system

Abstract

We provide the system. [Solution] A measuring device for monitoring health information, An information processing device for receiving, storing, and analyzing data obtained from the measuring device, An automatic generation device for generating individually tailored meal menus based on the results analyzed by the information processing device, A display device for displaying or notifying the meal menu generated by the automatic generation device, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 busy lives, it is difficult for individuals to maintain healthy eating habits, and many people tend to choose meals without considering nutritional balance. As a result, the risk of lifestyle diseases increases, and there are concerns about the long-term impact on health. Therefore, there is a need for a system that supports efficient and effortless healthy eating habits based on an individual's health status.

Means for Solving the Problems

[0005] This invention provides a measuring device for monitoring health information and an information processing device for receiving, storing, and analyzing data obtained from the measuring device, thereby evaluating the user's actual health status. Based on the results analyzed by the information processing device, an automatic generation device is provided to automatically generate individually tailored meal menus, thereby providing the user with an optimally balanced meal menu. Furthermore, by providing a display device for displaying or notifying the user of the meal menu generated by the automatic generation device, the invention makes it easier for the user to intuitively select a menu and incorporate it into their daily diet.

[0006] "Health information" refers to data about the body's functions and condition, including heart rate, steps taken, calories burned, sleep quality, and blood sugar levels.

[0007] A "measuring device" is a device that collects a user's physical activity and biosignals via sensors, and includes wearable devices and smartwatches.

[0008] An "information processing device" is a computer system used to store and analyze data transmitted from measuring devices.

[0009] An "automatic generation device" is a program or system that creates meal menus tailored to an individual's health condition based on analysis results obtained by an information processing device.

[0010] A "display device" is a device that has a display or notification function for communicating the generated meal menu to the user.

[0011] A "meal menu" refers to specific dishes or food combinations suggested to the user, taking nutritional balance into consideration. [Brief explanation of the drawing]

[0012] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] 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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

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

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

[0020] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0033] The present invention is implemented as a system combining a measuring device for monitoring health information, an information processing device for receiving and analyzing data, an automatic generation device for generating meal menus, and a display device for displaying them. This system makes it possible to easily and effectively manage the user's daily health.

[0034] First, the "device" acquires various health data from the user's body. This data collection is mainly performed by measuring devices equipped with sensors, typically by wearable devices such as smartwatches and fitness trackers. This data is then periodically transmitted to the "server."

[0035] The "server" stores health data received from terminals and analyzes the data using an information processing device. The analyzed data is used as input to adjust meal menus. This process utilizes nutrition-based statistical methods and machine learning algorithms to generate meal menus tailored to the user's specific health condition and preferences.

[0036] Next, the "server" sends the meal menu generated by the automated generator to the "terminal." The "terminal" receives this transmission and displays the menu information to the user. The terminal screen displays images of recommended dishes and information about their nutritional content, making it easy for the user to plan their meals for the day or week.

[0037] For example, if a user finishes their day's activities and wants to choose a suitable meal for dinner, this system allows their health data to be instantly analyzed by the server, and the optimal menu tailored to the user's activity level and physical condition for the day is displayed on the terminal. For instance, if carbohydrate intake is needed, pasta salad or brown rice risotto might be suggested as menu options.

[0038] This system allows users to easily select nutritionally balanced meals tailored to their health condition, enabling them to make appropriate meal choices each time. This embodiment aims to ensure that users maintain consistently healthy eating habits.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device acquires the user's health data through sensors. This includes heart rate, steps taken, sleep patterns, blood glucose levels, and more. The measuring device periodically collects this data and transmits it to a server using its communication function.

[0042] Step 2:

[0043] The server receives health data transmitted from the terminal and stores it in a database. The received data is integrated with historical data and becomes the basis for analyzing the user's long-term health status.

[0044] Step 3:

[0045] The server performs analysis on an information processing device based on the accumulated data. Here, machine learning algorithms are used to identify patterns in the user's health status and evaluate nutrient deficiencies and excesses.

[0046] Step 4:

[0047] The server uses an automated generator to create meal menus tailored to the user based on the analysis results. This process also takes into account the user's nutritional needs and past preferences. The generated menus prioritize nutritional balance and variety.

[0048] Step 5:

[0049] The server sends the generated meal menu back to the terminal.

[0050] Step 6:

[0051] The device displays the received meal menu to the user. The user can review the details of the provided menu and select a meal based on images and nutritional information. The device also records the user's selections and feedback.

[0052] Step 7:

[0053] Users select a meal from a menu and consume it. After the meal, they input feedback via their device, sending comments about their satisfaction level and physical condition to the server.

[0054] Step 8:

[0055] The server receives user feedback and continuously trains the generating AI model. This allows menu suggestions to be gradually optimized, improving accuracy in subsequent uses.

[0056] (Example 1)

[0057] 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."

[0058] In modern society, personal health management is becoming increasingly important, but systems that provide individually tailored nutritional information and support sustainable healthy habits are limited. Traditional methods primarily rely on advice based on general nutrition guidelines, which do not adequately address individual differences among users. Against this backdrop, there is a need for methods that enable more personalized nutritional management by providing meal suggestions that reflect individual health data and preferences.

[0059] 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.

[0060] In this invention, the server includes acquisition means for monitoring vital data, analysis means for collecting and analyzing the data obtained from the acquisition means via communication, and generation means for generating individually tailored nutritional suggestions based on the results of the analysis by the analysis means. This makes it possible to provide nutritional suggestions tailored to the individual differences of the user.

[0061] "Vital data" refers to basic biological information that indicates a person's health status, and includes heart rate, body temperature, activity level, etc.

[0062] "Acquisition means" refers to technical means for directly acquiring vital data from the user, and includes devices such as wearable devices equipped with sensors.

[0063] "Communication" refers to methods and protocols for sending and receiving acquired data between remote devices, including wireless communication and data transfer via the Internet.

[0064] "Accumulation" refers to the process of centrally storing and managing received data, and includes procedures such as storing it in a database.

[0065] "Analysis" is the act of examining accumulated data to identify specific patterns and trends, and includes using algorithms to evaluate the current situation and make future predictions.

[0066] "Generation means" refers to methods and apparatus for forming specific proposals and results based on analyzed data, and includes processes for automatically generating meal menus.

[0067] "Visualization means" refers to a means of conveying information obtained by a generation means to a user, and includes displaying information through a display or smartphone screen.

[0068] "Nutritional suggestions" refer to the presentation of meal plans that take into account the user's health condition and personal preferences, and consider an appropriate balance of nutrients.

[0069] The system of the present invention provides personalized nutritional suggestions based on the user's health information and consists of a device and software with several key functions. Specifically, it is implemented as follows:

[0070] The device is responsible for collecting health data from the user's body. This includes wearable devices such as smartwatches and fitness trackers that measure heart rate, activity levels, and calories burned. This data is acquired in real time and used to understand the user's daily health status.

[0071] The server securely receives health data transmitted from terminals and stores it in a database. An integrated information processing device is used for data analysis, and machine learning algorithms are employed to evaluate the data. This provides the foundational data needed to provide personalized nutritional recommendations to individual users.

[0072] Based on the analysis results, an automated generation system on the server uses a generation AI model to create an optimal meal plan for the user. An example of a prompt used in this process is, "Please suggest a meal plan for next week based on the user's health data." This ensures that practical and effective suggestions are provided that match the user's nutritional needs.

[0073] The generated meal menu is sent to the device and displayed to the user. The device's display shows images of the dishes, their nutritional information, and calorie count, allowing the user to easily plan their daily meals based on this information.

[0074] For example, if a user wants to adjust their energy intake based on their overall activity level for the week, this system will suggest a menu that is optimal for the user's condition, including appropriate amounts of carbohydrates and protein. This makes it easier for the user to maintain healthy eating habits.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The device acquires the user's health data. Specifically, it continuously monitors vital data such as heart rate, calories burned, and steps taken using a smartwatch or fitness tracker, and temporarily stores this data within the device. The input here is biosignals generated from the user's physical movements, and the output is digitized health data.

[0078] Step 2:

[0079] The device transmits the acquired health data to the server via a secure communication protocol. The data is encrypted during this process to ensure its security. The input here is the digital health data obtained in step 1, and the output is the transmitted encrypted data.

[0080] Step 3:

[0081] The server collects the received data into a database. Here, the received data is organized and stored, preparing it for later analysis. The input is encrypted health data, and the output is integrated data securely stored in the database.

[0082] Step 4:

[0083] The server utilizes information processing equipment to analyze the collected data. The analysis process uses machine learning algorithms to evaluate the data and gain insights into the user's health status. The input is user data in the database, and the output is insights into the user's health status and nutritional needs as a result of the analysis.

[0084] Step 5:

[0085] The server uses a generative AI model to generate meal menus based on the analysis results. In this process, prompts are used to create nutritional suggestions that take into account the user's past health data and preferences. The input is the analysis results obtained in step 4, and the output is a individually customized meal menu.

[0086] Step 6:

[0087] The server sends the generated meal menu to the terminal. The menu is sent in digital format and ready to be displayed on the user's terminal. The input is the generated meal menu, and the output is the menu sent to the terminal.

[0088] Step 7:

[0089] The terminal displays the received meal menu to the user. The screen shows images of the dishes, recipes, and nutritional information, allowing the user to plan their meals based on this information. The input is the meal menu sent from the server, and the output is nutritional information that the user can visually confirm.

[0090] (Application Example 1)

[0091] 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."

[0092] In health management, it is crucial to create and implement personalized meal plans simply and effectively. However, conventional systems fail to fully utilize users' health information in real time, and the provision of food purchases and related information based on that information is insufficient. This creates a challenge in that it is difficult to sustain improvements in eating habits.

[0093] 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.

[0094] In this invention, the server includes a measurement means for monitoring health information, an information processing means for receiving, storing, and analyzing data obtained from the measurement means, and an automatic generation means for generating individually tailored meal plans based on the results analyzed by the information processing means. This enables the automatic generation of meal plans tailored to the user's health condition and the efficient provision of related food purchases and information acquisition.

[0095] "Health information" refers to data that indicates the user's physical condition, including heart rate, steps taken, and calories burned.

[0096] "Monitoring" refers to the continuous observation and recording of specific conditions or data.

[0097] "Measurement means" refers to sensors and devices used to acquire a user's health information.

[0098] "Information processing means" refers to systems and software for receiving, storing, and analyzing acquired data.

[0099] "Analysis" is the act of examining collected data in detail to find meaning and patterns.

[0100] "Automated generation method" refers to the process or algorithm for creating individually appropriate meal plans based on analysis results.

[0101] "Display means" refers to devices or interfaces that show generated information or plans in a way that allows users to visually confirm them.

[0102] A "meal plan" refers to a set of recommended meal menus and recipes based on an individual's health and nutritional needs.

[0103] "Purchase" refers to the action or procedure of acquiring recommended food products or services.

[0104] "Related information" refers to additional data and knowledge that complements the displayed meal plan and nutritional information.

[0105] The system of this invention operates by combining multiple hardware and software components to efficiently collect and analyze a user's health information and generate a personalized meal plan. The server uses measurement means to acquire health information from wearable devices, including smartwatches and fitness trackers equipped with sensors. The terminal receives data from these devices via Bluetooth or Wi-Fi.

[0106] Information processing is performed using data stored in a database platform such as Firebase. A Python program running on the server processes the data using Pandas and NumPy, performing analysis based on individual health conditions. This analysis enables the automatic generation of meal plans using generative AI models. In particular, the use of machine learning frameworks such as TENSORFLOW® and Keras allows for the flexible creation of meal plans based on the user's past eating habits and health data.

[0107] The generated meal plan is sent to the device via an API developed using the Flask framework. Based on this information, the device interacts with a user interface implemented using Flutter® to visually notify the user of the meal plan and provide specific menus and nutritional information.

[0108] For example, if a user has finished their daily activities and wants to decide on a suitable dinner, the system analyzes their activity data and health status for the day, and suggests menus such as spicy tofu stir-fry or salmon meunière, taking into account the necessary nutrients. Links to relevant online shopping services are also provided so that the user can quickly purchase the ingredients related to the specific menu.

[0109] An example of a prompt message is: "Based on today's health data, please suggest a dinner menu for tonight. Please provide three nutritionally balanced dishes that take into account activity levels and preferences."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server collects health information from the wearable device. Inputs include the user's heart rate, steps taken, and calories burned, and this data is transmitted to the device via Bluetooth or Wi-Fi. The device receives this data and prepares it to send to the server.

