system

Abstract

We provide the system. [Solution] Means for obtaining user information, An analysis means that analyzes user information acquired by the acquisition means and generates optimal meal suggestions, An ordering means that automatically orders the necessary ingredients based on the meal suggestions generated by the analysis means, A guidance means that provides cooking guidance in accordance with the aforementioned meal suggestion, A proposal method for generating new culinary suggestions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order for individual users to select and cook appropriate meals according to their own health conditions and lifestyles, it is necessary to collect and process a huge amount of information, which causes a large time and psychological burden. In addition, the management of food ingredients and the exploration of new dishes are also complicated, and it is said to be difficult to maintain a healthy and varied diet. There is a need for a technology that solves such problems and supports users in leading an efficient and healthy diet.

Means for Solving the Problems

[0005] The present invention solves the above problems by including an acquisition means for acquiring user information, an analysis means for analyzing the acquired data and making meal suggestions, an ordering means for automatically ordering necessary ingredients, a guidance means for providing cooking guides, and a suggestion means for suggesting new dishes. As a result, users can receive optimal meal suggestions based on their health condition and lifestyle, and the management of ingredients and the cooking process are made more efficient. Consequently, users can maintain a healthy and diverse diet.

[0006] "User information" includes information such as the user's health status, eating habits, preferences, schedule, and refrigerator inventory information.

[0007] "Means of acquisition" refers to technical elements that provide interfaces and processes for collecting user information.

[0008] "Analysis means" refers to the process of processing acquired user information and generating optimal meal suggestions based on the user's health status and preferences.

[0009] "Ordering method" refers to a technological element that has the function of automatically ordering necessary ingredients online based on meal suggestions generated by the analysis method.

[0010] "Guidance means" refers to technical elements that provide users with cooking procedures according to meal suggestions, and support the user's cooking process.

[0011] A "suggestion tool" is a technological element that has the function of suggesting new dishes or cross-cultural dining experiences, taking into account the user's preferences and past eating history. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

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

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

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

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

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

[0020] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0033] This invention is a system that automatically generates an optimal meal plan tailored to each individual user and supports its implementation. The system can improve the user's diet in the following ways.

[0034] First, the system acquires user information such as health data, food preferences, and schedules from wearable devices, smartphones, and IoT sensors in the refrigerator. Specifically, it collects health information such as heart rate and activity levels, schedule information from the calendar, and information on the food inventory in the refrigerator.

[0035] The server then analyzes this information and generates a personalized meal plan based on the user's health status and nutritional needs. This plan includes the proportion of necessary nutrients and the types of foods to be consumed. Based on the analysis results, the menus for breakfast, lunch, and dinner are determined.

[0036] Based on the generated meal plan, the server checks the refrigerator inventory to identify any missing ingredients. Missing ingredients are automatically ordered through the online store, reducing the user's worry about running out of food. Orders are flexibly adjusted to match the user's specified delivery time and store preferences.

[0037] When a user cooks a meal for themselves, the device provides a cooking guide and works with smart cooking appliances to assist with the cooking process. The guide gives step-by-step instructions and automatically operates the cooking appliances as needed. This makes it easy for even novice cooks to prepare meals.

[0038] Finally, once the user finishes their meal, the device provides feedback based on the nutritional balance of the meal. Furthermore, the server analyzes the user's preferences and past eating patterns to periodically suggest new dishes and dishes from different cultures. This allows users to enjoy a diverse range of dining experiences.

[0039] This invention provides a comprehensive solution that optimizes meals with health in mind, automates purchasing, supports cooking, and offers a new dining experience, enriching users' lifestyles and supporting healthy eating habits.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server retrieves user health information, food preferences, schedules, and inventory information from wearable devices, smartphones, and IoT sensors in refrigerators. This includes heart rate, activity levels, scheduled activities, and the quantity and expiration dates of food items in the refrigerator.

[0043] Step 2:

[0044] The server analyzes the acquired user information and generates a meal plan based on the user's health status and preferences. For example, it might suggest a breakfast that provides energy to a user who is sleep-deprived.

[0045] Step 3:

[0046] The server compares the list of ingredients needed for the generated meal plan with the refrigerator's inventory to identify any missing ingredients. The missing ingredients are automatically ordered through the online store.

[0047] Step 4:

[0048] If the user chooses to cook for themselves, the device provides them with a cooking guide. The device displays the cooking steps in real time and works with smart cooking appliances to automatically control the temperature and timer.

[0049] Step 5:

[0050] The device provides the user with feedback on the nutritional balance after a meal, allowing them to confirm whether the meal met their health goals.

[0051] Step 6:

[0052] The server suggests new dishes and cross-cultural dining experiences based on the user's dining history and preferences. This gives users new dining options and allows them to enjoy a variety of cuisines without getting bored.

[0053] (Example 1)

[0054] 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."

[0055] Traditional meal management systems struggled to individually optimize meal plans based on users' health conditions and dietary preferences, and lacked sufficient functionality to automatically compensate for ingredient shortages. As a result, users had to expend considerable effort to maintain a healthy and diverse diet. Furthermore, the lack of support during cooking was a burden for novice cooks.

[0056] 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.

[0057] In this invention, the server includes receiving means for receiving user health information, preference information, and schedule information; generating means for generating an individualized meal plan based on the information received by the receiving means; and replenishment means for automatically ordering necessary ingredients based on the meal plan created by the generating means. This makes it possible to easily achieve a healthy and diverse diet while reducing the user's time and effort.

[0058] A "user" refers to an individual who uses the system to manage their diet.

[0059] "Health information" refers to data that indicates the user's physical condition, such as heart rate and activity level.

[0060] "Preference information" refers to data related to a user's food preferences and past eating history.

[0061] "Schedule information" refers to data that shows the user's schedule and appointments.

[0062] "Receiving means" refers to the components within the system for collecting the aforementioned information from the user.

[0063] "Generation means" refers to the components within a system that create personalized meal plans based on collected information.

[0064] A "meal plan" refers to a menu of meals and intake guidelines designed to meet the user's nutritional needs.

[0065] "Replenishment means" refers to a component within a system that automatically orders and procures necessary ingredients based on a meal plan.

[0066] "Adjustment mechanism" refers to a component within a system used to revise food order details based on inventory information in the refrigerator.

[0067] "Support measures" refer to components within a system that provide guidance on cooking and work in conjunction with cooking equipment to assist in the cooking process.

[0068] "Evaluation means" refers to the components within a system that provide post-meal feedback and suggest new dishes based on user preferences.

[0069] This invention is a system that provides optimal dietary management to users and is operated using multiple hardware devices.

[0070] Users acquire health information such as heart rate and activity levels using wearable devices and smartphones. This data is transmitted to a server via Bluetooth or Wi-Fi. Users can also input schedule information and dietary preferences using a smartphone application. This information is stored in a cloud database and used for analysis.

[0071] The server performs data analysis using software running on the cloud. This analysis utilizes generative AI models to create personalized meal plans based on each user's individual nutritional needs. Simultaneously, the server receives inventory information from IoT sensors in the refrigerator and automatically places orders using the online store's API if necessary ingredients are in short supply. These processes are managed by the server's scheduling software.

[0072] When a user is cooking, a device (such as a tablet) displays a cooking guide. This device works in conjunction with smart cooking appliances and can automatically operate them according to the instructions. For example, it can automatically set the oven temperature and help the cooking process proceed smoothly.

[0073] After a meal, the device provides feedback based on the nutritional balance of the meal, and the server analyzes the user's accumulated eating history and preferences to suggest new cooking experiences. This allows users to be exposed to diverse food cultures and enjoy a balanced diet.

[0074] For example, if a user requests a low-calorie, high-protein breakfast, the server will suggest a recipe combining oatmeal with protein powder and fruit. Another example of a prompt message is, "Please suggest a breakfast menu for health promotion."

[0075] This system makes it easier for users to maintain their daily health and improve their lifestyle by supporting improvements in their eating habits.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user obtains health information using a wearable device. This device measures heart rate and activity level and transfers the data to a smartphone. The input is heart rate and activity level data, and the output is a set of health information stored on the smartphone. This data is then sent to a server.

[0079] Step 2:

[0080] Users input schedule and preference information using a smartphone app. This information concerns the user's daily activities and dietary preferences. The input consists of schedule and preference information, while the output is a set of user information stored in a cloud database.

[0081] Step 3:

[0082] The server performs data analysis based on health information, preference information, and schedule information received from the user. This analysis utilizes a generative AI model to create a personalized meal plan for the user. The input is a set of user information, and the output is the personalized meal plan.

[0083] Step 4:

[0084] The server obtains inventory information from IoT sensors installed in the refrigerator. This information is compared with meal plans stored in the system to identify any missing ingredients. The inputs are meal plans and inventory information, and the output is a list of missing ingredients.

[0085] Step 5:

[0086] The server automatically orders missing ingredients via the online store. This process uses the online store's API to confirm the order based on user preferences and specified delivery times. The input is a list of missing ingredients, and the output is an order confirmation notification.

[0087] Step 6:

[0088] When the user begins cooking, the device displays a detailed cooking guide. The device guides the user through the steps and informs them of the next steps via voice and on-screen prompts. The input is the meal plan, and the output is a display of the cooking procedure.

[0089] Step 7:

[0090] The device works in conjunction with smart cooking appliances to assist the cooking process. For example, it automatically adjusts oven settings to ensure food is cooked properly. The input is the cooking procedure, and the output is the operation of the automatically controlled cooking appliance.

[0091] Step 8:

[0092] After a meal, the user receives a meal evaluation via their device. This evaluation provides feedback based on the balance of nutrients consumed. The input is the actual meal consumed, and the output is the nutritional evaluation feedback.

[0093] Step 9:

[0094] The server analyzes the user's eating history and preferences to suggest new culinary experiences and cross-cultural dishes. The input is past eating history and preference data, and the output is a suggested new meal plan.

[0095] (Application Example 1)

[0096] 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."

[0097] In today's busy lifestyle, maintaining a healthy and efficient diet tailored to individual users is not easy. Many factors are involved, including ingredient selection, nutritional balance considerations, and cooking time. In particular, optimizing these factors individually is difficult, so there is a need for a system that allows users to continue eating healthily without hassle.

[0098] 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.

[0099] In this invention, the server includes a device for acquiring user information, a processing device for analyzing the acquired user information and generating an optimal meal plan, an ordering device for automatically ordering necessary ingredients based on the generated meal plan, and a mechanism for efficiently providing meals and ingredients through a food delivery service in conjunction with health data. This enables the provision of personalized meal plans to individual users and the automatic ordering and delivery of ingredients and dishes.

[0100] A "device for acquiring user information" is a technological device used to collect user health data, preferences, schedule information, and other similar information.

[0101] A "processing device" is a computer system that uses acquired user information to perform analysis and generate personalized meal plans based on the user's health status and nutritional needs.

[0102] An "ordering device" is a system that automatically orders the necessary ingredients through online stores or delivery services based on a generated meal plan.

[0103] A "guidance device" is an information terminal that guides users through the procedures and tools to be used when preparing a planned meal.

[0104] A "suggestion device" is an information processing system that suggests new dishes based on the user's preferences and past eating history.

[0105] "Health data" refers to a collection of data that shows health-related information such as the user's heart rate, activity level, and nutritional status.

[0106] The "system provided through food delivery services" is a service infrastructure that efficiently delivers ingredients and prepared meals based on the user's meal plan through food delivery companies.

[0107] To implement this invention, a server and a user terminal play key roles. The server acquires information such as the user's health data, preferences, and schedule via wearable devices and IoT refrigerator sensors. This collected information is analyzed by the server's processing unit, and an optimized meal plan is generated for each user.

[0108] Based on this meal plan, the server has a system in place to automatically order the necessary ingredients through available online stores and delivery services. When the user cooks according to the plan, the terminal provides cooking instructions step by step, making it easy for even novice cooks to prepare meals. Specifically, using a smartphone or tablet, the user is visually guided through the recipe steps and cooking timings.

[0109] Furthermore, the server uses a generative AI model to suggest new dishes, taking into account the user's past eating history and preferences. This allows users to experience new food cultures and enrich their dietary habits. In addition, the server has established a system to quickly deliver meals and dishes according to the plan through a food delivery service.

[0110] For example, if a user prefers Japanese food and has a high activity level during the day, the server will suggest a nutritious Japanese meal plan. Ingredients are ordered in a timely manner, and a delivery service delivers them according to the user's schedule. The user can then prepare a healthy meal in a short time, receiving cooking instructions via their smartphone.

[0111] Example prompt to input into the generating AI model: "Please advise on how to build a system that suggests the optimal Japanese meal plan based on the user's health data and automatically orders the necessary ingredients."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server acquires health data, preferences, and schedule information from users via wearable devices and IoT refrigerator sensors. It receives user health status and food inventory information as input and stores it in a database. This serves as the basis for creating personalized meal plans for each user.

[0115] Step 2:

[0116] The server uses the acquired data to generate an optimal meal plan based on the user's health status and nutritional needs. User information acquired in step 1 is used as input, and an AI algorithm is used to determine the menu for each meal. The output is a meal plan optimized for the user.

[0117] Step 3:

[0118] The server automatically orders the necessary ingredients through online stores and delivery services based on the generated meal plan. It uses the meal plan and current ingredient inventory information as input to determine which ingredients need to be ordered. The output is an order list, which is sent to an external system.

[0119] Step 4:

[0120] The user's device displays cooking instructions according to the meal plan received from the server. The input is the server's optimized meal plan, and based on this, the device guides the user through visual cooking steps. The output displays specific instructions for the user to perform the cooking.

