system

Abstract

We provide the system. [Solution] An information input means for the user to input their meal history and physiological data, A data analysis means that analyzes input information and evaluates nutritional status, A plan generation means that automatically generates a meal plan using food available from the nearest food service provider based on the analysis results, An information display means that presents the generated meal plan to the user and provides instructions to immediately place an order, 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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, a busy lifestyle has become common, and it is difficult to maintain a healthy diet. In addition, many people tend to consume a diet with an unbalanced nutritional balance, so the health risk is increasing. Therefore, there is a need to plan and provide a diet with a good nutritional balance using ingredients that can be easily obtained at daily and nearby sales stores (for example, convenience stores), but such services have not yet been sufficiently provided.

Means for Solving the Problems

[0005] This invention provides a system that includes input means for users to input their meal history and physiological data, and a data analysis means that analyzes the data provided by the user to evaluate the user's nutritional status. Furthermore, it provides a system that includes a plan generation means that automatically generates a meal plan using ingredients available at a specific store based on the results. In addition, it includes a display means that presents the generated meal plan to the user, and an adjustment means that adjusts the data analysis means and the plan generation means based on feedback information from the user to improve the meal plan. This enables the user to maintain their health efficiently and effectively.

[0006] A "user" is an entity that uses the system to create meal plans and evaluate nutritional status.

[0007] "Meal history" refers to information about the types of meals a user has eaten in the past and the times they have eaten them.

[0008] "Physiological data" refers to information that indicates the user's physical condition, including fatigue levels, weight, and exercise levels.

[0009] An "input method" is an interface that allows users to provide the system with their meal history and physiological data.

[0010] The "data analysis means" refers to a system that analyzes the entered meal history and physiological data to evaluate the user's nutritional status.

[0011] The "plan generation means" is a device that has the function of automatically generating a meal plan using ingredients available at the sales store, based on the results of data analysis.

[0012] A "display means" is an interface for visually presenting the generated meal plan to the user.

[0013] "Adjustment means" refers to a function that improves the system's analysis and plan generation capabilities based on user feedback.

[0014] A "retail store" is a commercial facility where users can purchase food ingredients, and typically refers to convenience stores, supermarkets, and similar establishments. [Brief explanation of the drawing]

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

Embodiment for Implementing the Invention

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

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

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

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

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

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

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

[0023] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] This invention is a system that supports users' meal management, specifically enabling users to create healthy and balanced meal plans. This system functions through the coordinated efforts of three entities: the user, the terminal, and the server.

[0037] Users input their daily meal history and physiological data (e.g., foods consumed, time of day, weight, fatigue level, etc.) into a terminal. The terminal collects this information and sends it to a server. The server uses the received information to perform data analysis to evaluate the user's nutritional status. AI algorithms are used in the analysis to determine the user's nutrient deficiencies or excesses and to analyze trends from their meal history.

[0038] Next, the server automatically generates a meal plan tailored to the user using ingredients available at a specific store. This plan includes specific recipes, a list of ingredients to purchase, and calorie information. The generated meal plan is then visually presented to the user via their device.

[0039] Furthermore, this system receives feedback from users and adjusts its analysis and plan generation algorithms based on that information to continuously improve accuracy. For example, if a user provides feedback that a suggested meal plan contains too much food, the next suggestion will be adjusted accordingly.

[0040] As a concrete example, suppose a user is experiencing fatigue and is too busy to cook. When this user inputs their meal history and health data, the server analyzes this data and suggests simple, nutritious recipes that can be prepared in the shortest possible time, such as a "brown rice and bean salad." In this way, it becomes possible to eat efficiently and healthily.

[0041] This system functions as an important tool to improve users' health awareness and support them in maintaining their health in their daily lives.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users use their devices to input their meal history and physiological data. This data includes specific food names, amounts consumed, timing of consumption, current weight, and comments about their physical condition.

[0045] Step 2:

[0046] The terminal temporarily stores the data entered by the user and creates a data package for transmission to the server. During this process, it verifies the integrity of the data and checks for any incomplete information.

[0047] Step 3:

[0048] The server receives the data package sent from the terminal and saves it to the database. Simultaneously with saving, it starts analyzing the data via an AI algorithm.

[0049] Step 4:

[0050] The server uses an AI algorithm to assess the user's nutritional status. This includes analyzing which nutrients are deficient or in excess, and understanding their dietary trends. The algorithm also utilizes historical data to improve its accuracy.

[0051] Step 5:

[0052] Based on the analysis results, the server automatically generates a meal plan using ingredients available at a specific store. This plan includes recipes, a list of required ingredients, and calorie information, and is customized to suit the user's needs.

[0053] Step 6:

[0054] The terminal displays the meal plan received from the server to the user. The display uses a visual interface to ensure clarity and intuitive understanding for the user.

[0055] Step 7:

[0056] Users provide feedback on their meal plans. This feedback includes satisfaction levels and areas for improvement, and is taken into consideration when generating future meal plans.

[0057] Step 8:

[0058] The server receives feedback from the user and adjusts the AI ​​algorithm. This optimizes the analysis results and plan generation for subsequent sessions to better match the user's preferences.

[0059] (Example 1)

[0060] 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."

[0061] In modern society, maintaining a healthy and balanced diet is crucial, yet many people find it difficult to manage their nutrition properly amidst their busy daily lives. This challenge, especially when individuals lack the time or knowledge to plan their meals, leads to nutritional imbalances, overconsumption, and deficiencies in essential nutrients. Therefore, there is a need for flexible and efficient meal management systems tailored to the individual circumstances of each user.

[0062] 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.

[0063] In this invention, the server includes information input means, data analysis means using a computing device, and plan creation means. This allows users to easily and effectively receive a nutrition plan tailored to their health condition.

[0064] An "information input means" is a device or interface for users to input data such as the foods they have consumed, the time of their meals, their weight, and their perceived health.

[0065] "Data analysis means by a computing device" refers to a computer program or algorithm used to determine the excess or deficiency of nutrients in the user based on the input information.

[0066] A "plan creation method" refers to a procedure or device for automatically generating a nutrition plan tailored to the user's needs based on the analyzed results.

[0067] "Visual presentation means" refers to a device or tool for displaying a generated plan in a format that is easy for the user to understand.

[0068] An "information correction mechanism" is a system or process for collecting opinions and feedback from users and dynamically improving plans based on them.

[0069] "Geographic location data" refers to information indicating the user's current location and is used to identify the nearest source of materials.

[0070] "Purchasable ingredients" refers to food and nutritional products that are available in the user's region or specific location.

[0071] Modes for carrying out the invention

[0072] This invention is a nutrition management system that supports a healthy diet. This system consists of a terminal where the user inputs data on a daily basis, and a server that analyzes that data and generates a meal plan.

[0073] Users manually input data such as the foods they consume, meal times, weight, and perceived health using their devices. This data is collected through applications running on common mobile devices such as smartphones and tablets.

[0074] The terminal collects information and transmits it to the server via the internet. The server performs data analysis using computing devices that utilize programming languages ​​such as Python and R. The analysis uses algorithms that determine the user's nutritional deficiencies or excesses, employing generative AI model frameworks such as TENSORFLOW® and PyTorch.

[0075] Based on this analysis, the server generates an optimal meal plan for the user. This plan incorporates practical recipes and ingredient lists, utilizing the user's geographical location data to select ingredients available in their vicinity. The plan is visually presented on the device screen, making it easy to implement.

[0076] As an example of actual use, if a user inputs that they "recently get tired easily and are too busy to cook much," the system will suggest a nutritionally balanced recipe that can be prepared in a short time, such as "brown rice and bean salad." This suggestion will include specific cooking instructions, purchasing information, and even calorie information.

[0077] As an example of a prompt using a generative AI model, if you input "I've been feeling tired lately, please suggest some easy-to-prepare healthy recipes," the system will immediately analyze the data and provide an appropriate response. In this way, the present invention functions as a flexible meal management system tailored to individual users, supporting the maintenance of healthy lifestyle habits.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users input their dietary information and health status into the device. Specifically, they manually record the types of food consumed, meal times, weight, fatigue levels, etc., in the application they are using. The entered data is temporarily stored in a local database. This collects the user's health information and prepares the basic data for the next processing step.

[0081] Step 2:

[0082] The terminal sends the collected data to the server. HTTPS, a secure communication protocol, is used. The input data is transferred to the server via the network, preparing it for analysis. The transmitted data is processed on the remote server while maintaining security.

[0083] Step 3:

[0084] The server performs data analysis based on the received data. The computing unit utilizes a generative AI model to perform calculations to evaluate the user's nutritional balance. This analysis uses frameworks such as TensorFlow and PyTorch to determine the user's nutrient deficiencies or excesses and extract trends in their eating habits. As output, a nutritional assessment tailored to the user's situation is generated.

[0085] Step 4:

[0086] The server uses the analysis results to create a meal plan. This plan lists purchasable ingredients based on information combining the user's geographic location data and market data. Furthermore, a nutritionally balanced recipe combining these ingredients is automatically generated. The nutrition plan is generated as a result of calculations performed by a Python program.

[0087] Step 5:

[0088] The generated nutrition plan is provided to the user via their device. The information is displayed in a visually easy-to-understand interface, showing specific recipes, shopping lists, and required calorie information. This allows users to intuitively understand the content and apply it to their daily meals.

[0089] Step 6:

[0090] Users provide feedback on the presented nutrition plan. Specifically, they use the feedback function on their device to send comments such as "the portion size in the recipe is too large." This feedback information is analyzed on the server and reflected in the processing unit to be used in suggesting the next meal plan. New data analysis, including the feedback, contributes to improving the accuracy of the next output.

[0091] (Application Example 1)

[0092] 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."

[0093] In today's busy lifestyle, many consumers find it difficult to maintain a healthy and balanced diet. To address this problem, a system is needed that allows consumers to easily assess their nutritional status, create optimal meal plans, and quickly access those meals. Currently, the process of nutritional management and purchasing is time-consuming and burdensome for consumers. Furthermore, the meal plans offered often do not perfectly match individual needs.

[0094] 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.

[0095] In this invention, the server includes information input means for the user to input meal history and physiological data; data analysis means for analyzing the input information and evaluating the nutritional status; plan generation means for automatically generating a meal plan using foods available from the nearest food service provider based on the analysis results; and information display means for presenting the generated meal plan to the user and enabling immediate ordering. This allows the user to quickly select and order the optimal meal according to their health condition.

[0096] "Information input means" refers to devices or software that provide functions that allow users to easily input their dietary history and physiological data.

[0097] "Data analysis tools" refer to algorithms and software designed to evaluate nutritional status based on information entered by users.

[0098] A "plan generation method" refers to a process or system for automatically creating an optimal meal plan based on analysis results.

[0099] "Information display means" refers to devices or software equipped with an interface for visually communicating the generated meal plan to the user.

[0100] "Evaluation information" refers to feedback and opinions provided by users, which the system uses to provide more personalized services.

[0101] "Food and beverage providers" refers to stores and service providers that offer food or meals.

[0102] In this invention, users can input their daily dietary history and physiological data via an information input means using their smartphone or other portable information terminal. After data input, the terminal transmits this information to a server. The server evaluates the user's nutritional status using data analysis means equipped with TensorFlow or similar AI algorithms. Through these analyses, the server clarifies the user's eating habits and determines nutrient deficiencies or excesses as needed.

[0103] Subsequently, the server connects with a food database of the nearest food and beverage providers and creates an optimal meal plan based on the analysis results using a planning generation system. This meal plan includes menus available at partner restaurants and is visually presented to the user by an information display system. Based on the presented plan, the user can immediately order food through the application.

[0104] For example, if a user enters data indicating they "recently feel tired," the server's analysis might determine that they "need a high-protein, low-calorie diet." Based on this result, the server suggests menu items such as "grilled chicken salad" from its database, and the user can then order them. In this way, users can easily choose and consume meals based on their health condition. An example of a prompt message would be, "I've been feeling tired lately; please suggest a high-protein, low-calorie meal plan."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] Users use a terminal to input their meal history and physiological data. This data includes details of the foods consumed, the time of consumption, weight, and fatigue level. This data is then transmitted to the server via the information input system.

[0108] Step 2:

[0109] The server transmits the received data to the data analysis system. The data analysis system uses a generative AI model to evaluate the user's nutritional status. Based on the input of dietary history and physiological data, it determines the excess or deficiency of nutrients through data calculations and analyzes the trends in nutritional status. It generates the analysis results as output.

[0110] Step 3:

[0111] The server uses a plan generation mechanism to automatically generate an appropriate meal plan based on the analysis results. Input data includes nutritional status assessment results and menu information from local food and beverage providers. The plan generation mechanism retrieves food information available from the nearest food and beverage providers based on the user's geographical information and selects menus that match the analysis results. The output is a meal plan proposed to the user.

[0112] Step 4:

[0113] The server sends the generated meal plan to the terminal and presents it visually to the user through an information display. Based on this plan, the user reviews the suggested menu and provides feedback using prompts as needed. An example of a prompt would be, "I've been feeling tired lately, please suggest a high-protein, low-calorie meal plan."

[0114] Step 5:

[0115] When user feedback is received, the server sends the evaluation information to the adjustment mechanism, which then improves the data analysis and plan generation mechanisms. This process enables the provision of better meal plans based on the user's preferences and health status. The feedback is then incorporated into the generating AI model to improve future suggestions.