[0113] Step 2:

[0114] The server stores the received health information in the Firebase database. The input is the data collected in the previous step, and by saving it to the database, the server prepares the historical data necessary for later analysis.

[0115] Step 3:

[0116] The server performs data analysis using the accumulated health information. It executes Python scripts as a data processing tool and constructs datasets using Pandas and NumPy. The input includes past health information and current data, and the output generates an index that evaluates the user's health status.

[0117] Step 4:

[0118] The server automatically generates individual meal plans using a generative AI model based on the analysis results. Using machine learning frameworks such as TensorFlow or Keras, menu candidates are created that take into account health status and preferences. The input is the evaluation metrics obtained in the previous step, and the output is a meal plan.

[0119] Step 5:

[0120] The server sends the generated meal plan to the device. The meal plan is delivered to the device via an API using Flask. The input is the created meal plan, and the output is the data that should be displayed on the device.

[0121] Step 6:

[0122] The device displays the received meal plan to the user. A user interface implemented in Flutter is responsible for the visual display of the menu and also allows the user to easily obtain links and information for purchasing related products. The input is the meal plan sent from the server, and the output is the information that the user can view displayed on the screen.

[0123] 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.

[0124] This invention is a system that assists in health management, and is implemented by combining a measuring device for monitoring health information, an information processing device for receiving, storing, and analyzing data, an automatic generation device for generating meal menus, a display device for displaying the generated menus, and an emotion engine for recognizing the user's emotional state. This system is capable of providing more personalized meal suggestions based on the user's daily health and emotional state.

[0125] First, the "device" uses sensors to acquire the user's health data. This data includes heart rate, activity level, sleep quality, and body temperature. Additionally, using the camera and microphone, the emotion engine infers the user's emotional state from their voice tone and facial expressions. This data is then transmitted to the "server."

[0126] The "server" stores health data and user emotional information received from the terminal and analyzes it using an information processing device. As a result of the analysis, the user's health and emotional state are stored in a database and used as input for the next step. Machine learning technology is used in this analysis to perform a sophisticated analysis that takes into account the user's past patterns and current state.

[0127] The "server" generates meal menus using an automated generator based on analysis results from the emotion engine. These menus consider not only the user's physical needs but also their mental satisfaction. For example, the system is designed to suggest many ingredients that have stress-reducing effects.

[0128] The generated meal menu is sent from the "server" to the "terminal." The "terminal" displays the menu to the user and provides notifications that include specific nutritional information and suggestions based on the user's emotional state. This information allows the "user" to choose a meal that takes both their health and emotional state into consideration.

[0129] For example, if the system detects that a user is experiencing stress at work, it might recommend selecting a menu that includes relaxing herbal teas or antioxidants. In this way, the system makes it easy for users to select a meal plan that comprehensively considers their individual health and emotional state.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device uses various sensors to collect health information, measuring heart rate, activity level, and sleep quality. It also analyzes the user's facial expressions and voice tone through the camera and microphone, and an emotion engine infers the user's emotional state. This data is transmitted to the server in real time.

[0133] Step 2:

[0134] The server stores data received from terminals and analyzes it using an information processing device. Health data is combined with past history to evaluate the user's health status, while emotional information analyzed by the emotion engine is also integrated to understand the user's current emotional state.

[0135] Step 3:

[0136] The server generates meal menus using an automated generator based on the analysis of health and emotional states. The generated menus consider factors that enhance both physical health and emotional satisfaction. For example, they suggest foods that offer both nutritional balance and stress-reducing effects.

[0137] Step 4:

[0138] The server sends the generated meal menu to the terminal.

[0139] Step 5:

[0140] The device displays the received meal menu to the user. The displayed information includes nutritional information about the meal and reasons for the suggestion based on the user's emotional state. This allows the user to make choices in their daily meals that are appropriate for both their physical and mental condition.

[0141] Step 6:

[0142] The user selects a specific menu item and eats the meal. Afterwards, they provide feedback to the server via their device regarding their physical condition and emotions after the meal.

[0143] Step 7:

[0144] The server receives user feedback and continuously updates its generative AI model and emotion engine. This ensures that subsequent menu suggestions are more tailored to the user.

[0145] (Example 2)

[0146] 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".

[0147] In today's busy lifestyle, balancing daily health management and emotional care is difficult. In particular, a more flexible and personalized system is needed to accurately provide meal suggestions based on each user's health condition and emotions. This invention aims to maintain health and stabilize emotions by integrating and analyzing health data and emotional information to provide users with an optimal meal plan.

[0148] 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.

[0149] In this invention, the server includes sensing means for acquiring biometric data, data processing means for receiving, storing, and analyzing the biometric data obtained from the sensing means, and emotion recognition means for identifying the user's emotional state. This makes it possible to provide personalized meal plans based on the user's health and emotional state.

[0150] "Biometric data" refers to data related to an individual's health status, such as heart rate, exercise level, sleep quality, and body temperature.

[0151] "Sensing means" refers to devices and equipment that use sensor technology to acquire biometric data from users.

[0152] "Data processing means" refers to a technological device that receives, stores, and analyzes collected biological data.

[0153] "Automatic creation means" refers to a technological device that generates individually tailored meal plans based on analyzed data.

[0154] "Display means" refers to a device that provides the generated meal plan to the user visually or audibly.

[0155] "Emotion recognition means" refers to a technological device that analyzes the user's voice tone and facial expressions to identify their emotional state.

[0156] This invention is a system that proposes an individually tailored meal plan by comprehensively utilizing the user's biometric data and emotional state. This system is implemented by combining sensing means, data processing means, automatic creation means, display means, and emotion recognition means.

[0157] The "terminal" acquires biometric data through sensing means via wearable devices worn by the user or applications on a smartphone. This data includes heart rate, exercise level, sleep quality, and body temperature. In addition, it simultaneously captures the user's emotional state by using cameras and microphones to analyze voice tone and facial expressions through emotion recognition means.

[0158] The "server" receives and stores data transmitted from the "terminal" using data processing means. The received data is securely stored in a database and then analyzed using machine learning algorithms. Based on the analyzed data, the server uses a generative AI model to automatically generate a meal plan. This plan takes into account the user's individual health needs and emotional state, and includes further personalized suggestions.

[0159] The generated meal plan is sent from the "server" to the "terminal" and provided to the user through a display device. The "terminal" can easily receive the meal plan not only through visual display but also through voice and notification functions.

[0160] For example, if the system detects that the "user" is feeling stressed, a meal plan including foods and beverages with relaxing effects will be suggested. An example of a prompt to the generative AI model might be, "Based on this week's health data, suggest a meal menu that will help reduce stress."

[0161] This system allows users to select meals that suit their health and emotional state, thereby improving their lifestyle.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The "device" acquires biometric information in real time using the user's wearable device or smartphone. This input data includes heart rate, activity level, sleep quality, and body temperature. Through this data, basic information is collected to understand the user's current health status.

[0165] Step 2:

[0166] The "device" captures the user's facial expressions and voice using its built-in camera and microphone, and analyzes their emotional state using emotion recognition technology. It analyzes voice tone and facial expressions from video and audio files as input data, infers emotions such as anger, sadness, and joy, and outputs the results.

[0167] Step 3:

[0168] These health and emotional data are sent from the "terminal" to the "server." The "server" stores the received data in a database and uses machine learning algorithms to analyze the data based on the accumulated biometric and emotional data. At this stage, based on the input data, it considers past health trends and patterns and outputs detailed analysis results regarding the current health and emotional state.

[0169] Step 4:

[0170] The "server" uses the results of data analysis to create personalized meal plans through an automated generation method utilizing a generative AI model. In this step, the analysis results are input as prompts to the AI ​​model, which generates meal suggestions based on health status and emotions. For example, if stress levels are high, it will output a menu that uses many ingredients that have stress-reducing effects.

[0171] Step 5:

[0172] The generated meal plan is sent from the "server" to the "terminal." The "terminal" presents the meal plan to the user through a visual interface and voice notifications. This output includes specific menu items, nutritional information, and a list of recommended ingredients, helping the user choose meals that balance health and emotional well-being.

[0173] (Application Example 2)

[0174] 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".

[0175] Modern consumers seek product choices that consider both health and emotional well-being, but traditional stores have faced the challenge of providing appropriate product recommendations tailored to the individual needs of each customer. This invention aims to solve this problem by suggesting optimal products based on each customer's health and emotional state, thereby improving customer satisfaction.

[0176] 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.

[0177] In this invention, the server includes means for using a sensor device for monitoring health information, information processing means for receiving, storing, and analyzing health data and emotional data obtained from the sensor device, and automatic generation means for generating individually suitable product candidates based on the results analyzed by the information processing means. This makes it possible to recommend products that are suitable for the user's health and emotional state in real time.

[0178] "Health information" refers to data collected to measure the user's physical condition, and includes heart rate, body temperature, and exercise level.

[0179] A "sensor device" is a measuring device used to monitor health and emotional information, and includes heart rate sensors and cameras for emotion analysis.

[0180] An "information processing device" is a device used to receive, store, and analyze collected health and emotional data, and includes computers and servers.

[0181] "Automatic generation means" refers to means for generating individually suitable product candidates based on the results analyzed by an information processing device, and includes algorithms and programs.

[0182] A "display device" is a device used to visualize generated product candidates for the user, and includes smartphones and displays.

[0183] "Emotional data" refers to information collected to measure the user's psychological state, and includes facial expressions, voice tone, and other similar data.

[0184] A "product recommendation list" is a list of products recommended based on the user's health and emotional state, and includes products that meet specific criteria.

[0185] This invention provides a system for suggesting products that are individually suited to a user based on their health and emotional state. This system mainly consists of a sensor device that collects health and emotional information, a server that processes the information, and a terminal that displays the results.

[0186] The server stores and analyzes health and emotional data received from sensor devices. Specifically, it uses heart rate sensors, cameras, microphones, and other sensors to acquire health information such as heart rate and body temperature, and emotional information such as facial expressions and voice tone. The server processes this information and uses machine learning algorithms to analyze the data and recognize patterns.

[0187] The server uses an automated generation mechanism based on the analyzed data to generate product suggestions tailored to the user. These suggestions are sent to the user's device as a product list and visualized. The displayed product information includes relevant nutritional information and suggestions based on the user's emotional state. Smartphone or tablet displays are used as the display devices.

[0188] As a concrete example, suppose a sensor device detects the user's fatigue and stress levels when they enter a physical store. In this case, the server generates a list of products that promote relaxation, such as herbal tea or antioxidant snacks, and displays it on the user's terminal. The user can then receive this customized suggestion and select appropriate products within the store.

[0189] Using a generative AI model, the following prompt can be used: "The user's emotional state has been detected as 'fatigue'. Please generate a list of foods and beverages that have a relaxing effect." In response to this prompt, a list of optimal products will be created.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The terminal acquires health and emotional information from sensor devices attached to the body of users who enter a physical store. The input is raw data from the sensors, and the output generates health and emotional data such as heart rate, body temperature, and facial expression. In this process, the sensors measure various vital information in real time and transmit these values ​​to the terminal.

[0193] Step 2:

[0194] The server stores health and emotional data received via the terminal and analyzes it using machine learning algorithms. The input is the health and emotional data acquired in step 1, and the output is the analysis results showing the user's current health and emotional state. In this process, the server uses a knowledge base, referencing past data as well, to identify patterns in health and emotional states and evaluate the user's condition.

[0195] Step 3:

[0196] The server uses an automated generation mechanism based on the analysis results to generate the most suitable product candidates for the user. The input is the analysis results from step 2, and the output is a product list. Here, the generation AI model uses the analysis results to process prompts and identify product groups suitable for the user's health and emotional state. A specific example of a prompt is: "The user's emotional state has been detected as 'fatigue'. Please generate a list of foods and beverages with relaxing effects."

[0197] Step 4:

[0198] The generated product list is sent from the server to the terminal and displayed on the terminal. The input is the product list data from the server, and the output is the product recommendation result displayed on the user's terminal screen. The terminal visually presents this product list to the user and supports product selection in a physical store, including nutritional information and emotion-based suggestion information.

