system

Abstract

We provide the system. [Solution] An analysis means that receives meal data registered by the user and identifies ingredients based on said meal data, A means for obtaining the nutritional components of the identified food ingredient from a data warehouse, An evaluation method for assessing which nutrients a user should consume and which they should avoid based on the acquired nutritional information, A suggestion means that proposes healthy ingredients or dishes to the user based on the evaluation results, A generation means for generating a link to purchase the proposed food ingredients or supplements, A means of moving around within the user's living space and acquiring meal data, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Many modern people tend to have an unbalanced diet, and as a result, the cases where nutrient deficiencies and excesses have an adverse impact on health are increasing. In particular, a lifestyle that relies on convenience store lunches and eating out is likely to disrupt the nutritional balance. For such problems, there is a need for a technology that allows users to easily manage their own diet and obtain accurate information for improvement.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a system that analyzes meal data registered by users and identifies the nutritional components of food ingredients. Specifically, it uses an AI algorithm to perform image analysis and retrieves nutritional components from a database based on the obtained data. Furthermore, based on the retrieved nutritional components, it evaluates the nutrients that the user should consume and those that should be avoided, and suggests healthy foods and dishes. In addition, it generates links that allow users to purchase the suggested foods and supplements, making it easy for users to obtain the products. In this way, it provides a technology that efficiently supports users in managing their dietary habits.

[0006] A "user" is an individual who uses the system to record their food intake and manage their health.

[0007] "Meal data" refers to information about the meals a user has consumed, which is registered in the system through photos or manual input.

[0008] "Analysis means" refers to technical means that analyze registered meal data using AI algorithms to identify ingredients and extract nutritional components.

[0009] "Ingredients" refer to the individual foods and materials that make up a meal, and are the subject of nutritional component evaluation.

[0010] "Nutritional components" refer to indicators of the amount of energy and nutrients (protein, fat, carbohydrates, etc.) contained in food.

[0011] A "database" is an information management platform that structures and stores acquired information, making it quickly accessible.

[0012] "Evaluation methods" refer to technical means for analyzing a user's nutritional intake based on acquired nutritional components and providing health guidance.

[0013] A "suggestion method" is a function that presents healthy foods and dishes based on the nutrients the user should consume and those they should avoid.

[0014] "Link" refers to the URL that can access a specific web page or online store on the Internet.

[0015] "Generation means" refers to algorithms or technical means for creating the proposed information or links.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [[ID=4"] [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when 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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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.

[0020] 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.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system for efficiently managing a user's diet and supporting their health. Users utilize a dedicated application on a device such as a smartphone or tablet. Users can register their meal data by taking photos of the meals they have eaten or by manually entering the details of their meals.

[0038] Registered meal data is sent from the terminal to the server. The server analyzes the meal data using an AI algorithm. Specifically, the server identifies ingredients based on images and retrieves the nutritional components contained in each ingredient from a database. Then, based on the retrieved nutritional components, the server evaluates which nutrients the user needs to consume and which nutrients they should limit.

[0039] Based on the evaluation results, the server suggests healthy foods and dishes to the user. For example, if a comparison with past dietary data determines that the user is deficient in minerals, the server will suggest dishes using mineral-rich ingredients or supplements. Links to obtain the necessary ingredients and supplements online will also be generated.

[0040] These suggestions are displayed on the device, allowing users to easily find places to buy ingredients and supplements. Furthermore, by referring to the recommended menus, users can plan their next meals in a healthier way.

[0041] As a concrete example, when a user uploads a photo of their lunch, the server identifies it as a high-fat meal and suggests using fresh vegetables and protein-rich ingredients instead. Furthermore, if supplements are needed, a link is provided showing the best purchase options from multiple online retailers.

[0042] This invention makes it easier for users to balance their nutrition through their daily meals, enabling them to achieve a healthy lifestyle.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user opens a screen on their device to record their meals, taking a photo of the meal or entering the details in text. By pressing the registration button, the meal data is saved on the device.

[0046] Step 2:

[0047] The device sends the stored meal data along with the user's authentication information to the server. This transmission is encrypted for data security.

[0048] Step 3:

[0049] The server analyzes the received meal data using an AI algorithm. Image analysis technology is used to identify ingredients from meal images and determine the nutritional components of each ingredient.

[0050] Step 4:

[0051] The server retrieves nutritional data corresponding to the identified ingredients from the database and evaluates the overall nutritional balance of the meal. This evaluation also includes a comparison with the user's past meal history.

[0052] Step 5:

[0053] Based on the evaluation results, the server analyzes the nutrients necessary for the user's healthy diet and identifies any excesses or deficiencies. This then generates specific nutritional advice for the user.

[0054] Step 6:

[0055] The server suggests foods, dishes, and necessary supplements to the user. It also generates links to online stores where users can purchase the suggested items.

[0056] Step 7:

[0057] The terminal receives suggestion information from the server and notifies the user, displaying it on a dedicated application screen. This allows the user to review the suggestions and consider their next action.

[0058] Step 8:

[0059] Based on the information provided, users adjust their diet and decide to purchase ingredients and supplements through the suggested links. They also plan their next meal based on the displayed recipes.

[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] Conventional diet management systems have made it difficult for users to accurately record their meals and receive appropriate nutritional advice based on that data. Furthermore, they lack specific suggestions tailored to each user's nutritional status and support for purchasing appropriate ingredients quickly, which means they cannot adequately help users manage their health.

[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 an analysis means for receiving meal data registered by the user and identifying food based on the meal data; an acquisition means for obtaining the nutrients of the identified food from a data storage; an evaluation means for evaluating the nutrients the user should consume and the nutrients they should avoid based on the acquired nutrients; and a creation means for creating prompt statements using a generation AI model and optimizing the suggested content. This enables the user to intuitively and efficiently manage their diet and achieve a healthy lifestyle while maintaining nutritional balance.

[0065] "Analysis means" refers to a system that receives meal data registered by a user and has functions and technologies for identifying food based on said meal data.

[0066] "Acquisition means" refers to the process or function of obtaining the nutrients of food identified by the analysis means from a data storage facility.

[0067] "Evaluation methods" include mechanisms and technologies for evaluating which nutrients a user should consume and which they should limit, based on the acquired nutrients.

[0068] A "proposal method" refers to a system or technology that proposes healthy foods or dishes to users based on evaluation results.

[0069] "Generation means" refers to a function or technology that generates a pathway for users to obtain the proposed food or nutritional supplement.

[0070] "Creation method" refers to a process or function that uses a generative AI model to create prompt sentences and optimize the suggested content.

[0071] This invention is a system that efficiently manages a user's diet and supports a healthy lifestyle. Users can register their meal data using a dedicated application on a device such as a smartphone or tablet.

[0072] Users either take a photo of their meal or manually enter the details of their meal through a text field. This data is temporarily stored on the device and then sent to the server via the internet. The secure HTTPS protocol is used for communication.

[0073] The server analyzes received meal data using AI algorithms. Image recognition uses deep learning-based image processing software (e.g., TENSORFLOW®, PyTorch) to identify food items from registered photos. Manually entered data is analyzed using natural language processing techniques.

[0074] The food information obtained as a result of the analysis is used to retrieve nutrient information from the data storage. The database APIs used here include public database APIs that cover food data (e.g., FoodData Central API). Based on this data, the server evaluates the user's nutrient deficiencies or excesses.

[0075] The system uses a generative AI model to optimize suggestions for the user. An example of a generated prompt might be, "Based on your recent dietary data, please identify any missing nutrients and suggest which foods you should include in your next meal." The server also generates and provides the user with links to access relevant foods or nutritional supplements.

[0076] These suggestions are displayed on the device, making them easily viewable and accessible to users. This system allows users to review their daily diet and receive support in making healthier choices. For example, if a user uploads a photo of a high-fat lunch, the server analyzes it and suggests alternative dishes rich in fresh vegetables and protein. It also provides optimal options for purchasing relevant supplements.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user launches a dedicated application on a device such as a smartphone or tablet. The user either takes a photo of their meal or manually enters the details of their meal through a text field. The entered data, either as image data or text data of the meal, is temporarily stored within the application on the device.

[0080] Step 2:

[0081] The device converts the stored meal data into JSON format and sends it to the server via the internet. Privacy and security are ensured by using the HTTPS protocol for data transmission.

[0082] Step 3:

[0083] The server analyzes the received meal data. Image data is analyzed using image recognition models based on TensorFlow or PyTorch to identify food items. Text data is analyzed using natural language processing techniques to extract specific food information. The output is a list of identified foods.

[0084] Step 4:

[0085] Based on the identified food list, the server retrieves relevant nutrient information from a data storage source (e.g., FoodData Central API). This data processing then outputs specific data on the nutrients contained in each food item.

[0086] Step 5:

[0087] The server uses the acquired nutrient information to compare it with the user's past dietary history and evaluate whether there are any nutrient deficiencies or excesses. This process uses a generative AI model to ensure optimal evaluation. The output of the evaluation is a list of nutrients that should be consumed and nutrients that should be limited.

[0088] Step 6:

[0089] Based on the evaluation results, the server suggests healthy foods and recipes to the user. It optimizes the suggestions using prompts generated by a generative AI model. Specifically, it might generate suggestions such as, "Try broccoli as a food rich in minerals."

[0090] Step 7:

[0091] The terminal displays suggested information received from the server on the user interface. Based on this, the user plans their next meal and makes healthy choices. Links to suggested foods and nutritional supplements are provided, allowing the user to easily access and purchase them.

[0092] (Application Example 1)

[0093] 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."

[0094] Maintaining a healthy diet while keeping nutritional balance is crucial in modern life, but achieving this requires considerable effort and knowledge. In particular, properly collecting and analyzing dietary data and providing optimal dietary suggestions to individual users is difficult. Furthermore, systems used in the home may be burdensome for users to operate, and the accuracy of the information may be compromised. This invention aims to solve these problems and provide the most efficient and effective nutritional management.

[0095] 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.

[0096] In this invention, the server includes an analysis means for receiving meal data registered by the user and identifying ingredients based on the meal data, an acquisition means for obtaining the nutritional components of the identified ingredients from a data warehouse, and an evaluation means for evaluating the nutrients that the user should consume and the nutrients that they should avoid based on the acquired nutritional components. This makes it possible to move around within the user's living space and acquire meal data.

[0097] A "user" refers to a person who uses the system to register their dietary data and receive health suggestions.

[0098] "Meal data" refers to information about the meals consumed by the user, and includes image data and manually entered data.

[0099] "Analysis means" refers to elements that perform processing to identify ingredients based on received meal data.

[0100] "Acquisition means" refers to the element that processes data to retrieve the nutritional components of identified food ingredients from a data warehouse.

[0101] "Evaluation methods" refer to elements that process data based on acquired nutritional information to determine which nutrients are necessary for the user and which should be avoided.

[0102] "Suggestion methods" refer to elements that suggest healthy ingredients and recipes to users.

[0103] "Generation means" refers to elements that create links to purchase the proposed food ingredients or supplements.

[0104] "Living space" refers to the area where users conduct their daily lives and the environment in which a system for nutritional management is implemented.

[0105] The system that realizes this invention is designed to streamline the user's meal management and consists of several main components, including a server, a terminal, and a robot that moves around the living space.

[0106] After receiving meal data sent by the user, the server uses an AI algorithm to identify the ingredients. Specifically, it analyzes the images registered as meal data to identify the ingredients. Machine learning libraries such as TensorFlow are commonly used for this process. Nutritional information for the identified ingredients is retrieved from a database containing nutritional data. As an evaluation method, the server assesses the user's nutritional balance and determines whether there are any deficiencies or excesses.

[0107] On the device side, suggestions sent from the server are visually displayed via a user interface. Information on suggested healthy foods and links to corresponding online stores for purchase are provided. The interface operates on smartphones and tablets and is designed for easy user access.

[0108] Within the user's living space, a robot that supports nutritional management functions. The robot collects meal data during the user's mealtimes and uses this data to suggest improvements to their nutritional balance in the future. The robot also takes images and uploads them to the cloud, enabling analysis by a server.

[0109] As a concrete example, when a user eats french fries and steak for dinner, the robot takes a picture of the meal. The server uses AI to analyze the ingredients, and if it determines that the meal is high in fat, a message such as "We recommend adding a salad or smoothie with plenty of vegetables the next day to balance it out" will be displayed on the device.

