1944] Input: Health status information, diet-related information.

[1945] Processing: The server feeds this data into an AI algorithm to generate an optimized meal plan for the user.

[1946] Output: The generated meal plan is stored in a database and sent to the device.

[1947] Step 5:

[1948] The server sends the generated meal plan to the device and suggests it to the user.

[1949] Input: The generated meal plan.

[1950] Processing: The server sends the meal plan to the terminal, which notifies and displays it to the user.

[1951] Output: A meal plan suggestion notification is displayed to the user.

[1952] Step 6:

[1953] The user selects their preferred menu from the suggested meal plans.

[1954] Input: The menu selected by the user.

[1955] Processing: The device accepts the user's selection and sends it to the server, which uses a generation AI to generate a specific recipe based on the menu selection.

[1956] Output: The generated concrete recipe is sent to the terminal and displayed to the user.

[1957] Step 7:

[1958] The emotion engine recognizes the user's emotions.

[1959] Input: User's facial and voice data.

[1960] Processing: While the user is using the device, emotional data is collected in real time through the camera and microphone. The emotion engine analyzes this data to recognize the user's emotional state.

[1961] Output: The recognized emotional state data is sent to the server.

[1962] Step 8:

[1963] The server analyzes the emotional data recognized by the emotion engine and adjusts the meal plan.

[1964] Input: Recognized emotional state data.

[1965] Processing: The server analyzes the emotional data and generates a new meal plan that includes foods that have a relaxing effect if the user is feeling stressed.

[1966] Output: The adjusted meal plan is stored in the database and sent to the device.

[1967] Step 9:

[1968] The server arranges delivery to provide meals based on the optimized meal plan.

[1969] Enter: a tailored meal plan.

[1970] Processing: The server coordinates with the delivery service to arrange for the required meal to be delivered.

[1971] Output: A notification that delivery has been arranged will be displayed on the terminal.

[1972] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1973] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1974] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1975] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1976] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1977] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1978] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1979] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1980] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1981] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based ...

Claims

1. a means for obtaining health status information of a user; a means for obtaining information related to the user's diet; means for generating an optimized meal plan for a user based on the acquired health status information and diet-related information; a means for suggesting the generated meal plan to a user; means for generating specific recipes based on menu selections from the suggested meal plan by the user; a means for providing the generated recipe to a user; A system including:

2. The system of claim 1 , further comprising a wearable device that collects information about a user's daily activities, and optimizes a meal plan based on the collected information about the daily activities.

3. The system of claim 1 , further comprising means for obtaining nutritional information for meals by analyzing images of meals consumed by a user.

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