system

Abstract

We provide the system. [Solution] Means of receiving personal information, A means for generating nutritional advice and meal plans based on the aforementioned personal information, A means for processing image data to recognize food and analyze its nutritional components, A means for receiving activity data in real time and adjusting the meal plan based on that data, Means for displaying the aforementioned nutritional components and adjusted meal plan, Means of sharing data with medical professionals, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, appropriate nutrition management according to an individual's health status is important, but many people do not have the specialized knowledge or time for it. Under such circumstances, there is a need to provide personalized nutrition advice and diet plans according to individual health goals to improve health efficiently and sustainably. Also, the importance of dynamic nutrition management based on real-time activity data is increasing, but conventional systems have problems with immediacy and accuracy. Furthermore, data sharing for medical experts to quickly access an individual's health status and provide appropriate guidance is also insufficient.

Means for Solving the Problems

[0005] This invention provides a system that can receive personal information and generate nutritional advice and meal plans based on that information. Specifically, it includes means for recognizing food and processing image data to analyze nutritional components, and can receive activity data acquired from wearable devices in real time and adjust meal plans based on that data. It also has a function to display the obtained nutritional components and adjusted meal plans and to enable effective data sharing with medical professionals. The aim is to efficiently support individual health management and achieve sustainable nutritional improvement.

[0006] "Personal information" refers to information necessary for personalized nutrition management, such as a user's basic profile data, health goals, and preferred eating style.

[0007] "Nutritional advice" refers to specific guidance and suggestions regarding the intake of appropriate nutrients, based on an individual's health condition and goals.

[0008] A "meal plan" is a systematic outline of food choices and intake amounts for daily meals, tailored to individual needs and lifestyles.

[0009] "Image data" refers to visual information, such as photos of meals taken by users, that is used for recognizing food and analyzing its nutritional components.

[0010] "Activity data" refers to data about a user's physical activity (such as steps taken, exercise volume, and heart rate) acquired through wearable devices and other means.

[0011] "Real-time reception" refers to the process of instantly incorporating the latest activity data at that moment into the system and responding to it instantly.

[0012] A "medical professional" is someone, such as a doctor or nutritionist, who possesses specialized knowledge and qualifications in health and nutrition and is able to provide health advice and guidance to individuals.

[0013] "Sharing data" means transferring information from one device or system to another with the permission of its owner. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0018] 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.

[0019] 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, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is an advanced system for providing personalized nutritional management, designed to help users achieve their health goals. The system is operated via smartphones or other digital devices.

[0036] First, the user uses a device to enter basic personal information and health goals. The entered data is sent from the device to a server, which uses an AI algorithm to create nutritional advice and meal plans based on the individual's goals. Information considered at this stage includes the user's current health status, dietary preferences, allergies, and other lifestyle information.

[0037] Next, the user takes a picture of their meal using the device's camera. The captured image data is sent to a server, which uses image processing technology to recognize the food and analyze its nutritional components. The analyzed data is sent back to the device and presented to the user as feedback.

[0038] If a user is using a wearable device, the device continuously transmits activity data to the terminal. The terminal sends this data to a server, which dynamically adjusts the meal plan in real time. For example, if the user's activity level is higher than expected, an increase in energy intake commensurate with that activity will be recommended.

[0039] Furthermore, users can share nutritional information and health data with healthcare professionals as needed. This data is encrypted and securely transmitted to other healthcare systems via servers. This allows healthcare professionals to understand the individual patient's condition and provide more effective guidance.

[0040] For example, if a user wants to eat a high-protein meal after exercise, they can scan their meal with their device, and the server will calculate the protein content and immediately inform the user. Furthermore, based on the day's activity data, the system adjusts the plan and suggests supplements for necessary nutrients. In this way, the system enables flexible nutritional management tailored to individual lifestyles and health goals.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users enter basic personal information and health goals using their devices. This data includes age, gender, weight, height, dietary preferences, and allergy information.

[0044] Step 2:

[0045] The device sends the collected personal information to the server. The data is securely encrypted during transmission.

[0046] Step 3:

[0047] Based on the data received by the server, an AI algorithm is applied to generate nutritional advice and meal plans tailored to individual health goals.

[0048] Step 4:

[0049] The user takes a picture of their meal using their device's camera and sends the image data to the server via their device.

[0050] Step 5:

[0051] The server analyzes image data and uses image processing technology to recognize food items. It then calculates the nutritional components of the recognized food items.

[0052] Step 6:

[0053] The server sends the nutritional information from the analysis results to the terminal, and the terminal displays that information to the user.

[0054] Step 7:

[0055] If a user is using a wearable device, the device sends activity data to the terminal. The terminal then sends that data to the server.

[0056] Step 8:

[0057] The server analyzes real-time activity data received and adjusts the meal plan as needed. The adjustments are sent to the device and notified to the user.

[0058] Step 9:

[0059] If a user chooses to share nutritional information and health data with a healthcare professional, the device encrypts the data and sends it to the healthcare professional's system via a server.

[0060] (Example 1)

[0061] 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."

[0062] Currently, it is difficult to provide nutritional management that is appropriately adapted to individual health conditions and goals, and the sharing and analysis of information for this purpose is insufficient. Therefore, the challenge lies in providing dietary guidance that is optimized for each individual user.

[0063] 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.

[0064] In this invention, the server includes means for receiving personalized health information, means for generating nutritional guidance and dietary advice, and means for identifying food and analyzing its nutritional components. This enables the provision of appropriate dietary guidance to individual users and facilitates effective information sharing with healthcare professionals.

[0065] "Personalized health information" refers to detailed information about each individual's specific health status, goals, preferences, allergies, and other relevant factors.

[0066] "Nutritional guidance" refers to providing appropriate nutritional information based on the user's health condition and lifestyle.

[0067] "Dietary guidance" refers to creating an optimal meal plan for each individual user and providing information that includes specific dietary recommendations.

[0068] "Food identification" refers to the visual recognition and classification of different types of food.

[0069] "Nutritional elements" refer to components such as proteins, lipids, carbohydrates, vitamins, and minerals found in food.

[0070] "Visual data" refers to image information acquired through imaging devices such as cameras.

[0071] "Activity tracking" refers to data such as exercise volume and heart rate obtained from wearable devices.

[0072] "Body-worn devices" refer to devices that are worn on the body to collect biometric information and exercise data.

[0073] "Medical professionals" refers to experts engaged in occupations related to healthcare, such as doctors, nurses, and nutritionists.

[0074] To implement this invention, users utilize digital devices such as smartphones and tablets to input data related to their health status and goals. A dedicated application is installed on the device, providing the user interface. Users input their basic, personalized health information and record their daily meals.

[0075] The data transmitted from the terminals is stored on a server, which then uses a generative AI model to generate nutritional and dietary guidance. This model is built using open-source libraries (e.g., TENSORFLOW® and PyTorch) and is trained on a large amount of nutritional and dietary data.

[0076] When a user takes a picture of their meal, the device sends this visual data to a server. The server uses an image processing library (e.g., OpenCV) to identify the food and analyze its nutritional components. The results of this analysis are returned to the user's device, providing the user with real-time nutritional information.

[0077] Furthermore, when users use wearable devices to record their activity, the devices continuously transmit this data to a server. The server analyzes the activity data in real time and dynamically adjusts dietary guidance based on the results. This makes it possible to provide guidance on the optimal energy intake according to the user's activity level.

[0078] For example, if a user requests a high-protein meal after exercise, the following prompt could be input into the AI ​​model: "Please create a high-protein, low-calorie meal plan." The server would then provide dietary guidance appropriate to the user's health condition.

[0079] Thus, the invention combines advanced technologies to realize personalized nutrition and dietary management, providing support to users in achieving their health goals.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user launches an application on a digital device and enters personalized health information. This information includes age, weight, dietary preferences, and allergy information. This input data is temporarily stored on the device. When the user presses the "Send" button, the device sends the data to the server. The output on the server is data that has been stored and converted into a format that can be processed by an AI model.

[0083] Step 2:

[0084] The server activates an AI model based on the received personalized health information. The model generates nutritional and dietary guidance tailored to each user based on the prompt. In this process, the AI ​​model utilizes a large amount of stored dietary data to design the optimal meal plan. An example of a prompt might be, "Create a high-protein meal plan suitable for the user." The output of this step is appropriate nutritional and dietary guidance for each user.

[0085] Step 3:

[0086] The user takes a picture of the food using their device's camera before eating. The device compresses the image data to reduce the data load and sends it to the server. The server uses an image processing library to identify the food. The input is image data of the meal, and the output is information about the type of food and the nutritional elements it contains.

[0087] Step 4:

[0088] The server analyzes the nutritional information of identified foods and combines it with dietary guidance. Data processing on the server includes referencing nutrient databases and assessing their relevance to individualized health information. The output from the server to the terminal is detailed feedback provided to the user, indicating which nutrients the user is consuming.

[0089] Step 5:

[0090] If the user is wearing a body-worn device, the terminal receives activity data from it in real time and sends it to the server. The server performs calculations to dynamically adjust dietary guidance based on the activity data. The input is the activity data, and the output is the dynamically adjusted dietary guidance.

[0091] Step 6:

[0092] Users can view generated nutritional and dietary guidance through their devices. This information is regularly updated to reflect the user's activity level. Furthermore, it is possible to share this information with healthcare professionals as needed. Information is properly encrypted via the server, ensuring secure information sharing. The output provides continuous feedback and clinical support information to help users manage their health.

[0093] (Application Example 1)

[0094] 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."

[0095] In modern times, providing optimal nutritional management tailored to individual health conditions and lifestyles is extremely difficult. Furthermore, with the increasing diversity of dietary options, there is a need for systems that support users in ensuring they are consuming meals that meet their health goals. However, the challenge lies in the fact that current technology does not adequately meet these needs.

[0096] 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.

[0097] In this invention, the server includes means for receiving personal information, means for generating nutritional advice and meal plans, means for processing image information to recognize food and analyze its nutritional components, means for receiving activity information in real time and adjusting the meal plan based on that information, means for sharing information with medical professionals, and means for suggesting food selections and placing orders based on nutritional management information. This makes it possible to achieve a diet that suits individual health goals and to manage nutrition efficiently and conveniently.

[0098] "Personal information" refers to basic information about the user, which is data necessary for nutritional management and setting health goals.

[0099] "Nutritional advice" refers to dietary guidelines created based on the user's health condition and food preferences.

[0100] A "meal plan" is a specific combination of meals and a schedule suggested to help users achieve their health goals.

[0101] "Image information" refers to data taken by users using smartphones or other devices to photograph their meals, and is used to analyze the nutritional components of the food.

[0102] "Activity information" refers to data on the user's exercise volume and daily activities, which is acquired in real time through wearable devices and other means.

[0103] "Real-time adjustment" is a process that instantly modifies nutritional advice and meal plans based on the latest user information.

[0104] A "medical professional" is a person qualified to provide advice and diagnoses regarding a user's health condition and nutritional management.

[0105] "Food selection suggestions" refer to a list of specific foods recommended to the user based on their individual nutritional management information.

[0106] An "order" is the act of a user actually purchasing or requesting delivery of food items they have selected.

[0107] The system for carrying out this invention consists of a smartphone application, a server, and, if necessary, a wearable device.

[0108] First, the user enters personal information and health goals using a smartphone application. The device sends this information to a server, which uses a generative AI model to generate personalized nutritional advice and meal plans. This process uses nutritional algorithms to perform data calculations and propose the optimal diet for the user.

[0109] Next, the user takes a picture of the food with their smartphone camera while eating. The device sends the image data to a server, where image recognition technologies such as Google Cloud Vision API are used to identify the food and analyze its nutritional components. The results are fed back to the device in real time, allowing the user to understand what they are eating.

[0110] Furthermore, users wearing wearable devices continuously transmit activity information to their devices. This activity information is sent to a server, which adjusts the meal plan in real time to suit the user's lifestyle. For example, if more exercise than expected is recorded, the meal plan will incorporate an increase in energy intake.

[0111] By integrating with a food delivery application that also includes an ordering function, users can order suggested foods on the spot. This enables quick and accurate food selection based on nutritional management information.