[0121] Step 5:

[0122] The server uses an AI model to generate new dish suggestions, taking into account the user's past eating history and preferences. It uses the user's preference information and past eating history as input to generate new dish suggestions. The output is a list of suggested dishes, which is provided to the user.

[0123] Step 6:

[0124] In conjunction with food delivery services, the server quickly delivers meals and dishes according to the user's schedule and plan. Inputs are the user's order and schedule information, and the output is a delivery plan. The delivery service is then coordinated to deliver the meals at the specified time.

[0125] 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.

[0126] This invention is a system that proposes an optimal meal plan to each individual user based on information including their health and emotional state. To support a healthy and emotionally-driven diet for users, the invention is implemented using the following means.

[0127] First, the system acquires the user's vital data, activity level, emotional state, schedule, and refrigerator inventory information through wearable devices and smartphones. The emotion engine analyzes the user's facial expressions and voice data to infer their current emotions. Based on this data, if the user is feeling stressed, suggestions using foods with relaxation effects are made.

[0128] Next, the server analyzes past health and emotional data, preferences, and allergy information to generate a personalized meal plan. This ensures that the meal content not only reflects the user's preferences but also considers its potential to alleviate their current emotional state. For example, a depressed user might be offered meals that are expected to uplift their mood.

[0129] Based on the generated meal plan, the server checks the inventory in the refrigerator to identify any missing ingredients. It then automatically places an order using the online store. When ordering, users are also offered options for delivery timing, allowing for adjustments to fit their schedule.

[0130] When a user is cooking, the device provides a detailed cooking guide and works with smart cooking appliances to control temperature and cooking time. In particular, it can be set to play relaxing music while cooking, taking into account the user's emotional state. This allows users to enjoy cooking more comfortably.

[0131] As feedback, the device evaluates the nutritional balance after the user completes a meal and shares data with the emotion engine to optimize the next meal plan. Furthermore, the server uses this data to suggest new dishes or cross-cultural dining experiences to try next. This suggestion takes into account the user's fluctuating emotional state, broadening the range of choices.

[0132] This invention aims to improve not only the user's physical health management but also their psychological satisfaction, and is a system that enables diverse support in their dietary habits.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The server acquires user health and emotional data from wearable devices and smartphones. The emotion engine analyzes the user's facial expressions and voice data to determine their emotional state and extract information such as "high stress levels."

[0136] Step 2:

[0137] The server generates an optimal meal plan for the user based on acquired health and emotional data. In this process, ingredients and recipes are selected not only for their nutritional balance but also for their mood-enhancing effects. For example, it might suggest a vitamin-rich menu to promote fatigue recovery.

[0138] Step 3:

[0139] The server uses the generated meal plan as a basis to identify missing ingredients by cross-referencing it with inventory information obtained from refrigerator sensors. It then automatically orders the missing ingredients from an online store and arranges for delivery at the user's specified time.

[0140] Step 4:

[0141] When a user begins cooking, the device visually displays the cooking steps and guides them through the process in real time. The emotion engine suggests relaxing music and aromas based on the user's current emotions, providing a comfortable cooking environment.

[0142] Step 5:

[0143] After a meal is completed, the device provides the user with feedback on the nutritional balance of the meal and their emotional state for the day. The feedback includes specific details such as, "Today's meal was effective in reducing stress."

[0144] Step 6:

[0145] Based on the collected emotional and dining data, the server performs analysis to refine future meal recommendations, offering suggestions that broaden the user's choices by providing new dishes and cross-cultural dining experiences.

[0146] (Example 2)

[0147] 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".

[0148] In recent years, there has been a growing demand for personalized nutrition plans based on each user's health and emotional state. Conventional systems have faced challenges in acquiring and analyzing user data adequately, making it difficult to provide optimal meal suggestions. Furthermore, the lack of proper integration between automated ingredient procurement and cooking instructions resulted in low user convenience.

[0149] 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.

[0150] In this invention, the server includes a terminal for acquiring user data, a processing unit for analyzing the user data acquired by the terminal and generating an optimal nutrition plan, and means for automatically procuring necessary resources based on the nutrition plan generated by the processing unit. This makes it possible to provide personalized nutrition plans tailored to the user's health condition and preferences, thereby efficiently and effectively supporting the user's health management.

[0151] "User data" refers to information about an individual user's health status, emotional state, activity level, schedule, and preferences.

[0152] A "terminal" is a device used to acquire and communicate user data, and includes wearable devices and smartphones.

[0153] A "processing device" is a device that analyzes acquired user data and generates an optimal nutrition plan based on that analysis.

[0154] A "nutrition plan" is a meal plan that takes into account the user's health condition and preferences, and considers the content of meals and necessary nutrients.

[0155] "Resources" refers to the food ingredients and cooking supplies needed to implement a nutrition plan.

[0156] "Procurement" refers to the act of securing necessary resources, and includes processes such as ordering and purchasing.

[0157] This invention is a system that provides a nutrition plan optimized for individual users. This system mainly consists of collecting and analyzing user data, generating plans, procuring resources, and providing guidance to users.

[0158] The server first receives user data from the terminal. This terminal functions as a smartphone or wearable device, collecting information such as heart rate, body temperature, activity level, emotional state, and schedule. The wearable device monitors the user's daily health status and transmits the data to the server via the smartphone.

[0159] Next, the server uses a processing unit to analyze the received user data in detail. This analysis takes into account past health records, user preferences, and allergy information, and evaluates the user's current physical and emotional state to generate an optimal nutrition plan. For example, if a user has recently been experiencing stress, a meal plan using ingredients with relaxation-enhancing properties might be suggested to alleviate that stress.

[0160] Furthermore, based on the generated nutrition plan, the server automatically procures resources through an online platform. During this process, it can compare the inventory in the refrigerator and efficiently order any shortages. For resource procurement, the optimal delivery timing can be set to suit the user's lifestyle and schedule.

[0161] When a user is cooking, the device provides detailed instructions and works with smart cooking appliances to control the cooking process. For example, it can guide users in real time on the appropriate heating temperature and time, and it also has a function to play relaxing music according to their emotional state.

[0162] Furthermore, after meals, the device prompts the user for feedback. This feedback is used to evaluate nutritional balance and optimize the next nutritional plan. This provides continuous support for maintaining the user's health and improving their psychological well-being.

[0163] An example of a prompt message is to send a request to the AI ​​model in the format of, "Please suggest a suitable meal plan for a user who is feeling down." This allows the system to provide more personalized meal suggestions.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The device acquires data from the user. Its inputs include heart rate, body temperature, activity level, emotional state measured by a wearable device, and schedule information managed on a smartphone. By collecting this data, the device gains a real-time understanding of the user's current health and emotional state. Specifically, the device is configured to automatically send user data to a server at a fixed time each night. The output is a dataset representing the user's state.

[0167] Step 2:

[0168] The server receives user data transmitted from the terminal and performs analysis using a processing unit. The inputs used are collected health data, past health records, preferences, and allergy information. Data processing includes sentiment analysis to evaluate the user's current mental state and historical data analysis to identify trends. Specifically, the system utilizes an emotion engine to perform voice tone analysis and facial expression analysis to calculate stress levels. The output is the analysis result, taking into account the user's health and emotional state.

[0169] Step 3:

[0170] The server generates an optimal nutrition plan using a generative AI model based on the analysis results. Inputs include the analysis results and prompts that consider the user's nutritional needs and preferences (e.g., "Please suggest a meal plan using ingredients that help reduce stress"). Data calculations involve optimization within the model to determine the most suitable ingredients and menus for the user. This includes constructing a daily meal plan from specific food groups. The output is a nutrition plan presented as a meal suggestion.

[0171] Step 4:

[0172] The server compares the generated nutrition plan with inventory information to identify resource shortages. It uses current inventory data from the refrigerator as input. Data processing includes comparing the ingredients required for the nutrition plan with current inventory and listing the shortages. The output is a list of ingredients that need to be ordered.

[0173] Step 5:

[0174] The server sends an ingredient list to an online platform and automatically procures the resources. Using the identified list of missing ingredients as input, it generates orders via the API of a specific online store. Specifically, it performs calculations to evaluate the prices and delivery information of each store to make the optimal selection. The output is confirmation information that the order has been completed.

[0175] Step 6:

[0176] The terminal provides the user with a nutrition plan and a cooking guide. It uses the generated nutrition plan as input. Data calculations include step-by-step guidance for the necessary cooking steps and specific actions such as automatically controlling cooking temperature and time in conjunction with smart cooking appliances. Outputs include a cooking guide and progress display for the user.

[0177] Step 7:

[0178] The device collects feedback from the user after a meal and sends it to the server. The input is the user's feedback information, which is used to evaluate nutritional balance and emotional state. Specifically, it prompts the user for input through a feedback collection interface. The output is data used to optimize the next nutrition plan.

[0179] (Application Example 2)

[0180] 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".

[0181] In modern society, there is a demand for personalized meal planning that takes into account people's health and emotional states. However, conventional systems mainly make suggestions based solely on health information, and do not adequately consider emotional aspects. As a result, suggestions may be made that downplay the psychological impact of food, and people may not be able to achieve sufficient satisfaction. Furthermore, there is currently a lack of mechanisms to support the psychological stability of users during cooking. Therefore, a system is needed that proposes individualized meal plans based on both health and emotional information, and provides accompanying cooking support.

[0182] 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.

[0183] In this invention, the server includes an acquisition means for acquiring user information, an analysis means for analyzing the information acquired by the acquisition means and generating optimal meal suggestions, an ordering means for automatically ordering necessary ingredients based on the meal suggestions generated by the analysis means, an auxiliary means for providing psychological stability during cooking, and an optimization means for optimizing the meal plan based on the user's health and emotional state. This makes it possible to provide a meal plan that takes into account the user's health and emotional state, along with cooking support.

[0184] "User information" refers to data including the user's health status, emotional state, activity level, and food inventory information.

[0185] "Means of acquisition" refers to methods and devices for collecting user information from wearable devices and smartphones.

[0186] "Analysis means" refers to methods and devices for analyzing acquired user information and generating optimal meal suggestions based on that analysis.

[0187] "Ordering method" refers to a method or device for automatically ordering the necessary ingredients based on the generated meal suggestions.

[0188] "Guidance means" refers to methods or devices that provide users with cooking guides that follow meal suggestions.

[0189] "Supportive measures" refer to methods and devices that provide psychological stability during cooking and help users relax while cooking.

[0190] "Optimization means" refers to methods or devices for appropriately adjusting meal plans based on the user's health and emotional state.

[0191] The system that implements this application provides personalized meal suggestions based on the user's health and emotional state. User information is acquired through wearable devices and smartphones worn by the user. This information includes vital data, emotional data, and activity levels.

[0192] The server uses Bluetooth and various sensors to acquire this user information. The acquired data is aggregated through health information management platforms such as Google Fit® and Apple HealthKit. The server then uses analysis tools to generate optimal meal suggestions based on the user's health and emotional state. By using a generative AI model in this analysis, a more accurate personalized plan is provided.

[0193] The ordering system automatically orders the necessary ingredients from the online store based on the generated meal suggestions. Users can also select a delivery time that suits them, allowing for flexible service tailored to their schedules.

[0194] When a user is cooking, the device provides detailed cooking instructions using guidance devices. In addition, auxiliary devices stream music to help the user relax while cooking, via APIs such as Spotify. For example, if a user is feeling stressed, they can select music with a relaxing effect.

[0195] The server analyzes all acquired data through optimization mechanisms to further optimize future meal plans. The suggestion mechanism also introduces new dishes and cross-cultural dining experiences. This allows users to enrich their eating habits.

[0196] For example, when a user is feeling fatigued, it is possible to suggest an energy-boosting menu using vitamin-rich ingredients. An example of a prompt message for the generating AI model would be, "Please suggest a dinner menu that is effective for fatigue recovery for a woman in her 30s."

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] Retrieving user information

[0200] The server acquires vital and emotional data from the user's wearable device and smartphone via Bluetooth. Inputs include heart rate, steps, activity level, and voice data, which are aggregated through Google Fit® and Apple HealthKit. The output is raw data used for analysis.

[0201] Step 2:

[0202] Analysis of user information

[0203] The server processes the acquired user information using analytical tools. Using a generative AI model, the emotion engine analyzes voice data and facial expression data to identify the user's emotional state. The input is the acquired raw data, and the output is the analysis result indicating the user's health and emotional state.

[0204] Step 3:

[0205] Generating meal suggestions

[0206] The server generates optimal meal suggestions based on the analysis results. Utilizing analytical methods and a generation AI model, it selects ingredients and dishes that match the user's health and emotional state. The input is the analysis results from step 2, and the output is a personalized meal suggestion for the user.

[0207] Step 4:

[0208] Automated ordering of ingredients

[0209] The server orders the necessary ingredients from an online store based on the generated meal suggestions. Delivery is scheduled to coincide with the user's schedule, depending on the ordering method. The input is the meal suggestion, and the output is a list of ordered ingredients.

[0210] Step 5:

[0211] Cooking guide provided

[0212] The terminal displays a cooking guide via a guidance system while the user is cooking. It includes specific steps, timings, and temperature settings, making cooking easy for the user. The input is the meal suggestion, and the output is the on-screen cooking guide.

[0213] Step 6:

[0214] Providing psychological stability during cooking

[0215] The device uses auxiliary means to provide relaxation music while cooking. It selects and plays music suitable for the user via the Spotify API, etc. The input is the user's emotional state, and the output is a music stream.