[0116] 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.

[0117] This invention is a system that integrates user meal management and emotional recognition, taking into account the user's emotional state when they receive a healthy and balanced meal plan. This system operates through the coordinated efforts of three entities: the user, the terminal, and the server.

[0118] Users input their daily meal history and physiological data through the device. Physiological data includes weight, fatigue levels, and exercise levels. The device also senses the user's facial expressions and acquires emotional data. The device then organizes this information and sends it to the server.

[0119] Upon receiving the input data, the server first uses an AI algorithm to assess the user's nutritional status. Furthermore, an emotion engine analyzes the user's current emotional state and its impact on the meal plan. Based on this analysis, a meal plan is generated that takes into account both the user's physical and emotional well-being. The plan includes recipes using ingredients available at specific retail stores such as convenience stores, a shopping list, and calorie information.

[0120] The generated meal plan is presented to the user via the device. Here, it's necessary to design the interface to be more user-friendly and easy to understand, based on the user's emotional state. For example, for a user experiencing stress, it might be suggested to recommend recipes using ingredients with relaxing effects or to display encouraging messages.

[0121] After the user experiences the suggested meal plan, they provide feedback to the system. This feedback includes their satisfaction with the suggested plan and its impact on their emotions. The server uses this feedback to further adjust the AI ​​algorithm and plan generation methods, optimizing the next suggestion to be more personalized.

[0122] As a concrete example, suppose a user is experiencing stress at work and wants to maintain a balanced diet but doesn't want to spend a lot of time on it. The user's data is analyzed, and the server suggests recipes such as a simple avocado and chicken salad that include ingredients that can help the user relax. These suggestions are accompanied by messages designed to alleviate the user's stress, providing warm and supportive assistance.

[0123] Thus, the present invention provides an innovative meal management system that not only maintains the user's health but also takes emotional support into consideration.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The user uses the device to input their meal history and physiological data. This data includes specific food names, intake amounts, weight, exercise levels, and fatigue levels. The device also uses its built-in camera to detect the user's facial expressions and acquire emotional data.

[0127] Step 2:

[0128] The device transmits all data collected from the user (eating history, physiological data, emotional data) to the server. This data is collected with the user's consent and while ensuring privacy.

[0129] Step 3:

[0130] The server passes the received data to an AI algorithm, which analyzes the dietary history and physiological data. In particular, it assesses nutritional status and identifies any nutrient deficiencies.

[0131] Step 4:

[0132] The server uses an emotion engine to analyze the user's emotional state from their facial expressions. The analyzed emotional information is then used to adjust the meal plan.

[0133] Step 5:

[0134] Based on the analysis results, the server generates a meal plan optimized for the user. This plan includes recipes using ingredients available at specific stores, a list of ingredients to purchase, and calorie information. If the user's emotional state is negative, the plan is reinforced with specific ingredients or messages.

[0135] Step 6:

[0136] The terminal receives and generates a meal plan from the server, which is then displayed to the user. The display uses a user-friendly interface that is appropriate to the user's emotional state.

[0137] Step 7:

[0138] Users try out suggested meal plans and input feedback about their experience into a device. The feedback evaluates the feasibility, satisfaction, and emotional impact of the plan.

[0139] Step 8:

[0140] The server uses user feedback to adjust its AI algorithms and emotion engine, optimizing future suggestions for better results. This allows the system to continuously provide users with the most suitable meal plans.

[0141] (Example 2)

[0142] 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".

[0143] In modern society, maintaining a healthy lifestyle requires appropriate meal planning tailored to an individual's nutritional and emotional state. However, conventional meal management systems often fail to adequately consider the user's emotional state, leading to decreased user satisfaction and continued use. Furthermore, there is a need for suggestions based on the purchase location of ingredients, utilizing geographical location information to facilitate cooking.

[0144] 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.

[0145] In this invention, the server includes input means for the user to input information on their dietary history and physiological state, analysis means for analyzing the input information and evaluating their nutritional status, and sensing means for recognizing the user's facial expressions and obtaining information on their emotions. This makes it possible to provide a personalized meal plan that takes into account both the user's nutritional status and emotional state.

[0146] A "user" is the entity that uses the system to input their dietary history and physiological data.

[0147] "Meal history" refers to a record of food and drink that the user has consumed in the past.

[0148] "Menstrual information" refers to physical data such as weight, fatigue levels, and exercise levels.

[0149] "Input means" refers to devices or interfaces that allow users to register information into a system.

[0150] "Analysis means" refers to methods for processing input information and evaluating nutritional status and other related data.

[0151] "Nutritional status" refers to data that evaluates the balance of nutrients consumed by the user and their overall health.

[0152] "Sensing means" refers to devices and technologies that collect emotional information from the user's facial expressions, etc.

[0153] "Emotional information" refers to data that indicates the user's psychological and emotional state.

[0154] "Plan generation means" refers to methods and technologies for automatically creating appropriate meal plans based on analysis results and emotional data.

[0155] "Generative AI technology" refers to a method of generating information that meets specific conditions using artificial intelligence technology.

[0156] "An affinity-oriented display format" refers to a method of presenting information in a way that is easy to understand and approachable, depending on the user's emotional state.

[0157] "Feedback information" refers to opinions and data regarding users' satisfaction with the suggested meal plan and their usage results.

[0158] "Adjustment methods" refer to mechanisms for improving the overall system performance and proposed solutions based on feedback data.

[0159] "Geographic location" refers to information that indicates the user's current location.

[0160] "Places of sale" refers to places where users can purchase food ingredients, including supermarkets and convenience stores.

[0161] This invention is an advanced meal management system that combines user health management with emotion recognition. This system operates through the cooperation of three parties: the user, the terminal, and the server. The user first inputs their daily meal history and physiological information into the terminal. Specific physiological data includes weight, fatigue levels, and exercise levels.

[0162] In addition to the above, the device utilizes its built-in camera and sensors to detect the user's facial expressions and collect emotional data. Facial recognition software runs on the device, capturing emotional changes in real time. All of this information is organized by the device and sent to the server. Secure communication protocols such as TLS are used for data transmission, protecting the user's privacy.

[0163] The server uses the received physiological and emotional data to run an AI algorithm that evaluates the user's nutritional and emotional status. This analysis uses data analysis libraries such as Python to quantify nutritional status and derive an ideal nutritional intake pattern. Subsequently, emotional data is analyzed using an emotion engine to analyze how the user's psychological state affects their eating habits.

[0164] Based on the analysis results, the server uses generative AI technology to create a meal plan using ingredients available at specific locations. This meal plan includes recipes, a shopping list of ingredients, and calorie information, taking into account the user's health and emotional state. The generated plan is presented to the user via their device, with the display method tailored to the user's emotional state. For users who want to relax, recipes are presented with psychologically calming designs and messages.

[0165] For example, if a user is experiencing stress at work and wants to maintain a balanced diet but doesn't want to spend a lot of time cooking, the server will suggest a simple avocado and chicken salad recipe. This suggestion will include an encouraging message such as, "This salad has a relaxing effect and can help reduce stress."

[0166] An example of a prompt message given to a generative AI model is: "When the user is feeling stressed, suggest a simple recipe using ingredients that help them relax. The meal plan should include a list of available ingredients and a message to help them relax."

[0167] This system can help users lead more fulfilling lives by providing individually optimized support for both their physical and emotional well-being.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] Users input their daily meal history and physiological information via a terminal. This input data includes details of the food consumed, weight, fatigue levels, and exercise levels. The terminal organizes this information and temporarily stores it in a database. At this stage, the data format is standardized and prepared for subsequent analysis.

[0171] Step 2:

[0172] The device uses its built-in camera and sensors to detect the user's facial expressions and acquires emotional data using facial recognition technology. The input is image data of the user's facial expressions in each frame. This data is processed to calculate the user's emotion score and quantify the user's emotional state.

[0173] Step 3:

[0174] The terminal sends the data obtained in Step 1 and Step 2 to the server. During this process, the data is securely transmitted using encryption protocols such as TLS, ensuring network security.

[0175] Step 4:

[0176] The server analyzes the received physiological and emotional data. First, it uses an AI algorithm to evaluate the user's nutritional status. Dietary history and physiological data are used as data input. This allows for the calculation of nutrient intake balance and the quantification of health status.

[0177] Step 5:

[0178] The server then analyzes emotional data using an emotion engine. This analysis uses NLP techniques to further refine the emotion score and analyze how the user's current psychological state affects their eating habits. Combining these results, the server generates an overall assessment of the user's nutritional and emotional status as output.

[0179] Step 6:

[0180] The server uses generative AI technology to generate meal plans based on the analysis results. The input data consists of nutritional and emotional assessment results, and prompts are used as commands to the generative AI. The output is a meal plan suggestion tailored to the user's specific state, and includes ingredient lists and recipes.

[0181] Step 7:

[0182] The server sends the generated meal plan to the device. The device displays the suggested meal plan and encouraging messages to the user through an interface tailored to the user's emotional state. This allows the user to receive meal suggestions in an easy-to-understand and psychologically reassuring way.

[0183] (Application Example 2)

[0184] 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".

[0185] Conventional meal management systems were limited to nutritional assessments based on the user's physical data, and did not adequately address the user's emotional or psychological aspects. Therefore, it was difficult to provide effective meal plans that considered the balance between the user's mental state and the quality of their meals. Furthermore, they were insufficient in suggesting ingredients that matched the user's geographical location.

[0186] 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.

[0187] In this invention, the server includes means for acquiring user meal history and physiological information, means for analyzing information to evaluate nutritional status based on the input information, means for detecting the user's emotional state and automatically generating an emotionally conscious meal plan, and means for displaying the generated meal plan in a user-friendly manner according to the user's emotional state. This makes it possible to provide personalized meal plans that meet the user's physical and emotional needs.

[0188] "Acquisition means" refers to the operations and devices necessary for users to input their dietary history and physiological information. These means are designed to ensure that information is reliably collected from users.

[0189] "Information analysis means" refers to analytical techniques and algorithms for evaluating a user's nutritional status based on acquired dietary history and physiological information. This means allows for an accurate understanding of the user's health status.

[0190] "Recommendation methods" refer to technologies and methods used to detect a user's emotional state and automatically generate a meal plan that takes those emotions into consideration. This makes it possible to provide suggestions that meet the user's psychological needs.

[0191] "Display means" refers to devices and technologies that present the generated meal plan in a user-friendly manner according to the user's emotional state. This means plays a role in communicating the meal plan to the user in an easy-to-understand manner.

[0192] A "server" refers to a computer system that centrally manages various methods and performs tasks such as information analysis and meal plan generation. The server plays a crucial role in handling the entire process in a unified manner.

[0193] To implement this invention, a system must be built involving three parties: the user, the data acquisition terminal, and the server. The user inputs their daily meal history and physiological information using the data acquisition terminal. The data acquisition terminal has a built-in camera and sensors that detect the user's emotional state by sensing their facial expressions.

[0194] The server receives information transmitted from the acquisition terminal and uses information analysis tools to evaluate the user's nutritional status. The information analysis tools use image processing software such as OpenCV to recognize the user's facial expressions and perform emotional analysis. Based on the emotional state obtained from this analysis, the server uses recommendation tools to generate an individualized meal plan that takes emotions into consideration. AI algorithms are used for generation, employing programs implemented in programming languages ​​such as Python.

[0195] The display system plays the role of presenting the generated meal plan in a way that is adapted to the user's emotions. This presentation uses visual displays and audio output devices. For example, if it is determined that the user is seeking relaxation, the display system will show a recommendation message such as, "How about an avocado and chicken salad today?" and provide detailed instructions on how to prepare it. It can also play relaxing music.

[0196] For example, if a user is experiencing stress, the server will detect this and recommend a menu using vitamin B-rich foods that help reduce stress. An example of a prompt to the generating AI model could be, "What is the best meal plan for a user experiencing fatigue?" In this way, the system of the present invention can respond to the user's physical and emotional needs and provide healthy and personalized support.

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

[0198] Step 1:

[0199] The user uses a device to input their daily meal history and physiological information. The input method utilizes the touchscreen or voice input function of the smart device. The input in this step includes data such as a list of foods, intake amounts, weight, and exercise levels, and the output is raw data stored on the device.

[0200] Step 2:

[0201] The device uses cameras and sensors to capture the user's facial expressions and detect their emotional state. An emotion recognition model analyzes this image data and quantifies the user's current emotional state. Input is in the form of still images or videos, and output is quantified emotion data.

[0202] Step 3:

[0203] The terminal sends the data acquired in Step 1 and Step 2 to the server. After receiving this data, the server uses information analysis tools to comprehensively evaluate the user's nutritional and emotional state. The data analysis is performed using an algorithm, with the input being database-stored user information and the output being the evaluation results.

[0204] Step 4:

[0205] The server uses recommended methods to generate emotionally sensitive meal plans. An AI algorithm designs meal plans based on evaluation results. Specifically, it extracts and synthesizes appropriate menu and ingredient information from relevant databases. The input is the evaluation results from step 3, and the output is a proposed meal plan.

[0206] Step 5:

[0207] The server sends the generated meal plan to the terminal in a user-friendly format, and the terminal displays it. Specific actions include providing information in a visually clear and user-friendly manner, including through audio output. The input is the meal plan, and the output is the visual and auditory presentation to the user.