[0112] For example, if a user desires high-protein food after exercise, the application receives this information and immediately suggests items such as a "high-protein chicken breast salad," which the user can order with a single tap. In this case, an example of a prompt to the generating AI would be, "Please tell me what protein-rich foods I should eat after exercise."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user launches a smartphone application and enters personal information and health goals. This information includes age, gender, allergy information, and health goals (e.g., weight loss, muscle gain). The device then sends this data to the server.

[0116] Step 2:

[0117] The server uses an AI model based on the received information to create personalized nutritional advice for each user. During this process, the AI ​​calculates a meal plan based on the entered personal information and suggests recommended calorie and nutrient intakes. The results are displayed on the terminal and presented to the user.

[0118] Step 3:

[0119] The user takes a picture of the food using their smartphone camera at each meal. The device then transfers this image data to the server.

[0120] Step 4:

[0121] The server analyzes this image data using image recognition technologies such as the Google Cloud Vision API. Based on the input food image, it recognizes the food and extracts calorie and nutritional information. The results are sent to the terminal and displayed to the user as nutritional information.

[0122] Step 5:

[0123] If a user is wearing a wearable device, activity information is sent from that device to the terminal. The terminal then forwards this activity information to the server.

[0124] Step 6:

[0125] The server analyzes activity information in real time and dynamically adjusts the meal plan based on this analysis. For example, if activity levels increase, it calculates whether to increase calorie intake or the amount of specific nutrients. The adjusted meal plan is then sent to the terminal.

[0126] Step 7:

[0127] Users can utilize a food delivery service within the application. The server provides recommended food selections based on the adjusted meal plan, and users can choose their desired meals from these options and place an order.

[0128] Step 8:

[0129] If a user desires specific nutrients after exercise, they can use prompts to query the generating AI model. Prompts such as "Please tell me what protein-rich foods I should consume after exercise" allow the AI ​​to support appropriate food selection.

[0130] 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.

[0131] This invention provides advanced personalization features that take into account the user's emotional state by incorporating an emotion engine into an individualized nutrition management system. This system more effectively supports the user's health management through smartphones and other digital devices.

[0132] When a user enters personal information and health goals using their device, the device securely transmits this information to a server. Based on this information, the server generates nutritional advice and meal plans tailored to the user's goals. Users can also send image data to the server by taking pictures of their meals using their device's camera. The server uses image analysis technology to recognize the food and analyze its nutritional components, then sends the results back to the device.

[0133] When a user is wearing a wearable device, activity data obtained from that device is transmitted to the terminal in real time and then to a server. The server dynamically adjusts the meal plan based on this activity data and reflects the results back on the terminal.

[0134] Furthermore, the present invention incorporates an emotion engine that provides a function to analyze the user's emotional state in real time. This emotion engine analyzes the user's voice data and facial images to determine their emotional state. For example, if the user is feeling stressed, the system adjusts the meal plan based on that emotion and suggests foods and nutrients that can help alleviate stress.

[0135] For example, if a user is determined to be experiencing high levels of stress after work, the device will suggest a special dinner plan aimed at stress relief. This plan may include relaxing herbal teas or foods rich in magnesium. Furthermore, if the user wishes, the system can share this information with a medical professional for further advice.

[0136] As described above, this system enables flexible and intelligent nutritional management tailored to the user's lifestyle and emotional state.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The user uses their device to input basic personal information, health goals, and dietary preferences. This allows the system to obtain the user's baseline data.

[0140] Step 2:

[0141] The device sends the personal information it has collected to the server. The server then generates basic nutritional advice and meal plans based on this data.

[0142] Step 3:

[0143] The user uses their device's camera to take a picture of their meal for the day. The device then sends the captured image data to the server.

[0144] Step 4:

[0145] The server analyzes image data, uses AI technology to identify food components, and calculates their nutritional information.

[0146] Step 5:

[0147] The server calculates nutritional information and returns it to the terminal, which then provides that information to the user. The user can then view specific nutritional data.

[0148] Step 6:

[0149] If a user is using a wearable device, the device sends activity data to the terminal, which then sends it to the server.

[0150] Step 7:

[0151] The server analyzes activity data in real time and adjusts meal plans according to the user's activity level.

[0152] Step 8:

[0153] The device uses the user's voice data and facial image to activate the emotion engine and sends the data to the server.

[0154] Step 9:

[0155] The server uses an emotion engine to analyze the user's emotional state and determine conditions such as stress and anxiety.

[0156] Step 10:

[0157] The server adjusts the meal plan based on the user's emotional state and sends the adjustment results to the device. For example, if the user is under high stress, the server will suggest a menu that includes many ingredients that help relieve stress.

[0158] (Example 2)

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

[0160] Traditional nutrition management systems struggle to provide personalized recommendations that fully consider an individual's health and emotional state, and are particularly inadequate in adapting nutritional management based on emotional fluctuations. Furthermore, they have limitations in their ability to integrate information from multiple data sources and dynamically adjust plans.

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

[0162] In this invention, the server includes means for acquiring personal information, means for creating nutritional management information and intake plans, and means for analyzing emotional information and adapting meal suggestions. This enables a high degree of personalization that takes into account individual emotional and activity levels.

[0163] "Personal information" refers to data such as a user's health status, lifestyle, and goals, and is information necessary for the system to provide personalized suggestions to the user.

[0164] "Nutritional management information" refers to data used to provide users with instructions on the optimal balance of nutrient intake and dietary content.

[0165] A "nutrition plan" provides users with a specific plan regarding recommended meals and nutritional intake.

[0166] "Visual data" refers to images of food and related photographic data, which, when analyzed, can be used to identify food items and calculate their nutritional content.

[0167] "Activity information" refers to data that indicates the user's physical activity, including information about body movements and energy consumption.

[0168] "Emotional information" refers to data that indicates a user's emotional state, and includes information obtained particularly from voice and facial expressions.

[0169] A "specialist" refers to an individual or organization that possesses knowledge of nutrition and health management and can provide users with expert advice and guidance.

[0170] This system is designed to highly personalize users' individualized nutritional management. When users enter personal information using their smartphones or digital devices, this information is securely transmitted from the device to the server. The server uses the received information to generate nutritional management information and intake plans using an AI model. The nutritional management information provides customized advice based on the user's health goals.

[0171] Furthermore, when a user takes a picture of their meal using the device's camera, the visual data is sent to the server, where image analysis libraries such as TensorFlow are used to recognize the food and analyze its nutrients. By using a wearable device, the user sends activity information to the device in real time, which is also processed by the server. Based on this activity information, the server dynamically adjusts the intake plan as needed.

[0172] Furthermore, this system incorporates an emotion engine, enabling the analysis of emotional information using the user's voice data and facial images. For example, if the system determines that the user's stress level is high, it will suggest meals appropriate to that state. A specific suggestion might be to recommend foods with relaxing effects.

[0173] As a concrete example, when inputting prompts into the generative AI model, a sentence such as, "Generate a meal plan recommended for a user who is feeling stressed. Please provide detailed information about foods that can help alleviate stress," would be used. In this way, flexible nutritional management tailored to individual needs and emotional states is achieved.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] Users input personal information and health goals using their smartphones or digital devices. This input data includes target weight and allergy information. After this information is entered into the device, the device encrypts it and securely transmits it to the server.

[0177] Step 2:

[0178] The server receives personal information transmitted from the terminal and uses a generated AI model to create nutritional management information and intake plans. Specifically, it calculates appropriate calorie intake and nutrient distribution according to the received health goals. The output is nutritional advice and a meal plan tailored to the user.

[0179] Step 3:

[0180] When a user takes a picture of their food using their device's camera, the visual data is sent from the device to a server. The server processes the received image data, using technologies such as TensorFlow to recognize the type of food and analyze its nutrients. As a result of the analysis, detailed information about the food's components is output.

[0181] Step 4:

[0182] When a user is wearing a wearable device, the device transmits activity information to the terminal in real time. The terminal forwards this data to a server. The server analyzes the activity information and dynamically adjusts the intake plan based on the user's energy consumption. The adjusted plan is output and sent back to the terminal.

[0183] Step 5:

[0184] The device collects the user's voice data and facial images and sends them to the server as emotional information. The server uses an emotion engine to analyze the data and determine the user's emotional state. Based on these results, it adapts a meal plan and suggests foods that are effective in reducing stress.

[0185] Step 6:

[0186] The server generates a meal plan based on emotions and sends it to the device. The device presents this plan to the user and, if necessary, suggests a procedure to share information within the system with a medical professional. This procedure allows the user to receive feedback from the professional at any time.

[0187] (Application Example 2)

[0188] 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".

[0189] Modern consumers are required to maintain a healthy diet while quickly and easily obtaining meals that suit their emotional state. However, conventional systems struggle to provide meal suggestions that adequately consider individual health data and emotional states, and there is a lack of means to quickly obtain food based on such suggestions. Against this backdrop, there is a need for the automated generation of personalized meal plans based on health information and emotional states, and for integration with food services that provide them.

[0190] 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.

[0191] In this invention, the server includes means for receiving personal information, means for generating nutritional advice and meal plans, and means for analyzing emotional states. This enables the automatic generation of personalized meal plans based on the user's health information and emotional state, and the rapid provision of food services based on those plans.

[0192] "Personal information" refers to information about a user's health status, eating history, and lifestyle habits, and serves as the basic data for the system to personalize meal plans.

[0193] "Nutritional advice" refers to expert guidance on diet and nutrition provided according to the user's individual health condition and goals.

[0194] A "meal plan" is a specific menu and schedule of meals suggested based on the user's health goals and emotional state.

[0195] "Food" refers to the items that users consume based on their meal plan.

[0196] "Image information" refers to visual data of the food and ingredients consumed by the user, which the system uses to analyze nutritional components.

[0197] "Activity information" refers to data related to a user's physical activity and exercise, which is obtained from wearable devices and other sources.

[0198] "Emotional state" refers to data on the user's stress level and emotional fluctuations, and is an important factor when the system adjusts meal plans.

[0199] "Digital communication" refers to a means of sending and receiving information over a network, and is used when transmitting a user's meal plan to a food delivery service.

[0200] "Experts" refer to professionals such as healthcare workers and nutritionists who are qualified to provide advice on the user's health and emotional state.

[0201] The system for implementing the present invention consists of a digital network environment using the user's smartphone, a wearable device, and a server. The user inputs personal information through a smartphone application and provides data tailored to their health status and goals. This information is securely transmitted to the server via the terminal.

[0202] The server receives "personal information" and generates "nutritional advice" and "meal plans" based on it. This process utilizes a generative AI model to provide personalized recommendations. Furthermore, it uses "image information" and "audio data" collected using the device's camera and voice recognition functions to analyze the user's "emotional state." This data is crucial for determining the "emotional state" and dynamically adjusting the meal plan.

[0203] Activity information obtained in real time from smartphones and wearable devices (e.g., Fitbit) is also sent to the server and reflected in the adjustment of the meal plan. The resulting adjusted plan is displayed on the user's device and transmitted to the food delivery service via digital communication. This allows users to select and quickly obtain foods based on their health and emotional state.

[0204] For example, if a user is experiencing stress due to long working hours, the application can detect their emotional state, and the system will suggest a "meal plan" that includes ingredients with relaxing effects. The user can then review the suggestion and order the selected menu from a partner food service with a single button click.

[0205] An example of a prompt message might be: "Generate an optimal dinner plan using the user's emotional data and health information. Present a food delivery option that combines foods effective in reducing stress."

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] Users enter personal information into a smartphone application. The data entered includes the user's health goals, dietary history, and real-time emotional state data. This information is collected by the device and securely transmitted to a server.

[0209] Step 2:

[0210] Based on the received personal information, the server utilizes a generative AI model to generate nutritional advice and a basic meal plan. At this stage, the AI ​​model performs predictive analytics and data mining to suggest optimal meal choices based on the user's health goals and eating history. The output is a nutrition plan tailored to the user.

[0211] Step 3:

[0212] The device uses its built-in camera and voice recognition API to collect image and audio data from the user. This data is then sent to a server for real-time analysis of the user's emotional state. This analysis utilizes data processing technologies such as facial expression recognition and voice tone analysis.

[0213] Step 4:

[0214] The server analyzes the obtained emotional state data and dynamically adjusts the meal plan. This process uses an emotional analysis model to identify the user's current emotional state and determine nutrients and foods that can reduce stress. The output is the adjusted meal plan.