[0216] Step 7:

[0217] Optimizing your next meal plan

[0218] The server analyzes user feedback and acquired data to provide suggestions for optimizing future meal plans. Inputs are the user's meal history and feedback, while outputs are improved meal plans and new dish suggestions.

[0219] 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.

[0220] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention is a system that automatically generates an optimal meal plan tailored to each individual user and supports its implementation. The system can improve the user's diet in the following ways.

[0236] First, the system acquires user information such as health data, food preferences, and schedules from wearable devices, smartphones, and IoT sensors in the refrigerator. Specifically, it collects health information such as heart rate and activity levels, schedule information from the calendar, and information on the food inventory in the refrigerator.

[0237] The server then analyzes this information and generates a personalized meal plan based on the user's health status and nutritional needs. This plan includes the proportion of necessary nutrients and the types of foods to be consumed. Based on the analysis results, the menus for breakfast, lunch, and dinner are determined.

[0238] Based on the generated meal plan, the server checks the refrigerator inventory to identify any missing ingredients. Missing ingredients are automatically ordered through the online store, reducing the user's worry about running out of food. Orders are flexibly adjusted to match the user's specified delivery time and store preferences.

[0239] When a user cooks a meal for themselves, the device provides a cooking guide and works with smart cooking appliances to assist with the cooking process. The guide gives step-by-step instructions and automatically operates the cooking appliances as needed. This makes it easy for even novice cooks to prepare meals.

[0240] Finally, once the user finishes their meal, the device provides feedback based on the nutritional balance of the meal. Furthermore, the server analyzes the user's preferences and past eating patterns to periodically suggest new dishes and dishes from different cultures. This allows users to enjoy a diverse range of dining experiences.

[0241] This invention provides a comprehensive solution that optimizes meals with health in mind, automates purchasing, supports cooking, and offers a new dining experience, enriching users' lifestyles and supporting healthy eating habits.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The server retrieves user health information, food preferences, schedules, and inventory information from wearable devices, smartphones, and IoT sensors in refrigerators. This includes heart rate, activity levels, scheduled activities, and the quantity and expiration dates of food items in the refrigerator.

[0245] Step 2:

[0246] The server analyzes the acquired user information and generates a meal plan based on the user's health status and preferences. For example, it might suggest a breakfast that provides energy to a user who is sleep-deprived.

[0247] Step 3:

[0248] The server compares the list of ingredients needed for the generated meal plan with the refrigerator's inventory to identify any missing ingredients. The missing ingredients are automatically ordered through the online store.

[0249] Step 4:

[0250] If the user chooses to cook for themselves, the device provides them with a cooking guide. The device displays the cooking steps in real time and works with smart cooking appliances to automatically control the temperature and timer.

[0251] Step 5:

[0252] The device provides the user with feedback on the nutritional balance after a meal, allowing them to confirm whether the meal met their health goals.

[0253] Step 6:

[0254] The server suggests new dishes and cross-cultural dining experiences based on the user's dining history and preferences. This gives users new dining options and allows them to enjoy a variety of cuisines without getting bored.

[0255] (Example 1)

[0256] 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."

[0257] Traditional meal management systems struggled to individually optimize meal plans based on users' health conditions and dietary preferences, and lacked sufficient functionality to automatically compensate for ingredient shortages. As a result, users had to expend considerable effort to maintain a healthy and diverse diet. Furthermore, the lack of support during cooking was a burden for novice cooks.

[0258] 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.

[0259] In this invention, the server includes receiving means for receiving user health information, preference information, and schedule information; generating means for generating an individualized meal plan based on the information received by the receiving means; and replenishment means for automatically ordering necessary ingredients based on the meal plan created by the generating means. This makes it possible to easily achieve a healthy and diverse diet while reducing the user's time and effort.

[0260] A "user" refers to an individual who uses the system to manage their diet.

[0261] "Health information" refers to data that indicates the user's physical condition, such as heart rate and activity level.

[0262] "Preference information" refers to data related to a user's food preferences and past eating history.

[0263] "Schedule information" refers to data that shows the user's schedule and appointments.

[0264] "Receiving means" refers to the components within the system for collecting the aforementioned information from the user.

[0265] "Generation means" refers to the components within a system that create personalized meal plans based on collected information.

[0266] A "meal plan" refers to a menu of meals and intake guidelines designed to meet the user's nutritional needs.

[0267] "Replenishment means" refers to a component within a system that automatically orders and procures necessary ingredients based on a meal plan.

[0268] "Adjustment mechanism" refers to a component within a system used to revise food order details based on inventory information in the refrigerator.

[0269] "Support measures" refer to components within a system that provide guidance on cooking and work in conjunction with cooking equipment to assist in the cooking process.

[0270] "Evaluation means" refers to the components within a system that provide post-meal feedback and suggest new dishes based on user preferences.

[0271] This invention is a system that provides optimal dietary management to users and is operated using multiple hardware devices.

[0272] Users acquire health information such as heart rate and activity levels using wearable devices and smartphones. This data is transmitted to a server via Bluetooth or Wi-Fi. Users can also input schedule information and dietary preferences using a smartphone application. This information is stored in a cloud database and used for analysis.

[0273] The server performs data analysis using software running on the cloud. This analysis utilizes generative AI models to create personalized meal plans based on each user's individual nutritional needs. Simultaneously, the server receives inventory information from IoT sensors in the refrigerator and automatically places orders using the online store's API if necessary ingredients are in short supply. These processes are managed by the server's scheduling software.

[0274] When a user is cooking, a device (such as a tablet) displays a cooking guide. This device works in conjunction with smart cooking appliances and can automatically operate them according to the instructions. For example, it can automatically set the oven temperature and help the cooking process proceed smoothly.

[0275] After a meal, the device provides feedback based on the nutritional balance of the meal, and the server analyzes the user's accumulated eating history and preferences to suggest new cooking experiences. This allows users to be exposed to diverse food cultures and enjoy a balanced diet.

[0276] For example, if a user requests a low-calorie, high-protein breakfast, the server will suggest a recipe combining oatmeal with protein powder and fruit. Another example of a prompt message is, "Please suggest a breakfast menu for health promotion."

[0277] This system makes it easier for users to maintain their daily health and improve their lifestyle by supporting improvements in their eating habits.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The user obtains health information using a wearable device. This device measures heart rate and activity level and transfers the data to a smartphone. The input is heart rate and activity level data, and the output is a set of health information stored on the smartphone. This data is then sent to a server.

[0281] Step 2:

[0282] The user inputs schedule information and preference information through the smartphone app. This information relates to the user's daily activities and dietary preferences. The input is schedule information and preference information, and the output is a set of user information stored in the cloud database.

[0283] Step 3:

[0284] The server performs data analysis based on the health information, preference information, and schedule information received from the user. In this analysis, a generative AI model is used to create a personalized meal plan for the user. The input is a set of user information, and the output is a personalized meal plan.

[0285] Step 4:

[0286] The server obtains inventory information from the IoT sensors installed in the refrigerator. This information is compared with the meal plan stored in the system to identify shortages of required ingredients. The input is the meal plan and inventory information, and the output is a list of missing ingredients.

[0287] Step 5:

[0288] The server automatically places an order for the missing ingredients via an online store. In this process, the API of the online store is used to confirm the order based on the user's preferences and the specified delivery time. The input is a list of missing ingredients, and the output is a notification of order confirmation.

[0289] Step 6:

[0290] When the user starts cooking, the terminal displays a detailed cooking guide. The terminal shows the steps of the guide and notifies the user of the next step via voice or the screen. The input is the meal plan, and the output is the display of cooking steps.

[0291] Step 7:

[0292] The device works in conjunction with smart cooking appliances to assist the cooking process. For example, it automatically adjusts oven settings to ensure food is cooked properly. The input is the cooking procedure, and the output is the operation of the automatically controlled cooking appliance.

[0293] Step 8:

[0294] After a meal, the user receives a meal evaluation via their device. This evaluation provides feedback based on the balance of nutrients consumed. The input is the actual meal consumed, and the output is the nutritional evaluation feedback.

[0295] Step 9:

[0296] The server analyzes the user's eating history and preferences to suggest new culinary experiences and cross-cultural dishes. The input is past eating history and preference data, and the output is a suggested new meal plan.

[0297] (Application Example 1)

[0298] 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."

[0299] In today's busy lifestyle, maintaining a healthy and efficient diet tailored to individual users is not easy. Many factors are involved, including ingredient selection, nutritional balance considerations, and cooking time. In particular, optimizing these factors individually is difficult, so there is a need for a system that allows users to continue eating healthily without hassle.

[0300] 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.

[0301] In this invention, the server includes a device for acquiring user information, a processing device for analyzing the acquired user information and generating an optimal meal plan, an ordering device for automatically placing orders for necessary ingredients based on the generated meal plan, and a mechanism for efficiently providing meals and ingredients through cooperation with health data and a food delivery service. As a result, it becomes possible to provide an individualized meal plan for each user and to automatically order and deliver food ingredients and dishes.

[0302] The "device for acquiring user information" is a technical device used to collect the user's health data, preferences, schedule information, etc.

[0303] The "processing device" is a computer system that analyzes using the acquired user information and generates an individualized meal plan based on the health status and nutritional needs.

[0304] The "ordering device" is a system that automatically places orders for necessary ingredients through an online store or a delivery service based on the generated meal plan.

[0305] The "guidance device" is an information terminal that guides the procedures and tools to be used when the user cooks the planned meal.

[0306] The "proposal device" is an information processing system for proposing new dishes based on the user's preferences and past meal history.

[0307] The "health data" is a group of data indicating information related to health, such as the user's heart rate, activity level, and nutritional status.

[0308] The "mechanism for providing through a food delivery service" is a service infrastructure for efficiently delivering food ingredients and dishes based on the user's meal plan by a food delivery operator.

[0309] To implement this invention, a server and a user terminal play key roles. The server acquires information such as the user's health data, preferences, and schedule via wearable devices and IoT refrigerator sensors. This collected information is analyzed by the server's processing unit, and an optimized meal plan is generated for each user.

[0310] Based on this meal plan, the server has a system in place to automatically order the necessary ingredients through available online stores and delivery services. When the user cooks according to the plan, the terminal provides cooking instructions step by step, making it easy for even novice cooks to prepare meals. Specifically, using a smartphone or tablet, the user is visually guided through the recipe steps and cooking timings.

[0311] Furthermore, the server uses a generative AI model to suggest new dishes, taking into account the user's past eating history and preferences. This allows users to experience new food cultures and enrich their dietary habits. In addition, the server has established a system to quickly deliver meals and dishes according to the plan through a food delivery service.

[0312] For example, if a user prefers Japanese food and has a high activity level during the day, the server will suggest a nutritious Japanese meal plan. Ingredients are ordered in a timely manner, and a delivery service delivers them according to the user's schedule. The user can then prepare a healthy meal in a short time, receiving cooking instructions via their smartphone.

[0313] Example prompt to input into the generating AI model: "Please advise on how to build a system that suggests the optimal Japanese meal plan based on the user's health data and automatically orders the necessary ingredients."

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

[0315] Step 1:

[0316] The server acquires health data, preferences, and schedule information from users via wearable devices and IoT refrigerator sensors. It receives user health status and food inventory information as input and stores it in a database. This serves as the basis for creating personalized meal plans for each user.

[0317] Step 2:

[0318] The server uses the acquired data to generate an optimal meal plan based on the user's health status and nutritional needs. User information acquired in step 1 is used as input, and an AI algorithm is used to determine the menu for each meal. The output is a meal plan optimized for the user.

[0319] Step 3:

[0320] The server automatically orders the necessary ingredients through online stores and delivery services based on the generated meal plan. It uses the meal plan and current ingredient inventory information as input to determine which ingredients need to be ordered. The output is an order list, which is sent to an external system.

[0321] Step 4:

[0322] The user's device displays cooking instructions according to the meal plan received from the server. The input is the server's optimized meal plan, and based on this, the device guides the user through visual cooking steps. The output displays specific instructions for the user to perform the cooking.

[0323] Step 5:

[0324] The server uses an AI model to generate new dish suggestions, taking into account the user's past eating history and preferences. It uses the user's preference information and past eating history as input to generate new dish suggestions. The output is a list of suggested dishes, which is provided to the user.

[0325] Step 6:

[0326] In conjunction with food delivery services, the server quickly delivers meals and dishes according to the user's schedule and plan. Inputs are the user's order and schedule information, and the output is a delivery plan. The delivery service is then coordinated to deliver the meals at the specified time.

[0327] 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.

[0328] This invention is a system that proposes an optimal meal plan to each individual user based on information including their health and emotional state. To support a healthy and emotionally-driven diet for users, the invention is implemented using the following means.

[0329] First, the system acquires the user's vital data, activity level, emotional state, schedule, and refrigerator inventory information through wearable devices and smartphones. The emotion engine analyzes the user's facial expressions and voice data to infer their current emotions. Based on this data, if the user is feeling stressed, suggestions using foods with relaxation effects are made.

[0330] Next, the server analyzes past health and emotional data, preferences, and allergy information to generate a personalized meal plan. This ensures that the meal content not only reflects the user's preferences but also considers its potential to alleviate their current emotional state. For example, a depressed user might be offered meals that are expected to uplift their mood.

[0331] Based on the generated meal plan, the server checks the inventory in the refrigerator to identify any missing ingredients. It then automatically places an order using the online store. When ordering, users are also offered options for delivery timing, allowing for adjustments to fit their schedule.