[0208] Step 6:

[0209] Users provide feedback on their meal plan experience via their device. This feedback includes emotional impact and satisfaction levels. The device collects this data and sends it back to the server. The input is the user's feedback information, and the output is a new dataset received by the server.

[0210] Step 7:

[0211] The server uses feedback to update and adjust its algorithms and recommendation methods, optimizing subsequent suggestions to be more personalized. Specific actions include training the AI ​​model and improving prompt statements. Input is feedback data, and output is the improved suggestion system.

[0212] 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.

[0213] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention is a system that supports users' meal management, specifically enabling users to create healthy and balanced meal plans. This system functions through the coordinated efforts of three entities: the user, the terminal, and the server.

[0229] Users input their daily meal history and physiological data (e.g., foods consumed, time of day, weight, fatigue level, etc.) into a terminal. The terminal collects this information and sends it to a server. The server uses the received information to perform data analysis to evaluate the user's nutritional status. AI algorithms are used in the analysis to determine the user's nutrient deficiencies or excesses and to analyze trends from their meal history.

[0230] Next, the server automatically generates a meal plan tailored to the user using ingredients available at a specific store. This plan includes specific recipes, a list of ingredients to purchase, and calorie information. The generated meal plan is then visually presented to the user via their device.

[0231] Furthermore, this system receives feedback from users and adjusts its analysis and plan generation algorithms based on that information to continuously improve accuracy. For example, if a user provides feedback that a suggested meal plan contains too much food, the next suggestion will be adjusted accordingly.

[0232] As a concrete example, suppose a user is experiencing fatigue and is too busy to cook. When this user inputs their meal history and health data, the server analyzes this data and suggests simple, nutritious recipes that can be prepared in the shortest possible time, such as a "brown rice and bean salad." In this way, it becomes possible to eat efficiently and healthily.

[0233] This system functions as an important tool to improve users' health awareness and support them in maintaining their health in their daily lives.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] Users use their devices to input their meal history and physiological data. This data includes specific food names, amounts consumed, timing of consumption, current weight, and comments about their physical condition.

[0237] Step 2:

[0238] The terminal temporarily stores the data entered by the user and creates a data package for transmission to the server. During this process, it verifies the integrity of the data and checks for any incomplete information.

[0239] Step 3:

[0240] The server receives the data package sent from the terminal and saves it to the database. Simultaneously with saving, it starts analyzing the data via an AI algorithm.

[0241] Step 4:

[0242] The server uses an AI algorithm to assess the user's nutritional status. This includes analyzing which nutrients are deficient or in excess, and understanding their dietary trends. The algorithm also utilizes historical data to improve its accuracy.

[0243] Step 5:

[0244] Based on the analysis results, the server automatically generates a meal plan using ingredients available at a specific store. This plan includes recipes, a list of required ingredients, and calorie information, and is customized to suit the user's needs.

[0245] Step 6:

[0246] The terminal displays the meal plan received from the server to the user. The display uses a visual interface to ensure clarity and intuitive understanding for the user.

[0247] Step 7:

[0248] Users provide feedback on their meal plans. This feedback includes satisfaction levels and areas for improvement, and is taken into consideration when generating future meal plans.

[0249] Step 8:

[0250] The server receives feedback from the user and adjusts the AI ​​algorithm. This optimizes the analysis results and plan generation for subsequent sessions to better match the user's preferences.

[0251] (Example 1)

[0252] 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."

[0253] In modern society, maintaining a healthy and balanced diet is crucial, yet many people find it difficult to manage their nutrition properly amidst their busy daily lives. This challenge, especially when individuals lack the time or knowledge to plan their meals, leads to nutritional imbalances, overconsumption, and deficiencies in essential nutrients. Therefore, there is a need for flexible and efficient meal management systems tailored to the individual circumstances of each user.

[0254] 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.

[0255] In this invention, the server includes information input means, data analysis means using a computing device, and plan creation means. This allows users to easily and effectively receive a nutrition plan tailored to their health condition.

[0256] An "information input means" is a device or interface for users to input data such as the foods they have consumed, the time of their meals, their weight, and their perceived health.

[0257] "Data analysis means by a computing device" refers to a computer program or algorithm used to determine the excess or deficiency of nutrients in the user based on the input information.

[0258] A "plan creation method" refers to a procedure or device for automatically generating a nutrition plan tailored to the user's needs based on the analyzed results.

[0259] "Visual presentation means" refers to a device or tool for displaying a generated plan in a format that is easy for the user to understand.

[0260] An "information correction mechanism" is a system or process for collecting opinions and feedback from users and dynamically improving plans based on them.

[0261] "Geographic location data" refers to information indicating the user's current location and is used to identify the nearest source of materials.

[0262] "Purchasable ingredients" refers to food and nutritional products that are available in the user's region or specific location.

[0263] Modes for carrying out the invention

[0264] This invention is a nutrition management system that supports a healthy diet. This system consists of a terminal where the user inputs data on a daily basis, and a server that analyzes that data and generates a meal plan.

[0265] Users manually input data such as the foods they consume, meal times, weight, and perceived health using their devices. This data is collected through applications running on common mobile devices such as smartphones and tablets.

[0266] The terminal collects information and sends it to the server via the internet. The server performs data analysis using computing devices that utilize programming languages ​​such as Python and R. The analysis uses algorithms that determine the user's nutritional deficiencies or excesses, employing generative AI model frameworks such as TensorFlow and PyTorch.

[0267] Based on this analysis, the server generates an optimal meal plan for the user. This plan incorporates practical recipes and ingredient lists, utilizing the user's geographical location data to select ingredients available in their vicinity. The plan is visually presented on the device screen, making it easy to implement.

[0268] As an example of actual use, if a user inputs that they "recently get tired easily and are too busy to cook much," the system will suggest a nutritionally balanced recipe that can be prepared in a short time, such as "brown rice and bean salad." This suggestion will include specific cooking instructions, purchasing information, and even calorie information.

[0269] As an example of a prompt using a generative AI model, if you input "I've been feeling tired lately, please suggest some easy-to-prepare healthy recipes," the system will immediately analyze the data and provide an appropriate response. In this way, the present invention functions as a flexible meal management system tailored to individual users, supporting the maintenance of healthy lifestyle habits.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] Users input their dietary information and health status into the device. Specifically, they manually record the types of food consumed, meal times, weight, fatigue levels, etc., in the application they are using. The entered data is temporarily stored in a local database. This collects the user's health information and prepares the basic data for the next processing step.

[0273] Step 2:

[0274] The terminal sends the collected data to the server. HTTPS, a secure communication protocol, is used. The input data is transferred to the server via the network, preparing it for analysis. The transmitted data is processed on the remote server while maintaining security.

[0275] Step 3:

[0276] The server performs data analysis based on the received data. The computing unit utilizes a generative AI model to perform calculations to evaluate the user's nutritional balance. This analysis uses frameworks such as TensorFlow and PyTorch to determine the user's nutrient deficiencies or excesses and extract trends in their eating habits. As output, a nutritional assessment tailored to the user's situation is generated.

[0277] Step 4:

[0278] The server uses the analysis results to create a meal plan. This plan lists purchasable ingredients based on information combining the user's geographic location data and market data. Furthermore, a nutritionally balanced recipe combining these ingredients is automatically generated. The nutrition plan is generated as a result of calculations performed by a Python program.

[0279] Step 5:

[0280] The generated nutrition plan is provided to the user via their device. The information is displayed in a visually easy-to-understand interface, showing specific recipes, shopping lists, and required calorie information. This allows users to intuitively understand the content and apply it to their daily meals.

[0281] Step 6:

[0282] The user provides feedback on the presented nutrition plan. Specifically, using the feedback function of the terminal, the user sends opinions such as "the amount of the recipe is large". The feedback information is analyzed by the server and reflected in the computing device for utilization in the next meal plan proposal. The new data analysis including the feedback contributes to the improvement of the output accuracy for the next time.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In modern busy lives, many consumers find it difficult to consume a healthy and balanced diet. To address this problem, a system is needed that enables consumers to easily evaluate their nutritional status, create an optimal meal plan, and quickly obtain that meal. In the current method, the trouble of nutritional management and purchasing is significant, which is actually a burden on consumers. Moreover, the provided meal plans often do not fully meet individual needs.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0287] In this invention, the server includes an information input means for the user to input meal history and physiological data, a data analysis means for analyzing the input information and evaluating the nutritional status, a plan generation means for automatically generating a meal plan using foods available from nearby food service providers based on the analysis result, and an information display means for presenting the generated meal plan to the user and making it immediately orderable. Thereby, the user can quickly select and order an optimal meal according to their health condition.

[0288] The "information input means" refers to a device or software that provides a function for the user to easily input meal history and physiological data.

[0289] "Data analysis tools" refer to algorithms and software designed to evaluate nutritional status based on information entered by users.

[0290] A "plan generation method" refers to a process or system for automatically creating an optimal meal plan based on analysis results.

[0291] "Information display means" refers to devices or software equipped with an interface for visually communicating the generated meal plan to the user.

[0292] "Evaluation information" refers to feedback and opinions provided by users, which the system uses to provide more personalized services.

[0293] "Food and beverage providers" refers to stores and service providers that offer food or meals.

[0294] In this invention, users can input their daily dietary history and physiological data via an information input means using their smartphone or other portable information terminal. After data input, the terminal transmits this information to a server. The server evaluates the user's nutritional status using data analysis means equipped with TensorFlow or similar AI algorithms. Through these analyses, the server clarifies the user's eating habits and determines nutrient deficiencies or excesses as needed.

[0295] Subsequently, the server connects with a food database of the nearest food and beverage providers and creates an optimal meal plan based on the analysis results using a planning generation system. This meal plan includes menus available at partner restaurants and is visually presented to the user by an information display system. Based on the presented plan, the user can immediately order food through the application.

[0296] For example, if a user enters data indicating they "recently feel tired," the server's analysis might determine that they "need a high-protein, low-calorie diet." Based on this result, the server suggests menu items such as "grilled chicken salad" from its database, and the user can then order them. In this way, users can easily choose and consume meals based on their health condition. An example of a prompt message would be, "I've been feeling tired lately; please suggest a high-protein, low-calorie meal plan."

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] Users use a terminal to input their meal history and physiological data. This data includes details of the foods consumed, the time of consumption, weight, and fatigue level. This data is then transmitted to the server via the information input system.

[0300] Step 2:

[0301] The server transmits the received data to the data analysis system. The data analysis system uses a generative AI model to evaluate the user's nutritional status. Based on the input of dietary history and physiological data, it determines the excess or deficiency of nutrients through data calculations and analyzes the trends in nutritional status. It generates the analysis results as output.

[0302] Step 3:

[0303] The server uses a plan generation mechanism to automatically generate an appropriate meal plan based on the analysis results. Input data includes nutritional status assessment results and menu information from local food and beverage providers. The plan generation mechanism retrieves food information available from the nearest food and beverage providers based on the user's geographical information and selects menus that match the analysis results. The output is a meal plan proposed to the user.

[0304] Step 4:

[0305] The server transmits the generated meal plan to the terminal and visually presents it to the user through the information display means. Based on this plan, the user checks the proposed menu and inputs feedback using prompt sentences if necessary. As an example of a prompt sentence, "I'm often tired recently, so please propose a high-protein and low-calorie meal plan."

[0306] Step 5:

[0307] If there is feedback from the user, the server sends the evaluation information to the adjustment means to improve the data analysis means and the plan generation means. Through this process, it becomes possible to provide a better meal plan based on the user's preferences and health condition. Reflect the feedback in the generation AI model to improve the proposals after the next time.

[0308] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0309] The present invention is a system that integrates the user's meal management and emotion recognition, and also considers the emotional state when the user receives a healthy and balanced meal plan. This system operates with the cooperation of three entities: the user, the terminal, and the server.

[0310] The user inputs daily meal history and physiological data through the terminal. The physiological data includes weight, fatigue, amount of exercise, etc. In addition, the terminal also senses the user's expression and obtains emotion data. The terminal organizes this information and transmits it to the server.

[0311] Upon receiving the input data, the server first uses an AI algorithm to assess the user's nutritional status. Furthermore, an emotion engine analyzes the user's current emotional state and its impact on the meal plan. Based on this analysis, a meal plan is generated that takes into account both the user's physical and emotional well-being. The plan includes recipes using ingredients available at specific retail stores such as convenience stores, a shopping list, and calorie information.

[0312] The generated meal plan is presented to the user via the device. Here, it's necessary to design the interface to be more user-friendly and easy to understand, based on the user's emotional state. For example, for a user experiencing stress, it might be suggested to recommend recipes using ingredients with relaxing effects or to display encouraging messages.

[0313] After the user experiences the suggested meal plan, they provide feedback to the system. This feedback includes their satisfaction with the suggested plan and its impact on their emotions. The server uses this feedback to further adjust the AI ​​algorithm and plan generation methods, optimizing the next suggestion to be more personalized.

[0314] As a concrete example, suppose a user is experiencing stress at work and wants to maintain a balanced diet but doesn't want to spend a lot of time on it. The user's data is analyzed, and the server suggests recipes such as a simple avocado and chicken salad that include ingredients that can help the user relax. These suggestions are accompanied by messages designed to alleviate the user's stress, providing warm and supportive assistance.