[0110] An example of a prompt message for a generative AI model is, "Analyze the meal images registered by the user and generate meal suggestions based on nutritional balance."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] When a user eats, a terminal or robot takes a picture of the meal. The input is an image of the user's meal, which is sent to the server. The output is an image file that is registered as meal data. This image is pre-processed to identify the ingredients.

[0114] Step 2:

[0115] The server runs a generative AI model using the received images. During this process, the model performs image analysis to identify food ingredients and extract their nutritional information. The input is a pre-processed image of a meal, and the output is the type of food ingredient and its nutritional data. The food ingredients are then compared against a database, and the necessary information is organized.

[0116] Step 3:

[0117] The server uses nutritional data retrieved from the database to compare with the user's past nutritional intake history and identify nutrients that are in excess or deficient. The input is nutritional data for each food item and the user's dietary history, and the output is information on the identified nutritional imbalances.

[0118] Step 4:

[0119] The server generates personalized meal suggestions for the user based on identified nutritional balance information. The input is information about nutritional imbalances, and the output is a list of suggested healthy ingredients and dishes. These suggestions include specific menus and supplement recommendations.

[0120] Step 5:

[0121] The server generates online store links based on the suggestions it receives and sends them to the user's device. The input is a list of healthy food items, and the output is links to purchase them. Users can view this on their smartphones or tablets.

[0122] Step 6:

[0123] The terminal displays the suggested content and purchase links received from the server on the user interface. The input is the suggested data from the server, and the output is a display that the user can visually confirm. The user can use this as a reference for planning their next meal.

[0124] 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.

[0125] This invention provides a health support system that comprehensively manages a user's dietary habits, taking into account not only the content of their meals but also their emotional state. This system operates when the user uses a dedicated application on their device.

[0126] Users register their daily meals by taking photos or entering text descriptions. The registered data is sent to a server, where an AI algorithm analyzes the meal data and identifies ingredients. Based on this analysis, the server retrieves the nutritional components of the identified ingredients from a database and evaluates the overall nutritional balance of the meal.

[0127] A distinctive feature of this system is the incorporation of emotion recognition technology. The server analyzes the user's facial expressions and voice tone via the camera and sensors installed in the terminal to recognize their current emotional state. This information is then reflected in the suggestions for ingredients and dishes; for example, a user experiencing stress might be offered ingredients that promote relaxation or dishes that enhance feelings of happiness.

[0128] The suggested ingredients and recipes are displayed on the device along with appropriate purchase links, allowing users to easily buy related products online while being supported in making healthy choices. Furthermore, the system continuously improves the accuracy of its suggestions by accumulating data on changes in the user's emotions and analyzing their effects.

[0129] For example, if the server detects that a user is feeling stressed in the evening, it will suggest a dinner menu that is rich in calcium and has relaxing effects. This suggestion will also include recipes and recommended purchase links, making it easy for the user to put the suggestions into practice.

[0130] As described above, the present invention is a system that takes into account both the user's dietary content and emotional state, and supports the realization of a healthy diet that meets individual needs.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user launches a dedicated application on their device, takes a photo of their meal or enters details of the meal in text, and records the meal data.

[0134] Step 2:

[0135] The device sends recorded meal data to the server. This data is encrypted, ensuring security.

[0136] Step 3:

[0137] The server analyzes the submitted meal data. Using an AI algorithm, it identifies ingredients from images and retrieves the nutritional information for each ingredient from a database.

[0138] Step 4:

[0139] The server evaluates the overall nutritional balance of the meal and identifies nutrients that the user should either consume less of or avoid.

[0140] Step 5:

[0141] The user activates the emotion recognition function via their device, and the system recognizes their current emotional state by analyzing their facial expressions and voice using cameras and sensors.

[0142] Step 6:

[0143] The recognized emotional data is sent to the server, which analyzes the emotional state and forms a comprehensive recommendation in combination with nutritional assessment.

[0144] Step 7:

[0145] The server suggests healthy foods and dishes that take into account the user's nutritional and emotional state, and also suggests supplements if necessary. It also generates links to online stores along with the suggestions.

[0146] Step 8:

[0147] The device notifies the user of suggestions received from the server and displays them in an accessible manner. The user can use this information to adjust their diet and purchase products through the suggested links.

[0148] (Example 2)

[0149] 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".

[0150] While modern meal management systems focus on managing dietary data, they lack suggestions that take into account the user's emotional state. Therefore, there is a need to comprehensively manage the impact of daily eating habits on emotional state and support more personalized and healthy eating habits.

[0151] 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.

[0152] In this invention, the server includes an analysis means for receiving meal information registered by the user and identifying food based on that information, an acquisition means for obtaining nutritional data of the identified food, and a suggestion means for suggesting appropriate food based on the user's emotional state. This makes it possible to suggest a healthy diet that simultaneously considers the user's diet and emotional state.

[0153] A "user" is an individual who uses the system to register their dietary information and receive suggestions for healthy eating.

[0154] "Dietary information" refers to data that users input or photograph about their daily eating habits and register in the system.

[0155] "Food" refers to individual ingredients or dishes recognized within meal information.

[0156] "Nutritional data" refers to information about the nutrients and components contained in food.

[0157] "Analysis methods" refer to algorithms and technologies used to analyze meal information and the emotional state of users.

[0158] "Means of acquisition" refers to the function of extracting nutritional data about identified food items from the information source.

[0159] "Suggested method" refers to a mechanism for recommending healthy meals or foods to users based on analysis results.

[0160] "Emotional state" refers to the current emotional state of the user, as analyzed from their facial expressions and voice.

[0161] This invention is a health support system that comprehensively manages a user's eating habits along with their emotional state. Users utilize this system by using a dedicated application on a terminal to input or photograph meal information. The terminal transmits the meal information received from the user, along with data on the user's facial expressions and voice, to a server. The terminal is equipped with a high-resolution camera and microphone, which are used to collect detailed data.

[0162] The server utilizes AI technology to analyze meal information. Specifically, it uses machine learning libraries such as TensorFlow to analyze image data and interprets text-based meal information using natural language processing techniques. This allows the server to identify ingredients and retrieve their nutritional data from a database. The server also uses OpenCV and the Emotion API to analyze the user's facial expressions and voice to recognize their emotional state.

[0163] This system suggests healthy foods and meal plans tailored to the user based on analyzed dietary data and emotional state. The suggestions include food selections that take the user's emotional state into account; for example, a stressed user might be offered meals designed to promote relaxation. For instance, a stressed user might be advised, "We recommend chamomile tea and salmon for dinner. You can purchase it here."

[0164] The suggested information is displayed on the device along with links to purchase related products online, allowing users to easily make healthy choices. The server also stores the user's past data and uses it to improve the accuracy of future suggestions. This system enables users to achieve a personalized, healthy diet.

[0165] When using a generative AI model, a concrete example of a prompt might be, "Generate a relaxing meal menu to suggest to a user who is stressed." By utilizing this prompt, the system can generate more appropriate suggestions.

[0166] In summary, the present invention enables users to manage their diet and emotions in a balanced way, supporting a healthy lifestyle.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The device receives daily meal information from the user. The user registers their meals by taking photos or entering them in text format. The entered data includes either photos or text. This data is then prepared to be sent to the server for further analysis.

[0170] Step 2:

[0171] The device sends meal data to the server. The input is either a photo or text of the meal, which is then converted into a data format for transmission to the server. Encryption technology is used during data transmission to ensure data security.

[0172] Step 3:

[0173] The server analyzes the received meal data. First, AI technology is used to process image data. TensorFlow is used to identify food items within the image. The input is an image, and the output is a list of food names. Next, for text data, natural language processing is performed to extract information about the food items described. This result is output as a list of food names.

[0174] Step 4:

[0175] The server retrieves nutritional data for identified foods from its information sources. Specifically, it extracts the relevant nutritional information from the database based on the identified food name. The input is a list of food names, and the output is the corresponding nutritional data.

[0176] Step 5:

[0177] The server analyzes the user's facial expressions and voice data to understand their emotional state. It uses OpenCV and the Emotion API to analyze audio and video data sent from the terminal as input. As a result, the user's emotional state is output, providing information such as "stressed" or "happy."

[0178] Step 6:

[0179] Based on the analysis results, the server suggests foods that take into account the user's emotional state and nutritional balance. Here, a generative AI model is used, referencing past suggestion data and user history. The inputs are the analysis results and the emotional state, and the output is a suggestion of specific foods or dishes. For example, it might suggest a "relaxing meal for the evening."

[0180] Step 7:

[0181] The terminal receives food information suggested by the server and displays it to the user. The suggestions also include online purchase links, allowing the user to easily buy related products by clicking on them. The goal here is to expand the user's choices based on the suggested information.

[0182] This allows users to make meal choices that are tailored to their nutritional needs and emotional state.

[0183] (Application Example 2)

[0184] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0185] Maintaining a healthy diet is important for modern consumers, but daily busyness and information overload often make it difficult to choose meals that suit individual nutritional needs and emotional changes. Furthermore, the knowledge and effort required to choose meals that match one's emotional state can be a burden for many users.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0187] In this invention, the server includes an information processing device that identifies the user's emotional state and suggests food based on it, a data acquisition device that obtains the nutritional components of food ingredients from a recording medium, and an information processing device that identifies food ingredients. This makes it possible to suggest food according to the user's emotional state and to support the realization of a healthy diet that suits individual needs.

[0188] A "user" is an individual who uses this system, or an entity that inputs and manages information on their behalf.

[0189] "Meal data" refers to information about the food and drinks a user has consumed, and is registered as photos and text.

[0190] "Ingredients" refers to the specific names and types of food included in the meal data.

[0191] "Nutritional components" refer to the types and amounts of nutrients such as proteins, fats, carbohydrates, vitamins, and minerals contained in food.

[0192] A "recording medium" refers to a digital or physical medium used to store nutritional information or food ingredient information.

[0193] An "information processing device" refers to a computer or program used to analyze, evaluate, or make suggestions based on information obtained from a user.

[0194] A "data acquisition device" refers to a device or program that acquires necessary information from a recording medium and converts it into a format that can be used by an information processing device.

[0195] An "observation device" refers to a device or program that analyzes facial expressions and voice to identify the user's emotional state.

[0196] A "data generation device" refers to a device or program that creates links for purchasing suggested foods or health supplements online.

[0197] The system for implementing this invention relies primarily on a program that suggests healthy foods and dishes based on the user's emotional state. The system is implemented using the user's terminal and a central server.

[0198] The device is responsible for acquiring meal data from the user. Specifically, the user can either take a photo of their meal or input details of their meal in text. This data is collected through the device's built-in camera and text input interface and sent to a server for data processing.

[0199] The server uses an internally built AI model to analyze received meal data. This analysis identifies ingredients from images or text and retrieves corresponding nutritional data from the recording medium. TensorFlow, a well-known machine learning library, is used for the data analysis and suggestion generation process. In addition, to identify the user's emotional state, the server uses facial and voice data obtained from observation devices and processes it with an emotion recognition algorithm.

[0200] To enable suggestions that reflect the user's emotional state, the server generates purchase links for suggested food items and health supplements. This functionality is achieved through API data integration, connecting to corresponding online shopping platforms.

[0201] For example, if the server determines that a user is stressed, it will suggest foods that have a relaxing effect. These suggestions might include lavender tea or chamomile, and the user can easily purchase related products using the provided online links. Specific examples of prompts for the generating AI model include, "Please suggest a relaxing drink for the evening," or "Please list foods that are high in calcium."

[0202] In this way, the system takes into account both the user's daily diet and emotional state, and provides suggestions for a healthy diet tailored to their individual needs.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The device retrieves meal data from the user. The user either takes a photo of their meal or enters the details of their meal in text. The retrieved meal data is reviewed by the user and then sent from the device to the server.

[0206] Step 2:

[0207] The server analyzes the received meal data using an AI model. It identifies ingredients from the image or text data obtained as input. Machine learning techniques are used in this process, and an ingredient list is generated as output.

[0208] Step 3:

[0209] The server retrieves the nutritional information of the identified food ingredients from the storage medium. It issues a database query using the food ingredient list as input and retrieves the corresponding nutritional data as output.

[0210] Step 4:

[0211] The server receives facial and voice data from the user's device and analyzes the emotional state using an emotion recognition algorithm. Based on the input sensor data, the server then outputs the current emotional state.

[0212] Step 5:

[0213] The server integrates emotional states and nutritional information to suggest the most suitable ingredients and dishes for the user. This process uses a generative AI model to process input information and output optimal suggestion results.