[0215] Step 5:

[0216] The server uses real-time activity data acquired from wearable devices to make final adjustments to the meal plan. This activity data includes exercise volume and calorie expenditure, which are then used to further customize the plan. This results in an optimized meal plan.

[0217] Step 6:

[0218] The final meal plan is sent to the device and displayed to the user. The user can review this suggestion and order directly through the food delivery service from a customized menu according to their preferences. The data sent to the food delivery service via digital communication includes the selected menu and delivery information.

[0219] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0221] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0222] [Second Embodiment]

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

[0224] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0225] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0226] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0227] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0228] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0229] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0230] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0231] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0232] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0233] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0234] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0235] This invention is an advanced system for providing personalized nutritional management, designed to help users achieve their health goals. The system is operated via smartphones or other digital devices.

[0236] First, the user uses a device to enter basic personal information and health goals. The entered data is sent from the device to a server, which uses an AI algorithm to create nutritional advice and meal plans based on the individual's goals. Information considered at this stage includes the user's current health status, dietary preferences, allergies, and other lifestyle information.

[0237] Next, the user takes a picture of their meal using the device's camera. The captured image data is sent to a server, which uses image processing technology to recognize the food and analyze its nutritional components. The analyzed data is sent back to the device and presented to the user as feedback.

[0238] If a user is using a wearable device, the device continuously transmits activity data to the terminal. The terminal sends this data to a server, which dynamically adjusts the meal plan in real time. For example, if the user's activity level is higher than expected, an increase in energy intake commensurate with that activity will be recommended.

[0239] Furthermore, users can share nutritional information and health data with healthcare professionals as needed. This data is encrypted and securely transmitted to other healthcare systems via servers. This allows healthcare professionals to understand the individual patient's condition and provide more effective guidance.

[0240] For example, if a user wants to eat a high-protein meal after exercise, they can scan their meal with their device, and the server will calculate the protein content and immediately inform the user. Furthermore, based on the day's activity data, the system adjusts the plan and suggests supplements for necessary nutrients. In this way, the system enables flexible nutritional management tailored to individual lifestyles and health goals.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] Users enter basic personal information and health goals using their devices. This data includes age, gender, weight, height, dietary preferences, and allergy information.

[0244] Step 2:

[0245] The device sends the collected personal information to the server. The data is securely encrypted during transmission.

[0246] Step 3:

[0247] Based on the data received by the server, an AI algorithm is applied to generate nutritional advice and meal plans tailored to individual health goals.

[0248] Step 4:

[0249] The user takes a picture of their meal using their device's camera and sends the image data to the server via their device.

[0250] Step 5:

[0251] The server analyzes image data and uses image processing technology to recognize food items. It then calculates the nutritional components of the recognized food items.

[0252] Step 6:

[0253] The server sends the nutritional information from the analysis results to the terminal, and the terminal displays that information to the user.

[0254] Step 7:

[0255] If a user is using a wearable device, the device sends activity data to the terminal. The terminal then sends that data to the server.

[0256] Step 8:

[0257] The server analyzes real-time activity data received and adjusts the meal plan as needed. The adjustments are sent to the device and notified to the user.

[0258] Step 9:

[0259] If a user chooses to share nutritional information and health data with a healthcare professional, the device encrypts the data and sends it to the healthcare professional's system via a server.

[0260] (Example 1)

[0261] 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".

[0262] Currently, it is difficult to provide nutritional management that is appropriately adapted to individual health conditions and goals, and the sharing and analysis of information for this purpose is insufficient. Therefore, the challenge lies in providing dietary guidance that is optimized for each individual user.

[0263] 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.

[0264] In this invention, the server includes means for receiving personalized health information, means for generating nutritional guidance and dietary advice, and means for identifying food and analyzing its nutritional components. This enables the provision of appropriate dietary guidance to individual users and facilitates effective information sharing with healthcare professionals.

[0265] "Personalized health information" refers to detailed information about each individual's specific health status, goals, preferences, allergies, and other relevant factors.

[0266] "Nutritional guidance" refers to providing appropriate nutritional information based on the user's health condition and lifestyle.

[0267] "Dietary guidance" refers to creating an optimal meal plan for each individual user and providing information that includes specific dietary recommendations.

[0268] "Food identification" refers to the visual recognition and classification of different types of food.

[0269] "Nutritional elements" refer to components such as proteins, lipids, carbohydrates, vitamins, and minerals found in food.

[0270] "Visual data" refers to image information acquired through imaging devices such as cameras.

[0271] "Activity tracking" refers to data such as exercise volume and heart rate obtained from wearable devices.

[0272] "Body-worn devices" refer to devices that are worn on the body to collect biometric information and exercise data.

[0273] "Medical professionals" refers to experts engaged in occupations related to healthcare, such as doctors, nurses, and nutritionists.

[0274] To implement this invention, users utilize digital devices such as smartphones and tablets to input data related to their health status and goals. A dedicated application is installed on the device, providing the user interface. Users input their basic, personalized health information and record their daily meals.

[0275] The data transmitted from the terminals is stored on a server, which then uses a generative AI model to generate nutritional and dietary guidance. This model is built using open-source libraries (e.g., TensorFlow and PyTorch) and is trained on a large amount of nutritional and dietary data.

[0276] When a user takes a picture of their meal, the device sends this visual data to a server. The server uses an image processing library (e.g., OpenCV) to identify the food and analyze its nutritional components. The results of this analysis are returned to the user's device, providing the user with real-time nutritional information.

[0277] Furthermore, when users use wearable devices to record their activity, the devices continuously transmit this data to a server. The server analyzes the activity data in real time and dynamically adjusts dietary guidance based on the results. This makes it possible to provide guidance on the optimal energy intake according to the user's activity level.

[0278] For example, if a user requests a high-protein meal after exercise, the following prompt could be input into the AI ​​model: "Please create a high-protein, low-calorie meal plan." The server would then provide dietary guidance appropriate to the user's health condition.

[0279] Thus, the invention combines advanced technologies to realize personalized nutrition and dietary management, providing support to users in achieving their health goals.

[0280] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0281] Step 1:

[0282] The user launches an application on a digital device and enters personalized health information. This information includes age, weight, dietary preferences, and allergy information. This input data is temporarily stored on the device. When the user presses the "Send" button, the device sends the data to the server. The output on the server is data that has been stored and converted into a format that can be processed by an AI model.

[0283] Step 2:

[0284] The server activates an AI model generated based on the received personalized health information. Based on the prompt text, the model generates nutrition guidance and dietary guidance suitable for each user. At this time, the AI model utilizes a large amount of stored dietary data to design an optimal diet plan. Examples of prompt texts include "Please create a high-protein diet plan suitable for the user." The output of this step is appropriate nutrition guidance and dietary guidance for each user.

[0285] Step 3:

[0286] The user uses the camera of the terminal to take a picture of the food before eating. The terminal reduces the data load by compressing the image data and sends it to the server. The server performs food identification using an image processing library. The input is the image data of the meal, and the output is information on the type of food and the nutritional elements contained.

[0287] Step 4:

[0288] The server analyzes the nutritional element information of the identified food and combines it with the dietary guidance. The data calculations on the server include reference to the nutrient database and evaluation of the compatibility with the personalized health information. The output from the server to the terminal is detailed feedback provided to the user. This feedback indicates which nutrients the user is ingesting.

[0289] Step 5:

[0290] When the user is wearing a body-worn device, the terminal receives the activity record from it in real-time and sends it to the server. The server performs calculations to dynamically adjust the dietary guidance based on the activity data. The input is the activity record, and the output is the dynamically adjusted dietary guidance.

[0291] Step 6:

[0292] Users can view generated nutritional and dietary guidance through their devices. This information is regularly updated to reflect the user's activity level. Furthermore, it is possible to share this information with healthcare professionals as needed. Information is properly encrypted via the server, ensuring secure information sharing. The output provides continuous feedback and clinical support information to help users manage their health.

[0293] (Application Example 1)

[0294] 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."

[0295] In modern times, providing optimal nutritional management tailored to individual health conditions and lifestyles is extremely difficult. Furthermore, with the increasing diversity of dietary options, there is a need for systems that support users in ensuring they are consuming meals that meet their health goals. However, the challenge lies in the fact that current technology does not adequately meet these needs.

[0296] 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.

[0297] In this invention, the server includes means for receiving personal information, means for generating nutritional advice and meal plans, means for processing image information to recognize food and analyze its nutritional components, means for receiving activity information in real time and adjusting the meal plan based on that information, means for sharing information with medical professionals, and means for suggesting food selections and placing orders based on nutritional management information. This makes it possible to achieve a diet that suits individual health goals and to manage nutrition efficiently and conveniently.

[0298] "Personal information" refers to basic information about the user, which is data necessary for nutritional management and setting health goals.

[0299] "Nutritional advice" is a guideline for diet created based on the user's health condition and dietary preferences.

[0300] "Diet plan" is a combination and schedule of meals specifically proposed to assist the user in achieving their health goals.

[0301] "Image information" is data captured by the user using a smartphone or the like for the content of a meal, and is used to analyze the nutritional components of food.

[0302] "Activity information" is data on the user's exercise volume and daily activities, and is obtained in real time through wearable devices and the like.

[0303] "Real-time adjustment" is a process of immediately modifying nutritional advice and diet plans according to the latest user information.

[0304] "Medical professional" is a person with the qualification to provide advice and diagnosis regarding the user's health condition and nutritional management.

[0305] "Proposed food selection" is a list of specific foods recommended to the user based on individual nutritional management information.

[0306] "Order" is an act of actually purchasing or requesting delivery of the food selected by the user.

[0307] [[ID=3—]]

[0308] First, the user inputs personal information and health goals using a smartphone application. The terminal transmits this information to the server, and the server utilizes a generative AI model to generate personalized nutritional advice and diet plans. In this process, data calculations are performed using nutritional algorithms to propose an optimal diet for the user. ​

[0309] Next, the user takes a picture of the food with their smartphone camera while eating. The device sends the image data to a server, where image recognition technologies such as the Google Cloud Vision API are used to identify the food and analyze its nutritional components. The results are fed back to the device in real time, allowing the user to understand what they are eating.

[0310] Furthermore, users wearing wearable devices continuously transmit activity information to their devices. This activity information is sent to a server, which adjusts the meal plan in real time to suit the user's lifestyle. For example, if more exercise than expected is recorded, the meal plan will incorporate an increase in energy intake.

[0311] By integrating with a food delivery application that also includes an ordering function, users can order suggested foods on the spot. This enables quick and accurate food selection based on nutritional management information.

[0312] For example, if a user desires high-protein food after exercise, the application receives this information and immediately suggests items such as a "high-protein chicken breast salad," which the user can order with a single tap. In this case, an example of a prompt to the generating AI would be, "Please tell me what protein-rich foods I should eat after exercise."

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The user launches a smartphone application and enters personal information and health goals. This information includes age, gender, allergy information, and health goals (e.g., weight loss, muscle gain). The device then sends this data to the server.

[0316] Step 2:

[0317] The server uses an AI model based on the received information to create personalized nutritional advice for each user. During this process, the AI ​​calculates a meal plan based on the entered personal information and suggests recommended calorie and nutrient intakes. The results are displayed on the terminal and presented to the user.

[0318] Step 3:

[0319] The user takes a picture of the food using their smartphone camera at each meal. The device then transfers this image data to the server.

[0320] Step 4:

[0321] The server analyzes this image data using image recognition technologies such as the Google Cloud Vision API. Based on the input food image, it recognizes the food and extracts calorie and nutritional information. The results are sent to the terminal and displayed to the user as nutritional information.

[0322] Step 5:

[0323] If a user is wearing a wearable device, activity information is sent from that device to the terminal. The terminal then forwards this activity information to the server.

[0324] Step 6:

[0325] The server analyzes activity information in real time and dynamically adjusts the meal plan based on this analysis. For example, if activity levels increase, it calculates whether to increase calorie intake or the amount of specific nutrients. The adjusted meal plan is then sent to the terminal.

[0326] Step 7:

[0327] Users can utilize a food delivery service within the application. The server provides recommended food selections based on the adjusted meal plan, and users can choose their desired meals from these options and place an order.

[0328] Step 8:

[0329] If a user desires specific nutrients after exercise, they can use prompts to query the generating AI model. Prompts such as "Please tell me what protein-rich foods I should consume after exercise" allow the AI ​​to support appropriate food selection.