[0315] Thus, the present invention provides an innovative meal management system that not only maintains the user's health but also takes emotional support into consideration.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The user uses the device to input their meal history and physiological data. This data includes specific food names, intake amounts, weight, exercise levels, and fatigue levels. The device also uses its built-in camera to detect the user's facial expressions and acquire emotional data.

[0319] Step 2:

[0320] The device transmits all data collected from the user (eating history, physiological data, emotional data) to the server. This data is collected with the user's consent and while ensuring privacy.

[0321] Step 3:

[0322] The server passes the received data to an AI algorithm, which analyzes the dietary history and physiological data. In particular, it assesses nutritional status and identifies any nutrient deficiencies.

[0323] Step 4:

[0324] The server uses an emotion engine to analyze the user's emotional state from their facial expressions. The analyzed emotional information is then used to adjust the meal plan.

[0325] Step 5:

[0326] Based on the analysis results, the server generates a meal plan optimized for the user. This plan includes recipes using ingredients available at specific stores, a list of ingredients to purchase, and calorie information. If the user's emotional state is negative, the plan is reinforced with specific ingredients or messages.

[0327] Step 6:

[0328] The terminal receives and generates a meal plan from the server, which is then displayed to the user. The display uses a user-friendly interface that is appropriate to the user's emotional state.

[0329] Step 7:

[0330] Users try out suggested meal plans and input feedback about their experience into a device. The feedback evaluates the feasibility, satisfaction, and emotional impact of the plan.

[0331] Step 8:

[0332] The server uses user feedback to adjust its AI algorithms and emotion engine, optimizing future suggestions for better results. This allows the system to continuously provide users with the most suitable meal plans.

[0333] (Example 2)

[0334] 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".

[0335] In modern society, maintaining a healthy lifestyle requires appropriate meal planning tailored to an individual's nutritional and emotional state. However, conventional meal management systems often fail to adequately consider the user's emotional state, leading to decreased user satisfaction and continued use. Furthermore, there is a need for suggestions based on the purchase location of ingredients, utilizing geographical location information to facilitate cooking.

[0336] 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.

[0337] In this invention, the server includes input means for the user to input information on their dietary history and physiological state, analysis means for analyzing the input information and evaluating their nutritional status, and sensing means for recognizing the user's facial expressions and obtaining information on their emotions. This makes it possible to provide a personalized meal plan that takes into account both the user's nutritional status and emotional state.

[0338] A "user" is the entity that uses the system to input their dietary history and physiological data.

[0339] "Meal history" refers to a record of food and drink that the user has consumed in the past.

[0340] "Menstrual information" refers to physical data such as weight, fatigue levels, and exercise levels.

[0341] "Input means" refers to devices or interfaces that allow users to register information into a system.

[0342] "Analysis means" refers to methods for processing input information and evaluating nutritional status and other related data.

[0343] "Nutritional status" refers to data that evaluates the balance of nutrients consumed by the user and their overall health.

[0344] "Sensing means" refers to devices and technologies that collect emotional information from the user's facial expressions, etc.

[0345] "Emotional information" refers to data that indicates the user's psychological and emotional state.

[0346] "Plan generation means" refers to methods and technologies for automatically creating appropriate meal plans based on analysis results and emotional data.

[0347] "Generative AI technology" refers to a method of generating information that meets specific conditions using artificial intelligence technology.

[0348] "An affinity-oriented display format" refers to a method of presenting information in a way that is easy to understand and approachable, depending on the user's emotional state.

[0349] "Feedback information" refers to opinions and data regarding users' satisfaction with the suggested meal plan and their usage results.

[0350] "Adjustment methods" refer to mechanisms for improving the overall system performance and proposed solutions based on feedback data.

[0351] "Geographic location" refers to information that indicates the user's current location.

[0352] "Places of sale" refers to places where users can purchase food ingredients, including supermarkets and convenience stores.

[0353] This invention is an advanced meal management system that combines user health management with emotion recognition. This system operates through the cooperation of three parties: the user, the terminal, and the server. The user first inputs their daily meal history and physiological information into the terminal. Specific physiological data includes weight, fatigue levels, and exercise levels.

[0354] In addition to the above, the device utilizes its built-in camera and sensors to detect the user's facial expressions and collect emotional data. Facial recognition software runs on the device, capturing emotional changes in real time. All of this information is organized by the device and sent to the server. Secure communication protocols such as TLS are used for data transmission, protecting the user's privacy.

[0355] The server uses the received physiological and emotional data to run an AI algorithm that evaluates the user's nutritional and emotional status. This analysis uses data analysis libraries such as Python to quantify nutritional status and derive an ideal nutritional intake pattern. Subsequently, emotional data is analyzed using an emotion engine to analyze how the user's psychological state affects their eating habits.

[0356] Based on the analysis results, the server uses generative AI technology to create a meal plan using ingredients available at specific locations. This meal plan includes recipes, a shopping list of ingredients, and calorie information, taking into account the user's health and emotional state. The generated plan is presented to the user via their device, with the display method tailored to the user's emotional state. For users who want to relax, recipes are presented with psychologically calming designs and messages.

[0357] For example, if a user is experiencing stress at work and wants to maintain a balanced diet but doesn't want to spend a lot of time cooking, the server will suggest a simple avocado and chicken salad recipe. This suggestion will include an encouraging message such as, "This salad has a relaxing effect and can help reduce stress."

[0358] An example of a prompt message given to a generative AI model is: "When the user is feeling stressed, suggest a simple recipe using ingredients that help them relax. The meal plan should include a list of available ingredients and a message to help them relax."

[0359] This system can help users lead more fulfilling lives by providing individually optimized support for both their physical and emotional well-being.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] Users input their daily meal history and physiological information via a terminal. This input data includes details of the food consumed, weight, fatigue levels, and exercise levels. The terminal organizes this information and temporarily stores it in a database. At this stage, the data format is standardized and prepared for subsequent analysis.

[0363] Step 2:

[0364] The device uses its built-in camera and sensors to detect the user's facial expressions and acquires emotional data using facial recognition technology. The input is image data of the user's facial expressions in each frame. This data is processed to calculate the user's emotion score and quantify the user's emotional state.

[0365] Step 3:

[0366] The terminal sends the data obtained in Step 1 and Step 2 to the server. During this process, the data is securely transmitted using encryption protocols such as TLS, ensuring network security.

[0367] Step 4:

[0368] The server analyzes the received physiological and emotional data. First, it uses an AI algorithm to evaluate the user's nutritional status. Dietary history and physiological data are used as data input. This allows for the calculation of nutrient intake balance and the quantification of health status.

[0369] Step 5:

[0370] The server then analyzes emotional data using an emotion engine. This analysis uses NLP techniques to further refine the emotion score and analyze how the user's current psychological state affects their eating habits. Combining these results, the server generates an overall assessment of the user's nutritional and emotional status as output.

[0371] Step 6:

[0372] The server uses generative AI technology to generate meal plans based on the analysis results. The input data consists of nutritional and emotional assessment results, and prompts are used as commands to the generative AI. The output is a meal plan suggestion tailored to the user's specific state, and includes ingredient lists and recipes.

[0373] Step 7:

[0374] The server sends the generated meal plan to the device. The device displays the suggested meal plan and encouraging messages to the user through an interface tailored to the user's emotional state. This allows the user to receive meal suggestions in an easy-to-understand and psychologically reassuring way.

[0375] (Application Example 2)

[0376] 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."

[0377] Conventional meal management systems were limited to nutritional assessments based on the user's physical data, and did not adequately address the user's emotional or psychological aspects. Therefore, it was difficult to provide effective meal plans that considered the balance between the user's mental state and the quality of their meals. Furthermore, they were insufficient in suggesting ingredients that matched the user's geographical location.

[0378] 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.

[0379] In this invention, the server includes means for acquiring user meal history and physiological information, means for analyzing information to evaluate nutritional status based on the input information, means for detecting the user's emotional state and automatically generating an emotionally conscious meal plan, and means for displaying the generated meal plan in a user-friendly manner according to the user's emotional state. This makes it possible to provide personalized meal plans that meet the user's physical and emotional needs.

[0380] "Acquisition means" refers to the operations and devices necessary for users to input their dietary history and physiological information. These means are designed to ensure that information is reliably collected from users.

[0381] "Information analysis means" refers to analytical techniques and algorithms for evaluating a user's nutritional status based on acquired dietary history and physiological information. This means allows for an accurate understanding of the user's health status.

[0382] "Recommendation methods" refer to technologies and methods used to detect a user's emotional state and automatically generate a meal plan that takes those emotions into consideration. This makes it possible to provide suggestions that meet the user's psychological needs.

[0383] "Display means" refers to devices and technologies that present the generated meal plan in a user-friendly manner according to the user's emotional state. This means plays a role in communicating the meal plan to the user in an easy-to-understand manner.

[0384] A "server" refers to a computer system that centrally manages various methods and performs tasks such as information analysis and meal plan generation. The server plays a crucial role in handling the entire process in a unified manner.

[0385] To implement this invention, a system must be built involving three parties: the user, the data acquisition terminal, and the server. The user inputs their daily meal history and physiological information using the data acquisition terminal. The data acquisition terminal has a built-in camera and sensors that detect the user's emotional state by sensing their facial expressions.

[0386] The server receives information transmitted from the acquisition terminal and uses information analysis tools to evaluate the user's nutritional status. The information analysis tools use image processing software such as OpenCV to recognize the user's facial expressions and perform emotional analysis. Based on the emotional state obtained from this analysis, the server uses recommendation tools to generate an individualized meal plan that takes emotions into consideration. AI algorithms are used for generation, employing programs implemented in programming languages ​​such as Python.

[0387] The display system plays the role of presenting the generated meal plan in a way that is adapted to the user's emotions. This presentation uses visual displays and audio output devices. For example, if it is determined that the user is seeking relaxation, the display system will show a recommendation message such as, "How about an avocado and chicken salad today?" and provide detailed instructions on how to prepare it. It can also play relaxing music.

[0388] For example, if a user is experiencing stress, the server will detect this and recommend a menu using vitamin B-rich foods that help reduce stress. An example of a prompt to the generating AI model could be, "What is the best meal plan for a user experiencing fatigue?" In this way, the system of the present invention can respond to the user's physical and emotional needs and provide healthy and personalized support.

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The user uses a device to input their daily meal history and physiological information. The input method utilizes the touchscreen or voice input function of the smart device. The input in this step includes data such as a list of foods, intake amounts, weight, and exercise levels, and the output is raw data stored on the device.

[0392] Step 2:

[0393] The device uses cameras and sensors to capture the user's facial expressions and detect their emotional state. An emotion recognition model analyzes this image data and quantifies the user's current emotional state. Input is in the form of still images or videos, and output is quantified emotion data.

[0394] Step 3:

[0395] The terminal sends the data acquired in Step 1 and Step 2 to the server. After receiving this data, the server uses information analysis tools to comprehensively evaluate the user's nutritional and emotional state. The data analysis is performed using an algorithm, with the input being database-stored user information and the output being the evaluation results.

[0396] Step 4:

[0397] The server uses recommended methods to generate emotionally sensitive meal plans. An AI algorithm designs meal plans based on evaluation results. Specifically, it extracts and synthesizes appropriate menu and ingredient information from relevant databases. The input is the evaluation results from step 3, and the output is a proposed meal plan.

[0398] Step 5:

[0399] The server sends the generated meal plan to the terminal in a user-friendly format, and the terminal displays it. Specific actions include providing information in a visually clear and user-friendly manner, including through audio output. The input is the meal plan, and the output is the visual and auditory presentation to the user.

[0400] Step 6:

[0401] Users provide feedback on their meal plan experience via their device. This feedback includes emotional impact and satisfaction levels. The device collects this data and sends it back to the server. The input is the user's feedback information, and the output is a new dataset received by the server.

[0402] Step 7:

[0403] The server uses feedback to update and adjust its algorithms and recommendation methods, optimizing subsequent suggestions to be more personalized. Specific actions include training the AI ​​model and improving prompt statements. Input is feedback data, and output is the improved suggestion system.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] [Third Embodiment]

[0408] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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".

[0420] This invention is a system that supports users' meal management, specifically enabling users to create healthy and balanced meal plans. This system functions through the coordinated efforts of three entities: the user, the terminal, and the server.

[0421] Users input their daily meal history and physiological data (e.g., foods consumed, time of day, weight, fatigue level, etc.) into a terminal. The terminal collects this information and sends it to a server. The server uses the received information to perform data analysis to evaluate the user's nutritional status. AI algorithms are used in the analysis to determine the user's nutrient deficiencies or excesses and to analyze trends from their meal history.

[0422] Next, the server automatically generates a meal plan tailored to the user using ingredients available at a specific store. This plan includes specific recipes, a list of ingredients to purchase, and calorie information. The generated meal plan is then visually presented to the user via their device.

[0423] Furthermore, this system receives feedback from users and adjusts its analysis and plan generation algorithms based on that information to continuously improve accuracy. For example, if a user provides feedback that a suggested meal plan contains too much food, the next suggestion will be adjusted accordingly.

[0424] As a concrete example, suppose a user is experiencing fatigue and is too busy to cook. When this user inputs their meal history and health data, the server analyzes this data and suggests simple, nutritious recipes that can be prepared in the shortest possible time, such as a "brown rice and bean salad." In this way, it becomes possible to eat efficiently and healthily.