[0214] Step 6:

[0215] The server generates purchase links for the suggested ingredients and dishes. It connects with online shopping platforms using an API to generate purchase links associated with the suggested results and outputs them.

[0216] Step 7:

[0217] The server sends the suggested results, including a purchase link, to the terminal. The user can then use the provided link to purchase the product online. The returned link is confirmed, ensuring a smooth purchase process.

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

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

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

[0221] [Second Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0234] This invention is a system for efficiently managing a user's diet and supporting their health. Users utilize a dedicated application on a device such as a smartphone or tablet. Users can register their meal data by taking photos of the meals they have eaten or by manually entering the details of their meals.

[0235] Registered meal data is sent from the terminal to the server. The server analyzes the meal data using an AI algorithm. Specifically, the server identifies ingredients based on images and retrieves the nutritional components contained in each ingredient from a database. Then, based on the retrieved nutritional components, the server evaluates which nutrients the user needs to consume and which nutrients they should limit.

[0236] Based on the evaluation results, the server suggests healthy foods and dishes to the user. For example, if a comparison with past dietary data determines that the user is deficient in minerals, the server will suggest dishes using mineral-rich ingredients or supplements. Links to obtain the necessary ingredients and supplements online will also be generated.

[0237] These suggestions are displayed on the device, allowing users to easily find places to buy ingredients and supplements. Furthermore, by referring to the recommended menus, users can plan their next meals in a healthier way.

[0238] As a concrete example, when a user uploads a photo of their lunch, the server identifies it as a high-fat meal and suggests using fresh vegetables and protein-rich ingredients instead. Furthermore, if supplements are needed, a link is provided showing the best purchase options from multiple online retailers.

[0239] This invention makes it easier for users to balance their nutrition through their daily meals, enabling them to achieve a healthy lifestyle.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The user opens a screen on their device to record their meals, taking a photo of the meal or entering the details in text. By pressing the registration button, the meal data is saved on the device.

[0243] Step 2:

[0244] The device sends the stored meal data along with the user's authentication information to the server. This transmission is encrypted for data security.

[0245] Step 3:

[0246] The server analyzes the received meal data using an AI algorithm. Image analysis technology is used to identify ingredients from meal images and determine the nutritional components of each ingredient.

[0247] Step 4:

[0248] The server retrieves nutritional data corresponding to the identified ingredients from the database and evaluates the overall nutritional balance of the meal. This evaluation also includes a comparison with the user's past meal history.

[0249] Step 5:

[0250] Based on the evaluation results, the server analyzes the nutrients necessary for the user's healthy diet and identifies any excesses or deficiencies. This then generates specific nutritional advice for the user.

[0251] Step 6:

[0252] The server suggests foods, dishes, and necessary supplements to the user. It also generates links to online stores where users can purchase the suggested items.

[0253] Step 7:

[0254] The terminal receives suggestion information from the server and notifies the user, displaying it on a dedicated application screen. This allows the user to review the suggestions and consider their next action.

[0255] Step 8:

[0256] Based on the information provided, users adjust their diet and decide to purchase ingredients and supplements through the suggested links. They also plan their next meal based on the displayed recipes.

[0257] (Example 1)

[0258] 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."

[0259] Conventional diet management systems have made it difficult for users to accurately record their meals and receive appropriate nutritional advice based on that data. Furthermore, they lack specific suggestions tailored to each user's nutritional status and support for purchasing appropriate ingredients quickly, which means they cannot adequately help users manage their health.

[0260] 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.

[0261] In this invention, the server includes an analysis means for receiving meal data registered by the user and identifying food based on the meal data; an acquisition means for obtaining the nutrients of the identified food from a data storage; an evaluation means for evaluating the nutrients the user should consume and the nutrients they should avoid based on the acquired nutrients; and a creation means for creating prompt statements using a generation AI model and optimizing the suggested content. This enables the user to intuitively and efficiently manage their diet and achieve a healthy lifestyle while maintaining nutritional balance.

[0262] "Analysis means" refers to a system that receives meal data registered by a user and has functions and technologies for identifying food based on said meal data.

[0263] "Acquisition means" refers to the process or function of obtaining the nutrients of food identified by the analysis means from a data storage facility.

[0264] "Evaluation methods" include mechanisms and technologies for evaluating which nutrients a user should consume and which they should limit, based on the acquired nutrients.

[0265] A "proposal method" refers to a system or technology that proposes healthy foods or dishes to users based on evaluation results.

[0266] "Generation means" refers to a function or technology that generates a pathway for users to obtain the proposed food or nutritional supplement.

[0267] "Creation method" refers to a process or function that uses a generative AI model to create prompt sentences and optimize the suggested content.

[0268] This invention is a system that efficiently manages a user's diet and supports a healthy lifestyle. Users can register their meal data using a dedicated application on a device such as a smartphone or tablet.

[0269] Users either take a photo of their meal or manually enter the details of their meal through a text field. This data is temporarily stored on the device and then sent to the server via the internet. The secure HTTPS protocol is used for communication.

[0270] The server analyzes received meal data using AI algorithms. Image recognition uses deep learning-based image processing software (e.g., TensorFlow, PyTorch) to identify food items from registered photos. Manually entered data is analyzed using natural language processing techniques.

[0271] The food information obtained as a result of the analysis is used to retrieve nutrient information from the data storage. The database APIs used here include public database APIs that cover food data (e.g., FoodData Central API). Based on this data, the server evaluates the user's nutrient deficiencies or excesses.

[0272] The system uses a generative AI model to optimize suggestions for the user. An example of a generated prompt might be, "Based on your recent dietary data, please identify any missing nutrients and suggest which foods you should include in your next meal." The server also generates and provides the user with links to access relevant foods or nutritional supplements.

[0273] These suggestions are displayed on the device, making them easily viewable and accessible to users. This system allows users to review their daily diet and receive support in making healthier choices. For example, if a user uploads a photo of a high-fat lunch, the server analyzes it and suggests alternative dishes rich in fresh vegetables and protein. It also provides optimal options for purchasing relevant supplements.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The user launches a dedicated application on a device such as a smartphone or tablet. The user either takes a photo of their meal or manually enters the details of their meal through a text field. The entered data, either as image data or text data of the meal, is temporarily stored within the application on the device.

[0277] Step 2:

[0278] The terminal converts the saved meal data into JSON format and sends it to the server via the Internet. At this time, by using the HTTPS protocol for data transmission, privacy and security are ensured.

[0279] Step 3:

[0280] The server analyzes the received meal data. For the image data, an image recognition model using TensorFlow or PyTorch is executed to identify the food. For the text data, the content is analyzed by natural language processing technology to extract specific food information. What is output is a list of the identified foods.

[0281] Step 4:

[0282] Based on the list of identified foods, the server retrieves the corresponding nutrient information from a data repository (e.g., FoodData Central API). Through this data processing, specific data on the nutritional components contained in each food is output.

[0283] Step 5:

[0284] The server evaluates the sufficiency and deficiency of nutrients by comparing the obtained nutrient information with the user's past meal history. For this process, an optimal evaluation is performed using a generative AI model. The output of the evaluation is a list of nutrients to be ingested and nutrients to be restricted.

[0285] Step 6:

[0286] Based on the evaluation results, the server makes suggestions on healthy foods and recipes for the user. The content of the suggestions is optimized using the prompt text generated by the generative AI model. Specifically, suggestions such as "Please try broccoli as a food rich in minerals." are generated.

[0287] Step 7:

[0288] The terminal displays suggested information received from the server on the user interface. Based on this, the user plans their next meal and makes healthy choices. Links to suggested foods and nutritional supplements are provided, allowing the user to easily access and purchase them.

[0289] (Application Example 1)

[0290] 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."

[0291] Maintaining a healthy diet while keeping nutritional balance is crucial in modern life, but achieving this requires considerable effort and knowledge. In particular, properly collecting and analyzing dietary data and providing optimal dietary suggestions to individual users is difficult. Furthermore, systems used in the home may be burdensome for users to operate, and the accuracy of the information may be compromised. This invention aims to solve these problems and provide the most efficient and effective nutritional management.

[0292] 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.

[0293] In this invention, the server includes an analysis means for receiving meal data registered by the user and identifying ingredients based on the meal data, an acquisition means for obtaining the nutritional components of the identified ingredients from a data warehouse, and an evaluation means for evaluating the nutrients that the user should consume and the nutrients that they should avoid based on the acquired nutritional components. This makes it possible to move around within the user's living space and acquire meal data.

[0294] A "user" refers to a person who uses the system to register their dietary data and receive health suggestions.

[0295] "Meal data" refers to information about the meals consumed by the user, and includes image data and manually entered data.

[0296] "Analysis means" refers to elements that perform processing to identify ingredients based on received meal data.

[0297] "Acquisition means" refers to the element that processes data to retrieve the nutritional components of identified food ingredients from a data warehouse.

[0298] "Evaluation methods" refer to elements that process data based on acquired nutritional information to determine which nutrients are necessary for the user and which should be avoided.

[0299] "Suggestion methods" refer to elements that suggest healthy ingredients and recipes to users.

[0300] "Generation means" refers to elements that create links to purchase the proposed food ingredients or supplements.

[0301] "Living space" refers to the area where users conduct their daily lives and the environment in which a system for nutritional management is implemented.

[0302] The system that realizes this invention is designed to streamline the user's meal management and consists of several main components, including a server, a terminal, and a robot that moves around the living space.

[0303] After receiving meal data sent by the user, the server uses an AI algorithm to identify the ingredients. Specifically, it analyzes the images registered as meal data to identify the ingredients. Machine learning libraries such as TensorFlow are commonly used for this process. Nutritional information for the identified ingredients is retrieved from a database containing nutritional data. As an evaluation method, the server assesses the user's nutritional balance and determines whether there are any deficiencies or excesses.

[0304] On the terminal side, the proposals sent from the server are visually displayed via the user interface. Information on the proposed healthy foods and the purchase links of the online stores associated with them are provided. The interface operates on smartphones and tablets and is designed to be easily accessible to users.

[0305] Within the user's living space, a robot that supports nutrition management functions. The robot collects meal data in the scene of the user's meal and uses it to make suggestions for improving the next nutritional balance. This robot enables analysis by the server by taking pictures and uploading them to the cloud.

[0306] As a specific example, when the user consumes French fries and steak for dinner, the robot takes a picture of the meal. If food analysis by AI is performed on the server side and it is determined that it is a high-fat meal, a message such as "It is recommended to add a salad or smoothie rich in vegetables the next day to balance it." will be displayed on the terminal.

[0307] Examples of prompt sentences for the generative AI model include "Analyze the meal images registered by the user and generate meal proposals based on the nutritional balance."

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] When the user has a meal, the terminal or the robot takes a picture of the meal. The input is the user's meal image, which is sent to the server. The output is the image file registered as meal data. This image is pre-processed to identify the ingredients.

[0311] Step 2:

[0312] The server runs a generative AI model using the received images. During this process, the model performs image analysis to identify food ingredients and extract their nutritional information. The input is a pre-processed image of a meal, and the output is the type of food ingredient and its nutritional data. The food ingredients are then compared against a database, and the necessary information is organized.

[0313] Step 3:

[0314] The server uses nutritional data retrieved from the database to compare with the user's past nutritional intake history and identify nutrients that are in excess or deficient. The input is nutritional data for each food item and the user's dietary history, and the output is information on the identified nutritional imbalances.

[0315] Step 4:

[0316] The server generates personalized meal suggestions for the user based on identified nutritional balance information. The input is information about nutritional imbalances, and the output is a list of suggested healthy ingredients and dishes. These suggestions include specific menus and supplement recommendations.

[0317] Step 5:

[0318] The server generates online store links based on the suggestions it receives and sends them to the user's device. The input is a list of healthy food items, and the output is links to purchase them. Users can view this on their smartphones or tablets.

[0319] Step 6:

[0320] The terminal displays the suggested content and purchase links received from the server on the user interface. The input is the suggested data from the server, and the output is a display that the user can visually confirm. The user can use this as a reference for planning their next meal.

[0321] 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.

[0322] This invention provides a health support system that comprehensively manages a user's dietary habits, taking into account not only the content of their meals but also their emotional state. This system operates when the user uses a dedicated application on their device.

[0323] Users register their daily meals by taking photos or entering text descriptions. The registered data is sent to a server, where an AI algorithm analyzes the meal data and identifies ingredients. Based on this analysis, the server retrieves the nutritional components of the identified ingredients from a database and evaluates the overall nutritional balance of the meal.

