system

Abstract

Provide a system. 【Solution means】 Means for acquiring user's biological data and evaluating the health status, Means for recording the user's eating habits and preferences, Means for managing the supply status of food ingredients in real time, Means for generating a meal plan based on the acquired information and the user's health status and preferences, Means for automatically ordering the necessary food ingredients based on the meal plan, Means for notifying the user of the generated meal plan, Voice and visual display means for guiding the procedure of cooking at home, Means for cooperating with local food suppliers to procure fresh food ingredients, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern urban life, it is very difficult for users to maintain a healthy diet in their busy daily lives. In particular, optimizing diets based on health conditions, managing food inventories, and adjusting nutritional balances are complicated and require a lot of time and effort. Furthermore, there are also insufficient means for continuously exploring new cuisines and food cultures. In such a situation, there is a problem that it is difficult to achieve an effective diet that suits the lifestyle and health needs of individual users.

Means for Solving the Problems

[0005] 1]<\ This invention includes means for acquiring a user's biometric information and analyzing their health status. It also includes means for registering and recording the user's eating habits and preferences, and means for managing food inventory information in real time. Based on this information, it provides means for generating meal plans tailored to the user's health status and preferences, thereby suggesting optimal meal choices to the user. In addition, it includes means for automatically ordering necessary ingredients and notifying the user of the generated meal plan. Furthermore, it provides voice and augmented reality display means to support the cooking process, making it easier for the user to cook. Moreover, it enables integration with computer-controlled cooking appliances and automatically controls the cooking process, effectively solving the problem.

[0006] "Biometric information" refers to data collected to assess a user's health status, such as heart rate, body temperature, activity level, and sleep duration.

[0007] "Health status" refers to an indicator that shows the user's physical condition, stress level, and overall health, evaluated based on biometric information.

[0008] "Eating habits" refers to information such as the user's past meals, eating patterns, meal times, and meal frequency.

[0009] "Preferences" refer to a user's personal tastes regarding the types of food, seasonings, and cooking methods they enjoy consuming.

[0010] "Inventory information" refers to information such as the type, quantity, and expiration date of food items, obtained from IoT refrigerators and other management systems.

[0011] A "meal plan" is a suggestion of meals with appropriate nutritional balance that is automatically generated considering the user's health condition and preferences.

[0012] "Automatic ordering" is a process that automatically places orders from designated stores or suppliers when necessary ingredients are in short supply.

[0013] "Voice and augmented reality display means" refers to technical means for guiding users visually and audibly through cooking procedures and meal plan details.

[0014] A "computer-controlled cooking appliance" is a cooking device that is controlled by a computer to automatically control cooking time, temperature, and cooking process, thereby assisting the user in their cooking. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system for effectively managing a user's diet. Based on the user's biometric information, eating habits, preferences, and food inventory information, the system aims to propose and implement an optimal meal plan tailored to their health condition and needs.

[0037] First, the server collects biometric information from the user's wearable device. This information includes heart rate, body temperature, activity level, and sleep duration, and is used to assess the user's health. The device then provides an interface for the user to input their food preferences, allergy information, and past eating history. This records their eating habits and preferences.

[0038] Next, the server utilizes data from IoT refrigerators and other devices to manage food inventory information in real time. This includes the type, quantity, and expiration date of the food. This data is analyzed in combination with the user's health status and registered preferences. Based on this, the server automatically generates a meal plan with the optimal nutritional balance for the user.

[0039] The generated meal plan includes options for the user to choose from: cooking at home, delivery, or eating out. The device notifies the user of this meal plan, and the user can modify it according to their preferences. If the user chooses to cook at home, the device guides the user through the cooking process using voice and augmented reality displays. This also includes automated control of the cooking process through integration with smart cooking appliances.

[0040] For example, if a user requests a "healthy and quick lunch," the server will suggest a menu based on past data, such as "grilled chicken and mixed salad." If the necessary ingredients are not in sufficient stock, the system will automatically order them and ensure quick procurement. After the meal, the user can enter feedback into the terminal, which will improve the accuracy of future suggestions.

[0041] Thus, the present invention is a comprehensive system that improves the user's quality of life and supports the maintenance of a healthy diet.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server periodically retrieves the user's biometric information from the wearable device. This information includes heart rate, body temperature, activity level, and sleep duration.

[0045] Step 2:

[0046] The device provides an interface that allows users to input their eating habits, preferences, and allergy information. Users can use this interface to update their past eating history and preferences.

[0047] Step 3:

[0048] The server retrieves food inventory information from the IoT refrigerator. This information includes the type, quantity, and expiration date of the food items, recorded in real time.

[0049] Step 4:

[0050] The server analyzes collected biometric information, eating habits, and preference data to assess the user's health status. Based on these results, it generates a meal plan that takes into account appropriate nutritional balance.

[0051] Step 5:

[0052] The server verifies whether there are enough necessary ingredients based on the generated meal plan, and automatically places orders if there are any shortages.

[0053] Step 6:

[0054] The device notifies the user of a generated meal plan. The notification includes options for cooking at home, delivery, and eating out, and the user can choose according to their preference.

[0055] Step 7:

[0056] If the user chooses to cook their own meal, the device will guide them through the specific cooking steps using voice and augmented reality displays. During this process, the cooking process will be automatically controlled in conjunction with smart cooking appliances.

[0057] Step 8:

[0058] After cooking and enjoying their meal, users enter feedback on their satisfaction level and areas for improvement into their device.

[0059] Step 9:

[0060] The server collects feedback data from users and uses it to generate the next meal plan. This allows the system to continuously improve its accuracy.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] In modern society, busy people find it difficult to maintain a healthy diet. In particular, creating meal plans optimized for individual health needs and managing daily food shortages are challenges. Furthermore, there is a need for efficient cooking support, but a comprehensive system to meet these needs does not yet exist.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for acquiring the user's biometric information and analyzing their health status, means for registering and recording the user's eating habits and preferences, and means for managing the inventory information of available ingredients in real time. This enables the automatic adjustment of meal plans to meet the user's specific needs, making it possible to maintain a healthy and efficient diet.

[0066] "User biometric information" refers to data necessary to indicate a person's health status, such as heart rate, body temperature, activity level, and sleep duration.

[0067] "Dietary habits and preferences" refers to data that includes an individual's tendencies regarding the foods and dishes they prefer to consume on a daily basis, as well as allergy information related to specific foods.

[0068] "Available food inventory information" refers to data on current food storage, including information on the type, quantity, and expiration date of ingredients.

[0069] "Using generative models" refers to a method that uses machine learning algorithms to analyze collected data and automatically generate the optimal meal plan for the user.

[0070] "Methods for guiding the steps of cooking at home" refers to methods that use voice commands or augmented reality technology to guide users through the cooking process so that they can cook efficiently.

[0071] "Feedback data" refers to evaluation information about the dining experience provided by users, including data on taste evaluations and comments on the difficulty of preparation.

[0072] "Computer-controlled cooking equipment" refers to cooking devices that are managed by a program to automatically control the cooking process.

[0073] This invention is a comprehensive system that supports users in maintaining a healthy diet. The embodiments thereof are described below.

[0074] The server collects biometric information from the user's wearable device. This biometric information includes heart rate, body temperature, activity level, and sleep duration, and is acquired using communication methods such as Bluetooth and Wi-Fi. The collected data is analyzed on the server to evaluate the user's health status.

[0075] The terminal provides an interface for users to input their food preferences, allergy information, and past eating history. Specifically, a smartphone application or web interface is used. Users can select their favorite and avoided foods and record their past eating history. This information is sent to a server, and the database of eating habits and preferences is updated.

[0076] The server also acquires food inventory information from IoT refrigerators and other devices. Using IoT sensors, it manages the type, quantity, and expiration date of ingredients in real time. This inventory information is used when creating meal plans using a generative AI model.

[0077] The generative AI model takes into account the user's health status, eating habits, preferences, and inventory information to generate an optimal, nutritionally balanced meal plan. An example of a prompt phrase that can be used is, "Suggest an appropriate lunch menu based on the user's health status and ingredient inventory."

[0078] The device notifies the user of the generated meal plan. The plan includes options such as cooking at home, delivery, and eating out. The user receives this notification and can modify or select the plan according to their preferences.

[0079] For example, if a user requests a healthy and quick lunch, the server might suggest something like "grilled chicken and mixed salad." If the necessary ingredients are in short supply, the system automatically orders them, supporting quick procurement.

[0080] In general, the present invention aims to provide safe and healthy food and improve the lifestyle of busy users in particular.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server acquires biometric information from the user's wearable device. Input data includes heart rate, body temperature, activity level, and sleep duration. Using this input data, the server analyzes the user's health status, detects abnormalities, and evaluates daily changes in health, thereby outputting health indicators. Specifically, data is received via Bluetooth or Wi-Fi communication and stored in a database.

[0084] Step 2:

[0085] The device provides users with an interface for inputting their food preferences, allergy information, and meal history. This input includes manually selected preferred ingredients and allergy information. Based on this, the system updates the user's preference database and outputs data to be used for future menu suggestions. A specific example of this operation is selection on a smartphone application screen.

[0086] Step 3:

[0087] The server retrieves food inventory information from the IoT refrigerator. Inputs include food type, quantity, and expiration date. Based on this data, the inventory management system analyzes the expiration dates and shortages of food items, and outputs a real-time inventory list. IoT sensors automatically send information about each food item to the server, updating the list as needed.

[0088] Step 4:

[0089] The server uses a generative AI model to generate meal plans based on the user's health status, eating habits, preferences, and inventory information. For data processing, the generative AI model uses machine learning algorithms to calculate the optimal meal menu and outputs it as a suggested menu. Based on the example prompt, the generative model is activated with the command, "Suggest an appropriate lunch menu based on the user's health status and ingredient inventory."

[0090] Step 5:

[0091] The terminal notifies the user of the generated meal plan. The input is suggested menu information from the server, which is displayed on the user's terminal screen as options such as cooking at home, delivery, and eating out. Upon receiving this notification, the user reviews the options, makes selections and modifications according to their preferences, and receives output to confirm the final meal plan. Notifications are delivered via pop-ups or push notifications.

[0092] Step 6:

[0093] The device guides users who choose to cook their own meals through the cooking process. Input includes a confirmed meal menu and cooking procedure information, and based on this, it outputs the cooking process using voice commands and augmented reality displays. Users can use their smartphones to check how ingredients are being cut and the cooking progress during cooking, and it integrates with smart cooking devices to automate the cooking process. This process includes activating a voice assistant and using the camera for AR displays.

[0094] Step 7:

[0095] After finishing their meal, users enter feedback into their device. This feedback includes evaluations of the taste of the meal and the ease of preparation. This feedback is output as data that will be used to generate the next meal plan and is recorded in a database on the server side. Specifically, this involves entering feedback into an evaluation form on the app.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] In modern urban life, supporting residents' health management requires the proposal of meal plans that take into account individual health conditions and dietary preferences. However, many residents find it difficult to plan and implement healthy meals amidst their busy daily lives. Efficiently procuring fresh ingredients is also a challenge. Furthermore, there is a lack of cooking support to enable citizens to easily prepare healthy meals at home. An efficient system is needed to address these problems.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] This invention includes a server that includes means for acquiring user biometric data and evaluating their health status, means for recording user eating habits and preferences, and means for managing the supply status of ingredients in real time. This makes it possible to generate an optimal meal plan based on the health status and preferences of residents. Furthermore, by collaborating with local ingredient suppliers, fresh ingredients can be procured, and by coordinating with computer-controlled cooking appliances, self-catering can be made easier. These means realize a comprehensive support system that enables the promotion of the health of the entire community.

[0101] "Biometric data" refers to information about a user's physical activity, including heart rate, body temperature, and activity level.

[0102] "Health status" refers to information indicating the user's physical condition and is evaluated based on biometric data.

[0103] "Eating habits" refers to the patterns, timing, and types of meals a user typically consumes.

[0104] "Preferences" refer to the ingredients, seasonings, and eating styles that users enjoy.

[0105] "Supply status" refers to information regarding the stock, availability, and expiration dates of food ingredients.

[0106] A "meal plan" refers to a series of meals suggested to the user, taking into account their health condition and preferences.

[0107] "Automatic ordering" refers to the process where the system automatically orders the necessary ingredients.

[0108] "Notification" refers to informing the user about the generated meal plan.

[0109] "Visual display" refers to an interface that provides information to users visually.

[0110] "Local food suppliers" refers to businesses that supply food products within a specific region.

[0111] The "cooking process" refers to the series of operations that transform ingredients into a dish.

[0112] The system for implementing this invention primarily uses a server, a terminal, and multiple hardware devices. First, the server collects biometric data from the user's wearable device and evaluates the user's health status. This data includes specific information such as heart rate, body temperature, and activity level. The server also records data on the user's eating habits and preferences entered into the terminal and generates a meal plan based on this data.

[0113] This system is also connected to an IoT refrigerator that manages the supply status of ingredients in real time. The server automatically orders the necessary ingredients based on the ingredient information. The meal plan generated by the server is notified to the user's device. It provides an interface that guides users through cooking procedures with visual displays and voice prompts, supporting them when cooking for themselves.

[0114] For example, if a user requests a "simple and nutritious dinner for a busy day," the system suggests grilled salmon and quinoa salad. In this case, cooking instructions and guidance are provided visually and audibly through a smartphone app, and cooking can be automatically started via IoT-enabled appliances. In this way, the system helps users efficiently prepare healthy meals.