[0332] When a user is cooking, the device provides a detailed cooking guide and works with smart cooking appliances to control temperature and cooking time. In particular, it can be set to play relaxing music while cooking, taking into account the user's emotional state. This allows users to enjoy cooking more comfortably.

[0333] As feedback, the device evaluates the nutritional balance after the user completes a meal and shares data with the emotion engine to optimize the next meal plan. Furthermore, the server uses this data to suggest new dishes or cross-cultural dining experiences to try next. This suggestion takes into account the user's fluctuating emotional state, broadening the range of choices.

[0334] This invention aims to improve not only the user's physical health management but also their psychological satisfaction, and is a system that enables diverse support in their dietary habits.

[0335] The following describes the processing flow.

[0336] Step 1:

[0337] The server acquires user health and emotional data from wearable devices and smartphones. The emotion engine analyzes the user's facial expressions and voice data to determine their emotional state and extract information such as "high stress levels."

[0338] Step 2:

[0339] The server generates an optimal meal plan for the user based on acquired health and emotional data. In this process, ingredients and recipes are selected not only for their nutritional balance but also for their mood-enhancing effects. For example, it might suggest a vitamin-rich menu to promote fatigue recovery.

[0340] Step 3:

[0341] The server uses the generated meal plan as a basis to identify missing ingredients by cross-referencing it with inventory information obtained from refrigerator sensors. It then automatically orders the missing ingredients from an online store and arranges for delivery at the user's specified time.

[0342] Step 4:

[0343] When a user begins cooking, the device visually displays the cooking steps and guides them through the process in real time. The emotion engine suggests relaxing music and aromas based on the user's current emotions, providing a comfortable cooking environment.

[0344] Step 5:

[0345] After a meal is completed, the device provides the user with feedback on the nutritional balance of the meal and their emotional state for the day. The feedback includes specific details such as, "Today's meal was effective in reducing stress."

[0346] Step 6:

[0347] Based on the collected emotional and dining data, the server performs analysis to refine future meal recommendations, offering suggestions that broaden the user's choices by providing new dishes and cross-cultural dining experiences.

[0348] (Example 2)

[0349] 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".

[0350] In recent years, there has been a growing demand for personalized nutrition plans based on each user's health and emotional state. Conventional systems have faced challenges in acquiring and analyzing user data adequately, making it difficult to provide optimal meal suggestions. Furthermore, the lack of proper integration between automated ingredient procurement and cooking instructions resulted in low user convenience.

[0351] 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.

[0352] In this invention, the server includes a terminal for acquiring user data, a processing unit for analyzing the user data acquired by the terminal and generating an optimal nutrition plan, and means for automatically procuring necessary resources based on the nutrition plan generated by the processing unit. This makes it possible to provide personalized nutrition plans tailored to the user's health condition and preferences, thereby efficiently and effectively supporting the user's health management.

[0353] "User data" refers to information about an individual user's health status, emotional state, activity level, schedule, and preferences.

[0354] A "terminal" is a device used to acquire and communicate user data, and includes wearable devices and smartphones.

[0355] A "processing device" is a device that analyzes acquired user data and generates an optimal nutrition plan based on that analysis.

[0356] A "nutrition plan" is a meal plan that takes into account the user's health condition and preferences, and considers the content of meals and necessary nutrients.

[0357] "Resources" refers to the food ingredients and cooking supplies needed to implement a nutrition plan.

[0358] "Procurement" refers to the act of securing necessary resources, and includes processes such as ordering and purchasing.

[0359] This invention is a system that provides a nutrition plan optimized for individual users. This system mainly consists of collecting and analyzing user data, generating plans, procuring resources, and providing guidance to users.

[0360] The server first receives user data from the terminal. This terminal functions as a smartphone or wearable device, collecting information such as heart rate, body temperature, activity level, emotional state, and schedule. The wearable device monitors the user's daily health status and transmits the data to the server via the smartphone.

[0361] Next, the server uses a processing unit to analyze the received user data in detail. This analysis takes into account past health records, user preferences, and allergy information, and evaluates the user's current physical and emotional state to generate an optimal nutrition plan. For example, if a user has recently been experiencing stress, a meal plan using ingredients with relaxation-enhancing properties might be suggested to alleviate that stress.

[0362] Furthermore, based on the generated nutrition plan, the server automatically procures resources through an online platform. During this process, it can compare the inventory in the refrigerator and efficiently order any shortages. For resource procurement, the optimal delivery timing can be set to suit the user's lifestyle and schedule.

[0363] When a user is cooking, the device provides detailed instructions and works with smart cooking appliances to control the cooking process. For example, it can guide users in real time on the appropriate heating temperature and time, and it also has a function to play relaxing music according to their emotional state.

[0364] Furthermore, after meals, the device prompts the user for feedback. This feedback is used to evaluate nutritional balance and optimize the next nutritional plan. This provides continuous support for maintaining the user's health and improving their psychological well-being.

[0365] An example of a prompt message is to send a request to the AI ​​model in the format of, "Please suggest a suitable meal plan for a user who is feeling down." This allows the system to provide more personalized meal suggestions.

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] The device acquires data from the user. Its inputs include heart rate, body temperature, activity level, emotional state measured by a wearable device, and schedule information managed on a smartphone. By collecting this data, the device gains a real-time understanding of the user's current health and emotional state. Specifically, the device is configured to automatically send user data to a server at a fixed time each night. The output is a dataset representing the user's state.

[0369] Step 2:

[0370] The server receives user data transmitted from the terminal and performs analysis using a processing unit. The inputs used are collected health data, past health records, preferences, and allergy information. Data processing includes sentiment analysis to evaluate the user's current mental state and historical data analysis to identify trends. Specifically, the system utilizes an emotion engine to perform voice tone analysis and facial expression analysis to calculate stress levels. The output is the analysis result, taking into account the user's health and emotional state.

[0371] Step 3:

[0372] The server generates an optimal nutrition plan using a generative AI model based on the analysis results. Inputs include the analysis results and prompts that consider the user's nutritional needs and preferences (e.g., "Please suggest a meal plan using ingredients that help reduce stress"). Data calculations involve optimization within the model to determine the most suitable ingredients and menus for the user. This includes constructing a daily meal plan from specific food groups. The output is a nutrition plan presented as a meal suggestion.

[0373] Step 4:

[0374] The server compares the generated nutrition plan with inventory information to identify resource shortages. It uses current inventory data from the refrigerator as input. Data processing includes comparing the ingredients required for the nutrition plan with current inventory and listing the shortages. The output is a list of ingredients that need to be ordered.

[0375] Step 5:

[0376] The server sends an ingredient list to an online platform and automatically procures the resources. Using the identified list of missing ingredients as input, it generates orders via the API of a specific online store. Specifically, it performs calculations to evaluate the prices and delivery information of each store to make the optimal selection. The output is confirmation information that the order has been completed.

[0377] Step 6:

[0378] The terminal provides the user with a nutrition plan and a cooking guide. It uses the generated nutrition plan as input. Data calculations include step-by-step guidance for the necessary cooking steps and specific actions such as automatically controlling cooking temperature and time in conjunction with smart cooking appliances. Outputs include a cooking guide and progress display for the user.

[0379] Step 7:

[0380] The device collects feedback from the user after a meal and sends it to the server. The input is the user's feedback information, which is used to evaluate nutritional balance and emotional state. Specifically, it prompts the user for input through a feedback collection interface. The output is data used to optimize the next nutrition plan.

[0381] (Application Example 2)

[0382] 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."

[0383] In modern society, there is a demand for personalized meal planning that takes into account people's health and emotional states. However, conventional systems mainly make suggestions based solely on health information, and do not adequately consider emotional aspects. As a result, suggestions may be made that downplay the psychological impact of food, and people may not be able to achieve sufficient satisfaction. Furthermore, there is currently a lack of mechanisms to support the psychological stability of users during cooking. Therefore, a system is needed that proposes individualized meal plans based on both health and emotional information, and provides accompanying cooking support.

[0384] 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.

[0385] In this invention, the server includes an acquisition means for acquiring user information, an analysis means for analyzing the information acquired by the acquisition means and generating optimal meal suggestions, an ordering means for automatically ordering necessary ingredients based on the meal suggestions generated by the analysis means, an auxiliary means for providing psychological stability during cooking, and an optimization means for optimizing the meal plan based on the user's health and emotional state. This makes it possible to provide a meal plan that takes into account the user's health and emotional state, along with cooking support.

[0386] "User information" refers to data including the user's health status, emotional state, activity level, and food inventory information.

[0387] "Means of acquisition" refers to methods and devices for collecting user information from wearable devices and smartphones.

[0388] "Analysis means" refers to methods and devices for analyzing acquired user information and generating optimal meal suggestions based on that analysis.

[0389] "Ordering method" refers to a method or device for automatically ordering the necessary ingredients based on the generated meal suggestions.

[0390] "Guidance means" refers to methods or devices that provide users with cooking guides that follow meal suggestions.

[0391] "Supportive measures" refer to methods and devices that provide psychological stability during cooking and help users relax while cooking.

[0392] "Optimization means" refers to methods or devices for appropriately adjusting meal plans based on the user's health and emotional state.

[0393] The system that implements this application provides personalized meal suggestions based on the user's health and emotional state. User information is acquired through wearable devices and smartphones worn by the user. This information includes vital data, emotional data, and activity levels.

[0394] The server uses Bluetooth and various sensors to acquire this user information. The acquired data is aggregated through health information management platforms such as Google Fit and Apple HealthKit. The server then uses analysis tools to generate optimal meal suggestions based on the user's health and emotional state. By using a generative AI model in this analysis, a more accurate personalized plan is provided.

[0395] The ordering system automatically orders the necessary ingredients from the online store based on the generated meal suggestions. Users can also select a delivery time that suits them, allowing for flexible service tailored to their schedules.

[0396] When a user is cooking, the device provides detailed cooking instructions using guidance devices. In addition, auxiliary devices stream music to help the user relax while cooking, via APIs such as Spotify. For example, if a user is feeling stressed, they can select music with a relaxing effect.

[0397] The server analyzes all acquired data through optimization mechanisms to further optimize future meal plans. The suggestion mechanism also introduces new dishes and cross-cultural dining experiences. This allows users to enrich their eating habits.

[0398] For example, when a user is feeling fatigued, it is possible to suggest an energy-boosting menu using vitamin-rich ingredients. An example of a prompt message for the generating AI model would be, "Please suggest a dinner menu that is effective for fatigue recovery for a woman in her 30s."

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] Retrieving user information

[0402] The server acquires vital and emotional data from the user's wearable device and smartphone via Bluetooth. Inputs include heart rate, steps, activity levels, and voice data, which are aggregated through Google Fit and Apple HealthKit. The output is raw data used for analysis.

[0403] Step 2:

[0404] Analysis of user information

[0405] The server processes the acquired user information using analytical tools. Using a generative AI model, the emotion engine analyzes voice data and facial expression data to identify the user's emotional state. The input is the acquired raw data, and the output is the analysis result indicating the user's health and emotional state.

[0406] Step 3:

[0407] Generating meal suggestions

[0408] The server generates optimal meal suggestions based on the analysis results. Utilizing analytical methods and a generation AI model, it selects ingredients and dishes that match the user's health and emotional state. The input is the analysis results from step 2, and the output is a personalized meal suggestion for the user.

[0409] Step 4:

[0410] Automated ordering of ingredients

[0411] The server orders the necessary ingredients from an online store based on the generated meal suggestions. Delivery is scheduled to coincide with the user's schedule, depending on the ordering method. The input is the meal suggestion, and the output is a list of ordered ingredients.

[0412] Step 5:

[0413] Cooking guide provided

[0414] The terminal displays a cooking guide via a guidance system while the user is cooking. It includes specific steps, timings, and temperature settings, making cooking easy for the user. The input is the meal suggestion, and the output is the on-screen cooking guide.

[0415] Step 6:

[0416] Providing psychological stability during cooking

[0417] The device uses auxiliary means to provide relaxation music while cooking. It selects and plays music suitable for the user via the Spotify API, etc. The input is the user's emotional state, and the output is a music stream.

[0418] Step 7:

[0419] Optimizing your next meal plan

[0420] The server analyzes user feedback and acquired data to provide suggestions for optimizing future meal plans. Inputs are the user's meal history and feedback, while outputs are improved meal plans and new dish suggestions.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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".

[0437] This invention is a system that automatically generates an optimal meal plan tailored to each individual user and supports its implementation. The system can improve the user's diet in the following ways.

[0438] First, the system acquires user information such as health data, food preferences, and schedules from wearable devices, smartphones, and IoT sensors in the refrigerator. Specifically, it collects health information such as heart rate and activity levels, schedule information from the calendar, and information on the food inventory in the refrigerator.

[0439] The server then analyzes this information and generates a personalized meal plan based on the user's health status and nutritional needs. This plan includes the proportion of necessary nutrients and the types of foods to be consumed. Based on the analysis results, the menus for breakfast, lunch, and dinner are determined.

[0440] Based on the generated meal plan, the server checks the refrigerator inventory to identify any missing ingredients. Missing ingredients are automatically ordered through the online store, reducing the user's worry about running out of food. Orders are flexibly adjusted to match the user's specified delivery time and store preferences.

[0441] When a user cooks a meal for themselves, the device provides a cooking guide and works with smart cooking appliances to assist with the cooking process. The guide gives step-by-step instructions and automatically operates the cooking appliances as needed. This makes it easy for even novice cooks to prepare meals.

[0442] Finally, once the user finishes their meal, the device provides feedback based on the nutritional balance of the meal. Furthermore, the server analyzes the user's preferences and past eating patterns to periodically suggest new dishes and dishes from different cultures. This allows users to enjoy a diverse range of dining experiences.