[0324] A distinctive feature of this system is the incorporation of emotion recognition technology. The server analyzes the user's facial expressions and voice tone via the camera and sensors installed in the terminal to recognize their current emotional state. This information is then reflected in the suggestions for ingredients and dishes; for example, a user experiencing stress might be offered ingredients that promote relaxation or dishes that enhance feelings of happiness.

[0325] The suggested ingredients and recipes are displayed on the device along with appropriate purchase links, allowing users to easily buy related products online while being supported in making healthy choices. Furthermore, the system continuously improves the accuracy of its suggestions by accumulating data on changes in the user's emotions and analyzing their effects.

[0326] For example, if the server detects that a user is feeling stressed in the evening, it will suggest a dinner menu that is rich in calcium and has relaxing effects. This suggestion will also include recipes and recommended purchase links, making it easy for the user to put the suggestions into practice.

[0327] As described above, the present invention is a system that takes into account both the user's dietary content and emotional state, and supports the realization of a healthy diet that meets individual needs.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The user launches a dedicated application on their device, takes a photo of their meal or enters details of the meal in text, and records the meal data.

[0331] Step 2:

[0332] The device sends recorded meal data to the server. This data is encrypted, ensuring security.

[0333] Step 3:

[0334] The server analyzes the submitted meal data. Using an AI algorithm, it identifies ingredients from images and retrieves the nutritional information for each ingredient from a database.

[0335] Step 4:

[0336] The server evaluates the overall nutritional balance of the meal and identifies nutrients that the user should either consume less of or avoid.

[0337] Step 5:

[0338] The user activates the emotion recognition function via their device, and the system recognizes their current emotional state by analyzing their facial expressions and voice using cameras and sensors.

[0339] Step 6:

[0340] The recognized emotional data is sent to the server, which analyzes the emotional state and forms a comprehensive recommendation in combination with nutritional assessment.

[0341] Step 7:

[0342] The server suggests healthy foods and dishes that take into account the user's nutritional and emotional state, and also suggests supplements if necessary. It also generates links to online stores along with the suggestions.

[0343] Step 8:

[0344] The device notifies the user of suggestions received from the server and displays them in an accessible manner. The user can use this information to adjust their diet and purchase products through the suggested links.

[0345] (Example 2)

[0346] 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".

[0347] While modern meal management systems focus on managing dietary data, they lack suggestions that take into account the user's emotional state. Therefore, there is a need to comprehensively manage the impact of daily eating habits on emotional state and support more personalized and healthy eating habits.

[0348] 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.

[0349] In this invention, the server includes an analysis means for receiving meal information registered by the user and identifying food based on that information, an acquisition means for obtaining nutritional data of the identified food, and a suggestion means for suggesting appropriate food based on the user's emotional state. This makes it possible to suggest a healthy diet that simultaneously considers the user's diet and emotional state.

[0350] A "user" is an individual who uses the system to register their dietary information and receive suggestions for healthy eating.

[0351] "Dietary information" refers to data that users input or photograph about their daily eating habits and register in the system.

[0352] "Food" refers to individual ingredients or dishes recognized within meal information.

[0353] "Nutritional data" refers to information about the nutrients and components contained in food.

[0354] "Analysis methods" refer to algorithms and technologies used to analyze meal information and the emotional state of users.

[0355] "Means of acquisition" refers to the function of extracting nutritional data about identified food items from the information source.

[0356] "Suggested method" refers to a mechanism for recommending healthy meals or foods to users based on analysis results.

[0357] "Emotional state" refers to the current emotional state of the user, as analyzed from their facial expressions and voice.

[0358] This invention is a health support system that comprehensively manages a user's eating habits along with their emotional state. Users utilize this system by using a dedicated application on a terminal to input or photograph meal information. The terminal transmits the meal information received from the user, along with data on the user's facial expressions and voice, to a server. The terminal is equipped with a high-resolution camera and microphone, which are used to collect detailed data.

[0359] The server utilizes AI technology to analyze meal information. Specifically, it uses machine learning libraries such as TensorFlow to analyze image data and interprets text-based meal information using natural language processing techniques. This allows the server to identify ingredients and retrieve their nutritional data from a database. The server also uses OpenCV and the Emotion API to analyze the user's facial expressions and voice to recognize their emotional state.

[0360] This system suggests healthy foods and meal plans tailored to the user based on analyzed dietary data and emotional state. The suggestions include food selections that take the user's emotional state into account; for example, a stressed user might be offered meals designed to promote relaxation. For instance, a stressed user might be advised, "We recommend chamomile tea and salmon for dinner. You can purchase it here."

[0361] The suggested information is displayed on the device along with links to purchase related products online, allowing users to easily make healthy choices. The server also stores the user's past data and uses it to improve the accuracy of future suggestions. This system enables users to achieve a personalized, healthy diet.

[0362] When using a generative AI model, a concrete example of a prompt might be, "Generate a relaxing meal menu to suggest to a user who is stressed." By utilizing this prompt, the system can generate more appropriate suggestions.

[0363] In summary, the present invention enables users to manage their diet and emotions in a balanced way, supporting a healthy lifestyle.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The device receives daily meal information from the user. The user registers their meals by taking photos or entering them in text format. The entered data includes either photos or text. This data is then prepared to be sent to the server for further analysis.

[0367] Step 2:

[0368] The device sends meal data to the server. The input is either a photo or text of the meal, which is then converted into a data format for transmission to the server. Encryption technology is used during data transmission to ensure data security.

[0369] Step 3:

[0370] The server analyzes the received meal data. First, AI technology is used to process image data. TensorFlow is used to identify food items within the image. The input is an image, and the output is a list of food names. Next, for text data, natural language processing is performed to extract information about the food items described. This result is output as a list of food names.

[0371] Step 4:

[0372] The server retrieves nutritional data for identified foods from its information sources. Specifically, it extracts the relevant nutritional information from the database based on the identified food name. The input is a list of food names, and the output is the corresponding nutritional data.

[0373] Step 5:

[0374] The server analyzes the user's facial expressions and voice data to understand their emotional state. It uses OpenCV and the Emotion API to analyze audio and video data sent from the terminal as input. As a result, the user's emotional state is output, providing information such as "stressed" or "happy."

[0375] Step 6:

[0376] Based on the analysis results, the server suggests foods that take into account the user's emotional state and nutritional balance. Here, a generative AI model is used, referencing past suggestion data and user history. The inputs are the analysis results and the emotional state, and the output is a suggestion of specific foods or dishes. For example, it might suggest a "relaxing meal for the evening."

[0377] Step 7:

[0378] The terminal receives food information suggested by the server and displays it to the user. The suggestions also include online purchase links, allowing the user to easily buy related products by clicking on them. The goal here is to expand the user's choices based on the suggested information.

[0379] This allows users to make meal choices that are tailored to their nutritional needs and emotional state.

[0380] (Application Example 2)

[0381] 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."

[0382] Maintaining a healthy diet is important for modern consumers, but daily busyness and information overload often make it difficult to choose meals that suit individual nutritional needs and emotional changes. Furthermore, the knowledge and effort required to choose meals that match one's emotional state can be a burden for many users.

[0383] 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.

[0384] In this invention, the server includes an information processing device that identifies the user's emotional state and suggests food based on it, a data acquisition device that obtains the nutritional components of food ingredients from a recording medium, and an information processing device that identifies food ingredients. This makes it possible to suggest food according to the user's emotional state and to support the realization of a healthy diet that suits individual needs.

[0385] A "user" is an individual who uses this system, or an entity that inputs and manages information on their behalf.

[0386] "Meal data" refers to information about the food and drinks a user has consumed, and is registered as photos and text.

[0387] "Ingredients" refers to the specific names and types of food included in the meal data.

[0388] "Nutritional components" refer to the types and amounts of nutrients such as proteins, fats, carbohydrates, vitamins, and minerals contained in food.

[0389] A "recording medium" refers to a digital or physical medium used to store nutritional information or food ingredient information.

[0390] An "information processing device" refers to a computer or program used to analyze, evaluate, or make suggestions based on information obtained from a user.

[0391] A "data acquisition device" refers to a device or program that acquires necessary information from a recording medium and converts it into a format that can be used by an information processing device.

[0392] An "observation device" refers to a device or program that analyzes facial expressions and voice to identify the user's emotional state.

[0393] A "data generation device" refers to a device or program that creates links for purchasing suggested foods or health supplements online.

[0394] The system for implementing this invention relies primarily on a program that suggests healthy foods and dishes based on the user's emotional state. The system is implemented using the user's terminal and a central server.

[0395] The device is responsible for acquiring meal data from the user. Specifically, the user can either take a photo of their meal or input details of their meal in text. This data is collected through the device's built-in camera and text input interface and sent to a server for data processing.

[0396] The server uses an internally built AI model to analyze received meal data. This analysis identifies ingredients from images or text and retrieves corresponding nutritional data from the recording medium. TensorFlow, a well-known machine learning library, is used for the data analysis and suggestion generation process. In addition, to identify the user's emotional state, the server uses facial and voice data obtained from observation devices and processes it with an emotion recognition algorithm.

[0397] To enable suggestions that reflect the user's emotional state, the server generates purchase links for suggested food items and health supplements. This functionality is achieved through API data integration, connecting to corresponding online shopping platforms.

[0398] For example, if the server determines that a user is stressed, it will suggest foods that have a relaxing effect. These suggestions might include lavender tea or chamomile, and the user can easily purchase related products using the provided online links. Specific examples of prompts for the generating AI model include, "Please suggest a relaxing drink for the evening," or "Please list foods that are high in calcium."

[0399] In this way, the system takes into account both the user's daily diet and emotional state, and provides suggestions for a healthy diet tailored to their individual needs.

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The device retrieves meal data from the user. The user either takes a photo of their meal or enters the details of their meal in text. The retrieved meal data is reviewed by the user and then sent from the device to the server.

[0403] Step 2:

[0404] The server analyzes the received meal data using an AI model. It identifies ingredients from the image or text data obtained as input. Machine learning techniques are used in this process, and an ingredient list is generated as output.

[0405] Step 3:

[0406] The server retrieves the nutritional information of the identified food ingredients from the storage medium. It issues a database query using the food ingredient list as input and retrieves the corresponding nutritional data as output.

[0407] Step 4:

[0408] The server receives facial and voice data from the user's device and analyzes the emotional state using an emotion recognition algorithm. Based on the input sensor data, the server then outputs the current emotional state.

[0409] Step 5:

[0410] The server integrates emotional states and nutritional information to suggest the most suitable ingredients and dishes for the user. This process uses a generative AI model to process input information and output optimal suggestion results.

[0411] Step 6:

[0412] The server generates purchase links for the suggested ingredients and dishes. It connects with online shopping platforms using an API to generate purchase links associated with the suggested results and outputs them.

[0413] Step 7:

[0414] The server sends the suggested results, including a purchase link, to the terminal. The user can then use the provided link to purchase the product online. The returned link is confirmed, ensuring a smooth purchase process.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] This invention is a system for efficiently managing a user's diet and supporting their health. Users utilize a dedicated application on a device such as a smartphone or tablet. Users can register their meal data by taking photos of the meals they have eaten or by manually entering the details of their meals.

[0432] Registered meal data is sent from the terminal to the server. The server analyzes the meal data using an AI algorithm. Specifically, the server identifies ingredients based on images and retrieves the nutritional components contained in each ingredient from a database. Then, based on the retrieved nutritional components, the server evaluates which nutrients the user needs to consume and which nutrients they should limit.

[0433] Based on the evaluation results, the server suggests healthy foods and dishes to the user. For example, if a comparison with past dietary data determines that the user is deficient in minerals, the server will suggest dishes using mineral-rich ingredients or supplements. Links to obtain the necessary ingredients and supplements online will also be generated.

[0434] These suggestions are displayed on the device, allowing users to easily find places to buy ingredients and supplements. Furthermore, by referring to the recommended menus, users can plan their next meals in a healthier way.

[0435] As a concrete example, when a user uploads a photo of their lunch, the server identifies it as a high-fat meal and suggests using fresh vegetables and protein-rich ingredients instead. Furthermore, if supplements are needed, a link is provided showing the best purchase options from multiple online retailers.

[0436] This invention makes it easier for users to balance their nutrition through their daily meals, enabling them to achieve a healthy lifestyle.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The user opens a screen on their device to record their meals, taking a photo of the meal or entering the details in text. By pressing the registration button, the meal data is saved on the device.

[0440] Step 2:

[0441] The device sends the stored meal data along with the user's authentication information to the server. This transmission is encrypted for data security.