[0115] The software used includes "pandas" and "numpy" for analyzing health data, "Scikit-learn" for suggestion functions, and web frameworks such as "Flask" and "FastAPI" for communication with wearable devices and IoT devices. It also has a function to record resident feedback and incorporate it into the generation of the next meal plan. By utilizing the generation AI model and inputting prompts such as "Please suggest a simple and nutritious dinner recipe suitable for a busy day, with salmon as the main ingredient," it can generate an effective meal plan.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server acquires biometric data from the user's wearable device. Specifically, it collects data such as heart rate, body temperature, and activity level from the device in real time. Based on this data, it generates an initial dataset for evaluating the user's health status. Here, the input is biometric data from the device, and the output is evaluation data indicating the health status.

[0119] Step 2:

[0120] The device collects data from users regarding their eating habits and preferences. Users input their preferred foods, allergy information, and past meal history through the application interface. Based on this information, the device updates an individual dietary database. The input here is user preference data, and the output is an updated user profile.

[0121] Step 3:

[0122] The server monitors and manages the supply status of food ingredients through devices such as IoT refrigerators. It collects data on the type, quantity, and expiration date of ingredients, and uses this data to identify ingredients that are in short supply. The input here is inventory data from refrigerators, and the output is a list of ingredients that are in short supply.

[0123] Step 4:

[0124] The server integrates collected health status data, dietary habit data, and supply status data to generate a customized meal plan for each user. Using a generation AI model, it formulates the optimal menu based on the input data. The input here is all the data from the previous step, and the output is the recommended meal plan.

[0125] Step 5:

[0126] The server automatically orders the necessary ingredients from local suppliers based on the generated meal plan. It generates an ingredient list according to the generated plan and places orders through its network of suppliers. The input here is the meal plan, and the output is an order form or order list.

[0127] Step 6:

[0128] The device notifies the user of recommended meal plans and guides them through the necessary cooking steps. It utilizes voice instructions and augmented reality (AR) to provide users with the information they need when cooking at home. The input here is the generated meal plan, and the output is a user-friendly notification to the user.

[0129] Step 7:

[0130] After the user has consumed a meal, the device collects feedback and uses it to generate the next meal plan. It records the user's satisfaction level, new preferences, and any discovered allergies, updating the dataset for the next plan. The input for this step is the user's feedback, and the output is the updated preference data.

[0131] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0132] This invention enables more personalized responses by incorporating an emotion engine into a meal management system based on the user's biometric information and eating habits. The emotion engine recognizes the user's everyday emotions and reflects them in the generation and adjustment of meal plans.

[0133] First, the server acquires biometric information from the wearable device and continuously monitors the user's health status. This data includes heart rate, body temperature, activity level, and sleep patterns. Next, the device provides an interface for the user to input and edit their eating habits, preferences, and allergy information. This information includes details such as personal food preferences and foods to avoid.

[0134] The introduction of an emotion engine allows the system to recognize the user's daily emotional state from their facial expressions and tone of voice, and analyze this information in combination with biometric data. For example, if the system determines that the user is stressed, it will suggest relaxing herbal teas or foods that not only provide nutritional value but also lift the user's spirits.

[0135] Furthermore, the server checks the inventory status of ingredients and uses an automated ordering module to quickly replenish any missing ingredients. This ensures that planned meals are always feasible.

[0136] Notifications to the user are delivered via the device. The device uses a voice assistant and AR guidance to suggest meal plan options to the user. Emotional data is used here to prioritize and present options that are best suited to the user's mood.

[0137] For example, if a user wants to approach their next meal in a cheerful mood, the system will suggest colorful and visually appealing dishes. Furthermore, when positive emotions are detected, options to introduce new recipes or foreign culinary traditions to support the user's exploration of food culture are also included.

[0138] By integrating an emotional engine into this system, we aim to provide a more engaging eating experience that considers not only the user's physical health but also their psychological satisfaction.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The server periodically retrieves the user's biometric information from the wearable device. This information includes heart rate, body temperature, activity level, and sleep patterns.

[0142] Step 2:

[0143] The terminal provides an interface for users to input and edit their eating habits, preferences, and allergy information. Users use the terminal to enter this information and update it according to their preferences and needs.

[0144] Step 3:

[0145] The emotion engine analyzes the user's facial expressions and voice tone to recognize their emotional state in real time. This recognized emotional data is then comprehensively evaluated in conjunction with the user's health status.

[0146] Step 4:

[0147] The server generates an optimal meal plan tailored to the user's health condition and current emotions, based on acquired biometric information, eating habits, preferences, and emotional data. In doing so, it suggests ingredients that promote relaxation and visually appealing dishes based on the user's emotions.

[0148] Step 5:

[0149] The server retrieves the latest food inventory information from the IoT refrigerator and automatically places orders when it detects any missing ingredients based on the generated meal plan. The ordering process is carried out quickly to trusted suppliers.

[0150] Step 6:

[0151] The device notifies the user of a generated meal plan. This notification includes emotionally conscious options for home cooking, delivery, and dining out. The user can choose their preferred plan from the presented options.

[0152] Step 7:

[0153] If the user chooses to cook for themselves, the device uses a voice assistant and augmented reality display to guide them through the cooking process. This includes features that automatically control cooking temperature and time using smart cooking appliances.

[0154] Step 8:

[0155] After cooking, users enjoy their meal and, upon completion, input feedback on their satisfaction level and suggestions for future meals into their device. This feedback is then used to generate future meal plans.

[0156] Step 9:

[0157] The server accumulates user feedback and sentiment data, and uses this to continuously personalize plans, thereby improving system accuracy and user satisfaction.

[0158] (Example 2)

[0159] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0160] In modern society, there is a growing demand for automated meal plans optimized for individual health conditions, eating habits, and emotional states. However, existing systems fail to adequately integrate these factors into meal management, making it difficult to simultaneously improve user health and psychological satisfaction. To solve this problem, a more personalized and flexible meal management system is needed.

[0161] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0162] In this invention, the server includes means for acquiring biometric data and analyzing health status, means for registering and storing eating habits and preferences, and means for recognizing daily emotional states using emotion analysis means. This makes it possible to provide a personalized meal plan optimized for the user's health and emotional state, thereby increasing psychological satisfaction.

[0163] "Biometric data" refers to physical information such as a user's heart rate, body temperature, activity level, and sleep patterns, and is data used to evaluate their health status by continuously acquiring this information.

[0164] "Means of analyzing health status" refers to methods of analyzing biometric data acquired using sensors and algorithms to understand a user's current health status and physical changes.

[0165] "Means for registering eating habits and preferences" refers to a method for accumulating information on ingredients that users like and should avoid, as well as past meal records, and saving each user's individual eating habits in a database.

[0166] "Emotional analysis methods" refer to techniques that analyze everyday emotions by recognizing a user's facial expressions and tone of voice, and using these to determine their psychological state.

[0167] "Methods for generating meal plans" refers to methods for creating meal schedules that combine appropriate ingredients and menus, taking into account the user's health condition, eating habits, and emotions.

[0168] "Automatic ordering methods" refer to a system that uses an online platform to automatically order any missing ingredients based on inventory information, thereby ensuring that the necessary ingredients are secured.

[0169] "Augmented reality display means" refers to a method that uses technology to overlay digital information onto the real world to provide users with a visual representation of meal preparation steps and the finished dish.

[0170] In implementing this invention, the user first wears a wearable device and transmits their biometric data to a server. The server acquires this data and runs a dedicated algorithm to analyze heart rate, body temperature, activity level, and sleep patterns. This makes it possible to monitor the user's health status in real time.

[0171] Next, the device provides the user with an interface to input their eating habits and preferences. The user can launch the application and register allergy information, preferred foods, foods to avoid, and more. This information is stored in a database and used to generate meal plans.

[0172] For emotion analysis, the device captures the user's facial expressions and voice tone using its camera and microphone. The server then uses natural language processing technology and emotion analysis algorithms to infer the user's emotional state from this data.

[0173] Based on this visual and emotional data, the server uses a generative AI model to create individually customized meal plans. This model is used to recommend the optimal ingredients and menus tailored to the user's health, preferences, and emotions.

[0174] For example, if a user is feeling stressed, the system can suggest menu items that include chamomile tea, which has a relaxing effect, or chocolate, which enhances feelings of happiness. If positive emotions are detected, it may also offer recipes for exotic dishes to encourage exploration of new culinary experiences.

[0175] An example of a prompt might be, "Suggest the best dinner for the user based on their current health and emotional state." In this way, the goal is to provide a more personalized dining experience through advanced analysis using diverse user data.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The server receives biometric data from the wearable device. This data includes heart rate, body temperature, activity level, and sleep patterns. To analyze the input biometric data, the server applies algorithms to understand the user's health status. The output is an assessment of the user's current health status.

[0179] Step 2:

[0180] The terminal provides an interface for users to input information about their eating habits, preferences, and allergies. When a user enters this information into the application, the terminal saves it to a database. It receives the user's eating habits as input and the saved data as output.

[0181] Step 3:

[0182] The device uses a camera and microphone to scan the user's facial expressions and voice tone. It acquires the captured visual and audio data as input and sends it to the server. Based on this, the server uses an emotion analysis algorithm to recognize the user's emotional state. An evaluation result regarding the user's emotional state is generated as output.

[0183] Step 4:

[0184] The server integrates collected biometric data, eating habits, and emotional states, and uses a generative AI model to create a meal plan. Using the integrated user information as input, it generates a individually customized meal plan. The output is a meal plan optimized for the user.

[0185] Step 5:

[0186] The server sends the generated meal plan to the device. The device presents the meal plan to the user using voice assistant or AR guidance functions. It receives the meal plan from the server as input and provides the user with information visually and audibly as output.

[0187] Step 6:

[0188] The server manages ingredient inventory information and automatically places orders as needed. It takes ingredient inventory data as input and generates a list of ingredients that are running low. The output is the execution of online orders.

[0189] Step 7:

[0190] The terminal provides a method for users to input feedback on the meal plans they receive. It receives user feedback and sends that information to the server to be used in generating the next meal plan. It receives user feedback as input and provides evaluation data as output to help with future planning.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] While conventional meal management systems can manage health and register preferences based on users' biometric information, they have the challenge of not being able to flexibly suggest or adjust meals according to the user's emotional state. Furthermore, there is a need for methods that can provide a more personalized and fulfilling dining experience by taking emotions into consideration.

[0194] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0195] In this invention, the server includes means for acquiring the user's physiological characteristics and analyzing their health status, a system for managing food inventory information in real time, and an emotion analysis engine for analyzing the user's daily emotional state and adjusting the nutrition plan based on that. This enables more personalized meal suggestions that simultaneously consider the user's health status and emotions.

[0196] "User physiological characteristics" refer to biological numerical data such as heart rate and body temperature obtained from individual users.

[0197] "Means for analyzing health status" refers to a method for analyzing a user's health status based on acquired physiological characteristics.

[0198] "Food inventory information" refers to data regarding the quantity and remaining amount of food ingredients, beverages, and other similar items.

[0199] A "real-time management system" is a system designed to process and update data that changes moment by moment in real time.

[0200] "Everyday emotional states" refer to the emotional fluctuations, such as joy and stress, that users experience on a daily basis.

[0201] "Adjusting a nutrition plan" means changing or optimizing the content of meals according to the user's condition.

[0202] An "emotion analysis engine" is a mechanism that recognizes emotions from a user's facial expressions, tone of voice, and other factors, and processes that information.

[0203] To realize this invention, the server first needs to acquire the user's physiological characteristics from a wearable device. This includes information such as heart rate and body temperature, and devices such as Apple Watch and Fitbit can be used. The acquired physiological characteristics are analyzed using dedicated data analysis software to determine the user's health status, and the results are reflected in the food inventory management system.

[0204] The device captures the user's everyday emotional state through a system equipped with voice recognition technology (e.g., Amazon Alexa or Google Assistant). An emotion analysis engine is used to analyze the user's facial expressions and tone of voice in real time. If the system determines that the user is in a specific emotional state, the emotion analysis results are used to adjust the nutrition plan, and the optimal nutrition plan is suggested each time.

[0205] As a concrete example, when the server detects a user's peaceful mood while watching a movie on a Sunday afternoon, it suggests a meal menu designed to soothe that user. This is a plan tailored to the user's health condition and emotions at that time.

[0206] An example of a prompt is, "Design an algorithm that suggests the most suitable food and drinks for the situation based on the user's physiological characteristics and emotional data." This is an attempt to achieve optimal meal suggestions by utilizing a generative AI model that combines these elements.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The server acquires physiological characteristics such as heart rate and body temperature from wearable devices in real time. The input is biometric data from the wearable device, and the output is an analysis result indicating the user's health status. As part of the data processing, calculations are performed to evaluate the user's health status based on heart rate and body temperature.

[0210] Step 2:

[0211] The device uses speech recognition technology to analyze the user's voice tone and facial expressions, and inputs the data into an emotion analysis engine. The input is the user's voice and visual data, and the output is analytical information indicating the user's emotional state. The voice and video data are processed in real time, and calculations are performed to quantify emotions.

[0212] Step 3:

[0213] The server integrates the results of user physiological characteristic analysis and emotional analysis to generate a nutrition plan that takes into account the user's health status and emotions. The input is health status data and emotional analysis data, and the output is a personalized nutrition suggestion. The acquired data is compared, and a generative AI model is used to create a meal plan that provides the optimal nutritional balance.

[0214] Step 4:

[0215] The server notifies the user's device with meal suggestions based on the generated nutrition plan. The input is the generated nutrition plan, and the output is a notification of specific meal suggestions to the user. Augmented reality displays and audio guides are used when making notifications, providing intuitive information that appeals to both sight and hearing.