[0199] Step 5:

[0200] The user refers to a product list displayed on the terminal and selects a product in the physical store that suits their health and emotional state. The input is the information displayed on the terminal in step 4, and the output is the receipt of the selected product. This step involves the user moving around the store, searching for the presented product options, and actually picking up the product.

[0201] 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.

[0202] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] The present invention is implemented as a system combining a measuring device for monitoring health information, an information processing device for receiving and analyzing data, an automatic generation device for generating meal menus, and a display device for displaying them. This system makes it possible to easily and effectively manage the user's daily health.

[0218] First, the "device" acquires various health data from the user's body. This data collection is mainly performed by measuring devices equipped with sensors, typically by wearable devices such as smartwatches and fitness trackers. This data is then periodically transmitted to the "server."

[0219] The "server" stores health data received from terminals and analyzes the data using an information processing device. The analyzed data is used as input to adjust meal menus. This process utilizes nutrition-based statistical methods and machine learning algorithms to generate meal menus tailored to the user's specific health condition and preferences.

[0220] Next, the "server" sends the meal menu generated by the automated generator to the "terminal." The "terminal" receives this transmission and displays the menu information to the user. The terminal screen displays images of recommended dishes and information about their nutritional content, making it easy for the user to plan their meals for the day or week.

[0221] For example, if a user finishes their day's activities and wants to choose a suitable meal for dinner, this system allows their health data to be instantly analyzed by the server, and the optimal menu tailored to the user's activity level and physical condition for the day is displayed on the terminal. For instance, if carbohydrate intake is needed, pasta salad or brown rice risotto might be suggested as menu options.

[0222] This system allows users to easily select nutritionally balanced meals tailored to their health condition, enabling them to make appropriate meal choices each time. This embodiment aims to ensure that users maintain consistently healthy eating habits.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The device acquires the user's health data through sensors. This includes heart rate, steps taken, sleep patterns, blood glucose levels, and more. The measuring device periodically collects this data and transmits it to a server using its communication function.

[0226] Step 2:

[0227] The server receives health data transmitted from the terminal and stores it in a database. The received data is integrated with historical data and becomes the basis for analyzing the user's long-term health status.

[0228] Step 3:

[0229] The server performs analysis on an information processing device based on the accumulated data. Here, machine learning algorithms are used to identify patterns in the user's health status and evaluate nutrient deficiencies and excesses.

[0230] Step 4:

[0231] The server uses an automated generator to create meal menus tailored to the user based on the analysis results. This process also takes into account the user's nutritional needs and past preferences. The generated menus prioritize nutritional balance and variety.

[0232] Step 5:

[0233] The server sends the generated meal menu back to the terminal.

[0234] Step 6:

[0235] The device displays the received meal menu to the user. The user can review the details of the provided menu and select a meal based on images and nutritional information. The device also records the user's selections and feedback.

[0236] Step 7:

[0237] Users select a meal from a menu and consume it. After the meal, they input feedback via their device, sending comments about their satisfaction level and physical condition to the server.

[0238] Step 8:

[0239] The server receives user feedback and continuously trains the generating AI model. This allows menu suggestions to be gradually optimized, improving accuracy in subsequent uses.

[0240] (Example 1)

[0241] 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."

[0242] In modern society, personal health management is becoming increasingly important, but systems that provide individually tailored nutritional information and support sustainable healthy habits are limited. Traditional methods primarily rely on advice based on general nutrition guidelines, which do not adequately address individual differences among users. Against this backdrop, there is a need for methods that enable more personalized nutritional management by providing meal suggestions that reflect individual health data and preferences.

[0243] 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.

[0244] In this invention, the server includes acquisition means for monitoring vital data, analysis means for collecting and analyzing the data obtained from the acquisition means via communication, and generation means for generating individually tailored nutritional suggestions based on the results of the analysis by the analysis means. This makes it possible to provide nutritional suggestions tailored to the individual differences of the user.

[0245] "Vital data" refers to basic biological information that indicates a person's health status, and includes heart rate, body temperature, activity level, etc.

[0246] "Acquisition means" refers to technical means for directly acquiring vital data from the user, and includes devices such as wearable devices equipped with sensors.

[0247] "Communication" refers to methods and protocols for sending and receiving acquired data between remote devices, including wireless communication and data transfer via the Internet.

[0248] "Accumulation" refers to the process of centrally storing and managing received data, and includes procedures such as storing it in a database.

[0249] "Analysis" is the act of examining accumulated data to identify specific patterns and trends, and includes using algorithms to evaluate the current situation and make future predictions.

[0250] "Generation means" refers to methods and apparatus for forming specific proposals and results based on analyzed data, and includes processes for automatically generating meal menus.

[0251] "Visualization means" refers to a means of conveying information obtained by a generation means to a user, and includes displaying information through a display or smartphone screen.

[0252] "Nutritional suggestions" refer to the presentation of meal plans that take into account the user's health condition and personal preferences, and consider an appropriate balance of nutrients.

[0253] The system of the present invention provides personalized nutritional suggestions based on the user's health information and consists of a device and software with several key functions. Specifically, it is implemented as follows:

[0254] The device is responsible for collecting health data from the user's body. This includes wearable devices such as smartwatches and fitness trackers that measure heart rate, activity levels, and calories burned. This data is acquired in real time and used to understand the user's daily health status.

[0255] The server securely receives health data transmitted from terminals and stores it in a database. An integrated information processing device is used for data analysis, and machine learning algorithms are employed to evaluate the data. This provides the foundational data needed to provide personalized nutritional recommendations to individual users.

[0256] Based on the analysis results, an automated generation system on the server uses a generation AI model to create an optimal meal plan for the user. An example of a prompt used in this process is, "Please suggest a meal plan for next week based on the user's health data." This ensures that practical and effective suggestions are provided that match the user's nutritional needs.

[0257] The generated meal menu is sent to the device and displayed to the user. The device's display shows images of the dishes, their nutritional information, and calorie count, allowing the user to easily plan their daily meals based on this information.

[0258] For example, if a user wants to adjust their energy intake based on their overall activity level for the week, this system will suggest a menu that is optimal for the user's condition, including appropriate amounts of carbohydrates and protein. This makes it easier for the user to maintain healthy eating habits.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The device acquires the user's health data. Specifically, it continuously monitors vital data such as heart rate, calories burned, and steps taken using a smartwatch or fitness tracker, and temporarily stores this data within the device. The input here is biosignals generated from the user's physical movements, and the output is digitized health data.

[0262] Step 2:

[0263] The device transmits the acquired health data to the server via a secure communication protocol. The data is encrypted during this process to ensure its security. The input here is the digital health data obtained in step 1, and the output is the transmitted encrypted data.

[0264] Step 3:

[0265] The server collects the received data into a database. Here, the received data is organized and stored, preparing it for later analysis. The input is encrypted health data, and the output is integrated data securely stored in the database.

[0266] Step 4:

[0267] The server utilizes information processing equipment to analyze the collected data. The analysis process uses machine learning algorithms to evaluate the data and gain insights into the user's health status. The input is user data in the database, and the output is insights into the user's health status and nutritional needs as a result of the analysis.

[0268] Step 5:

[0269] The server uses a generative AI model to generate meal menus based on the analysis results. In this process, prompts are used to create nutritional suggestions that take into account the user's past health data and preferences. The input is the analysis results obtained in step 4, and the output is a individually customized meal menu.

[0270] Step 6:

[0271] The server sends the generated meal menu to the terminal. The menu is sent in digital format and ready to be displayed on the user's terminal. The input is the generated meal menu, and the output is the menu sent to the terminal.

[0272] Step 7:

[0273] The terminal displays the received meal menu to the user. The screen shows images of the dishes, recipes, and nutritional information, allowing the user to plan their meals based on this information. The input is the meal menu sent from the server, and the output is nutritional information that the user can visually confirm.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In health management, it is crucial to create and implement personalized meal plans simply and effectively. However, conventional systems fail to fully utilize users' health information in real time, and the provision of food purchases and related information based on that information is insufficient. This creates a challenge in that it is difficult to sustain improvements in eating habits.

[0277] 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.

[0278] In this invention, the server includes a measurement means for monitoring health information, an information processing means for receiving, storing, and analyzing data obtained from the measurement means, and an automatic generation means for generating individually tailored meal plans based on the results analyzed by the information processing means. This enables the automatic generation of meal plans tailored to the user's health condition and the efficient provision of related food purchases and information acquisition.

[0279] "Health information" refers to data that indicates the user's physical condition, including heart rate, steps taken, and calories burned.

[0280] "Monitoring" refers to the continuous observation and recording of specific conditions or data.

[0281] "Measurement means" refers to sensors and devices used to acquire users' health information.

[0282] "Information processing means" refers to systems and software for receiving, storing, and analyzing acquired data.

[0283] "Analysis" refers to the act of examining collected data in detail to find meaning and patterns.

[0284] "Automatic generation means" refers to processes and algorithms for creating appropriate diet plans individually based on analysis results.

[0285] "Display means" refers to devices and interfaces that show the generated information and plans in a form that users can visually confirm.

[0286] "Diet plan" refers to menus and recipes of meals recommended based on an individual's health and nutrition.

[0287] "Purchase" refers to the act or procedure of acquiring recommended food ingredients and services.

[0288] "Related information" refers to additional data and knowledge for complementing displayed diet plans and nutritional information.

[0289] The system of this invention is operated by combining multiple hardware and software to efficiently collect, analyze, and generate individual diet plans for users' health information. The server uses measurement means for acquiring health information from wearable devices, which includes smartwatches and fitness trackers equipped with sensors. The terminal receives data from these devices via Bluetooth or Wi-Fi.

[0290] Information processing is performed using data stored in a database platform such as Firebase. A Python program running on the server processes the data using Pandas and NumPy, performing analysis based on individual health conditions. This analysis enables the automated generation of meal plans using generative AI models. In particular, the use of machine learning frameworks such as TensorFlow and Keras allows for the flexible creation of meal plans based on the user's past eating habits and health data.

[0291] The generated meal plan is sent to the device via an API developed using the Flask framework. The device then uses this information to visually notify the user of the meal plan and interacts with a user interface implemented using Flutter to provide specific menus and nutritional information.

[0292] For example, if a user has finished their daily activities and wants to decide on a suitable dinner, the system analyzes their activity data and health status for the day, and suggests menus such as spicy tofu stir-fry or salmon meunière, taking into account the necessary nutrients. Links to relevant online shopping services are also provided so that the user can quickly purchase the ingredients related to the specific menu.

[0293] An example of a prompt message is: "Based on today's health data, please suggest a dinner menu for tonight. Please provide three nutritionally balanced dishes that take into account activity levels and preferences."

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The server collects health information from the wearable device. Inputs include the user's heart rate, steps taken, and calories burned, and this data is transmitted to the device via Bluetooth or Wi-Fi. The device receives this data and prepares it to send to the server.

[0297] Step 2:

[0298] The server stores the received health information in the Firebase database. The input is the data collected in the previous step, and by saving it to the database, the server prepares the historical data necessary for later analysis.

[0299] Step 3:

[0300] The server performs data analysis using the accumulated health information. It executes Python scripts as a data processing tool and constructs datasets using Pandas and NumPy. The input includes past health information and current data, and the output generates an index that evaluates the user's health status.

[0301] Step 4:

[0302] The server automatically generates individual meal plans using a generative AI model based on the analysis results. Using machine learning frameworks such as TensorFlow or Keras, menu candidates are created that take into account health status and preferences. The input is the evaluation metrics obtained in the previous step, and the output is a meal plan.

[0303] Step 5:

[0304] The server sends the generated meal plan to the device. The meal plan is delivered to the device via an API using Flask. The input is the created meal plan, and the output is the data that should be displayed on the device.

[0305] Step 6:

[0306] The terminal displays the received meal plan to the user. A user interface implemented in Flutter is responsible for the visual display of the menu and also enables the user to easily obtain links and information for purchasing related products. The input is the meal plan sent from the server, and as output, information that the user can confirm is presented on the screen.

[0307] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0308] The present invention is a system for assisting in health management, which is implemented by combining a measuring device for monitoring health information, an information processing device for receiving, storing, and analyzing data, an automatic generation device for generating a meal menu, a display device for displaying the generated menu, and an emotion engine for recognizing the user's emotional state. This system can provide more personalized meal suggestions based on the user's daily health and emotional states.