[0442] Step 3:

[0443] The server analyzes the received meal data using an AI algorithm. Image analysis technology is used to identify ingredients from meal images and determine the nutritional components of each ingredient.

[0444] Step 4:

[0445] The server retrieves nutritional data corresponding to the identified ingredients from the database and evaluates the overall nutritional balance of the meal. This evaluation also includes a comparison with the user's past meal history.

[0446] Step 5:

[0447] Based on the evaluation results, the server analyzes the nutrients necessary for the user's healthy diet and identifies any excesses or deficiencies. This then generates specific nutritional advice for the user.

[0448] Step 6:

[0449] The server suggests foods, dishes, and necessary supplements to the user. It also generates links to online stores where users can purchase the suggested items.

[0450] Step 7:

[0451] The terminal receives suggestion information from the server and notifies the user, displaying it on a dedicated application screen. This allows the user to review the suggestions and consider their next action.

[0452] Step 8:

[0453] Based on the information provided, users adjust their diet and decide to purchase ingredients and supplements through the suggested links. They also plan their next meal based on the displayed recipes.

[0454] (Example 1)

[0455] 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."

[0456] Conventional diet management systems have made it difficult for users to accurately record their meals and receive appropriate nutritional advice based on that data. Furthermore, they lack specific suggestions tailored to each user's nutritional status and support for purchasing appropriate ingredients quickly, which means they cannot adequately help users manage their health.

[0457] 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.

[0458] In this invention, the server includes an analysis means for receiving meal data registered by the user and identifying food based on the meal data; an acquisition means for obtaining the nutrients of the identified food from a data storage; an evaluation means for evaluating the nutrients the user should consume and the nutrients they should avoid based on the acquired nutrients; and a creation means for creating prompt statements using a generation AI model and optimizing the suggested content. This enables the user to intuitively and efficiently manage their diet and achieve a healthy lifestyle while maintaining nutritional balance.

[0459] "Analysis means" refers to a system that receives meal data registered by a user and has functions and technologies for identifying food based on said meal data.

[0460] "Acquisition means" refers to the process or function of obtaining the nutrients of food identified by the analysis means from a data storage facility.

[0461] "Evaluation methods" include mechanisms and technologies for evaluating which nutrients a user should consume and which they should limit, based on the acquired nutrients.

[0462] A "proposal method" refers to a system or technology that proposes healthy foods or dishes to users based on evaluation results.

[0463] "Generation means" refers to a function or technology that generates a pathway for users to obtain the proposed food or nutritional supplement.

[0464] "Creation method" refers to a process or function that uses a generative AI model to create prompt sentences and optimize the suggested content.

[0465] This invention is a system that efficiently manages a user's diet and supports a healthy lifestyle. Users can register their meal data using a dedicated application on a device such as a smartphone or tablet.

[0466] Users either take a photo of their meal or manually enter the details of their meal through a text field. This data is temporarily stored on the device and then sent to the server via the internet. The secure HTTPS protocol is used for communication.

[0467] The server analyzes received meal data using AI algorithms. Image recognition uses deep learning-based image processing software (e.g., TensorFlow, PyTorch) to identify food items from registered photos. Manually entered data is analyzed using natural language processing techniques.

[0468] The food information obtained as a result of the analysis is used to retrieve nutrient information from the data storage. The database APIs used here include public database APIs that cover food data (e.g., FoodData Central API). Based on this data, the server evaluates the user's nutrient deficiencies or excesses.

[0469] The system uses a generative AI model to optimize suggestions for the user. An example of a generated prompt might be, "Based on your recent dietary data, please identify any missing nutrients and suggest which foods you should include in your next meal." The server also generates and provides the user with links to access relevant foods or nutritional supplements.

[0470] These suggestions are displayed on the device, making them easily viewable and accessible to users. This system allows users to review their daily diet and receive support in making healthier choices. For example, if a user uploads a photo of a high-fat lunch, the server analyzes it and suggests alternative dishes rich in fresh vegetables and protein. It also provides optimal options for purchasing relevant supplements.

[0471] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0472] Step 1:

[0473] The user launches a dedicated application on a device such as a smartphone or tablet. The user either takes a photo of their meal or manually enters the details of their meal through a text field. The entered data, either as image data or text data of the meal, is temporarily stored within the application on the device.

[0474] Step 2:

[0475] The device converts the stored meal data into JSON format and sends it to the server via the internet. Privacy and security are ensured by using the HTTPS protocol for data transmission.

[0476] Step 3:

[0477] The server analyzes the received meal data. Image data is analyzed using image recognition models based on TensorFlow or PyTorch to identify food items. Text data is analyzed using natural language processing techniques to extract specific food information. The output is a list of identified foods.

[0478] Step 4:

[0479] Based on the identified food list, the server retrieves relevant nutrient information from a data storage source (e.g., FoodData Central API). This data processing then outputs specific data on the nutrients contained in each food item.

[0480] Step 5:

[0481] The server uses the acquired nutrient information to compare it with the user's past dietary history and evaluate whether there are any nutrient deficiencies or excesses. This process uses a generative AI model to ensure optimal evaluation. The output of the evaluation is a list of nutrients that should be consumed and nutrients that should be limited.

[0482] Step 6:

[0483] Based on the evaluation results, the server suggests healthy foods and recipes to the user. It optimizes the suggestions using prompts generated by a generative AI model. Specifically, it might generate suggestions such as, "Try broccoli as a food rich in minerals."

[0484] Step 7:

[0485] The terminal displays suggested information received from the server on the user interface. Based on this, the user plans their next meal and makes healthy choices. Links to suggested foods and nutritional supplements are provided, allowing the user to easily access and purchase them.

[0486] (Application Example 1)

[0487] 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."

[0488] Maintaining a healthy diet while keeping nutritional balance is crucial in modern life, but achieving this requires considerable effort and knowledge. In particular, properly collecting and analyzing dietary data and providing optimal dietary suggestions to individual users is difficult. Furthermore, systems used in the home may be burdensome for users to operate, and the accuracy of the information may be compromised. This invention aims to solve these problems and provide the most efficient and effective nutritional management.

[0489] 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.

[0490] In this invention, the server includes an analysis means for receiving meal data registered by the user and identifying ingredients based on the meal data, an acquisition means for obtaining the nutritional components of the identified ingredients from a data warehouse, and an evaluation means for evaluating the nutrients that the user should consume and the nutrients that they should avoid based on the acquired nutritional components. This makes it possible to move around within the user's living space and acquire meal data.

[0491] A "user" refers to a person who uses the system to register their dietary data and receive health suggestions.

[0492] "Meal data" refers to information about the meals consumed by the user, and includes image data and manually entered data.

[0493] "Analysis means" refers to elements that perform processing to identify ingredients based on received meal data.

[0494] "Acquisition means" refers to the element that processes data to retrieve the nutritional components of identified food ingredients from a data warehouse.

[0495] "Evaluation methods" refer to elements that process data based on acquired nutritional information to determine which nutrients are necessary for the user and which should be avoided.

[0496] "Suggestion methods" refer to elements that suggest healthy ingredients and recipes to users.

[0497] "Generation means" refers to elements that create links to purchase the proposed food ingredients or supplements.

[0498] "Living space" refers to the area where users conduct their daily lives and the environment in which a system for nutritional management is implemented.

[0499] The system that realizes this invention is designed to streamline the user's meal management and consists of several main components, including a server, a terminal, and a robot that moves around the living space.

[0500] After receiving meal data sent by the user, the server uses an AI algorithm to identify the ingredients. Specifically, it analyzes the images registered as meal data to identify the ingredients. Machine learning libraries such as TensorFlow are commonly used for this process. Nutritional information for the identified ingredients is retrieved from a database containing nutritional data. As an evaluation method, the server assesses the user's nutritional balance and determines whether there are any deficiencies or excesses.

[0501] On the device side, suggestions sent from the server are visually displayed via a user interface. Information on suggested healthy foods and links to corresponding online stores for purchase are provided. The interface operates on smartphones and tablets and is designed for easy user access.

[0502] Within the user's living space, a robot that supports nutritional management functions. The robot collects meal data during the user's mealtimes and uses this data to suggest improvements to their nutritional balance in the future. The robot also takes images and uploads them to the cloud, enabling analysis by a server.

[0503] As a concrete example, when a user eats french fries and steak for dinner, the robot takes a picture of the meal. The server uses AI to analyze the ingredients, and if it determines that the meal is high in fat, a message such as "We recommend adding a salad or smoothie with plenty of vegetables the next day to balance it out" will be displayed on the device.

[0504] An example of a prompt message for a generative AI model is, "Analyze the meal images registered by the user and generate meal suggestions based on nutritional balance."

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] When a user eats, a terminal or robot takes a picture of the meal. The input is an image of the user's meal, which is sent to the server. The output is an image file that is registered as meal data. This image is pre-processed to identify the ingredients.

[0508] Step 2:

[0509] The server runs a generative AI model using the received images. During this process, the model performs image analysis to identify food ingredients and extract their nutritional information. The input is a pre-processed image of a meal, and the output is the type of food ingredient and its nutritional data. The food ingredients are then compared against a database, and the necessary information is organized.

[0510] Step 3:

[0511] The server uses nutritional data retrieved from the database to compare with the user's past nutritional intake history and identify nutrients that are in excess or deficient. The input is nutritional data for each food item and the user's dietary history, and the output is information on the identified nutritional imbalances.

[0512] Step 4:

[0513] The server generates personalized meal suggestions for the user based on identified nutritional balance information. The input is information about nutritional imbalances, and the output is a list of suggested healthy ingredients and dishes. These suggestions include specific menus and supplement recommendations.

[0514] Step 5:

[0515] The server generates online store links based on the suggestions it receives and sends them to the user's device. The input is a list of healthy food items, and the output is links to purchase them. Users can view this on their smartphones or tablets.

[0516] Step 6:

[0517] The terminal displays the suggested content and purchase links received from the server on the user interface. The input is the suggested data from the server, and the output is a display that the user can visually confirm. The user can use this as a reference for planning their next meal.

[0518] 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.

[0519] This invention provides a health support system that comprehensively manages a user's dietary habits, taking into account not only the content of their meals but also their emotional state. This system operates when the user uses a dedicated application on their device.

[0520] Users register their daily meals by taking photos or entering text descriptions. The registered data is sent to a server, where an AI algorithm analyzes the meal data and identifies ingredients. Based on this analysis, the server retrieves the nutritional components of the identified ingredients from a database and evaluates the overall nutritional balance of the meal.

[0521] A distinctive feature of this system is the incorporation of emotion recognition technology. The server analyzes the user's facial expressions and voice tone via the camera and sensors installed in the terminal to recognize their current emotional state. This information is then reflected in the suggestions for ingredients and dishes; for example, a user experiencing stress might be offered ingredients that promote relaxation or dishes that enhance feelings of happiness.

[0522] The suggested ingredients and recipes are displayed on the device along with appropriate purchase links, allowing users to easily buy related products online while being supported in making healthy choices. Furthermore, the system continuously improves the accuracy of its suggestions by accumulating data on changes in the user's emotions and analyzing their effects.

[0523] For example, if the server detects that a user is feeling stressed in the evening, it will suggest a dinner menu that is rich in calcium and has relaxing effects. This suggestion will also include recipes and recommended purchase links, making it easy for the user to put the suggestions into practice.

[0524] As described above, the present invention is a system that takes into account both the user's dietary content and emotional state, and supports the realization of a healthy diet that meets individual needs.

[0525] The following describes the processing flow.

[0526] Step 1:

[0527] The user launches a dedicated application on their device, takes a photo of their meal or enters details of the meal in text, and records the meal data.

[0528] Step 2:

[0529] The device sends recorded meal data to the server. This data is encrypted, ensuring security.

[0530] Step 3:

[0531] The server analyzes the submitted meal data. Using an AI algorithm, it identifies ingredients from images and retrieves the nutritional information for each ingredient from a database.

[0532] Step 4:

[0533] The server evaluates the overall nutritional balance of the meal and identifies nutrients that the user should either consume less of or avoid.

[0534] Step 5:

[0535] The user activates the emotion recognition function via their device, and the system recognizes their current emotional state by analyzing their facial expressions and voice using cameras and sensors.

[0536] Step 6:

[0537] The recognized emotional data is sent to the server, which analyzes the emotional state and forms a comprehensive recommendation in combination with nutritional assessment.