[0425] This system functions as an important tool to improve users' health awareness and support them in maintaining their health in their daily lives.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] Users use their devices to input their meal history and physiological data. This data includes specific food names, amounts consumed, timing of consumption, current weight, and comments about their physical condition.

[0429] Step 2:

[0430] The terminal temporarily stores the data entered by the user and creates a data package for transmission to the server. During this process, it verifies the integrity of the data and checks for any incomplete information.

[0431] Step 3:

[0432] The server receives the data package sent from the terminal and saves it to the database. Simultaneously with saving, it starts analyzing the data via an AI algorithm.

[0433] Step 4:

[0434] The server uses an AI algorithm to assess the user's nutritional status. This includes analyzing which nutrients are deficient or in excess, and understanding their dietary trends. The algorithm also utilizes historical data to improve its accuracy.

[0435] Step 5:

[0436] Based on the analysis results, the server automatically generates a meal plan using ingredients available at a specific store. This plan includes recipes, a list of required ingredients, and calorie information, and is customized to suit the user's needs.

[0437] Step 6:

[0438] The terminal displays the meal plan received from the server to the user. The display uses a visual interface to ensure clarity and intuitive understanding for the user.

[0439] Step 7:

[0440] Users provide feedback on their meal plans. This feedback includes satisfaction levels and areas for improvement, and is taken into consideration when generating future meal plans.

[0441] Step 8:

[0442] The server receives feedback from the user and adjusts the AI ​​algorithm. This optimizes the analysis results and plan generation for subsequent sessions to better match the user's preferences.

[0443] (Example 1)

[0444] 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."

[0445] In modern society, maintaining a healthy and balanced diet is crucial, yet many people find it difficult to manage their nutrition properly amidst their busy daily lives. This challenge, especially when individuals lack the time or knowledge to plan their meals, leads to nutritional imbalances, overconsumption, and deficiencies in essential nutrients. Therefore, there is a need for flexible and efficient meal management systems tailored to the individual circumstances of each user.

[0446] 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.

[0447] In this invention, the server includes information input means, data analysis means using a computing device, and plan creation means. This allows users to easily and effectively receive a nutrition plan tailored to their health condition.

[0448] An "information input means" is a device or interface for users to input data such as the foods they have consumed, the time of their meals, their weight, and their perceived health.

[0449] "Data analysis means by a computing device" refers to a computer program or algorithm used to determine the excess or deficiency of nutrients in the user based on the input information.

[0450] A "plan creation method" refers to a procedure or device for automatically generating a nutrition plan tailored to the user's needs based on the analyzed results.

[0451] "Visual presentation means" refers to a device or tool for displaying a generated plan in a format that is easy for the user to understand.

[0452] An "information correction mechanism" is a system or process for collecting opinions and feedback from users and dynamically improving plans based on them.

[0453] "Geographic location data" refers to information indicating the user's current location and is used to identify the nearest source of materials.

[0454] "Purchasable ingredients" refers to food and nutritional products that are available in the user's region or specific location.

[0455] Modes for carrying out the invention

[0456] This invention is a nutrition management system that supports a healthy diet. This system consists of a terminal where the user inputs data on a daily basis, and a server that analyzes that data and generates a meal plan.

[0457] Users manually input data such as the foods they consume, meal times, weight, and perceived health using their devices. This data is collected through applications running on common mobile devices such as smartphones and tablets.

[0458] The terminal collects information and sends it to the server via the internet. The server performs data analysis using computing devices that utilize programming languages ​​such as Python and R. The analysis uses algorithms that determine the user's nutritional deficiencies or excesses, employing generative AI model frameworks such as TensorFlow and PyTorch.

[0459] Based on this analysis, the server generates an optimal meal plan for the user. This plan incorporates practical recipes and ingredient lists, utilizing the user's geographical location data to select ingredients available in their vicinity. The plan is visually presented on the device screen, making it easy to implement.

[0460] As an example of actual use, if a user inputs that they "recently get tired easily and are too busy to cook much," the system will suggest a nutritionally balanced recipe that can be prepared in a short time, such as "brown rice and bean salad." This suggestion will include specific cooking instructions, purchasing information, and even calorie information.

[0461] As an example of a prompt using a generative AI model, if you input "I've been feeling tired lately, please suggest some easy-to-prepare healthy recipes," the system will immediately analyze the data and provide an appropriate response. In this way, the present invention functions as a flexible meal management system tailored to individual users, supporting the maintenance of healthy lifestyle habits.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] Users input their dietary information and health status into the device. Specifically, they manually record the types of food consumed, meal times, weight, fatigue levels, etc., in the application they are using. The entered data is temporarily stored in a local database. This collects the user's health information and prepares the basic data for the next processing step.

[0465] Step 2:

[0466] The terminal sends the collected data to the server. HTTPS, a secure communication protocol, is used. The input data is transferred to the server via the network, preparing it for analysis. The transmitted data is processed on the remote server while maintaining security.

[0467] Step 3:

[0468] The server performs data analysis based on the received data. The computing unit utilizes a generative AI model to perform calculations to evaluate the user's nutritional balance. This analysis uses frameworks such as TensorFlow and PyTorch to determine the user's nutrient deficiencies or excesses and extract trends in their eating habits. As output, a nutritional assessment tailored to the user's situation is generated.

[0469] Step 4:

[0470] The server uses the analysis results to create a meal plan. This plan lists purchasable ingredients based on information combining the user's geographic location data and market data. Furthermore, a nutritionally balanced recipe combining these ingredients is automatically generated. The nutrition plan is generated as a result of calculations performed by a Python program.

[0471] Step 5:

[0472] The generated nutrition plan is provided to the user via their device. The information is displayed in a visually easy-to-understand interface, showing specific recipes, shopping lists, and required calorie information. This allows users to intuitively understand the content and apply it to their daily meals.

[0473] Step 6:

[0474] Users provide feedback on the presented nutrition plan. Specifically, they use the feedback function on their device to send comments such as "the portion size in the recipe is too large." This feedback information is analyzed on the server and reflected in the processing unit to be used in suggesting the next meal plan. New data analysis, including the feedback, contributes to improving the accuracy of the next output.

[0475] (Application Example 1)

[0476] 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."

[0477] In today's busy lifestyle, many consumers find it difficult to maintain a healthy and balanced diet. To address this problem, a system is needed that allows consumers to easily assess their nutritional status, create optimal meal plans, and quickly access those meals. Currently, the process of nutritional management and purchasing is time-consuming and burdensome for consumers. Furthermore, the meal plans offered often do not perfectly match individual needs.

[0478] 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.

[0479] In this invention, the server includes information input means for the user to input meal history and physiological data; data analysis means for analyzing the input information and evaluating the nutritional status; plan generation means for automatically generating a meal plan using foods available from the nearest food service provider based on the analysis results; and information display means for presenting the generated meal plan to the user and enabling immediate ordering. This allows the user to quickly select and order the optimal meal according to their health condition.

[0480] "Information input means" refers to devices or software that provide functions that allow users to easily input their dietary history and physiological data.

[0481] "Data analysis tools" refer to algorithms and software designed to evaluate nutritional status based on information entered by users.

[0482] A "plan generation method" refers to a process or system for automatically creating an optimal meal plan based on analysis results.

[0483] "Information display means" refers to devices or software equipped with an interface for visually communicating the generated meal plan to the user.

[0484] "Evaluation information" refers to feedback and opinions provided by users, which the system uses to provide more personalized services.

[0485] "Food and beverage providers" refers to stores and service providers that offer food or meals.

[0486] In this invention, users can input their daily dietary history and physiological data via an information input means using their smartphone or other portable information terminal. After data input, the terminal transmits this information to a server. The server evaluates the user's nutritional status using data analysis means equipped with TensorFlow or similar AI algorithms. Through these analyses, the server clarifies the user's eating habits and determines nutrient deficiencies or excesses as needed.

[0487] Subsequently, the server connects with a food database of the nearest food and beverage providers and creates an optimal meal plan based on the analysis results using a planning generation system. This meal plan includes menus available at partner restaurants and is visually presented to the user by an information display system. Based on the presented plan, the user can immediately order food through the application.

[0488] For example, if a user enters data indicating they "recently feel tired," the server's analysis might determine that they "need a high-protein, low-calorie diet." Based on this result, the server suggests menu items such as "grilled chicken salad" from its database, and the user can then order them. In this way, users can easily choose and consume meals based on their health condition. An example of a prompt message would be, "I've been feeling tired lately; please suggest a high-protein, low-calorie meal plan."

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] Users use a terminal to input their meal history and physiological data. This data includes details of the foods consumed, the time of consumption, weight, and fatigue level. This data is then transmitted to the server via the information input system.

[0492] Step 2:

[0493] The server transmits the received data to the data analysis system. The data analysis system uses a generative AI model to evaluate the user's nutritional status. Based on the input of dietary history and physiological data, it determines the excess or deficiency of nutrients through data calculations and analyzes the trends in nutritional status. It generates the analysis results as output.

[0494] Step 3:

[0495] The server uses a plan generation mechanism to automatically generate an appropriate meal plan based on the analysis results. Input data includes nutritional status assessment results and menu information from local food and beverage providers. The plan generation mechanism retrieves food information available from the nearest food and beverage providers based on the user's geographical information and selects menus that match the analysis results. The output is a meal plan proposed to the user.

[0496] Step 4:

[0497] The server sends the generated meal plan to the terminal and presents it visually to the user through an information display. Based on this plan, the user reviews the suggested menu and provides feedback using prompts as needed. An example of a prompt would be, "I've been feeling tired lately, please suggest a high-protein, low-calorie meal plan."

[0498] Step 5:

[0499] When user feedback is received, the server sends the evaluation information to the adjustment mechanism, which then improves the data analysis and plan generation mechanisms. This process enables the provision of better meal plans based on the user's preferences and health status. The feedback is then incorporated into the generating AI model to improve future suggestions.

[0500] 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.

[0501] This invention is a system that integrates user meal management and emotional recognition, taking into account the user's emotional state when they receive a healthy and balanced meal plan. This system operates through the coordinated efforts of three entities: the user, the terminal, and the server.

[0502] Users input their daily meal history and physiological data through the device. Physiological data includes weight, fatigue levels, and exercise levels. The device also senses the user's facial expressions and acquires emotional data. The device then organizes this information and sends it to the server.

[0503] Upon receiving the input data, the server first uses an AI algorithm to assess the user's nutritional status. Furthermore, an emotion engine analyzes the user's current emotional state and its impact on the meal plan. Based on this analysis, a meal plan is generated that takes into account both the user's physical and emotional well-being. The plan includes recipes using ingredients available at specific retail stores such as convenience stores, a shopping list, and calorie information.

[0504] The generated meal plan is presented to the user via the device. Here, it's necessary to design the interface to be more user-friendly and easy to understand, based on the user's emotional state. For example, for a user experiencing stress, it might be suggested to recommend recipes using ingredients with relaxing effects or to display encouraging messages.

[0505] After the user experiences the suggested meal plan, they provide feedback to the system. This feedback includes their satisfaction with the suggested plan and its impact on their emotions. The server uses this feedback to further adjust the AI ​​algorithm and plan generation methods, optimizing the next suggestion to be more personalized.

[0506] As a concrete example, suppose a user is experiencing stress at work and wants to maintain a balanced diet but doesn't want to spend a lot of time on it. The user's data is analyzed, and the server suggests recipes such as a simple avocado and chicken salad that include ingredients that can help the user relax. These suggestions are accompanied by messages designed to alleviate the user's stress, providing warm and supportive assistance.

[0507] Thus, the present invention provides an innovative meal management system that not only maintains the user's health but also takes emotional support into consideration.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The user uses the device to input their meal history and physiological data. This data includes specific food names, intake amounts, weight, exercise levels, and fatigue levels. The device also uses its built-in camera to detect the user's facial expressions and acquire emotional data.

[0511] Step 2:

[0512] The device transmits all data collected from the user (eating history, physiological data, emotional data) to the server. This data is collected with the user's consent and while ensuring privacy.

[0513] Step 3:

[0514] The server passes the received data to an AI algorithm, which analyzes the dietary history and physiological data. In particular, it assesses nutritional status and identifies any nutrient deficiencies.

[0515] Step 4:

[0516] The server uses an emotion engine to analyze the user's emotional state from their facial expressions. The analyzed emotional information is then used to adjust the meal plan.

[0517] Step 5:

[0518] Based on the analysis results, the server generates a meal plan optimized for the user. This plan includes recipes using ingredients available at specific stores, a list of ingredients to purchase, and calorie information. If the user's emotional state is negative, the plan is reinforced with specific ingredients or messages.

[0519] Step 6:

[0520] The terminal receives and generates a meal plan from the server, which is then displayed to the user. The display uses a user-friendly interface that is appropriate to the user's emotional state.

[0521] Step 7:

[0522] Users try out suggested meal plans and input feedback about their experience into a device. The feedback evaluates the feasibility, satisfaction, and emotional impact of the plan.

[0523] Step 8:

[0524] The server uses user feedback to adjust its AI algorithms and emotion engine, optimizing future suggestions for better results. This allows the system to continuously provide users with the most suitable meal plans.

[0525] (Example 2)

[0526] 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."