[0216] Step 5:

[0217] Users receive meal suggestions and input feedback into their device, which is then reflected in future meal suggestions. The input is user feedback data, and the output is reference information for generating the next nutrition plan. By analyzing the feedback and updating the database for future suggestions, more accurate personalization becomes possible.

[0218] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0219] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0220] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0224] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0225] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0226] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0227] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0228] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0229] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0230] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0231] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0232] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0233] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0234] This invention is a system for effectively managing a user's diet. Based on the user's biometric information, eating habits, preferences, and food inventory information, the system aims to propose and implement an optimal meal plan tailored to their health condition and needs.

[0235] First, the server collects biometric information from the user's wearable device. This information includes heart rate, body temperature, activity level, and sleep duration, and is used to assess the user's health. The device then provides an interface for the user to input their food preferences, allergy information, and past eating history. This records their eating habits and preferences.

[0236] Next, the server utilizes data from IoT refrigerators and other devices to manage food inventory information in real time. This includes the type, quantity, and expiration date of the food. This data is analyzed in combination with the user's health status and registered preferences. Based on this, the server automatically generates a meal plan with the optimal nutritional balance for the user.

[0237] The generated meal plan includes options for the user to choose from: cooking at home, delivery, or eating out. The device notifies the user of this meal plan, and the user can modify it according to their preferences. If the user chooses to cook at home, the device guides the user through the cooking process using voice and augmented reality displays. This also includes automated control of the cooking process through integration with smart cooking appliances.

[0238] For example, if a user requests a "healthy and quick lunch," the server will suggest a menu based on past data, such as "grilled chicken and mixed salad." If the necessary ingredients are not in sufficient stock, the system will automatically order them and ensure quick procurement. After the meal, the user can enter feedback into the terminal, which will improve the accuracy of future suggestions.

[0239] Thus, the present invention is a comprehensive system that improves the user's quality of life and supports the maintenance of a healthy diet.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The server periodically retrieves the user's biometric information from the wearable device. This information includes heart rate, body temperature, activity level, and sleep duration.

[0243] Step 2:

[0244] The device provides an interface that allows users to input their eating habits, preferences, and allergy information. Users can use this interface to update their past eating history and preferences.

[0245] Step 3:

[0246] The server retrieves food inventory information from the IoT refrigerator. This information includes the type, quantity, and expiration date of the food items, recorded in real time.

[0247] Step 4:

[0248] The server analyzes collected biometric information, eating habits, and preference data to assess the user's health status. Based on these results, it generates a meal plan that takes into account appropriate nutritional balance.

[0249] Step 5:

[0250] The server verifies whether there are enough necessary ingredients based on the generated meal plan, and automatically places orders if there are any shortages.

[0251] Step 6:

[0252] The device notifies the user of a generated meal plan. The notification includes options for cooking at home, delivery, and eating out, and the user can choose according to their preference.

[0253] Step 7:

[0254] If the user chooses to cook their own meal, the device will guide them through the specific cooking steps using voice and augmented reality displays. During this process, the cooking process will be automatically controlled in conjunction with smart cooking appliances.

[0255] Step 8:

[0256] After cooking and enjoying their meal, users enter feedback on their satisfaction level and areas for improvement into their device.

[0257] Step 9:

[0258] The server collects feedback data from users and uses it to generate the next meal plan. This allows the system to continuously improve its accuracy.

[0259] (Example 1)

[0260] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0261] In modern society, busy people find it difficult to maintain a healthy diet. In particular, creating meal plans optimized for individual health needs and managing daily food shortages are challenges. Furthermore, there is a need for efficient cooking support, but a comprehensive system to meet these needs does not yet exist.

[0262] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0263] In this invention, the server includes means for acquiring the user's biometric information and analyzing their health status, means for registering and recording the user's eating habits and preferences, and means for managing the inventory information of available ingredients in real time. This enables the automatic adjustment of meal plans to meet the user's specific needs, making it possible to maintain a healthy and efficient diet.

[0264] "User biometric information" refers to data necessary to indicate a person's health status, such as heart rate, body temperature, activity level, and sleep duration.

[0265] "Dietary habits and preferences" refers to data that includes an individual's tendencies regarding the foods and dishes they prefer to consume on a daily basis, as well as allergy information related to specific foods.

[0266] "Available food inventory information" refers to data on current food storage, including information on the type, quantity, and expiration date of ingredients.

[0267] "Using generative models" refers to a method that uses machine learning algorithms to analyze collected data and automatically generate the optimal meal plan for the user.

[0268] "Methods for guiding the steps of cooking at home" refers to methods that use voice commands or augmented reality technology to guide users through the cooking process so that they can cook efficiently.

[0269] "Feedback data" refers to evaluation information about the dining experience provided by users, including data on taste evaluations and comments on the difficulty of preparation.

[0270] "Computer-controlled cooking equipment" refers to cooking devices that are managed by a program to automatically control the cooking process.

[0271] This invention is a comprehensive system that supports users in maintaining a healthy diet. The embodiments thereof are described below.

[0272] The server collects biometric information from the user's wearable device. This biometric information includes heart rate, body temperature, activity level, and sleep duration, and is acquired using communication methods such as Bluetooth and Wi-Fi. The collected data is analyzed on the server to evaluate the user's health status.

[0273] The terminal provides an interface for users to input their food preferences, allergy information, and past eating history. Specifically, a smartphone application or web interface is used. Users can select their favorite and avoided foods and record their past eating history. This information is sent to a server, and the database of eating habits and preferences is updated.

[0274] The server also acquires food inventory information from IoT refrigerators and other devices. Using IoT sensors, it manages the type, quantity, and expiration date of ingredients in real time. This inventory information is used when creating meal plans using a generative AI model.

[0275] The generative AI model takes into account the user's health status, eating habits, preferences, and inventory information to generate an optimal, nutritionally balanced meal plan. An example of a prompt phrase that can be used is, "Suggest an appropriate lunch menu based on the user's health status and ingredient inventory."

[0276] The device notifies the user of the generated meal plan. The plan includes options such as cooking at home, delivery, and eating out. The user receives this notification and can modify or select the plan according to their preferences.

[0277] For example, if a user requests a healthy and quick lunch, the server might suggest something like "grilled chicken and mixed salad." If the necessary ingredients are in short supply, the system automatically orders them, supporting quick procurement.

[0278] In general, the present invention aims to provide safe and healthy food and improve the lifestyle of busy users in particular.

[0279] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0280] Step 1:

[0281] The server acquires biometric information from the user's wearable device. Input data includes heart rate, body temperature, activity level, and sleep duration. Using this input data, the server analyzes the user's health status, detects abnormalities, and evaluates daily changes in health, thereby outputting health indicators. Specifically, data is received via Bluetooth or Wi-Fi communication and stored in a database.

[0282] Step 2:

[0283] The terminal provides an interface for the user to input food preferences, allergy information, and dietary history. The input includes the preferred ingredients and allergy information manually selected by the user. Based on this, the user's preference database is updated, and data for use in future menu suggestions is output. Specific operations include selections on the application screen of a smartphone.

[0284] Step 3:

[0285] The server obtains the inventory information of the ingredients from the IoT refrigerator. The inputs are the type of ingredients, quantity, and expiration date. Based on these data, the inventory management system analyzes the expiration date and shortage items of the ingredients and outputs a real-time inventory list. The IoT sensor automatically sends the information of each ingredient to the server for timely update.

[0286] Step 4:

[0287] The server uses a generative AI model to generate a meal plan with the user's health status, eating habits, preferences, and inventory information as inputs. As data processing, the generative AI model uses machine learning algorithms to calculate an optimal meal menu and outputs it as a proposed menu. Based on an example of a prompt sentence, the generative model is activated with "Propose an appropriate lunch menu based on the user's health status and ingredient inventory."

[0288] Step 5:

[0289] The terminal notifies the user of the generated meal plan. The input is the proposed menu information from the server, which is displayed on the user's terminal screen as options such as cooking at home, delivery, and eating out. The user who receives this notification checks the options, makes selections or modifications according to their preferences, and obtains an output that finalizes the meal plan. The notification is carried out via pop-up or push notifications.

[0290] Step 6:

[0291] The device guides users who choose to cook their own meals through the cooking process. Input includes a confirmed meal menu and cooking procedure information, and based on this, it outputs the cooking process using voice commands and augmented reality displays. Users can use their smartphones to check how ingredients are being cut and the cooking progress during cooking, and it integrates with smart cooking devices to automate the cooking process. This process includes activating a voice assistant and using the camera for AR displays.

[0292] Step 7:

[0293] After finishing their meal, users enter feedback into their device. This feedback includes evaluations of the taste of the meal and the ease of preparation. This feedback is output as data that will be used to generate the next meal plan and is recorded in a database on the server side. Specifically, this involves entering feedback into an evaluation form on the app.

[0294] (Application Example 1)

[0295] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0296] In modern urban life, supporting residents' health management requires the proposal of meal plans that take into account individual health conditions and dietary preferences. However, many residents find it difficult to plan and implement healthy meals amidst their busy daily lives. Efficiently procuring fresh ingredients is also a challenge. Furthermore, there is a lack of cooking support to enable citizens to easily prepare healthy meals at home. An efficient system is needed to address these problems.

[0297] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0298] In this invention, the server includes means for acquiring the user's biological data and evaluating the health status, means for recording the user's eating habits and preferences, and means for managing the supply situation of food ingredients in real time. As a result, it becomes possible to generate an optimal meal plan based on the health status and preferences of the residents. Also, by collaborating with local food suppliers, fresh food ingredients can be procured, and home cooking can be facilitated through collaboration with computer-controlled cooking appliances. These means realize a comprehensive support system that enables the promotion of the health of all residents.

[0299] "Biological data" refers to information related to the user's physical activities and includes heart rate, body temperature, activity level, etc.

[0300] "Health status" is information indicating the user's physical condition and is evaluated based on biological data.

[0301] "Eating habits" refer to the pattern, time zone, type, etc. of the meals that the user usually consumes.

[0302] "Preferences" refer to the food ingredients, seasonings, and meal formats that the user likes.

[0303] "Supply situation" refers to information related to the inventory, availability, expiration date, etc. of food ingredients.

[0304] "Meal plan" refers to a series of meal contents proposed considering the user's health status and preferences.

[0305] "Automatic ordering" refers to the process in which the system automatically orders the necessary food ingredients.

[0306] "Notification" refers to informing the user of the generated meal plan.

[0307] "Visual display" refers to an interface that provides information to the user visually.

[0308] "Local food suppliers" refers to businesses that supply food products within a specific region.

[0309] The "cooking process" refers to the series of operations that transform ingredients into a dish.

[0310] The system for implementing this invention primarily uses a server, a terminal, and multiple hardware devices. First, the server collects biometric data from the user's wearable device and evaluates the user's health status. This data includes specific information such as heart rate, body temperature, and activity level. The server also records data on the user's eating habits and preferences entered into the terminal and generates a meal plan based on this data.

[0311] This system is also connected to an IoT refrigerator that manages the supply status of ingredients in real time. The server automatically orders the necessary ingredients based on the ingredient information. The meal plan generated by the server is notified to the user's device. It provides an interface that guides users through cooking procedures with visual displays and voice prompts, supporting them when cooking for themselves.

[0312] For example, if a user requests a "simple and nutritious dinner for a busy day," the system suggests grilled salmon and quinoa salad. In this case, cooking instructions and guidance are provided visually and audibly through a smartphone app, and cooking can be automatically started via IoT-enabled appliances. In this way, the system helps users efficiently prepare healthy meals.

[0313] The software used includes "pandas" and "numpy" for analyzing health data, "Scikit-learn" for suggestion functions, and web frameworks such as "Flask" and "FastAPI" for communication with wearable devices and IoT devices. It also has a function to record resident feedback and incorporate it into the generation of the next meal plan. By utilizing the generation AI model and inputting prompts such as "Please suggest a simple and nutritious dinner recipe suitable for a busy day, with salmon as the main ingredient," it can generate an effective meal plan.

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The server acquires biometric data from the user's wearable device. Specifically, it collects data such as heart rate, body temperature, and activity level from the device in real time. Based on this data, it generates an initial dataset for evaluating the user's health status. Here, the input is biometric data from the device, and the output is evaluation data indicating the health status.

[0317] Step 2:

[0318] The device collects data from users regarding their eating habits and preferences. Users input their preferred foods, allergy information, and past meal history through the application interface. Based on this information, the device updates an individual dietary database. The input here is user preference data, and the output is an updated user profile.

[0319] Step 3:

[0320] The server monitors and manages the supply status of food ingredients through devices such as IoT refrigerators. It collects data on the type, quantity, and expiration date of ingredients, and uses this data to identify ingredients that are in short supply. The input here is inventory data from refrigerators, and the output is a list of ingredients that are in short supply.

[0321] Step 4:

[0322] The server integrates collected health status data, dietary habit data, and supply status data to generate a customized meal plan for each user. Using a generation AI model, it formulates the optimal menu based on the input data. The input here is all the data from the previous step, and the output is the recommended meal plan.

[0323] Step 5:

[0324] The server automatically orders the necessary ingredients from local suppliers based on the generated meal plan. It generates an ingredient list according to the generated plan and places orders through its network of suppliers. The input here is the meal plan, and the output is an order form or order list.

[0325] Step 6:

[0326] The device notifies the user of recommended meal plans and guides them through the necessary cooking steps. It utilizes voice instructions and augmented reality (AR) to provide users with the information they need when cooking at home. The input here is the generated meal plan, and the output is a user-friendly notification to the user.