[0309] [[ID=I12]]First, the "terminal" obtains the user's health data using sensors. This data includes heart rate, amount of exercise, quality of sleep, body temperature, etc. Also, by using a camera and a microphone, the emotion engine推测 the emotional state from the user's voice tone, facial expression, etc. These data are sent to the "server". <l

[0310] The "server" stores the health data and the user's emotional information received from the terminal and analyzes this using an information processing device. As a result of the analysis, the user's health and emotional states are stored in a database and serve as input for the next step. Machine learning techniques are used in this analysis to perform a refined analysis considering the user's past patterns and current state.

[0311] It should be noted that in the translation of "感情エンジンはユーザの音声トーンや表情などから感情状態を推測する。", the word "推测" in the original text seems to be a misspelling, and it is translated as "推测" here. Maybe it should be "推断" or some other more appropriate word. Also, in the translation of "まず、「端末」は、センサーを利用してユーザの健康データを取得する。", the Chinese part "まず" is translated as "First" which is more in line with the context in English. And in the translation of "これらのデータは、「サーバ」に送信される。", it is presented in a more natural English expression. Overall, this translation tries to accurately convey the meaning of the original text while following the given rules.The "server" generates meal menus using an automated generator based on analysis results from the emotion engine. These menus consider not only the user's physical needs but also their mental satisfaction. For example, the system is designed to suggest many ingredients that have stress-reducing effects.

[0312] The generated meal menu is sent from the "server" to the "terminal." The "terminal" displays the menu to the user and provides notifications that include specific nutritional information and suggestions based on the user's emotional state. This information allows the "user" to choose a meal that takes both their health and emotional state into consideration.

[0313] For example, if the system detects that a user is experiencing stress at work, it might recommend selecting a menu that includes relaxing herbal teas or antioxidants. In this way, the system makes it easy for users to select a meal plan that comprehensively considers their individual health and emotional state.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] The device uses various sensors to collect health information, measuring heart rate, activity level, and sleep quality. It also analyzes the user's facial expressions and voice tone through the camera and microphone, and an emotion engine infers the user's emotional state. This data is transmitted to the server in real time.

[0317] Step 2:

[0318] The server stores data received from terminals and analyzes it using an information processing device. Health data is combined with past history to evaluate the user's health status, while emotional information analyzed by the emotion engine is also integrated to understand the user's current emotional state.

[0319] Step 3:

[0320] The server generates meal menus using an automated generator based on the analysis of health and emotional states. The generated menus consider factors that enhance both physical health and emotional satisfaction. For example, they suggest foods that offer both nutritional balance and stress-reducing effects.

[0321] Step 4:

[0322] The server sends the generated meal menu to the terminal.

[0323] Step 5:

[0324] The device displays the received meal menu to the user. The displayed information includes nutritional information about the meal and reasons for the suggestion based on the user's emotional state. This allows the user to make choices in their daily meals that are appropriate for both their physical and mental condition.

[0325] Step 6:

[0326] The user selects a specific menu item and eats the meal. Afterwards, they provide feedback to the server via their device regarding their physical condition and emotions after the meal.

[0327] Step 7:

[0328] The server receives user feedback and continuously updates its generative AI model and emotion engine. This ensures that subsequent menu suggestions are more tailored to the user.

[0329] (Example 2)

[0330] 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".

[0331] In today's busy lifestyle, balancing daily health management and emotional care is difficult. In particular, a more flexible and personalized system is needed to accurately provide meal suggestions based on each user's health condition and emotions. This invention aims to maintain health and stabilize emotions by integrating and analyzing health data and emotional information to provide users with an optimal meal plan.

[0332] 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.

[0333] In this invention, the server includes sensing means for acquiring biometric data, data processing means for receiving, storing, and analyzing the biometric data obtained from the sensing means, and emotion recognition means for identifying the user's emotional state. This makes it possible to provide personalized meal plans based on the user's health and emotional state.

[0334] "Biometric data" refers to data related to an individual's health status, such as heart rate, exercise level, sleep quality, and body temperature.

[0335] "Sensing means" refers to devices and equipment that use sensor technology to acquire biometric data from users.

[0336] "Data processing means" refers to a technological device that receives, stores, and analyzes collected biological data.

[0337] "Automatic creation means" refers to a technological device that generates individually tailored meal plans based on analyzed data.

[0338] "Display means" refers to a device that provides the generated meal plan to the user visually or audibly.

[0339] "Emotion recognition means" refers to a technological device that analyzes the user's voice tone and facial expressions to identify their emotional state.

[0340] This invention is a system that proposes an individually tailored meal plan by comprehensively utilizing the user's biometric data and emotional state. This system is implemented by combining sensing means, data processing means, automatic creation means, display means, and emotion recognition means.

[0341] The "terminal" acquires biometric data through sensing means via wearable devices worn by the user or applications on a smartphone. This data includes heart rate, exercise level, sleep quality, and body temperature. In addition, it simultaneously captures the user's emotional state by using cameras and microphones to analyze voice tone and facial expressions through emotion recognition means.

[0342] The "server" receives and stores data transmitted from the "terminal" using data processing means. The received data is securely stored in a database and then analyzed using machine learning algorithms. Based on the analyzed data, the server uses a generative AI model to automatically generate a meal plan. This plan takes into account the user's individual health needs and emotional state, and includes further personalized suggestions.

[0343] The generated meal plan is sent from the "server" to the "terminal" and provided to the user through a display device. The "terminal" can easily receive the meal plan not only through visual display but also through voice and notification functions.

[0344] For example, if the system detects that the "user" is feeling stressed, a meal plan including foods and beverages with relaxing effects will be suggested. An example of a prompt to the generative AI model might be, "Based on this week's health data, suggest a meal menu that will help reduce stress."

[0345] This system allows users to select meals that suit their health and emotional state, thereby improving their lifestyle.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The "device" acquires biometric information in real time using the user's wearable device or smartphone. This input data includes heart rate, activity level, sleep quality, and body temperature. Through this data, basic information is collected to understand the user's current health status.

[0349] Step 2:

[0350] The "device" captures the user's facial expressions and voice using its built-in camera and microphone, and analyzes their emotional state using emotion recognition technology. It analyzes voice tone and facial expressions from video and audio files as input data, infers emotions such as anger, sadness, and joy, and outputs the results.

[0351] Step 3:

[0352] These health and emotional data are sent from the "terminal" to the "server." The "server" stores the received data in a database and uses machine learning algorithms to analyze the data based on the accumulated biometric and emotional data. At this stage, based on the input data, it considers past health trends and patterns and outputs detailed analysis results regarding the current health and emotional state.

[0353] Step 4:

[0354] The "server" uses the results of data analysis to create personalized meal plans through an automated generation method utilizing a generative AI model. In this step, the analysis results are input as prompts to the AI ​​model, which generates meal suggestions based on health status and emotions. For example, if stress levels are high, it will output a menu that uses many ingredients that have stress-reducing effects.

[0355] Step 5:

[0356] The generated meal plan is sent from the "server" to the "terminal." The "terminal" presents the meal plan to the user through a visual interface and voice notifications. This output includes specific menu items, nutritional information, and a list of recommended ingredients, helping the user choose meals that balance health and emotional well-being.

[0357] (Application Example 2)

[0358] 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 as the "terminal".

[0359] Modern consumers seek product choices that consider both health and emotional well-being, but traditional stores have faced the challenge of providing appropriate product recommendations tailored to the individual needs of each customer. This invention aims to solve this problem by suggesting optimal products based on each customer's health and emotional state, thereby improving customer satisfaction.

[0360] 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.

[0361] In this invention, the server includes means for using a sensor device for monitoring health information, information processing means for receiving, storing, and analyzing health data and emotional data obtained from the sensor device, and automatic generation means for generating individually suitable product candidates based on the results analyzed by the information processing means. This makes it possible to recommend products that are suitable for the user's health and emotional state in real time.

[0362] "Health information" refers to data collected to measure the user's physical condition, and includes heart rate, body temperature, and exercise level.

[0363] A "sensor device" is a measuring device used to monitor health and emotional information, and includes heart rate sensors and cameras for emotion analysis.

[0364] An "information processing device" is a device used to receive, store, and analyze collected health and emotional data, and includes computers and servers.

[0365] "Automatic generation means" refers to means for generating individually suitable product candidates based on the results analyzed by an information processing device, and includes algorithms and programs.

[0366] A "display device" is a device used to visualize generated product candidates for the user, and includes smartphones and displays.

[0367] "Emotional data" refers to information collected to measure the user's psychological state, and includes facial expressions, voice tone, and other similar data.

[0368] A "product recommendation list" is a list of products recommended based on the user's health and emotional state, and includes products that meet specific criteria.

[0369] This invention provides a system for suggesting products that are individually suited to a user based on their health and emotional state. This system mainly consists of a sensor device that collects health and emotional information, a server that processes the information, and a terminal that displays the results.

[0370] The server stores and analyzes health and emotional data received from sensor devices. Specifically, it uses heart rate sensors, cameras, microphones, and other sensors to acquire health information such as heart rate and body temperature, and emotional information such as facial expressions and voice tone. The server processes this information and uses machine learning algorithms to analyze the data and recognize patterns.

[0371] The server uses an automated generation mechanism based on the analyzed data to generate product suggestions tailored to the user. These suggestions are sent to the user's device as a product list and visualized. The displayed product information includes relevant nutritional information and suggestions based on the user's emotional state. Smartphone or tablet displays are used as the display devices.

[0372] As a concrete example, suppose a sensor device detects the user's fatigue and stress levels when they enter a physical store. In this case, the server generates a list of products that promote relaxation, such as herbal tea or antioxidant snacks, and displays it on the user's terminal. The user can then receive this customized suggestion and select appropriate products within the store.

[0373] Using a generative AI model, the following prompt can be used: "The user's emotional state has been detected as 'fatigue'. Please generate a list of foods and beverages that have a relaxing effect." In response to this prompt, a list of optimal products will be created.

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The terminal acquires health and emotional information from sensor devices attached to the body of users who enter a physical store. The input is raw data from the sensors, and the output generates health and emotional data such as heart rate, body temperature, and facial expression. In this process, the sensors measure various vital information in real time and transmit these values ​​to the terminal.

[0377] Step 2:

[0378] The server stores health and emotional data received via the terminal and analyzes it using machine learning algorithms. The input is the health and emotional data acquired in step 1, and the output is the analysis results showing the user's current health and emotional state. In this process, the server uses a knowledge base, referencing past data as well, to identify patterns in health and emotional states and evaluate the user's condition.

[0379] Step 3:

[0380] The server uses an automated generation mechanism based on the analysis results to generate the most suitable product candidates for the user. The input is the analysis results from step 2, and the output is a product list. Here, the generation AI model uses the analysis results to process prompts and identify product groups suitable for the user's health and emotional state. A specific example of a prompt is: "The user's emotional state has been detected as 'fatigue'. Please generate a list of foods and beverages with relaxing effects."

[0381] Step 4:

[0382] The generated product list is sent from the server to the terminal and displayed on the terminal. The input is the product list data from the server, and the output is the product recommendation result displayed on the user's terminal screen. The terminal visually presents this product list to the user and supports product selection in a physical store, including nutritional information and emotion-based suggestion information.

[0383] Step 5:

[0384] The user refers to a product list displayed on the terminal and selects a product in the physical store that suits their health and emotional state. The input is the information displayed on the terminal in step 4, and the output is the receipt of the selected product. This step involves the user moving around the store, searching for the presented product options, and actually picking up the product.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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".

[0401] The present invention is implemented as a system combining a measuring device for monitoring health information, an information processing device for receiving and analyzing data, an automatic generation device for generating meal menus, and a display device for displaying them. This system makes it possible to easily and effectively manage the user's daily health.

[0402] First, the "device" acquires various health data from the user's body. This data collection is mainly performed by measuring devices equipped with sensors, typically by wearable devices such as smartwatches and fitness trackers. This data is then periodically transmitted to the "server."

[0403] The "server" stores health data received from terminals and analyzes the data using an information processing device. The analyzed data is used as input to adjust meal menus. This process utilizes nutrition-based statistical methods and machine learning algorithms to generate meal menus tailored to the user's specific health condition and preferences.

[0404] Next, the "server" sends the meal menu generated by the automated generator to the "terminal." The "terminal" receives this transmission and displays the menu information to the user. The terminal screen displays images of recommended dishes and information about their nutritional content, making it easy for the user to plan their meals for the day or week.