[0330] 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.

[0331] This invention provides advanced personalization features that take into account the user's emotional state by incorporating an emotion engine into an individualized nutrition management system. This system more effectively supports the user's health management through smartphones and other digital devices.

[0332] When a user enters personal information and health goals using their device, the device securely transmits this information to a server. Based on this information, the server generates nutritional advice and meal plans tailored to the user's goals. Users can also send image data to the server by taking pictures of their meals using their device's camera. The server uses image analysis technology to recognize the food and analyze its nutritional components, then sends the results back to the device.

[0333] When a user is wearing a wearable device, activity data obtained from that device is transmitted to the terminal in real time and then to a server. The server dynamically adjusts the meal plan based on this activity data and reflects the results back on the terminal.

[0334] Furthermore, the present invention incorporates an emotion engine that provides a function to analyze the user's emotional state in real time. This emotion engine analyzes the user's voice data and facial images to determine their emotional state. For example, if the user is feeling stressed, the system adjusts the meal plan based on that emotion and suggests foods and nutrients that can help alleviate stress.

[0335] For example, if a user is determined to be experiencing high levels of stress after work, the device will suggest a special dinner plan aimed at stress relief. This plan may include relaxing herbal teas or foods rich in magnesium. Furthermore, if the user wishes, the system can share this information with a medical professional for further advice.

[0336] As described above, this system enables flexible and intelligent nutritional management tailored to the user's lifestyle and emotional state.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] The user uses their device to input basic personal information, health goals, and dietary preferences. This allows the system to obtain the user's baseline data.

[0340] Step 2:

[0341] The device sends the personal information it has collected to the server. The server then generates basic nutritional advice and meal plans based on this data.

[0342] Step 3:

[0343] The user uses their device's camera to take a picture of their meal for the day. The device then sends the captured image data to the server.

[0344] Step 4:

[0345] The server analyzes image data, uses AI technology to identify food components, and calculates their nutritional information.

[0346] Step 5:

[0347] The server calculates nutritional information and returns it to the terminal, which then provides that information to the user. The user can then view specific nutritional data.

[0348] Step 6:

[0349] If a user is using a wearable device, the device sends activity data to the terminal, which then sends it to the server.

[0350] Step 7:

[0351] The server analyzes activity data in real time and adjusts meal plans according to the user's activity level.

[0352] Step 8:

[0353] The device uses the user's voice data and facial image to activate the emotion engine and sends the data to the server.

[0354] Step 9:

[0355] The server uses an emotion engine to analyze the user's emotional state and determine conditions such as stress and anxiety.

[0356] Step 10:

[0357] The server adjusts the meal plan based on the user's emotional state and sends the adjustment results to the device. For example, if the user is under high stress, the server will suggest a menu that includes many ingredients that help relieve stress.

[0358] (Example 2)

[0359] 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".

[0360] Traditional nutrition management systems struggle to provide personalized recommendations that fully consider an individual's health and emotional state, and are particularly inadequate in adapting nutritional management based on emotional fluctuations. Furthermore, they have limitations in their ability to integrate information from multiple data sources and dynamically adjust plans.

[0361] 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.

[0362] In this invention, the server includes means for acquiring personal information, means for creating nutritional management information and intake plans, and means for analyzing emotional information and adapting meal suggestions. This enables a high degree of personalization that takes into account individual emotional and activity levels.

[0363] "Personal information" refers to data such as a user's health status, lifestyle, and goals, and is information necessary for the system to provide personalized suggestions to the user.

[0364] "Nutritional management information" refers to data used to provide users with instructions on the optimal balance of nutrient intake and dietary content.

[0365] A "nutrition plan" provides users with a specific plan regarding recommended meals and nutritional intake.

[0366] "Visual data" refers to images of food and related photographic data, which, when analyzed, can be used to identify food items and calculate their nutritional content.

[0367] "Activity information" refers to data that indicates the user's physical activity, including information about body movements and energy consumption.

[0368] "Emotional information" refers to data that indicates a user's emotional state, and includes information obtained particularly from voice and facial expressions.

[0369] A "specialist" refers to an individual or organization that possesses knowledge of nutrition and health management and can provide users with expert advice and guidance.

[0370] This system is designed to highly personalize users' individualized nutritional management. When users enter personal information using their smartphones or digital devices, this information is securely transmitted from the device to the server. The server uses the received information to generate nutritional management information and intake plans using an AI model. The nutritional management information provides customized advice based on the user's health goals.

[0371] Furthermore, when a user takes a picture of their meal using the device's camera, the visual data is sent to the server, where image analysis libraries such as TensorFlow are used to recognize the food and analyze its nutrients. By using a wearable device, the user sends activity information to the device in real time, which is also processed by the server. Based on this activity information, the server dynamically adjusts the intake plan as needed.

[0372] Furthermore, this system incorporates an emotion engine, enabling the analysis of emotional information using the user's voice data and facial images. For example, if the system determines that the user's stress level is high, it will suggest meals appropriate to that state. A specific suggestion might be to recommend foods with relaxing effects.

[0373] As a concrete example, when inputting prompts into the generative AI model, a sentence such as, "Generate a meal plan recommended for a user who is feeling stressed. Please provide detailed information about foods that can help alleviate stress," would be used. In this way, flexible nutritional management tailored to individual needs and emotional states is achieved.

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

[0375] Step 1:

[0376] Users input personal information and health goals using their smartphones or digital devices. This input data includes target weight and allergy information. After this information is entered into the device, the device encrypts it and securely transmits it to the server.

[0377] Step 2:

[0378] The server receives personal information transmitted from the terminal and uses a generated AI model to create nutritional management information and intake plans. Specifically, it calculates appropriate calorie intake and nutrient distribution according to the received health goals. The output is nutritional advice and a meal plan tailored to the user.

[0379] Step 3:

[0380] When a user takes a picture of their food using their device's camera, the visual data is sent from the device to a server. The server processes the received image data, using technologies such as TensorFlow to recognize the type of food and analyze its nutrients. As a result of the analysis, detailed information about the food's components is output.

[0381] Step 4:

[0382] When a user is wearing a wearable device, the device transmits activity information to the terminal in real time. The terminal forwards this data to a server. The server analyzes the activity information and dynamically adjusts the intake plan based on the user's energy consumption. The adjusted plan is output and sent back to the terminal.

[0383] Step 5:

[0384] The device collects the user's voice data and facial images and sends them to the server as emotional information. The server uses an emotion engine to analyze the data and determine the user's emotional state. Based on these results, it adapts a meal plan and suggests foods that are effective in reducing stress.

[0385] Step 6:

[0386] The server generates a meal plan based on emotions and sends it to the device. The device presents this plan to the user and, if necessary, suggests a procedure to share information within the system with a medical professional. This procedure allows the user to receive feedback from the professional at any time.

[0387] (Application Example 2)

[0388] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0389] Modern consumers are required to maintain a healthy diet while quickly and easily obtaining meals that suit their emotional state. However, conventional systems struggle to provide meal suggestions that adequately consider individual health data and emotional states, and there is a lack of means to quickly obtain food based on such suggestions. Against this backdrop, there is a need for the automated generation of personalized meal plans based on health information and emotional states, and for integration with food services that provide them.

[0390] 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.

[0391] In this invention, the server includes means for receiving personal information, means for generating nutritional advice and meal plans, and means for analyzing emotional states. This enables the automatic generation of personalized meal plans based on the user's health information and emotional state, and the rapid provision of food services based on those plans.

[0392] "Personal information" refers to information about a user's health status, eating history, and lifestyle habits, and serves as the basic data for the system to personalize meal plans.

[0393] "Nutritional advice" refers to expert guidance on diet and nutrition provided according to the user's individual health condition and goals.

[0394] A "meal plan" is a specific menu and schedule of meals suggested based on the user's health goals and emotional state.

[0395] "Food" refers to the items that users consume based on their meal plan.

[0396] "Image information" refers to visual data of the food and ingredients consumed by the user, which the system uses to analyze nutritional components.

[0397] "Activity information" refers to data related to a user's physical activity and exercise, which is obtained from wearable devices and other sources.

[0398] "Emotional state" refers to data on the user's stress level and emotional fluctuations, and is an important factor when the system adjusts meal plans.

[0399] "Digital communication" refers to a means of sending and receiving information over a network, and is used when transmitting a user's meal plan to a food delivery service.

[0400] "Experts" refer to professionals such as healthcare workers and nutritionists who are qualified to provide advice on the user's health and emotional state.

[0401] The system for implementing the present invention consists of a digital network environment using the user's smartphone, a wearable device, and a server. The user inputs personal information through a smartphone application and provides data tailored to their health status and goals. This information is securely transmitted to the server via the terminal.

[0402] The server receives "personal information" and generates "nutritional advice" and "meal plans" based on it. This process utilizes a generative AI model to provide personalized recommendations. Furthermore, it uses "image information" and "audio data" collected using the device's camera and voice recognition functions to analyze the user's "emotional state." This data is crucial for determining the "emotional state" and dynamically adjusting the meal plan.

[0403] Activity information obtained in real time from smartphones and wearable devices (e.g., Fitbit) is also sent to the server and reflected in the adjustment of the meal plan. The resulting adjusted plan is displayed on the user's device and transmitted to the food delivery service via digital communication. This allows users to select and quickly obtain foods based on their health and emotional state.

[0404] For example, if a user is experiencing stress due to long working hours, the application can detect their emotional state, and the system will suggest a "meal plan" that includes ingredients with relaxing effects. The user can then review the suggestion and order the selected menu from a partner food service with a single button click.

[0405] An example of a prompt message might be: "Generate an optimal dinner plan using the user's emotional data and health information. Present a food delivery option that combines foods effective in reducing stress."

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] Users enter personal information into a smartphone application. The data entered includes the user's health goals, dietary history, and real-time emotional state data. This information is collected by the device and securely transmitted to a server.

[0409] Step 2:

[0410] Based on the received personal information, the server utilizes a generative AI model to generate nutritional advice and a basic meal plan. At this stage, the AI ​​model performs predictive analytics and data mining to suggest optimal meal choices based on the user's health goals and eating history. The output is a nutrition plan tailored to the user.

[0411] Step 3:

[0412] The device uses its built-in camera and voice recognition API to collect image and audio data from the user. This data is then sent to a server for real-time analysis of the user's emotional state. This analysis utilizes data processing technologies such as facial expression recognition and voice tone analysis.

[0413] Step 4:

[0414] The server analyzes the obtained emotional state data and dynamically adjusts the meal plan. This process uses an emotional analysis model to identify the user's current emotional state and determine nutrients and foods that can reduce stress. The output is the adjusted meal plan.

[0415] Step 5:

[0416] The server uses real-time activity data acquired from wearable devices to make final adjustments to the meal plan. This activity data includes exercise volume and calorie expenditure, which are then used to further customize the plan. This results in an optimized meal plan.

[0417] Step 6:

[0418] The final meal plan is sent to the device and displayed to the user. The user can review this suggestion and order directly through the food delivery service from a customized menu according to their preferences. The data sent to the food delivery service via digital communication includes the selected menu and delivery information.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] This invention is an advanced system for providing personalized nutritional management, designed to help users achieve their health goals. The system is operated via smartphones or other digital devices.

[0436] First, the user uses a device to enter basic personal information and health goals. The entered data is sent from the device to a server, which uses an AI algorithm to create nutritional advice and meal plans based on the individual's goals. Information considered at this stage includes the user's current health status, dietary preferences, allergies, and other lifestyle information.

[0437] Next, the user takes a picture of their meal using the device's camera. The captured image data is sent to a server, which uses image processing technology to recognize the food and analyze its nutritional components. The analyzed data is sent back to the device and presented to the user as feedback.

[0438] If a user is using a wearable device, the device continuously transmits activity data to the terminal. The terminal sends this data to a server, which dynamically adjusts the meal plan in real time. For example, if the user's activity level is higher than expected, an increase in energy intake commensurate with that activity will be recommended.

[0439] Furthermore, users can share nutritional information and health data with healthcare professionals as needed. This data is encrypted and securely transmitted to other healthcare systems via servers. This allows healthcare professionals to understand the individual patient's condition and provide more effective guidance.