[0327] Step 7:

[0328] After the user has consumed a meal, the device collects feedback and uses it to generate the next meal plan. It records the user's satisfaction level, new preferences, and any discovered allergies, updating the dataset for the next plan. The input for this step is the user's feedback, and the output is the updated preference data.

[0329] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0330] This invention enables more personalized responses by incorporating an emotion engine into a meal management system based on the user's biometric information and eating habits. The emotion engine recognizes the user's everyday emotions and reflects them in the generation and adjustment of meal plans.

[0331] First, the server acquires biometric information from the wearable device and continuously monitors the user's health status. This data includes heart rate, body temperature, activity level, and sleep patterns. Next, the device provides an interface for the user to input and edit their eating habits, preferences, and allergy information. This information includes details such as personal food preferences and foods to avoid.

[0332] The introduction of an emotion engine allows the system to recognize the user's daily emotional state from their facial expressions and tone of voice, and analyze this information in combination with biometric data. For example, if the system determines that the user is stressed, it will suggest relaxing herbal teas or foods that not only provide nutritional value but also lift the user's spirits.

[0333] Furthermore, the server checks the inventory status of ingredients and uses an automated ordering module to quickly replenish any missing ingredients. This ensures that planned meals are always feasible.

[0334] Notifications to the user are delivered via the device. The device uses a voice assistant and AR guidance to suggest meal plan options to the user. Emotional data is used here to prioritize and present options that are best suited to the user's mood.

[0335] For example, if a user wants to approach their next meal in a cheerful mood, the system will suggest colorful and visually appealing dishes. Furthermore, when positive emotions are detected, options to introduce new recipes or foreign culinary traditions to support the user's exploration of food culture are also included.

[0336] By integrating an emotional engine into this system, we aim to provide a more engaging eating experience that considers not only the user's physical health but also their psychological satisfaction.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] The server periodically retrieves the user's biometric information from the wearable device. This information includes heart rate, body temperature, activity level, and sleep patterns.

[0340] Step 2:

[0341] The terminal provides an interface for users to input and edit their eating habits, preferences, and allergy information. Users use the terminal to enter this information and update it according to their preferences and needs.

[0342] Step 3:

[0343] The emotion engine analyzes the user's facial expressions and voice tone to recognize their emotional state in real time. This recognized emotional data is then comprehensively evaluated in conjunction with the user's health status.

[0344] Step 4:

[0345] The server generates an optimal meal plan tailored to the user's health condition and current emotions, based on acquired biometric information, eating habits, preferences, and emotional data. In doing so, it suggests ingredients that promote relaxation and visually appealing dishes based on the user's emotions.

[0346] Step 5:

[0347] The server retrieves the latest food inventory information from the IoT refrigerator and automatically places orders when it detects any missing ingredients based on the generated meal plan. The ordering process is carried out quickly to trusted suppliers.

[0348] Step 6:

[0349] The device notifies the user of a generated meal plan. This notification includes emotionally conscious options for home cooking, delivery, and dining out. The user can choose their preferred plan from the presented options.

[0350] Step 7:

[0351] If the user chooses to cook for themselves, the device uses a voice assistant and augmented reality display to guide them through the cooking process. This includes features that automatically control cooking temperature and time using smart cooking appliances.

[0352] Step 8:

[0353] After cooking, users enjoy their meal and, upon completion, input feedback on their satisfaction level and suggestions for future meals into their device. This feedback is then used to generate future meal plans.

[0354] Step 9:

[0355] The server accumulates user feedback and sentiment data, and uses this to continuously personalize plans, thereby improving system accuracy and user satisfaction.

[0356] (Example 2)

[0357] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0358] In modern society, there is a growing demand for automated meal plans optimized for individual health conditions, eating habits, and emotional states. However, existing systems fail to adequately integrate these factors into meal management, making it difficult to simultaneously improve user health and psychological satisfaction. To solve this problem, a more personalized and flexible meal management system is needed.

[0359] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0360] In this invention, the server includes means for acquiring biometric data and analyzing health status, means for registering and storing eating habits and preferences, and means for recognizing daily emotional states using emotion analysis means. This makes it possible to provide a personalized meal plan optimized for the user's health and emotional state, thereby increasing psychological satisfaction.

[0361] "Biometric data" refers to physical information such as a user's heart rate, body temperature, activity level, and sleep patterns, and is data used to evaluate their health status by continuously acquiring this information.

[0362] "Means of analyzing health status" refers to methods of analyzing biometric data acquired using sensors and algorithms to understand a user's current health status and physical changes.

[0363] "Means for registering eating habits and preferences" refers to a method for accumulating information on ingredients that users like and should avoid, as well as past meal records, and saving each user's individual eating habits in a database.

[0364] "Emotional analysis methods" refer to techniques that analyze everyday emotions by recognizing a user's facial expressions and tone of voice, and using these to determine their psychological state.

[0365] "Methods for generating meal plans" refers to methods for creating meal schedules that combine appropriate ingredients and menus, taking into account the user's health condition, eating habits, and emotions.

[0366] "Automatic ordering methods" refer to a system that uses an online platform to automatically order any missing ingredients based on inventory information, thereby ensuring that the necessary ingredients are secured.

[0367] "Augmented reality display means" refers to a method that uses technology to overlay digital information onto the real world to provide users with a visual representation of meal preparation steps and the finished dish.

[0368] In implementing this invention, the user first wears a wearable device and transmits their biometric data to a server. The server acquires this data and runs a dedicated algorithm to analyze heart rate, body temperature, activity level, and sleep patterns. This makes it possible to monitor the user's health status in real time.

[0369] Next, the device provides the user with an interface to input their eating habits and preferences. The user can launch the application and register allergy information, preferred foods, foods to avoid, and more. This information is stored in a database and used to generate meal plans.

[0370] For emotion analysis, the device captures the user's facial expressions and voice tone using its camera and microphone. The server then uses natural language processing technology and emotion analysis algorithms to infer the user's emotional state from this data.

[0371] Based on this visual and emotional data, the server uses a generative AI model to create individually customized meal plans. This model is used to recommend the optimal ingredients and menus tailored to the user's health, preferences, and emotions.

[0372] For example, if a user is feeling stressed, the system can suggest menu items that include chamomile tea, which has a relaxing effect, or chocolate, which enhances feelings of happiness. If positive emotions are detected, it may also offer recipes for exotic dishes to encourage exploration of new culinary experiences.

[0373] An example of a prompt might be, "Suggest the best dinner for the user based on their current health and emotional state." In this way, the goal is to provide a more personalized dining experience through advanced analysis using diverse user data.

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] The server receives biometric data from the wearable device. This data includes heart rate, body temperature, activity level, and sleep patterns. To analyze the input biometric data, the server applies algorithms to understand the user's health status. The output is an assessment of the user's current health status.

[0377] Step 2:

[0378] The terminal provides an interface for users to input information about their eating habits, preferences, and allergies. When a user enters this information into the application, the terminal saves it to a database. It receives the user's eating habits as input and the saved data as output.

[0379] Step 3:

[0380] The device uses a camera and microphone to scan the user's facial expressions and voice tone. It acquires the captured visual and audio data as input and sends it to the server. Based on this, the server uses an emotion analysis algorithm to recognize the user's emotional state. An evaluation result regarding the user's emotional state is generated as output.

[0381] Step 4:

[0382] The server integrates collected biometric data, eating habits, and emotional states, and uses a generative AI model to create a meal plan. Using the integrated user information as input, it generates a individually customized meal plan. The output is a meal plan optimized for the user.

[0383] Step 5:

[0384] The server sends the generated meal plan to the device. The device presents the meal plan to the user using voice assistant or AR guidance functions. It receives the meal plan from the server as input and provides the user with information visually and audibly as output.

[0385] Step 6:

[0386] The server manages ingredient inventory information and automatically places orders as needed. It takes ingredient inventory data as input and generates a list of ingredients that are running low. The output is the execution of online orders.

[0387] Step 7:

[0388] The terminal provides a method for users to input feedback on the meal plans they receive. It receives user feedback and sends that information to the server to be used in generating the next meal plan. It receives user feedback as input and provides evaluation data as output to help with future planning.

[0389] (Application Example 2)

[0390] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0391] While conventional meal management systems can manage health and register preferences based on users' biometric information, they have the challenge of not being able to flexibly suggest or adjust meals according to the user's emotional state. Furthermore, there is a need for methods that can provide a more personalized and fulfilling dining experience by taking emotions into consideration.

[0392] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0393] In this invention, the server includes means for acquiring the user's physiological characteristics and analyzing their health status, a system for managing food inventory information in real time, and an emotion analysis engine for analyzing the user's daily emotional state and adjusting the nutrition plan based on that. This enables more personalized meal suggestions that simultaneously consider the user's health status and emotions.

[0394] "User physiological characteristics" refer to biological numerical data such as heart rate and body temperature obtained from individual users.

[0395] "Means for analyzing health status" refers to a method for analyzing a user's health status based on acquired physiological characteristics.

[0396] "Food inventory information" refers to data regarding the quantity and remaining amount of food ingredients, beverages, and other similar items.

[0397] A "real-time management system" is a system designed to process and update data that changes moment by moment in real time.

[0398] "Everyday emotional states" refer to the emotional fluctuations, such as joy and stress, that users experience on a daily basis.

[0399] "Adjusting a nutrition plan" means changing or optimizing the content of meals according to the user's condition.

[0400] An "emotion analysis engine" is a mechanism that recognizes emotions from a user's facial expressions, tone of voice, and other factors, and processes that information.

[0401] To realize this invention, the server first needs to acquire the user's physiological characteristics from a wearable device. This includes information such as heart rate and body temperature, and devices such as Apple Watch and Fitbit can be used. The acquired physiological characteristics are analyzed using dedicated data analysis software to determine the user's health status, and the results are reflected in the food inventory management system.

[0402] The device captures the user's everyday emotional state through a system equipped with voice recognition technology (e.g., Amazon Alexa or Google Assistant). An emotion analysis engine is used to analyze the user's facial expressions and tone of voice in real time. If the system determines that the user is in a specific emotional state, the emotion analysis results are used to adjust the nutrition plan, and the optimal nutrition plan is suggested each time.

[0403] As a concrete example, when the server detects a user's peaceful mood while watching a movie on a Sunday afternoon, it suggests a meal menu designed to soothe that user. This is a plan tailored to the user's health condition and emotions at that time.

[0404] An example of a prompt is, "Design an algorithm that suggests the most suitable food and drinks for the situation based on the user's physiological characteristics and emotional data." This is an attempt to achieve optimal meal suggestions by utilizing a generative AI model that combines these elements.

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] The server acquires physiological characteristics such as heart rate and body temperature from wearable devices in real time. The input is biometric data from the wearable device, and the output is an analysis result indicating the user's health status. As part of the data processing, calculations are performed to evaluate the user's health status based on heart rate and body temperature.

[0408] Step 2:

[0409] The device uses speech recognition technology to analyze the user's voice tone and facial expressions, and inputs the data into an emotion analysis engine. The input is the user's voice and visual data, and the output is analytical information indicating the user's emotional state. The voice and video data are processed in real time, and calculations are performed to quantify emotions.

[0410] Step 3:

[0411] The server integrates the results of user physiological characteristic analysis and emotional analysis to generate a nutrition plan that takes into account the user's health status and emotions. The input is health status data and emotional analysis data, and the output is a personalized nutrition suggestion. The acquired data is compared, and a generative AI model is used to create a meal plan that provides the optimal nutritional balance.

[0412] Step 4:

[0413] The server notifies the user's device with meal suggestions based on the generated nutrition plan. The input is the generated nutrition plan, and the output is a notification of specific meal suggestions to the user. Augmented reality displays and audio guides are used when making notifications, providing intuitive information that appeals to both sight and hearing.

[0414] Step 5:

[0415] Users receive meal suggestions and input feedback into their device, which is then reflected in future meal suggestions. The input is user feedback data, and the output is reference information for generating the next nutrition plan. By analyzing the feedback and updating the database for future suggestions, more accurate personalization becomes possible.

[0416] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0417] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0418] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0422] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0423] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0424] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0425] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0426] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0427] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0428] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0429] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0430] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0431] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0432] This invention is a system for effectively managing a user's diet. Based on the user's biometric information, eating habits, preferences, and food inventory information, the system aims to propose and implement an optimal meal plan tailored to their health condition and needs.

[0433] First, the server collects biometric information from the user's wearable device. This information includes heart rate, body temperature, activity level, and sleep duration, and is used to assess the user's health. The device then provides an interface for the user to input their food preferences, allergy information, and past eating history. This records their eating habits and preferences.

[0434] Next, the server utilizes data from IoT refrigerators and other devices to manage food inventory information in real time. This includes the type, quantity, and expiration date of the food. This data is analyzed in combination with the user's health status and registered preferences. Based on this, the server automatically generates a meal plan with the optimal nutritional balance for the user.

[0435] The generated meal plan includes options for the user to choose from: cooking at home, delivery, or eating out. The device notifies the user of this meal plan, and the user can modify it according to their preferences. If the user chooses to cook at home, the device guides the user through the cooking process using voice and augmented reality displays. This also includes automated control of the cooking process through integration with smart cooking appliances.

[0436] For example, if a user requests a "healthy and quick lunch," the server will suggest a menu based on past data, such as "grilled chicken and mixed salad." If the necessary ingredients are not in sufficient stock, the system will automatically order them and ensure quick procurement. After the meal, the user can enter feedback into the terminal, which will improve the accuracy of future suggestions.