[0538] Step 7:

[0539] The server suggests healthy foods and dishes that take into account the user's nutritional and emotional state, and also suggests supplements if necessary. It also generates links to online stores along with the suggestions.

[0540] Step 8:

[0541] The device notifies the user of suggestions received from the server and displays them in an accessible manner. The user can use this information to adjust their diet and purchase products through the suggested links.

[0542] (Example 2)

[0543] 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."

[0544] While modern meal management systems focus on managing dietary data, they lack suggestions that take into account the user's emotional state. Therefore, there is a need to comprehensively manage the impact of daily eating habits on emotional state and support more personalized and healthy eating habits.

[0545] 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.

[0546] In this invention, the server includes an analysis means for receiving meal information registered by the user and identifying food based on that information, an acquisition means for obtaining nutritional data of the identified food, and a suggestion means for suggesting appropriate food based on the user's emotional state. This makes it possible to suggest a healthy diet that simultaneously considers the user's diet and emotional state.

[0547] A "user" is an individual who uses the system to register their dietary information and receive suggestions for healthy eating.

[0548] "Dietary information" refers to data that users input or photograph about their daily eating habits and register in the system.

[0549] "Food" refers to individual ingredients or dishes recognized within meal information.

[0550] "Nutritional data" refers to information about the nutrients and components contained in food.

[0551] "Analysis methods" refer to algorithms and technologies used to analyze meal information and the emotional state of users.

[0552] "Means of acquisition" refers to the function of extracting nutritional data about identified food items from the information source.

[0553] "Suggested method" refers to a mechanism for recommending healthy meals or foods to users based on analysis results.

[0554] "Emotional state" refers to the current emotional state of the user, as analyzed from their facial expressions and voice.

[0555] This invention is a health support system that comprehensively manages a user's eating habits along with their emotional state. Users utilize this system by using a dedicated application on a terminal to input or photograph meal information. The terminal transmits the meal information received from the user, along with data on the user's facial expressions and voice, to a server. The terminal is equipped with a high-resolution camera and microphone, which are used to collect detailed data.

[0556] The server utilizes AI technology to analyze meal information. Specifically, it uses machine learning libraries such as TensorFlow to analyze image data and interprets text-based meal information using natural language processing techniques. This allows the server to identify ingredients and retrieve their nutritional data from a database. The server also uses OpenCV and the Emotion API to analyze the user's facial expressions and voice to recognize their emotional state.

[0557] This system suggests healthy foods and meal plans tailored to the user based on analyzed dietary data and emotional state. The suggestions include food selections that take the user's emotional state into account; for example, a stressed user might be offered meals designed to promote relaxation. For instance, a stressed user might be advised, "We recommend chamomile tea and salmon for dinner. You can purchase it here."

[0558] The suggested information is displayed on the device along with links to purchase related products online, allowing users to easily make healthy choices. The server also stores the user's past data and uses it to improve the accuracy of future suggestions. This system enables users to achieve a personalized, healthy diet.

[0559] When using a generative AI model, a concrete example of a prompt might be, "Generate a relaxing meal menu to suggest to a user who is stressed." By utilizing this prompt, the system can generate more appropriate suggestions.

[0560] In summary, the present invention enables users to manage their diet and emotions in a balanced way, supporting a healthy lifestyle.

[0561] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0562] Step 1:

[0563] The device receives daily meal information from the user. The user registers their meals by taking photos or entering them in text format. The entered data includes either photos or text. This data is then prepared to be sent to the server for further analysis.

[0564] Step 2:

[0565] The device sends meal data to the server. The input is either a photo or text of the meal, which is then converted into a data format for transmission to the server. Encryption technology is used during data transmission to ensure data security.

[0566] Step 3:

[0567] The server analyzes the received meal data. First, AI technology is used to process image data. TensorFlow is used to identify food items within the image. The input is an image, and the output is a list of food names. Next, for text data, natural language processing is performed to extract information about the food items described. This result is output as a list of food names.

[0568] Step 4:

[0569] The server retrieves nutritional data for identified foods from its information sources. Specifically, it extracts the relevant nutritional information from the database based on the identified food name. The input is a list of food names, and the output is the corresponding nutritional data.

[0570] Step 5:

[0571] The server analyzes the user's facial expressions and voice data to understand their emotional state. It uses OpenCV and the Emotion API to analyze audio and video data sent from the terminal as input. As a result, the user's emotional state is output, providing information such as "stressed" or "happy."

[0572] Step 6:

[0573] Based on the analysis results, the server suggests foods that take into account the user's emotional state and nutritional balance. Here, a generative AI model is used, referencing past suggestion data and user history. The inputs are the analysis results and the emotional state, and the output is a suggestion of specific foods or dishes. For example, it might suggest a "relaxing meal for the evening."

[0574] Step 7:

[0575] The terminal receives food information suggested by the server and displays it to the user. The suggestions also include online purchase links, allowing the user to easily buy related products by clicking on them. The goal here is to expand the user's choices based on the suggested information.

[0576] This allows users to make meal choices that are tailored to their nutritional needs and emotional state.

[0577] (Application Example 2)

[0578] 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."

[0579] Maintaining a healthy diet is important for modern consumers, but daily busyness and information overload often make it difficult to choose meals that suit individual nutritional needs and emotional changes. Furthermore, the knowledge and effort required to choose meals that match one's emotional state can be a burden for many users.

[0580] 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.

[0581] In this invention, the server includes an information processing device that identifies the user's emotional state and suggests food based on it, a data acquisition device that obtains the nutritional components of food ingredients from a recording medium, and an information processing device that identifies food ingredients. This makes it possible to suggest food according to the user's emotional state and to support the realization of a healthy diet that suits individual needs.

[0582] A "user" is an individual who uses this system, or an entity that inputs and manages information on their behalf.

[0583] "Meal data" refers to information about the food and drinks a user has consumed, and is registered as photos and text.

[0584] "Ingredients" refers to the specific names and types of food included in the meal data.

[0585] "Nutritional components" refer to the types and amounts of nutrients such as proteins, fats, carbohydrates, vitamins, and minerals contained in food.

[0586] A "recording medium" refers to a digital or physical medium used to store nutritional information or food ingredient information.

[0587] An "information processing device" refers to a computer or program used to analyze, evaluate, or make suggestions based on information obtained from a user.

[0588] A "data acquisition device" refers to a device or program that acquires necessary information from a recording medium and converts it into a format that can be used by an information processing device.

[0589] An "observation device" refers to a device or program that analyzes facial expressions and voice to identify the user's emotional state.

[0590] A "data generation device" refers to a device or program that creates links for purchasing suggested foods or health supplements online.

[0591] The system for implementing this invention relies primarily on a program that suggests healthy foods and dishes based on the user's emotional state. The system is implemented using the user's terminal and a central server.

[0592] The device is responsible for acquiring meal data from the user. Specifically, the user can either take a photo of their meal or input details of their meal in text. This data is collected through the device's built-in camera and text input interface and sent to a server for data processing.

[0593] The server uses an internally built AI model to analyze received meal data. This analysis identifies ingredients from images or text and retrieves corresponding nutritional data from the recording medium. TensorFlow, a well-known machine learning library, is used for the data analysis and suggestion generation process. In addition, to identify the user's emotional state, the server uses facial and voice data obtained from observation devices and processes it with an emotion recognition algorithm.

[0594] To enable suggestions that reflect the user's emotional state, the server generates purchase links for suggested food items and health supplements. This functionality is achieved through API data integration, connecting to corresponding online shopping platforms.

[0595] For example, if the server determines that a user is stressed, it will suggest foods that have a relaxing effect. These suggestions might include lavender tea or chamomile, and the user can easily purchase related products using the provided online links. Specific examples of prompts for the generating AI model include, "Please suggest a relaxing drink for the evening," or "Please list foods that are high in calcium."

[0596] In this way, the system takes into account both the user's daily diet and emotional state, and provides suggestions for a healthy diet tailored to their individual needs.

[0597] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0598] Step 1:

[0599] The device retrieves meal data from the user. The user either takes a photo of their meal or enters the details of their meal in text. The retrieved meal data is reviewed by the user and then sent from the device to the server.

[0600] Step 2:

[0601] The server analyzes the received meal data using an AI model. It identifies ingredients from the image or text data obtained as input. Machine learning techniques are used in this process, and an ingredient list is generated as output.

[0602] Step 3:

[0603] The server retrieves the nutritional information of the identified food ingredients from the storage medium. It issues a database query using the food ingredient list as input and retrieves the corresponding nutritional data as output.

[0604] Step 4:

[0605] The server receives facial and voice data from the user's device and analyzes the emotional state using an emotion recognition algorithm. Based on the input sensor data, the server then outputs the current emotional state.

[0606] Step 5:

[0607] The server integrates emotional states and nutritional information to suggest the most suitable ingredients and dishes for the user. This process uses a generative AI model to process input information and output optimal suggestion results.

[0608] Step 6:

[0609] The server generates purchase links for the suggested ingredients and dishes. It connects with online shopping platforms using an API to generate purchase links associated with the suggested results and outputs them.

[0610] Step 7:

[0611] The server sends the suggested results, including a purchase link, to the terminal. The user can then use the provided link to purchase the product online. The returned link is confirmed, ensuring a smooth purchase process.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] [Fourth Embodiment]

[0616] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0617] 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.

[0618] 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).

[0619] 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.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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".

[0629] This invention is a system for efficiently managing a user's diet and supporting their health. Users utilize a dedicated application on a device such as a smartphone or tablet. Users can register their meal data by taking photos of the meals they have eaten or by manually entering the details of their meals.

[0630] Registered meal data is sent from the terminal to the server. The server analyzes the meal data using an AI algorithm. Specifically, the server identifies ingredients based on images and retrieves the nutritional components contained in each ingredient from a database. Then, based on the retrieved nutritional components, the server evaluates which nutrients the user needs to consume and which nutrients they should limit.

[0631] Based on the evaluation results, the server suggests healthy foods and dishes to the user. For example, if a comparison with past dietary data determines that the user is deficient in minerals, the server will suggest dishes using mineral-rich ingredients or supplements. Links to obtain the necessary ingredients and supplements online will also be generated.

[0632] These suggestions are displayed on the device, allowing users to easily find places to buy ingredients and supplements. Furthermore, by referring to the recommended menus, users can plan their next meals in a healthier way.

[0633] As a concrete example, when a user uploads a photo of their lunch, the server identifies it as a high-fat meal and suggests using fresh vegetables and protein-rich ingredients instead. Furthermore, if supplements are needed, a link is provided showing the best purchase options from multiple online retailers.

[0634] This invention makes it easier for users to balance their nutrition through their daily meals, enabling them to achieve a healthy lifestyle.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The user opens a screen on their device to record their meals, taking a photo of the meal or entering the details in text. By pressing the registration button, the meal data is saved on the device.

[0638] Step 2:

[0639] The device sends the stored meal data along with the user's authentication information to the server. This transmission is encrypted for data security.

[0640] Step 3:

[0641] The server analyzes the received meal data using an AI algorithm. Image analysis technology is used to identify ingredients from meal images and determine the nutritional components of each ingredient.

[0642] Step 4:

[0643] The server retrieves nutritional data corresponding to the identified ingredients from the database and evaluates the overall nutritional balance of the meal. This evaluation also includes a comparison with the user's past meal history.

[0644] Step 5:

[0645] Based on the evaluation results, the server analyzes the nutrients necessary for the user's healthy diet and identifies any excesses or deficiencies. This then generates specific nutritional advice for the user.

[0646] Step 6:

[0647] The server suggests foods, dishes, and necessary supplements to the user. It also generates links to online stores where users can purchase the suggested items.

[0648] Step 7:

[0649] The terminal receives suggestion information from the server and notifies the user, displaying it on a dedicated application screen. This allows the user to review the suggestions and consider their next action.

[0650] Step 8:

[0651] Based on the information provided, users adjust their diet and decide to purchase ingredients and supplements through the suggested links. They also plan their next meal based on the displayed recipes.

[0652] (Example 1)

[0653] 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".

[0654] Conventional diet management systems have made it difficult for users to accurately record their meals and receive appropriate nutritional advice based on that data. Furthermore, they lack specific suggestions tailored to each user's nutritional status and support for purchasing appropriate ingredients quickly, which means they cannot adequately help users manage their health.

[0655] 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.