[0443] This invention provides a comprehensive solution that optimizes meals with health in mind, automates purchasing, supports cooking, and offers a new dining experience, enriching users' lifestyles and supporting healthy eating habits.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] The server retrieves user health information, food preferences, schedules, and inventory information from wearable devices, smartphones, and IoT sensors in refrigerators. This includes heart rate, activity levels, scheduled activities, and the quantity and expiration dates of food items in the refrigerator.

[0447] Step 2:

[0448] The server analyzes the acquired user information and generates a meal plan based on the user's health status and preferences. For example, it might suggest a breakfast that provides energy to a user who is sleep-deprived.

[0449] Step 3:

[0450] The server compares the list of ingredients needed for the generated meal plan with the refrigerator's inventory to identify any missing ingredients. The missing ingredients are automatically ordered through the online store.

[0451] Step 4:

[0452] If the user chooses to cook for themselves, the device provides them with a cooking guide. The device displays the cooking steps in real time and works with smart cooking appliances to automatically control the temperature and timer.

[0453] Step 5:

[0454] The device provides the user with feedback on the nutritional balance after a meal, allowing them to confirm whether the meal met their health goals.

[0455] Step 6:

[0456] The server suggests new dishes and cross-cultural dining experiences based on the user's dining history and preferences. This gives users new dining options and allows them to enjoy a variety of cuisines without getting bored.

[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] Traditional meal management systems struggled to individually optimize meal plans based on users' health conditions and dietary preferences, and lacked sufficient functionality to automatically compensate for ingredient shortages. As a result, users had to expend considerable effort to maintain a healthy and diverse diet. Furthermore, the lack of support during cooking was a burden for novice cooks.

[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 receiving means for receiving user health information, preference information, and schedule information; generating means for generating an individualized meal plan based on the information received by the receiving means; and replenishment means for automatically ordering necessary ingredients based on the meal plan created by the generating means. This makes it possible to easily achieve a healthy and diverse diet while reducing the user's time and effort.

[0462] A "user" refers to an individual who uses the system to manage their diet.

[0463] "Health information" refers to data that indicates the user's physical condition, such as heart rate and activity level.

[0464] "Preference information" refers to data related to a user's food preferences and past eating history.

[0465] "Schedule information" refers to data that shows the user's schedule and appointments.

[0466] "Receiving means" refers to the components within the system for collecting the aforementioned information from the user.

[0467] "Generation means" refers to the components within a system that create personalized meal plans based on collected information.

[0468] A "meal plan" refers to a menu of meals and intake guidelines designed to meet the user's nutritional needs.

[0469] "Replenishment means" refers to a component within a system that automatically orders and procures necessary ingredients based on a meal plan.

[0470] "Adjustment mechanism" refers to a component within a system used to revise food order details based on inventory information in the refrigerator.

[0471] "Support measures" refer to components within a system that provide guidance on cooking and work in conjunction with cooking equipment to assist in the cooking process.

[0472] "Evaluation means" refers to the components within a system that provide post-meal feedback and suggest new dishes based on user preferences.

[0473] This invention is a system that provides optimal dietary management to users and is operated using multiple hardware devices.

[0474] Users acquire health information such as heart rate and activity levels using wearable devices and smartphones. This data is transmitted to a server via Bluetooth or Wi-Fi. Users can also input schedule information and dietary preferences using a smartphone application. This information is stored in a cloud database and used for analysis.

[0475] The server performs data analysis using software running on the cloud. This analysis utilizes generative AI models to create personalized meal plans based on each user's individual nutritional needs. Simultaneously, the server receives inventory information from IoT sensors in the refrigerator and automatically places orders using the online store's API if necessary ingredients are in short supply. These processes are managed by the server's scheduling software.

[0476] When a user is cooking, a device (such as a tablet) displays a cooking guide. This device works in conjunction with smart cooking appliances and can automatically operate them according to the instructions. For example, it can automatically set the oven temperature and help the cooking process proceed smoothly.

[0477] After a meal, the device provides feedback based on the nutritional balance of the meal, and the server analyzes the user's accumulated eating history and preferences to suggest new cooking experiences. This allows users to be exposed to diverse food cultures and enjoy a balanced diet.

[0478] For example, if a user requests a low-calorie, high-protein breakfast, the server will suggest a recipe combining oatmeal with protein powder and fruit. Another example of a prompt message is, "Please suggest a breakfast menu for health promotion."

[0479] This system makes it easier for users to maintain their daily health and improve their lifestyle by supporting improvements in their eating habits.

[0480] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0481] Step 1:

[0482] The user obtains health information using a wearable device. This device measures heart rate and activity level and transfers the data to a smartphone. The input is heart rate and activity level data, and the output is a set of health information stored on the smartphone. This data is then sent to a server.

[0483] Step 2:

[0484] Users input schedule and preference information using a smartphone app. This information concerns the user's daily activities and dietary preferences. The input consists of schedule and preference information, while the output is a set of user information stored in a cloud database.

[0485] Step 3:

[0486] The server performs data analysis based on health information, preference information, and schedule information received from the user. This analysis utilizes a generative AI model to create a personalized meal plan for the user. The input is a set of user information, and the output is the personalized meal plan.

[0487] Step 4:

[0488] The server obtains inventory information from IoT sensors installed in the refrigerator. This information is compared with meal plans stored in the system to identify any missing ingredients. The inputs are meal plans and inventory information, and the output is a list of missing ingredients.

[0489] Step 5:

[0490] The server automatically orders missing ingredients via the online store. This process uses the online store's API to confirm the order based on user preferences and specified delivery times. The input is a list of missing ingredients, and the output is an order confirmation notification.

[0491] Step 6:

[0492] When the user begins cooking, the device displays a detailed cooking guide. The device guides the user through the steps and informs them of the next steps via voice and on-screen prompts. The input is the meal plan, and the output is a display of the cooking procedure.

[0493] Step 7:

[0494] The device works in conjunction with smart cooking appliances to assist the cooking process. For example, it automatically adjusts oven settings to ensure food is cooked properly. The input is the cooking procedure, and the output is the operation of the automatically controlled cooking appliance.

[0495] Step 8:

[0496] After a meal, the user receives a meal evaluation via their device. This evaluation provides feedback based on the balance of nutrients consumed. The input is the actual meal consumed, and the output is the nutritional evaluation feedback.

[0497] Step 9:

[0498] The server analyzes the user's eating history and preferences to suggest new culinary experiences and cross-cultural dishes. The input is past eating history and preference data, and the output is a suggested new meal plan.

[0499] (Application Example 1)

[0500] 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."

[0501] In today's busy lifestyle, maintaining a healthy and efficient diet tailored to individual users is not easy. Many factors are involved, including ingredient selection, nutritional balance considerations, and cooking time. In particular, optimizing these factors individually is difficult, so there is a need for a system that allows users to continue eating healthily without hassle.

[0502] 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.

[0503] In this invention, the server includes a device for acquiring user information, a processing device for analyzing the acquired user information and generating an optimal meal plan, an ordering device for automatically ordering necessary ingredients based on the generated meal plan, and a mechanism for efficiently providing meals and ingredients through a food delivery service in conjunction with health data. This enables the provision of personalized meal plans to individual users and the automatic ordering and delivery of ingredients and dishes.

[0504] A "device for acquiring user information" is a technological device used to collect user health data, preferences, schedule information, and other similar information.

[0505] A "processing device" is a computer system that uses acquired user information to perform analysis and generate personalized meal plans based on the user's health status and nutritional needs.

[0506] An "ordering device" is a system that automatically orders the necessary ingredients through online stores or delivery services based on a generated meal plan.

[0507] A "guidance device" is an information terminal that guides users through the procedures and tools to be used when preparing a planned meal.

[0508] A "suggestion device" is an information processing system that suggests new dishes based on the user's preferences and past eating history.

[0509] "Health data" refers to a collection of data that shows health-related information such as the user's heart rate, activity level, and nutritional status.

[0510] The "system provided through food delivery services" is a service infrastructure that efficiently delivers ingredients and prepared meals based on the user's meal plan through food delivery companies.

[0511] To implement this invention, a server and a user terminal play key roles. The server acquires information such as the user's health data, preferences, and schedule via wearable devices and IoT refrigerator sensors. This collected information is analyzed by the server's processing unit, and an optimized meal plan is generated for each user.

[0512] Based on this meal plan, the server has a system in place to automatically order the necessary ingredients through available online stores and delivery services. When the user cooks according to the plan, the terminal provides cooking instructions step by step, making it easy for even novice cooks to prepare meals. Specifically, using a smartphone or tablet, the user is visually guided through the recipe steps and cooking timings.

[0513] Furthermore, the server uses a generative AI model to suggest new dishes, taking into account the user's past eating history and preferences. This allows users to experience new food cultures and enrich their dietary habits. In addition, the server has established a system to quickly deliver meals and dishes according to the plan through a food delivery service.

[0514] For example, if a user prefers Japanese food and has a high activity level during the day, the server will suggest a nutritious Japanese meal plan. Ingredients are ordered in a timely manner, and a delivery service delivers them according to the user's schedule. The user can then prepare a healthy meal in a short time, receiving cooking instructions via their smartphone.

[0515] Example prompt to input into the generating AI model: "Please advise on how to build a system that suggests the optimal Japanese meal plan based on the user's health data and automatically orders the necessary ingredients."

[0516] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0517] Step 1:

[0518] The server acquires health data, preferences, and schedule information from users via wearable devices and IoT refrigerator sensors. It receives user health status and food inventory information as input and stores it in a database. This serves as the basis for creating personalized meal plans for each user.

[0519] Step 2:

[0520] The server uses the acquired data to generate an optimal meal plan based on the user's health status and nutritional needs. User information acquired in step 1 is used as input, and an AI algorithm is used to determine the menu for each meal. The output is a meal plan optimized for the user.

[0521] Step 3:

[0522] The server automatically orders the necessary ingredients through online stores and delivery services based on the generated meal plan. It uses the meal plan and current ingredient inventory information as input to determine which ingredients need to be ordered. The output is an order list, which is sent to an external system.

[0523] Step 4:

[0524] The user's device displays cooking instructions according to the meal plan received from the server. The input is the server's optimized meal plan, and based on this, the device guides the user through visual cooking steps. The output displays specific instructions for the user to perform the cooking.

[0525] Step 5:

[0526] The server uses an AI model to generate new dish suggestions, taking into account the user's past eating history and preferences. It uses the user's preference information and past eating history as input to generate new dish suggestions. The output is a list of suggested dishes, which is provided to the user.

[0527] Step 6:

[0528] In conjunction with food delivery services, the server quickly delivers meals and dishes according to the user's schedule and plan. Inputs are the user's order and schedule information, and the output is a delivery plan. The delivery service is then coordinated to deliver the meals at the specified time.

[0529] 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.

[0530] This invention is a system that proposes an optimal meal plan to each individual user based on information including their health and emotional state. To support a healthy and emotionally-driven diet for users, the invention is implemented using the following means.

[0531] First, the system acquires the user's vital data, activity level, emotional state, schedule, and refrigerator inventory information through wearable devices and smartphones. The emotion engine analyzes the user's facial expressions and voice data to infer their current emotions. Based on this data, if the user is feeling stressed, suggestions using foods with relaxation effects are made.

[0532] Next, the server analyzes past health and emotional data, preferences, and allergy information to generate a personalized meal plan. This ensures that the meal content not only reflects the user's preferences but also considers its potential to alleviate their current emotional state. For example, a depressed user might be offered meals that are expected to uplift their mood.

[0533] Based on the generated meal plan, the server checks the inventory in the refrigerator to identify any missing ingredients. It then automatically places an order using the online store. When ordering, users are also offered options for delivery timing, allowing for adjustments to fit their schedule.

[0534] When a user is cooking, the device provides a detailed cooking guide and works with smart cooking appliances to control temperature and cooking time. In particular, it can be set to play relaxing music while cooking, taking into account the user's emotional state. This allows users to enjoy cooking more comfortably.

[0535] As feedback, the device evaluates the nutritional balance after the user completes a meal and shares data with the emotion engine to optimize the next meal plan. Furthermore, the server uses this data to suggest new dishes or cross-cultural dining experiences to try next. This suggestion takes into account the user's fluctuating emotional state, broadening the range of choices.

[0536] This invention aims to improve not only the user's physical health management but also their psychological satisfaction, and is a system that enables diverse support in their dietary habits.

[0537] The following describes the processing flow.

[0538] Step 1:

[0539] The server acquires user health and emotional data from wearable devices and smartphones. The emotion engine analyzes the user's facial expressions and voice data to determine their emotional state and extract information such as "high stress levels."

[0540] Step 2:

[0541] The server generates an optimal meal plan for the user based on acquired health and emotional data. In this process, ingredients and recipes are selected not only for their nutritional balance but also for their mood-enhancing effects. For example, it might suggest a vitamin-rich menu to promote fatigue recovery.

[0542] Step 3:

[0543] The server uses the generated meal plan as a basis to identify missing ingredients by cross-referencing it with inventory information obtained from refrigerator sensors. It then automatically orders the missing ingredients from an online store and arranges for delivery at the user's specified time.

[0544] Step 4:

[0545] When a user begins cooking, the device visually displays the cooking steps and guides them through the process in real time. The emotion engine suggests relaxing music and aromas based on the user's current emotions, providing a comfortable cooking environment.