[0437] Thus, the present invention is a comprehensive system that improves the user's quality of life and supports the maintenance of a healthy diet.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The server periodically retrieves the user's biometric information from the wearable device. This information includes heart rate, body temperature, activity level, and sleep duration.

[0441] Step 2:

[0442] The device provides an interface that allows users to input their eating habits, preferences, and allergy information. Users can use this interface to update their past eating history and preferences.

[0443] Step 3:

[0444] The server retrieves food inventory information from the IoT refrigerator. This information includes the type, quantity, and expiration date of the food items, recorded in real time.

[0445] Step 4:

[0446] The server analyzes collected biometric information, eating habits, and preference data to assess the user's health status. Based on these results, it generates a meal plan that takes into account appropriate nutritional balance.

[0447] Step 5:

[0448] The server verifies whether there are enough necessary ingredients based on the generated meal plan, and automatically places orders if there are any shortages.

[0449] Step 6:

[0450] The device notifies the user of a generated meal plan. The notification includes options for cooking at home, delivery, and eating out, and the user can choose according to their preference.

[0451] Step 7:

[0452] If the user chooses to cook their own meal, the device will guide them through the specific cooking steps using voice and augmented reality displays. During this process, the cooking process will be automatically controlled in conjunction with smart cooking appliances.

[0453] Step 8:

[0454] After cooking and enjoying their meal, users enter feedback on their satisfaction level and areas for improvement into their device.

[0455] Step 9:

[0456] The server collects feedback data from users and uses it to generate the next meal plan. This allows the system to continuously improve its accuracy.

[0457] (Example 1)

[0458] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0459] In modern society, busy people find it difficult to maintain a healthy diet. In particular, creating meal plans optimized for individual health needs and managing daily food shortages are challenges. Furthermore, there is a need for efficient cooking support, but a comprehensive system to meet these needs does not yet exist.

[0460] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0461] In this invention, the server includes means for acquiring the user's biometric information and analyzing their health status, means for registering and recording the user's eating habits and preferences, and means for managing the inventory information of available ingredients in real time. This enables the automatic adjustment of meal plans to meet the user's specific needs, making it possible to maintain a healthy and efficient diet.

[0462] "User biometric information" refers to data necessary to indicate a person's health status, such as heart rate, body temperature, activity level, and sleep duration.

[0463] "Dietary habits and preferences" refers to data that includes an individual's tendencies regarding the foods and dishes they prefer to consume on a daily basis, as well as allergy information related to specific foods.

[0464] "Available food inventory information" refers to data on current food storage, including information on the type, quantity, and expiration date of ingredients.

[0465] "Using generative models" refers to a method that uses machine learning algorithms to analyze collected data and automatically generate the optimal meal plan for the user.

[0466] "Methods for guiding the steps of cooking at home" refers to methods that use voice commands or augmented reality technology to guide users through the cooking process so that they can cook efficiently.

[0467] "Feedback data" refers to evaluation information about the dining experience provided by users, including data on taste evaluations and comments on the difficulty of preparation.

[0468] "Computer-controlled cooking equipment" refers to cooking devices that are managed by a program to automatically control the cooking process.

[0469] This invention is a comprehensive system that supports users in maintaining a healthy diet. The embodiments thereof are described below.

[0470] The server collects biometric information from the user's wearable device. This biometric information includes heart rate, body temperature, activity level, and sleep duration, and is acquired using communication methods such as Bluetooth and Wi-Fi. The collected data is analyzed on the server to evaluate the user's health status.

[0471] The terminal provides an interface for users to input their food preferences, allergy information, and past eating history. Specifically, a smartphone application or web interface is used. Users can select their favorite and avoided foods and record their past eating history. This information is sent to a server, and the database of eating habits and preferences is updated.

[0472] The server also acquires food inventory information from IoT refrigerators and other devices. Using IoT sensors, it manages the type, quantity, and expiration date of ingredients in real time. This inventory information is used when creating meal plans using a generative AI model.

[0473] The generative AI model takes into account the user's health status, eating habits, preferences, and inventory information to generate an optimal, nutritionally balanced meal plan. An example of a prompt phrase that can be used is, "Suggest an appropriate lunch menu based on the user's health status and ingredient inventory."

[0474] The device notifies the user of the generated meal plan. The plan includes options such as cooking at home, delivery, and eating out. The user receives this notification and can modify or select the plan according to their preferences.

[0475] For example, if a user requests a healthy and quick lunch, the server might suggest something like "grilled chicken and mixed salad." If the necessary ingredients are in short supply, the system automatically orders them, supporting quick procurement.

[0476] In general, the present invention aims to provide safe and healthy food and improve the lifestyle of busy users in particular.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The server acquires biometric information from the user's wearable device. Input data includes heart rate, body temperature, activity level, and sleep duration. Using this input data, the server analyzes the user's health status, detects abnormalities, and evaluates daily changes in health, thereby outputting health indicators. Specifically, data is received via Bluetooth or Wi-Fi communication and stored in a database.

[0480] Step 2:

[0481] The device provides users with an interface for inputting their food preferences, allergy information, and meal history. This input includes manually selected preferred ingredients and allergy information. Based on this, the system updates the user's preference database and outputs data to be used for future menu suggestions. A specific example of this operation is selection on a smartphone application screen.

[0482] Step 3:

[0483] The server retrieves food inventory information from the IoT refrigerator. Inputs include food type, quantity, and expiration date. Based on this data, the inventory management system analyzes the expiration dates and shortages of food items, and outputs a real-time inventory list. IoT sensors automatically send information about each food item to the server, updating the list as needed.

[0484] Step 4:

[0485] The server uses a generative AI model to generate meal plans based on the user's health status, eating habits, preferences, and inventory information. For data processing, the generative AI model uses machine learning algorithms to calculate the optimal meal menu and outputs it as a suggested menu. Based on the example prompt, the generative model is activated with the command, "Suggest an appropriate lunch menu based on the user's health status and ingredient inventory."

[0486] Step 5:

[0487] The terminal notifies the user of the generated meal plan. The input is suggested menu information from the server, which is displayed on the user's terminal screen as options such as cooking at home, delivery, and eating out. Upon receiving this notification, the user reviews the options, makes selections and modifications according to their preferences, and receives output to confirm the final meal plan. Notifications are delivered via pop-ups or push notifications.

[0488] Step 6:

[0489] The device guides users who choose to cook their own meals through the cooking process. Input includes a confirmed meal menu and cooking procedure information, and based on this, it outputs the cooking process using voice commands and augmented reality displays. Users can use their smartphones to check how ingredients are being cut and the cooking progress during cooking, and it integrates with smart cooking devices to automate the cooking process. This process includes activating a voice assistant and using the camera for AR displays.

[0490] Step 7:

[0491] After finishing their meal, users enter feedback into their device. This feedback includes evaluations of the taste of the meal and the ease of preparation. This feedback is output as data that will be used to generate the next meal plan and is recorded in a database on the server side. Specifically, this involves entering feedback into an evaluation form on the app.

[0492] (Application Example 1)

[0493] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0494] In modern urban life, supporting residents' health management requires the proposal of meal plans that take into account individual health conditions and dietary preferences. However, many residents find it difficult to plan and implement healthy meals amidst their busy daily lives. Efficiently procuring fresh ingredients is also a challenge. Furthermore, there is a lack of cooking support to enable citizens to easily prepare healthy meals at home. An efficient system is needed to address these problems.

[0495] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0496] This invention includes a server that includes means for acquiring user biometric data and evaluating their health status, means for recording user eating habits and preferences, and means for managing the supply status of ingredients in real time. This makes it possible to generate an optimal meal plan based on the health status and preferences of residents. Furthermore, by collaborating with local ingredient suppliers, fresh ingredients can be procured, and by coordinating with computer-controlled cooking appliances, self-catering can be made easier. These means realize a comprehensive support system that enables the promotion of the health of the entire community.

[0497] "Biometric data" refers to information about a user's physical activity, including heart rate, body temperature, and activity level.

[0498] "Health status" refers to information indicating the user's physical condition and is evaluated based on biometric data.

[0499] "Eating habits" refers to the patterns, timing, and types of meals a user typically consumes.

[0500] "Preferences" refer to the ingredients, seasonings, and eating styles that users enjoy.

[0501] "Supply status" refers to information regarding the stock, availability, and expiration dates of food ingredients.

[0502] A "meal plan" refers to a series of meals suggested to the user, taking into account their health condition and preferences.

[0503] "Automatic ordering" refers to the process where the system automatically orders the necessary ingredients.

[0504] "Notification" refers to informing the user about the generated meal plan.

[0505] "Visual display" refers to an interface that provides information to users visually.

[0506] "Local food suppliers" refers to businesses that supply food products within a specific region.

[0507] The "cooking process" refers to the series of operations that transform ingredients into a dish.

[0508] The system for implementing this invention primarily uses a server, a terminal, and multiple hardware devices. First, the server collects biometric data from the user's wearable device and evaluates the user's health status. This data includes specific information such as heart rate, body temperature, and activity level. The server also records data on the user's eating habits and preferences entered into the terminal and generates a meal plan based on this data.

[0509] This system is also connected to an IoT refrigerator that manages the supply status of ingredients in real time. The server automatically orders the necessary ingredients based on the ingredient information. The meal plan generated by the server is notified to the user's device. It provides an interface that guides users through cooking procedures with visual displays and voice prompts, supporting them when cooking for themselves.

[0510] For example, if a user requests a "simple and nutritious dinner for a busy day," the system suggests grilled salmon and quinoa salad. In this case, cooking instructions and guidance are provided visually and audibly through a smartphone app, and cooking can be automatically started via IoT-enabled appliances. In this way, the system helps users efficiently prepare healthy meals.

[0511] The software used includes "pandas" and "numpy" for analyzing health data, "Scikit-learn" for suggestion functions, and web frameworks such as "Flask" and "FastAPI" for communication with wearable devices and IoT devices. It also has a function to record resident feedback and incorporate it into the generation of the next meal plan. By utilizing the generation AI model and inputting prompts such as "Please suggest a simple and nutritious dinner recipe suitable for a busy day, with salmon as the main ingredient," it can generate an effective meal plan.

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The server acquires biometric data from the user's wearable device. Specifically, it collects data such as heart rate, body temperature, and activity level from the device in real time. Based on this data, it generates an initial dataset for evaluating the user's health status. Here, the input is biometric data from the device, and the output is evaluation data indicating the health status.

[0515] Step 2:

[0516] The device collects data from users regarding their eating habits and preferences. Users input their preferred foods, allergy information, and past meal history through the application interface. Based on this information, the device updates an individual dietary database. The input here is user preference data, and the output is an updated user profile.

[0517] Step 3:

[0518] The server monitors and manages the supply status of food ingredients through devices such as IoT refrigerators. It collects data on the type, quantity, and expiration date of ingredients, and uses this data to identify ingredients that are in short supply. The input here is inventory data from refrigerators, and the output is a list of ingredients that are in short supply.

[0519] Step 4:

[0520] The server integrates collected health status data, dietary habit data, and supply status data to generate a customized meal plan for each user. Using a generation AI model, it formulates the optimal menu based on the input data. The input here is all the data from the previous step, and the output is the recommended meal plan.

[0521] Step 5:

[0522] The server automatically orders the necessary ingredients from local suppliers based on the generated meal plan. It generates an ingredient list according to the generated plan and places orders through its network of suppliers. The input here is the meal plan, and the output is an order form or order list.

[0523] Step 6:

[0524] The device notifies the user of recommended meal plans and guides them through the necessary cooking steps. It utilizes voice instructions and augmented reality (AR) to provide users with the information they need when cooking at home. The input here is the generated meal plan, and the output is a user-friendly notification to the user.

[0525] Step 7:

[0526] After the user has consumed a meal, the device collects feedback and uses it to generate the next meal plan. It records the user's satisfaction level, new preferences, and any discovered allergies, updating the dataset for the next plan. The input for this step is the user's feedback, and the output is the updated preference data.

[0527] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0528] This invention enables more personalized responses by incorporating an emotion engine into a meal management system based on the user's biometric information and eating habits. The emotion engine recognizes the user's everyday emotions and reflects them in the generation and adjustment of meal plans.

[0529] First, the server acquires biometric information from the wearable device and continuously monitors the user's health status. This data includes heart rate, body temperature, activity level, and sleep patterns. Next, the device provides an interface for the user to input and edit their eating habits, preferences, and allergy information. This information includes details such as personal food preferences and foods to avoid.

[0530] The introduction of an emotion engine allows the system to recognize the user's daily emotional state from their facial expressions and tone of voice, and analyze this information in combination with biometric data. For example, if the system determines that the user is stressed, it will suggest relaxing herbal teas or foods that not only provide nutritional value but also lift the user's spirits.

[0531] Furthermore, the server checks the inventory status of ingredients and uses an automated ordering module to quickly replenish any missing ingredients. This ensures that planned meals are always feasible.

[0532] Notifications to the user are delivered via the device. The device uses a voice assistant and AR guidance to suggest meal plan options to the user. Emotional data is used here to prioritize and present options that are best suited to the user's mood.

[0533] For example, if a user wants to approach their next meal in a cheerful mood, the system will suggest colorful and visually appealing dishes. Furthermore, when positive emotions are detected, options to introduce new recipes or foreign culinary traditions to support the user's exploration of food culture are also included.

[0534] By integrating an emotional engine into this system, we aim to provide a more engaging eating experience that considers not only the user's physical health but also their psychological satisfaction.

[0535] The following describes the processing flow.

[0536] Step 1:

[0537] The server periodically retrieves the user's biometric information from the wearable device. This information includes heart rate, body temperature, activity level, and sleep patterns.