[0656] In this invention, the server includes an analysis means for receiving meal data registered by the user and identifying food based on the meal data; an acquisition means for obtaining the nutrients of the identified food from a data storage; an evaluation means for evaluating the nutrients the user should consume and the nutrients they should avoid based on the acquired nutrients; and a creation means for creating prompt statements using a generation AI model and optimizing the suggested content. This enables the user to intuitively and efficiently manage their diet and achieve a healthy lifestyle while maintaining nutritional balance.

[0657] "Analysis means" refers to a system that receives meal data registered by a user and has functions and technologies for identifying food based on said meal data.

[0658] "Acquisition means" refers to the process or function of obtaining the nutrients of food identified by the analysis means from a data storage facility.

[0659] "Evaluation methods" include mechanisms and technologies for evaluating which nutrients a user should consume and which they should limit, based on the acquired nutrients.

[0660] A "proposal method" refers to a system or technology that proposes healthy foods or dishes to users based on evaluation results.

[0661] "Generation means" refers to a function or technology that generates a pathway for users to obtain the proposed food or nutritional supplement.

[0662] "Creation method" refers to a process or function that uses a generative AI model to create prompt sentences and optimize the suggested content.

[0663] This invention is a system that efficiently manages a user's diet and supports a healthy lifestyle. Users can register their meal data using a dedicated application on a device such as a smartphone or tablet.

[0664] Users either take a photo of their meal or manually enter the details of their meal through a text field. This data is temporarily stored on the device and then sent to the server via the internet. The secure HTTPS protocol is used for communication.

[0665] The server analyzes received meal data using AI algorithms. Image recognition uses deep learning-based image processing software (e.g., TensorFlow, PyTorch) to identify food items from registered photos. Manually entered data is analyzed using natural language processing techniques.

[0666] The food information obtained as a result of the analysis is used to retrieve nutrient information from the data storage. The database APIs used here include public database APIs that cover food data (e.g., FoodData Central API). Based on this data, the server evaluates the user's nutrient deficiencies or excesses.

[0667] The system uses a generative AI model to optimize suggestions for the user. An example of a generated prompt might be, "Based on your recent dietary data, please identify any missing nutrients and suggest which foods you should include in your next meal." The server also generates and provides the user with links to access relevant foods or nutritional supplements.

[0668] These suggestions are displayed on the device, making them easily viewable and accessible to users. This system allows users to review their daily diet and receive support in making healthier choices. For example, if a user uploads a photo of a high-fat lunch, the server analyzes it and suggests alternative dishes rich in fresh vegetables and protein. It also provides optimal options for purchasing relevant supplements.

[0669] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0670] Step 1:

[0671] The user launches a dedicated application on a device such as a smartphone or tablet. The user either takes a photo of their meal or manually enters the details of their meal through a text field. The entered data, either as image data or text data of the meal, is temporarily stored within the application on the device.

[0672] Step 2:

[0673] The device converts the stored meal data into JSON format and sends it to the server via the internet. Privacy and security are ensured by using the HTTPS protocol for data transmission.

[0674] Step 3:

[0675] The server analyzes the received meal data. Image data is analyzed using image recognition models based on TensorFlow or PyTorch to identify food items. Text data is analyzed using natural language processing techniques to extract specific food information. The output is a list of identified foods.

[0676] Step 4:

[0677] Based on the identified food list, the server retrieves relevant nutrient information from a data storage source (e.g., FoodData Central API). This data processing then outputs specific data on the nutrients contained in each food item.

[0678] Step 5:

[0679] The server uses the acquired nutrient information to compare it with the user's past dietary history and evaluate whether there are any nutrient deficiencies or excesses. This process uses a generative AI model to ensure optimal evaluation. The output of the evaluation is a list of nutrients that should be consumed and nutrients that should be limited.

[0680] Step 6:

[0681] Based on the evaluation results, the server suggests healthy foods and recipes to the user. It optimizes the suggestions using prompts generated by a generative AI model. Specifically, it might generate suggestions such as, "Try broccoli as a food rich in minerals."

[0682] Step 7:

[0683] The terminal displays suggested information received from the server on the user interface. Based on this, the user plans their next meal and makes healthy choices. Links to suggested foods and nutritional supplements are provided, allowing the user to easily access and purchase them.

[0684] (Application Example 1)

[0685] 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".

[0686] Maintaining a healthy diet while keeping nutritional balance is crucial in modern life, but achieving this requires considerable effort and knowledge. In particular, properly collecting and analyzing dietary data and providing optimal dietary suggestions to individual users is difficult. Furthermore, systems used in the home may be burdensome for users to operate, and the accuracy of the information may be compromised. This invention aims to solve these problems and provide the most efficient and effective nutritional management.

[0687] 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.

[0688] In this invention, the server includes an analysis means for receiving meal data registered by the user and identifying ingredients based on the meal data, an acquisition means for obtaining the nutritional components of the identified ingredients from a data warehouse, and an evaluation means for evaluating the nutrients that the user should consume and the nutrients that they should avoid based on the acquired nutritional components. This makes it possible to move around within the user's living space and acquire meal data.

[0689] A "user" refers to a person who uses the system to register their dietary data and receive health suggestions.

[0690] "Meal data" refers to information about the meals consumed by the user, and includes image data and manually entered data.

[0691] "Analysis means" refers to elements that perform processing to identify ingredients based on received meal data.

[0692] "Acquisition means" refers to the element that processes data to retrieve the nutritional components of identified food ingredients from a data warehouse.

[0693] "Evaluation methods" refer to elements that process data based on acquired nutritional information to determine which nutrients are necessary for the user and which should be avoided.

[0694] "Suggestion methods" refer to elements that suggest healthy ingredients and recipes to users.

[0695] "Generation means" refers to elements that create links to purchase the proposed food ingredients or supplements.

[0696] "Living space" refers to the area where users conduct their daily lives and the environment in which a system for nutritional management is implemented.

[0697] The system that realizes this invention is designed to streamline the user's meal management and consists of several main components, including a server, a terminal, and a robot that moves around the living space.

[0698] After receiving meal data sent by the user, the server uses an AI algorithm to identify the ingredients. Specifically, it analyzes the images registered as meal data to identify the ingredients. Machine learning libraries such as TensorFlow are commonly used for this process. Nutritional information for the identified ingredients is retrieved from a database containing nutritional data. As an evaluation method, the server assesses the user's nutritional balance and determines whether there are any deficiencies or excesses.

[0699] On the device side, suggestions sent from the server are visually displayed via a user interface. Information on suggested healthy foods and links to corresponding online stores for purchase are provided. The interface operates on smartphones and tablets and is designed for easy user access.

[0700] Within the user's living space, a robot that supports nutritional management functions. The robot collects meal data during the user's mealtimes and uses this data to suggest improvements to their nutritional balance in the future. The robot also takes images and uploads them to the cloud, enabling analysis by a server.

[0701] As a concrete example, when a user eats french fries and steak for dinner, the robot takes a picture of the meal. The server uses AI to analyze the ingredients, and if it determines that the meal is high in fat, a message such as "We recommend adding a salad or smoothie with plenty of vegetables the next day to balance it out" will be displayed on the device.

[0702] An example of a prompt message for a generative AI model is, "Analyze the meal images registered by the user and generate meal suggestions based on nutritional balance."

[0703] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0704] Step 1:

[0705] When a user eats, a terminal or robot takes a picture of the meal. The input is an image of the user's meal, which is sent to the server. The output is an image file that is registered as meal data. This image is pre-processed to identify the ingredients.

[0706] Step 2:

[0707] The server runs a generative AI model using the received images. During this process, the model performs image analysis to identify food ingredients and extract their nutritional information. The input is a pre-processed image of a meal, and the output is the type of food ingredient and its nutritional data. The food ingredients are then compared against a database, and the necessary information is organized.

[0708] Step 3:

[0709] The server uses nutritional data retrieved from the database to compare with the user's past nutritional intake history and identify nutrients that are in excess or deficient. The input is nutritional data for each food item and the user's dietary history, and the output is information on the identified nutritional imbalances.

[0710] Step 4:

[0711] The server generates personalized meal suggestions for the user based on identified nutritional balance information. The input is information about nutritional imbalances, and the output is a list of suggested healthy ingredients and dishes. These suggestions include specific menus and supplement recommendations.

[0712] Step 5:

[0713] The server generates online store links based on the suggestions it receives and sends them to the user's device. The input is a list of healthy food items, and the output is links to purchase them. Users can view this on their smartphones or tablets.

[0714] Step 6:

[0715] The terminal displays the suggested content and purchase links received from the server on the user interface. The input is the suggested data from the server, and the output is a display that the user can visually confirm. The user can use this as a reference for planning their next meal.

[0716] 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.

[0717] This invention provides a health support system that comprehensively manages a user's dietary habits, taking into account not only the content of their meals but also their emotional state. This system operates when the user uses a dedicated application on their device.

[0718] Users register their daily meals by taking photos or entering text descriptions. The registered data is sent to a server, where an AI algorithm analyzes the meal data and identifies ingredients. Based on this analysis, the server retrieves the nutritional components of the identified ingredients from a database and evaluates the overall nutritional balance of the meal.

[0719] A distinctive feature of this system is the incorporation of emotion recognition technology. The server analyzes the user's facial expressions and voice tone via the camera and sensors installed in the terminal to recognize their current emotional state. This information is then reflected in the suggestions for ingredients and dishes; for example, a user experiencing stress might be offered ingredients that promote relaxation or dishes that enhance feelings of happiness.

[0720] The suggested ingredients and recipes are displayed on the device along with appropriate purchase links, allowing users to easily buy related products online while being supported in making healthy choices. Furthermore, the system continuously improves the accuracy of its suggestions by accumulating data on changes in the user's emotions and analyzing their effects.

[0721] For example, if the server detects that a user is feeling stressed in the evening, it will suggest a dinner menu that is rich in calcium and has relaxing effects. This suggestion will also include recipes and recommended purchase links, making it easy for the user to put the suggestions into practice.

[0722] As described above, the present invention is a system that takes into account both the user's dietary content and emotional state, and supports the realization of a healthy diet that meets individual needs.

[0723] The following describes the processing flow.

[0724] Step 1:

[0725] The user launches a dedicated application on their device, takes a photo of their meal or enters details of the meal in text, and records the meal data.

[0726] Step 2:

[0727] The device sends recorded meal data to the server. This data is encrypted, ensuring security.

[0728] Step 3:

[0729] The server analyzes the submitted meal data. Using an AI algorithm, it identifies ingredients from images and retrieves the nutritional information for each ingredient from a database.

[0730] Step 4:

[0731] The server evaluates the overall nutritional balance of the meal and identifies nutrients that the user should either consume less of or avoid.

[0732] Step 5:

[0733] The user activates the emotion recognition function via their device, and the system recognizes their current emotional state by analyzing their facial expressions and voice using cameras and sensors.

[0734] Step 6:

[0735] The recognized emotional data is sent to the server, which analyzes the emotional state and forms a comprehensive recommendation in combination with nutritional assessment.

[0736] Step 7:

[0737] The server suggests healthy foods and dishes that take into account the user's nutritional and emotional state, and also suggests supplements if necessary. It also generates links to online stores along with the suggestions.

[0738] Step 8:

[0739] The device notifies the user of suggestions received from the server and displays them in an accessible manner. The user can use this information to adjust their diet and purchase products through the suggested links.

[0740] (Example 2)

[0741] 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".

[0742] While modern meal management systems focus on managing dietary data, they lack suggestions that take into account the user's emotional state. Therefore, there is a need to comprehensively manage the impact of daily eating habits on emotional state and support more personalized and healthy eating habits.

[0743] 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.

[0744] In this invention, the server includes an analysis means for receiving meal information registered by the user and identifying food based on that information, an acquisition means for obtaining nutritional data of the identified food, and a suggestion means for suggesting appropriate food based on the user's emotional state. This makes it possible to suggest a healthy diet that simultaneously considers the user's diet and emotional state.

[0745] A "user" is an individual who uses the system to register their dietary information and receive suggestions for healthy eating.

[0746] "Dietary information" refers to data that users input or photograph about their daily eating habits and register in the system.

[0747] "Food" refers to individual ingredients or dishes recognized within meal information.

[0748] "Nutritional data" refers to information about the nutrients and components contained in food.

[0749] "Analysis methods" refer to algorithms and technologies used to analyze meal information and the emotional state of users.

[0750] "Means of acquisition" refers to the function of extracting nutritional data about identified food items from the information source.

[0751] "Suggested method" refers to a mechanism for recommending healthy meals or foods to users based on analysis results.

[0752] "Emotional state" refers to the current emotional state of the user, as analyzed from their facial expressions and voice.