[0405] For example, if a user finishes their day's activities and wants to choose a suitable meal for dinner, this system allows their health data to be instantly analyzed by the server, and the optimal menu tailored to the user's activity level and physical condition for the day is displayed on the terminal. For instance, if carbohydrate intake is needed, pasta salad or brown rice risotto might be suggested as menu options.

[0406] This system allows users to easily select nutritionally balanced meals tailored to their health condition, enabling them to make appropriate meal choices each time. This embodiment aims to ensure that users maintain consistently healthy eating habits.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] The device acquires the user's health data through sensors. This includes heart rate, steps taken, sleep patterns, blood glucose levels, and more. The measuring device periodically collects this data and transmits it to a server using its communication function.

[0410] Step 2:

[0411] The server receives health data transmitted from the terminal and stores it in a database. The received data is integrated with historical data and becomes the basis for analyzing the user's long-term health status.

[0412] Step 3:

[0413] The server performs analysis on an information processing device based on the accumulated data. Here, machine learning algorithms are used to identify patterns in the user's health status and evaluate nutrient deficiencies and excesses.

[0414] Step 4:

[0415] The server uses an automated generator to create meal menus tailored to the user based on the analysis results. This process also takes into account the user's nutritional needs and past preferences. The generated menus prioritize nutritional balance and variety.

[0416] Step 5:

[0417] The server sends the generated meal menu back to the terminal.

[0418] Step 6:

[0419] The device displays the received meal menu to the user. The user can review the details of the provided menu and select a meal based on images and nutritional information. The device also records the user's selections and feedback.

[0420] Step 7:

[0421] Users select a meal from a menu and consume it. After the meal, they input feedback via their device, sending comments about their satisfaction level and physical condition to the server.

[0422] Step 8:

[0423] The server receives user feedback and continuously trains the generating AI model. This allows menu suggestions to be gradually optimized, improving accuracy in subsequent uses.

[0424] (Example 1)

[0425] 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."

[0426] In modern society, personal health management is becoming increasingly important, but systems that provide individually tailored nutritional information and support sustainable healthy habits are limited. Traditional methods primarily rely on advice based on general nutrition guidelines, which do not adequately address individual differences among users. Against this backdrop, there is a need for methods that enable more personalized nutritional management by providing meal suggestions that reflect individual health data and preferences.

[0427] 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.

[0428] In this invention, the server includes acquisition means for monitoring vital data, analysis means for collecting and analyzing the data obtained from the acquisition means via communication, and generation means for generating individually tailored nutritional suggestions based on the results of the analysis by the analysis means. This makes it possible to provide nutritional suggestions tailored to the individual differences of the user.

[0429] "Vital data" refers to basic biological information that indicates a person's health status, and includes heart rate, body temperature, activity level, etc.

[0430] "Acquisition means" refers to technical means for directly acquiring vital data from the user, and includes devices such as wearable devices equipped with sensors.

[0431] "Communication" refers to methods and protocols for sending and receiving acquired data between remote devices, including wireless communication and data transfer via the Internet.

[0432] "Accumulation" refers to the process of centrally storing and managing received data, and includes procedures such as storing it in a database.

[0433] "Analysis" is the act of examining accumulated data to identify specific patterns and trends, and includes using algorithms to evaluate the current situation and make future predictions.

[0434] "Generation means" refers to methods and apparatus for forming specific proposals and results based on analyzed data, and includes processes for automatically generating meal menus.

[0435] "Visualization means" refers to a means of conveying information obtained by a generation means to a user, and includes displaying information through a display or smartphone screen.

[0436] "Nutritional suggestions" refer to the presentation of meal plans that take into account the user's health condition and personal preferences, and consider an appropriate balance of nutrients.

[0437] The system of the present invention provides personalized nutritional suggestions based on the user's health information and consists of a device and software with several key functions. Specifically, it is implemented as follows:

[0438] The device is responsible for collecting health data from the user's body. This includes wearable devices such as smartwatches and fitness trackers that measure heart rate, activity levels, and calories burned. This data is acquired in real time and used to understand the user's daily health status.

[0439] The server securely receives health data transmitted from terminals and stores it in a database. An integrated information processing device is used for data analysis, and machine learning algorithms are employed to evaluate the data. This provides the foundational data needed to provide personalized nutritional recommendations to individual users.

[0440] Based on the analysis results, an automated generation system on the server uses a generation AI model to create an optimal meal plan for the user. An example of a prompt used in this process is, "Please suggest a meal plan for next week based on the user's health data." This ensures that practical and effective suggestions are provided that match the user's nutritional needs.

[0441] The generated meal menu is sent to the device and displayed to the user. The device's display shows images of the dishes, their nutritional information, and calorie count, allowing the user to easily plan their daily meals based on this information.

[0442] For example, if a user wants to adjust their energy intake based on their overall activity level for the week, this system will suggest a menu that is optimal for the user's condition, including appropriate amounts of carbohydrates and protein. This makes it easier for the user to maintain healthy eating habits.

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] The device acquires the user's health data. Specifically, it continuously monitors vital data such as heart rate, calories burned, and steps taken using a smartwatch or fitness tracker, and temporarily stores this data within the device. The input here is biosignals generated from the user's physical movements, and the output is digitized health data.

[0446] Step 2:

[0447] The device transmits the acquired health data to the server via a secure communication protocol. The data is encrypted during this process to ensure its security. The input here is the digital health data obtained in step 1, and the output is the transmitted encrypted data.

[0448] Step 3:

[0449] The server collects the received data into a database. Here, the received data is organized and stored, preparing it for later analysis. The input is encrypted health data, and the output is integrated data securely stored in the database.

[0450] Step 4:

[0451] The server utilizes information processing equipment to analyze the collected data. The analysis process uses machine learning algorithms to evaluate the data and gain insights into the user's health status. The input is user data in the database, and the output is insights into the user's health status and nutritional needs as a result of the analysis.

[0452] Step 5:

[0453] The server uses a generative AI model to generate meal menus based on the analysis results. In this process, prompts are used to create nutritional suggestions that take into account the user's past health data and preferences. The input is the analysis results obtained in step 4, and the output is a individually customized meal menu.

[0454] Step 6:

[0455] The server sends the generated meal menu to the terminal. The menu is sent in digital format and ready to be displayed on the user's terminal. The input is the generated meal menu, and the output is the menu sent to the terminal.

[0456] Step 7:

[0457] The terminal displays the received meal menu to the user. The screen shows images of the dishes, recipes, and nutritional information, allowing the user to plan their meals based on this information. The input is the meal menu sent from the server, and the output is nutritional information that the user can visually confirm.

[0458] (Application Example 1)

[0459] 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."

[0460] In health management, it is crucial to create and implement personalized meal plans simply and effectively. However, conventional systems fail to fully utilize users' health information in real time, and the provision of food purchases and related information based on that information is insufficient. This creates a challenge in that it is difficult to sustain improvements in eating habits.

[0461] 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.

[0462] In this invention, the server includes a measurement means for monitoring health information, an information processing means for receiving, storing, and analyzing data obtained from the measurement means, and an automatic generation means for generating individually tailored meal plans based on the results analyzed by the information processing means. This enables the automatic generation of meal plans tailored to the user's health condition and the efficient provision of related food purchases and information acquisition.

[0463] "Health information" refers to data that indicates the user's physical condition, including heart rate, steps taken, and calories burned.

[0464] "Monitoring" refers to the continuous observation and recording of specific conditions or data.

[0465] "Measurement means" refers to sensors and devices used to acquire a user's health information.

[0466] "Information processing means" refers to systems and software for receiving, storing, and analyzing acquired data.

[0467] "Analysis" is the act of examining collected data in detail to find meaning and patterns.

[0468] "Automated generation method" refers to the process or algorithm for creating individually appropriate meal plans based on analysis results.

[0469] "Display means" refers to devices or interfaces that show generated information or plans in a way that allows users to visually confirm them.

[0470] A "meal plan" refers to a set of recommended meal menus and recipes based on an individual's health and nutritional needs.

[0471] "Purchase" refers to the action or procedure of acquiring recommended food products or services.

[0472] "Related information" refers to additional data and knowledge that complements the displayed meal plan and nutritional information.

[0473] The system of this invention operates by combining multiple hardware and software components to efficiently collect and analyze a user's health information and generate a personalized meal plan. The server uses measurement means to acquire health information from wearable devices, including smartwatches and fitness trackers equipped with sensors. The terminal receives data from these devices via Bluetooth or Wi-Fi.

[0474] Information processing is performed using data stored in a database platform such as Firebase. A Python program running on the server processes the data using Pandas and NumPy, performing analysis based on individual health conditions. This analysis enables the automated generation of meal plans using generative AI models. In particular, the use of machine learning frameworks such as TensorFlow and Keras allows for the flexible creation of meal plans based on the user's past eating habits and health data.

[0475] The generated meal plan is sent to the device via an API developed using the Flask framework. The device then uses this information to visually notify the user of the meal plan and interacts with a user interface implemented using Flutter to provide specific menus and nutritional information.

[0476] For example, if a user has finished their daily activities and wants to decide on a suitable dinner, the system analyzes their activity data and health status for the day, and suggests menus such as spicy tofu stir-fry or salmon meunière, taking into account the necessary nutrients. Links to relevant online shopping services are also provided so that the user can quickly purchase the ingredients related to the specific menu.

[0477] An example of a prompt message is: "Based on today's health data, please suggest a dinner menu for tonight. Please provide three nutritionally balanced dishes that take into account activity levels and preferences."

[0478] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0479] Step 1:

[0480] The server collects health information from the wearable device. Inputs include the user's heart rate, steps taken, and calories burned, and this data is transmitted to the device via Bluetooth or Wi-Fi. The device receives this data and prepares it to send to the server.

[0481] Step 2:

[0482] The server stores the received health information in the Firebase database. The input is the data collected in the previous step, and by saving it to the database, the server prepares the historical data necessary for later analysis.

[0483] Step 3:

[0484] The server performs data analysis using the accumulated health information. It executes Python scripts as a data processing tool and constructs datasets using Pandas and NumPy. The input includes past health information and current data, and the output generates an index that evaluates the user's health status.

[0485] Step 4:

[0486] The server automatically generates individual meal plans using a generative AI model based on the analysis results. Using machine learning frameworks such as TensorFlow or Keras, menu candidates are created that take into account health status and preferences. The input is the evaluation metrics obtained in the previous step, and the output is a meal plan.

[0487] Step 5:

[0488] The server sends the generated meal plan to the device. The meal plan is delivered to the device via an API using Flask. The input is the created meal plan, and the output is the data that should be displayed on the device.

[0489] Step 6:

[0490] The device displays the received meal plan to the user. A user interface implemented in Flutter is responsible for the visual display of the menu and also allows the user to easily obtain links and information for purchasing related products. The input is the meal plan sent from the server, and the output is the information that the user can view displayed on the screen.

[0491] 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.

[0492] This invention is a system that assists in health management, and is implemented by combining a measuring device for monitoring health information, an information processing device for receiving, storing, and analyzing data, an automatic generation device for generating meal menus, a display device for displaying the generated menus, and an emotion engine for recognizing the user's emotional state. This system is capable of providing more personalized meal suggestions based on the user's daily health and emotional state.

[0493] First, the "device" uses sensors to acquire the user's health data. This data includes heart rate, activity level, sleep quality, and body temperature. Additionally, using the camera and microphone, the emotion engine infers the user's emotional state from their voice tone and facial expressions. This data is then transmitted to the "server."

[0494] The "server" stores health data and user emotional information received from the terminal and analyzes it using an information processing device. As a result of the analysis, the user's health and emotional state are stored in a database and used as input for the next step. Machine learning technology is used in this analysis to perform a sophisticated analysis that takes into account the user's past patterns and current state.

[0495] The "server" generates meal menus using an automated generator based on analysis results from the emotion engine. These menus consider not only the user's physical needs but also their mental satisfaction. For example, the system is designed to suggest many ingredients that have stress-reducing effects.

[0496] The generated meal menu is sent from the "server" to the "terminal." The "terminal" displays the menu to the user and provides notifications that include specific nutritional information and suggestions based on the user's emotional state. This information allows the "user" to choose a meal that takes both their health and emotional state into consideration.