[0546] Step 5:

[0547] After a meal is completed, the device provides the user with feedback on the nutritional balance of the meal and their emotional state for the day. The feedback includes specific details such as, "Today's meal was effective in reducing stress."

[0548] Step 6:

[0549] Based on the collected emotional and dining data, the server performs analysis to refine future meal recommendations, offering suggestions that broaden the user's choices by providing new dishes and cross-cultural dining experiences.

[0550] (Example 2)

[0551] 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."

[0552] In recent years, there has been a growing demand for personalized nutrition plans based on each user's health and emotional state. Conventional systems have faced challenges in acquiring and analyzing user data adequately, making it difficult to provide optimal meal suggestions. Furthermore, the lack of proper integration between automated ingredient procurement and cooking instructions resulted in low user convenience.

[0553] 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.

[0554] In this invention, the server includes a terminal for acquiring user data, a processing unit for analyzing the user data acquired by the terminal and generating an optimal nutrition plan, and means for automatically procuring necessary resources based on the nutrition plan generated by the processing unit. This makes it possible to provide personalized nutrition plans tailored to the user's health condition and preferences, thereby efficiently and effectively supporting the user's health management.

[0555] "User data" refers to information about an individual user's health status, emotional state, activity level, schedule, and preferences.

[0556] A "terminal" is a device used to acquire and communicate user data, and includes wearable devices and smartphones.

[0557] A "processing device" is a device that analyzes acquired user data and generates an optimal nutrition plan based on that analysis.

[0558] A "nutrition plan" is a meal plan that takes into account the user's health condition and preferences, and considers the content of meals and necessary nutrients.

[0559] "Resources" refers to the food ingredients and cooking supplies needed to implement a nutrition plan.

[0560] "Procurement" refers to the act of securing necessary resources, and includes processes such as ordering and purchasing.

[0561] This invention is a system that provides a nutrition plan optimized for individual users. This system mainly consists of collecting and analyzing user data, generating plans, procuring resources, and providing guidance to users.

[0562] The server first receives user data from the terminal. This terminal functions as a smartphone or wearable device, collecting information such as heart rate, body temperature, activity level, emotional state, and schedule. The wearable device monitors the user's daily health status and transmits the data to the server via the smartphone.

[0563] Next, the server uses a processing unit to analyze the received user data in detail. This analysis takes into account past health records, user preferences, and allergy information, and evaluates the user's current physical and emotional state to generate an optimal nutrition plan. For example, if a user has recently been experiencing stress, a meal plan using ingredients with relaxation-enhancing properties might be suggested to alleviate that stress.

[0564] Furthermore, based on the generated nutrition plan, the server automatically procures resources through an online platform. During this process, it can compare the inventory in the refrigerator and efficiently order any shortages. For resource procurement, the optimal delivery timing can be set to suit the user's lifestyle and schedule.

[0565] When a user is cooking, the device provides detailed instructions and works with smart cooking appliances to control the cooking process. For example, it can guide users in real time on the appropriate heating temperature and time, and it also has a function to play relaxing music according to their emotional state.

[0566] Furthermore, after meals, the device prompts the user for feedback. This feedback is used to evaluate nutritional balance and optimize the next nutritional plan. This provides continuous support for maintaining the user's health and improving their psychological well-being.

[0567] An example of a prompt message is to send a request to the AI ​​model in the format of, "Please suggest a suitable meal plan for a user who is feeling down." This allows the system to provide more personalized meal suggestions.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] The device acquires data from the user. Its inputs include heart rate, body temperature, activity level, emotional state measured by a wearable device, and schedule information managed on a smartphone. By collecting this data, the device gains a real-time understanding of the user's current health and emotional state. Specifically, the device is configured to automatically send user data to a server at a fixed time each night. The output is a dataset representing the user's state.

[0571] Step 2:

[0572] The server receives user data transmitted from the terminal and performs analysis using a processing unit. The inputs used are collected health data, past health records, preferences, and allergy information. Data processing includes sentiment analysis to evaluate the user's current mental state and historical data analysis to identify trends. Specifically, the system utilizes an emotion engine to perform voice tone analysis and facial expression analysis to calculate stress levels. The output is the analysis result, taking into account the user's health and emotional state.

[0573] Step 3:

[0574] The server generates an optimal nutrition plan using a generative AI model based on the analysis results. Inputs include the analysis results and prompts that consider the user's nutritional needs and preferences (e.g., "Please suggest a meal plan using ingredients that help reduce stress"). Data calculations involve optimization within the model to determine the most suitable ingredients and menus for the user. This includes constructing a daily meal plan from specific food groups. The output is a nutrition plan presented as a meal suggestion.

[0575] Step 4:

[0576] The server compares the generated nutrition plan with inventory information to identify resource shortages. It uses current inventory data from the refrigerator as input. Data processing includes comparing the ingredients required for the nutrition plan with current inventory and listing the shortages. The output is a list of ingredients that need to be ordered.

[0577] Step 5:

[0578] The server sends an ingredient list to an online platform and automatically procures the resources. Using the identified list of missing ingredients as input, it generates orders via the API of a specific online store. Specifically, it performs calculations to evaluate the prices and delivery information of each store to make the optimal selection. The output is confirmation information that the order has been completed.

[0579] Step 6:

[0580] The terminal provides the user with a nutrition plan and a cooking guide. It uses the generated nutrition plan as input. Data calculations include step-by-step guidance for the necessary cooking steps and specific actions such as automatically controlling cooking temperature and time in conjunction with smart cooking appliances. Outputs include a cooking guide and progress display for the user.

[0581] Step 7:

[0582] The device collects feedback from the user after a meal and sends it to the server. The input is the user's feedback information, which is used to evaluate nutritional balance and emotional state. Specifically, it prompts the user for input through a feedback collection interface. The output is data used to optimize the next nutrition plan.

[0583] (Application Example 2)

[0584] 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."

[0585] In modern society, there is a demand for personalized meal planning that takes into account people's health and emotional states. However, conventional systems mainly make suggestions based solely on health information, and do not adequately consider emotional aspects. As a result, suggestions may be made that downplay the psychological impact of food, and people may not be able to achieve sufficient satisfaction. Furthermore, there is currently a lack of mechanisms to support the psychological stability of users during cooking. Therefore, a system is needed that proposes individualized meal plans based on both health and emotional information, and provides accompanying cooking support.

[0586] 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.

[0587] In this invention, the server includes an acquisition means for acquiring user information, an analysis means for analyzing the information acquired by the acquisition means and generating optimal meal suggestions, an ordering means for automatically ordering necessary ingredients based on the meal suggestions generated by the analysis means, an auxiliary means for providing psychological stability during cooking, and an optimization means for optimizing the meal plan based on the user's health and emotional state. This makes it possible to provide a meal plan that takes into account the user's health and emotional state, along with cooking support.

[0588] "User information" refers to data including the user's health status, emotional state, activity level, and food inventory information.

[0589] "Means of acquisition" refers to methods and devices for collecting user information from wearable devices and smartphones.

[0590] "Analysis means" refers to methods and devices for analyzing acquired user information and generating optimal meal suggestions based on that analysis.

[0591] "Ordering method" refers to a method or device for automatically ordering the necessary ingredients based on the generated meal suggestions.

[0592] "Guidance means" refers to methods or devices that provide users with cooking guides that follow meal suggestions.

[0593] "Supportive measures" refer to methods and devices that provide psychological stability during cooking and help users relax while cooking.

[0594] "Optimization means" refers to methods or devices for appropriately adjusting meal plans based on the user's health and emotional state.

[0595] The system that implements this application provides personalized meal suggestions based on the user's health and emotional state. User information is acquired through wearable devices and smartphones worn by the user. This information includes vital data, emotional data, and activity levels.

[0596] The server uses Bluetooth and various sensors to acquire this user information. The acquired data is aggregated through health information management platforms such as Google Fit and Apple HealthKit. The server then uses analysis tools to generate optimal meal suggestions based on the user's health and emotional state. By using a generative AI model in this analysis, a more accurate personalized plan is provided.

[0597] The ordering system automatically orders the necessary ingredients from the online store based on the generated meal suggestions. Users can also select a delivery time that suits them, allowing for flexible service tailored to their schedules.

[0598] When a user is cooking, the device provides detailed cooking instructions using guidance devices. In addition, auxiliary devices stream music to help the user relax while cooking, via APIs such as Spotify. For example, if a user is feeling stressed, they can select music with a relaxing effect.

[0599] The server analyzes all acquired data through optimization mechanisms to further optimize future meal plans. The suggestion mechanism also introduces new dishes and cross-cultural dining experiences. This allows users to enrich their eating habits.

[0600] For example, when a user is feeling fatigued, it is possible to suggest an energy-boosting menu using vitamin-rich ingredients. An example of a prompt message for the generating AI model would be, "Please suggest a dinner menu that is effective for fatigue recovery for a woman in her 30s."

[0601] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0602] Step 1:

[0603] Retrieving user information

[0604] The server acquires vital and emotional data from the user's wearable device and smartphone via Bluetooth. Inputs include heart rate, steps, activity levels, and voice data, which are aggregated through Google Fit and Apple HealthKit. The output is raw data used for analysis.

[0605] Step 2:

[0606] Analysis of user information

[0607] The server processes the acquired user information using analytical tools. Using a generative AI model, the emotion engine analyzes voice data and facial expression data to identify the user's emotional state. The input is the acquired raw data, and the output is the analysis result indicating the user's health and emotional state.

[0608] Step 3:

[0609] Generating meal suggestions

[0610] The server generates optimal meal suggestions based on the analysis results. Utilizing analytical methods and a generation AI model, it selects ingredients and dishes that match the user's health and emotional state. The input is the analysis results from step 2, and the output is a personalized meal suggestion for the user.

[0611] Step 4:

[0612] Automated ordering of ingredients

[0613] The server orders the necessary ingredients from an online store based on the generated meal suggestions. Delivery is scheduled to coincide with the user's schedule, depending on the ordering method. The input is the meal suggestion, and the output is a list of ordered ingredients.

[0614] Step 5:

[0615] Cooking guide provided

[0616] The terminal displays a cooking guide via a guidance system while the user is cooking. It includes specific steps, timings, and temperature settings, making cooking easy for the user. The input is the meal suggestion, and the output is the on-screen cooking guide.

[0617] Step 6:

[0618] Providing psychological stability during cooking

[0619] The device uses auxiliary means to provide relaxation music while cooking. It selects and plays music suitable for the user via the Spotify API, etc. The input is the user's emotional state, and the output is a music stream.

[0620] Step 7:

[0621] Optimizing your next meal plan

[0622] The server analyzes user feedback and acquired data to provide suggestions for optimizing future meal plans. Inputs are the user's meal history and feedback, while outputs are improved meal plans and new dish suggestions.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] [Fourth Embodiment]

[0627] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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).

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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".

[0640] This invention is a system that automatically generates an optimal meal plan tailored to each individual user and supports its implementation. The system can improve the user's diet in the following ways.

[0641] First, the system acquires user information such as health data, food preferences, and schedules from wearable devices, smartphones, and IoT sensors in the refrigerator. Specifically, it collects health information such as heart rate and activity levels, schedule information from the calendar, and information on the food inventory in the refrigerator.

[0642] The server then analyzes this information and generates a personalized meal plan based on the user's health status and nutritional needs. This plan includes the proportion of necessary nutrients and the types of foods to be consumed. Based on the analysis results, the menus for breakfast, lunch, and dinner are determined.

[0643] Based on the generated meal plan, the server checks the refrigerator inventory to identify any missing ingredients. Missing ingredients are automatically ordered through the online store, reducing the user's worry about running out of food. Orders are flexibly adjusted to match the user's specified delivery time and store preferences.

[0644] When a user cooks a meal for themselves, the device provides a cooking guide and works with smart cooking appliances to assist with the cooking process. The guide gives step-by-step instructions and automatically operates the cooking appliances as needed. This makes it easy for even novice cooks to prepare meals.

[0645] Finally, once the user finishes their meal, the device provides feedback based on the nutritional balance of the meal. Furthermore, the server analyzes the user's preferences and past eating patterns to periodically suggest new dishes and dishes from different cultures. This allows users to enjoy a diverse range of dining experiences.

[0646] This invention provides a comprehensive solution that optimizes meals with health in mind, automates purchasing, supports cooking, and offers a new dining experience, enriching users' lifestyles and supporting healthy eating habits.

[0647] The following describes the processing flow.

[0648] Step 1:

[0649] The server retrieves user health information, food preferences, schedules, and inventory information from wearable devices, smartphones, and IoT sensors in refrigerators. This includes heart rate, activity levels, scheduled activities, and the quantity and expiration dates of food items in the refrigerator.

[0650] Step 2:

[0651] The server analyzes the acquired user information and generates a meal plan based on the user's health status and preferences. For example, it might suggest a breakfast that provides energy to a user who is sleep-deprived.

[0652] Step 3:

[0653] The server compares the list of ingredients needed for the generated meal plan with the refrigerator's inventory to identify any missing ingredients. The missing ingredients are automatically ordered through the online store.

[0654] Step 4:

[0655] If the user chooses to cook for themselves, the device provides them with a cooking guide. The device displays the cooking steps in real time and works with smart cooking appliances to automatically control the temperature and timer.

[0656] Step 5:

[0657] The device provides the user with feedback on the nutritional balance after a meal, allowing them to confirm whether the meal met their health goals.

[0658] Step 6:

[0659] The server suggests new dishes and cross-cultural dining experiences based on the user's dining history and preferences. This gives users new dining options and allows them to enjoy a variety of cuisines without getting bored.

[0660] (Example 1)

[0661] 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".