[0440] For example, if a user wants to eat a high-protein meal after exercise, they can scan their meal with their device, and the server will calculate the protein content and immediately inform the user. Furthermore, based on the day's activity data, the system adjusts the plan and suggests supplements for necessary nutrients. In this way, the system enables flexible nutritional management tailored to individual lifestyles and health goals.

[0441] The following describes the processing flow.

[0442] Step 1:

[0443] Users enter basic personal information and health goals using their devices. This data includes age, gender, weight, height, dietary preferences, and allergy information.

[0444] Step 2:

[0445] The device sends the collected personal information to the server. The data is securely encrypted during transmission.

[0446] Step 3:

[0447] Based on the data received by the server, an AI algorithm is applied to generate nutritional advice and meal plans tailored to individual health goals.

[0448] Step 4:

[0449] The user takes a picture of their meal using their device's camera and sends the image data to the server via their device.

[0450] Step 5:

[0451] The server analyzes image data and uses image processing technology to recognize food items. It then calculates the nutritional components of the recognized food items.

[0452] Step 6:

[0453] The server sends the nutritional information from the analysis results to the terminal, and the terminal displays that information to the user.

[0454] Step 7:

[0455] If a user is using a wearable device, the device sends activity data to the terminal. The terminal then sends that data to the server.

[0456] Step 8:

[0457] The server analyzes real-time activity data received and adjusts the meal plan as needed. The adjustments are sent to the device and notified to the user.

[0458] Step 9:

[0459] If a user chooses to share nutritional information and health data with a healthcare professional, the device encrypts the data and sends it to the healthcare professional's system via a server.

[0460] (Example 1)

[0461] 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."

[0462] Currently, it is difficult to provide nutritional management that is appropriately adapted to individual health conditions and goals, and the sharing and analysis of information for this purpose is insufficient. Therefore, the challenge lies in providing dietary guidance that is optimized for each individual user.

[0463] 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.

[0464] In this invention, the server includes means for receiving personalized health information, means for generating nutritional guidance and dietary advice, and means for identifying food and analyzing its nutritional components. This enables the provision of appropriate dietary guidance to individual users and facilitates effective information sharing with healthcare professionals.

[0465] "Personalized health information" refers to detailed information about each individual's specific health status, goals, preferences, allergies, and other relevant factors.

[0466] "Nutritional guidance" refers to providing appropriate nutritional information based on the user's health condition and lifestyle.

[0467] "Dietary guidance" refers to creating an optimal meal plan for each individual user and providing information that includes specific dietary recommendations.

[0468] "Food identification" refers to the visual recognition and classification of different types of food.

[0469] "Nutritional elements" refer to components such as proteins, lipids, carbohydrates, vitamins, and minerals found in food.

[0470] "Visual data" refers to image information acquired through imaging devices such as cameras.

[0471] "Activity tracking" refers to data such as exercise volume and heart rate obtained from wearable devices.

[0472] "Body-worn devices" refer to devices that are worn on the body to collect biometric information and exercise data.

[0473] "Medical professionals" refers to experts engaged in occupations related to healthcare, such as doctors, nurses, and nutritionists.

[0474] To implement this invention, users utilize digital devices such as smartphones and tablets to input data related to their health status and goals. A dedicated application is installed on the device, providing the user interface. Users input their basic, personalized health information and record their daily meals.

[0475] The data transmitted from the terminals is stored on a server, which then uses a generative AI model to generate nutritional and dietary guidance. This model is built using open-source libraries (e.g., TensorFlow and PyTorch) and is trained on a large amount of nutritional and dietary data.

[0476] When a user takes a picture of their meal, the device sends this visual data to a server. The server uses an image processing library (e.g., OpenCV) to identify the food and analyze its nutritional components. The results of this analysis are returned to the user's device, providing the user with real-time nutritional information.

[0477] Furthermore, when users use wearable devices to record their activity, the devices continuously transmit this data to a server. The server analyzes the activity data in real time and dynamically adjusts dietary guidance based on the results. This makes it possible to provide guidance on the optimal energy intake according to the user's activity level.

[0478] For example, if a user requests a high-protein meal after exercise, the following prompt could be input into the AI ​​model: "Please create a high-protein, low-calorie meal plan." The server would then provide dietary guidance appropriate to the user's health condition.

[0479] Thus, the invention combines advanced technologies to realize personalized nutrition and dietary management, providing support to users in achieving their health goals.

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

[0481] Step 1:

[0482] The user launches an application on a digital device and enters personalized health information. This information includes age, weight, dietary preferences, and allergy information. This input data is temporarily stored on the device. When the user presses the "Send" button, the device sends the data to the server. The output on the server is data that has been stored and converted into a format that can be processed by an AI model.

[0483] Step 2:

[0484] The server activates an AI model based on the received personalized health information. The model generates nutritional and dietary guidance tailored to each user based on the prompt. In this process, the AI ​​model utilizes a large amount of stored dietary data to design the optimal meal plan. An example of a prompt might be, "Create a high-protein meal plan suitable for the user." The output of this step is appropriate nutritional and dietary guidance for each user.

[0485] Step 3:

[0486] The user takes a picture of the food using their device's camera before eating. The device compresses the image data to reduce the data load and sends it to the server. The server uses an image processing library to identify the food. The input is image data of the meal, and the output is information about the type of food and the nutritional elements it contains.

[0487] Step 4:

[0488] The server analyzes the nutritional information of identified foods and combines it with dietary guidance. Data processing on the server includes referencing nutrient databases and assessing their relevance to individualized health information. The output from the server to the terminal is detailed feedback provided to the user, indicating which nutrients the user is consuming.

[0489] Step 5:

[0490] If the user is wearing a body-worn device, the terminal receives activity data from it in real time and sends it to the server. The server performs calculations to dynamically adjust dietary guidance based on the activity data. The input is the activity data, and the output is the dynamically adjusted dietary guidance.

[0491] Step 6:

[0492] Users can view generated nutritional and dietary guidance through their devices. This information is regularly updated to reflect the user's activity level. Furthermore, it is possible to share this information with healthcare professionals as needed. Information is properly encrypted via the server, ensuring secure information sharing. The output provides continuous feedback and clinical support information to help users manage their health.

[0493] (Application Example 1)

[0494] 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."

[0495] In modern times, providing optimal nutritional management tailored to individual health conditions and lifestyles is extremely difficult. Furthermore, with the increasing diversity of dietary options, there is a need for systems that support users in ensuring they are consuming meals that meet their health goals. However, the challenge lies in the fact that current technology does not adequately meet these needs.

[0496] 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.

[0497] In this invention, the server includes means for receiving personal information, means for generating nutritional advice and meal plans, means for processing image information to recognize food and analyze its nutritional components, means for receiving activity information in real time and adjusting the meal plan based on that information, means for sharing information with medical professionals, and means for suggesting food selections and placing orders based on nutritional management information. This makes it possible to achieve a diet that suits individual health goals and to manage nutrition efficiently and conveniently.

[0498] "Personal information" refers to basic information about the user, which is data necessary for nutritional management and setting health goals.

[0499] "Nutritional advice" refers to dietary guidelines created based on the user's health condition and food preferences.

[0500] A "meal plan" is a specific combination of meals and a schedule suggested to help users achieve their health goals.

[0501] "Image information" refers to data taken by users using smartphones or other devices to photograph their meals, and is used to analyze the nutritional components of the food.

[0502] "Activity information" refers to data on the user's exercise volume and daily activities, which is acquired in real time through wearable devices and other means.

[0503] "Real-time adjustment" is a process that instantly modifies nutritional advice and meal plans based on the latest user information.

[0504] A "medical professional" is a person qualified to provide advice and diagnoses regarding a user's health condition and nutritional management.

[0505] "Food selection suggestions" refer to a list of specific foods recommended to the user based on their individual nutritional management information.

[0506] An "order" is the act of a user actually purchasing or requesting delivery of food items they have selected.

[0507] The system for carrying out this invention consists of a smartphone application, a server, and, if necessary, a wearable device.

[0508] First, the user enters personal information and health goals using a smartphone application. The device sends this information to a server, which uses a generative AI model to generate personalized nutritional advice and meal plans. This process uses nutritional algorithms to perform data calculations and propose the optimal diet for the user.

[0509] Next, the user takes a picture of the food with their smartphone camera while eating. The device sends the image data to a server, where image recognition technologies such as the Google Cloud Vision API are used to identify the food and analyze its nutritional components. The results are fed back to the device in real time, allowing the user to understand what they are eating.

[0510] Furthermore, users wearing wearable devices continuously transmit activity information to their devices. This activity information is sent to a server, which adjusts the meal plan in real time to suit the user's lifestyle. For example, if more exercise than expected is recorded, the meal plan will incorporate an increase in energy intake.

[0511] By integrating with a food delivery application that also includes an ordering function, users can order suggested foods on the spot. This enables quick and accurate food selection based on nutritional management information.

[0512] For example, if a user desires high-protein food after exercise, the application receives this information and immediately suggests items such as a "high-protein chicken breast salad," which the user can order with a single tap. In this case, an example of a prompt to the generating AI would be, "Please tell me what protein-rich foods I should eat after exercise."

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] The user launches a smartphone application and enters personal information and health goals. This information includes age, gender, allergy information, and health goals (e.g., weight loss, muscle gain). The device then sends this data to the server.

[0516] Step 2:

[0517] The server uses an AI model based on the received information to create personalized nutritional advice for each user. During this process, the AI ​​calculates a meal plan based on the entered personal information and suggests recommended calorie and nutrient intakes. The results are displayed on the terminal and presented to the user.

[0518] Step 3:

[0519] The user takes a picture of the food using their smartphone camera at each meal. The device then transfers this image data to the server.

[0520] Step 4:

[0521] The server analyzes this image data using image recognition technologies such as the Google Cloud Vision API. Based on the input food image, it recognizes the food and extracts calorie and nutritional information. The results are sent to the terminal and displayed to the user as nutritional information.

[0522] Step 5:

[0523] If a user is wearing a wearable device, activity information is sent from that device to the terminal. The terminal then forwards this activity information to the server.

[0524] Step 6:

[0525] The server analyzes activity information in real time and dynamically adjusts the meal plan based on this analysis. For example, if activity levels increase, it calculates whether to increase calorie intake or the amount of specific nutrients. The adjusted meal plan is then sent to the terminal.

[0526] Step 7:

[0527] Users can utilize a food delivery service within the application. The server provides recommended food selections based on the adjusted meal plan, and users can choose their desired meals from these options and place an order.

[0528] Step 8:

[0529] If a user desires specific nutrients after exercise, they can use prompts to query the generating AI model. Prompts such as "Please tell me what protein-rich foods I should consume after exercise" allow the AI ​​to support appropriate food selection.

[0530] 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.

[0531] This invention provides advanced personalization features that take into account the user's emotional state by incorporating an emotion engine into an individualized nutrition management system. This system more effectively supports the user's health management through smartphones and other digital devices.

[0532] When a user enters personal information and health goals using their device, the device securely transmits this information to a server. Based on this information, the server generates nutritional advice and meal plans tailored to the user's goals. Users can also send image data to the server by taking pictures of their meals using their device's camera. The server uses image analysis technology to recognize the food and analyze its nutritional components, then sends the results back to the device.

[0533] When a user is wearing a wearable device, activity data obtained from that device is transmitted to the terminal in real time and then to a server. The server dynamically adjusts the meal plan based on this activity data and reflects the results back on the terminal.

[0534] Furthermore, the present invention incorporates an emotion engine that provides a function to analyze the user's emotional state in real time. This emotion engine analyzes the user's voice data and facial images to determine their emotional state. For example, if the user is feeling stressed, the system adjusts the meal plan based on that emotion and suggests foods and nutrients that can help alleviate stress.

[0535] For example, if a user is determined to be experiencing high levels of stress after work, the device will suggest a special dinner plan aimed at stress relief. This plan may include relaxing herbal teas or foods rich in magnesium. Furthermore, if the user wishes, the system can share this information with a medical professional for further advice.

[0536] As described above, this system enables flexible and intelligent nutritional management tailored to the user's lifestyle and emotional state.

[0537] The following describes the processing flow.

[0538] Step 1:

[0539] The user uses their device to input basic personal information, health goals, and dietary preferences. This allows the system to obtain the user's baseline data.