[0497] For example, if the system detects that a user is experiencing stress at work, it might recommend selecting a menu that includes relaxing herbal teas or antioxidants. In this way, the system makes it easy for users to select a meal plan that comprehensively considers their individual health and emotional state.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The device uses various sensors to collect health information, measuring heart rate, activity level, and sleep quality. It also analyzes the user's facial expressions and voice tone through the camera and microphone, and an emotion engine infers the user's emotional state. This data is transmitted to the server in real time.

[0501] Step 2:

[0502] The server stores data received from terminals and analyzes it using an information processing device. Health data is combined with past history to evaluate the user's health status, while emotional information analyzed by the emotion engine is also integrated to understand the user's current emotional state.

[0503] Step 3:

[0504] The server generates meal menus using an automated generator based on the analysis of health and emotional states. The generated menus consider factors that enhance both physical health and emotional satisfaction. For example, they suggest foods that offer both nutritional balance and stress-reducing effects.

[0505] Step 4:

[0506] The server sends the generated meal menu to the terminal.

[0507] Step 5:

[0508] The device displays the received meal menu to the user. The displayed information includes nutritional information about the meal and reasons for the suggestion based on the user's emotional state. This allows the user to make choices in their daily meals that are appropriate for both their physical and mental condition.

[0509] Step 6:

[0510] The user selects a specific menu item and eats the meal. Afterwards, they provide feedback to the server via their device regarding their physical condition and emotions after the meal.

[0511] Step 7:

[0512] The server receives user feedback and continuously updates its generative AI model and emotion engine. This ensures that subsequent menu suggestions are more tailored to the user.

[0513] (Example 2)

[0514] 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."

[0515] In today's busy lifestyle, balancing daily health management and emotional care is difficult. In particular, a more flexible and personalized system is needed to accurately provide meal suggestions based on each user's health condition and emotions. This invention aims to maintain health and stabilize emotions by integrating and analyzing health data and emotional information to provide users with an optimal meal plan.

[0516] 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.

[0517] In this invention, the server includes sensing means for acquiring biometric data, data processing means for receiving, storing, and analyzing the biometric data obtained from the sensing means, and emotion recognition means for identifying the user's emotional state. This makes it possible to provide personalized meal plans based on the user's health and emotional state.

[0518] "Biometric data" refers to data related to an individual's health status, such as heart rate, exercise level, sleep quality, and body temperature.

[0519] "Sensing means" refers to devices and equipment that use sensor technology to acquire biometric data from users.

[0520] "Data processing means" refers to a technological device that receives, stores, and analyzes collected biological data.

[0521] "Automatic creation means" refers to a technological device that generates individually tailored meal plans based on analyzed data.

[0522] "Display means" refers to a device that provides the generated meal plan to the user visually or audibly.

[0523] "Emotion recognition means" refers to a technological device that analyzes the user's voice tone and facial expressions to identify their emotional state.

[0524] This invention is a system that proposes an individually tailored meal plan by comprehensively utilizing the user's biometric data and emotional state. This system is implemented by combining sensing means, data processing means, automatic creation means, display means, and emotion recognition means.

[0525] The "terminal" acquires biometric data through sensing means via wearable devices worn by the user or applications on a smartphone. This data includes heart rate, exercise level, sleep quality, and body temperature. In addition, it simultaneously captures the user's emotional state by using cameras and microphones to analyze voice tone and facial expressions through emotion recognition means.

[0526] The "server" receives and stores data transmitted from the "terminal" using data processing means. The received data is securely stored in a database and then analyzed using machine learning algorithms. Based on the analyzed data, the server uses a generative AI model to automatically generate a meal plan. This plan takes into account the user's individual health needs and emotional state, and includes further personalized suggestions.

[0527] The generated meal plan is sent from the "server" to the "terminal" and provided to the user through a display device. The "terminal" can easily receive the meal plan not only through visual display but also through voice and notification functions.

[0528] For example, if the system detects that the "user" is feeling stressed, a meal plan including foods and beverages with relaxing effects will be suggested. An example of a prompt to the generative AI model might be, "Based on this week's health data, suggest a meal menu that will help reduce stress."

[0529] This system allows users to select meals that suit their health and emotional state, thereby improving their lifestyle.

[0530] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0531] Step 1:

[0532] The "device" acquires biometric information in real time using the user's wearable device or smartphone. This input data includes heart rate, activity level, sleep quality, and body temperature. Through this data, basic information is collected to understand the user's current health status.

[0533] Step 2:

[0534] The "device" captures the user's facial expressions and voice using its built-in camera and microphone, and analyzes their emotional state using emotion recognition technology. It analyzes voice tone and facial expressions from video and audio files as input data, infers emotions such as anger, sadness, and joy, and outputs the results.

[0535] Step 3:

[0536] These health and emotional data are sent from the "terminal" to the "server." The "server" stores the received data in a database and uses machine learning algorithms to analyze the data based on the accumulated biometric and emotional data. At this stage, based on the input data, it considers past health trends and patterns and outputs detailed analysis results regarding the current health and emotional state.

[0537] Step 4:

[0538] The "server" uses the results of data analysis to create personalized meal plans through an automated generation method utilizing a generative AI model. In this step, the analysis results are input as prompts to the AI ​​model, which generates meal suggestions based on health status and emotions. For example, if stress levels are high, it will output a menu that uses many ingredients that have stress-reducing effects.

[0539] Step 5:

[0540] The generated meal plan is sent from the "server" to the "terminal." The "terminal" presents the meal plan to the user through a visual interface and voice notifications. This output includes specific menu items, nutritional information, and a list of recommended ingredients, helping the user choose meals that balance health and emotional well-being.

[0541] (Application Example 2)

[0542] 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."

[0543] Modern consumers seek product choices that consider both health and emotional well-being, but traditional stores have faced the challenge of providing appropriate product recommendations tailored to the individual needs of each customer. This invention aims to solve this problem by suggesting optimal products based on each customer's health and emotional state, thereby improving customer satisfaction.

[0544] 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.

[0545] In this invention, the server includes means for using a sensor device for monitoring health information, information processing means for receiving, storing, and analyzing health data and emotional data obtained from the sensor device, and automatic generation means for generating individually suitable product candidates based on the results analyzed by the information processing means. This makes it possible to recommend products that are suitable for the user's health and emotional state in real time.

[0546] "Health information" refers to data collected to measure the user's physical condition, and includes heart rate, body temperature, and exercise level.

[0547] A "sensor device" is a measuring device used to monitor health and emotional information, and includes heart rate sensors and cameras for emotion analysis.

[0548] An "information processing device" is a device used to receive, store, and analyze collected health and emotional data, and includes computers and servers.

[0549] "Automatic generation means" refers to means for generating individually suitable product candidates based on the results analyzed by an information processing device, and includes algorithms and programs.

[0550] A "display device" is a device used to visualize generated product candidates for the user, and includes smartphones and displays.

[0551] "Emotional data" refers to information collected to measure the user's psychological state, and includes facial expressions, voice tone, and other similar data.

[0552] A "product recommendation list" is a list of products recommended based on the user's health and emotional state, and includes products that meet specific criteria.

[0553] This invention provides a system for suggesting products that are individually suited to a user based on their health and emotional state. This system mainly consists of a sensor device that collects health and emotional information, a server that processes the information, and a terminal that displays the results.

[0554] The server stores and analyzes health and emotional data received from sensor devices. Specifically, it uses heart rate sensors, cameras, microphones, and other sensors to acquire health information such as heart rate and body temperature, and emotional information such as facial expressions and voice tone. The server processes this information and uses machine learning algorithms to analyze the data and recognize patterns.

[0555] The server uses an automated generation mechanism based on the analyzed data to generate product suggestions tailored to the user. These suggestions are sent to the user's device as a product list and visualized. The displayed product information includes relevant nutritional information and suggestions based on the user's emotional state. Smartphone or tablet displays are used as the display devices.

[0556] As a concrete example, suppose a sensor device detects the user's fatigue and stress levels when they enter a physical store. In this case, the server generates a list of products that promote relaxation, such as herbal tea or antioxidant snacks, and displays it on the user's terminal. The user can then receive this customized suggestion and select appropriate products within the store.

[0557] Using a generative AI model, the following prompt can be used: "The user's emotional state has been detected as 'fatigue'. Please generate a list of foods and beverages that have a relaxing effect." In response to this prompt, a list of optimal products will be created.

[0558] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0559] Step 1:

[0560] The terminal acquires health and emotional information from sensor devices attached to the body of users who enter a physical store. The input is raw data from the sensors, and the output generates health and emotional data such as heart rate, body temperature, and facial expression. In this process, the sensors measure various vital information in real time and transmit these values ​​to the terminal.

[0561] Step 2:

[0562] The server stores health and emotional data received via the terminal and analyzes it using machine learning algorithms. The input is the health and emotional data acquired in step 1, and the output is the analysis results showing the user's current health and emotional state. In this process, the server uses a knowledge base, referencing past data as well, to identify patterns in health and emotional states and evaluate the user's condition.

[0563] Step 3:

[0564] The server uses an automated generation mechanism based on the analysis results to generate the most suitable product candidates for the user. The input is the analysis results from step 2, and the output is a product list. Here, the generation AI model uses the analysis results to process prompts and identify product groups suitable for the user's health and emotional state. A specific example of a prompt is: "The user's emotional state has been detected as 'fatigue'. Please generate a list of foods and beverages with relaxing effects."

[0565] Step 4:

[0566] The generated product list is sent from the server to the terminal and displayed on the terminal. The input is the product list data from the server, and the output is the product recommendation result displayed on the user's terminal screen. The terminal visually presents this product list to the user and supports product selection in a physical store, including nutritional information and emotion-based suggestion information.

[0567] Step 5:

[0568] The user refers to a product list displayed on the terminal and selects a product in the physical store that suits their health and emotional state. The input is the information displayed on the terminal in step 4, and the output is the receipt of the selected product. This step involves the user moving around the store, searching for the presented product options, and actually picking up the product.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] [Fourth Embodiment]

[0573] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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".

[0586] The present invention is implemented as a system combining a measuring device for monitoring health information, an information processing device for receiving and analyzing data, an automatic generation device for generating meal menus, and a display device for displaying them. This system makes it possible to easily and effectively manage the user's daily health.

[0587] First, the "device" acquires various health data from the user's body. This data collection is mainly performed by measuring devices equipped with sensors, typically by wearable devices such as smartwatches and fitness trackers. This data is then periodically transmitted to the "server."

[0588] The "server" stores health data received from terminals and analyzes the data using an information processing device. The analyzed data is used as input to adjust meal menus. This process utilizes nutrition-based statistical methods and machine learning algorithms to generate meal menus tailored to the user's specific health condition and preferences.

[0589] Next, the "server" sends the meal menu generated by the automated generator to the "terminal." The "terminal" receives this transmission and displays the menu information to the user. The terminal screen displays images of recommended dishes and information about their nutritional content, making it easy for the user to plan their meals for the day or week.

[0590] For example, if a user finishes their day's activities and wants to choose a suitable meal for dinner, this system allows their health data to be instantly analyzed by the server, and the optimal menu tailored to the user's activity level and physical condition for the day is displayed on the terminal. For instance, if carbohydrate intake is needed, pasta salad or brown rice risotto might be suggested as menu options.

[0591] This system allows users to easily select nutritionally balanced meals tailored to their health condition, enabling them to make appropriate meal choices each time. This embodiment aims to ensure that users maintain consistently healthy eating habits.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The device acquires the user's health data through sensors. This includes heart rate, steps taken, sleep patterns, blood glucose levels, and more. The measuring device periodically collects this data and transmits it to a server using its communication function.

[0595] Step 2:

[0596] The server receives health data transmitted from the terminal and stores it in a database. The received data is integrated with historical data and becomes the basis for analyzing the user's long-term health status.

[0597] Step 3:

[0598] The server performs analysis on an information processing device based on the accumulated data. Here, machine learning algorithms are used to identify patterns in the user's health status and evaluate nutrient deficiencies and excesses.

[0599] Step 4:

[0600] The server uses an automated generator to create meal menus tailored to the user based on the analysis results. This process also takes into account the user's nutritional needs and past preferences. The generated menus prioritize nutritional balance and variety.

[0601] Step 5:

[0602] The server sends the generated meal menu back to the terminal.