[0527] In modern society, maintaining a healthy lifestyle requires appropriate meal planning tailored to an individual's nutritional and emotional state. However, conventional meal management systems often fail to adequately consider the user's emotional state, leading to decreased user satisfaction and continued use. Furthermore, there is a need for suggestions based on the purchase location of ingredients, utilizing geographical location information to facilitate cooking.

[0528] 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.

[0529] In this invention, the server includes input means for the user to input information on their dietary history and physiological state, analysis means for analyzing the input information and evaluating their nutritional status, and sensing means for recognizing the user's facial expressions and obtaining information on their emotions. This makes it possible to provide a personalized meal plan that takes into account both the user's nutritional status and emotional state.

[0530] A "user" is the entity that uses the system to input their dietary history and physiological data.

[0531] "Meal history" refers to a record of food and drink that the user has consumed in the past.

[0532] "Menstrual information" refers to physical data such as weight, fatigue levels, and exercise levels.

[0533] "Input means" refers to devices or interfaces that allow users to register information into a system.

[0534] "Analysis means" refers to methods for processing input information and evaluating nutritional status and other related data.

[0535] "Nutritional status" refers to data that evaluates the balance of nutrients consumed by the user and their overall health.

[0536] "Sensing means" refers to devices and technologies that collect emotional information from the user's facial expressions, etc.

[0537] "Emotional information" refers to data that indicates the user's psychological and emotional state.

[0538] "Plan generation means" refers to methods and technologies for automatically creating appropriate meal plans based on analysis results and emotional data.

[0539] "Generative AI technology" refers to a method of generating information that meets specific conditions using artificial intelligence technology.

[0540] "An affinity-oriented display format" refers to a method of presenting information in a way that is easy to understand and approachable, depending on the user's emotional state.

[0541] "Feedback information" refers to opinions and data regarding users' satisfaction with the suggested meal plan and their usage results.

[0542] "Adjustment methods" refer to mechanisms for improving the overall system performance and proposed solutions based on feedback data.

[0543] "Geographic location" refers to information that indicates the user's current location.

[0544] "Places of sale" refers to places where users can purchase food ingredients, including supermarkets and convenience stores.

[0545] This invention is an advanced meal management system that combines user health management with emotion recognition. This system operates through the cooperation of three parties: the user, the terminal, and the server. The user first inputs their daily meal history and physiological information into the terminal. Specific physiological data includes weight, fatigue levels, and exercise levels.

[0546] In addition to the above, the device utilizes its built-in camera and sensors to detect the user's facial expressions and collect emotional data. Facial recognition software runs on the device, capturing emotional changes in real time. All of this information is organized by the device and sent to the server. Secure communication protocols such as TLS are used for data transmission, protecting the user's privacy.

[0547] The server uses the received physiological and emotional data to run an AI algorithm that evaluates the user's nutritional and emotional status. This analysis uses data analysis libraries such as Python to quantify nutritional status and derive an ideal nutritional intake pattern. Subsequently, emotional data is analyzed using an emotion engine to analyze how the user's psychological state affects their eating habits.

[0548] Based on the analysis results, the server uses generative AI technology to create a meal plan using ingredients available at specific locations. This meal plan includes recipes, a shopping list of ingredients, and calorie information, taking into account the user's health and emotional state. The generated plan is presented to the user via their device, with the display method tailored to the user's emotional state. For users who want to relax, recipes are presented with psychologically calming designs and messages.

[0549] For example, if a user is experiencing stress at work and wants to maintain a balanced diet but doesn't want to spend a lot of time cooking, the server will suggest a simple avocado and chicken salad recipe. This suggestion will include an encouraging message such as, "This salad has a relaxing effect and can help reduce stress."

[0550] An example of a prompt message given to a generative AI model is: "When the user is feeling stressed, suggest a simple recipe using ingredients that help them relax. The meal plan should include a list of available ingredients and a message to help them relax."

[0551] This system can help users lead more fulfilling lives by providing individually optimized support for both their physical and emotional well-being.

[0552] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0553] Step 1:

[0554] Users input their daily meal history and physiological information via a terminal. This input data includes details of the food consumed, weight, fatigue levels, and exercise levels. The terminal organizes this information and temporarily stores it in a database. At this stage, the data format is standardized and prepared for subsequent analysis.

[0555] Step 2:

[0556] The device uses its built-in camera and sensors to detect the user's facial expressions and acquires emotional data using facial recognition technology. The input is image data of the user's facial expressions in each frame. This data is processed to calculate the user's emotion score and quantify the user's emotional state.

[0557] Step 3:

[0558] The terminal sends the data obtained in Step 1 and Step 2 to the server. During this process, the data is securely transmitted using encryption protocols such as TLS, ensuring network security.

[0559] Step 4:

[0560] The server analyzes the received physiological and emotional data. First, it uses an AI algorithm to evaluate the user's nutritional status. Dietary history and physiological data are used as data input. This allows for the calculation of nutrient intake balance and the quantification of health status.

[0561] Step 5:

[0562] The server then analyzes emotional data using an emotion engine. This analysis uses NLP techniques to further refine the emotion score and analyze how the user's current psychological state affects their eating habits. Combining these results, the server generates an overall assessment of the user's nutritional and emotional status as output.

[0563] Step 6:

[0564] The server uses generative AI technology to generate meal plans based on the analysis results. The input data consists of nutritional and emotional assessment results, and prompts are used as commands to the generative AI. The output is a meal plan suggestion tailored to the user's specific state, and includes ingredient lists and recipes.

[0565] Step 7:

[0566] The server sends the generated meal plan to the device. The device displays the suggested meal plan and encouraging messages to the user through an interface tailored to the user's emotional state. This allows the user to receive meal suggestions in an easy-to-understand and psychologically reassuring way.

[0567] (Application Example 2)

[0568] 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."

[0569] Conventional meal management systems were limited to nutritional assessments based on the user's physical data, and did not adequately address the user's emotional or psychological aspects. Therefore, it was difficult to provide effective meal plans that considered the balance between the user's mental state and the quality of their meals. Furthermore, they were insufficient in suggesting ingredients that matched the user's geographical location.

[0570] 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.

[0571] In this invention, the server includes means for acquiring user meal history and physiological information, means for analyzing information to evaluate nutritional status based on the input information, means for detecting the user's emotional state and automatically generating an emotionally conscious meal plan, and means for displaying the generated meal plan in a user-friendly manner according to the user's emotional state. This makes it possible to provide personalized meal plans that meet the user's physical and emotional needs.

[0572] "Acquisition means" refers to the operations and devices necessary for users to input their dietary history and physiological information. These means are designed to ensure that information is reliably collected from users.

[0573] "Information analysis means" refers to analytical techniques and algorithms for evaluating a user's nutritional status based on acquired dietary history and physiological information. This means allows for an accurate understanding of the user's health status.

[0574] "Recommendation methods" refer to technologies and methods used to detect a user's emotional state and automatically generate a meal plan that takes those emotions into consideration. This makes it possible to provide suggestions that meet the user's psychological needs.

[0575] "Display means" refers to devices and technologies that present the generated meal plan in a user-friendly manner according to the user's emotional state. This means plays a role in communicating the meal plan to the user in an easy-to-understand manner.

[0576] A "server" refers to a computer system that centrally manages various methods and performs tasks such as information analysis and meal plan generation. The server plays a crucial role in handling the entire process in a unified manner.

[0577] To implement this invention, a system must be built involving three parties: the user, the data acquisition terminal, and the server. The user inputs their daily meal history and physiological information using the data acquisition terminal. The data acquisition terminal has a built-in camera and sensors that detect the user's emotional state by sensing their facial expressions.

[0578] The server receives information transmitted from the acquisition terminal and uses information analysis tools to evaluate the user's nutritional status. The information analysis tools use image processing software such as OpenCV to recognize the user's facial expressions and perform emotional analysis. Based on the emotional state obtained from this analysis, the server uses recommendation tools to generate an individualized meal plan that takes emotions into consideration. AI algorithms are used for generation, employing programs implemented in programming languages ​​such as Python.

[0579] The display system plays the role of presenting the generated meal plan in a way that is adapted to the user's emotions. This presentation uses visual displays and audio output devices. For example, if it is determined that the user is seeking relaxation, the display system will show a recommendation message such as, "How about an avocado and chicken salad today?" and provide detailed instructions on how to prepare it. It can also play relaxing music.

[0580] For example, if a user is experiencing stress, the server will detect this and recommend a menu using vitamin B-rich foods that help reduce stress. An example of a prompt to the generating AI model could be, "What is the best meal plan for a user experiencing fatigue?" In this way, the system of the present invention can respond to the user's physical and emotional needs and provide healthy and personalized support.

[0581] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0582] Step 1:

[0583] The user uses a device to input their daily meal history and physiological information. The input method utilizes the touchscreen or voice input function of the smart device. The input in this step includes data such as a list of foods, intake amounts, weight, and exercise levels, and the output is raw data stored on the device.

[0584] Step 2:

[0585] The device uses cameras and sensors to capture the user's facial expressions and detect their emotional state. An emotion recognition model analyzes this image data and quantifies the user's current emotional state. Input is in the form of still images or videos, and output is quantified emotion data.

[0586] Step 3:

[0587] The terminal sends the data acquired in Step 1 and Step 2 to the server. After receiving this data, the server uses information analysis tools to comprehensively evaluate the user's nutritional and emotional state. The data analysis is performed using an algorithm, with the input being database-stored user information and the output being the evaluation results.

[0588] Step 4:

[0589] The server uses recommended methods to generate emotionally sensitive meal plans. An AI algorithm designs meal plans based on evaluation results. Specifically, it extracts and synthesizes appropriate menu and ingredient information from relevant databases. The input is the evaluation results from step 3, and the output is a proposed meal plan.

[0590] Step 5:

[0591] The server sends the generated meal plan to the terminal in a user-friendly format, and the terminal displays it. Specific actions include providing information in a visually clear and user-friendly manner, including through audio output. The input is the meal plan, and the output is the visual and auditory presentation to the user.

[0592] Step 6:

[0593] Users provide feedback on their meal plan experience via their device. This feedback includes emotional impact and satisfaction levels. The device collects this data and sends it back to the server. The input is the user's feedback information, and the output is a new dataset received by the server.

[0594] Step 7:

[0595] The server uses feedback to update and adjust its algorithms and recommendation methods, optimizing subsequent suggestions to be more personalized. Specific actions include training the AI ​​model and improving prompt statements. Input is feedback data, and output is the improved suggestion system.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] [Fourth Embodiment]

[0600] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0601] 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.

[0602] 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).

[0603] 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.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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".

[0613] This invention is a system that supports users' meal management, specifically enabling users to create healthy and balanced meal plans. This system functions through the coordinated efforts of three entities: the user, the terminal, and the server.

[0614] Users input their daily meal history and physiological data (e.g., foods consumed, time of day, weight, fatigue level, etc.) into a terminal. The terminal collects this information and sends it to a server. The server uses the received information to perform data analysis to evaluate the user's nutritional status. AI algorithms are used in the analysis to determine the user's nutrient deficiencies or excesses and to analyze trends from their meal history.

[0615] Next, the server automatically generates a meal plan tailored to the user using ingredients available at a specific store. This plan includes specific recipes, a list of ingredients to purchase, and calorie information. The generated meal plan is then visually presented to the user via their device.

[0616] Furthermore, this system receives feedback from users and adjusts its analysis and plan generation algorithms based on that information to continuously improve accuracy. For example, if a user provides feedback that a suggested meal plan contains too much food, the next suggestion will be adjusted accordingly.

[0617] As a concrete example, suppose a user is experiencing fatigue and is too busy to cook. When this user inputs their meal history and health data, the server analyzes this data and suggests simple, nutritious recipes that can be prepared in the shortest possible time, such as a "brown rice and bean salad." In this way, it becomes possible to eat efficiently and healthily.

[0618] This system functions as an important tool to improve users' health awareness and support them in maintaining their health in their daily lives.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] Users use their devices to input their meal history and physiological data. This data includes specific food names, amounts consumed, timing of consumption, current weight, and comments about their physical condition.

[0622] Step 2:

[0623] The terminal temporarily stores the data entered by the user and creates a data package for transmission to the server. During this process, it verifies the integrity of the data and checks for any incomplete information.

[0624] Step 3:

[0625] The server receives the data package sent from the terminal and saves it to the database. Simultaneously with saving, it starts analyzing the data via an AI algorithm.

[0626] Step 4:

[0627] The server uses an AI algorithm to assess the user's nutritional status. This includes analyzing which nutrients are deficient or in excess, and understanding their dietary trends. The algorithm also utilizes historical data to improve its accuracy.

[0628] Step 5:

[0629] Based on the analysis results, the server automatically generates a meal plan using ingredients available at a specific store. This plan includes recipes, a list of required ingredients, and calorie information, and is customized to suit the user's needs.

[0630] Step 6:

[0631] The terminal displays the meal plan received from the server to the user. The display uses a visual interface to ensure clarity and intuitive understanding for the user.

[0632] Step 7:

[0633] Users provide feedback on their meal plans. This feedback includes satisfaction levels and areas for improvement, and is taken into consideration when generating future meal plans.

[0634] Step 8:

[0635] The server receives feedback from the user and adjusts the AI ​​algorithm. This optimizes the analysis results and plan generation for subsequent sessions to better match the user's preferences.

[0636] (Example 1)

[0637] 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".