[0540] Step 2:

[0541] The device sends the personal information it has collected to the server. The server then generates basic nutritional advice and meal plans based on this data.

[0542] Step 3:

[0543] The user uses their device's camera to take a picture of their meal for the day. The device then sends the captured image data to the server.

[0544] Step 4:

[0545] The server analyzes image data, uses AI technology to identify food components, and calculates their nutritional information.

[0546] Step 5:

[0547] The server calculates nutritional information and returns it to the terminal, which then provides that information to the user. The user can then view specific nutritional data.

[0548] Step 6:

[0549] If a user is using a wearable device, the device sends activity data to the terminal, which then sends it to the server.

[0550] Step 7:

[0551] The server analyzes activity data in real time and adjusts meal plans according to the user's activity level.

[0552] Step 8:

[0553] The device uses the user's voice data and facial image to activate the emotion engine and sends the data to the server.

[0554] Step 9:

[0555] The server uses an emotion engine to analyze the user's emotional state and determine conditions such as stress and anxiety.

[0556] Step 10:

[0557] The server adjusts the meal plan based on the user's emotional state and sends the adjustment results to the device. For example, if the user is under high stress, the server will suggest a menu that includes many ingredients that help relieve stress.

[0558] (Example 2)

[0559] 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."

[0560] Traditional nutrition management systems struggle to provide personalized recommendations that fully consider an individual's health and emotional state, and are particularly inadequate in adapting nutritional management based on emotional fluctuations. Furthermore, they have limitations in their ability to integrate information from multiple data sources and dynamically adjust plans.

[0561] 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.

[0562] In this invention, the server includes means for acquiring personal information, means for creating nutritional management information and intake plans, and means for analyzing emotional information and adapting meal suggestions. This enables a high degree of personalization that takes into account individual emotional and activity levels.

[0563] "Personal information" refers to data such as a user's health status, lifestyle, and goals, and is information necessary for the system to provide personalized suggestions to the user.

[0564] "Nutritional management information" refers to data used to provide users with instructions on the optimal balance of nutrient intake and dietary content.

[0565] A "nutrition plan" provides users with a specific plan regarding recommended meals and nutritional intake.

[0566] "Visual data" refers to images of food and related photographic data, which, when analyzed, can be used to identify food items and calculate their nutritional content.

[0567] "Activity information" refers to data that indicates the user's physical activity, including information about body movements and energy consumption.

[0568] "Emotional information" refers to data that indicates a user's emotional state, and includes information obtained particularly from voice and facial expressions.

[0569] A "specialist" refers to an individual or organization that possesses knowledge of nutrition and health management and can provide users with expert advice and guidance.

[0570] This system is designed to highly personalize users' individualized nutritional management. When users enter personal information using their smartphones or digital devices, this information is securely transmitted from the device to the server. The server uses the received information to generate nutritional management information and intake plans using an AI model. The nutritional management information provides customized advice based on the user's health goals.

[0571] Furthermore, when a user takes a picture of their meal using the device's camera, the visual data is sent to the server, where image analysis libraries such as TensorFlow are used to recognize the food and analyze its nutrients. By using a wearable device, the user sends activity information to the device in real time, which is also processed by the server. Based on this activity information, the server dynamically adjusts the intake plan as needed.

[0572] Furthermore, this system incorporates an emotion engine, enabling the analysis of emotional information using the user's voice data and facial images. For example, if the system determines that the user's stress level is high, it will suggest meals appropriate to that state. A specific suggestion might be to recommend foods with relaxing effects.

[0573] As a concrete example, when inputting prompts into the generative AI model, a sentence such as, "Generate a meal plan recommended for a user who is feeling stressed. Please provide detailed information about foods that can help alleviate stress," would be used. In this way, flexible nutritional management tailored to individual needs and emotional states is achieved.

[0574] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0575] Step 1:

[0576] Users input personal information and health goals using their smartphones or digital devices. This input data includes target weight and allergy information. After this information is entered into the device, the device encrypts it and securely transmits it to the server.

[0577] Step 2:

[0578] The server receives personal information transmitted from the terminal and uses a generated AI model to create nutritional management information and intake plans. Specifically, it calculates appropriate calorie intake and nutrient distribution according to the received health goals. The output is nutritional advice and a meal plan tailored to the user.

[0579] Step 3:

[0580] When a user takes a picture of their food using their device's camera, the visual data is sent from the device to a server. The server processes the received image data, using technologies such as TensorFlow to recognize the type of food and analyze its nutrients. As a result of the analysis, detailed information about the food's components is output.

[0581] Step 4:

[0582] When a user is wearing a wearable device, the device transmits activity information to the terminal in real time. The terminal forwards this data to a server. The server analyzes the activity information and dynamically adjusts the intake plan based on the user's energy consumption. The adjusted plan is output and sent back to the terminal.

[0583] Step 5:

[0584] The device collects the user's voice data and facial images and sends them to the server as emotional information. The server uses an emotion engine to analyze the data and determine the user's emotional state. Based on these results, it adapts a meal plan and suggests foods that are effective in reducing stress.

[0585] Step 6:

[0586] The server generates a meal plan based on emotions and sends it to the device. The device presents this plan to the user and, if necessary, suggests a procedure to share information within the system with a medical professional. This procedure allows the user to receive feedback from the professional at any time.

[0587] (Application Example 2)

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

[0589] Modern consumers are required to maintain a healthy diet while quickly and easily obtaining meals that suit their emotional state. However, conventional systems struggle to provide meal suggestions that adequately consider individual health data and emotional states, and there is a lack of means to quickly obtain food based on such suggestions. Against this backdrop, there is a need for the automated generation of personalized meal plans based on health information and emotional states, and for integration with food services that provide them.

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

[0591] In this invention, the server includes means for receiving personal information, means for generating nutritional advice and meal plans, and means for analyzing emotional states. This enables the automatic generation of personalized meal plans based on the user's health information and emotional state, and the rapid provision of food services based on those plans.

[0592] "Personal information" refers to information about a user's health status, eating history, and lifestyle habits, and serves as the basic data for the system to personalize meal plans.

[0593] "Nutritional advice" refers to expert guidance on diet and nutrition provided according to the user's individual health condition and goals.

[0594] A "meal plan" is a specific menu and schedule of meals suggested based on the user's health goals and emotional state.

[0595] "Food" refers to the items that users consume based on their meal plan.

[0596] "Image information" refers to visual data of the food and ingredients consumed by the user, which the system uses to analyze nutritional components.

[0597] "Activity information" refers to data related to a user's physical activity and exercise, which is obtained from wearable devices and other sources.

[0598] "Emotional state" refers to data on the user's stress level and emotional fluctuations, and is an important factor when the system adjusts meal plans.

[0599] "Digital communication" refers to a means of sending and receiving information over a network, and is used when transmitting a user's meal plan to a food delivery service.

[0600] "Experts" refer to professionals such as healthcare workers and nutritionists who are qualified to provide advice on the user's health and emotional state.

[0601] The system for implementing the present invention consists of a digital network environment using the user's smartphone, a wearable device, and a server. The user inputs personal information through a smartphone application and provides data tailored to their health status and goals. This information is securely transmitted to the server via the terminal.

[0602] The server receives "personal information" and generates "nutritional advice" and "meal plans" based on it. This process utilizes a generative AI model to provide personalized recommendations. Furthermore, it uses "image information" and "audio data" collected using the device's camera and voice recognition functions to analyze the user's "emotional state." This data is crucial for determining the "emotional state" and dynamically adjusting the meal plan.

[0603] Activity information obtained in real time from smartphones and wearable devices (e.g., Fitbit) is also sent to the server and reflected in the adjustment of the meal plan. The resulting adjusted plan is displayed on the user's device and transmitted to the food delivery service via digital communication. This allows users to select and quickly obtain foods based on their health and emotional state.

[0604] For example, if a user is experiencing stress due to long working hours, the application can detect their emotional state, and the system will suggest a "meal plan" that includes ingredients with relaxing effects. The user can then review the suggestion and order the selected menu from a partner food service with a single button click.

[0605] An example of a prompt message might be: "Generate an optimal dinner plan using the user's emotional data and health information. Present a food delivery option that combines foods effective in reducing stress."

[0606] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0607] Step 1:

[0608] Users enter personal information into a smartphone application. The data entered includes the user's health goals, dietary history, and real-time emotional state data. This information is collected by the device and securely transmitted to a server.

[0609] Step 2:

[0610] Based on the received personal information, the server utilizes a generative AI model to generate nutritional advice and a basic meal plan. At this stage, the AI ​​model performs predictive analytics and data mining to suggest optimal meal choices based on the user's health goals and eating history. The output is a nutrition plan tailored to the user.

[0611] Step 3:

[0612] The device uses its built-in camera and voice recognition API to collect image and audio data from the user. This data is then sent to a server for real-time analysis of the user's emotional state. This analysis utilizes data processing technologies such as facial expression recognition and voice tone analysis.

[0613] Step 4:

[0614] The server analyzes the obtained emotional state data and dynamically adjusts the meal plan. This process uses an emotional analysis model to identify the user's current emotional state and determine nutrients and foods that can reduce stress. The output is the adjusted meal plan.

[0615] Step 5:

[0616] The server uses real-time activity data acquired from wearable devices to make final adjustments to the meal plan. This activity data includes exercise volume and calorie expenditure, which are then used to further customize the plan. This results in an optimized meal plan.

[0617] Step 6:

[0618] The final meal plan is sent to the device and displayed to the user. The user can review this suggestion and order directly through the food delivery service from a customized menu according to their preferences. The data sent to the food delivery service via digital communication includes the selected menu and delivery information.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] [Fourth Embodiment]

[0623] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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).

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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".

[0636] This invention is an advanced system for providing personalized nutritional management, designed to help users achieve their health goals. The system is operated via smartphones or other digital devices.

[0637] First, the user uses a device to enter basic personal information and health goals. The entered data is sent from the device to a server, which uses an AI algorithm to create nutritional advice and meal plans based on the individual's goals. Information considered at this stage includes the user's current health status, dietary preferences, allergies, and other lifestyle information.

[0638] Next, the user takes a picture of their meal using the device's camera. The captured image data is sent to a server, which uses image processing technology to recognize the food and analyze its nutritional components. The analyzed data is sent back to the device and presented to the user as feedback.

[0639] If a user is using a wearable device, the device continuously transmits activity data to the terminal. The terminal sends this data to a server, which dynamically adjusts the meal plan in real time. For example, if the user's activity level is higher than expected, an increase in energy intake commensurate with that activity will be recommended.

[0640] Furthermore, users can share nutritional information and health data with healthcare professionals as needed. This data is encrypted and securely transmitted to other healthcare systems via servers. This allows healthcare professionals to understand the individual patient's condition and provide more effective guidance.

[0641] For example, if a user wants to eat a high-protein meal after exercise, they can scan their meal with their device, and the server will calculate the protein content and immediately inform the user. Furthermore, based on the day's activity data, the system adjusts the plan and suggests supplements for necessary nutrients. In this way, the system enables flexible nutritional management tailored to individual lifestyles and health goals.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] Users enter basic personal information and health goals using their devices. This data includes age, gender, weight, height, dietary preferences, and allergy information.

[0645] Step 2:

[0646] The device sends the collected personal information to the server. The data is securely encrypted during transmission.

[0647] Step 3:

[0648] Based on the data received by the server, an AI algorithm is applied to generate nutritional advice and meal plans tailored to individual health goals.

[0649] Step 4:

[0650] The user takes a picture of their meal using their device's camera and sends the image data to the server via their device.

[0651] Step 5:

[0652] The server analyzes image data and uses image processing technology to recognize food items. It then calculates the nutritional components of the recognized food items.

[0653] Step 6:

[0654] The server sends the nutritional information from the analysis results to the terminal, and the terminal displays that information to the user.

[0655] Step 7:

[0656] If a user is using a wearable device, the device sends activity data to the terminal. The terminal then sends that data to the server.

[0657] Step 8:

[0658] The server analyzes real-time activity data received and adjusts the meal plan as needed. The adjustments are sent to the device and notified to the user.

[0659] Step 9:

[0660] If a user chooses to share nutritional information and health data with a healthcare professional, the device encrypts the data and sends it to the healthcare professional's system via a server.

[0661] (Example 1)

[0662] 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".