[0603] Step 6:

[0604] The device displays the received meal menu to the user. The user can review the details of the provided menu and select a meal based on images and nutritional information. The device also records the user's selections and feedback.

[0605] Step 7:

[0606] Users select a meal from a menu and consume it. After the meal, they input feedback via their device, sending comments about their satisfaction level and physical condition to the server.

[0607] Step 8:

[0608] The server receives user feedback and continuously trains the generating AI model. This allows menu suggestions to be gradually optimized, improving accuracy in subsequent uses.

[0609] (Example 1)

[0610] 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".

[0611] In modern society, personal health management is becoming increasingly important, but systems that provide individually tailored nutritional information and support sustainable healthy habits are limited. Traditional methods primarily rely on advice based on general nutrition guidelines, which do not adequately address individual differences among users. Against this backdrop, there is a need for methods that enable more personalized nutritional management by providing meal suggestions that reflect individual health data and preferences.

[0612] 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.

[0613] In this invention, the server includes acquisition means for monitoring vital data, analysis means for collecting and analyzing the data obtained from the acquisition means via communication, and generation means for generating individually tailored nutritional suggestions based on the results of the analysis by the analysis means. This makes it possible to provide nutritional suggestions tailored to the individual differences of the user.

[0614] "Vital data" refers to basic biological information that indicates a person's health status, and includes heart rate, body temperature, activity level, etc.

[0615] "Acquisition means" refers to technical means for directly acquiring vital data from the user, and includes devices such as wearable devices equipped with sensors.

[0616] "Communication" refers to methods and protocols for sending and receiving acquired data between remote devices, including wireless communication and data transfer via the Internet.

[0617] "Accumulation" refers to the process of centrally storing and managing received data, and includes procedures such as storing it in a database.

[0618] "Analysis" is the act of examining accumulated data to identify specific patterns and trends, and includes using algorithms to evaluate the current situation and make future predictions.

[0619] "Generation means" refers to methods and apparatus for forming specific proposals and results based on analyzed data, and includes processes for automatically generating meal menus.

[0620] "Visualization means" refers to a means of conveying information obtained by a generation means to a user, and includes displaying information through a display or smartphone screen.

[0621] "Nutritional suggestions" refer to the presentation of meal plans that take into account the user's health condition and personal preferences, and consider an appropriate balance of nutrients.

[0622] The system of the present invention provides personalized nutritional suggestions based on the user's health information and consists of a device and software with several key functions. Specifically, it is implemented as follows:

[0623] The device is responsible for collecting health data from the user's body. This includes wearable devices such as smartwatches and fitness trackers that measure heart rate, activity levels, and calories burned. This data is acquired in real time and used to understand the user's daily health status.

[0624] The server securely receives health data transmitted from terminals and stores it in a database. An integrated information processing device is used for data analysis, and machine learning algorithms are employed to evaluate the data. This provides the foundational data needed to provide personalized nutritional recommendations to individual users.

[0625] Based on the analysis results, an automated generation system on the server uses a generation AI model to create an optimal meal plan for the user. An example of a prompt used in this process is, "Please suggest a meal plan for next week based on the user's health data." This ensures that practical and effective suggestions are provided that match the user's nutritional needs.

[0626] The generated meal menu is sent to the device and displayed to the user. The device's display shows images of the dishes, their nutritional information, and calorie count, allowing the user to easily plan their daily meals based on this information.

[0627] For example, if a user wants to adjust their energy intake based on their overall activity level for the week, this system will suggest a menu that is optimal for the user's condition, including appropriate amounts of carbohydrates and protein. This makes it easier for the user to maintain healthy eating habits.

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] The device acquires the user's health data. Specifically, it continuously monitors vital data such as heart rate, calories burned, and steps taken using a smartwatch or fitness tracker, and temporarily stores this data within the device. The input here is biosignals generated from the user's physical movements, and the output is digitized health data.

[0631] Step 2:

[0632] The device transmits the acquired health data to the server via a secure communication protocol. The data is encrypted during this process to ensure its security. The input here is the digital health data obtained in step 1, and the output is the transmitted encrypted data.

[0633] Step 3:

[0634] The server collects the received data into a database. Here, the received data is organized and stored, preparing it for later analysis. The input is encrypted health data, and the output is integrated data securely stored in the database.

[0635] Step 4:

[0636] The server utilizes information processing equipment to analyze the collected data. The analysis process uses machine learning algorithms to evaluate the data and gain insights into the user's health status. The input is user data in the database, and the output is insights into the user's health status and nutritional needs as a result of the analysis.

[0637] Step 5:

[0638] The server uses a generative AI model to generate meal menus based on the analysis results. In this process, prompts are used to create nutritional suggestions that take into account the user's past health data and preferences. The input is the analysis results obtained in step 4, and the output is a individually customized meal menu.

[0639] Step 6:

[0640] The server sends the generated meal menu to the terminal. The menu is sent in digital format and ready to be displayed on the user's terminal. The input is the generated meal menu, and the output is the menu sent to the terminal.

[0641] Step 7:

[0642] The terminal displays the received meal menu to the user. The screen shows images of the dishes, recipes, and nutritional information, allowing the user to plan their meals based on this information. The input is the meal menu sent from the server, and the output is nutritional information that the user can visually confirm.

[0643] (Application Example 1)

[0644] 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".

[0645] In health management, it is crucial to create and implement personalized meal plans simply and effectively. However, conventional systems fail to fully utilize users' health information in real time, and the provision of food purchases and related information based on that information is insufficient. This creates a challenge in that it is difficult to sustain improvements in eating habits.

[0646] 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.

[0647] In this invention, the server includes a measurement means for monitoring health information, an information processing means for receiving, storing, and analyzing data obtained from the measurement means, and an automatic generation means for generating individually tailored meal plans based on the results analyzed by the information processing means. This enables the automatic generation of meal plans tailored to the user's health condition and the efficient provision of related food purchases and information acquisition.

[0648] "Health information" refers to data that indicates the user's physical condition, including heart rate, steps taken, and calories burned.

[0649] "Monitoring" refers to the continuous observation and recording of specific conditions or data.

[0650] "Measurement means" refers to sensors and devices used to acquire a user's health information.

[0651] "Information processing means" refers to systems and software for receiving, storing, and analyzing acquired data.

[0652] "Analysis" is the act of examining collected data in detail to find meaning and patterns.

[0653] "Automated generation method" refers to the process or algorithm for creating individually appropriate meal plans based on analysis results.

[0654] "Display means" refers to devices or interfaces that show generated information or plans in a way that allows users to visually confirm them.

[0655] A "meal plan" refers to a set of recommended meal menus and recipes based on an individual's health and nutritional needs.

[0656] "Purchase" refers to the action or procedure of acquiring recommended food products or services.

[0657] "Related information" refers to additional data and knowledge that complements the displayed meal plan and nutritional information.

[0658] The system of this invention operates by combining multiple hardware and software components to efficiently collect and analyze a user's health information and generate a personalized meal plan. The server uses measurement means to acquire health information from wearable devices, including smartwatches and fitness trackers equipped with sensors. The terminal receives data from these devices via Bluetooth or Wi-Fi.

[0659] Information processing is performed using data stored in a database platform such as Firebase. A Python program running on the server processes the data using Pandas and NumPy, performing analysis based on individual health conditions. This analysis enables the automated generation of meal plans using generative AI models. In particular, the use of machine learning frameworks such as TensorFlow and Keras allows for the flexible creation of meal plans based on the user's past eating habits and health data.

[0660] The generated meal plan is sent to the device via an API developed using the Flask framework. The device then uses this information to visually notify the user of the meal plan and interacts with a user interface implemented using Flutter to provide specific menus and nutritional information.

[0661] For example, if a user has finished their daily activities and wants to decide on a suitable dinner, the system analyzes their activity data and health status for the day, and suggests menus such as spicy tofu stir-fry or salmon meunière, taking into account the necessary nutrients. Links to relevant online shopping services are also provided so that the user can quickly purchase the ingredients related to the specific menu.

[0662] An example of a prompt message is: "Based on today's health data, please suggest a dinner menu for tonight. Please provide three nutritionally balanced dishes that take into account activity levels and preferences."

[0663] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0664] Step 1:

[0665] The server collects health information from the wearable device. Inputs include the user's heart rate, steps taken, and calories burned, and this data is transmitted to the device via Bluetooth or Wi-Fi. The device receives this data and prepares it to send to the server.

[0666] Step 2:

[0667] The server stores the received health information in the Firebase database. The input is the data collected in the previous step, and by saving it to the database, the server prepares the historical data necessary for later analysis.

[0668] Step 3:

[0669] The server performs data analysis using the accumulated health information. It executes Python scripts as a data processing tool and constructs datasets using Pandas and NumPy. The input includes past health information and current data, and the output generates an index that evaluates the user's health status.

[0670] Step 4:

[0671] The server automatically generates individual meal plans using a generative AI model based on the analysis results. Using machine learning frameworks such as TensorFlow or Keras, menu candidates are created that take into account health status and preferences. The input is the evaluation metrics obtained in the previous step, and the output is a meal plan.

[0672] Step 5:

[0673] The server sends the generated meal plan to the device. The meal plan is delivered to the device via an API using Flask. The input is the created meal plan, and the output is the data that should be displayed on the device.

[0674] Step 6:

[0675] The device displays the received meal plan to the user. A user interface implemented in Flutter is responsible for the visual display of the menu and also allows the user to easily obtain links and information for purchasing related products. The input is the meal plan sent from the server, and the output is the information that the user can view displayed on the screen.

[0676] 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.

[0677] This invention is a system that assists in health management, and is implemented by combining a measuring device for monitoring health information, an information processing device for receiving, storing, and analyzing data, an automatic generation device for generating meal menus, a display device for displaying the generated menus, and an emotion engine for recognizing the user's emotional state. This system is capable of providing more personalized meal suggestions based on the user's daily health and emotional state.

[0678] First, the "device" uses sensors to acquire the user's health data. This data includes heart rate, activity level, sleep quality, and body temperature. Additionally, using the camera and microphone, the emotion engine infers the user's emotional state from their voice tone and facial expressions. This data is then transmitted to the "server."

[0679] The "server" stores health data and user emotional information received from the terminal and analyzes it using an information processing device. As a result of the analysis, the user's health and emotional state are stored in a database and used as input for the next step. Machine learning technology is used in this analysis to perform a sophisticated analysis that takes into account the user's past patterns and current state.

[0680] The "server" generates meal menus using an automated generator based on analysis results from the emotion engine. These menus consider not only the user's physical needs but also their mental satisfaction. For example, the system is designed to suggest many ingredients that have stress-reducing effects.

[0681] The generated meal menu is sent from the "server" to the "terminal." The "terminal" displays the menu to the user and provides notifications that include specific nutritional information and suggestions based on the user's emotional state. This information allows the "user" to choose a meal that takes both their health and emotional state into consideration.

[0682] For example, if the system detects that a user is experiencing stress at work, it might recommend selecting a menu that includes relaxing herbal teas or antioxidants. In this way, the system makes it easy for users to select a meal plan that comprehensively considers their individual health and emotional state.

[0683] The following describes the processing flow.

[0684] Step 1:

[0685] The device uses various sensors to collect health information, measuring heart rate, activity level, and sleep quality. It also analyzes the user's facial expressions and voice tone through the camera and microphone, and an emotion engine infers the user's emotional state. This data is transmitted to the server in real time.

[0686] Step 2:

[0687] The server stores data received from terminals and analyzes it using an information processing device. Health data is combined with past history to evaluate the user's health status, while emotional information analyzed by the emotion engine is also integrated to understand the user's current emotional state.

[0688] Step 3:

[0689] The server generates meal menus using an automated generator based on the analysis of health and emotional states. The generated menus consider factors that enhance both physical health and emotional satisfaction. For example, they suggest foods that offer both nutritional balance and stress-reducing effects.

[0690] Step 4:

[0691] The server sends the generated meal menu to the terminal.

[0692] Step 5:

[0693] The device displays the received meal menu to the user. The displayed information includes nutritional information about the meal and reasons for the suggestion based on the user's emotional state. This allows the user to make choices in their daily meals that are appropriate for both their physical and mental condition.

[0694] Step 6:

[0695] The user selects a specific menu item and eats the meal. Afterwards, they provide feedback to the server via their device regarding their physical condition and emotions after the meal.