[0538] Step 2:

[0539] The terminal provides an interface for users to input and edit their eating habits, preferences, and allergy information. Users use the terminal to enter this information and update it according to their preferences and needs.

[0540] Step 3:

[0541] The emotion engine analyzes the user's facial expressions and voice tone to recognize their emotional state in real time. This recognized emotional data is then comprehensively evaluated in conjunction with the user's health status.

[0542] Step 4:

[0543] The server generates an optimal meal plan tailored to the user's health condition and current emotions, based on acquired biometric information, eating habits, preferences, and emotional data. In doing so, it suggests ingredients that promote relaxation and visually appealing dishes based on the user's emotions.

[0544] Step 5:

[0545] The server retrieves the latest food inventory information from the IoT refrigerator and automatically places orders when it detects any missing ingredients based on the generated meal plan. The ordering process is carried out quickly to trusted suppliers.

[0546] Step 6:

[0547] The device notifies the user of a generated meal plan. This notification includes emotionally conscious options for home cooking, delivery, and dining out. The user can choose their preferred plan from the presented options.

[0548] Step 7:

[0549] If the user chooses to cook for themselves, the device uses a voice assistant and augmented reality display to guide them through the cooking process. This includes features that automatically control cooking temperature and time using smart cooking appliances.

[0550] Step 8:

[0551] After cooking, users enjoy their meal and, upon completion, input feedback on their satisfaction level and suggestions for future meals into their device. This feedback is then used to generate future meal plans.

[0552] Step 9:

[0553] The server accumulates user feedback and sentiment data, and uses this to continuously personalize plans, thereby improving system accuracy and user satisfaction.

[0554] (Example 2)

[0555] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0556] In modern society, there is a growing demand for automated meal plans optimized for individual health conditions, eating habits, and emotional states. However, existing systems fail to adequately integrate these factors into meal management, making it difficult to simultaneously improve user health and psychological satisfaction. To solve this problem, a more personalized and flexible meal management system is needed.

[0557] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0558] In this invention, the server includes means for acquiring biometric data and analyzing health status, means for registering and storing eating habits and preferences, and means for recognizing daily emotional states using emotion analysis means. This makes it possible to provide a personalized meal plan optimized for the user's health and emotional state, thereby increasing psychological satisfaction.

[0559] "Biometric data" refers to physical information such as a user's heart rate, body temperature, activity level, and sleep patterns, and is data used to evaluate their health status by continuously acquiring this information.

[0560] "Means of analyzing health status" refers to methods of analyzing biometric data acquired using sensors and algorithms to understand a user's current health status and physical changes.

[0561] "Means for registering eating habits and preferences" refers to a method for accumulating information on ingredients that users like and should avoid, as well as past meal records, and saving each user's individual eating habits in a database.

[0562] "Emotional analysis methods" refer to techniques that analyze everyday emotions by recognizing a user's facial expressions and tone of voice, and using these to determine their psychological state.

[0563] "Methods for generating meal plans" refers to methods for creating meal schedules that combine appropriate ingredients and menus, taking into account the user's health condition, eating habits, and emotions.

[0564] "Automatic ordering methods" refer to a system that uses an online platform to automatically order any missing ingredients based on inventory information, thereby ensuring that the necessary ingredients are secured.

[0565] "Augmented reality display means" refers to a method that uses technology to overlay digital information onto the real world to provide users with a visual representation of meal preparation steps and the finished dish.

[0566] In implementing this invention, the user first wears a wearable device and transmits their biometric data to a server. The server acquires this data and runs a dedicated algorithm to analyze heart rate, body temperature, activity level, and sleep patterns. This makes it possible to monitor the user's health status in real time.

[0567] Next, the device provides the user with an interface to input their eating habits and preferences. The user can launch the application and register allergy information, preferred foods, foods to avoid, and more. This information is stored in a database and used to generate meal plans.

[0568] For emotion analysis, the device captures the user's facial expressions and voice tone using its camera and microphone. The server then uses natural language processing technology and emotion analysis algorithms to infer the user's emotional state from this data.

[0569] Based on this visual and emotional data, the server uses a generative AI model to create individually customized meal plans. This model is used to recommend the optimal ingredients and menus tailored to the user's health, preferences, and emotions.

[0570] For example, if a user is feeling stressed, the system can suggest menu items that include chamomile tea, which has a relaxing effect, or chocolate, which enhances feelings of happiness. If positive emotions are detected, it may also offer recipes for exotic dishes to encourage exploration of new culinary experiences.

[0571] An example of a prompt might be, "Suggest the best dinner for the user based on their current health and emotional state." In this way, the goal is to provide a more personalized dining experience through advanced analysis using diverse user data.

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] The server receives biometric data from the wearable device. This data includes heart rate, body temperature, activity level, and sleep patterns. To analyze the input biometric data, the server applies algorithms to understand the user's health status. The output is an assessment of the user's current health status.

[0575] Step 2:

[0576] The terminal provides an interface for users to input information about their eating habits, preferences, and allergies. When a user enters this information into the application, the terminal saves it to a database. It receives the user's eating habits as input and the saved data as output.

[0577] Step 3:

[0578] The device uses a camera and microphone to scan the user's facial expressions and voice tone. It acquires the captured visual and audio data as input and sends it to the server. Based on this, the server uses an emotion analysis algorithm to recognize the user's emotional state. An evaluation result regarding the user's emotional state is generated as output.

[0579] Step 4:

[0580] The server integrates collected biometric data, eating habits, and emotional states, and uses a generative AI model to create a meal plan. Using the integrated user information as input, it generates a individually customized meal plan. The output is a meal plan optimized for the user.

[0581] Step 5:

[0582] The server sends the generated meal plan to the device. The device presents the meal plan to the user using voice assistant or AR guidance functions. It receives the meal plan from the server as input and provides the user with information visually and audibly as output.

[0583] Step 6:

[0584] The server manages ingredient inventory information and automatically places orders as needed. It takes ingredient inventory data as input and generates a list of ingredients that are running low. The output is the execution of online orders.

[0585] Step 7:

[0586] The terminal provides a method for users to input feedback on the meal plans they receive. It receives user feedback and sends that information to the server to be used in generating the next meal plan. It receives user feedback as input and provides evaluation data as output to help with future planning.

[0587] (Application Example 2)

[0588] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0589] While conventional meal management systems can manage health and register preferences based on users' biometric information, they have the challenge of not being able to flexibly suggest or adjust meals according to the user's emotional state. Furthermore, there is a need for methods that can provide a more personalized and fulfilling dining experience by taking emotions into consideration.

[0590] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0591] In this invention, the server includes means for acquiring the user's physiological characteristics and analyzing their health status, a system for managing food inventory information in real time, and an emotion analysis engine for analyzing the user's daily emotional state and adjusting the nutrition plan based on that. This enables more personalized meal suggestions that simultaneously consider the user's health status and emotions.

[0592] "User physiological characteristics" refer to biological numerical data such as heart rate and body temperature obtained from individual users.

[0593] "Means for analyzing health status" refers to a method for analyzing a user's health status based on acquired physiological characteristics.

[0594] "Food inventory information" refers to data regarding the quantity and remaining amount of food ingredients, beverages, and other similar items.

[0595] A "real-time management system" is a system designed to process and update data that changes moment by moment in real time.

[0596] "Everyday emotional states" refer to the emotional fluctuations, such as joy and stress, that users experience on a daily basis.

[0597] "Adjusting a nutrition plan" means changing or optimizing the content of meals according to the user's condition.

[0598] An "emotion analysis engine" is a mechanism that recognizes emotions from a user's facial expressions, tone of voice, and other factors, and processes that information.

[0599] To realize this invention, the server first needs to acquire the user's physiological characteristics from a wearable device. This includes information such as heart rate and body temperature, and devices such as Apple Watch and Fitbit can be used. The acquired physiological characteristics are analyzed using dedicated data analysis software to determine the user's health status, and the results are reflected in the food inventory management system.

[0600] The device captures the user's everyday emotional state through a system equipped with voice recognition technology (e.g., Amazon Alexa or Google Assistant). An emotion analysis engine is used to analyze the user's facial expressions and tone of voice in real time. If the system determines that the user is in a specific emotional state, the emotion analysis results are used to adjust the nutrition plan, and the optimal nutrition plan is suggested each time.

[0601] As a concrete example, when the server detects a user's peaceful mood while watching a movie on a Sunday afternoon, it suggests a meal menu designed to soothe that user. This is a plan tailored to the user's health condition and emotions at that time.

[0602] An example of a prompt is, "Design an algorithm that suggests the most suitable food and drinks for the situation based on the user's physiological characteristics and emotional data." This is an attempt to achieve optimal meal suggestions by utilizing a generative AI model that combines these elements.

[0603] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0604] Step 1:

[0605] The server acquires physiological characteristics such as heart rate and body temperature from wearable devices in real time. The input is biometric data from the wearable device, and the output is an analysis result indicating the user's health status. As part of the data processing, calculations are performed to evaluate the user's health status based on heart rate and body temperature.

[0606] Step 2:

[0607] The device uses speech recognition technology to analyze the user's voice tone and facial expressions, and inputs the data into an emotion analysis engine. The input is the user's voice and visual data, and the output is analytical information indicating the user's emotional state. The voice and video data are processed in real time, and calculations are performed to quantify emotions.

[0608] Step 3:

[0609] The server integrates the results of user physiological characteristic analysis and emotional analysis to generate a nutrition plan that takes into account the user's health status and emotions. The input is health status data and emotional analysis data, and the output is a personalized nutrition suggestion. The acquired data is compared, and a generative AI model is used to create a meal plan that provides the optimal nutritional balance.

[0610] Step 4:

[0611] The server notifies the user's device with meal suggestions based on the generated nutrition plan. The input is the generated nutrition plan, and the output is a notification of specific meal suggestions to the user. Augmented reality displays and audio guides are used when making notifications, providing intuitive information that appeals to both sight and hearing.

[0612] Step 5:

[0613] Users receive meal suggestions and input feedback into their device, which is then reflected in future meal suggestions. The input is user feedback data, and the output is reference information for generating the next nutrition plan. By analyzing the feedback and updating the database for future suggestions, more accurate personalization becomes possible.

[0614] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0615] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0616] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0617] [Fourth Embodiment]

[0618] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0619] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0620] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0621] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0622] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0623] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0624] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0625] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0626] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0627] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0628] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0629] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0630] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0631] This invention is a system for effectively managing a user's diet. Based on the user's biometric information, eating habits, preferences, and food inventory information, the system aims to propose and implement an optimal meal plan tailored to their health condition and needs.

[0632] First, the server collects biometric information from the user's wearable device. This information includes heart rate, body temperature, activity level, and sleep duration, and is used to assess the user's health. The device then provides an interface for the user to input their food preferences, allergy information, and past eating history. This records their eating habits and preferences.

[0633] Next, the server utilizes data from IoT refrigerators and other devices to manage food inventory information in real time. This includes the type, quantity, and expiration date of the food. This data is analyzed in combination with the user's health status and registered preferences. Based on this, the server automatically generates a meal plan with the optimal nutritional balance for the user.

[0634] The generated meal plan includes options for the user to choose from: cooking at home, delivery, or eating out. The device notifies the user of this meal plan, and the user can modify it according to their preferences. If the user chooses to cook at home, the device guides the user through the cooking process using voice and augmented reality displays. This also includes automated control of the cooking process through integration with smart cooking appliances.

[0635] For example, if a user requests a "healthy and quick lunch," the server will suggest a menu based on past data, such as "grilled chicken and mixed salad." If the necessary ingredients are not in sufficient stock, the system will automatically order them and ensure quick procurement. After the meal, the user can enter feedback into the terminal, which will improve the accuracy of future suggestions.

[0636] Thus, the present invention is a comprehensive system that improves the user's quality of life and supports the maintenance of a healthy diet.

[0637] The following describes the processing flow.

[0638] Step 1:

[0639] The server periodically retrieves the user's biometric information from the wearable device. This information includes heart rate, body temperature, activity level, and sleep duration.

[0640] Step 2:

[0641] The device provides an interface that allows users to input their eating habits, preferences, and allergy information. Users can use this interface to update their past eating history and preferences.

[0642] Step 3:

[0643] The server retrieves food inventory information from the IoT refrigerator. This information includes the type, quantity, and expiration date of the food items, recorded in real time.

[0644] Step 4:

[0645] The server analyzes collected biometric information, eating habits, and preference data to assess the user's health status. Based on these results, it generates a meal plan that takes into account appropriate nutritional balance.

[0646] Step 5:

[0647] The server verifies whether there are enough necessary ingredients based on the generated meal plan, and automatically places orders if there are any shortages.

[0648] Step 6:

[0649] The device notifies the user of a generated meal plan. The notification includes options for cooking at home, delivery, and eating out, and the user can choose according to their preference.

[0650] Step 7:

[0651] If the user chooses to cook their own meal, the device will guide them through the specific cooking steps using voice and augmented reality displays. During this process, the cooking process will be automatically controlled in conjunction with smart cooking appliances.

[0652] Step 8:

[0653] After cooking and enjoying their meal, users enter feedback on their satisfaction level and areas for improvement into their device.

[0654] Step 9:

[0655] The server collects feedback data from users and uses it to generate the next meal plan. This allows the system to continuously improve its accuracy.

[0656] (Example 1)

[0657] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0658] In modern society, busy people find it difficult to maintain a healthy diet. In particular, creating meal plans optimized for individual health needs and managing daily food shortages are challenges. Furthermore, there is a need for efficient cooking support, but a comprehensive system to meet these needs does not yet exist.