[0662] Traditional meal management systems struggled to individually optimize meal plans based on users' health conditions and dietary preferences, and lacked sufficient functionality to automatically compensate for ingredient shortages. As a result, users had to expend considerable effort to maintain a healthy and diverse diet. Furthermore, the lack of support during cooking was a burden for novice cooks.

[0663] 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.

[0664] In this invention, the server includes receiving means for receiving user health information, preference information, and schedule information; generating means for generating an individualized meal plan based on the information received by the receiving means; and replenishment means for automatically ordering necessary ingredients based on the meal plan created by the generating means. This makes it possible to easily achieve a healthy and diverse diet while reducing the user's time and effort.

[0665] A "user" refers to an individual who uses the system to manage their diet.

[0666] "Health information" refers to data that indicates the user's physical condition, such as heart rate and activity level.

[0667] "Preference information" refers to data related to a user's food preferences and past eating history.

[0668] "Schedule information" refers to data that shows the user's schedule and appointments.

[0669] "Receiving means" refers to the components within the system for collecting the aforementioned information from the user.

[0670] "Generation means" refers to the components within a system that create personalized meal plans based on collected information.

[0671] A "meal plan" refers to a menu of meals and intake guidelines designed to meet the user's nutritional needs.

[0672] "Replenishment means" refers to a component within a system that automatically orders and procures necessary ingredients based on a meal plan.

[0673] "Adjustment mechanism" refers to a component within a system used to revise food order details based on inventory information in the refrigerator.

[0674] "Support measures" refer to components within a system that provide guidance on cooking and work in conjunction with cooking equipment to assist in the cooking process.

[0675] "Evaluation means" refers to the components within a system that provide post-meal feedback and suggest new dishes based on user preferences.

[0676] This invention is a system that provides optimal dietary management to users and is operated using multiple hardware devices.

[0677] Users acquire health information such as heart rate and activity levels using wearable devices and smartphones. This data is transmitted to a server via Bluetooth or Wi-Fi. Users can also input schedule information and dietary preferences using a smartphone application. This information is stored in a cloud database and used for analysis.

[0678] The server performs data analysis using software running on the cloud. This analysis utilizes generative AI models to create personalized meal plans based on each user's individual nutritional needs. Simultaneously, the server receives inventory information from IoT sensors in the refrigerator and automatically places orders using the online store's API if necessary ingredients are in short supply. These processes are managed by the server's scheduling software.

[0679] When a user is cooking, a device (such as a tablet) displays a cooking guide. This device works in conjunction with smart cooking appliances and can automatically operate them according to the instructions. For example, it can automatically set the oven temperature and help the cooking process proceed smoothly.

[0680] After a meal, the device provides feedback based on the nutritional balance of the meal, and the server analyzes the user's accumulated eating history and preferences to suggest new cooking experiences. This allows users to be exposed to diverse food cultures and enjoy a balanced diet.

[0681] For example, if a user requests a low-calorie, high-protein breakfast, the server will suggest a recipe combining oatmeal with protein powder and fruit. Another example of a prompt message is, "Please suggest a breakfast menu for health promotion."

[0682] This system makes it easier for users to maintain their daily health and improve their lifestyle by supporting improvements in their eating habits.

[0683] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0684] Step 1:

[0685] The user obtains health information using a wearable device. This device measures heart rate and activity level and transfers the data to a smartphone. The input is heart rate and activity level data, and the output is a set of health information stored on the smartphone. This data is then sent to a server.

[0686] Step 2:

[0687] Users input schedule and preference information using a smartphone app. This information concerns the user's daily activities and dietary preferences. The input consists of schedule and preference information, while the output is a set of user information stored in a cloud database.

[0688] Step 3:

[0689] The server performs data analysis based on health information, preference information, and schedule information received from the user. This analysis utilizes a generative AI model to create a personalized meal plan for the user. The input is a set of user information, and the output is the personalized meal plan.

[0690] Step 4:

[0691] The server obtains inventory information from IoT sensors installed in the refrigerator. This information is compared with meal plans stored in the system to identify any missing ingredients. The inputs are meal plans and inventory information, and the output is a list of missing ingredients.

[0692] Step 5:

[0693] The server automatically orders missing ingredients via the online store. This process uses the online store's API to confirm the order based on user preferences and specified delivery times. The input is a list of missing ingredients, and the output is an order confirmation notification.

[0694] Step 6:

[0695] When the user begins cooking, the device displays a detailed cooking guide. The device guides the user through the steps and informs them of the next steps via voice and on-screen prompts. The input is the meal plan, and the output is a display of the cooking procedure.

[0696] Step 7:

[0697] The device works in conjunction with smart cooking appliances to assist the cooking process. For example, it automatically adjusts oven settings to ensure food is cooked properly. The input is the cooking procedure, and the output is the operation of the automatically controlled cooking appliance.

[0698] Step 8:

[0699] After a meal, the user receives a meal evaluation via their device. This evaluation provides feedback based on the balance of nutrients consumed. The input is the actual meal consumed, and the output is the nutritional evaluation feedback.

[0700] Step 9:

[0701] The server analyzes the user's eating history and preferences to suggest new culinary experiences and cross-cultural dishes. The input is past eating history and preference data, and the output is a suggested new meal plan.

[0702] (Application Example 1)

[0703] 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".

[0704] In today's busy lifestyle, maintaining a healthy and efficient diet tailored to individual users is not easy. Many factors are involved, including ingredient selection, nutritional balance considerations, and cooking time. In particular, optimizing these factors individually is difficult, so there is a need for a system that allows users to continue eating healthily without hassle.

[0705] 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.

[0706] In this invention, the server includes a device for acquiring user information, a processing device for analyzing the acquired user information and generating an optimal meal plan, an ordering device for automatically ordering necessary ingredients based on the generated meal plan, and a mechanism for efficiently providing meals and ingredients through a food delivery service in conjunction with health data. This enables the provision of personalized meal plans to individual users and the automatic ordering and delivery of ingredients and dishes.

[0707] A "device for acquiring user information" is a technological device used to collect user health data, preferences, schedule information, and other similar information.

[0708] A "processing device" is a computer system that uses acquired user information to perform analysis and generate personalized meal plans based on the user's health status and nutritional needs.

[0709] An "ordering device" is a system that automatically orders the necessary ingredients through online stores or delivery services based on a generated meal plan.

[0710] A "guidance device" is an information terminal that guides users through the procedures and tools to be used when preparing a planned meal.

[0711] A "suggestion device" is an information processing system that suggests new dishes based on the user's preferences and past eating history.

[0712] "Health data" refers to a collection of data that shows health-related information such as the user's heart rate, activity level, and nutritional status.

[0713] The "system provided through food delivery services" is a service infrastructure that efficiently delivers ingredients and prepared meals based on the user's meal plan through food delivery companies.

[0714] To implement this invention, a server and a user terminal play key roles. The server acquires information such as the user's health data, preferences, and schedule via wearable devices and IoT refrigerator sensors. This collected information is analyzed by the server's processing unit, and an optimized meal plan is generated for each user.

[0715] Based on this meal plan, the server has a system in place to automatically order the necessary ingredients through available online stores and delivery services. When the user cooks according to the plan, the terminal provides cooking instructions step by step, making it easy for even novice cooks to prepare meals. Specifically, using a smartphone or tablet, the user is visually guided through the recipe steps and cooking timings.

[0716] Furthermore, the server uses a generative AI model to suggest new dishes, taking into account the user's past eating history and preferences. This allows users to experience new food cultures and enrich their dietary habits. In addition, the server has established a system to quickly deliver meals and dishes according to the plan through a food delivery service.

[0717] For example, if a user prefers Japanese food and has a high activity level during the day, the server will suggest a nutritious Japanese meal plan. Ingredients are ordered in a timely manner, and a delivery service delivers them according to the user's schedule. The user can then prepare a healthy meal in a short time, receiving cooking instructions via their smartphone.

[0718] Example prompt to input into the generating AI model: "Please advise on how to build a system that suggests the optimal Japanese meal plan based on the user's health data and automatically orders the necessary ingredients."

[0719] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0720] Step 1:

[0721] The server acquires health data, preferences, and schedule information from users via wearable devices and IoT refrigerator sensors. It receives user health status and food inventory information as input and stores it in a database. This serves as the basis for creating personalized meal plans for each user.

[0722] Step 2:

[0723] The server uses the acquired data to generate an optimal meal plan based on the user's health status and nutritional needs. User information acquired in step 1 is used as input, and an AI algorithm is used to determine the menu for each meal. The output is a meal plan optimized for the user.

[0724] Step 3:

[0725] The server automatically orders the necessary ingredients through online stores and delivery services based on the generated meal plan. It uses the meal plan and current ingredient inventory information as input to determine which ingredients need to be ordered. The output is an order list, which is sent to an external system.

[0726] Step 4:

[0727] The user's device displays cooking instructions according to the meal plan received from the server. The input is the server's optimized meal plan, and based on this, the device guides the user through visual cooking steps. The output displays specific instructions for the user to perform the cooking.

[0728] Step 5:

[0729] The server uses an AI model to generate new dish suggestions, taking into account the user's past eating history and preferences. It uses the user's preference information and past eating history as input to generate new dish suggestions. The output is a list of suggested dishes, which is provided to the user.

[0730] Step 6:

[0731] In conjunction with food delivery services, the server quickly delivers meals and dishes according to the user's schedule and plan. Inputs are the user's order and schedule information, and the output is a delivery plan. The delivery service is then coordinated to deliver the meals at the specified time.

[0732] 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.

[0733] This invention is a system that proposes an optimal meal plan to each individual user based on information including their health and emotional state. To support a healthy and emotionally-driven diet for users, the invention is implemented using the following means.

[0734] First, the system acquires the user's vital data, activity level, emotional state, schedule, and refrigerator inventory information through wearable devices and smartphones. The emotion engine analyzes the user's facial expressions and voice data to infer their current emotions. Based on this data, if the user is feeling stressed, suggestions using foods with relaxation effects are made.

[0735] Next, the server analyzes past health and emotional data, preferences, and allergy information to generate a personalized meal plan. This ensures that the meal content not only reflects the user's preferences but also considers its potential to alleviate their current emotional state. For example, a depressed user might be offered meals that are expected to uplift their mood.

[0736] Based on the generated meal plan, the server checks the inventory in the refrigerator to identify any missing ingredients. It then automatically places an order using the online store. When ordering, users are also offered options for delivery timing, allowing for adjustments to fit their schedule.

[0737] When a user is cooking, the device provides a detailed cooking guide and works with smart cooking appliances to control temperature and cooking time. In particular, it can be set to play relaxing music while cooking, taking into account the user's emotional state. This allows users to enjoy cooking more comfortably.

[0738] As feedback, the device evaluates the nutritional balance after the user completes a meal and shares data with the emotion engine to optimize the next meal plan. Furthermore, the server uses this data to suggest new dishes or cross-cultural dining experiences to try next. This suggestion takes into account the user's fluctuating emotional state, broadening the range of choices.

[0739] This invention aims to improve not only the user's physical health management but also their psychological satisfaction, and is a system that enables diverse support in their dietary habits.

[0740] The following describes the processing flow.

[0741] Step 1:

[0742] The server acquires user health and emotional data from wearable devices and smartphones. The emotion engine analyzes the user's facial expressions and voice data to determine their emotional state and extract information such as "high stress levels."

[0743] Step 2:

[0744] The server generates an optimal meal plan for the user based on acquired health and emotional data. In this process, ingredients and recipes are selected not only for their nutritional balance but also for their mood-enhancing effects. For example, it might suggest a vitamin-rich menu to promote fatigue recovery.

[0745] Step 3:

[0746] The server uses the generated meal plan as a basis to identify missing ingredients by cross-referencing it with inventory information obtained from refrigerator sensors. It then automatically orders the missing ingredients from an online store and arranges for delivery at the user's specified time.

[0747] Step 4:

[0748] When a user begins cooking, the device visually displays the cooking steps and guides them through the process in real time. The emotion engine suggests relaxing music and aromas based on the user's current emotions, providing a comfortable cooking environment.

[0749] Step 5:

[0750] After a meal is completed, the device provides the user with feedback on the nutritional balance of the meal and their emotional state for the day. The feedback includes specific details such as, "Today's meal was effective in reducing stress."

[0751] Step 6:

[0752] Based on the collected emotional and dining data, the server performs analysis to refine future meal recommendations, offering suggestions that broaden the user's choices by providing new dishes and cross-cultural dining experiences.

[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 recent years, there has been a growing demand for personalized nutrition plans based on each user's health and emotional state. Conventional systems have faced challenges in acquiring and analyzing user data adequately, making it difficult to provide optimal meal suggestions. Furthermore, the lack of proper integration between automated ingredient procurement and cooking instructions resulted in low user convenience.

[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 a terminal for acquiring user data, a processing unit for analyzing the user data acquired by the terminal and generating an optimal nutrition plan, and means for automatically procuring necessary resources based on the nutrition plan generated by the processing unit. This makes it possible to provide personalized nutrition plans tailored to the user's health condition and preferences, thereby efficiently and effectively supporting the user's health management.

[0758] "User data" refers to information about an individual user's health status, emotional state, activity level, schedule, and preferences.

[0759] A "terminal" is a device used to acquire and communicate user data, and includes wearable devices and smartphones.

[0760] A "processing device" is a device that analyzes acquired user data and generates an optimal nutrition plan based on that analysis.

[0761] A "nutrition plan" is a meal plan that takes into account the user's health condition and preferences, and considers the content of meals and necessary nutrients.