[0638] In modern society, maintaining a healthy and balanced diet is crucial, yet many people find it difficult to manage their nutrition properly amidst their busy daily lives. This challenge, especially when individuals lack the time or knowledge to plan their meals, leads to nutritional imbalances, overconsumption, and deficiencies in essential nutrients. Therefore, there is a need for flexible and efficient meal management systems tailored to the individual circumstances of each user.

[0639] 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.

[0640] In this invention, the server includes information input means, data analysis means using a computing device, and plan creation means. This allows users to easily and effectively receive a nutrition plan tailored to their health condition.

[0641] An "information input means" is a device or interface for users to input data such as the foods they have consumed, the time of their meals, their weight, and their perceived health.

[0642] "Data analysis means by a computing device" refers to a computer program or algorithm used to determine the excess or deficiency of nutrients in the user based on the input information.

[0643] A "plan creation method" refers to a procedure or device for automatically generating a nutrition plan tailored to the user's needs based on the analyzed results.

[0644] "Visual presentation means" refers to a device or tool for displaying a generated plan in a format that is easy for the user to understand.

[0645] An "information correction mechanism" is a system or process for collecting opinions and feedback from users and dynamically improving plans based on them.

[0646] "Geographic location data" refers to information indicating the user's current location and is used to identify the nearest source of materials.

[0647] "Purchasable ingredients" refers to food and nutritional products that are available in the user's region or specific location.

[0648] Modes for carrying out the invention

[0649] This invention is a nutrition management system that supports a healthy diet. This system consists of a terminal where the user inputs data on a daily basis, and a server that analyzes that data and generates a meal plan.

[0650] Users manually input data such as the foods they consume, meal times, weight, and perceived health using their devices. This data is collected through applications running on common mobile devices such as smartphones and tablets.

[0651] The terminal collects information and sends it to the server via the internet. The server performs data analysis using computing devices that utilize programming languages ​​such as Python and R. The analysis uses algorithms that determine the user's nutritional deficiencies or excesses, employing generative AI model frameworks such as TensorFlow and PyTorch.

[0652] Based on this analysis, the server generates an optimal meal plan for the user. This plan incorporates practical recipes and ingredient lists, utilizing the user's geographical location data to select ingredients available in their vicinity. The plan is visually presented on the device screen, making it easy to implement.

[0653] As an example of actual use, if a user inputs that they "recently get tired easily and are too busy to cook much," the system will suggest a nutritionally balanced recipe that can be prepared in a short time, such as "brown rice and bean salad." This suggestion will include specific cooking instructions, purchasing information, and even calorie information.

[0654] As an example of a prompt using a generative AI model, if you input "I've been feeling tired lately, please suggest some easy-to-prepare healthy recipes," the system will immediately analyze the data and provide an appropriate response. In this way, the present invention functions as a flexible meal management system tailored to individual users, supporting the maintenance of healthy lifestyle habits.

[0655] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0656] Step 1:

[0657] Users input their dietary information and health status into the device. Specifically, they manually record the types of food consumed, meal times, weight, fatigue levels, etc., in the application they are using. The entered data is temporarily stored in a local database. This collects the user's health information and prepares the basic data for the next processing step.

[0658] Step 2:

[0659] The terminal sends the collected data to the server. HTTPS, a secure communication protocol, is used. The input data is transferred to the server via the network, preparing it for analysis. The transmitted data is processed on the remote server while maintaining security.

[0660] Step 3:

[0661] The server performs data analysis based on the received data. The computing unit utilizes a generative AI model to perform calculations to evaluate the user's nutritional balance. This analysis uses frameworks such as TensorFlow and PyTorch to determine the user's nutrient deficiencies or excesses and extract trends in their eating habits. As output, a nutritional assessment tailored to the user's situation is generated.

[0662] Step 4:

[0663] The server uses the analysis results to create a meal plan. This plan lists purchasable ingredients based on information combining the user's geographic location data and market data. Furthermore, a nutritionally balanced recipe combining these ingredients is automatically generated. The nutrition plan is generated as a result of calculations performed by a Python program.

[0664] Step 5:

[0665] The generated nutrition plan is provided to the user via their device. The information is displayed in a visually easy-to-understand interface, showing specific recipes, shopping lists, and required calorie information. This allows users to intuitively understand the content and apply it to their daily meals.

[0666] Step 6:

[0667] Users provide feedback on the presented nutrition plan. Specifically, they use the feedback function on their device to send comments such as "the portion size in the recipe is too large." This feedback information is analyzed on the server and reflected in the processing unit to be used in suggesting the next meal plan. New data analysis, including the feedback, contributes to improving the accuracy of the next output.

[0668] (Application Example 1)

[0669] 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".

[0670] In today's busy lifestyle, many consumers find it difficult to maintain a healthy and balanced diet. To address this problem, a system is needed that allows consumers to easily assess their nutritional status, create optimal meal plans, and quickly access those meals. Currently, the process of nutritional management and purchasing is time-consuming and burdensome for consumers. Furthermore, the meal plans offered often do not perfectly match individual needs.

[0671] 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.

[0672] In this invention, the server includes information input means for the user to input meal history and physiological data; data analysis means for analyzing the input information and evaluating the nutritional status; plan generation means for automatically generating a meal plan using foods available from the nearest food service provider based on the analysis results; and information display means for presenting the generated meal plan to the user and enabling immediate ordering. This allows the user to quickly select and order the optimal meal according to their health condition.

[0673] "Information input means" refers to devices or software that provide functions that allow users to easily input their dietary history and physiological data.

[0674] "Data analysis tools" refer to algorithms and software designed to evaluate nutritional status based on information entered by users.

[0675] A "plan generation method" refers to a process or system for automatically creating an optimal meal plan based on analysis results.

[0676] "Information display means" refers to devices or software equipped with an interface for visually communicating the generated meal plan to the user.

[0677] "Evaluation information" refers to feedback and opinions provided by users, which the system uses to provide more personalized services.

[0678] "Food and beverage providers" refers to stores and service providers that offer food or meals.

[0679] In this invention, users can input their daily dietary history and physiological data via an information input means using their smartphone or other portable information terminal. After data input, the terminal transmits this information to a server. The server evaluates the user's nutritional status using data analysis means equipped with TensorFlow or similar AI algorithms. Through these analyses, the server clarifies the user's eating habits and determines nutrient deficiencies or excesses as needed.

[0680] Subsequently, the server connects with a food database of the nearest food and beverage providers and creates an optimal meal plan based on the analysis results using a planning generation system. This meal plan includes menus available at partner restaurants and is visually presented to the user by an information display system. Based on the presented plan, the user can immediately order food through the application.

[0681] For example, if a user enters data indicating they "recently feel tired," the server's analysis might determine that they "need a high-protein, low-calorie diet." Based on this result, the server suggests menu items such as "grilled chicken salad" from its database, and the user can then order them. In this way, users can easily choose and consume meals based on their health condition. An example of a prompt message would be, "I've been feeling tired lately; please suggest a high-protein, low-calorie meal plan."

[0682] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0683] Step 1:

[0684] Users use a terminal to input their meal history and physiological data. This data includes details of the foods consumed, the time of consumption, weight, and fatigue level. This data is then transmitted to the server via the information input system.

[0685] Step 2:

[0686] The server transmits the received data to the data analysis system. The data analysis system uses a generative AI model to evaluate the user's nutritional status. Based on the input of dietary history and physiological data, it determines the excess or deficiency of nutrients through data calculations and analyzes the trends in nutritional status. It generates the analysis results as output.

[0687] Step 3:

[0688] The server uses a plan generation mechanism to automatically generate an appropriate meal plan based on the analysis results. Input data includes nutritional status assessment results and menu information from local food and beverage providers. The plan generation mechanism retrieves food information available from the nearest food and beverage providers based on the user's geographical information and selects menus that match the analysis results. The output is a meal plan proposed to the user.

[0689] Step 4:

[0690] The server sends the generated meal plan to the terminal and presents it visually to the user through an information display. Based on this plan, the user reviews the suggested menu and provides feedback using prompts as needed. An example of a prompt would be, "I've been feeling tired lately, please suggest a high-protein, low-calorie meal plan."

[0691] Step 5:

[0692] When user feedback is received, the server sends the evaluation information to the adjustment mechanism, which then improves the data analysis and plan generation mechanisms. This process enables the provision of better meal plans based on the user's preferences and health status. The feedback is then incorporated into the generating AI model to improve future suggestions.

[0693] 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.

[0694] This invention is a system that integrates user meal management and emotional recognition, taking into account the user's emotional state when they receive a healthy and balanced meal plan. This system operates through the coordinated efforts of three entities: the user, the terminal, and the server.

[0695] Users input their daily meal history and physiological data through the device. Physiological data includes weight, fatigue levels, and exercise levels. The device also senses the user's facial expressions and acquires emotional data. The device then organizes this information and sends it to the server.

[0696] Upon receiving the input data, the server first uses an AI algorithm to assess the user's nutritional status. Furthermore, an emotion engine analyzes the user's current emotional state and its impact on the meal plan. Based on this analysis, a meal plan is generated that takes into account both the user's physical and emotional well-being. The plan includes recipes using ingredients available at specific retail stores such as convenience stores, a shopping list, and calorie information.

[0697] The generated meal plan is presented to the user via the device. Here, it's necessary to design the interface to be more user-friendly and easy to understand, based on the user's emotional state. For example, for a user experiencing stress, it might be suggested to recommend recipes using ingredients with relaxing effects or to display encouraging messages.

[0698] After the user experiences the suggested meal plan, they provide feedback to the system. This feedback includes their satisfaction with the suggested plan and its impact on their emotions. The server uses this feedback to further adjust the AI ​​algorithm and plan generation methods, optimizing the next suggestion to be more personalized.

[0699] As a concrete example, suppose a user is experiencing stress at work and wants to maintain a balanced diet but doesn't want to spend a lot of time on it. The user's data is analyzed, and the server suggests recipes such as a simple avocado and chicken salad that include ingredients that can help the user relax. These suggestions are accompanied by messages designed to alleviate the user's stress, providing warm and supportive assistance.

[0700] Thus, the present invention provides an innovative meal management system that not only maintains the user's health but also takes emotional support into consideration.

[0701] The following describes the processing flow.

[0702] Step 1:

[0703] The user uses the device to input their meal history and physiological data. This data includes specific food names, intake amounts, weight, exercise levels, and fatigue levels. The device also uses its built-in camera to detect the user's facial expressions and acquire emotional data.

[0704] Step 2:

[0705] The device transmits all data collected from the user (eating history, physiological data, emotional data) to the server. This data is collected with the user's consent and while ensuring privacy.

[0706] Step 3:

[0707] The server passes the received data to an AI algorithm, which analyzes the dietary history and physiological data. In particular, it assesses nutritional status and identifies any nutrient deficiencies.

[0708] Step 4:

[0709] The server uses an emotion engine to analyze the user's emotional state from their facial expressions. The analyzed emotional information is then used to adjust the meal plan.

[0710] Step 5:

[0711] Based on the analysis results, the server generates a meal plan optimized for the user. This plan includes recipes using ingredients available at specific stores, a list of ingredients to purchase, and calorie information. If the user's emotional state is negative, the plan is reinforced with specific ingredients or messages.

[0712] Step 6:

[0713] The terminal receives and generates a meal plan from the server, which is then displayed to the user. The display uses a user-friendly interface that is appropriate to the user's emotional state.

[0714] Step 7:

[0715] Users try out suggested meal plans and input feedback about their experience into a device. The feedback evaluates the feasibility, satisfaction, and emotional impact of the plan.

[0716] Step 8:

[0717] The server uses user feedback to adjust its AI algorithms and emotion engine, optimizing future suggestions for better results. This allows the system to continuously provide users with the most suitable meal plans.

[0718] (Example 2)

[0719] 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".

[0720] In modern society, maintaining a healthy lifestyle requires appropriate meal planning tailored to an individual's nutritional and emotional state. However, conventional meal management systems often fail to adequately consider the user's emotional state, leading to decreased user satisfaction and continued use. Furthermore, there is a need for suggestions based on the purchase location of ingredients, utilizing geographical location information to facilitate cooking.

[0721] 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.

[0722] In this invention, the server includes input means for the user to input information on their dietary history and physiological state, analysis means for analyzing the input information and evaluating their nutritional status, and sensing means for recognizing the user's facial expressions and obtaining information on their emotions. This makes it possible to provide a personalized meal plan that takes into account both the user's nutritional status and emotional state.

[0723] A "user" is the entity that uses the system to input their dietary history and physiological data.

[0724] "Meal history" refers to a record of food and drink that the user has consumed in the past.

[0725] "Menstrual information" refers to physical data such as weight, fatigue levels, and exercise levels.

[0726] "Input means" refers to devices or interfaces that allow users to register information into a system.

[0727] "Analysis means" refers to methods for processing input information and evaluating nutritional status and other related data.

[0728] "Nutritional status" refers to data that evaluates the balance of nutrients consumed by the user and their overall health.

[0729] "Sensing means" refers to devices and technologies that collect emotional information from the user's facial expressions, etc.

[0730] "Emotional information" refers to data that indicates the user's psychological and emotional state.

[0731] "Plan generation means" refers to methods and technologies for automatically creating appropriate meal plans based on analysis results and emotional data.