[0659] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0660] In this invention, the server includes means for acquiring the user's biometric information and analyzing their health status, means for registering and recording the user's eating habits and preferences, and means for managing the inventory information of available ingredients in real time. This enables the automatic adjustment of meal plans to meet the user's specific needs, making it possible to maintain a healthy and efficient diet.

[0661] "User biometric information" refers to data necessary to indicate a person's health status, such as heart rate, body temperature, activity level, and sleep duration.

[0662] "Dietary habits and preferences" refers to data that includes an individual's tendencies regarding the foods and dishes they prefer to consume on a daily basis, as well as allergy information related to specific foods.

[0663] "Available food inventory information" refers to data on current food storage, including information on the type, quantity, and expiration date of ingredients.

[0664] "Using generative models" refers to a method that uses machine learning algorithms to analyze collected data and automatically generate the optimal meal plan for the user.

[0665] "Methods for guiding the steps of cooking at home" refers to methods that use voice commands or augmented reality technology to guide users through the cooking process so that they can cook efficiently.

[0666] "Feedback data" refers to evaluation information about the dining experience provided by users, including data on taste evaluations and comments on the difficulty of preparation.

[0667] "Computer-controlled cooking equipment" refers to cooking devices that are managed by a program to automatically control the cooking process.

[0668] This invention is a comprehensive system that supports users in maintaining a healthy diet. The embodiments thereof are described below.

[0669] The server collects biometric information from the user's wearable device. This biometric information includes heart rate, body temperature, activity level, and sleep duration, and is acquired using communication methods such as Bluetooth and Wi-Fi. The collected data is analyzed on the server to evaluate the user's health status.

[0670] The terminal provides an interface for users to input their food preferences, allergy information, and past eating history. Specifically, a smartphone application or web interface is used. Users can select their favorite and avoided foods and record their past eating history. This information is sent to a server, and the database of eating habits and preferences is updated.

[0671] The server also acquires food inventory information from IoT refrigerators and other devices. Using IoT sensors, it manages the type, quantity, and expiration date of ingredients in real time. This inventory information is used when creating meal plans using a generative AI model.

[0672] The generative AI model takes into account the user's health status, eating habits, preferences, and inventory information to generate an optimal, nutritionally balanced meal plan. An example of a prompt phrase that can be used is, "Suggest an appropriate lunch menu based on the user's health status and ingredient inventory."

[0673] The device notifies the user of the generated meal plan. The plan includes options such as cooking at home, delivery, and eating out. The user receives this notification and can modify or select the plan according to their preferences.

[0674] For example, if a user requests a healthy and quick lunch, the server might suggest something like "grilled chicken and mixed salad." If the necessary ingredients are in short supply, the system automatically orders them, supporting quick procurement.

[0675] In general, the present invention aims to provide safe and healthy food and improve the lifestyle of busy users in particular.

[0676] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0677] Step 1:

[0678] The server acquires biometric information from the user's wearable device. Input data includes heart rate, body temperature, activity level, and sleep duration. Using this input data, the server analyzes the user's health status, detects abnormalities, and evaluates daily changes in health, thereby outputting health indicators. Specifically, data is received via Bluetooth or Wi-Fi communication and stored in a database.

[0679] Step 2:

[0680] The device provides users with an interface for inputting their food preferences, allergy information, and meal history. This input includes manually selected preferred ingredients and allergy information. Based on this, the system updates the user's preference database and outputs data to be used for future menu suggestions. A specific example of this operation is selection on a smartphone application screen.

[0681] Step 3:

[0682] The server retrieves food inventory information from the IoT refrigerator. Inputs include food type, quantity, and expiration date. Based on this data, the inventory management system analyzes the expiration dates and shortages of food items, and outputs a real-time inventory list. IoT sensors automatically send information about each food item to the server, updating the list as needed.

[0683] Step 4:

[0684] The server uses a generative AI model to generate meal plans based on the user's health status, eating habits, preferences, and inventory information. For data processing, the generative AI model uses machine learning algorithms to calculate the optimal meal menu and outputs it as a suggested menu. Based on the example prompt, the generative model is activated with the command, "Suggest an appropriate lunch menu based on the user's health status and ingredient inventory."

[0685] Step 5:

[0686] The terminal notifies the user of the generated meal plan. The input is suggested menu information from the server, which is displayed on the user's terminal screen as options such as cooking at home, delivery, and eating out. Upon receiving this notification, the user reviews the options, makes selections and modifications according to their preferences, and receives output to confirm the final meal plan. Notifications are delivered via pop-ups or push notifications.

[0687] Step 6:

[0688] The device guides users who choose to cook their own meals through the cooking process. Input includes a confirmed meal menu and cooking procedure information, and based on this, it outputs the cooking process using voice commands and augmented reality displays. Users can use their smartphones to check how ingredients are being cut and the cooking progress during cooking, and it integrates with smart cooking devices to automate the cooking process. This process includes activating a voice assistant and using the camera for AR displays.

[0689] Step 7:

[0690] After finishing their meal, users enter feedback into their device. This feedback includes evaluations of the taste of the meal and the ease of preparation. This feedback is output as data that will be used to generate the next meal plan and is recorded in a database on the server side. Specifically, this involves entering feedback into an evaluation form on the app.

[0691] (Application Example 1)

[0692] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0693] In modern urban life, supporting residents' health management requires the proposal of meal plans that take into account individual health conditions and dietary preferences. However, many residents find it difficult to plan and implement healthy meals amidst their busy daily lives. Efficiently procuring fresh ingredients is also a challenge. Furthermore, there is a lack of cooking support to enable citizens to easily prepare healthy meals at home. An efficient system is needed to address these problems.

[0694] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0695] This invention includes a server that includes means for acquiring user biometric data and evaluating their health status, means for recording user eating habits and preferences, and means for managing the supply status of ingredients in real time. This makes it possible to generate an optimal meal plan based on the health status and preferences of residents. Furthermore, by collaborating with local ingredient suppliers, fresh ingredients can be procured, and by coordinating with computer-controlled cooking appliances, self-catering can be made easier. These means realize a comprehensive support system that enables the promotion of the health of the entire community.

[0696] "Biometric data" refers to information about a user's physical activity, including heart rate, body temperature, and activity level.

[0697] "Health status" refers to information indicating the user's physical condition and is evaluated based on biometric data.

[0698] "Eating habits" refers to the patterns, timing, and types of meals a user typically consumes.

[0699] "Preferences" refer to the ingredients, seasonings, and eating styles that users enjoy.

[0700] "Supply status" refers to information regarding the stock, availability, and expiration dates of food ingredients.

[0701] A "meal plan" refers to a series of meals suggested to the user, taking into account their health condition and preferences.

[0702] "Automatic ordering" refers to the process where the system automatically orders the necessary ingredients.

[0703] "Notification" refers to informing the user about the generated meal plan.

[0704] "Visual display" refers to an interface that provides information to users visually.

[0705] "Local food suppliers" refers to businesses that supply food products within a specific region.

[0706] The "cooking process" refers to the series of operations that transform ingredients into a dish.

[0707] The system for implementing this invention primarily uses a server, a terminal, and multiple hardware devices. First, the server collects biometric data from the user's wearable device and evaluates the user's health status. This data includes specific information such as heart rate, body temperature, and activity level. The server also records data on the user's eating habits and preferences entered into the terminal and generates a meal plan based on this data.

[0708] This system is also connected to an IoT refrigerator that manages the supply status of ingredients in real time. The server automatically orders the necessary ingredients based on the ingredient information. The meal plan generated by the server is notified to the user's device. It provides an interface that guides users through cooking procedures with visual displays and voice prompts, supporting them when cooking for themselves.

[0709] For example, if a user requests a "simple and nutritious dinner for a busy day," the system suggests grilled salmon and quinoa salad. In this case, cooking instructions and guidance are provided visually and audibly through a smartphone app, and cooking can be automatically started via IoT-enabled appliances. In this way, the system helps users efficiently prepare healthy meals.

[0710] The software used includes "pandas" and "numpy" for analyzing health data, "Scikit-learn" for suggestion functions, and web frameworks such as "Flask" and "FastAPI" for communication with wearable devices and IoT devices. It also has a function to record resident feedback and incorporate it into the generation of the next meal plan. By utilizing the generation AI model and inputting prompts such as "Please suggest a simple and nutritious dinner recipe suitable for a busy day, with salmon as the main ingredient," it can generate an effective meal plan.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The server acquires biometric data from the user's wearable device. Specifically, it collects data such as heart rate, body temperature, and activity level from the device in real time. Based on this data, it generates an initial dataset for evaluating the user's health status. Here, the input is biometric data from the device, and the output is evaluation data indicating the health status.

[0714] Step 2:

[0715] The device collects data from users regarding their eating habits and preferences. Users input their preferred foods, allergy information, and past meal history through the application interface. Based on this information, the device updates an individual dietary database. The input here is user preference data, and the output is an updated user profile.

[0716] Step 3:

[0717] The server monitors and manages the supply status of food ingredients through devices such as IoT refrigerators. It collects data on the type, quantity, and expiration date of ingredients, and uses this data to identify ingredients that are in short supply. The input here is inventory data from refrigerators, and the output is a list of ingredients that are in short supply.

[0718] Step 4:

[0719] The server integrates collected health status data, dietary habit data, and supply status data to generate a customized meal plan for each user. Using a generation AI model, it formulates the optimal menu based on the input data. The input here is all the data from the previous step, and the output is the recommended meal plan.

[0720] Step 5:

[0721] The server automatically orders the necessary ingredients from local suppliers based on the generated meal plan. It generates an ingredient list according to the generated plan and places orders through its network of suppliers. The input here is the meal plan, and the output is an order form or order list.

[0722] Step 6:

[0723] The device notifies the user of recommended meal plans and guides them through the necessary cooking steps. It utilizes voice instructions and augmented reality (AR) to provide users with the information they need when cooking at home. The input here is the generated meal plan, and the output is a user-friendly notification to the user.

[0724] Step 7:

[0725] After the user has consumed a meal, the device collects feedback and uses it to generate the next meal plan. It records the user's satisfaction level, new preferences, and any discovered allergies, updating the dataset for the next plan. The input for this step is the user's feedback, and the output is the updated preference data.

[0726] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0727] This invention enables more personalized responses by incorporating an emotion engine into a meal management system based on the user's biometric information and eating habits. The emotion engine recognizes the user's everyday emotions and reflects them in the generation and adjustment of meal plans.

[0728] First, the server acquires biometric information from the wearable device and continuously monitors the user's health status. This data includes heart rate, body temperature, activity level, and sleep patterns. Next, the device provides an interface for the user to input and edit their eating habits, preferences, and allergy information. This information includes details such as personal food preferences and foods to avoid.

[0729] The introduction of an emotion engine allows the system to recognize the user's daily emotional state from their facial expressions and tone of voice, and analyze this information in combination with biometric data. For example, if the system determines that the user is stressed, it will suggest relaxing herbal teas or foods that not only provide nutritional value but also lift the user's spirits.

[0730] Furthermore, the server checks the inventory status of ingredients and uses an automated ordering module to quickly replenish any missing ingredients. This ensures that planned meals are always feasible.

[0731] Notifications to the user are delivered via the device. The device uses a voice assistant and AR guidance to suggest meal plan options to the user. Emotional data is used here to prioritize and present options that are best suited to the user's mood.

[0732] For example, if a user wants to approach their next meal in a cheerful mood, the system will suggest colorful and visually appealing dishes. Furthermore, when positive emotions are detected, options to introduce new recipes or foreign culinary traditions to support the user's exploration of food culture are also included.

[0733] By integrating an emotional engine into this system, we aim to provide a more engaging eating experience that considers not only the user's physical health but also their psychological satisfaction.

[0734] The following describes the processing flow.

[0735] Step 1:

[0736] The server periodically retrieves the user's biometric information from the wearable device. This information includes heart rate, body temperature, activity level, and sleep patterns.

[0737] Step 2:

[0738] The terminal provides an interface for users to input and edit their eating habits, preferences, and allergy information. Users use the terminal to enter this information and update it according to their preferences and needs.

[0739] Step 3:

[0740] The emotion engine analyzes the user's facial expressions and voice tone to recognize their emotional state in real time. This recognized emotional data is then comprehensively evaluated in conjunction with the user's health status.

[0741] Step 4:

[0742] The server generates an optimal meal plan tailored to the user's health condition and current emotions, based on acquired biometric information, eating habits, preferences, and emotional data. In doing so, it suggests ingredients that promote relaxation and visually appealing dishes based on the user's emotions.

[0743] Step 5:

[0744] The server retrieves the latest food inventory information from the IoT refrigerator and automatically places orders when it detects any missing ingredients based on the generated meal plan. The ordering process is carried out quickly to trusted suppliers.

[0745] Step 6:

[0746] The device notifies the user of a generated meal plan. This notification includes emotionally conscious options for home cooking, delivery, and dining out. The user can choose their preferred plan from the presented options.

[0747] Step 7:

[0748] If the user chooses to cook for themselves, the device uses a voice assistant and augmented reality display to guide them through the cooking process. This includes features that automatically control cooking temperature and time using smart cooking appliances.

[0749] Step 8:

[0750] After cooking, users enjoy their meal and, upon completion, input feedback on their satisfaction level and suggestions for future meals into their device. This feedback is then used to generate future meal plans.

[0751] Step 9:

[0752] The server accumulates user feedback and sentiment data, and uses this to continuously personalize plans, thereby improving system accuracy and user satisfaction.