[0762] "Resources" refers to the food ingredients and cooking supplies needed to implement a nutrition plan.

[0763] "Procurement" refers to the act of securing necessary resources, and includes processes such as ordering and purchasing.

[0764] This invention is a system that provides a nutrition plan optimized for individual users. This system mainly consists of collecting and analyzing user data, generating plans, procuring resources, and providing guidance to users.

[0765] The server first receives user data from the terminal. This terminal functions as a smartphone or wearable device, collecting information such as heart rate, body temperature, activity level, emotional state, and schedule. The wearable device monitors the user's daily health status and transmits the data to the server via the smartphone.

[0766] Next, the server uses a processing unit to analyze the received user data in detail. This analysis takes into account past health records, user preferences, and allergy information, and evaluates the user's current physical and emotional state to generate an optimal nutrition plan. For example, if a user has recently been experiencing stress, a meal plan using ingredients with relaxation-enhancing properties might be suggested to alleviate that stress.

[0767] Furthermore, based on the generated nutrition plan, the server automatically procures resources through an online platform. During this process, it can compare the inventory in the refrigerator and efficiently order any shortages. For resource procurement, the optimal delivery timing can be set to suit the user's lifestyle and schedule.

[0768] When a user is cooking, the device provides detailed instructions and works with smart cooking appliances to control the cooking process. For example, it can guide users in real time on the appropriate heating temperature and time, and it also has a function to play relaxing music according to their emotional state.

[0769] Furthermore, after meals, the device prompts the user for feedback. This feedback is used to evaluate nutritional balance and optimize the next nutritional plan. This provides continuous support for maintaining the user's health and improving their psychological well-being.

[0770] An example of a prompt message is to send a request to the AI ​​model in the format of, "Please suggest a suitable meal plan for a user who is feeling down." This allows the system to provide more personalized meal suggestions.

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

[0772] Step 1:

[0773] The device acquires data from the user. Its inputs include heart rate, body temperature, activity level, emotional state measured by a wearable device, and schedule information managed on a smartphone. By collecting this data, the device gains a real-time understanding of the user's current health and emotional state. Specifically, the device is configured to automatically send user data to a server at a fixed time each night. The output is a dataset representing the user's state.

[0774] Step 2:

[0775] The server receives user data transmitted from the terminal and performs analysis using a processing unit. The inputs used are collected health data, past health records, preferences, and allergy information. Data processing includes sentiment analysis to evaluate the user's current mental state and historical data analysis to identify trends. Specifically, the system utilizes an emotion engine to perform voice tone analysis and facial expression analysis to calculate stress levels. The output is the analysis result, taking into account the user's health and emotional state.

[0776] Step 3:

[0777] The server generates an optimal nutrition plan using a generative AI model based on the analysis results. Inputs include the analysis results and prompts that consider the user's nutritional needs and preferences (e.g., "Please suggest a meal plan using ingredients that help reduce stress"). Data calculations involve optimization within the model to determine the most suitable ingredients and menus for the user. This includes constructing a daily meal plan from specific food groups. The output is a nutrition plan presented as a meal suggestion.

[0778] Step 4:

[0779] The server compares the generated nutrition plan with inventory information to identify resource shortages. It uses current inventory data from the refrigerator as input. Data processing includes comparing the ingredients required for the nutrition plan with current inventory and listing the shortages. The output is a list of ingredients that need to be ordered.

[0780] Step 5:

[0781] The server sends an ingredient list to an online platform and automatically procures the resources. Using the identified list of missing ingredients as input, it generates orders via the API of a specific online store. Specifically, it performs calculations to evaluate the prices and delivery information of each store to make the optimal selection. The output is confirmation information that the order has been completed.

[0782] Step 6:

[0783] The terminal provides the user with a nutrition plan and a cooking guide. It uses the generated nutrition plan as input. Data calculations include step-by-step guidance for the necessary cooking steps and specific actions such as automatically controlling cooking temperature and time in conjunction with smart cooking appliances. Outputs include a cooking guide and progress display for the user.

[0784] Step 7:

[0785] The device collects feedback from the user after a meal and sends it to the server. The input is the user's feedback information, which is used to evaluate nutritional balance and emotional state. Specifically, it prompts the user for input through a feedback collection interface. The output is data used to optimize the next nutrition plan.

[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] In modern society, there is a demand for personalized meal planning that takes into account people's health and emotional states. However, conventional systems mainly make suggestions based solely on health information, and do not adequately consider emotional aspects. As a result, suggestions may be made that downplay the psychological impact of food, and people may not be able to achieve sufficient satisfaction. Furthermore, there is currently a lack of mechanisms to support the psychological stability of users during cooking. Therefore, a system is needed that proposes individualized meal plans based on both health and emotional information, and provides accompanying cooking support.

[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 an acquisition means for acquiring user information, an analysis means for analyzing the information acquired by the acquisition means and generating optimal meal suggestions, an ordering means for automatically ordering necessary ingredients based on the meal suggestions generated by the analysis means, an auxiliary means for providing psychological stability during cooking, and an optimization means for optimizing the meal plan based on the user's health and emotional state. This makes it possible to provide a meal plan that takes into account the user's health and emotional state, along with cooking support.

[0791] "User information" refers to data including the user's health status, emotional state, activity level, and food inventory information.

[0792] "Means of acquisition" refers to methods and devices for collecting user information from wearable devices and smartphones.

[0793] "Analysis means" refers to methods and devices for analyzing acquired user information and generating optimal meal suggestions based on that analysis.

[0794] "Ordering method" refers to a method or device for automatically ordering the necessary ingredients based on the generated meal suggestions.

[0795] "Guidance means" refers to methods or devices that provide users with cooking guides that follow meal suggestions.

[0796] "Supportive measures" refer to methods and devices that provide psychological stability during cooking and help users relax while cooking.

[0797] "Optimization means" refers to methods or devices for appropriately adjusting meal plans based on the user's health and emotional state.

[0798] The system that implements this application provides personalized meal suggestions based on the user's health and emotional state. User information is acquired through wearable devices and smartphones worn by the user. This information includes vital data, emotional data, and activity levels.

[0799] The server uses Bluetooth and various sensors to acquire this user information. The acquired data is aggregated through health information management platforms such as Google Fit and Apple HealthKit. The server then uses analysis tools to generate optimal meal suggestions based on the user's health and emotional state. By using a generative AI model in this analysis, a more accurate personalized plan is provided.

[0800] The ordering system automatically orders the necessary ingredients from the online store based on the generated meal suggestions. Users can also select a delivery time that suits them, allowing for flexible service tailored to their schedules.

[0801] When a user is cooking, the device provides detailed cooking instructions using guidance devices. In addition, auxiliary devices stream music to help the user relax while cooking, via APIs such as Spotify. For example, if a user is feeling stressed, they can select music with a relaxing effect.

[0802] The server analyzes all acquired data through optimization mechanisms to further optimize future meal plans. The suggestion mechanism also introduces new dishes and cross-cultural dining experiences. This allows users to enrich their eating habits.

[0803] For example, when a user is feeling fatigued, it is possible to suggest an energy-boosting menu using vitamin-rich ingredients. An example of a prompt message for the generating AI model would be, "Please suggest a dinner menu that is effective for fatigue recovery for a woman in her 30s."

[0804] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0805] Step 1:

[0806] Retrieving user information

[0807] The server acquires vital and emotional data from the user's wearable device and smartphone via Bluetooth. Inputs include heart rate, steps, activity levels, and voice data, which are aggregated through Google Fit and Apple HealthKit. The output is raw data used for analysis.

[0808] Step 2:

[0809] Analysis of user information

[0810] The server processes the acquired user information using analytical tools. Using a generative AI model, the emotion engine analyzes voice data and facial expression data to identify the user's emotional state. The input is the acquired raw data, and the output is the analysis result indicating the user's health and emotional state.

[0811] Step 3:

[0812] Generating meal suggestions

[0813] The server generates optimal meal suggestions based on the analysis results. Utilizing analytical methods and a generation AI model, it selects ingredients and dishes that match the user's health and emotional state. The input is the analysis results from step 2, and the output is a personalized meal suggestion for the user.

[0814] Step 4:

[0815] Automated ordering of ingredients

[0816] The server orders the necessary ingredients from an online store based on the generated meal suggestions. Delivery is scheduled to coincide with the user's schedule, depending on the ordering method. The input is the meal suggestion, and the output is a list of ordered ingredients.

[0817] Step 5:

[0818] Cooking guide provided

[0819] The terminal displays a cooking guide via a guidance system while the user is cooking. It includes specific steps, timings, and temperature settings, making cooking easy for the user. The input is the meal suggestion, and the output is the on-screen cooking guide.

[0820] Step 6:

[0821] Providing psychological stability during cooking

[0822] The device uses auxiliary means to provide relaxation music while cooking. It selects and plays music suitable for the user via the Spotify API, etc. The input is the user's emotional state, and the output is a music stream.

[0823] Step 7:

[0824] Optimizing your next meal plan

[0825] The server analyzes user feedback and acquired data to provide suggestions for optimizing future meal plans. Inputs are the user's meal history and feedback, while outputs are improved meal plans and new dish suggestions.

[0826] 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.

[0827] 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.

[0828] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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."

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] The following is further disclosed regarding the embodiments described above.

[0848] (Claim 1)

[0849] Means for obtaining user information,

[0850] An analysis means that analyzes user information acquired by the acquisition means and generates optimal meal suggestions,

[0851] An ordering means that automatically orders the necessary ingredients based on the meal suggestions generated by the analysis means,

[0852] A guidance means that provides cooking guidance in accordance with the aforementioned meal suggestion,

[0853] A proposal method for generating new culinary suggestions,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, which evaluates the user's health status and optimizes dietary suggestions based on acquired user information.

[0857] (Claim 3)

[0858] The system according to claim 1, wherein the proposed means takes into account the user's preferences and past eating history to propose a new food culture.

[0859] "Example 1"

[0860] (Claim 1)

[0861] A receiving means for receiving user health information, preference information, and schedule information,

[0862] A generation means that generates an individualized meal plan based on the information received by the receiving means,

[0863] A replenishment means that automatically orders the necessary ingredients based on the meal plan created by the generation means,

[0864] An adjustment means that monitors inventory information in the refrigerator and adjusts the order contents according to the inventory status of the said ingredients,

[0865] A support system that provides detailed guidance during cooking and works in conjunction with cooking equipment to assist the cooking process,

[0866] An evaluation method that provides feedback based on nutritional balance after a meal and suggests new dishes,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, which evaluates the user's health status and optimizes the meal plan based on acquired user information.

[0870] (Claim 3)

[0871] The system according to claim 1, wherein the evaluation means takes into account the user's preferences and past eating history to propose a new food culture.

[0872] "Application Example 1"

[0873] (Claim 1)

[0874] A device for acquiring user information,

[0875] A processing device that analyzes user information acquired by the aforementioned device and generates an optimal meal plan,

[0876] An ordering device that automatically orders the necessary ingredients based on the meal plan generated by the processing device,

[0877] A guidance device that provides cooking instructions according to the aforementioned meal plan,

[0878] A suggestion device that generates new cooking suggestions,

[0879] A system that efficiently provides meals and ingredients through a food delivery service, linked with health data,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, which evaluates the user's health status, optimizes their meal plan, and provides cooking guidance based on acquired user information.

[0883] (Claim 3)

[0884] The system according to claim 1, wherein the proposed device takes into account the user's preferences and past eating history, proposes a new food culture, and supports the automation of meal provision.

[0885] "Example 2 of combining an emotion engine"

[0886] (Claim 1)

[0887] A terminal that acquires user data,

[0888] A processing device that analyzes user data acquired by the aforementioned terminal and generates an optimal nutrition plan,

[0889] A means for automatically procuring necessary resources based on a nutrition plan generated by the aforementioned processing device,

[0890] A guidance device that provides cooking procedures according to the aforementioned nutrition plan,

[0891] A generator that produces new nutritional suggestions,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, which evaluates the user's physical condition and optimizes the nutrition plan based on acquired user data.

[0895] (Claim 3)

[0896] The system according to claim 1, wherein the generating device takes into account the user's preferences and past nutritional history to propose new eating habits.

[0897] "Application example 2 when combining with an emotional engine"

[0898] (Claim 1)

[0899] Means for obtaining user information,

[0900] An analysis means that analyzes user information acquired by the acquisition means and generates optimal meal suggestions,

[0901] An ordering means that automatically orders the necessary ingredients based on the meal suggestions generated by the analysis means,

[0902] A guidance means that provides cooking guidance in accordance with the aforementioned meal suggestion,

[0903] Supportive means to provide psychological stability during cooking,

[0904] An optimization method for optimizing meal plans based on health and emotional state,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, which evaluates the user's health and emotional state based on acquired user information and optimizes meal suggestions.

[0908] (Claim 3)

[0909] The system according to claim 1, wherein the proposed means takes into account the user's preferences, past eating history, and emotional state to propose a new food culture experience. [Explanation of symbols]

[0910] 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. Means for obtaining user information, An analysis means that analyzes user information acquired by the acquisition means and generates optimal meal suggestions, An ordering means that automatically orders the necessary ingredients based on the meal suggestions generated by the analysis means, A guidance means that provides cooking guidance in accordance with the aforementioned meal suggestion, A proposal method for generating new culinary suggestions, A system that includes this.

2. The system according to claim 1, which evaluates the user's health status and optimizes dietary suggestions based on acquired user information.

3. The system according to claim 1, wherein the proposed means takes into account the user's preferences and past eating history to propose a new food culture.

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