[0753] This invention is a health support system that comprehensively manages a user's eating habits along with their emotional state. Users utilize this system by using a dedicated application on a terminal to input or photograph meal information. The terminal transmits the meal information received from the user, along with data on the user's facial expressions and voice, to a server. The terminal is equipped with a high-resolution camera and microphone, which are used to collect detailed data.

[0754] The server utilizes AI technology to analyze meal information. Specifically, it uses machine learning libraries such as TensorFlow to analyze image data and interprets text-based meal information using natural language processing techniques. This allows the server to identify ingredients and retrieve their nutritional data from a database. The server also uses OpenCV and the Emotion API to analyze the user's facial expressions and voice to recognize their emotional state.

[0755] This system suggests healthy foods and meal plans tailored to the user based on analyzed dietary data and emotional state. The suggestions include food selections that take the user's emotional state into account; for example, a stressed user might be offered meals designed to promote relaxation. For instance, a stressed user might be advised, "We recommend chamomile tea and salmon for dinner. You can purchase it here."

[0756] The suggested information is displayed on the device along with links to purchase related products online, allowing users to easily make healthy choices. The server also stores the user's past data and uses it to improve the accuracy of future suggestions. This system enables users to achieve a personalized, healthy diet.

[0757] When using a generative AI model, a concrete example of a prompt might be, "Generate a relaxing meal menu to suggest to a user who is stressed." By utilizing this prompt, the system can generate more appropriate suggestions.

[0758] In summary, the present invention enables users to manage their diet and emotions in a balanced way, supporting a healthy lifestyle.

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] The device receives daily meal information from the user. The user registers their meals by taking photos or entering them in text format. The entered data includes either photos or text. This data is then prepared to be sent to the server for further analysis.

[0762] Step 2:

[0763] The device sends meal data to the server. The input is either a photo or text of the meal, which is then converted into a data format for transmission to the server. Encryption technology is used during data transmission to ensure data security.

[0764] Step 3:

[0765] The server analyzes the received meal data. First, AI technology is used to process image data. TensorFlow is used to identify food items within the image. The input is an image, and the output is a list of food names. Next, for text data, natural language processing is performed to extract information about the food items described. This result is output as a list of food names.

[0766] Step 4:

[0767] The server retrieves nutritional data for identified foods from its information sources. Specifically, it extracts the relevant nutritional information from the database based on the identified food name. The input is a list of food names, and the output is the corresponding nutritional data.

[0768] Step 5:

[0769] The server analyzes the user's facial expressions and voice data to understand their emotional state. It uses OpenCV and the Emotion API to analyze audio and video data sent from the terminal as input. As a result, the user's emotional state is output, providing information such as "stressed" or "happy."

[0770] Step 6:

[0771] Based on the analysis results, the server suggests foods that take into account the user's emotional state and nutritional balance. Here, a generative AI model is used, referencing past suggestion data and user history. The inputs are the analysis results and the emotional state, and the output is a suggestion of specific foods or dishes. For example, it might suggest a "relaxing meal for the evening."

[0772] Step 7:

[0773] The terminal receives food information suggested by the server and displays it to the user. The suggestions also include online purchase links, allowing the user to easily buy related products by clicking on them. The goal here is to expand the user's choices based on the suggested information.

[0774] This allows users to make meal choices that are tailored to their nutritional needs and emotional state.

[0775] (Application Example 2)

[0776] 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".

[0777] Maintaining a healthy diet is important for modern consumers, but daily busyness and information overload often make it difficult to choose meals that suit individual nutritional needs and emotional changes. Furthermore, the knowledge and effort required to choose meals that match one's emotional state can be a burden for many users.

[0778] 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.

[0779] In this invention, the server includes an information processing device that identifies the user's emotional state and suggests food based on it, a data acquisition device that obtains the nutritional components of food ingredients from a recording medium, and an information processing device that identifies food ingredients. This makes it possible to suggest food according to the user's emotional state and to support the realization of a healthy diet that suits individual needs.

[0780] A "user" is an individual who uses this system, or an entity that inputs and manages information on their behalf.

[0781] "Meal data" refers to information about the food and drinks a user has consumed, and is registered as photos and text.

[0782] "Ingredients" refers to the specific names and types of food included in the meal data.

[0783] "Nutritional components" refer to the types and amounts of nutrients such as proteins, fats, carbohydrates, vitamins, and minerals contained in food.

[0784] A "recording medium" refers to a digital or physical medium used to store nutritional information or food ingredient information.

[0785] An "information processing device" refers to a computer or program used to analyze, evaluate, or make suggestions based on information obtained from a user.

[0786] A "data acquisition device" refers to a device or program that acquires necessary information from a recording medium and converts it into a format that can be used by an information processing device.

[0787] An "observation device" refers to a device or program that analyzes facial expressions and voice to identify the user's emotional state.

[0788] A "data generation device" refers to a device or program that creates links for purchasing suggested foods or health supplements online.

[0789] The system for implementing this invention relies primarily on a program that suggests healthy foods and dishes based on the user's emotional state. The system is implemented using the user's terminal and a central server.

[0790] The device is responsible for acquiring meal data from the user. Specifically, the user can either take a photo of their meal or input details of their meal in text. This data is collected through the device's built-in camera and text input interface and sent to a server for data processing.

[0791] The server uses an internally built AI model to analyze received meal data. This analysis identifies ingredients from images or text and retrieves corresponding nutritional data from the recording medium. TensorFlow, a well-known machine learning library, is used for the data analysis and suggestion generation process. In addition, to identify the user's emotional state, the server uses facial and voice data obtained from observation devices and processes it with an emotion recognition algorithm.

[0792] To enable suggestions that reflect the user's emotional state, the server generates purchase links for suggested food items and health supplements. This functionality is achieved through API data integration, connecting to corresponding online shopping platforms.

[0793] For example, if the server determines that a user is stressed, it will suggest foods that have a relaxing effect. These suggestions might include lavender tea or chamomile, and the user can easily purchase related products using the provided online links. Specific examples of prompts for the generating AI model include, "Please suggest a relaxing drink for the evening," or "Please list foods that are high in calcium."

[0794] In this way, the system takes into account both the user's daily diet and emotional state, and provides suggestions for a healthy diet tailored to their individual needs.

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The device retrieves meal data from the user. The user either takes a photo of their meal or enters the details of their meal in text. The retrieved meal data is reviewed by the user and then sent from the device to the server.

[0798] Step 2:

[0799] The server analyzes the received meal data using an AI model. It identifies ingredients from the image or text data obtained as input. Machine learning techniques are used in this process, and an ingredient list is generated as output.

[0800] Step 3:

[0801] The server retrieves the nutritional information of the identified food ingredients from the storage medium. It issues a database query using the food ingredient list as input and retrieves the corresponding nutritional data as output.

[0802] Step 4:

[0803] The server receives facial and voice data from the user's device and analyzes the emotional state using an emotion recognition algorithm. Based on the input sensor data, the server then outputs the current emotional state.

[0804] Step 5:

[0805] The server integrates emotional states and nutritional information to suggest the most suitable ingredients and dishes for the user. This process uses a generative AI model to process input information and output optimal suggestion results.

[0806] Step 6:

[0807] The server generates purchase links for the suggested ingredients and dishes. It connects with online shopping platforms using an API to generate purchase links associated with the suggested results and outputs them.

[0808] Step 7:

[0809] The server sends the suggested results, including a purchase link, to the terminal. The user can then use the provided link to purchase the product online. The returned link is confirmed, ensuring a smooth purchase process.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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."

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0831] The following is further disclosed regarding the embodiments described above.

[0832] (Claim 1)

[0833] An analysis means that receives meal data registered by the user and identifies ingredients based on said meal data,

[0834] A means for obtaining the nutritional components of the identified food ingredient from a database,

[0835] An evaluation method for assessing which nutrients a user should consume and which they should avoid based on the acquired nutritional information,

[0836] A suggestion means that proposes healthy ingredients or dishes to the user based on the evaluation results,

[0837] A generation means for generating a link to purchase the proposed food ingredients or supplements,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, comprising identification means for comparing the user's past meal history with current meal data to identify deficient or excessive nutrients.

[0841] (Claim 3)

[0842] The system according to claim 1, comprising an analysis means for analyzing images of food using an AI algorithm and identifying ingredients.

[0843] "Example 1"

[0844] (Claim 1)

[0845] An analysis means that receives meal data registered by a user and identifies food based on said meal data,

[0846] A means for obtaining the nutrients of the identified food from a data storage facility,

[0847] An evaluation method for assessing which nutrients a user should consume and which they should limit based on the acquired nutrients,

[0848] A suggestion means that proposes healthy foods or dishes to the user based on the evaluation results,

[0849] A generating means for generating a route through which the proposed food or nutritional supplement can be obtained,

[0850] A method for creating prompt sentences using a generative AI model and optimizing the suggested content,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, comprising identification means for comparing the user's past meal data history with current meal data to identify any deficient or excess nutrients.

[0854] (Claim 3)

[0855] The system according to claim 1, comprising an analysis means for analyzing images of food using an AI algorithm and identifying food items.

[0856] "Application Example 1"

[0857] (Claim 1)

[0858] An analysis means that receives meal data registered by the user and identifies ingredients based on said meal data,

[0859] A means for obtaining the nutritional components of the identified food ingredient from a data warehouse,

[0860] An evaluation method for assessing which nutrients a user should consume and which they should avoid based on the acquired nutritional information,

[0861] A suggestion means that proposes healthy ingredients or dishes to the user based on the evaluation results,

[0862] A generation means for generating a link to purchase the proposed food ingredients or supplements,

[0863] A means of moving around within the user's living space and acquiring meal data,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, comprising identification means for comparing the user's past meal history with current meal data to identify deficient or excessive nutrients.

[0867] (Claim 3)

[0868] The system according to claim 1, comprising an analysis means for analyzing images of food using an AI algorithm and identifying ingredients.

[0869] "Example 2 of combining an emotion engine"

[0870] (Claim 1)

[0871] An analysis means that receives meal information registered by a user and identifies food based on said meal information,

[0872] An acquisition means for obtaining nutritional data of the identified food from the information source,

[0873] An evaluation method for evaluating the nutrients that a user should consume and the nutrients they should limit based on acquired nutritional data,

[0874] A suggestion means that proposes healthy foods or meal plans to the user based on the evaluation results,

[0875] A generation means for generating a link to purchase the proposed food or health product,

[0876] An analytical method that analyzes the user's facial expressions and voice data to understand their emotional state,

[0877] A suggestion means that proposes food or meals suitable for the user based on the emotional state,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, comprising identification means for comparing the user's past dietary information history with current dietary information to identify deficient or excessive nutrients.

[0881] (Claim 3)

[0882] The system according to claim 1, comprising an analysis means for analyzing images of food using AI technology and identifying food items.

[0883] "Application example 2 when combining with an emotional engine"

[0884] (Claim 1)

[0885] An information processing device that receives meal data registered by a user and identifies ingredients based on said meal data,

[0886] A data acquisition device for obtaining the nutritional components of the identified food from a recording medium,

[0887] An information processing device that evaluates the nutrients a user should consume and the nutrients they should avoid based on the acquired nutritional information,

[0888] An information processing device that suggests healthy foods or dishes to the user based on the evaluation results,

[0889] A data generation device that generates links to purchase proposed foods or health supplements,

[0890] An observation device that analyzes the user's facial expressions and voice data to identify their emotional state,

[0891] An information processing device that suggests food based on the emotional state,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, comprising an information processing device that compares the user's past dietary information history with current dietary information and identifies deficient or excess nutrients.

[0895] (Claim 3)

[0896] The system according to claim 1, comprising an information processing device that uses artificial intelligence technology to analyze images of a meal and identify ingredients. [Explanation of Symbols]

[0897] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An analysis means that receives meal data registered by the user and identifies ingredients based on said meal data, A means for obtaining the nutritional components of the identified food ingredient from a data warehouse, An evaluation method for assessing which nutrients a user should consume and which they should avoid based on the acquired nutritional information, A suggestion means that proposes healthy ingredients or dishes to the user based on the evaluation results, A generation means for generating a link to purchase the proposed food ingredients or supplements, A means of moving around within the user's living space and acquiring meal data, A system that includes this.

2. The system according to claim 1, comprising identification means for comparing the user's past meal data history with current meal data to identify deficient or excessive nutrients.

3. The system according to claim 1, comprising an analysis means for analyzing images of food using an AI algorithm and identifying ingredients.

Images