[0753] (Example 2)

[0754] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0755] In modern society, there is a growing demand for automated meal plans optimized for individual health conditions, eating habits, and emotional states. However, existing systems fail to adequately integrate these factors into meal management, making it difficult to simultaneously improve user health and psychological satisfaction. To solve this problem, a more personalized and flexible meal management system is needed.

[0756] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0757] In this invention, the server includes means for acquiring biometric data and analyzing health status, means for registering and storing eating habits and preferences, and means for recognizing daily emotional states using emotion analysis means. This makes it possible to provide a personalized meal plan optimized for the user's health and emotional state, thereby increasing psychological satisfaction.

[0758] "Biometric data" refers to physical information such as a user's heart rate, body temperature, activity level, and sleep patterns, and is data used to evaluate their health status by continuously acquiring this information.

[0759] "Means of analyzing health status" refers to methods of analyzing biometric data acquired using sensors and algorithms to understand a user's current health status and physical changes.

[0760] "Means for registering eating habits and preferences" refers to a method for accumulating information on ingredients that users like and should avoid, as well as past meal records, and saving each user's individual eating habits in a database.

[0761] "Emotional analysis methods" refer to techniques that analyze everyday emotions by recognizing a user's facial expressions and tone of voice, and using these to determine their psychological state.

[0762] "Methods for generating meal plans" refers to methods for creating meal schedules that combine appropriate ingredients and menus, taking into account the user's health condition, eating habits, and emotions.

[0763] "Automatic ordering methods" refer to a system that uses an online platform to automatically order any missing ingredients based on inventory information, thereby ensuring that the necessary ingredients are secured.

[0764] "Augmented reality display means" refers to a method that uses technology to overlay digital information onto the real world to provide users with a visual representation of meal preparation steps and the finished dish.

[0765] In implementing this invention, the user first wears a wearable device and transmits their biometric data to a server. The server acquires this data and runs a dedicated algorithm to analyze heart rate, body temperature, activity level, and sleep patterns. This makes it possible to monitor the user's health status in real time.

[0766] Next, the device provides the user with an interface to input their eating habits and preferences. The user can launch the application and register allergy information, preferred foods, foods to avoid, and more. This information is stored in a database and used to generate meal plans.

[0767] For emotion analysis, the device captures the user's facial expressions and voice tone using its camera and microphone. The server then uses natural language processing technology and emotion analysis algorithms to infer the user's emotional state from this data.

[0768] Based on this visual and emotional data, the server uses a generative AI model to create individually customized meal plans. This model is used to recommend the optimal ingredients and menus tailored to the user's health, preferences, and emotions.

[0769] For example, if a user is feeling stressed, the system can suggest menu items that include chamomile tea, which has a relaxing effect, or chocolate, which enhances feelings of happiness. If positive emotions are detected, it may also offer recipes for exotic dishes to encourage exploration of new culinary experiences.

[0770] An example of a prompt might be, "Suggest the best dinner for the user based on their current health and emotional state." In this way, the goal is to provide a more personalized dining experience through advanced analysis using diverse user data.

[0771] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0772] Step 1:

[0773] The server receives biometric data from the wearable device. This data includes heart rate, body temperature, activity level, and sleep patterns. To analyze the input biometric data, the server applies algorithms to understand the user's health status. The output is an assessment of the user's current health status.

[0774] Step 2:

[0775] The terminal provides an interface for users to input information about their eating habits, preferences, and allergies. When a user enters this information into the application, the terminal saves it to a database. It receives the user's eating habits as input and the saved data as output.

[0776] Step 3:

[0777] The device uses a camera and microphone to scan the user's facial expressions and voice tone. It acquires the captured visual and audio data as input and sends it to the server. Based on this, the server uses an emotion analysis algorithm to recognize the user's emotional state. An evaluation result regarding the user's emotional state is generated as output.

[0778] Step 4:

[0779] The server integrates collected biometric data, eating habits, and emotional states, and uses a generative AI model to create a meal plan. Using the integrated user information as input, it generates a individually customized meal plan. The output is a meal plan optimized for the user.

[0780] Step 5:

[0781] The server sends the generated meal plan to the device. The device presents the meal plan to the user using voice assistant or AR guidance functions. It receives the meal plan from the server as input and provides the user with information visually and audibly as output.

[0782] Step 6:

[0783] The server manages ingredient inventory information and automatically places orders as needed. It takes ingredient inventory data as input and generates a list of ingredients that are running low. The output is the execution of online orders.

[0784] Step 7:

[0785] The terminal provides a method for users to input feedback on the meal plans they receive. It receives user feedback and sends that information to the server to be used in generating the next meal plan. It receives user feedback as input and provides evaluation data as output to help with future planning.

[0786] (Application Example 2)

[0787] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0788] While conventional meal management systems can manage health and register preferences based on users' biometric information, they have the challenge of not being able to flexibly suggest or adjust meals according to the user's emotional state. Furthermore, there is a need for methods that can provide a more personalized and fulfilling dining experience by taking emotions into consideration.

[0789] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0790] In this invention, the server includes means for acquiring the user's physiological characteristics and analyzing their health status, a system for managing food inventory information in real time, and an emotion analysis engine for analyzing the user's daily emotional state and adjusting the nutrition plan based on that. This enables more personalized meal suggestions that simultaneously consider the user's health status and emotions.

[0791] "User physiological characteristics" refer to biological numerical data such as heart rate and body temperature obtained from individual users.

[0792] "Means for analyzing health status" refers to a method for analyzing a user's health status based on acquired physiological characteristics.

[0793] "Food inventory information" refers to data regarding the quantity and remaining amount of food ingredients, beverages, and other similar items.

[0794] A "real-time management system" is a system designed to process and update data that changes moment by moment in real time.

[0795] "Everyday emotional states" refer to the emotional fluctuations, such as joy and stress, that users experience on a daily basis.

[0796] "Adjusting a nutrition plan" means changing or optimizing the content of meals according to the user's condition.

[0797] An "emotion analysis engine" is a mechanism that recognizes emotions from a user's facial expressions, tone of voice, and other factors, and processes that information.

[0798] To realize this invention, the server first needs to acquire the user's physiological characteristics from a wearable device. This includes information such as heart rate and body temperature, and devices such as Apple Watch and Fitbit can be used. The acquired physiological characteristics are analyzed using dedicated data analysis software to determine the user's health status, and the results are reflected in the food inventory management system.

[0799] The device captures the user's everyday emotional state through a system equipped with voice recognition technology (e.g., Amazon Alexa or Google Assistant). An emotion analysis engine is used to analyze the user's facial expressions and tone of voice in real time. If the system determines that the user is in a specific emotional state, the emotion analysis results are used to adjust the nutrition plan, and the optimal nutrition plan is suggested each time.

[0800] As a concrete example, when the server detects a user's peaceful mood while watching a movie on a Sunday afternoon, it suggests a meal menu designed to soothe that user. This is a plan tailored to the user's health condition and emotions at that time.

[0801] An example of a prompt is, "Design an algorithm that suggests the most suitable food and drinks for the situation based on the user's physiological characteristics and emotional data." This is an attempt to achieve optimal meal suggestions by utilizing a generative AI model that combines these elements.

[0802] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0803] Step 1:

[0804] The server acquires physiological characteristics such as heart rate and body temperature from wearable devices in real time. The input is biometric data from the wearable device, and the output is an analysis result indicating the user's health status. As part of the data processing, calculations are performed to evaluate the user's health status based on heart rate and body temperature.

[0805] Step 2:

[0806] The device uses speech recognition technology to analyze the user's voice tone and facial expressions, and inputs the data into an emotion analysis engine. The input is the user's voice and visual data, and the output is analytical information indicating the user's emotional state. The voice and video data are processed in real time, and calculations are performed to quantify emotions.

[0807] Step 3:

[0808] The server integrates the results of user physiological characteristic analysis and emotional analysis to generate a nutrition plan that takes into account the user's health status and emotions. The input is health status data and emotional analysis data, and the output is a personalized nutrition suggestion. The acquired data is compared, and a generative AI model is used to create a meal plan that provides the optimal nutritional balance.

[0809] Step 4:

[0810] The server notifies the user's device with meal suggestions based on the generated nutrition plan. The input is the generated nutrition plan, and the output is a notification of specific meal suggestions to the user. Augmented reality displays and audio guides are used when making notifications, providing intuitive information that appeals to both sight and hearing.

[0811] Step 5:

[0812] Users receive meal suggestions and input feedback into their device, which is then reflected in future meal suggestions. The input is user feedback data, and the output is reference information for generating the next nutrition plan. By analyzing the feedback and updating the database for future suggestions, more accurate personalization becomes possible.

[0813] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0814] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0815] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0816] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0817] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0818] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0819] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0820] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0821] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0822] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0823] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0824] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0825] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0826] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0827] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0828] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0829] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0830] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0831] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0832] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0833] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0834] The following is further disclosed regarding the embodiments described above.

[0835] (Claim 1)

[0836] A means of acquiring a user's biometric information and analyzing their health status,

[0837] A means of registering and recording the user's eating habits and preferences,

[0838] A means of managing food inventory information in real time,

[0839] A means for generating a meal plan tailored to the user's health status and preferences based on acquired data,

[0840] A method for automatically ordering necessary ingredients based on a meal plan,

[0841] A means of notifying the user of the generated meal plan,

[0842] Audio and augmented reality display means for guiding the user through the cooking process,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, comprising means for recording user feedback data and reflecting it in generating the next meal plan.

[0846] (Claim 3)

[0847] The system according to claim 1, comprising means for automatically controlling the cooking process in cooperation with computer-controlled cooking appliances.

[0848]

[0849] "Example 1"

[0850] (Claim 1)

[0851] A means of acquiring a user's biometric information and analyzing their health status,

[0852] A means of registering and recording the user's eating habits and preferences,

[0853] A means of managing real-time inventory information of available ingredients,

[0854] A means of using a generative model to generate meal plans tailored to the user's health status and preferences based on acquired data,

[0855] A means of automatically ordering the necessary ingredients based on the generated meal plan,

[0856] A means of notifying the user of the generated meal plan and allowing them to make a selection,

[0857] A means of guiding users through the cooking process using voice and augmented reality,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, which provides a function to record user feedback data and reflect it in the generation of the next meal plan.

[0861] (Claim 3)

[0862] The system according to claim 1, which provides a function to automatically control the cooking process in cooperation with computer-controlled cooking equipment.

[0863] "Application Example 1"

[0864] (Claim 1)

[0865] A means of acquiring users' biometric data and evaluating their health status,

[0866] A means of recording the user's eating habits and preferences,

[0867] A means of managing the supply status of ingredients in real time,

[0868] A means for generating a meal plan based on the user's health status and preferences, based on the acquired information,

[0869] A method for automatically ordering necessary ingredients based on a meal plan,

[0870] A means of notifying the user of the generated meal plan,

[0871] Audio and visual display means to guide the steps of cooking at home,

[0872] In collaboration with local food suppliers, we have a means of procuring fresh ingredients,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, comprising means for recording residents' feedback data and reflecting it in the generation of the next meal plan.

[0876] (Claim 3)

[0877] The system according to claim 1, comprising means for automatically controlling the cooking process in cooperation with computer-controlled cooking appliances.

[0878] "Example 2 of combining an emotion engine"

[0879] (Claim 1)

[0880] A means of acquiring a user's biometric data and analyzing their health status,

[0881] A means for registering and remembering the user's eating habits and preferences,

[0882] A means of recognizing the user's everyday emotional state using emotion analysis means,

[0883] A means of managing the inventory status of ingredients in real time,

[0884] A means for generating a meal plan tailored to the user's health and emotional state based on acquired data,

[0885] A method for automatically ordering necessary ingredients based on a meal plan,

[0886] A means of notifying and suggesting the generated meal plan to the user,

[0887] Audio and augmented reality display means for guiding the user through the self-scanning process,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, further comprising means for recording user feedback data and reflecting it in the generation of the next meal plan.

[0891] (Claim 3)

[0892] The system according to claim 1, comprising means for automatically controlling cooking procedures in cooperation with computer-controlled cooking equipment.

[0893] "Application example 2 when combining with an emotional engine"

[0894] (Claim 1)

[0895] A means of acquiring the user's physiological characteristics and analyzing their health status,

[0896] A device for registering and recording a user's eating habits and preferences,

[0897] A system for managing food inventory information in real time,

[0898] A means of generating a nutrition plan tailored to the user's health status and preferences based on acquired data,

[0899] A system that automatically orders the necessary ingredients based on a nutrition plan,

[0900] A system that notifies the user of the generated nutrition plan,

[0901] A device that provides audio and augmented reality displays to guide the user through the cooking process,

[0902] An emotion analysis engine that analyzes the user's daily emotional state and adjusts meal plans based on that analysis,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, further comprising a function to record user feedback data and reflect it in the generation of the next nutrition plan.

[0906] (Claim 3)

[0907] The system according to claim 1, comprising a function for automatically controlling the cooking process in cooperation with a computer-controlled cooking device. [Explanation of Symbols]

[0908] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring a user's biometric data and evaluating their health status, A means of recording the user's eating habits and preferences, A means of managing the supply status of ingredients in real time, A means for generating a meal plan based on the user's health status and preferences, based on the acquired information, A method for automatically ordering necessary ingredients based on a meal plan, A means of notifying the user of the generated meal plan, Audio and visual display means to guide the cooking procedure, In collaboration with local food suppliers, we have a means of procuring fresh ingredients, A system that includes this.

2. The system according to claim 1, comprising means for recording residents' feedback data and reflecting it in the generation of the next meal plan.

3. The system according to claim 1, comprising means for automatically controlling the cooking process in cooperation with computer-controlled cooking appliances.

Images