[0663] Currently, it is difficult to provide nutritional management that is appropriately adapted to individual health conditions and goals, and the sharing and analysis of information for this purpose is insufficient. Therefore, the challenge lies in providing dietary guidance that is optimized for each individual user.

[0664] 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.

[0665] In this invention, the server includes means for receiving personalized health information, means for generating nutritional guidance and dietary advice, and means for identifying food and analyzing its nutritional components. This enables the provision of appropriate dietary guidance to individual users and facilitates effective information sharing with healthcare professionals.

[0666] "Personalized health information" refers to detailed information about each individual's specific health status, goals, preferences, allergies, and other relevant factors.

[0667] "Nutritional guidance" refers to providing appropriate nutritional information based on the user's health condition and lifestyle.

[0668] "Dietary guidance" refers to creating an optimal meal plan for each individual user and providing information that includes specific dietary recommendations.

[0669] "Food identification" refers to the visual recognition and classification of different types of food.

[0670] "Nutritional elements" refer to components such as proteins, lipids, carbohydrates, vitamins, and minerals found in food.

[0671] "Visual data" refers to image information acquired through imaging devices such as cameras.

[0672] "Activity tracking" refers to data such as exercise volume and heart rate obtained from wearable devices.

[0673] "Body-worn devices" refer to devices that are worn on the body to collect biometric information and exercise data.

[0674] "Medical professionals" refers to experts engaged in occupations related to healthcare, such as doctors, nurses, and nutritionists.

[0675] To implement this invention, users utilize digital devices such as smartphones and tablets to input data related to their health status and goals. A dedicated application is installed on the device, providing the user interface. Users input their basic, personalized health information and record their daily meals.

[0676] The data transmitted from the terminals is stored on a server, which then uses a generative AI model to generate nutritional and dietary guidance. This model is built using open-source libraries (e.g., TensorFlow and PyTorch) and is trained on a large amount of nutritional and dietary data.

[0677] When a user takes a picture of their meal, the device sends this visual data to a server. The server uses an image processing library (e.g., OpenCV) to identify the food and analyze its nutritional components. The results of this analysis are returned to the user's device, providing the user with real-time nutritional information.

[0678] Furthermore, when users use wearable devices to record their activity, the devices continuously transmit this data to a server. The server analyzes the activity data in real time and dynamically adjusts dietary guidance based on the results. This makes it possible to provide guidance on the optimal energy intake according to the user's activity level.

[0679] For example, if a user requests a high-protein meal after exercise, the following prompt could be input into the AI ​​model: "Please create a high-protein, low-calorie meal plan." The server would then provide dietary guidance appropriate to the user's health condition.

[0680] Thus, the invention combines advanced technologies to realize personalized nutrition and dietary management, providing support to users in achieving their health goals.

[0681] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0682] Step 1:

[0683] The user launches an application on a digital device and enters personalized health information. This information includes age, weight, dietary preferences, and allergy information. This input data is temporarily stored on the device. When the user presses the "Send" button, the device sends the data to the server. The output on the server is data that has been stored and converted into a format that can be processed by an AI model.

[0684] Step 2:

[0685] The server activates an AI model based on the received personalized health information. The model generates nutritional and dietary guidance tailored to each user based on the prompt. In this process, the AI ​​model utilizes a large amount of stored dietary data to design the optimal meal plan. An example of a prompt might be, "Create a high-protein meal plan suitable for the user." The output of this step is appropriate nutritional and dietary guidance for each user.

[0686] Step 3:

[0687] The user takes a picture of the food using their device's camera before eating. The device compresses the image data to reduce the data load and sends it to the server. The server uses an image processing library to identify the food. The input is image data of the meal, and the output is information about the type of food and the nutritional elements it contains.

[0688] Step 4:

[0689] The server analyzes the nutritional information of identified foods and combines it with dietary guidance. Data processing on the server includes referencing nutrient databases and assessing their relevance to individualized health information. The output from the server to the terminal is detailed feedback provided to the user, indicating which nutrients the user is consuming.

[0690] Step 5:

[0691] If the user is wearing a body-worn device, the terminal receives activity data from it in real time and sends it to the server. The server performs calculations to dynamically adjust dietary guidance based on the activity data. The input is the activity data, and the output is the dynamically adjusted dietary guidance.

[0692] Step 6:

[0693] Users can view generated nutritional and dietary guidance through their devices. This information is regularly updated to reflect the user's activity level. Furthermore, it is possible to share this information with healthcare professionals as needed. Information is properly encrypted via the server, ensuring secure information sharing. The output provides continuous feedback and clinical support information to help users manage their health.

[0694] (Application Example 1)

[0695] 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".

[0696] In modern times, providing optimal nutritional management tailored to individual health conditions and lifestyles is extremely difficult. Furthermore, with the increasing diversity of dietary options, there is a need for systems that support users in ensuring they are consuming meals that meet their health goals. However, the challenge lies in the fact that current technology does not adequately meet these needs.

[0697] 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.

[0698] In this invention, the server includes means for receiving personal information, means for generating nutritional advice and meal plans, means for processing image information to recognize food and analyze its nutritional components, means for receiving activity information in real time and adjusting the meal plan based on that information, means for sharing information with medical professionals, and means for suggesting food selections and placing orders based on nutritional management information. This makes it possible to achieve a diet that suits individual health goals and to manage nutrition efficiently and conveniently.

[0699] "Personal information" refers to basic information about the user, which is data necessary for nutritional management and setting health goals.

[0700] "Nutritional advice" refers to dietary guidelines created based on the user's health condition and food preferences.

[0701] A "meal plan" is a specific combination of meals and a schedule suggested to help users achieve their health goals.

[0702] "Image information" refers to data taken by users using smartphones or other devices to photograph their meals, and is used to analyze the nutritional components of the food.

[0703] "Activity information" refers to data on the user's exercise volume and daily activities, which is acquired in real time through wearable devices and other means.

[0704] "Real-time adjustment" is a process that instantly modifies nutritional advice and meal plans based on the latest user information.

[0705] A "medical professional" is a person qualified to provide advice and diagnoses regarding a user's health condition and nutritional management.

[0706] "Food selection suggestions" refer to a list of specific foods recommended to the user based on their individual nutritional management information.

[0707] An "order" is the act of a user actually purchasing or requesting delivery of food items they have selected.

[0708] The system for carrying out this invention consists of a smartphone application, a server, and, if necessary, a wearable device.

[0709] First, the user enters personal information and health goals using a smartphone application. The device sends this information to a server, which uses a generative AI model to generate personalized nutritional advice and meal plans. This process uses nutritional algorithms to perform data calculations and propose the optimal diet for the user.

[0710] Next, the user takes a picture of the food with their smartphone camera while eating. The device sends the image data to a server, where image recognition technologies such as the Google Cloud Vision API are used to identify the food and analyze its nutritional components. The results are fed back to the device in real time, allowing the user to understand what they are eating.

[0711] Furthermore, users wearing wearable devices continuously transmit activity information to their devices. This activity information is sent to a server, which adjusts the meal plan in real time to suit the user's lifestyle. For example, if more exercise than expected is recorded, the meal plan will incorporate an increase in energy intake.

[0712] By integrating with a food delivery application that also includes an ordering function, users can order suggested foods on the spot. This enables quick and accurate food selection based on nutritional management information.

[0713] For example, if a user desires high-protein food after exercise, the application receives this information and immediately suggests items such as a "high-protein chicken breast salad," which the user can order with a single tap. In this case, an example of a prompt to the generating AI would be, "Please tell me what protein-rich foods I should eat after exercise."

[0714] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0715] Step 1:

[0716] The user launches a smartphone application and enters personal information and health goals. This information includes age, gender, allergy information, and health goals (e.g., weight loss, muscle gain). The device then sends this data to the server.

[0717] Step 2:

[0718] The server uses an AI model based on the received information to create personalized nutritional advice for each user. During this process, the AI ​​calculates a meal plan based on the entered personal information and suggests recommended calorie and nutrient intakes. The results are displayed on the terminal and presented to the user.

[0719] Step 3:

[0720] The user takes a picture of the food using their smartphone camera at each meal. The device then transfers this image data to the server.

[0721] Step 4:

[0722] The server analyzes this image data using image recognition technologies such as the Google Cloud Vision API. Based on the input food image, it recognizes the food and extracts calorie and nutritional information. The results are sent to the terminal and displayed to the user as nutritional information.

[0723] Step 5:

[0724] If a user is wearing a wearable device, activity information is sent from that device to the terminal. The terminal then forwards this activity information to the server.

[0725] Step 6:

[0726] The server analyzes activity information in real time and dynamically adjusts the meal plan based on this analysis. For example, if activity levels increase, it calculates whether to increase calorie intake or the amount of specific nutrients. The adjusted meal plan is then sent to the terminal.

[0727] Step 7:

[0728] Users can utilize a food delivery service within the application. The server provides recommended food selections based on the adjusted meal plan, and users can choose their desired meals from these options and place an order.

[0729] Step 8:

[0730] If a user desires specific nutrients after exercise, they can use prompts to query the generating AI model. Prompts such as "Please tell me what protein-rich foods I should consume after exercise" allow the AI ​​to support appropriate food selection.

[0731] 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.

[0732] This invention provides advanced personalization features that take into account the user's emotional state by incorporating an emotion engine into an individualized nutrition management system. This system more effectively supports the user's health management through smartphones and other digital devices.

[0733] When a user enters personal information and health goals using their device, the device securely transmits this information to a server. Based on this information, the server generates nutritional advice and meal plans tailored to the user's goals. Users can also send image data to the server by taking pictures of their meals using their device's camera. The server uses image analysis technology to recognize the food and analyze its nutritional components, then sends the results back to the device.

[0734] When a user is wearing a wearable device, activity data obtained from that device is transmitted to the terminal in real time and then to a server. The server dynamically adjusts the meal plan based on this activity data and reflects the results back on the terminal.

[0735] Furthermore, the present invention incorporates an emotion engine that provides a function to analyze the user's emotional state in real time. This emotion engine analyzes the user's voice data and facial images to determine their emotional state. For example, if the user is feeling stressed, the system adjusts the meal plan based on that emotion and suggests foods and nutrients that can help alleviate stress.

[0736] For example, if a user is determined to be experiencing high levels of stress after work, the device will suggest a special dinner plan aimed at stress relief. This plan may include relaxing herbal teas or foods rich in magnesium. Furthermore, if the user wishes, the system can share this information with a medical professional for further advice.

[0737] As described above, this system enables flexible and intelligent nutritional management tailored to the user's lifestyle and emotional state.

[0738] The following describes the processing flow.

[0739] Step 1:

[0740] The user uses their device to input basic personal information, health goals, and dietary preferences. This allows the system to obtain the user's baseline data.

[0741] Step 2:

[0742] The device sends the personal information it has collected to the server. The server then generates basic nutritional advice and meal plans based on this data.

[0743] Step 3:

[0744] The user uses their device's camera to take a picture of their meal for the day. The device then sends the captured image data to the server.

[0745] Step 4:

[0746] The server analyzes image data, uses AI technology to identify food components, and calculates their nutritional information.

[0747] Step 5:

[0748] The server calculates nutritional information and returns it to the terminal, which then provides that information to the user. The user can then view specific nutritional data.

[0749] Step 6:

[0750] If a user is using a wearable device, the device sends activity data to the terminal, which then sends it to the server.

[0751] Step 7:

[0752] The server analyzes activity data in real time and adjusts meal plans according to the user's activity level.

[0753] Step 8:

[0754] The device uses the user's voice data and facial image to activate the emotion engine and sends the data to the server.

[0755] Step 9:

[0756] The server uses an emotion engine to analyze the user's emotional state and determine conditions such as stress and anxiety.

[0757] Step 10:

[0758] The server adjusts the meal plan based on the user's emotional state and sends the adjustment results to the device. For example, if the user is under high stress, the server will suggest a menu that includes many ingredients that help relieve stress.

[0759] (Example 2)

[0760] 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".

[0761] Traditional nutrition management systems struggle to provide personalized recommendations that fully consider an individual's health and emotional state, and are particularly inadequate in adapting nutritional management based on emotional fluctuations. Furthermore, they have limitations in their ability to integrate information from multiple data sources and dynamically adjust plans.

[0762] 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.