[0732] "Generative AI technology" refers to a method of generating information that meets specific conditions using artificial intelligence technology.

[0733] "An affinity-oriented display format" refers to a method of presenting information in a way that is easy to understand and approachable, depending on the user's emotional state.

[0734] "Feedback information" refers to opinions and data regarding users' satisfaction with the suggested meal plan and their usage results.

[0735] "Adjustment methods" refer to mechanisms for improving the overall system performance and proposed solutions based on feedback data.

[0736] "Geographic location" refers to information that indicates the user's current location.

[0737] "Places of sale" refers to places where users can purchase food ingredients, including supermarkets and convenience stores.

[0738] This invention is an advanced meal management system that combines user health management with emotion recognition. This system operates through the cooperation of three parties: the user, the terminal, and the server. The user first inputs their daily meal history and physiological information into the terminal. Specific physiological data includes weight, fatigue levels, and exercise levels.

[0739] In addition to the above, the device utilizes its built-in camera and sensors to detect the user's facial expressions and collect emotional data. Facial recognition software runs on the device, capturing emotional changes in real time. All of this information is organized by the device and sent to the server. Secure communication protocols such as TLS are used for data transmission, protecting the user's privacy.

[0740] The server uses the received physiological and emotional data to run an AI algorithm that evaluates the user's nutritional and emotional status. This analysis uses data analysis libraries such as Python to quantify nutritional status and derive an ideal nutritional intake pattern. Subsequently, emotional data is analyzed using an emotion engine to analyze how the user's psychological state affects their eating habits.

[0741] Based on the analysis results, the server uses generative AI technology to create a meal plan using ingredients available at specific locations. This meal plan includes recipes, a shopping list of ingredients, and calorie information, taking into account the user's health and emotional state. The generated plan is presented to the user via their device, with the display method tailored to the user's emotional state. For users who want to relax, recipes are presented with psychologically calming designs and messages.

[0742] For example, if a user is experiencing stress at work and wants to maintain a balanced diet but doesn't want to spend a lot of time cooking, the server will suggest a simple avocado and chicken salad recipe. This suggestion will include an encouraging message such as, "This salad has a relaxing effect and can help reduce stress."

[0743] An example of a prompt message given to a generative AI model is: "When the user is feeling stressed, suggest a simple recipe using ingredients that help them relax. The meal plan should include a list of available ingredients and a message to help them relax."

[0744] This system can help users lead more fulfilling lives by providing individually optimized support for both their physical and emotional well-being.

[0745] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0746] Step 1:

[0747] Users input their daily meal history and physiological information via a terminal. This input data includes details of the food consumed, weight, fatigue levels, and exercise levels. The terminal organizes this information and temporarily stores it in a database. At this stage, the data format is standardized and prepared for subsequent analysis.

[0748] Step 2:

[0749] The device uses its built-in camera and sensors to detect the user's facial expressions and acquires emotional data using facial recognition technology. The input is image data of the user's facial expressions in each frame. This data is processed to calculate the user's emotion score and quantify the user's emotional state.

[0750] Step 3:

[0751] The terminal sends the data obtained in Step 1 and Step 2 to the server. During this process, the data is securely transmitted using encryption protocols such as TLS, ensuring network security.

[0752] Step 4:

[0753] The server analyzes the received physiological and emotional data. First, it uses an AI algorithm to evaluate the user's nutritional status. Dietary history and physiological data are used as data input. This allows for the calculation of nutrient intake balance and the quantification of health status.

[0754] Step 5:

[0755] The server then analyzes emotional data using an emotion engine. This analysis uses NLP techniques to further refine the emotion score and analyze how the user's current psychological state affects their eating habits. Combining these results, the server generates an overall assessment of the user's nutritional and emotional status as output.

[0756] Step 6:

[0757] The server uses generative AI technology to generate meal plans based on the analysis results. The input data consists of nutritional and emotional assessment results, and prompts are used as commands to the generative AI. The output is a meal plan suggestion tailored to the user's specific state, and includes ingredient lists and recipes.

[0758] Step 7:

[0759] The server sends the generated meal plan to the device. The device displays the suggested meal plan and encouraging messages to the user through an interface tailored to the user's emotional state. This allows the user to receive meal suggestions in an easy-to-understand and psychologically reassuring way.

[0760] (Application Example 2)

[0761] 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".

[0762] Conventional meal management systems were limited to nutritional assessments based on the user's physical data, and did not adequately address the user's emotional or psychological aspects. Therefore, it was difficult to provide effective meal plans that considered the balance between the user's mental state and the quality of their meals. Furthermore, they were insufficient in suggesting ingredients that matched the user's geographical location.

[0763] 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.

[0764] In this invention, the server includes means for acquiring user meal history and physiological information, means for analyzing information to evaluate nutritional status based on the input information, means for detecting the user's emotional state and automatically generating an emotionally conscious meal plan, and means for displaying the generated meal plan in a user-friendly manner according to the user's emotional state. This makes it possible to provide personalized meal plans that meet the user's physical and emotional needs.

[0765] "Acquisition means" refers to the operations and devices necessary for users to input their dietary history and physiological information. These means are designed to ensure that information is reliably collected from users.

[0766] "Information analysis means" refers to analytical techniques and algorithms for evaluating a user's nutritional status based on acquired dietary history and physiological information. This means allows for an accurate understanding of the user's health status.

[0767] "Recommendation methods" refer to technologies and methods used to detect a user's emotional state and automatically generate a meal plan that takes those emotions into consideration. This makes it possible to provide suggestions that meet the user's psychological needs.

[0768] "Display means" refers to devices and technologies that present the generated meal plan in a user-friendly manner according to the user's emotional state. This means plays a role in communicating the meal plan to the user in an easy-to-understand manner.

[0769] A "server" refers to a computer system that centrally manages various methods and performs tasks such as information analysis and meal plan generation. The server plays a crucial role in handling the entire process in a unified manner.

[0770] To implement this invention, a system must be built involving three parties: the user, the data acquisition terminal, and the server. The user inputs their daily meal history and physiological information using the data acquisition terminal. The data acquisition terminal has a built-in camera and sensors that detect the user's emotional state by sensing their facial expressions.

[0771] The server receives information transmitted from the acquisition terminal and uses information analysis tools to evaluate the user's nutritional status. The information analysis tools use image processing software such as OpenCV to recognize the user's facial expressions and perform emotional analysis. Based on the emotional state obtained from this analysis, the server uses recommendation tools to generate an individualized meal plan that takes emotions into consideration. AI algorithms are used for generation, employing programs implemented in programming languages ​​such as Python.

[0772] The display system plays the role of presenting the generated meal plan in a way that is adapted to the user's emotions. This presentation uses visual displays and audio output devices. For example, if it is determined that the user is seeking relaxation, the display system will show a recommendation message such as, "How about an avocado and chicken salad today?" and provide detailed instructions on how to prepare it. It can also play relaxing music.

[0773] For example, if a user is experiencing stress, the server will detect this and recommend a menu using vitamin B-rich foods that help reduce stress. An example of a prompt to the generating AI model could be, "What is the best meal plan for a user experiencing fatigue?" In this way, the system of the present invention can respond to the user's physical and emotional needs and provide healthy and personalized support.

[0774] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0775] Step 1:

[0776] The user uses a device to input their daily meal history and physiological information. The input method utilizes the touchscreen or voice input function of the smart device. The input in this step includes data such as a list of foods, intake amounts, weight, and exercise levels, and the output is raw data stored on the device.

[0777] Step 2:

[0778] The device uses cameras and sensors to capture the user's facial expressions and detect their emotional state. An emotion recognition model analyzes this image data and quantifies the user's current emotional state. Input is in the form of still images or videos, and output is quantified emotion data.

[0779] Step 3:

[0780] The terminal sends the data acquired in Step 1 and Step 2 to the server. After receiving this data, the server uses information analysis tools to comprehensively evaluate the user's nutritional and emotional state. The data analysis is performed using an algorithm, with the input being database-stored user information and the output being the evaluation results.

[0781] Step 4:

[0782] The server uses recommended methods to generate emotionally sensitive meal plans. An AI algorithm designs meal plans based on evaluation results. Specifically, it extracts and synthesizes appropriate menu and ingredient information from relevant databases. The input is the evaluation results from step 3, and the output is a proposed meal plan.

[0783] Step 5:

[0784] The server sends the generated meal plan to the terminal in a user-friendly format, and the terminal displays it. Specific actions include providing information in a visually clear and user-friendly manner, including through audio output. The input is the meal plan, and the output is the visual and auditory presentation to the user.

[0785] Step 6:

[0786] Users provide feedback on their meal plan experience via their device. This feedback includes emotional impact and satisfaction levels. The device collects this data and sends it back to the server. The input is the user's feedback information, and the output is a new dataset received by the server.

[0787] Step 7:

[0788] The server uses feedback to update and adjust its algorithms and recommendation methods, optimizing subsequent suggestions to be more personalized. Specific actions include training the AI ​​model and improving prompt statements. Input is feedback data, and output is the improved suggestion system.

[0789] 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.

[0790] 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.

[0791] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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."

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] The following is further disclosed regarding the embodiments described above.

[0811] (Claim 1)

[0812] An input means for the user to enter their meal history and physiological data,

[0813] A data analysis means that analyzes input data and evaluates nutritional status,

[0814] A plan generation means that automatically generates a meal plan using ingredients available at a specific store based on the analysis results,

[0815] A display means for presenting the generated meal plan to the user,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising an adjustment means for collecting user feedback information and adjusting the data analysis means and plan generation means to improve the meal plan.

[0819] (Claim 3)

[0820] The system according to claim 1, further comprising means for obtaining information on the nearest store based on the user's geographical location and generating a meal plan using ingredients available at that store.

[0821] "Example 1"

[0822] (Claim 1)

[0823] An information input method for users to input data including the foods they have consumed, the time of meals, their weight, and their perceived health,

[0824] A data analysis means using a computing device to determine the excess or deficiency of nutrients in the user, based on the input information,

[0825] A planning method that automatically generates a nutrition plan using locally available ingredients based on the analysis results,

[0826] A visual presentation means that provides the generated plan to the user via a display device,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, further comprising information modification means for improving meal plans by collecting user feedback and dynamically changing data analysis means and plan creation means.

[0830] (Claim 3)

[0831] The system according to claim 1, further comprising means for obtaining information on the nearest available ingredients based on the user's geographical location data and generating a nutrition plan using said ingredients.

[0832] "Application Example 1"

[0833] (Claim 1)

[0834] An information input means for the user to input their meal history and physiological data,

[0835] A data analysis means that analyzes input information and evaluates nutritional status,

[0836] A plan generation means that automatically generates a meal plan using food available from the nearest food service provider based on the analysis results,

[0837] An information display means that presents the generated meal plan to the user and provides instructions to immediately place an order,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, further comprising an adjustment means for collecting evaluation information from users and adjusting the data analysis means and the plan generation means to improve the meal plan.

[0841] (Claim 3)

[0842] The system according to claim 1, further comprising means for obtaining information on the nearest food and beverage provider based on the user's geographical location and generating a meal plan using food available from that provider.

[0843] "Example 2 of combining an emotion engine"

[0844] (Claim 1)

[0845] An input means for the user to enter information about their dietary history and menstrual cycle,

[0846] An analytical means for analyzing input information and evaluating nutritional status,

[0847] A sensing means for recognizing the user's facial expressions and obtaining information about their emotions,

[0848] A plan generation means that automatically generates a meal plan using ingredients available at a specific sales location based on analysis results and emotions,

[0849] A means of generating detailed meal plans using generative AI technology,

[0850] A display means that presents the generated meal plan in a display format that is compatible with the user's emotional state,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, further comprising an adjustment means for collecting user feedback information and adjusting the analysis means, emotion recognition means, and plan generation means to improve the meal plan.

[0854] (Claim 3)

[0855] The system according to claim 1, further comprising means for obtaining information on the nearest sales location based on the user's geographical location and generating a meal plan using ingredients available at that location.

[0856] "Application example 2 when combining with an emotional engine"

[0857] (Claim 1)

[0858] A means for users to input their dietary history and physiological information,

[0859] An information analysis means for evaluating nutritional status based on input information,

[0860] A recommendation system for detecting the user's emotional state and automatically generating an emotionally sensitive meal plan,

[0861] A display means that presents the generated meal plan in a user-friendly manner according to the user's emotional state,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, further comprising an optimization means for collecting user evaluation information and adjusting the information analysis means and recommendation means to improve meal plans that take emotional support into consideration.

[0865] (Claim 3)

[0866] The system according to claim 1, further comprising means for obtaining information on the nearest sales facility based on the user's geographical location, generating a meal plan using ingredients available at that facility, and providing emotional support to the user. [Explanation of Symbols]

[0867] 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. An information input means for the user to input their meal history and physiological data, A data analysis means that analyzes input information and evaluates nutritional status, A plan generation means that automatically generates a meal plan using food available from the nearest food service provider based on the analysis results, An information display means that presents the generated meal plan to the user and provides instructions to immediately place an order, A system that includes this.

2. The system according to claim 1, further comprising an adjustment means for collecting evaluation information from users and adjusting the data analysis means and the plan generation means to improve the meal plan.

3. The system according to claim 1, further comprising means for obtaining information on the nearest food and beverage provider based on the user's geographical location and generating a meal plan using food available from that provider.

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