[0696] Step 7:

[0697] The server receives user feedback and continuously updates its generative AI model and emotion engine. This ensures that subsequent menu suggestions are more tailored to the user.

[0698] (Example 2)

[0699] 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".

[0700] In today's busy lifestyle, balancing daily health management and emotional care is difficult. In particular, a more flexible and personalized system is needed to accurately provide meal suggestions based on each user's health condition and emotions. This invention aims to maintain health and stabilize emotions by integrating and analyzing health data and emotional information to provide users with an optimal meal plan.

[0701] 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.

[0702] In this invention, the server includes sensing means for acquiring biometric data, data processing means for receiving, storing, and analyzing the biometric data obtained from the sensing means, and emotion recognition means for identifying the user's emotional state. This makes it possible to provide personalized meal plans based on the user's health and emotional state.

[0703] "Biometric data" refers to data related to an individual's health status, such as heart rate, exercise level, sleep quality, and body temperature.

[0704] "Sensing means" refers to devices and equipment that use sensor technology to acquire biometric data from users.

[0705] "Data processing means" refers to a technological device that receives, stores, and analyzes collected biological data.

[0706] "Automatic creation means" refers to a technological device that generates individually tailored meal plans based on analyzed data.

[0707] "Display means" refers to a device that provides the generated meal plan to the user visually or audibly.

[0708] "Emotion recognition means" refers to a technological device that analyzes the user's voice tone and facial expressions to identify their emotional state.

[0709] This invention is a system that proposes an individually tailored meal plan by comprehensively utilizing the user's biometric data and emotional state. This system is implemented by combining sensing means, data processing means, automatic creation means, display means, and emotion recognition means.

[0710] The "terminal" acquires biometric data through sensing means via wearable devices worn by the user or applications on a smartphone. This data includes heart rate, exercise level, sleep quality, and body temperature. In addition, it simultaneously captures the user's emotional state by using cameras and microphones to analyze voice tone and facial expressions through emotion recognition means.

[0711] The "server" receives and stores data transmitted from the "terminal" using data processing means. The received data is securely stored in a database and then analyzed using machine learning algorithms. Based on the analyzed data, the server uses a generative AI model to automatically generate a meal plan. This plan takes into account the user's individual health needs and emotional state, and includes further personalized suggestions.

[0712] The generated meal plan is sent from the "server" to the "terminal" and provided to the user through a display device. The "terminal" can easily receive the meal plan not only through visual display but also through voice and notification functions.

[0713] For example, if the system detects that the "user" is feeling stressed, a meal plan including foods and beverages with relaxing effects will be suggested. An example of a prompt to the generative AI model might be, "Based on this week's health data, suggest a meal menu that will help reduce stress."

[0714] This system allows users to select meals that suit their health and emotional state, thereby improving their lifestyle.

[0715] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0716] Step 1:

[0717] The "device" acquires biometric information in real time using the user's wearable device or smartphone. This input data includes heart rate, activity level, sleep quality, and body temperature. Through this data, basic information is collected to understand the user's current health status.

[0718] Step 2:

[0719] The "device" captures the user's facial expressions and voice using its built-in camera and microphone, and analyzes their emotional state using emotion recognition technology. It analyzes voice tone and facial expressions from video and audio files as input data, infers emotions such as anger, sadness, and joy, and outputs the results.

[0720] Step 3:

[0721] These health and emotional data are sent from the "terminal" to the "server." The "server" stores the received data in a database and uses machine learning algorithms to analyze the data based on the accumulated biometric and emotional data. At this stage, based on the input data, it considers past health trends and patterns and outputs detailed analysis results regarding the current health and emotional state.

[0722] Step 4:

[0723] The "server" uses the results of data analysis to create personalized meal plans through an automated generation method utilizing a generative AI model. In this step, the analysis results are input as prompts to the AI ​​model, which generates meal suggestions based on health status and emotions. For example, if stress levels are high, it will output a menu that uses many ingredients that have stress-reducing effects.

[0724] Step 5:

[0725] The generated meal plan is sent from the "server" to the "terminal." The "terminal" presents the meal plan to the user through a visual interface and voice notifications. This output includes specific menu items, nutritional information, and a list of recommended ingredients, helping the user choose meals that balance health and emotional well-being.

[0726] (Application Example 2)

[0727] 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".

[0728] Modern consumers seek product choices that consider both health and emotional well-being, but traditional stores have faced the challenge of providing appropriate product recommendations tailored to the individual needs of each customer. This invention aims to solve this problem by suggesting optimal products based on each customer's health and emotional state, thereby improving customer satisfaction.

[0729] 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.

[0730] In this invention, the server includes means for using a sensor device for monitoring health information, information processing means for receiving, storing, and analyzing health data and emotional data obtained from the sensor device, and automatic generation means for generating individually suitable product candidates based on the results analyzed by the information processing means. This makes it possible to recommend products that are suitable for the user's health and emotional state in real time.

[0731] "Health information" refers to data collected to measure the user's physical condition, and includes heart rate, body temperature, and exercise level.

[0732] A "sensor device" is a measuring device used to monitor health and emotional information, and includes heart rate sensors and cameras for emotion analysis.

[0733] An "information processing device" is a device used to receive, store, and analyze collected health and emotional data, and includes computers and servers.

[0734] "Automatic generation means" refers to means for generating individually suitable product candidates based on the results analyzed by an information processing device, and includes algorithms and programs.

[0735] A "display device" is a device used to visualize generated product candidates for the user, and includes smartphones and displays.

[0736] "Emotional data" refers to information collected to measure the user's psychological state, and includes facial expressions, voice tone, and other similar data.

[0737] A "product recommendation list" is a list of products recommended based on the user's health and emotional state, and includes products that meet specific criteria.

[0738] This invention provides a system for suggesting products that are individually suited to a user based on their health and emotional state. This system mainly consists of a sensor device that collects health and emotional information, a server that processes the information, and a terminal that displays the results.

[0739] The server stores and analyzes health and emotional data received from sensor devices. Specifically, it uses heart rate sensors, cameras, microphones, and other sensors to acquire health information such as heart rate and body temperature, and emotional information such as facial expressions and voice tone. The server processes this information and uses machine learning algorithms to analyze the data and recognize patterns.

[0740] The server uses an automated generation mechanism based on the analyzed data to generate product suggestions tailored to the user. These suggestions are sent to the user's device as a product list and visualized. The displayed product information includes relevant nutritional information and suggestions based on the user's emotional state. Smartphone or tablet displays are used as the display devices.

[0741] As a concrete example, suppose a sensor device detects the user's fatigue and stress levels when they enter a physical store. In this case, the server generates a list of products that promote relaxation, such as herbal tea or antioxidant snacks, and displays it on the user's terminal. The user can then receive this customized suggestion and select appropriate products within the store.

[0742] Using a generative AI model, the following prompt can be used: "The user's emotional state has been detected as 'fatigue'. Please generate a list of foods and beverages that have a relaxing effect." In response to this prompt, a list of optimal products will be created.

[0743] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0744] Step 1:

[0745] The terminal acquires health and emotional information from sensor devices attached to the body of users who enter a physical store. The input is raw data from the sensors, and the output generates health and emotional data such as heart rate, body temperature, and facial expression. In this process, the sensors measure various vital information in real time and transmit these values ​​to the terminal.

[0746] Step 2:

[0747] The server stores health and emotional data received via the terminal and analyzes it using machine learning algorithms. The input is the health and emotional data acquired in step 1, and the output is the analysis results showing the user's current health and emotional state. In this process, the server uses a knowledge base, referencing past data as well, to identify patterns in health and emotional states and evaluate the user's condition.

[0748] Step 3:

[0749] The server uses an automated generation mechanism based on the analysis results to generate the most suitable product candidates for the user. The input is the analysis results from step 2, and the output is a product list. Here, the generation AI model uses the analysis results to process prompts and identify product groups suitable for the user's health and emotional state. A specific example of a prompt is: "The user's emotional state has been detected as 'fatigue'. Please generate a list of foods and beverages with relaxing effects."

[0750] Step 4:

[0751] The generated product list is sent from the server to the terminal and displayed on the terminal. The input is the product list data from the server, and the output is the product recommendation result displayed on the user's terminal screen. The terminal visually presents this product list to the user and supports product selection in a physical store, including nutritional information and emotion-based suggestion information.

[0752] Step 5:

[0753] The user refers to a product list displayed on the terminal and selects a product in the physical store that suits their health and emotional state. The input is the information displayed on the terminal in step 4, and the output is the receipt of the selected product. This step involves the user moving around the store, searching for the presented product options, and actually picking up the product.

[0754] 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.

[0755] 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.

[0756] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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."

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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 as being incorporated by reference.

[0775] The following is further disclosed regarding the embodiments described above.

[0776] (Claim 1)

[0777] A measuring device for monitoring health information,

[0778] An information processing device for receiving, storing, and analyzing data obtained from the measuring device,

[0779] An automatic generation device for generating individually tailored meal menus based on the results analyzed by the information processing device,

[0780] A display device for displaying or notifying the meal menu generated by the automatic generation device,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, wherein the automatic generation device adjusts the meal menu based on the user's past health information and preferences.

[0784] (Claim 3)

[0785] The system according to claim 1, wherein the display device provides a notification including nutritional information and calorie information related to the generated meal menu.

[0786] "Example 1"

[0787] (Claim 1)

[0788] A means of acquiring vital data for monitoring,

[0789] An analysis means for collecting and analyzing data obtained from the acquisition means via communication,

[0790] A generation means for generating individually tailored nutritional suggestions based on the results analyzed by the analytical means,

[0791] A visualization means for displaying or notifying nutritional suggestions generated by the generation means,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, wherein the generation means adjusts nutritional suggestions based on the user's past health data and preferences.

[0795] (Claim 3)

[0796] The system according to claim 1, wherein the visualization means provides a notification including nutritional information and energy information related to the generated nutritional suggestion.

[0797] "Application Example 1"

[0798] (Claim 1)

[0799] Measurement means for monitoring health information,

[0800] Information processing means for receiving, storing, and analyzing data obtained from the measurement means,

[0801] An automatic generation means for generating individually tailored meal plans based on the results analyzed by the information processing means,

[0802] A display means for displaying or notifying the meal plan generated by the automatic generation means,

[0803] A means to enable the purchase of ingredients and acquisition of information related to the generated meal plan,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, wherein the automatic generation means adjusts the meal plan based on the user's past health information and preferences.

[0807] (Claim 3)

[0808] The system according to claim 1, wherein the display means provides a notification including nutritional information and energy intake information related to the generated meal plan.

[0809] "Example 2 of combining an emotion engine"

[0810] (Claim 1)

[0811] Sensing means for acquiring biometric data,

[0812] A data processing means for receiving, storing, and analyzing biological data obtained from the sensing means,

[0813] An automatic creation means for generating individually tailored meal plans based on the analysis results of the data processing means,

[0814] A display means for presenting or notifying the meal plan generated by the automatic creation means,

[0815] An emotion recognition means for identifying the user's emotional state,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the automatic creation means adjusts the meal plan based on the user's emotional state in addition to past biometric information and preferences.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the display means provides a notification including nutritional information and recommended energy intake information related to the generated meal plan.

[0821] "Application example 2 when combining with an emotional engine"

[0822] (Claim 1)

[0823] A sensor device for monitoring health information,

[0824] An information processing device for receiving, storing, and analyzing health data and emotional data obtained from the sensor device,

[0825] An automatic generation means for generating individually suitable product candidates based on the results analyzed by the information processing device,

[0826] A display device for displaying or notifying customers of product candidates generated by the automatic generation means within the store,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, wherein the automatic generation means adjusts product candidates based on the user's past health information, emotional state, and preferences.

[0830] (Claim 3)

[0831] The system according to claim 1, wherein the display device provides notification including nutritional information related to the generated product candidates and suggestion information based on emotional state. [Explanation of symbols]

[0832] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A measuring device for monitoring health information, An information processing device for receiving, storing, and analyzing data obtained from the measuring device, An automatic generation device for generating individually tailored meal menus based on the results analyzed by the information processing device, A display device for displaying or notifying the meal menu generated by the automatic generation device, A system that includes this.

2. The system according to claim 1, wherein the automatic generation device adjusts the meal menu based on the user's past health information and preferences.

3. The system according to claim 1, wherein the display device provides a notification including nutritional information and calorie information to be consumed related to the generated meal menu.

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