[0763] In this invention, the server includes means for acquiring personal information, means for creating nutritional management information and intake plans, and means for analyzing emotional information and adapting meal suggestions. This enables a high degree of personalization that takes into account individual emotional and activity levels.

[0764] "Personal information" refers to data such as a user's health status, lifestyle, and goals, and is information necessary for the system to provide personalized suggestions to the user.

[0765] "Nutritional management information" refers to data used to provide users with instructions on the optimal balance of nutrient intake and dietary content.

[0766] A "nutrition plan" provides users with a specific plan regarding recommended meals and nutritional intake.

[0767] "Visual data" refers to images of food and related photographic data, which, when analyzed, can be used to identify food items and calculate their nutritional content.

[0768] "Activity information" refers to data that indicates the user's physical activity, including information about body movements and energy consumption.

[0769] "Emotional information" refers to data that indicates a user's emotional state, and includes information obtained particularly from voice and facial expressions.

[0770] A "specialist" refers to an individual or organization that possesses knowledge of nutrition and health management and can provide users with expert advice and guidance.

[0771] This system is designed to highly personalize users' individualized nutritional management. When users enter personal information using their smartphones or digital devices, this information is securely transmitted from the device to the server. The server uses the received information to generate nutritional management information and intake plans using an AI model. The nutritional management information provides customized advice based on the user's health goals.

[0772] Furthermore, when a user takes a picture of their meal using the device's camera, the visual data is sent to the server, where image analysis libraries such as TensorFlow are used to recognize the food and analyze its nutrients. By using a wearable device, the user sends activity information to the device in real time, which is also processed by the server. Based on this activity information, the server dynamically adjusts the intake plan as needed.

[0773] Furthermore, this system incorporates an emotion engine, enabling the analysis of emotional information using the user's voice data and facial images. For example, if the system determines that the user's stress level is high, it will suggest meals appropriate to that state. A specific suggestion might be to recommend foods with relaxing effects.

[0774] As a concrete example, when inputting prompts into the generative AI model, a sentence such as, "Generate a meal plan recommended for a user who is feeling stressed. Please provide detailed information about foods that can help alleviate stress," would be used. In this way, flexible nutritional management tailored to individual needs and emotional states is achieved.

[0775] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0776] Step 1:

[0777] Users input personal information and health goals using their smartphones or digital devices. This input data includes target weight and allergy information. After this information is entered into the device, the device encrypts it and securely transmits it to the server.

[0778] Step 2:

[0779] The server receives personal information transmitted from the terminal and uses a generated AI model to create nutritional management information and intake plans. Specifically, it calculates appropriate calorie intake and nutrient distribution according to the received health goals. The output is nutritional advice and a meal plan tailored to the user.

[0780] Step 3:

[0781] When a user takes a picture of their food using their device's camera, the visual data is sent from the device to a server. The server processes the received image data, using technologies such as TensorFlow to recognize the type of food and analyze its nutrients. As a result of the analysis, detailed information about the food's components is output.

[0782] Step 4:

[0783] When a user is wearing a wearable device, the device transmits activity information to the terminal in real time. The terminal forwards this data to a server. The server analyzes the activity information and dynamically adjusts the intake plan based on the user's energy consumption. The adjusted plan is output and sent back to the terminal.

[0784] Step 5:

[0785] The device collects the user's voice data and facial images and sends them to the server as emotional information. The server uses an emotion engine to analyze the data and determine the user's emotional state. Based on these results, it adapts a meal plan and suggests foods that are effective in reducing stress.

[0786] Step 6:

[0787] The server generates a meal plan based on emotions and sends it to the device. The device presents this plan to the user and, if necessary, suggests a procedure to share information within the system with a medical professional. This procedure allows the user to receive feedback from the professional at any time.

[0788] (Application Example 2)

[0789] 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".

[0790] Modern consumers are required to maintain a healthy diet while quickly and easily obtaining meals that suit their emotional state. However, conventional systems struggle to provide meal suggestions that adequately consider individual health data and emotional states, and there is a lack of means to quickly obtain food based on such suggestions. Against this backdrop, there is a need for the automated generation of personalized meal plans based on health information and emotional states, and for integration with food services that provide them.

[0791] 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.

[0792] In this invention, the server includes means for receiving personal information, means for generating nutritional advice and meal plans, and means for analyzing emotional states. This enables the automatic generation of personalized meal plans based on the user's health information and emotional state, and the rapid provision of food services based on those plans.

[0793] "Personal information" refers to information about a user's health status, eating history, and lifestyle habits, and serves as the basic data for the system to personalize meal plans.

[0794] "Nutritional advice" refers to expert guidance on diet and nutrition provided according to the user's individual health condition and goals.

[0795] A "meal plan" is a specific menu and schedule of meals suggested based on the user's health goals and emotional state.

[0796] "Food" refers to the items that users consume based on their meal plan.

[0797] "Image information" refers to visual data of the food and ingredients consumed by the user, which the system uses to analyze nutritional components.

[0798] "Activity information" refers to data related to a user's physical activity and exercise, which is obtained from wearable devices and other sources.

[0799] "Emotional state" refers to data on the user's stress level and emotional fluctuations, and is an important factor when the system adjusts meal plans.

[0800] "Digital communication" refers to a means of sending and receiving information over a network, and is used when transmitting a user's meal plan to a food delivery service.

[0801] "Experts" refer to professionals such as healthcare workers and nutritionists who are qualified to provide advice on the user's health and emotional state.

[0802] The system for implementing the present invention consists of a digital network environment using the user's smartphone, a wearable device, and a server. The user inputs personal information through a smartphone application and provides data tailored to their health status and goals. This information is securely transmitted to the server via the terminal.

[0803] The server receives "personal information" and generates "nutritional advice" and "meal plans" based on it. This process utilizes a generative AI model to provide personalized recommendations. Furthermore, it uses "image information" and "audio data" collected using the device's camera and voice recognition functions to analyze the user's "emotional state." This data is crucial for determining the "emotional state" and dynamically adjusting the meal plan.

[0804] Activity information obtained in real time from smartphones and wearable devices (e.g., Fitbit) is also sent to the server and reflected in the adjustment of the meal plan. The resulting adjusted plan is displayed on the user's device and transmitted to the food delivery service via digital communication. This allows users to select and quickly obtain foods based on their health and emotional state.

[0805] For example, if a user is experiencing stress due to long working hours, the application can detect their emotional state, and the system will suggest a "meal plan" that includes ingredients with relaxing effects. The user can then review the suggestion and order the selected menu from a partner food service with a single button click.

[0806] An example of a prompt message might be: "Generate an optimal dinner plan using the user's emotional data and health information. Present a food delivery option that combines foods effective in reducing stress."

[0807] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0808] Step 1:

[0809] Users enter personal information into a smartphone application. The data entered includes the user's health goals, dietary history, and real-time emotional state data. This information is collected by the device and securely transmitted to a server.

[0810] Step 2:

[0811] Based on the received personal information, the server utilizes a generative AI model to generate nutritional advice and a basic meal plan. At this stage, the AI ​​model performs predictive analytics and data mining to suggest optimal meal choices based on the user's health goals and eating history. The output is a nutrition plan tailored to the user.

[0812] Step 3:

[0813] The device uses its built-in camera and voice recognition API to collect image and audio data from the user. This data is then sent to a server for real-time analysis of the user's emotional state. This analysis utilizes data processing technologies such as facial expression recognition and voice tone analysis.

[0814] Step 4:

[0815] The server analyzes the obtained emotional state data and dynamically adjusts the meal plan. This process uses an emotional analysis model to identify the user's current emotional state and determine nutrients and foods that can reduce stress. The output is the adjusted meal plan.

[0816] Step 5:

[0817] The server uses real-time activity data acquired from wearable devices to make final adjustments to the meal plan. This activity data includes exercise volume and calorie expenditure, which are then used to further customize the plan. This results in an optimized meal plan.

[0818] Step 6:

[0819] The final meal plan is sent to the device and displayed to the user. The user can review this suggestion and order directly through the food delivery service from a customized menu according to their preferences. The data sent to the food delivery service via digital communication includes the selected menu and delivery information.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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."

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] The following is further disclosed regarding the embodiments described above.

[0842] (Claim 1)

[0843] Means of receiving personal information,

[0844] A means for generating nutritional advice and meal plans based on the aforementioned personal information,

[0845] A means for processing image data to recognize food and analyze its nutritional components,

[0846] A means for receiving activity data in real time and adjusting the meal plan based on that data,

[0847] Means for displaying the aforementioned nutritional components and adjusted meal plan,

[0848] Means of sharing data with medical professionals,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, further comprising means for performing image resizing and format conversion as preprocessing in the processing of the aforementioned image data.

[0852] (Claim 3)

[0853] The system according to claim 1, wherein the activity data is acquired from a wearable device.

[0854] "Example 1"

[0855] (Claim 1)

[0856] Means for receiving personalized health information,

[0857] A means for generating nutritional guidance and dietary guidance based on the aforementioned individualized health information,

[0858] A means for processing visual data to identify food and analyze its nutritional elements,

[0859] A means for receiving activity records in real time and modifying the dietary guidance based on those records,

[0860] Means for displaying the aforementioned nutritional elements and modified dietary guidance,

[0861] Means of sharing information with medical professionals,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, further comprising means for reducing and converting the format of visual information as preprocessing in the processing of the aforementioned visual data.

[0865] (Claim 3)

[0866] The system according to claim 1, wherein the activity record is obtained from a body-worn device.

[0867] "Application Example 1"

[0868] (Claim 1)

[0869] Means of receiving personal information,

[0870] A means for generating nutritional advice and meal plans based on the aforementioned personal information,

[0871] A means for processing image information to recognize food and analyze its nutritional components,

[0872] A means for receiving activity information in real time and adjusting the meal plan based on that information,

[0873] Means for displaying the aforementioned nutritional components and adjusted meal plan,

[0874] Means of sharing information with medical professionals,

[0875] A means of suggesting food choices and placing orders based on nutritional management information,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, further comprising means for resizing and formatting an image as a preprocessing step in the processing of the image information.

[0879] (Claim 3)

[0880] The system according to claim 1, wherein the activity information is obtained from a wearable device.

[0881] "Example 2 of combining an emotion engine"

[0882] (Claim 1)

[0883] Means of acquiring personal information,

[0884] A means of creating nutritional management information and intake plans based on the aforementioned personal information,

[0885] A means for processing visual data to identify food and analyze its nutrients,

[0886] A means for dynamically acquiring activity information and adjusting the intake plan based on that information,

[0887] Means for presenting the aforementioned nutrients and adjusted intake plans,

[0888] A means of analyzing emotional information and adapting meal suggestions based on the analysis results,

[0889] Means of sharing information with experts,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, further comprising means for performing dimensional adjustment and format conversion of the data before analysis in the processing of the aforementioned visual data.

[0893] (Claim 3)

[0894] The system according to claim 1, wherein the activity information is collected from a wearable device.

[0895] "Application example 2 when combining with an emotional engine"

[0896] (Claim 1)

[0897] Means of receiving personal information,

[0898] A means for generating nutritional advice and meal plans based on the aforementioned personal information,

[0899] A means for processing image information to identify food and analyze its nutritional components,

[0900] A means for receiving activity information in real time and adjusting the meal plan based on that information,

[0901] Means for displaying the aforementioned nutritional components and adjusted meal plan,

[0902] A means for analyzing the user's emotional state and adjusting the meal plan based on that state,

[0903] A means of transmitting a meal plan to a food delivery service via digital communication and presenting a food-based suggestion,

[0904] Means of sharing information with experts,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, further comprising means for resizing and formatting an image as a preprocessing step in the processing of the image information.

[0908] (Claim 3)

[0909] The system according to claim 1, wherein the activity information is obtained from a portable health monitoring device. [Explanation of Symbols]

[0910] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of receiving personal information, A means for generating nutritional advice and meal plans based on the aforementioned personal information, A means for processing image data to recognize food and analyze its nutritional components, A means for receiving activity data in real time and adjusting the meal plan based on that data, Means for displaying the aforementioned nutritional components and adjusted meal plan, Means of sharing data with medical professionals, A system that includes this.

2. The system according to claim 1, further comprising means for performing image resizing and format conversion as preprocessing in the processing of the aforementioned image data.

3. The system according to claim 1, wherein the activity data is acquired from a wearable device.

Images